To see the other types of publications on this topic, follow the link: Pattern-making validation.
Journal articles on the topic 'Pattern-making validation'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Pattern-making validation.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Zhu, Yan, Yan Cui, Lu Zhang, and Yong Mei Liu. "Automatic Pattern Generation of Female Pants Based on Garment CAD." Advanced Materials Research 627 (December 2012): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amr.627.510.
Full textAbstract:
Abstract. This article studies on automatic pattern generation of female pants. The authors analyze and summarize the structure of the female pants, and divide plants into different units. Then the authors extract shape and structure factors form the every element of plants, parameterize the factors, build modules with Modasoft system. At last, the authors test the validation of every module by changing the value of variables, and combines operation of different modules. These modules save the time of pattern-making significantly and improve efficiency. With these modules, technical personnel avoid large number of repetitive work. The automatic pattern generation is the trend of garment CAD.
2
Zhang, Liang. "A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems." Sensors 20, no. 20 (October 21, 2020): 5947. http://dx.doi.org/10.3390/s20205947.
Full textAbstract:
Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.
3
Fernandes, Carina, Ana Ribeiro Gonçalves, Rita Pasion, Fernando Ferreira-Santos, Tiago Oliveira Paiva, Joana Melo e Castro, Fernando Barbosa, Isabel Pavão Martins, and João Marques-Teixeira. "European Portuguese adaptation and validation of dilemmas used to assess moral decision-making." Trends in Psychiatry and Psychotherapy 40, no. 1 (April 5, 2018): 38–46. http://dx.doi.org/10.1590/2237-6089-2017-0022.
Full textAbstract:
Abstract Objective To adapt and validate a widely used set of moral dilemmas to European Portuguese, which can be applied to assess decision-making. Moreover, the classical formulation of the dilemmas was compared with a more focused moral probe. Finally, a shorter version of the moral scenarios was tested. Methods The Portuguese version of the set of moral dilemmas was tested in 53 individuals from several regions of Portugal. In a second study, an alternative way of questioning on moral dilemmas was tested in 41 participants. Finally, the shorter version of the moral dilemmas was tested in 137 individuals. Results Results evidenced no significant differences between English and Portuguese versions. Also, asking whether actions are “morally acceptable” elicited less utilitarian responses than the original question, although without reaching statistical significance. Finally, all tested versions of moral dilemmas exhibited the same pattern of responses, suggesting that the fundamental elements to the moral decision-making were preserved. Conclusions We found evidence of cross-cultural validity for moral dilemmas. However, the moral focus might affect utilitarian/deontological judgments.
4
Yosanti, Anggia Sekarini. "PENGEMBANGAN MEDIA VIDEO PEMBELAJARAN PEMBUATAN POLA DASAR BADAN WANITA DENGAN SISTEM DRAPING." KELUARGA: Jurnal Ilmiah Pendidikan Kesejahteraan Keluarga 5, no. 2 (November 22, 2019): 410. http://dx.doi.org/10.30738/keluarga.v5i2.5158.
Full textAbstract:
Purose of the study to find out the steps to delevlop videomedia making body pattern woman with draping system. Research development using team theory puslijaknov which includes 5 stages namely product analsyis to be developed, develop the intial product, validation exprest and revisions, small scale trials and revisions, large-scale trials and products end. Reserarch instruments use sheet use of media in learning. Techique descriptive analysis. Research result shows the development of video media learning through: product analysis that is requirements for the type of developments. Step observations to reveal the use of the syllabus analysis and analysis of developing media, the intial product is the process of making a video validation process for experst to find out the feasibility of the media, test small scale implementation of learning use video that was revoked 2 observer with good results, large scale test implementation of learning using video which was observed by observers with the results are very good.
5
Chidlow, Justin A., Colin A. Simpfendorfer, and Garry R. Russ. "Variable growth band deposition leads to age and growth uncertainty in the western wobbegong shark, Orectolobus hutchinsi." Marine and Freshwater Research 58, no. 9 (2007): 856. http://dx.doi.org/10.1071/mf06249.
Full textAbstract:
Age and growth parameters of Orectolobus hutchinsi were estimated using micro-radiographs of sectioned vertebrae from 182 wild caught individuals. Two fluorochrome marker dyes, calcein and oxytetracycline, were used to validate the timing and periodicity of vertebral band formation in nine individuals held in the laboratory for between 423 and 472 days. Growth bands were difficult to interpret and final counts were obtained from only 98 (53.8%) individuals ranging in total length (TL) from 63 to 146 cm. The timing of growth band formation in the vertebrae of captive animals had no predictable temporal pattern, with formation occurring during all seasons of the year, making age validation difficult. Growth band formation was hypothesised to be influenced by non-periodic changes in centrum or somatic growth rather than seasonal growth, as observed in many other elasmobranch species. Growth rates of nine O. hutchinsi held in captivity varied considerably, ranging from 3.5 cm year–1 to 13.8 cm year–1 in total length (mean = 7.03 cm year–1). Although the periodicity of vertebral band formation in captive animals did not support a synchronous annual pattern, captive growth rates matched those predicted when an annual band pattern was assumed for wild caught individuals. Von Bertalanffy growth parameters estimated from vertebral analysis assuming an annual banding pattern and a mean size of birth of 24.1 cm were: L∞ = 149.45 cm and K = 0.117 year–1 for both sexes combined. These results illustrate the fundamental importance of validating the periodicity of growth band formation in elasmobranch age and growth studies as it has considerable implications for the management of fisheries that exploit shark and ray species that may exhibit asynchronous growth band deposition.
6
Ang, Soon, Linn Van Dyne, Christine Koh, K. Yee Ng, Klaus J. Templer, Cheryl Tay, and N. Anand Chandrasekar. "Cultural Intelligence: Its Measurement and Effects on Cultural Judgment and Decision Making, Cultural Adaptation and Task Performance." Management and Organization Review 3, no. 3 (November 2007): 335–71. http://dx.doi.org/10.1111/j.1740-8784.2007.00082.x.
Full textAbstract:
We enhance the theoretical precision of cultural intelligence (CQ: capability to function effectively in culturally diverse settings) by developing and testing a model that posits differential relationships between the four CQ, dimensions (metacognitive, cognitive, motivational and behavioural) and three intercultural effectiveness outcomes (cultural judgment and decision making, cultural adaptation and task performance in culturally diverse settings). Before testing the model, we describe development and cross-validation (N = 1,360) of the multidimensional cultural intelligence scale (CQS) across samples, time and country. We then describe three substantive studies (N = 794) in field and educational development settings across two national contexts, the USA and Singapore. The results demonstrate a consistent pattern of relationships where metacognitive CQ and cognitive CQ predicted cultural judgment and decision making; motivational CQ and behavioural CQ predicted cultural adaptation; and metacognitive CQ and behavioural CQ predicted task performance. We discuss theoretical and practical implications of our model and findings.
7
Wang, Tao, and Hai Chen. "Parcel-Level Land Use Decision-Making of Farmers is Influenced by Neighborhood, Kinship, and Socioeconomic Conditions." Applied Mechanics and Materials 651-653 (September 2014): 1205–15. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1205.
Full textAbstract:
Farmers are the direct users of agricultural land and their decision-making affects the agricultural landscape pattern. The influencing factors for farmer land use decision-making were studied, and a method for elucidating the micro-mechanism of the multi-agent and cellular automata models was proposed. Mengcha village is located in Mizhi County of Shaanxi Province in northwest China. The neighborhoods in the village, as well as the kinship networks and socioeconomic conditions of the farmers, were chosen for the calculation of neighborhood similarity (NBSLY), kinship similarity (KSSLY), and socioeconomic similarity (SESLY). At the parcel level, planted crops figure importantly in farmer decision-making and are expressed by parcel similarity (PCSLY). On the basis of the similarity values and two-dimensional tables of NBSLY-PCSLY, KSSLY-PCSLY, and SESLY-PCSLY, (1) NBSLY was weakly correlated with farmer decision-making (PCSLY), which did not diminish with distance between neighboring buildings in the village. (2) For KSSLY, brotherhood accounted for a considerable proportion of decision-making with 68.92% of brotherhoods having similar or pre-similar decision-making. KSSLY imposed considerable influence on farmer decision-making. (3) Farmer decision-making was correlated with SESLY. With increasing SESLY, PCSLY showed an increasing then decreasing tendency. The 2007 results were verified using 2008 data, and the validation yielded identical results for these years. Farmer decision-making is the result of interaction among many factors, and the comprehensive exploration of this issue necessitates support by detailed micro-data.
8
Kicklighter, Taz, Mary Barnum, Paul R. Geisler, and Malissa Martin. "Validation of the Quantitative Diagnostic Thinking Inventory for Athletic Training: A Pilot Study." Athletic Training Education Journal 11, no. 1 (January 1, 2016): 58–67. http://dx.doi.org/10.4085/110158.
Full textAbstract:
Context: The cognitive process of making a clinical decision lies somewhere on a continuum between novices using hypothetico-deductive reasoning and experts relying more on case pattern recognition. Although several methods exist for measuring facets of clinical reasoning in specific situations, none have been experimentally applied, as of yet, to the profession of athletic training. The Diagnostic Thinking Inventory (DTI) has been used with medical doctors and medical students to determine their level of clinical reasoning as it applies to diagnosis making. Objective: To validate the DTI for Athletic Training (DTI-AT) and associated interview questions for use in the field of athletic training. Design: Mixed methodology. Setting: Online inventory and Skype-based interviews. Patients or Other Participants: Convenience sample of 25 senior-level athletic training students. Main Outcome Measure(s): Participants completed an online version of the DTI-AT which rated clinical reasoning tendencies on a 6-point Likert-type scale. Quantitative analysis consisted of determining means and ranges of scores along with reliability of total scores and subset scores. Randomly selected participants were interviewed online in order to provide validity of interview questions that were used to determine personal and professional activities that are either thought to enhance or hinder clinical reasoning. A secondary purpose was to solicit specific feedback that may enhance our understanding of the modified DTI. Results: A strong reliability was found for total DTI (r(41) = 0.846) and an acceptable reliability for flexibility in thinking (r(21) = 0.731) and structure of memory (r(20) = 0.771). Conclusions: The modifications of the DTI-AT demonstrated strong reliability and face validity. The DTI-AT may be an effective tool for determining clinical reasoning of athletic training students.
