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Yu, Kai Ming, and Tony Chun Tung Lau. "Practical problem solving by TRIZ enriched with weighted average scoring." International Journal of Product Development 4, no. 1/2 (2007): 80. http://dx.doi.org/10.1504/ijpd.2007.011535.
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Kovach, Alexandra E., Dedrick E. Moulton, Walton D. Plummer, William D. Dupont, and M. Cristina Pacheco. "Correlation of Endoscopic and Histologic Severity Scores in Pediatric Ulcerative Colitis at First Presentation." Pediatric and Developmental Pathology 22, no. 2 (2018): 106–11. http://dx.doi.org/10.1177/1093526618803736.
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Detailed histologic scoring systems have been developed for the assessment of disease activity in ulcerative colitis. Literature from adult patients has shown some correlation between endoscopy and histology, and reproducibility of histologic scoring systems has also been supported. The effectiveness of endoscopic appearance at predicting histologic scores in pediatric patients has not been well studied, and none of the histologic scoring systems used in adults have had interobserver reproducibility assessed in pediatric patients. We reviewed endoscopic images and concurrent biopsies using Mayo and Geboes scores from the distal colon and rectum in untreated pediatric patients at the presentation of presumed ulcerative colitis based on clinical and endoscopic findings. Interobserver concordance was calculated by weighted-kappa statistic. The averaged histologic scores were compared to endoscopy scores using Spearman’s coefficient. Correlation between endoscopic score and each histologic score was weakly to moderately positive, whereas interobserver agreement for histologic scores was fair to moderate, suggesting that the Geboes scoring system has value in pediatric patients. For each histologic parameter, the average score was lower than the average endoscopic score. Examination of larger pediatric cohorts, treated patients, correlations of clinical outcomes with individual histologic parameters, and alternate scoring systems may contextualize these findings.
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Wang, Wei. "Machine Learning-Based Intelligent Scoring of College English Teaching in the Field of Natural Language Processing." Computational Intelligence and Neuroscience 2022 (August 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/2754626.
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The current education evaluation is limited not only to the mode of simplification, indexing, and datafication, but also to the scientific nature of college teaching evaluation. This work firstly conducts a theoretical analysis of natural language processing technology, analyzes the related technologies of intelligent scoring, designs a systematic process for intelligent scoring of college English teaching, and finally conducts theoretical research on the Naive Bayesian algorithm in machine learning. In addition, the error of intelligent scoring of English teaching in colleges and universities and the accuracy of scoring and classification are analyzed and researched. The results show that the error between manual scoring and machine scoring is basically about 2 points and the minimum error of intelligent scoring in college English teaching under machine scoring can reach 0 points. There is a certain bias in manual scoring, and scoring on the machine can reduce the generation of this error. The Naive Bayes algorithm has the highest classification accuracy on the college intelligent scoring dataset, which is 76.43%. The weighted Naive Bayes algorithm has been improved in the classification accuracy of college English teaching intelligent scoring, with an average accuracy rate of 74.87%. To sum up, the weighted Naive Bayes algorithm has better performance in the classification accuracy of college English intelligent scoring. This work has a significant effect on the scoring of the college intelligent teaching scoring system under natural language processing and the classification of college teaching intelligence scoring under the Naive Bayes algorithm, which can improve the efficiency of college teaching scoring.
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Gill, Jaspreet Kaur, Rubens Copia Sperandio, Tuan Hoang Nguyen, and Urban Emmenegger. "Toxicity-benefit analysis of advanced prostate cancer trials using weighted toxicity scoring." Journal of Clinical Oncology 42, no. 4_suppl (2024): 110. http://dx.doi.org/10.1200/jco.2024.42.4_suppl.110.
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110 Background: The weighted toxicity score (WTS) is a metric suitable for comparing the toxicity burden in experimental versus control arms of randomized controlled trials (RCTs). When compared against clinical endpoints, the WTS can be used to evaluate the cost versus benefit of anti-cancer agents. This study sought to apply the WTS to prostate cancer (PC) clinical trials. Methods: Select phase 3 PC RCTs with reported adverse event (AE) data were compiled. The WTS was computed for each trial arm and compared to reported hazard ratios (HRs) of primary and/or secondary endpoints (i.e., overall survival (OS) and progression-free survival (PFS)). Average WTSs for the experimental versus control arms were used to calculate the toxicity differential between treatment arms. Average HRs for OS and PFS were used to compare efficacy. Results: Sixteen RCTs were analyzed (investigational agents: androgen-receptor signaling inhibitors (ARSi) [n = 4], poly (ADP-ribose) polymerase inhibitors (PARPi) monotherapy [n = 3], ARSi + PARPi as well as ARSi + ARSi combination therapies [n = 3 each], and triple therapy [n=3]). The median number of distinct reported AEs across all studies was 18 (range, 9-61). Overall, toxicity and efficacy were greater among experimental than control arms (6.62 versus 4.00 median WTS; median HR for OS 0.71, median HR for PFS 0.61). The triple therapy studies observed the lowest increase in toxicity by adding an ARSi to control therapy (10.4%), associated with 52% lower risk of progression, and 29% lower risk of death. Comparably, the ARSi + PARPi trials noted the highest increase in toxicity (79.7%), with 32% lower risk of progression and 12% lower risk of death. The PARPi monotherapy, ARSi monotherapy, and ARSi + ARSi combination therapy studies reported increased toxicity due to experimental therapy in descending order (Table). Conclusions: The WTS enables toxicity versus benefit assessment for anti-cancer regimens. Clinical application of this metric may facilitate individualized treatment planning in advanced PC management. [Table: see text]
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Sidik, Galih Kurniawan, and Taufik Djatna. "A FAIRNESS MODEL BASED ON INTERVAL TYPE-2 FUZZY SET FOR ISLAMIC FINANCING SCORING IN INDONESIA." Airlangga International Journal of Islamic Economics and Finance 1, no. 1 (2018): 43. http://dx.doi.org/10.20473/aijief.v1i1.10431.
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Conventional credit scoring model could lead to serious and unfair problems because in certain case it would incriminate one party in financing. Islamic financing scoring model complies with Sharia rules and ensures fairness among parties. Currently, there are no certain rules on Islamic financing scoring model which lead to subjective judgments. In the subjective judgments, words could mean different things to different people. Thus, this paper proposed and deployed models for scoring of default risk level by using Interval Type-2 Fuzzy Set model to support the subjective judgments in maintaining Sharia rules. Installment amount and the sum of delay period has used as variables for that scoring. Interval Type-2 Fuzzy Set model was proposed to support the subjective judgments in maintaining Sharia rules. Beginning delay period also used as a weight to the risk scoring results. Besides that, this paper also proposed the method for computing real loss value. It has used as a basis for fines computation according to default risk level, bad debt expense, and installment weighted average.