9
Yan, Aijun, Hairuo Song, and Pu Wang. "Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction." International Journal on Artificial Intelligence Tools 25, no. 02 (April 2016): 1550032. http://dx.doi.org/10.1142/s0218213015500323.
Full textAbstract:
Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.
10
Gaur, Shishir, K. Srinivasa Raju, D. Nagesh Kumar, and Mayank Bajpai. "Multicriterion decision making in groundwater planning." Journal of Hydroinformatics 23, no. 3 (March 4, 2021): 627–38. http://dx.doi.org/10.2166/hydro.2021.122.
Full textAbstract:
Abstract The groundwater planning problems are often multiobjective. Due to conflicting objectives and non-linearity of the variables involved, several feasible solutions may have to be evolved rather than single optimal solution. In this study, the simulation model built on an Analytic Element Method (AEM) and the optimization model built on a Non-dominated Sorting Genetic Algorithm (NSGA-II) were coupled and applied to study a part of the Dore river catchment, France. The maximization of discharge, the minimization of pumping cost and the minimization of piping cost are the three objectives considered. 2105 non-dominated groundwater planning strategies were generated. K-Means cluster analysis was employed to classify the strategies, and clustering was performed for 3 to 25 clusters. A cluster validation technique, namely Davies–Bouldin (DB) index, was employed to find the optimal number of clusters of groundwater strategies which were found to be 20. Multicriterion Decision-Making (MCDM) techniques, namely VIKOR and TOPSIS, were developed to rank the 20 representative strategies. Both these decision-making techniques preferred representative strategy A5 (piping cost, pumping cost and discharge respectively of 880,000 Euro, 679,000 Euro and 1,263.1 m3/s). The sensitivity analysis of parameter v in VIKOR suggested that there were changes in ranking pattern for various values of v. However, the first position remained unchanged.
11
Lin, Shu Yu, and Hsiao Lin Teng. "Predicting BEOL Key Qualities by Mahalanobis-Taguchi System – An Example of Taiwan’s Semiconductor." Applied Mechanics and Materials 548-549 (April 2014): 1201–5. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1201.
Full textAbstract:
Taguchi Gen'ichi introduced Mahalanobis-Taguchi System (MTS) which is in combination with the concepts of quality engineering and Mahalanobis Distance (MD). The MTS is proposed as diagnosis and forecasting method using multivariate measurement scale with its intention to help policy maker as basis for decision making. This study applies MTS approach in a manufacturing process to reduce a set of parameters, at the same time there will be a pattern, which can forecast and identify important parameters, constructed by MTS method. Through this pattern can minimize unimportant inspection in process and save unnecessary time and cost. The primary goal to structure a measuring scale which makes accurate forecasting in multidimensional system. The case study in this paper reviews the planarity of back-end process in 8-inch silicon wafers on the purpose to construct a pattern of reduced set of parameters. In this paper, using thirty-two current variables as reference space and furthermore reducing the variables to seven parameters in order to predict defective items. As a result, it has still good discriminant accuracy. If validation of the reduced-set parameters is reliable with its good discriminant accuracy, it means that the company in this case study can built defective items warning of the pattern parameters in back-end process because this approach of selecting parameters is feasible.
12
Sasikala, Mrs M., Ms D. Deepika, and Mr S. Shiva Shankar. "Pattern Identification and Predictions in Data Analysis." International Journal Of Engineering And Computer Science 7, no. 03 (March 5, 2018): 23686–91. http://dx.doi.org/10.18535/ijecs/v7i3.05.
Full textAbstract:
Data Mining is an analytic process to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. The main target of data mining application is prediction. Predictive data mining is important and it has the most direct business applications in world. The paper briefly explains the process of data mining which consists of three stages: (1) the Initial exploration, (2) Pattern identification with validation, and (3) Deployment (application of the model to new data in order to generate predictions). Data Mining is being done for Patterns and Relationships recognitions in Data analysis, with an emphasis on large Observational data bases. From a statistical perspective Data Mining is viewed as computer automated exploratory data analytical system for large sets of data and it has huge Research challenges in India and abroad as well. Machine learning methods form the core of Data Mining and Decision tree learning. Data mining work is integrated within an existing user environment, including the works that already make use of data warehousing and Online Analytical Processing (OLAP). The paper describes how data mining tools predict future trends and behavior which allows in making proactive knowledge-driven decisions.
13
Zhang, Junyu, Dafang Fu, Christian Urich, and Rajendra Singh. "Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning." Sustainability 10, no. 12 (December 5, 2018): 4600. http://dx.doi.org/10.3390/su10124600.
Full textAbstract:
In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman’s Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making.
14
Nickmilder, Charles, Anthony Tedde, Isabelle Dufrasne, Françoise Lessire, Bernard Tychon, Yannick Curnel, Jérome Bindelle, and Hélène Soyeurt. "Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations." Remote Sensing 13, no. 3 (January 25, 2021): 408. http://dx.doi.org/10.3390/rs13030408.
Full textAbstract:
Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.
15
Yin, Xin, Ke Shu Hu, Shen Xin Lu, Bei Tang, and Zhenggang Ren. "A novel pretreatment model identifying high-risk of refractoriness after transcatheter arterial chemoembolization in unresectable hepatocellular carcinoma." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16641-e16641. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16641.
Full textAbstract:
e16641 Background: Refractoriness to transcatheter arterial chemoembolization is common during the therapeutic process of hepatocellular carcinoma, which is an intractable issue and may compromise the prognosis. We aim to establish a pre-treatment model to identify patients with high risks of refractoriness. Methods: From 2010 to 2016, 824 patients who had initially underwent at least two sessions of transcatheter arterial chemoembolization in Zhongshan Hospital, Fudan University were retrospectively enrolled. These patients were randomly allocated into a training cohort and a validation cohort. The pre-treatment scoring model was established based on the clinical and radiological variables using logistic regression and nomogram. The discrimination and calibration of the model were also evaluated. Results: Logistic regression identified vascularization pattern, ALBI grade, serum alpha-fetoprotein level, serum γ- glutamyl transpeptidase level and major tumor size as the key parameters related to refractoriness. The p-TACE model was established using these variables (risk score range: 0-19.5). Patients were divided into six risk subgroups based on their scores ( < 4, ≥4, ≥7, ≥10, ≥13, ≥16). The discriminative ability, as determined by the area under receiver operating characteristic curve was 0.784 (95% confidence interval: 0.741-0.827) in the training cohort and 0.743 (95% confidence interval: 0.696-0.789) in the validation cohort. Moreover, satisfactory calibration was confirmed by Hosmer-Lemeshow test with P values of 0.767 and 0.913 in the training cohort and validation cohort. Conclusions: This study presents a pre-treatment model to identify patients with high risks of refractoriness after transcatheter arterial chemoembolization and shed light on clinical decision making.
16
Balusa, Bhanu Chander, and Amit Kumar Gorai. "Development of Fuzzy Pattern Recognition Model for Underground Metal Mining Method Selection." International Journal of Ambient Computing and Intelligence 12, no. 4 (October 2021): 64–78. http://dx.doi.org/10.4018/ijaci.2021100104.
Full textAbstract:
Selection of underground metal mining method is a crucial task for the mining industry to excavate the ore deposit with proper safety and economy. The objective of the proposed study is to demonstrate the application of a fuzzy pattern recognition model for the decision-making of the most favourable underground metal mining method for a typical ore deposit. The model considers eight factors (shape, depth, dip, rock mass rating [RMR] of ore zone, RMR of footwall, RMR of hanging wall, thickness of the ore body, grade distribution), which influence the mining method, as input variables. The weights of these factors were determined using the analytic hierarchy process (AHP). The study used the pair-wise comparison method to determine the relative membership degrees of qualitative and quantitative criteria as well as weights of the criteria set. The model validation was done with the deposit characteristics of Uranium Corporation of India Limited (UCIL), Tummalapalle mine selected. The weighted distances for easiest to adopt are found to be 0.1436, 0.0230, 0.0497, 0.2085, 0.0952, 0.1228, and 0.1274, respectively, for block caving, sublevel stoping, sublevel caving, room and pillar, shrinkage stoping, cut and fill stoping, and squares set stoping. The results indicate that the room and pillar mining method is having the maximum weighted distance value for the given ore deposit characteristics and thus assigned the first rank. It was observed that the mining method selected using fuzzy pattern recognition model and the actual mining method adopted to extract the ore deposit are the same.
17
Yuni Artini, Putu Candra, Ida Bagus Nyoman Sudria, and Ngadiran Kartowasono. "PENGEMBANGAN PERANGKAT PEMBELAJARAN PADA POKOK BAHASAN LARUTAN PENYANGGA DENGAN POLA DEDUKTIF." Jurnal Pendidikan Kimia Indonesia 3, no. 2 (October 11, 2019): 77. http://dx.doi.org/10.23887/jpk.v3i2.21131.
Full textAbstract:
AbstrakPenelitian dan pengembangan pendidikan (R&D) ini bertujuan (1) mengembangkan dan mendeskripsikan karakteristik perangkat pembelajaran pola deduktif pada pokok bahasan larutan penyangga, (2) mendeskripsikan validitas perangkat pembelajaran yang dikembangkan dari hasil penilaian dan masukan ahli dan praktisi. R&D mengikuti prosedur Borg dan Gall yang dibatasi sampai tahap validasi produk dan uji keterbacaan. Data hasil penelitian ini meliputi hasil analisis kebutuhan, rancangan prototipe perangkat pembelajaran, pembuatan perangkat pembelajaran, validasi produk, dan deskripsi karakteristik perangkat pembelajaran larutan penyangga dengan pola deduktif. Hasil-hasil penelitian dianalisis secara kualitatif. Hasil analisis kebutuhan menunjukkan diperlukannya perangkat pembelajaran dengan pendekatan saintifik pola deduktif. Produk yang dihasilkan berupa prototipe perangkat pembelajaran larutan penyangga dengan pola deduktif meliputi RPP, LKS, teks materi pelajaran, dan instrumen penilaian. Karakteristik RPP, LKS, dan teks materi pelajaran yang dikembangkan adalah menyajikan isi dengan urutan konsep dan tahapan penalaran yang sama (deduktif). Hasil validasi menunjukkan sebagian besar aspek perangkat pembelajaran mendapat rata-rata penilaian dengan kategori baik dari validator. Hasil uji keterbacaan pada siswa SMA juga menunjukkan tingkat keterbacaan LKS, teks materi, dan tes hasil belajar dalam kategori baik. Dengan demikian, perangkat pembelajaran larutan penyangga dengan pola deduktif yang telah dikembangkan memiliki validitas memadai (baik).Kata-kata kunci: perangkat pembelajaran, pendekatan saintifik, pola deduktif, larutan penyangga AbstractThis education research and development (R&D) was aimed to (1) develop and describe the characteristic of deductive learning tools on the topic of buffer solution, (2) describe the validity of developed learning tools from validators’ assessment results. R&D follow procedures by Borg and Gall that is limited until product validation and readabilty test. Results of the research were need assessment, planning of learning tools prototype, making of learning tools, validation data, and characteristic description of deductive learning tools. The research results were analyzed qualitatively. The need assessment result show that scientific deductive learning tools were needed. The products produced were scientific deductive learning tools which included lesson plan, worksheet, reading text, and assessment instrument. The characteristic of lesson plan, worksheet, and reading text are presenting content with same concept arrangement and reasoning step (deductive). Validation result show that most aspects of developed learning tools got good category judgement from validators. The readability test result also showed that level of students understanding on developed learning tools was in good category. Therefore, deductive learning tools on the topic of buffer solution that had been developed have good validity. Keywords: learning tools, scientific approach, deductive pattern, buffer solution
18
Batista, Pedro Velloso Gomes, Marx Leandro Naves Silva, Fabio Arnaldo Pomar Avalos, Marcelo Silva de Oliveira, Michele Duarte de Menezes, and Nilton Curi. "Hybrid kriging methods for interpolating sparse river bathymetry point data." Ciência e Agrotecnologia 41, no. 4 (July 2017): 402–12. http://dx.doi.org/10.1590/1413-70542017414008617.
Full textAbstract:
ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.
19
Martin, Jhon Hernandez, Oscar Heli Bejarano, Edwin Yamith Martínez, Luis A. Parra Piñeros, Jairo Alberto Romero, Fran Eduard Perez, and Phillipe Meziat Castro. "Análisis biomecánico del pie protésico Bioc-dm2." I+D Tecnológico 15, no. 1 (January 31, 2019): 80–86. http://dx.doi.org/10.33412/idt.v15.1.2102.
Full textAbstract:
Walking is one of the aspects directly compromising human wellbeing, as it has a physical and emotional impact in daily life.For this study, we delve into the challenge of improving some walking conditions in a patient suffering lower limb loss, specifically at transtibialor transfemoral levels. Given that our purpose was the analysis, design and manufacture of a lower-limb prosthetic component, which fills the needsfor functionality, it became necessary to build a foot with all the quality standards associated to each and all movements required to form thecomplex fundamental pattern of walking. Besides, this foot should also easily endure weight, daily use and physical characteristics of the patientobject of this study. When performing physical validation and during human walk, a proper response is observed in terms of mechanics, materialsand dynamics of the component, thus making evident proper construction and assembly. On the other hand, it is feasible that design and verificationof the component provided a competitive element, as compared to existing elements currently in the market. The previous situation generated theneed for verification from the National Institute for Medications and Food (INVIMA), as well as the revision of the use replying device, forcomponent verification, in accordance with ISO 10328.
20
Zulkarnaen, Harits Farras, and Sukmawati Nur Endah. "Quickpropagation Architecture Optimization Based on Input Pattern for Exchange Rate Prediction from Rupiah to US Dollar." Scientific Journal of Informatics 5, no. 2 (November 29, 2018): 171–84. http://dx.doi.org/10.15294/sji.v5i2.15889.
Full textAbstract:
Money exchange between countries was done by using exchange rates. One of the examples was the exchange between Rupiah and US Dollar. Exchange rates prediction to US Dollar was an attempt to assist all related economic actors to avoid losses during the process of decision making. The prediction could be done by using artificial neural network method. Quickpropagation was one of artificial neural network models considered suitable for prediction. Quickpropagation network architecture consisted of input layer, hidden layer, and output layer. The input layer of quickpropagation architecture could be determined by using autoregression (AR) for the input pattern. In this research, the authors aim to optimize the quickpropagation network architecture method using Nguyen-Widrow weight initialization to predict the Rupiah exchange rate to US Dollar. The research data were the exchange rate from the BI website from May 2017 to July 2017 with a total of 57 data. The test was performed by using K-Fold Cross Validation with k = 11 values for data without AR and k = 8 for AR data. The results show that quickpropagation method using AR has better performance than quickpropagation method without AR in terms of MSE training and testing. The best parameters are in alpha 0,6 and hidden neuron 5, with MSE training value 0,03272 and MSE testing 0,02873 for selling rate and at alpha 0,9 and hidden neuron 5, with MSE training value 0,03297 and MSE testing 0,02828 for buying rate with maximal epoch 100.000 and target error 0,05.
21
Nguyen, Andy N. D., Jitakshi De, Jacqueline Nguyen, Anthony Padula, and Zhenhong Qu. "A Teaching Database for Diagnosis of Hematologic Neoplasms Using Immunophenotyping by Flow Cytometry." Archives of Pathology & Laboratory Medicine 132, no. 5 (May 1, 2008): 829–37. http://dx.doi.org/10.5858/2008-132-829-atdfdo.
Full textAbstract:
Abstract Context.—In the diagnosis of lymphomas and leukemias, flow cytometry has been considered an essential addition to morphology and immunohistochemistry. The interpretation of immunophenotyping results by flow cytometry involves pattern recognition of different hematologic neoplasms that may have similar immunologic marker profiles. An important factor that creates difficulty in the interpretation process is the lack of consistency in marker expression for a particular neoplasm. For this reason, a definitive diagnostic pattern is usually not available for each specific neoplasm. Consequently, there is a need for decision support tools to assist pathology trainees in learning flow cytometric diagnosis of leukemia and lymphoma. Objective.—Development of a Web-enabled relational database integrated with decision-making tools for teaching flow cytometric diagnosis of hematologic neoplasms. Design.—This database has a knowledge base containing patterns of 44 markers for 37 hematologic neoplasms. We have obtained immunophenotyping data published in the scientific literature and incorporated them into a mathematical algorithm that is integrated to the database for differential diagnostic purposes. The algorithm takes into account the incidence of positive and negative expression of each marker for each disorder. Results.—Validation of this algorithm was performed using 92 clinical cases accumulated from 2 different medical centers. The database also incorporates the latest World Health Organization classification for hematologic neoplasms. Conclusions.—The algorithm developed in this database shows significant improvement in diagnostic accuracy over our previous database prototype. This Web-based database is proposed to be a useful public resource for teaching pathology trainees flow cytometric diagnosis.
22
Junqueira Gouvêa Silva, Maria Alice, Tadayuki Yanagi Junior, Raquel Silva de Moura, Patrícia Ferreira Ponciano Ferraz, Bruna Pontara Vilas Boas Ribeiro, and Marcelo Bahuti. "USE OF THE FUZZY CLUSTERING ALGORITHM FOR PATTERN RECOGNITION IN FEED CONSUMPTION DATA OF PURE NEW ZEALAND WHITE RABBITS EXPOSED TO VARIED THERMAL CHALLENGES." Theoretical and Applied Engineering 4, no. 2 (April 2, 2020): 9–14. http://dx.doi.org/10.31422/taae.v4i2.19.
Full textAbstract:
The performance of New Zealand White rabbits (NZW) is directly associated with to ambiance-related factors because they present high sensitivity to high-temperature conditions. The objective of the present work was to use the Fuzzy C-Means (FCM) clustering algorithm for pattern recognition in daily feed consumption (CDR) of NZW rabbits exposed to different thermal challenges. The experiment was carried out in four air-conditioned wind tunnels installed in a laboratory. Twenty-four pure rabbits of the NZW breed aged 30 to 37 days were used. The experiment was carried out in two stages with a period of seven days each, and, at each stage, four dry bulb temperatures (20°C, 24ºC, 28ºC and 32ºC) were tested from the 30th day of the rabbits’ life. Data on CDR (kilo, kg day-1) were obtained by weighing the quantities supplied and the leftovers obtained daily from each rabbit in each treatment. Afterward, the Fuzzy C-Means algorithm (FCM) was used to classify the results. Also, to validate the analysis, the validation indexes were applied to indicate in which quantities of clusters the best partition results were obtained for this database. Thus, FCM cluster analysis was set up as a methodology capable of providing information on the thermal comfort of NZB rabbits in a precise and non-invasive way, which could assist the producer in decision-making.
23
Kostić, Srđan, Milan Stojković, Stevan Prohaska, and Nebojša Vasović. "Modeling of river flow rate as a function of rainfall and temperature using response surface methodology based on historical time series." Journal of Hydroinformatics 18, no. 4 (January 4, 2016): 651–65. http://dx.doi.org/10.2166/hydro.2016.153.
Full textAbstract:
In the present paper we propose a new model of monthly river flow rate as a simple nonlinear function of air temperature and rainfall. Response surface methodology is used to analyze the observed monthly flow rates from 1950 to 1990 for Great Morava River, as the largest domestic river in Serbia. Obtained results indicate significant linear and quadratic effect of both individual factors, while two-factor interactions show significantly smaller influence, indicating occurrence of maximum flow rate for low temperature and high rainfall regime. Statistical reliability of the proposed model is verified by internal and external validation, the latter of which included comparison of predicted and observed values from 1991 to 2012. It is shown that predicted flow rates exhibit a similar statistical pattern as observed ones, with a satisfying value of Nash–Sutcliffe coefficient (NSE = 0.73), although the derived model cannot capture well the highest flow rates. Obtained results further indicate the sequence of residuals represents random time series, which is confirmed by appropriate test statistics and surrogate data testing. The advantage of using the derived model for hydrological simulations in river basins instead of existing ones lies in its explicit mathematical form, making it suitable for quick and reliable estimation and prediction of monthly flow rates.