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Li, Li, and Sui Tian. "Empirical Analysis of Multi-factor Stock Selection Model Based on Weight Assignment of Momentum and Discrete Degree." BCP Business & Management 19 (May 31, 2022): 151–57. http://dx.doi.org/10.54691/bcpbm.v19i.737.
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Through the stock selection of a large number of literature, we found that factors contribute differently at different market styles or stages, and the factor weight is different. Therefore, in this paper, the factors were preliminarily screened by IC mean and IR equal weight scoring method and T test, and then the screened factors were tested by correlation test and sorting method to obtain the final factors. After five methods, such as factor equal weight, momentum and dispersion comprehensive average weight, were tested and compared, we found that the method of momentum and dispersion comprehensive average weight on the effect of the stock selection is more excellent than other methods, so we chose the momentum and discrete degree of comprehensive weighted average approach to dynamic weighting of each stock, the last stock through empowerment scoring method selection. In this way, the speed of market change and the impact of industry on factors are better considered, and the te-test effect is better.
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Fowler, Peter V., Azza H. Al-Ani, and John M. D. Thompson. "Comparison of Reliability of Categorical and Continuous Scales for Radiographic Assessments of Bone Infill Following Secondary Alveolar Bone Grafting." Cleft Palate-Craniofacial Journal 55, no. 2 (2017): 269–75. http://dx.doi.org/10.1177/1055665617723922.
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Objectives: To investigate examiner reliability of scoring intraoral radiographs of bone infill following secondary bone grafting using a categorical scale (modified Kindelan Index) and a 10-cm visual analog scale (VAS). To investigate the level of clinical experience on the reliability of these assessments. Design: Retrospective study involving 10 clinicians of varying clinical experience who were blind to the patient’s identity. Setting: All grafting was carried out within the New Zealand public hospital service. Patients: Fifty-eight grafts (48 UCLP and 5 BCLP) were assessed with 14 duplicated radiographs randomly added to the sample, making a total of 72 radiographs for assessment. Main Outcome Measures: Weighted kappa was used for intra- and interrater examiner reliability for the categorical scale and correlations for the VAS. Results: Relatively poor intrarater examiner agreement for categorical scoring (median 0.46) was found. Clinicians with greater experience generally recording higher intrarater weighted kappa. The intrarater correlations for the continuous scale (median 0.89) suggest better consistency regardless of the level of experience. The interrater relationships were generally low, with an average of weighted kappa of 0.25 while the overall average VAS correlation was 0.49. For the majority of scorers, there was a relationship between the VAS and the Kindelan Index, with lower VAS scores related to worse Kindelan scores. Conclusions: The use of a VAS produced better intra- and interexaminer reliability than the categorical Kindelan Index and was more consistent despite the level of clinical experience. Further evaluation of the validity of the VAS for the assessment of bone infill is warranted.
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Swandewi, Ni Kadek, I Gede Sumadi, and Nina Indra Kristiana. "Persepsi Masyarakat Lokal Terhadap Festival Jatiluwih Cultural Week (JCW)." Journal of Tourism and Interdiciplinary Studies 4, no. 1 (2024): 86–91. http://dx.doi.org/10.51713/jotis.2024.4114.
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The Jatiluwih Cultural Week is a festival held by the village of Jatiluwih, located in the Tabanan Regency of Bali. Jatiluwih is a destination designated as a cultural heritage site by UNESCO and serves as the venue for the annual Jatiluwih Festival. This study aims to understand the perceptions of local residents who participate as visitors in JCW regarding the various dimensions of the event during the Jatiluwih Cultural Week festival. The research utilized a quantitative descriptive method with a questionnaire as the instrument, and 103 respondents were sampled using the Slovin formula. The measurement scale of the instrument used a Likert scale, which was then processed into average values or weighted mean scores. It was found that the perception of the community regarding the six event dimensions showed that in terms of transportation, access, and guest reception, the average score was 3.33, indicating it was less satisfactory. Similarly, the entertainment and facilities variables also received less satisfactory scores of 3.34 and 3.32, respectively. However, three other dimensions of the event were perceived positively, with marketing materials scoring 4.04, Culinary Offerings scoring 4.01, and Atmosphere and Decoration scoring 3.98.
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Wang, Yehao. "A Brief Analysis of Students' Chinese-style Competitive Behavior." Modern Economics & Management Forum 5, no. 1 (2024): 117. http://dx.doi.org/10.32629/memf.v5i1.1735.
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This academic paper delves into the intriguing competitive behaviors and phenomena exhibited by Chinese students. It provides a preliminary analysis of the root causes of competitive psychology, considering both the advantages and disadvantages of resulting social phenomena — specifically, over-competition and under-competition — from cultural and sociological perspectives. Utilizing Daxing District in Beijing as a case study, the paper identifies six influential factors and constructs a competitiveness model through expert scoring and a moving weighted average method.
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Yang, Yi, Mengqi Jie, and Jiajie Pan. "Multi-Source Data-Driven Personalized Recommendation and Decision-Making for Automobile Products Based on Basic Uncertain Information Order Weighted Average Operator." Sustainability 17, no. 9 (2025): 4078. https://doi.org/10.3390/su17094078.
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The extensive electronic word-of-mouth (eWOM) data generated by consumers encapsulates authentic product experience information. By leveraging advanced data analysis technologies, enterprises can extract sustainable consumer behavior preference knowledge, thereby supporting the optimization of their marketing and management strategies. However, existing data-driven product ranking processes predominantly focus on single-source eWOM data and rarely mine product insights from a multi-source perspective. Moreover, the quality of eWOM data cannot be overlooked. Consequently, this study uses automobile products as a case example and integrates rating eWOM data, complaint eWOM data, and safety test data to construct a multi-source data-driven personalized product ranking recommendation algorithm. Specifically, an evaluation index system is established for each of the three data types. To model information quality, these data are transformed into basic uncertain information (BUI), which incorporates scoring information and credibility metrics. The XLNet model is employed to convert complaint text data into scoring data, and three targeted credibility evaluation models are developed to assess the reliability of the three data types. Subsequently, BUI is aggregated using the BUI ordered weighted average (BUIOWA) aggregation operator. Based on this, a personalized product ranking method aligned with user preferences is proposed, offering consumers recommendation results that match their preferences. Finally, using automobile products as an illustrative example, this study elucidates the multi-source data-driven personalized product recommendation process and provides managerial implications for enterprises.
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Sun, Jingbo, Tianbao Song, Jihua Song, and Weiming Peng. "Improving Automated Essay Scoring by Prompt Prediction and Matching." Entropy 24, no. 9 (2022): 1206. http://dx.doi.org/10.3390/e24091206.
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Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on downstream tasks, and many studies have attempted to use pre-training and then fine-tuning mechanisms in an essay scoring system. However, obtaining better features such as prompts by the pre-trained encoder is critical but not fully studied. In this paper, we create a prompt feature fusion method that is better suited for fine-tuning. Besides, we use multi-task learning by designing two auxiliary tasks, prompt prediction and prompt matching, to obtain better features. The experimental results show that both auxiliary tasks can improve model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder produces the best results, with Quadratic Weighted Kappa improving 2.5% and Pearson’s Correlation Coefficient improving 2% on average across all results on the HSK dataset.