24
Sriporn, Krit, Cheng-Fa Tsai, Chia-En Tsai, and Paohsi Wang. "Analyzing Malaria Disease Using Effective Deep Learning Approach." Diagnostics 10, no. 10 (September 24, 2020): 744. http://dx.doi.org/10.3390/diagnostics10100744.
Full textAbstract:
Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.
25
Son, Hyojoo, and Changwan Kim. "A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity." Sustainability 12, no. 8 (April 13, 2020): 3103. http://dx.doi.org/10.3390/su12083103.
Full textAbstract:
Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.
26
Zhou, Yue, Jie Xue, Songchao Chen, Yin Zhou, Zongzheng Liang, Nan Wang, and Zhou Shi. "Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging." Remote Sensing 12, no. 1 (December 25, 2019): 85. http://dx.doi.org/10.3390/rs12010085.
Full textAbstract:
Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land.
27
Backes, Yara, Matthijs P. Schwartz, Frank ter Borg, Frank H. J. Wolfhagen, John N. Groen, Wouter H. de Vos tot Nederveen Cappel, Jeroen van Bergeijk, et al. "Multicentre prospective evaluation of real-time optical diagnosis of T1 colorectal cancer in large non-pedunculated colorectal polyps using narrow band imaging (the OPTICAL study)." Gut 68, no. 2 (January 3, 2018): 271–79. http://dx.doi.org/10.1136/gutjnl-2017-314723.
Full textAbstract:
ObjectiveThis study evaluated the preresection accuracy of optical diagnosis of T1 colorectal cancer (CRC) in large non-pedunculated colorectal polyps (LNPCPs).DesignIn this multicentre prospective study, endoscopists predicted the histology during colonoscopy in consecutive patients with LNPCPs using a standardised procedure for optical assessment. The presence of morphological features assessed with white light, and vascular and surface pattern with narrow-band imaging (NBI) were recorded, together with the optical diagnosis, the confidence level of prediction and the recommended treatment. A risk score chart was developed and validated using a multivariable mixed effects binary logistic least absolute shrinkage and selection (LASSO) model.ResultsAmong 343 LNPCPs, 47 cancers were found (36 T1 CRCs and 11 ≥T2 CRCs), of which 11 T1 CRCs were superficial invasive T1 CRCs (23.4% of all malignant polyps). Sensitivity and specificity for optical diagnosis of T1 CRC were 78.7% (95% CI 64.3 to 89.3) and 94.2% (95% CI 90.9 to 96.6), and 63.3% (95% CI 43.9 to 80.1) and 99.0% (95% CI 97.1 to 100.0) for optical diagnosis of endoscopically unresectable lesions (ie, ≥T1 CRC with deep invasion), respectively. A LASSO-derived model using white light and NBI features discriminated T1 CRCs from non-invasive polyps with a cross-validation area under the curve (AUC) of 0.85 (95% CI 0.80 to 0.90). This model was validated in a temporal validation set of 100 LNPCPs (AUC of 0.81; 95% CI 0.66 to 0.96).ConclusionOur study provides insights in the preresection accuracy of optical diagnosis of T1 CRC. Sensitivity is still limited, so further studies will show how the risk score chart could be improved and finally used for clinical decision making with regard to the type of endoresection to be used and whether to proceed to surgery instead of endoscopy.Trial registration numberNTR5561.
28
Uka, Arban, Albana Ndreu Halili, Xhoena Polisi, Ali O. Topal, Gent Imeraj, and Nihal E. Vrana. "Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy." Cells Tissues Organs 210, no. 2 (2021): 77–104. http://dx.doi.org/10.1159/000512969.
Full textAbstract:
Medical imaging is a growing field that has stemmed from the need to conduct noninvasive diagnosis, monitoring, and analysis of biological systems. With the developments and advances in the medical field and the new techniques that are used in the intervention of diseases, very soon the prevalence of implanted biomedical devices will be even more significant. The implanted materials in a biological system are used in diverse fields, which require lengthy evaluation and validation processes. However, currently the evaluation of the toxicity of biomaterials has not been fully automated yet. Moreover, image analysis is an integral part of biomaterial research, but it is not within the core capacities of a significant portion of biomaterial scientists, which results in the use of predominantly ready-made tools. The detailed image analysis can be conducted once all the relevant parameters including the inherent characteristics of image acquisition techniques are considered. Herein, we cover the currently used image analysis-based techniques for assessment of biomaterial/cell interaction with a specific focus on unstained brightfield microscopy acquired mostly in but not limited to microfluidic systems, which serve as multiparametric sensing platforms for noninvasive experimental measurements. We present the major imaging acquisition techniques that enable point-of-care testing when incorporated with microfluidic cells, discuss the constraints enforced by the geometry of the system and the material that is analyzed, and the challenges that rise in the image analysis when unstained cell imaging is employed. Emerging techniques such as utilization of machine learning and cell-specific pattern recognition algorithms and potential future directions are discussed. Automation and optimization of biomaterial assessment can facilitate the discovery of novel biomaterials together with making the validation of biomedical innovations cheaper and faster.
29
Ikusemiju, T. M., and O. B. Osinubi. "Improving the cultural and historical tourism resources for sustainable development in Ondo State – A survey of Idanre Hills and resort centre, Idanre." Nigerian Journal of Environmental Sciences and Technology 4, no. 1 (March 2020): 197–203. http://dx.doi.org/10.36263/nijest.2020.01.0182.
Full textAbstract:
It has been observed that necessary attention has not been given to Idanre hills and resort centre as tourists’ attraction and its sustainability is being threatened. Thus, this paper revealed that many studies have been carried out on Idanre hills and resort centre but had only focused on its beautifications, geo – tourism potentials, landscape management, maintenance and tourists’ patronage pattern. Hence, the objectives of this study revealed possible strategies of making the cultural and historical tourism attraction of Idanre hills and resort centre sustainable for both this generation and the future generation and how these strategies can specifically be of economic and social benefits to the residents of Idanre community for sustainable livelihood. The study adopted oral interview, personal observation and administration of questionnaire for data collection of which 200 questionnaires were administered and 188 were returned representing 94.00%. Meanwhile, the questionnaires were presented and analyzed with the use of simple percentage method while percentage point of t – test distribution (One – tailed) analysis was adopted in validation of the hypothesis. The result indicated that t (calculated) was 0.13 and was greater than t (tabulated) of -2.92; thus, the null hypothesis was rejected, while the alternative hypothesis was accepted, which states that there are promising economic and social benefits of specifically developing and sustaining the cultural and historical tourism resources of Idanre hills and resort centre. The study concluded that if Ondo State Government and relevant tourism stakeholders should put the necessary strategies in making the cultural and historical tourism resources of Idanre hills and resort a haven in place; its development will have specific economic and social benefits such as economic stability and social integration etc on the residents of Idanre community and Ondo State at large by stimulating its local economy both directly and indirectly through multiplier effects.
30
Kizhisseri, Mohamed I., Mohamed M. Mohamed, Walid El-Shorbagy, Rezaul Chowdhury, and Adrian McDonald. "Development of a dynamic water budget model for Abu Dhabi Emirate, UAE." PLOS ONE 16, no. 1 (January 27, 2021): e0245140. http://dx.doi.org/10.1371/journal.pone.0245140.
Full textAbstract:
In this study, a dynamic water budget model is developed for the Emirate of Abu Dhabi (EAD) in the United Arab Emirates (UAE). The model, called Abu Dhabi Water Budget Model (ADWBM), accounts for a number of drivers such as population growth, economic growth, consumption pattern and climatic factors. Model formulation, calibration, validation as well as simulation results for two future situations are presented in this paper. The two water simulations discuss demand-side options in response to different future water conditions until 2050. The first simulation, namely, baseline (BL) simulation examined water balance in the emirate assuming no change in both water production and consumption. BL simulation results highlight the expected shortages in water resources assuming no modification in the supply side. The second simulation, a more conservative and practical simulation considering water conservation options and sustainable improvements to the supply side was developed to achieve a balanced water budget by reducing the baseline consumption rates. The results show that a significant demand reduction is needed in all demand sectors, reaching 60% in the potable sectors and above 70% in non-potable sectors. Overall, results show that the ADWBM can be used as a numerical tool to produce accurate figures of water supply and demand for the sake of planning and decision making in the water sector of the EAD until 2050.
31
Deering, Sean, and Carl Stepnowsky. "101 Measurement of Tapping During the Interstimulus Interval as a Validation Metric for the 3-Minute Psychomotor Vigilance Test." Sleep 44, Supplement_2 (May 1, 2021): A41—A42. http://dx.doi.org/10.1093/sleep/zsab072.100.
Full textAbstract:
Abstract Introduction The Psychomotor Vigilance Test is a well-validated measure of sustained attention used to assess daytime alertness in sleep research studies.1 It is commonly used in a variety of research settings due to its high sensitivity to sleep loss and absence of learning effects,2 making it an ideal tool to assess objective alertness. As some types of sleep research transition out of controlled laboratory environments, tools like the PVT require modification to maximize their reliability. The validation of the 3-minute version (PVT-B) against the 10-minute PVT is an example of this modification.3 However, considerable work is needed to improve trust in the utility of the PVT-B in and outside of traditional laboratory settings. Methods We carefully analyzed data from a mobile-based version of the PVT-B, noting responses that occurred during the interstimulus interval which were termed “wrong taps.” Wrong taps indicated that participants were not performing the task as instructed. In some cases, wrong taps occurred across multiple trials of the same PVT block, indicative of participants repeatedly tapping the screen throughout the task to minimize response times. A comprehensive examination of wrong taps was carried out in order to identify instances where this pattern emerged. Results A total of 1,338,538 PVT-B trials from 7,028 participants were examined to determine the number of wrong taps present across all trials. While 91.7% of PVT-B trials were free of wrong taps, 8.3% of PVT-B trials contained 1 or more wrong taps and 5.2% contained 2 or more wrong taps. It appears that a maximum of one wrong tap per trial is acceptable and trials containing 2 or more should be excluded to maximize PVT data quality. Conclusion Utilizing a metric like wrong taps can help identify individuals taking the PVT-B who are tapping the screen multiple times prior to stimulus display. Closely examining this metric can help to ensure the validity of PVT-B administrations. Two possible uses of the metric could be to provide feedback during training trials and to remove trials where this strategy was employed. Support (if any) This analysis was supported by the VA San Diego Healthcare System Research Service.