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Li, Taoying, Linlin Jin, Zebin Wu, and Yan Chen. "Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve." Information 10, no. 4 (2019): 130. http://dx.doi.org/10.3390/info10040130.
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The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.
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Thiruselvam, Indrani, and James B. Hoelzle. "Refined Measurement of Verbal Learning and Memory: Application of Item Response Theory to California Verbal Learning Test – Second Edition (CVLT-II) Learning Trials." Archives of Clinical Neuropsychology 35, no. 1 (2019): 90–104. http://dx.doi.org/10.1093/arclin/acy097.
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Abstract Objective This study was conducted to investigate how items contained within the California Verbal Learning Test – Second Edition (CVLT-II; Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (2000). California Verbal Learning Test (2nd ed.). San Antonio, TX: Psychological Corporation) learning trials function and to evaluate whether weighted scoring approaches might improve quantification of verbal memory. Method Archival data from 577 individuals (338 healthy young adults, 239 medical or psychiatric patients referred to a neuropsychology clinic) were obtained and evaluated using item response theory. Results The serial position effect was evident across trials, but was most evident in Trials 1, 2, and 3. CVLT-II Trial 5 was the most effective among the five learning trials in quantifying verbal memory, although it was most effective when measuring lower memory ability levels. In contrast, CVLT-II Trial 1 items had, on average, the highest difficulty levels. Various weighted scoring approaches did not appear incrementally helpful in improving prediction of memory performance. Conclusion Specific items and trials differentially discriminate between examinees with low levels of memory ability; it is important to thoroughly evaluate item properties of tests used in clinical decision-making.
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Chandler, Ariel E., R. Kannan Mutharasan, Lia Amelia, Matthew B. Carson, Denise M. Scholtens, and Nicholas D. Soulakis. "Risk Adjusting Health Care Provider Collaboration Networks." Methods of Information in Medicine 58, no. 02/03 (2019): 071–78. http://dx.doi.org/10.1055/s-0039-1694990.
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Abstract Objectives The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives. Methods We extracted 4 years of patient, provider, and activity data related to cardiology discharge workflow. A Weibull model was developed to predict the risk of unplanned 30-day readmission. A provider–patient bipartite network was used to connect providers by shared patient encounters. We built collaboration networks and calculated the Shared Positive Outcome Ratio (SPOR) to quantify the relationship between providers by the relative rate of patient outcomes, using both risk-adjusted readmission rates and unadjusted readmission rates. The effect of risk adjustment on the calculation of the SPOR metric was quantified using a permutation test and descriptive statistics. Results Comparing the collaboration networks consisting of 2,359 provider pairs, we found that SPOR values with risk-adjusted outcomes are significantly different than unadjusted readmission as an outcome measure (p-value = 0.025). The two networks classified the same provider pairs as high-scoring 51.5% of the time, and the same low scoring provider pairs 85.6% of the time. The observed differences in patient demographics and disease characteristics between high-scoring and low-scoring provider pairs were reduced by applying the risk-adjusted model. The risk-adjusted model reduced the average variation across each individual's SPOR scored provider connections. Conclusions Risk adjusting unplanned readmission in a collaboration network has an effect on SPOR-weighted edges, especially on classifying high-scoring SPOR provider pairs. The risk-adjusted model reduces the variance of providers' connections and balances shared patient characteristics between low- and high-scoring provider pairs. This indicates that the risk-adjusted SPOR edges better measure the impact of collaboration on readmissions by accounting for patients' risk of readmission.
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Davis-Cheshire, Rhonda, Savannah Bennington, Allison Hartsek, Teresa Kelly, Janeene Marinelli, and Amanda Perez. "The Impact of Weighted Blanket Use on Adults with Sensory Sensitivity and Insomnia." Occupational Therapy International 2023 (December 20, 2023): 1–13. http://dx.doi.org/10.1155/2023/3109388.
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Purpose. This study’s purpose was to determine the impact of weighted blanket use on moderate to severe insomnia in adults with sensory sensitivity greater than the average population. Methods. For this study, a four-week, single-case, multiple-participant ABA study design was used. Through convenience sampling, four participants scoring 15 or greater on the Insomnia Severity Index (ISI), which categorizes them as having moderate to severe insomnia, and much more than most people in sensory sensitivity on the Adolescent/Adult Sensory Profile were recruited. First, seven-day baseline sleep data was gathered, followed by two weeks of weighted blanket use, concluding with a seven-day withdrawal phase. Additional outcome measures included: Tuck and Snooze Survey, Consensus Sleep Diary Morning, and Additional Sleep Diary Questions. Data analysis included visual analysis, mean comparisons, Tau-U calculations, and pre- to post-ISI category comparisons. Results. All participants’ ISI scores were categorized as one level less severe postintervention. All participants demonstrated increased sleep quality, and three participants showed an increase in sleep duration based on individual mean comparisons between baseline and intervention phases. Conclusion. Weighted blankets appear beneficial in reducing insomnia severity in adults with much more than the average population sensory sensitivity. In addition, those with self-reported anxiety may have increased benefit from this intervention.
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Ma, Lei, Hongtao Si, Manyi Li, et al. "Influence of Land Use Types on Soil Properties and Soil Quality in Karst Regions of Southwest China." Agronomy 14, no. 5 (2024): 882. http://dx.doi.org/10.3390/agronomy14050882.
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Establishing a suitable and useful soil quality index (SQI) is the key to accurately evaluating changes in soil quality (SQ) under different land use types. In the present study, a suitable and useful SQI using a minimum data set (MDS) with two scoring methods (linear scoring method and nonlinear scoring method) and two additive models (simple additive model with same weighting value and weighted additive model with significant different weighting value) was established to compare SQ under different land uses in Longtan valley. Soil samples were collected under one dryland (DRYL), one paddy (PADD), one orchard (GRA), and one natural forest (FORE), and 13 soil properties were measured. The four land use types had the same soil type and similar environmental conditions. Land use types had significant effects on the measured 12 soil properties. The top two principal components in Principal Component Analysis were chosen, and their cumulative variance was more than 90%. Soil indicators of soil labile carbon, C/N ratio, and microaggregates were chosen as members of MDS in this study. Significant (p < 0.001) positive correlations among the four establishing SQIs were found. The values of the sensitive index ranged from 47.17% to 82.12% for the four SQIs, and the SQI established using the nonlinear scoring method and weighted additive model (SQI-NLW) had the highest values. Among the four land use types, the four SQIs had similar change trends, and the average values of SQ under FORE (0.73) and PADD (0.68) were significantly higher than those under GRA (0.54) and DRYL (0.43). These results indicated that the SQI-NLW was an effective and precise tool to assess SQ under different land uses in similar regions, and the FORE and PADD were the suitable land use types for the sustainable use of soils in karst regions.
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Ghabrial, Emad, Hester JS Van den Berg, and Susanna M. Du Plessis. "Primary surgery effect on dental arch relationships of patients born with unilateral cleft lip and palate using the GOSLON yardstick index." South African Dental Journal 76, no. 5 (2021): 245–49. http://dx.doi.org/10.17159/10.17159/2519-0105/2021/v76no5a2.
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To score dental arch development using the Great Ormond Street, London, and Oslo (GOSLON) yardstick index, following primary surgery in patients with a complete unilateral cleft lip and palate (UCLP), and to compare the outcome score with the GOSLON score of Cleft Care UK (CCUK) as well as with the Clinical Standards Advisory Group (CSAG), United Kingdom.Study models of patients (average age 12 years) with a non-syndromic complete UCLP, who had been surgically treated at the University of Pretoria Facial Cleft Deformity (UPFCD) clinic. They were assessed using the GOSLON yardstick index by certified raters from the Dental School in Dundee, Scotland. The mean outcome ratings were calculated from the scoring of 27 sets of plaster models. The other scoring rounds were used to calculate intraand inter-observer agreement using Cohen's weighted kappa and Fleiss's multi-rater kappa. There were strong intra- and inter-observer agreement, with a weighted kappa of 0.92. The Facial Cleft Deformity (FCD) clinic data showed a good treatment outcome with a mean GOSLON rating of 2.85 compared to a rating of 3.2 for the CSAG and 2.62 for the CCUK cohort studies. The UPFCD clinic primary surgical protocol displayed a good treatment outcome rating, in line with that of the CCUK cohort and better than the CSAG results.
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Oluwadele, Deborah, Yashik Singh, and Timothy Adeliyi. "Operationalizing a Weighted Performance Scoring Model for Sustainable e-Learning in Medical Education: Insights from Expert Judgement." Electronic Journal of e-Learning 22, no. 8 (2024): 24–40. http://dx.doi.org/10.34190/ejel.22.8.3427.
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Validation is needed for any newly developed model or framework because it requires several real-life applications. The investment made into e-learning in medical education is daunting, as is the expectation for a positive return on investment. The medical education domain requires data-wise implementation of e-learning as the debate continues about the fitness of e-learning in medical education. The domain seldom employs frameworks or models to evaluate students' performance in e-learning contexts. However, when utilized, the Kirkpatrick evaluation model is a common choice. This model has faced significant criticism for its failure to incorporate constructs that assess technology and its influence on learning. This paper aims to assess the efficiency of a model developed to determine the effectiveness of e-learning in medical education, specifically targeting student performance. The model was validated through Delphi-based Expert Judgement Techniques (EJT), and Cronbach's alpha was used to determine the reliability of the proposed model. Simple Correspondence Analysis (SCA) was used to measure if stability is reached among experts. Fourteen experts, professors, senior lecturers, and researchers with an average of 12 years of experience in designing and evaluating students' performance in e-learning in medical education participated in the evaluation of the model based on two rounds of questionnaires developed to operationalize the constructs of the model. During the first round, the model had 64 % agreement from all experts; however, 100% agreement was achieved after the second round, with all statements achieving an average of 52% strong agreement and 48% agreement from all 14 experts; the evaluation dimension had the most substantial agreements, next to the design dimension. The results suggest that the model is valid and may be applied as Key Performance Metrics when designing and evaluating e-learning courses in medical education.
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Singla, Yaman Kumar, Sriram Krishna, Rajiv Ratn Shah, and Changyou Chen. "Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12835–43. http://dx.doi.org/10.1609/aaai.v36i11.21563.
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Automated Scoring (AS), the natural language processing task of scoring essays and speeches in an educational testing setting, is growing in popularity and being deployed across contexts from government examinations to companies providing language proficiency services. However, existing systems either forgo human raters entirely, thus harming the reliability of the test, or score every response by both human and machine thereby increasing costs. We target the spectrum of possible solutions in between, making use of both humans and machines to provide a higher quality test while keeping costs reasonable to democratize access to AS. In this work, we propose a combination of the existing paradigms, sampling responses to be scored by humans intelligently. We propose reward sampling and observe significant gains in accuracy (19.80% increase on average) and quadratic weighted kappa (QWK) (25.60% on average) with a relatively small human budget (30% samples) using our proposed sampling. The accuracy increase observed using standard random and importance sampling baselines are 8.6% and 12.2% respectively. Furthermore, we demonstrate the system's model agnostic nature by measuring its performance on a variety of models currently deployed in an AS setting as well as pseudo models. Finally, we propose an algorithm to estimate the accuracy/QWK with statistical guarantees (Our code is available at https://git.io/J1IOy).
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Jini, Jojo Stephen, and P. Prabu. "Detecting the magnitude of depression in Twitter users using sentiment analysis." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 3247–55. https://doi.org/10.11591/ijece.v9i4.pp3247-3255.
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Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data.Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together. Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data.
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Bocklage, Therese, Virgilius Cornea, Caylin Hickey, et al. "Ki-67 Testing in Breast Cancer: Assessing Variability With Scoring Methods and Specimen Types and the Potential Subsequent Impact on Therapy Eligibility." Applied Immunohistochemistry & Molecular Morphology 32, no. 3 (2024): 119–24. http://dx.doi.org/10.1097/pai.0000000000001188.
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Abemaciclib was originally FDA approved for patients with ER-positive/HER2-negative breast cancer with Ki-67 expression ≥20%. However, there were no guidelines provided on which specimen to test or which scoring method to use. We performed a comprehensive study evaluating the variation in Ki-67 expression in breast specimens from 50 consecutive patients who could have been eligible for abemaciclib therapy. Three pathologists with breast expertise each performed a blinded review with 3 different manual scoring methods [estimated (EST), unweighted (UNW), and weighted (WT) (WT recommended by the International Ki-67 in Breast Cancer Working Group)]. Quantitative image analysis (QIA) using the HALO platform was also performed. Three different specimen types [core needle biopsy (CNB) (n=63), resection (RES) (n=52), and axillary lymph node metastasis (ALN) (n=50)] were evaluated for each patient. The average Ki-67 for all specimens was 14.68% for EST, 14.46% for UNW, 14.15% for WT, and 11.15% for QIA. For the manual methods, the range between the lowest and highest Ki-67 for each specimen between the 3 pathologists was 8.44 for EST, 5.94 for WT, and 5.93 for UNW. The WT method limited interobserver variability with ICC1=0.959 (EST ICC1=0.922 and UNW=0.949). Using the aforementioned cutoff of Ki-67 ≥20% versus <20% to determine treatment eligibility, the averaged EST method yields 20 of 50 patients (40%) who would have been treatment-eligible, versus 15 (30%) for the UNW, 17 (34%) for the WT, and 12 (24%) for the QIA. There was no statistically significant difference in Ki-67 among the 3 specimen types. The average Ki-67 difference was 4.36 for CNB vs RES, 6.95 for CNB versus ALN, and RES versus ALN (P=0.93, 0.99, and 0.94, respectively). Our study concludes that further refinement in Ki-67 scoring is advisable to reduce clinically significant variation.