32
Chattaraman, Veena, Wi-Suk Kwon, Wanda Eugene, and Juan E. Gilbert. "Developing and Validating a Naturalistic Decision Model for Intelligent Language-Based Decision Aids." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 176–77. http://dx.doi.org/10.1177/1541931213601528.
Full textAbstract:
People make mundane and critical consumption decisions every day using choice processes that are inherently constructive in nature, where preferences emerge ‘on the spot’ or ‘on the go’ using multiple strategies based on the task at hand (Bettman, Luce, & Payne, 1998; Sproule & Archer, 2000). This implies that applying a single, invariant algorithm will not solve decision problems that humans face (Tversky, Sattath, & Slovic, 1988). Instead, consumers need adaptive, multi-strategy decision aids since they shift between multiple strategies in a single decision as they acquire increasing information during the decision-making process (Bettman et al., 1998). This paper puts forth a cognitive computing approach to develop and validate a naturalistic decision model for designing language-based, mobile decision-aids (MoDA©) based on adaptive and intelligent information retrieval and multi-decision strategy use. The approach integrates established psychological theories, Elaboration Likelihood Model (ELM) and Construal Level Theory (CLT), to develop the scientific base for predicting decision-making under contingencies. ELM delineates whether human information processing is effortful or heuristic based on a person’s ability and motivation to engage in an object-relevant elaboration (Petty & Cacioppo, 1981). CLT determines whether the cognitive construal of the decision object is abstract or concrete based on psychological distance (Liberman, Trope, & Wakslak, 2007). Integrating the derivatives of these theories, the Human-Elaboration-Object-Construal (H-E-O-C) Contingency Decision Model’s central thesis is that the decision-making strategy employed by a decision-maker can be predicted by using natural language cues to infer the extent of human elaboration (low-high) on the decision and the type of knowledge (abstract-concrete) possessed on the decision object. Specifically, an extensive (vs. limited) decision strategy is likely to be employed when human elaboration revealed through natural language cues is high (vs. low). Further, an attribute-based (vs. alternative-based) strategy may be employed when the cognitive representation of the decision object is abstract (vs. concrete). Based on this theorizing, the H-E-O-C Contingency Decision Model can predict the use of four common decision strategies that systematically differ based on the amount (extensive vs. limited) and pattern (attribute- vs. alternative-based) of processing: Lexicographic or LEX (limited, attribute-based processing), Satisficing or SAT (limited, alternative-based processing), Elimination-by-Aspects or EBA (extensive, attribute-based processing), and Weighted Adding or WADD (extensive, alternative-based processing) (Bettman et al., 1998). To validate the H-E-O-C Contingency Decision Model, we conducted observational studies that simulated in-store purchase decision-making with real consumers. A total of 48 shopping sessions (n = 48) were held in a simulation home improvement retail store, and decision-making dialog between consumers and a customer service agent (trained research assistant) was recorded using wearable voice recorders. To ensure that there were fairly equal numbers of consumers who were either motivated or not to elaborate on their decisions, we created two shopping conditions – low risk (replacement AC filter purchase) and high risk (AC filter purchase to address allergy and asthma). The recorded decision dialogs were first transcribed verbatim, resulting 48 units of analysis, which were then analyzed using the grounded theory approach through open and axial coding processes (Corbin & Strauss, 1990). The open coding first identified the construal level, which was followed by axial coding to infer the decision strategy (LEX, EBA, SAT, or WADD) employed by the consumer at the initial and final stages of decision-making. This process was conducted by two coders with adequate inter-coder reliability. Two different coders coded the transcripts for the elaboration level (low vs. high) of the consumer based on specific definitions, with adequate inter-coder reliability. The H-E-O-C Contingency Decision Model proposes that high elaboration consumers will employ either WADD or EBA, whereas low elaboration consumers will employ either SAT or LEX. This proposition was supported in over 80% of the decision transcripts, offering an important validation of the framework. The main contribution of the H-E-O-C Contingency Decision Model is that it is derived from universal psychological constructs and predicts decision-making strategies that apply to many types of products and services related to healthcare, education, and finance that are characterized by attributes and alternatives. This ensures its broad applicability across a wide variety of disciplines and use cases.
33
Ahmad, Hussaan, and Nasir Hayat. "Markov chain based modelling and prediction of natural gas allocation structure in Pakistan." International Journal of Energy Sector Management 14, no. 5 (March 30, 2020): 911–33. http://dx.doi.org/10.1108/ijesm-12-2019-0002.
Full textAbstract:
Purpose The purpose of this paper is to analyze the historical gas allocation pattern for seeking appropriate arrangement and utilization of potentially insufficient natural gas supply available in Pakistan up to 2030. Design/methodology/approach This study presents Markov chain-based modeling of historical gas allocation data followed by its validation through error evaluation. Structural prediction using classical Chapman–Kolmogorov method and varying-order polynomial regression in the historical transition matrices are presented. Findings Markov chain model reproduces the terminal state vector with 99.8 per cent accuracy, thus demonstrating its validity for capturing the history. Lower order polynomial regression results in better structural prediction compared with higher order ones in terms of closeness with Markov approach-based prediction. Research limitations/implications The data belongs to a certain geographic region with specific gas demand and supply profile. The proposition may be tested further by researchers to check the validity for other comparable structural predictions/analyses. Practical implications This study can facilitate policy-making in the field of natural gas allocation and management in Pakistan specifically and other comparable countries generally. Originality/value Two major literature gaps filled through this study are: first, Markov chain model becomes stationary when projected using Chapman–Kolmogorov relation in terms of a fixed, average transition matrix resulting in an equilibrium state after a finite number of future steps. Second, most of the previous studies analyze various gas consumption sectors individually, thus lacking integrated gas allocation policy.
34
Michalkiewicz, Mieczyslaw, Teresa Michalkiewicz, Aron M. Geurts, Richard J. Roman, Glenn R. Slocum, Oded Singer, Dorothee Weihrauch, et al. "Efficient transgenic rat production by a lentiviral vector." American Journal of Physiology-Heart and Circulatory Physiology 293, no. 1 (July 2007): H881—H894. http://dx.doi.org/10.1152/ajpheart.00060.2007.
Full textAbstract:
A lentiviral construct for an enhanced green fluorescent protein (eGFP) driven by a chicken β-actin promoter, cytomegalovirus enhancer, and intronic sequences from rabbit β-globin (CAG) was used to produce transgenic lines of rats for evaluation of the usefulness of this approach in gene function studies. Fertilized eggs were collected from inbred Dahl S and outbred Sprague-Dawley rats, and ∼100 pl of concentrated virus were microinjected into the perivitrelline space of one-cell embryos. Of 121 embryos injected, 60 pups (49.6%) were born. Transgenic rates averaged 22% in Dahl S and 14% in Sprague-Dawley rats. Copy number ranged from one to four in the founders, and the inheritance of the transgene in a subsequent F1population was 48.2%. The small number of insertion sites enabled us to derive inbred transgenic lines with a single copy of the transgene within one generation. Sequencing of each transgene insertion site revealed that they inserted as single copies with a preference for the introns of genes. The CAG promoter drove high levels of eGFP expression in brain, kidney, heart, and vasculature, making it very suitable for exploring the cardiovascular function of newly discovered genes. The pattern of eGFP expression was similar across five different F1transgenic lines, indicating that the expression of the transgene was independent of its chromosomal position. Thus lentiviral transgenesis provides a powerful tool for the production of transgenic inbred rats and will enhance the usefulness of this species in gene discovery and target validation studies.
35
Weng, Xiaohui, Xiangyu Luan, Cheng Kong, Zhiyong Chang, Yinwu Li, Shujun Zhang, Salah Al-Majeed, and Yingkui Xiao. "A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies." Journal of Sensors 2020 (September 21, 2020): 1–14. http://dx.doi.org/10.1155/2020/8838535.
Full textAbstract:
The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).
36
Rukmana, Asep Nana, and Bambang Siswoyo. "Penerapan Linier Discriminant Analysis pada Klasifiksi Distress Binsin Perbankan." ETHOS (Jurnal Penelitian dan Pengabdian) 7, no. 2 (June 28, 2019): 224–32. http://dx.doi.org/10.29313/ethos.v7i2.4554.
Full textAbstract:
Abstract. Linear discrimant machine learning analysis is part of artificial intelligence that can learn from past data, recognize patterns to get optimal solutions. The prediction of the bankruptcy of a Sharia public bank company in Indonesia is very important. Modeling Machine Learning with five input-output models can be implemented between financial ratio variables against bankruptcy. Overall, a linear discrimant analysis algorithm is able to train data to build patterns of input-output relationships and modeling behavior well. Every company certainly wants an appropriate and efficient decision making. Linear discrimant analysis builds predictive models using financial ratio variables as predictors. In this study the model can recognize well the pattern of financial ratios with the results of model validation in the form of means square error 8% and coefficient terminated 98%.Abstrak. Linier discrimant analisys machine learning merupakan bagian dari kecerdasan buatan yang dapat belajar dari data masa lalu, mengenali pola untuk mendapatkan solusi yang optimal. Prediksi kebangkrutan suatu perusahaan bank umum Syariah di Indonesia sangat penting. Modeling Machine Learning dengan lima model input-output dapat diimplementasikan antara variabel rasio keuangan terhadap kebangkrutan. Secara keseluruhan, algoritma linier discrimant analysis mampu melatih data untuk membangun pengenalan pola hubungan input-output dan perilaku pemodelan dengan baik. Setiap perusahaan tentu menginginkan sebuah pengambilan keputusan yang tepat dan efisien. Linier discrimant analisis membangun model prediksi menggunakan variabel rasio keuangan sebagai prediktor. Pada penelitian ini model dapat mengenal dengan baik pengenalan pola rasio keuangan dengan hasil validasi model berupa means square error 8% dan koefesien diterminasi 98%.
37
Stilwell, Abby R., Donald C. Rundquist, David B. Marx, and Gary L. Hein. "Differential Spatial Gradients of Wheat Streak Mosaic Virus into Winter Wheat from a Central Mite-Virus Source." Plant Disease 103, no. 2 (February 2019): 338–44. http://dx.doi.org/10.1094/pdis-01-18-0025-re.
Full textAbstract:
The wheat curl mite (WCM), Aceria tosichella Keifer, transmits three potentially devastating viruses to winter wheat. An increased understanding of mite movement and subsequent virus spread through the landscape is necessary to estimate the risk of epidemics by the virus in winter wheat. Owing to the small size of WCMs, their dispersal via wind is hard to monitor; however, the viruses they transmit produce symptoms that can be detected with remote sensing. The objective of this study was to characterize the spatial dispersal of the virus from a central mite-virus source. Virus infection gradients were measured spatially by using aerial remote sensing, ground measurements, geostatistics, and a geographic information system between 2006 and 2009. The red edge position vegetation index as measured via aerial imagery was significantly correlated with in-field biophysical measurements. The occurrence of virus symptoms extended differentially in all directions from mite-virus source plots, and predictions from cokriging revealed an oval pattern surrounding the source but displaced to the southeast. The variable dispersal in different directions appeared to be influenced by the mite source density and wind direction and speed, but temperature also seemed likely to have affected mite spread. The spatial spread revealed in this study may be used to estimate the potential sphere of influence of mite-infested volunteer wheat in production fields. These risk parameter estimates require further validation, but they may potentially aid growers in making better virus management decisions regarding differential virus spread potential away from a central source.
38
Petrovic-Subic, Narcisa, Miroslav Kojic, Slobodan Jankovic, and Srdjan Stefanovic. "Performance of a calculator for diagnosing the cause of liver damage." Srpski arhiv za celokupno lekarstvo 147, no. 1-2 (2019): 27–33. http://dx.doi.org/10.2298/sarh180504064p.
Full textAbstract:
Introduction/Objective. Making a calculator that would recognize patterns of abnormal liver function tests and link them to the most probable etiology could help clinicians in their initial orientation towards a definitive diagnosis in patients with liver damage. The aim of our study was to design, construct, and validate a calculator that based on a pattern of abnormalities in liver function tests of a patient with liver damage would propose the most probable etiology. Methods. Patterns of abnormal liver function tests for certain etiology of liver damage were extracted from distributions of actual values taken from reports in medical literature about patients whose etiology of liver damage was proven by reliable diagnostic tests. After setting up the calculator with the patterns extracted, its diagnostic value was checked under real-life conditions, on a sample of patients with liver damage whose etiology was established by the gold standard of diagnostics (biopsy or else). The calculator validation study was carried out at the Military Medical Academy in Belgrade during a two-year period (2015?2016). Results. For all tested diagnoses, the calculator demonstrated a highly significant difference between the area under the receiver-operator curves? values and the value of 0.5 (p < 0.001), and high level of sensitivity (more than 90%, except for the model for chronic hepatitis) as well as relatively high specificity (more than 75%) were noted, indicating good ability of the calculator to detect etiology of liver damage. Conclusion. New calculators showed satisfactory sensitivity and specificity for revealing major liver damage etiologies.
39
Ryu, Jihan, Emese Sükei, Agnes Norbury, Shelley H Liu, Juan José Campaña-Montes, Enrique Baca-Garcia, Antonio Artés, and M. Mercedes Perez-Rodriguez. "Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study." JMIR Mental Health 8, no. 9 (September 15, 2021): e30833. http://dx.doi.org/10.2196/30833.
Full textAbstract:
Background Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.
40
Piessevaux, Hubert, Marc Buyse, Michael Schlichting, Eric Van Cutsem, Carsten Bokemeyer, Steffen Heeger, and Sabine Tejpar. "Use of Early Tumor Shrinkage to Predict Long-Term Outcome in Metastatic Colorectal Cancer Treated With Cetuximab." Journal of Clinical Oncology 31, no. 30 (October 20, 2013): 3764–75. http://dx.doi.org/10.1200/jco.2012.42.8532.
Full textAbstract:
PurposeEarly tumor shrinkage (ETS) is associated with long-term outcome in patients with chemorefractory metastatic colorectal cancer (mCRC) receiving cetuximab. This association was investigated in the first-line setting in the randomized CRYSTAL and OPUS mCRC trials, after controlling for KRAS tumor mutation status.MethodsRadiologic assessments at week 8 were used to calculate the relative change in the sum of the longest diameters of the target lesions. Time-dependent receiver operating characteristics provided Cτ-indices (time-dependent c-index). Cox regression models and subpopulation treatment effect pattern plot analysis investigated associations between ETS (radiologic tumor size decrease at week 8) and survival and progression-free survival (PFS).ResultsIn both trials, in patients with KRAS wild-type mCRC, Cτ values for PFS and survival were higher (P < .001) in those receiving chemotherapy plus cetuximab versus chemotherapy alone, indicating a stronger predictive value of ETS for long-term outcome in these patients. In the CRYSTAL and OPUS trials, respectively, the cutoff value of ETS ≥ 20% (v < 20%) identified patients with KRAS wild-type mCRC receiving chemotherapy plus cetuximab with longer PFS (medians 14.1 v 7.3 months, hazard ratio [HR] = 0.32; P < .001, and medians 11.9 v 5.7 months, HR = 0.22; P < .001) and survival (medians 30.0 v 18.6 months, HR = 0.53; P < .001 and medians 26.0 v 15.7 months, HR = 0.43; P = .006).ConclusionETS was significantly associated with long-term outcome in patients with KRAS wild-type mCRC treated first-line with chemotherapy plus cetuximab. Validation in prospective trials is required to assess the value of this on-treatment marker in the clinical decision-making process.
41
Kimura, Konobu, Yoko Tabe, Tomohiko Ai, Akihiko Matsuzaki, Kumiko Nishibe, Miki Ebihara, Kimiko Kaniyu, Ikki Takehara, Kinya Uchihashi, and Akimichi Ohsaka. "A Novel Automated Image Analysis System Using Deep Convolutional Neural Networks to Diagnose MDS." Blood 134, Supplement_1 (November 13, 2019): 4670. http://dx.doi.org/10.1182/blood-2019-129524.
Full textAbstract:
Detection of dysmorphic cells in peripheral blood (PB) smears is essential for diagnosis of hematological malignancies. Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic stem cell disorders that can lead to acute leukemia. Although examinations of bone marrow aspiration and biopsy as well as chromosomal and genetic tests are essential to diagnose these disorders, conventional tests such as complete blood count (CBC) and peripheral blood (PB) smear examinations remain to be initial diagnostic work ups. Detection of dysplastic cells in PB smears and evaluation of CBCs are particularly useful for rapid screening. Thanks to the recent advancement of computational and laboratory technologies, routine manual microscopic examinations have been replaced by automated hematology analyzers in many hematology laboratories. However, detection of dysmorphic cells is still challenging. Therefore, more sophisticated image recognition systems need to be developed. In this study, we developed a novel MDS diagnostic system using PB smears. The system consists of a convolutional neural network (CNN)-based image recognition deep learning system (DLS) and an EGB-based decision-making algorithm (XGBoost). All PB smears were prepared at Juntendo University Hospital. The slides were stained with May Grunwald-Giemsa using a fully automated slide-maker. A total of 703,970 digitalized cell images were collected. First, we trained the CNN-based image-recognition system using 695,030 blood cell images taken from 3,261 PB smears of which 1,165 were obtained from patients with hematological disorders. The hematological disorders included MDS (n=94 cases), myeloproliferative neoplasms (n=127), acute myeloid leukemia (n= 38), acute lymphoblastic leukemia (ALL, n=27), malignant lymphoma (n=324), multiple myeloma (n=82) and AA (n=42). Of all images, these 695,030 images were used to train the CNN-based image-recognition system (Fig 1), and rest of the images (n=8,940) were used for validation. The datasets were classified into 17 cell types and 97 abnormal morphological features by two board-certified laboratory technologists and one senior hematopathologist using the morphological criteria of the Clinical and Laboratory Standards Institute (CLSI) H20-A2 guideline and the 2017 WHO classification of myeloid neoplasms and acute leukemia. After accumulating the image patterns using the training datasets, the performance of the DLS was evaluated using the validation datasets that were prepared for testing the DLS. The internal features learnt by the DLS using t-distributed Stochastic Neighbor Embedding (t-SNE)(Fig 2). In this context, our CNN-based image-recognition system exhibited a sensitivity of >93.5% and a specificity of >96.0% when classifying cells in subsets of the validation datasets. We then created an automated MDS diagnostic system by combining the CNN-based image-recognition system with a form of XGBoost. To establish diagnostic algorithm, the training datasets obtained from 75 MDS and 36 Aplastic Anemia (AA) cases were used for learning of the cell image pattern for each disease. The diagnosis of all datasets was validated by independent hematopathologists using clinical information, laboratory, flow cytometric, and genetic data, and bone marrow aspiration and biopsy findings. The accuracy of the system was tested using validation datasets (26 MDS and 11 AA cases). The system differentiated MDS from AA with the sensitivity and specificity of 96.2% and 100%, respectively (AUC 0.990). In conclusion, this is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated image diagnostic systems for various hematological disorders. Currently, we are collecting more data to improve the accuracy. Disclosures Kimura: Sysmex Corporation: Employment. Takehara:Sysmex Corporation: Employment. Uchihashi:Sysmex Corporation: Employment.
42
Dibi, Wilfried G., Beaulys Fotso, Casimir Y. Brou, Jeremie T. Zoueu, Adolphe Zeze, and Jocelyne Bosson. "Fluorescence and Reflectance Spectroscopy for Early Detection of Different Mycorrhized Plantain Plants." Applied Physics Research 8, no. 3 (April 19, 2016): 17. http://dx.doi.org/10.5539/apr.v8n3p17.