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Naz, Sumera, Muhammad Akram, Mohammed M. Ali Al-Shamiri, and Muhammad Ramzan Saeed. "Evaluation of Network Security Service Provider Using 2-Tuple Linguistic Complex q -Rung Orthopair Fuzzy COPRAS Method." Complexity 2022 (June 26, 2022): 1–27. http://dx.doi.org/10.1155/2022/4523287.
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In recent years, network security has become a major concern. Using the Internet to store and analyze data has become an integral aspect of the production and operation of many new and traditional enterprises. However, many enterprises lack the necessary resources to secure information security, and selecting the best network security service provider has become a real issue for many enterprises. This research introduces a novel decision-making method utilizing the 2-tuple linguistic complex q-rung orthopair fuzzy numbers (2TLCq-ROFNs) to tackle this issue. We propose the 2TLCq-ROF concept by combining the complex q-rung orthopair fuzzy set with 2-tuple linguistic terms, including the fundamental definition, operational rules, scoring, and accuracy functions. Aggregation operators are the fundamental mathematical approach used to combine various inputs into a single output. Taking into account the interaction between the attributes, we develop the 2TLCq-ROF Hamacher (2TLCq-ROFH) operators by using the innovative operational rules. These operators include the 2TLCq-ROFH weighted average (2TLCq-ROFHWA), 2TLCq-ROFH ordered weighted average (2TLCq-ROFHOWA), 2TLCq-ROFH hybrid average (2TLCq-ROFHHA), 2TLCq-ROFH weighted geometric (2TLCq-ROFHWG), 2TLCq-ROFH ordered weighted geometric (2TLCq-ROFHOWG), and 2TLCq-ROFH hybrid geometric (2TLCq-ROFHHG) operators. In addition, we talk about the properties of 2TLCq-ROFH operators such as idempotency, commutativity, monotonicity, and boundedness and also examine their spatial cases. To tackle the problems of the 2TLCq-ROF multiattribute group decision-making (MAGDM) environment, we develop a novel approach according to the COPRAS (complex proportional assessment) model. Finally, to validate the feasibility of the given strategy, we employ a quantitative example related to select the best network security service provider. In comparison with existing approaches, the developed decision-making algorithm is most extensively used and reduces the loss of information.
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Liu, K. K., C. H. Li, Y. P. Cai, M. Xu, and X. H. Xia. "Comprehensive evaluation of water resources security in the Yellow River basin based on a fuzzy multi-attribute decision analysis approach." Hydrology and Earth System Sciences 18, no. 5 (2014): 1605–23. http://dx.doi.org/10.5194/hess-18-1605-2014.
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Abstract. In this paper, a fuzzy multi-attribute decision analysis approach (FMADAA) was developed for supporting the evaluation of water resources security in nine provinces within the Yellow River basin. A numerical approximation system and a modified left–right scoring approach were adopted to cope with the uncertainties in the acquired information. Also, four conventional multi-attribute decision analysis (MADA) methods were implemented in the evaluation model for impact evaluation, including simple weighted addition (SWA), weighted product (WP), cooperative game theory (CGT) and technique for order preference by similarity to ideal solution (TOPSIS). Moreover, several aggregation methods including average ranking procedure, Borda and Copeland methods were used to integrate the ranking results, helping rank the water resources security in those nine provinces as well as improving reliability of evaluation results. The ranking results showed that the water resources security of the entire basin was in critical condition, including the insecurity and absolute insecurity states, especially in Shanxi, Inner Mongolia and Ningxia provinces in which water resources were lower than the average quantity in China. Hence, the improvement of water eco-environment statuses in the above-mentioned provinces should be prioritized in the future planning of the Yellow River basin.
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Liu, K. K., C. H. Li, Y. P. Cai, M. Xu, and X. H. Xia. "Comprehensive evaluation of water resources security in the Yellow River basin based on a Fuzzy Multi-Attribute Decision Analysis Approach." Hydrology and Earth System Sciences Discussions 11, no. 1 (2014): 371–410. http://dx.doi.org/10.5194/hessd-11-371-2014.
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Abstract. In this paper, a Fuzzy Multi-Attribute Decision Analysis Approach (FMADAA) was adopted in water resources security evaluation for the nine provinces in the Yellow River basin in 2006. A numerical approximation system and a modified left-right scoring approach were adopted to cope with the uncertainties in the acquired information. Four multi-attribute decision making methods were implemented in the evaluation model for impact evaluation, including simple weighted addition (SWA), weighted product (WP), cooperative game theory (CGT) and technique for order preference by similarity to ideal solution (TOPSIS) which could be used for helping rank the water resources security in those nine provinces as well as the criteria alternatives. Moreover, several aggregation methods including average ranking procedure, borda and copeland methods were used to integrate the ranking results. The ranking results showed that the water resources security of the entire basin is in critical, insecurity and absolute insecurity state, especially in Shanxi, Inner Mongolia and Ningxia provinces in which water resources were lower than the average quantity in China. Hence, future planning of the Yellow River basin should mainly focus on the improvement of water eco-environment status in the provinces above.
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Ibrahim, Hariwan Z., Tareq M. Al-Shami, and Abdelwaheb Mhemdi. "Applications of nth Power Root Fuzzy Sets in Multicriteria Decision Making." Journal of Mathematics 2023 (March 29, 2023): 1–14. http://dx.doi.org/10.1155/2023/1487724.
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An nth power root fuzzy set is a useful extension of a fuzzy set for expressing uncertain data. Because of their wider range of showing membership grades, nth power root fuzzy sets can cover more ambiguous situations than intuitionistic fuzzy sets. In this article, we present several novel operations on nth power root fuzzy sets, as well as their various features. Besides, we develop a new weighted aggregated operator, namely, nth power root fuzzy weighted power average (nPR-FWPA) over nth power root fuzzy sets to deal with choice information and show some of their basic properties. In addition, we define a scoring function for nth power root fuzzy sets ranking. Furthermore, we use this operator to determine the optimal location for constructing a home and demonstrate how we may choose the best alternative by comparing aggregate outputs using score values. Finally, we compare the nPR-FWPA operator outcomes to those of other well-known operators.
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Li, Yibo, Guoqin Yao, Wangyang Shen, Zhan Wang, Cheng Guo, and Xiwu Jia. "Optimizing the quality of bran-fortified stewing noodles using extruded wheat bran and improvers." Italian Journal of Food Science 36, no. 2 (2024): 1–12. http://dx.doi.org/10.15586/ijfs.v36i2.2496.