Full textAbstract:
<p class="1Body">Sustainable agriculture with use of Arbuscular Mycorrhizal Fungi (AMF) is an emerging farm management that improves crops nutrient and water use efficiency. Decision making on the effect of AMF is still dependent on agronomic diagnosis which is long, tedious, expensive and destructive. This study demonstrates the applicability of proximal fluorescence and reflectance spectroscopy for evaluating and detecting at early stage distinct types of mycorrhized plantain from two cultivars (<em>Musa paradisiaca</em>).</p><p class="1Body">Visible-near infrared (400-1000 nm) reflectance and fluorescence data were collected from control and three levels mycorrhized plants designed in randomized and complete block under greenhouse conditions. Two spectral measurements at a week interval were performed on plant leaves by using an USB spectrometer mounted with an Arduino-based LED driver clip.</p>A new normalized reflectance water NWI5 index shows with Datt5 alone highly significant differences at P<0.001 respectively for Orishele and fhia21 cultivars. dNIRmin920_980, NDVI3 and GI reflectance index are significant at P<0.01. Seven other reflectance and 3 fluorescence indices ANTH, FRF_R and NBI_R are significant at P<0.05. The two first principal components for each cultivar spectral features explaining 94.1 % of variance were used to build predictive classification models. LogitBoost algorithm indicates accuracy of 90.27% on stratified cross-validation and 87.5% on test split. Our results confirm that fluorescence and reflectance spectroscopy is a valuable tool for early assessment of mycorrhization success rate evaluation and pattern recognition. They also show promise for the development of non-destructive and cost-effective detectors in monitoring crops under biofertilizers with arbuscular mycorrhizae.
43
Li, Jingxin, Hongqi Zhang, and Erqi Xu. "Spatialization of Actual Grain Crop Yield Coupled with Cultivation Systems and Multiple Factors: From Survey Data to Grid." Agronomy 10, no. 5 (May 11, 2020): 675. http://dx.doi.org/10.3390/agronomy10050675.
Full textAbstract:
The spatialization of actual grain crop yield helps to understand the spatial heterogeneity of yield and support for the precise farming and targeted farmland management. However, inadequate consideration and quantification of anthropogenic factors affecting the estimation of actual yield distribution easily cause uncertainties in recent researches. Here, we developed a new grain crop yield spatialization (GCYS) model in order to downscale the yield from county to grid scale. The GCYS model is composed of four modules: (a) cultivated land Net Primary Productivity (NPP) calculation module, (b) comprehensive agricultural system construction module, (c) key factors establishment module, and (d) integration and validation module. Its novelty is to realize the actual grain crop yield spatialization from county scale to grid scale by quantifying and spatializing the comprehensive agricultural system when considering the diversity of cultivated structure and field management factors. Taking Guizhou and Guangxi Karst Mountains Region as a study-area, the GCYS model is developed and tested. The determination coefficients of regression analysis between agricultural survey data and spatialization results of paddy rice yield calculated by our model reach 0.72 and 0.76 in 2000 and 2015, respectively (p < 0.01). The results visualize the spatial pattern of actual grain crop yield at the grid scale, which show a gradually decreasing trend from southeast to northwest. With an increase in potential yield and improvement of field management technologies, the actual average yield of grain crops per unit increased form 4745.10 kg/ha of 2000 to 5592.89 kg/ha of 2015. Especially in high-yield zones in southeast area, mechanized cultivation became the dominated factor, rather than chemical fertilizer application. This demonstrates that our model can provide a reference for agricultural resource utilization and policy-making.
44
Banda, Talent, and Muthukrishnavellaisamy Kumarasamy. "Application of Multivariate Statistical Analysis in the Development of a Surrogate Water Quality Index (WQI) for South African Watersheds." Water 12, no. 6 (June 2, 2020): 1584. http://dx.doi.org/10.3390/w12061584.
Full textAbstract:
Water quality indices (WQIs) are customarily associated with heavy data input demand, making them more rigorous and bulky. Such burdensome attributes are too taxing, time-consuming, and command a significant amount of resources to implement, which discourages their application and directly influences water resource monitoring. It is then imperative to focus on developing compatible, simpler, and less-demanding WQI tools, but with equally matching computational ability. Surrogate models are the best fitting, conforming to the prescribed features and scope. Therefore, this study attempts to provide a surrogate WQI as an alternative water quality monitoring tool that requires fewer inputs, minimal effort, and marginal resources to function. Accordingly, multivariate statistical techniques which include principal component analysis (PCA), hierarchical clustering analysis (HCA) and multiple linear regression (MLR) are applied primarily to determine four proxy variables and establish relevant model coefficients. As a result, chlorophyll-a, electrical conductivity, pondus Hydrogenium and turbidity are the final four proxy variables retained. A vital feature of the proposed surrogate index is that the input parameters qualify for inclusion into remote monitoring systems; henceforth, the model can be applied in remote monitoring programs. Reflecting on the model validation results, the proposed surrogate WQI is considered scientifically stable, with a minimum magnitude of divergence from the ideal water quality values. More importantly, the model displayed a predictive pattern identical to the ideal graph, matching on both index scores and classification values. The established surrogate model is an important milestone with the potential of promoting water resource monitoring and assisting in capturing of spatial and temporal changes in South African river catchments. This paper aims at outlining the methods used in developing the surrogate water quality index and document the results achieved.
45
Brehm, Merel, Sicco A. Bus, Jaap Harlaar, and Frans Nollet. "A candidate core set of outcome measures based on the international classification of functioning, disability and health for clinical studies on lower limb orthoses." Prosthetics and Orthotics International 35, no. 3 (September 2011): 269–77. http://dx.doi.org/10.1177/0309364611413496.
Full textAbstract:
Background: Although many core sets of measurement concepts have been published in the literature, this has not been done for the field of lower limb orthoses. Objectives: This paper provides an overview of the measurement concepts that are relevant in lower limb orthotic evaluations, and it proposes a candidate Core Set of outcome measures to be used in clinical studies on ankle-foot orthoses (AFOs) and knee-ankle-foot orthoses (KAFOs). Study Design: Literature review. Methods: The International Classification of Functioning, Disability and Health (ICF) was used as framework to select relevant concepts. Results and conclusion: Measurement concepts covering all ICF levels of functioning were identified as relevant for the Core Set, including functions of the joints and bones (b710–b729), muscle functions (b730–b749), gait pattern functions (b770), walking (b450), moving around in different locations (d460), and daily-life functioning (d5–d9). Further validation of this candidate Core Set through a formal decision-making process is needed to obtain consensus among experts in the field. Based on such a consensus, the next step will be to systematically review the literature and identify those measurement instruments that are best suited to assess the proposed concepts, based on their psychometric properties in a given sample and context. Thereafter, we suggest that this ICF Core Set of measurement instruments should be applied in orthotic studies on AFOs and KAFOs in ambulatory patients with gait problems. Clinical relevance Although many ICF Core Sets have been published, this has not been done for the field of lower limb orthoses. We feel that such a Core Set is urgently needed, to enable comparison of results, and establish evidence on the efficacy of orthotic treatment, which will improve patient care.
46
Bissanum, Rassanee, Sitthichok Chaichulee, Rawikant Kamolphiwong, Raphatphorn Navakanitworakul, and Kanyanatt Kanokwiroon. "Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning." Journal of Personalized Medicine 11, no. 9 (September 1, 2021): 881. http://dx.doi.org/10.3390/jpm11090881.
Full textAbstract:
Triple negative breast cancer (TNBC) lacks well-defined molecular targets and is highly heterogenous, making treatment challenging. Using gene expression analysis, TNBC has been classified into four different subtypes: basal-like immune-activated (BLIA), basal-like immune-suppressed (BLIS), mesenchymal (MES), and luminal androgen receptor (LAR). However, there is currently no standardized method for classifying TNBC subtypes. We attempted to define a gene signature for each subtype, and to develop a classification method based on machine learning (ML) for TNBC subtyping. In these experiments, gene expression microarray data for TNBC patients were downloaded from the Gene Expression Omnibus database. Differentially expressed genes unique to 198 known TNBC cases were identified and selected as a training gene set to train in seven different classification models. We produced a training set consisting of 719 DEGs selected from uniquely expressed genes of all four subtypes. The highest average accuracy of classification of the BLIA, BLIS, MES, and LAR subtypes was achieved by the SVM algorithm (accuracy 95–98.8%; AUC 0.99–1.00). For model validation, we used 334 samples of unknown TNBC subtypes, of which 97 (29.04%), 73 (21.86%), 39 (11.68%) and 59 (17.66%) were predicted to be BLIA, BLIS, MES, and LAR, respectively. However, 66 TNBC samples (19.76%) could not be assigned to any subtype. These samples contained only three upregulated genes (EN1, PROM1, and CCL2). Each TNBC subtype had a unique gene expression pattern, which was confirmed by identification of DEGs and pathway analysis. These results indicated that our training gene set was suitable for development of classification models, and that the SVM algorithm could classify TNBC into four unique subtypes. Accurate and consistent classification of the TNBC subtypes is essential for personalized treatment and prognosis of TNBC.
47
Condomines, Maud, Dirk Hose, Thierry Reme, John de Vos, Guilhem Requirand, Jean-Francois Rossi, Hartmunt Goldschmidt, and Bernard Klein. "Gene Expression Profiling and Real-Time PCR Analyses Make It Possible To Identify Novel Potential Cancer-Testis Antigens in Multiple Myeloma." Blood 110, no. 11 (November 16, 2007): 1793. http://dx.doi.org/10.1182/blood.v110.11.1793.1793.