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Aimed to improve the tensile properties, color, and sensory quality of bran-fortified stewing noodles using bran extrusion and improvers. A single-factor experiment and Box–Behnken design response surface experiment were designed, and the multi-index comprehensive weighted scoring method was applied. The single-factor experiment indicated high-quality bran-fortified stewing noodles when wheat gluten, glucose oxidase, and guar gum contents were 10%, 40 mg/kg, and 0.3%, respectively. The response surface experiment showed that the comprehensive weighted score in the response surface optimal group was maximum (86.33 points) when wheat gluten, glucose oxidase, and guar gum contents were 5%, 40 mg/kg, and 0.2%, respectively. The verified model experiment showed that the average comprehensive weighted score was 89.31±0.49 points, indicating that the model was feasible to improve the quality of bran-fortified stewing noodles. The optimal formula involved wheat gluten, glucose oxidase, and guar gum contents of 6.67%, 28 mg/kg, and 0.22%, respectively. Bran extrusion and improvers can improve the quality of bran-fortified stewing noodles. The findings may serve as a reference for the quality improvement and industrial production of fiber-rich stewing noodles.
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Nongthombam, Sunanda, Natasha R. Marak, Chenxiang Rimchi N. Marak, Pavana Kumar, Chingakham Basanti Devi, and Puspita Das. "Exploring Barriers to the Consumption of Neglected and Underutilized Green Leafy Vegetables in Rural and Urban Populations of the Tribal Regions of Garo Hills, Meghalaya, India." Archives of Current Research International 24, no. 11 (2024): 509–18. http://dx.doi.org/10.9734/acri/2024/v24i11990.
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Aim: To identify and analyze the barriers to the consumption of neglected and underutilized Green Leafy Vegetables (GLVs) in urban and rural settings of Garo Hills, Meghalaya, focusing on key determinants influencing consumption patterns. Study Design: Cross-sectional survey-based study. Place and Duration of Study: Urban and rural areas of Garo Hills, Meghalaya, conducted from January to May 2024. Methodology: A total of 90 respondents, 45 each from urban and rural areas, were surveyed using a structured questionnaire. The study assessed five key determinants: availability, perception of taste/flavor, knowledge of nutritional benefits, difficulty in preparation, and cost/affordability. Weighted averages were calculated to rank the barriers based on their perceived significance in each setting. Data were analyzed using descriptive statistics to identify differences in consumption patterns and barriers across the two settings. Results: Limited availability emerged as the most significant barrier for urban respondents, with a weighted average score of 7.5, highlighting poor access to GLVs in urban markets. In contrast, rural respondents identified a lack of knowledge about nutritional benefits as the primary barrier, scoring a weighted average of 13.2, indicating a critical need for awareness campaigns. Taste perception influenced consumption, ranking as the second-highest barrier in rural areas (4.7) and third in urban areas (5.6). Preparation difficulties were ranked second by urban respondents (5.7) and third by rural respondents (4.1). Cost and affordability were the least significant barriers in both settings, suggesting that financial constraints play a minor role compared to other factors. Conclusion: The study reveals significant differences in the barriers to GLV consumption in urban and rural Garo Hills. Addressing limited availability in urban areas and enhancing knowledge of nutritional benefits in rural areas are essential for improving GLV consumption. Tailored interventions are required to mitigate these barriers and promote the inclusion of GLVs in diets.
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Wang, Kun. "State assessment of 750kV electric energy metering device based on grey theory and fuzzy analytic hierarchy process." Journal of Physics: Conference Series 2264, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1742-6596/2264/1/012011.
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Abstract It is necessary to evaluate the operation state of 750kV electric energy metering device to predict stop loss. On the basis of previous studies, the state indexes and evaluation methods of electric energy metering devices are improved: the quantitative state indexes are described by nonlinear mathematical scoring model, the qualitative state indexes are clustered by grey theory, and the state weight of each index is determined based on the improved analytic hierarchy process, Finally, the state evaluation results are obtained with the help of weighted average model and fuzzy mathematics theory. The improved state evaluation strategy is more perfect and practical, and realizes the all-round supervision of the operation state of the electric energy metering device.
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Gassenmaier, Tobias, Thomas Allmendinger, Andreas S. Kunz, et al. "In vitro evaluation of a new iterative reconstruction algorithm for dose reduction in coronary artery calcium scoring." Acta Radiologica Open 6, no. 5 (2017): 205846011771068. http://dx.doi.org/10.1177/2058460117710682.
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Background Coronary artery calcium (CAC) scoring is a widespread tool for cardiac risk assessment in asymptomatic patients and accompanying possible adverse effects, i.e. radiation exposure, should be as low as reasonably achievable. Purpose To evaluate a new iterative reconstruction (IR) algorithm for dose reduction of in vitro coronary artery calcium scoring at different tube currents. Material and Methods An anthropomorphic calcium scoring phantom was scanned in different configurations simulating slim, average-sized, and large patients. A standard calcium scoring protocol was performed on a third-generation dual-source CT at 120 kVp tube voltage. Reference tube current was 80 mAs as standard and stepwise reduced to 60, 40, 20, and 10 mAs. Images were reconstructed with weighted filtered back projection (wFBP) and a new version of an established IR kernel at different strength levels. Calcifications were quantified calculating Agatston and volume scores. Subjective image quality was visualized with scans of an ex vivo human heart. Results In general, Agatston and volume scores remained relatively stable between 80 and 40 mAs and increased at lower tube currents, particularly in the medium and large phantom. IR reduced this effect, as both Agatston and volume scores decreased with increasing levels of IR compared to wFBP ( P < 0.001). Depending on selected parameters, radiation dose could be lowered by up to 86% in the large size phantom when selecting a reference tube current of 10 mAs with resulting Agatston levels close to the reference settings. Conclusion New iterative reconstruction kernels may allow for reduction in tube current for established Agatston scoring protocols and consequently for substantial reduction in radiation exposure.
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Liu, Xun, Zhiyuan Xue, Zhenhan Ding, and Siyu Chen. "Sustainability Assessment of Municipal Infrastructure Projects Based on Continuous Interval Argumentation Ordered Weighted Average (C-OWA) and Cloud Models." Sustainability 15, no. 6 (2023): 4706. http://dx.doi.org/10.3390/su15064706.