Full textAbstract:
Abstract The identification of novel tumor-associated antigens is critical for the development of immunotherapeutic strategies. Cancer-testis (CT) antigens represent attractive targets due to their restricted pattern of expression. More than 90 CT genes have been previously classified into four categories according to their expression profiles: testis-restricted (expression in testis and tumor samples only), “tissue restricted” (mRNA detected in 2 or fewer non-gametogenic tissues), “differentially expressed” (mRNA detected in three to six non-gametogenic tissues), and “ubiquitously expressed”. Among those, we previously reported that 18 CT genes were expressed by primary myeloma cells (MMC) of more than 10% of patients with multiple myeloma (MM). This study aimed at finding novel putative CT genes expressed in MM using cDNA microarray analysis and real-time RT-PCR validation. Gene expression profiles of 5 testis samples, 64 MMC, 7 normal memory B cell (MB), 7 normal bone marrow plasma cell samples and 23 normal tissue samples available on a public database were obtained using Affymetrix U133AB microarrays. Out of 45000 probe sets of Affymetrix U133 AB chips, we selected 16982 probe sets which had a “Present” Affymetrix Call in MMC of at least 6/64 patients and in 3/5 testis samples. In order to select genes with a similar pattern of expression than the known CT genes, we developed 4 independent filters making it possible to keep a high number of known CT genes while decreasing the total number of probe sets. Firstly, 2514 of 16982 probe sets had a ratio of the mean signal in MMC with a Present call / mean signal in MB > 2.5. Secondly, 541 of these 2514 probe sets had a Present call in less than 7 of the 23 normal tissues. Thirdly, 333 of these 541 probe sets had a ratio of the mean signal in MMC with a Present call / mean signal in MMC with an Absent call > 2.5. Fourthly, we removed genes whose expression profiles were discordant with different probe sets or discordant with data of the literature. The final probe set list contains 88 probe sets which include 13 of 18 known CT genes reported in MM, thus resulting in a 190-fold enrichment. The expression in 13 normal tissues and in MM samples of 21 out of these 75 putative novel CT genes was investigated by real time RT-PCR. Seven genes were ubiquitously expressed or poorly expressed in MMC samples and further deleted. According to the previously defined CT gene categories, we found one novel “testis-restricted” (TEX14), 8 “tissue-restricted” and 5 “differentially expressed” CT genes. Immunogenicity of one gene product - IGSF11 - was already demonstrated in other cancers by identifying a T-cell epitope. Two genes - NLGN4X and FAM133A - are located in X chromosome and 2 genes - CTNNA2 and FAM133A - are expressed only in brain and testis. In conclusion, by analyzing gene expression patterns with Affymetrix microarrays, we found 75 novel putative CT antigen candidates expressed in MMC of 10 to 100% of patients. Real time RT-PCR validation made it possible to confirm the CT status of 14 genes out of the 21 tested. Further studies are warranted to determine their immunogenicity.
48
Mangaonkar, Abhishek, Vamsi Kota, Amyn Rojiani, Anand Jillella, and Ravindra B. Kolhe. "Utility and Impact Of Early t(15;17) Identification By Fluorescence In Situ Hybridization (FISH) In Clinical Decision Making For Patients With Acute Promyelocytic Leukemia (APL)." Blood 122, no. 21 (November 15, 2013): 4968. http://dx.doi.org/10.1182/blood.v122.21.4968.4968.
Full textAbstract:
Abstract Background Acute Promyelocytic Leukemia (APL) is a highly curable malignancy with most large co-operative group studies showing greater than 90% long term survival. Despite this, recent studies looking at survival in population-based studies suggest that approximately 30% of patients with APL die during induction. This has been confirmed in large population-based studies in Sweden and the US. A recent analysis of Surveillance, Epidemiology & End Results (SEER) data from 13 population-based cancer registries with 1400 APL patients in the US showed that 17% of all patients and 24% of patients > 55 years of age die within one month of diagnosis. Swedish registry data also showed that 29% of patients died within 30 days of diagnosis with most of them dying in the first two weeks. Early initiation of treatment is critical in improving outcomes. Early demonstration of t(15;17) will lead to more accurate decision making regarding treatment especially in the setting when patients are referred from rural areas or treated in academic institutions where confirmation is essential for research protocol purposes. The aim of this project was to validate earlier time frames for the Abbott Molecular Vysis LSI PML/RARA FISH probe (ASR 6-16 hrs). Materials and Methods 15 patients (AML-M0, M1, leukocytosis, NHL, pregnancy loss & APL) were selected for validating various hybridization (hybe) times for FISH probe. After preparing the slides, they were chemically aged by treatment of 2XSSC at 73°C for 2 minutes. 2uL of probe (Abbott Molecular Vysis LSI PML/RARA) was applied to each sample after dehydrating with an ethanol series (70%, 85% & 90%) for 1 minute each. The slides were then placed in the Thermobrite for 2, 4, 6 hours and overnight to denature and hybridize. After each hybe the slides were treated with 0.4xSSC/0.3%NP-40 for 2 minutes at 73°C and later immersed in 0.2xSSC/0.3% NP-40 for 1 minute at room temp. 2uL of DAPI Antifade SS was added with a micropipette onto each hybridized area, and a coverslip was applied. 200 cells with robust signals were identified and calculated. Hybe efficiency was graded as acceptable (good), intermediate (okay) and unacceptable (poor). Expected normal signal pattern was two red and two green signals (2R2G), and the expected most common abnormal signal pattern was two fusion (yellow) signals, one red and one green (2F1R1G) and/or one fusion, one red and one green (1F1R1G). Results The specificity of the probe ranged from 84% at two hours, 86% at four hours, 84% at 6 hours, and 87% for overnight hybe. The sensitivity increased from 79% at two hours, 80% at four hours, 81% at 6 hours, to 87% for overnight hybe, which was expected. Conclusion and Discussion Majority of the APL patients who survive the induction phase are cured with a low chance of relapse. Early mortality continues to be a significant problem with deaths attributable to bleeding, differentiation syndrome and infection. Coagulopathy abnormalities are a hallmark of this disorder and can be rapidly corrected with early institution of treatment. Also, in patients with co-morbid conditions that would exclude the use of anthracyclines, arsenic is the treatment of choice and starting of arsenic is not recommended unless cytogenetic diagnosis is confirmed. The utility of early t(15;17) detection by FISH helps in initiating treatment early and may lead to improved outcomes. This can be easily achieved by shortening the timeframe for FISH results. Based on the validation studies the current recommendations are to read FISH results at the 4 hour incubation mark for a preliminary diagnosis and confirmation with overnight hybridization. Disclosures: Kota: Teva: Speakers Bureau; Ariad: Advisory board, Advisory board Other.
49
Dissanayake, DMSLB, Takehiro Morimoto, Yuji Murayama, and Manjula Ranagalage. "The Impact of landscape composition for urban heat island intensity in Addis Ababa City using Landsat data (1986–2016)." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-63-2019.
Full textAbstract:
<p><strong>Abstract.</strong> Exploring changes in land use and land cover (LULC) in the city area and its surrounding is important to understand the variation of surface urban heat island (SUHI) and surface urban heat island intensity (SUHII). The SUHII can be calculated based on the local climate zone by using land use and land cover compossition of the city and based on the urban rural zone . The objective of this research is to examine the spatiotemporal changes of LULC and the impact of its composition for the formation of SUHI in Addis Ababa City, Ethiopia based on the urban rural zones.</p><p> The mean center of the central business district of the Addis Ababa City was considered as the central point of the study area. We represented the 30&thinsp;km&thinsp;&times;&thinsp;30&thinsp;km geographical area as a study area with a 15km radius from the central point. As data sources, multi-temporal satellite data provided by the United States Geological Survey (USGS) were used in respect to the years of 1986, 2001, and 2016. In the methodology, we first completed the classification of LULC by using pixel-oriented method for the three years and the validation of the classification has been made. For the classification five LULC classes were identified such as forest, impervious surface, grass land, bare land and crop land. Afterward, land surface temperature (LST) has been computed for three years respectively. Finally, urban rural gradient zones (URGZs) have been generated as a set of polygons with 210m distance in each zone from the central point of the study area. In order to evaluate the SUHII along the URGZs in respect to the LULC, the following analyses were accomplished: (i) the relationship between mean LST and composition of the LULC was computed, (ii) the SUHII was calculated based on the LST variation of main LULC categories and the temperature difference between URGZs, (iii) multi-temporal and multi-directional SUHII was computed, and (iv) linear regression analyses were used to assess the correlations of the mean LST with composition of LULC.</p><p> The results of the analyses show that (i) distribution pattern of SUHII has changed over the study period as results of changes in LULC, and (ii) mean LST gradually declines from city centre to outside of the city , then it can be seen increasing trends due to the effect of bare lands in rural area. This pattern can be seen over the three years as the result of multi-directional approach. The methodology presented will be able to apply other cities which are showing similar growth pattern by making necessary calibration, and our finding can be used as a proxy indicator to introduce appropriate landscape and town planning in a sustainable viewpoint in Addis Ababa City.</p>
50
Eggener, Scott E., R. Bryan Rumble, Andrew J. Armstrong, Todd M. Morgan, Tony Crispino, Philip Cornford, Theodorus van der Kwast, et al. "Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline." Journal of Clinical Oncology 38, no. 13 (May 1, 2020): 1474–94. http://dx.doi.org/10.1200/jco.19.02768.
Full textAbstract:
PURPOSE This guideline provides recommendations for available tissue-based prostate cancer biomarkers geared toward patient selection for active surveillance, identification of clinically significant disease, choice of postprostatectomy adjuvant versus salvage radiotherapy, and to address emerging questions such as the relative value of tissue biomarkers compared with magnetic resonance imaging. METHODS An ASCO multidisciplinary Expert Panel, with representatives from the European Association of Urology, American Urological Association, and the College of American Pathologists, conducted a systematic literature review of localized prostate cancer biomarker studies between January 2013 and January 2019. Numerous tissue-based molecular biomarkers were evaluated for their prognostic capabilities and potential for improving management decisions. Here, the Panel makes recommendations regarding the clinical use and indications of these biomarkers. RESULTS Of 555 studies identified, 77 were selected for inclusion plus 32 additional references selected by the Expert Panel. Few biomarkers had rigorous testing involving multiple cohorts and only 5 of these tests are commercially available currently: Onco type Dx Prostate, Prolaris, Decipher, Decipher PORTOS, and ProMark. With various degrees of value and validation, multiple biomarkers have been shown to refine risk stratification and can be considered for select men to improve management decisions. There is a paucity of prospective studies assessing short- and long-term outcomes of patients when these markers are integrated into clinical decision making. RECOMMENDATIONS Tissue-based molecular biomarkers (evaluating the sample with the highest volume of the highest Gleason pattern) may improve risk stratification when added to standard clinical parameters, but the Expert Panel endorses their use only in situations in which the assay results, when considered as a whole with routine clinical factors, are likely to affect a clinical decision. These assays are not recommended for routine use as they have not been prospectively tested or shown to improve long-term outcomes—for example, quality of life, need for treatment, or survival. Additional information is available at www.asco.org/genitourinary-cancer-guidelines .