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The goals of sustainable development are constantly negatively impacted by infrastructure initiatives. The importance of these projects in advancing the economic, social, and civilizational growth of the country will, however, prevent their construction from being stopped. The overall construction of the project is related to the scientific and unbiased assessment of an infrastructure project’s sustainability throughout the decision-making stage. Based on the references documents, this paper establishes an index system for evaluating an infrastructure project’s sustainability from three aspects: environment, economy, and society. In the assessment process, the cloud model was used to describe the various attribute values of infrastructure project sustainability, which achieved the uncertainty measures for infrastructure project sustainability, and a cloud model-based assessment method for infrastructure project sustainability was proposed by modifying the attribute value by the penalty factor. Finally, an assessment method for infrastructure project sustainability based on the cloud model was proposed after the attribute values were modified by using a continuous interval argument ordered weighted average (C-OWA) operator. The model carries out an overall sustainability assessment by generating a synthesized cloud with the weight to calculate the similarity of assessment factors, which takes the randomness, fuzziness, and uncertainty of expert qualitative assessment into account, and uses the analytic hierarchy process (AHP) method and the C-OWA operator to determine the weight of the sustainable index and the aggregation of the expert scoring interval. A case study was conducted to clarify how this strategy was applied. The study provides a valuable and useful tool for the operational stage to assess the achievability of municipal infrastructure projects.
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Sangsrichan, Chaiwat, Patcharida Sungtrisearn, Nopadon Kronprasert, Auttawit Upayokin, and Preda Pichayapan. "Context sensitive transit oriented development assessment: AHP weighted TOD standards for regional railway hubs in Thailand." Decision Science Letters 14, no. 3 (2025): 677–88. https://doi.org/10.5267/j.dsl.2025.3.012.
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This study develops a context-specific Transit-Oriented Development (TOD) evaluation framework for Thailand's regional railway hubs by integrating the Analytic Hierarchy Process (AHP) with established TOD Standards. Through expert-based pairwise comparisons, we determined that transit accessibility (19.1%), connectivity (15.0%), and walkability (14.1%) represent priority criteria for the Thai context, contrasting with the uniform weighting system of international standards. We applied this AHP-weighted framework to assess six regional railway stations: Chiang Mai, Phitsanulok, Nakhon Ratchasima, Khon Kaen, Pattaya, and Hat Yai Junction. Comparative analysis revealed that Hat Yai Junction achieved the highest TOD potential ranking under both standard (74/100) and AHP-weighted (79.7/100) methods, followed by Chiang Mai (72/100 standard; 78.8/100 weighted). The most notable scoring differential appeared in Nakhon Ratchasima (69/100 vs. 78.4/100), demonstrating the significant impact of context-sensitive weighting. All stations showed common weaknesses in cycling infrastructure (average 3.2/5) and car use reduction metrics while achieving the highest scores in transit accessibility criteria. Station-specific evaluation identified targeted improvement priorities: enhancing cycling networks in Chiang Mai, improving pedestrian infrastructure in Phitsanulok, and increasing block connectivity in Pattaya. This contextualized framework gives planners a practical tool for prioritizing TOD investments in Thailand's regional centers.
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Hsiung, Wan-Chun, and Pi-Heng Chung. "Exploring the Determinants of Success among Ladies Golfers by DEA-SBM Model." International Journal of Strategic Decision Sciences 5, no. 3 (2014): 87–98. http://dx.doi.org/10.4018/ijsds.2014070105.
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Different from previous studies focusing on the skill performance in professional golf, this study utilize the non-oriented SBM model of DEA to evaluate the performance of the Ladies Professional Golf Association Tournament players. DEA is used to assess the overall efficiencies, as well as to conduct slack variable analysis of players' performance in 2008 based on the LPGA official web statistical data. In this study, four inputs (including Average Non-green Shots, Putts Per GIR, Sand Saves, and Average Driving Distance) and two outputs (Scoring Average and Official Money Ranking) are selected as the performance index of LPGA players. The results confirm that the most efficient players are not necessarily the top players on the list of official money ranking but also the lower positions. Inefficient players could advance their games varied by skills in lowering the strokes and raising their earnings as improving range suggested. Skill competencies of professional golf players seemed to be weighted differently and players shall choose the right direction for improvement to advance their career earning efficiently on the tours.
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Sun, Qian, Qian Wang, and Yuan Chao Zhang. "Multi-Objective Evaluation of PV." Applied Mechanics and Materials 484-485 (January 2014): 639–43. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.639.
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Access to distribution grid for distributed photovoltaic (short for PV),technically,it determines solutions in the economic evaluation method.The method combines with comprehensive scoring method,Access to the distribution grid will be different from each program in economic construction,load flow,voltage,power quality,supply reliability and other aspects .Also the transformation of the distribution grid to rate the degree by the weighted average method that determines each PV access solutions the final performance of grid PV Construction method of the process,mainly AHP through index calculation,the access of PV program is evaluated to determine the economic and technical level access solution.This study will greatly enhance the PV grid security that helps PV in our country to develop PV.
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Kasemset, Chompoonoot, Takron Opassuwan, Thanakit Tangsittikhun, and Nititada Chaiyajina. "Application of Simulation Technique for Improving Plant Layout in Ceramic Factory." Production Engineering Archives 29, no. 2 (2023): 186–94. http://dx.doi.org/10.30657/pea.2023.29.22.
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Abstract This study aims to design and improve the plant layout of a ceramic factory by adopting Systematic Layout Planning (SLP) and the simulation technique. A ceramic company in northern Thailand is selected as a case study. Three ceramic products including roof tiles, wall tiles and dishware are studied due to their highest production volume. Through the SLP approach, information regarding the number of departments and machines, the area of the plant, the frequency of movement and the distance between each department is collected for the analysis of the relationship between departments. Two plant layout designs are then proposed; the first one is derived from the Computerized Relationship Layout Planning algorithm (CORELAP), and the second one is the process layout. For selecting the most appropriate layout design, five criteria are considered including total distance, the average total process time of each unit produced, ease of movement, material flow and safety. To determine the distance and the average total process time per unit, Distance-Based Scoring and simulation techniques are conducted while the ease of movement, material flow and safety are rated based on whether the company satisfies each criterion. Employing the weight scoring technique, the results report that the CORELAP layout is the most suitable for further implementation due to its highest weighted score equal to 2.536 while the process layout receives 2.386. Implementing the CORELAP layout can reduce the total distance by 16.76% while the average total process time per unit of the CORELAP layout is not significantly different at the significance level of 0.05 as compared to the existing layout.
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Paterson, Q., R. Hartmann, R. Woods, L. Martin, and B. Thoma. "MP23: Giving medical students what they deserve - a rigorous, equitable and defensible CaRMS selection process." CJEM 21, S1 (2019): S50. http://dx.doi.org/10.1017/cem.2019.158.
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Innovation Concept: The fairness of the Canadian Residency Matching Service (CaRMS) selection process has been called into question by rising rates of unmatched medical students and reports of bias and subjectivity. We outline how the University of Saskatchewan Royal College emergency medicine program evaluates CaRMS applications in a standardized, rigorous, equitable and defensible manner. Methods: Our CaRMS applicant evaluation methods were first utilized in the 2017 CaRMS cycle, based on published Best Practices, and have been refined yearly to ensure validity, standardization, defensibility, rigour, and to improve the speed and flow of data processing. To determine the reliability of the total application scores for each rater, single measures intraclass correlation coefficients (ICCs) were calculated using a random effects model in 2017 and 2018. Curriculum, Tool or Material: A secure, online spreadsheet was created that includes applicant names, reviewer assignments, data entry boxes, and formulas. Each file reviewer entered data in a dedicated sheet within the document. Each application was reviewed by two staff physicians and two to four residents. File reviewers used a standardized, criterion-based scoring rubric for each application component. The file score for each reviewer-applicant pair was converted into a z-score based on each reviewer's distribution of scores. Z-scores of all reviewers for a single applicant were then combined by weighted average, with the group of staff and group of residents each being weighted to represent half of the final file score. The ICC for the total raw scores improved from 0.38 (poor) in 2017 to 0.52 (moderate) in 2018. The data from each reviewer was amalgamated into a master sheet where applicants were sorted by final file score and heat-mapped to offer a visual aid regarding differences in ratings. Conclusion: Our innovation uses heat-mapped and formula-populated spreadsheets, scoring rubrics, and z-scores to normalize variation in scoring trends between reviewers. We believe this approach provides a rigorous, defensible, and reproducible process by which Canadian residency programs can appraise applicants and create a rank order list.
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Jaremko, Jacob L., Omar Azmat, Robert G. Lambert, et al. "Validation of a Knowledge Transfer Tool for the Knee Inflammation MRI Scoring System for Bone Marrow Lesions According to the OMERACT Filter: Data from the Osteoarthritis Initiative." Journal of Rheumatology 44, no. 11 (2017): 1718–22. http://dx.doi.org/10.3899/jrheum.161102.
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Objective.To assess feasibility and reliability of scoring bone marrow lesions (BML) on knee magnetic resonance imaging (MRI) in osteoarthritis using the Outcome Measures in Rheumatology Knee Inflammation MRI Scoring System (KIMRISS), with a Web-based interface and online training with real-time iterative calibration.Methods.Six readers new to the KIMRISS (3 radiologists, 3 rheumatologists) scored sagittal T2-weighted fat-saturated MRI in 20 subjects randomly selected from the Osteoarthritis Initiative data, at baseline and 1-year followup. In the KIMRISS, the reader moves a transparent overlay grid within a Web-based interface to fit bones, then clicks or touches each region containing BML per slice, to score 1 if BML is present. Regional and total scores are automatically calculated. Outcomes include the interreader intraclass correlation coefficients (ICC) and the smallest detectable change (SDC).Results.Scoring took 3–12 min per scan and all readers rated the process as moderately to very user friendly. Despite a low BML burden (average score 2.8% of maximum possible) and small changes, interobserver reliability was moderate to high for BML status and change in the femur and tibia (ICC 0.78–0.88). Four readers also scored the patella reliably, whereas 2 readers were outliers, likely because of image artifacts. SDC of 1.5–5.6 represented 0.7% of the maximum possible score.Conclusion.We confirmed feasibility of knee BML scoring by new readers using interactive training and a Web-based touch-sensitive overlay system, finding high reliability and sensitivity to change. Further work will include adjustments to training materials regarding patellar scoring, and study in therapeutic trial datasets with higher burden of BML and larger changes.
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Andersen, Jakob Kristian Holm, Jannik Skyttegaard Pedersen, Martin Sundahl Laursen, et al. "Neural networks for automatic scoring of arthritis disease activity on ultrasound images." RMD Open 5, no. 1 (2019): e000891. http://dx.doi.org/10.1136/rmdopen-2018-000891.
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BackgroundThe development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of patients with inflammatory arthritis. The variation in interpretation of disease activity on US images can affect diagnosis, treatment and outcomes in clinical trials. We, therefore, set out to investigate if we could utilise neural network architecture for the interpretation of disease activity on Doppler US images, using the OESS scoring system.MethodsTwo state-of-the-art neural networks were used to extract information from 1342 Doppler US images from patients with rheumatoid arthritis (RA). One neural network divided images as either healthy (Doppler OESS score 0 or 1) or diseased (Doppler OESS score 2 or 3). The other to score images across all four of the OESS systems Doppler US scores (0–3). The neural networks were hereafter tested on a new set of RA Doppler US images (n=176). Agreement between rheumatologist’s scores and network scores was measured with the kappa statistic.ResultsFor the neural network assessing healthy/diseased score, the highest accuracies compared with an expert rheumatologist were 86.4% and 86.9% with a sensitivity of 0.864 and 0.875 and specificity of 0.864 and 0.864, respectively. The other neural network developed to four class Doppler OESS scoring achieved an average per class accuracy of 75.0% and a quadratically weighted kappa score of 0.84.ConclusionThis study is the first to show that neural network technology can be used in the scoring of disease activity on Doppler US images according to the OESS system.
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Chen, Jiazun, Yikuan Xia, and Jun Gao. "CommunityAF: An Example-Based Community Search Method via Autoregressive Flow." Proceedings of the VLDB Endowment 16, no. 10 (2023): 2565–77. http://dx.doi.org/10.14778/3603581.3603595.
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Example-based community search utilizes hidden patterns of given examples rather than explicit rules, reducing users' burden and enhancing flexibility. However, existing works face challenges such as low scalability, high training cost, and improper termination during the search. Aiming at tackling all these issues, this paper proposes a community search framework named CommunityAF with three well-designed components. The first is a GNN (graph neural network) component that combines community-aware structure features to incrementally learn node embeddings over a large graph for the other two components. The second is an autoregres-sive flow-based generation component designed for fast training and model stability. The third is a scoring component that evaluates the communities and provides scores for a stable termination. Moreover, to show that CommunityAF has the sufficient expressive power to cover the rules, we demonstrate that the scoring component with node features weighted by degree-related factors is able to mimic the existing structure-based community metrics. We introduce a square ranking loss to guide the training of the scoring component, and further devise a flexible termination strategy based on the inferred score change pattern over a sequence of candidate communities using beam search. We compare CommunityAF with four different categories of community search methods on six real-world datasets. The results illustrate that CommunityAF outperforms these community search methods, and achieves an average 15.3% improvement in effectiveness and 4x to 20x speedups on different datasets relative to the state-of-the-art generative method.
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Marei, Ahmed A., Mohamed Reda Rady, Hazem Mostafa Kamal, William C. Welch, and Mohamed A. Hafez. "Prognostic Indices of Surgical Outcome in Cervical Spondylotic Myelopathy: A Clinical Prospective Study." Open Access Macedonian Journal of Medical Sciences 9, B (2021): 438–43. http://dx.doi.org/10.3889/oamjms.2021.6009.
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BACKGROUND: One of the most common causes of spinal cord dysfunction is cervical spondylotic myelopathy (CSM) especially in the elderly. Prognostic indices can aid the surgeon preoperatively to detect the patients’ prognosis. AIM: The aim of the work is to better assess patients and to find possible indicators for post-operative improvement or deterioration in CSM patients. METHODS: Forty patients with multiple levels CSM, admitted and operated on in the Neurosurgery Department of Cairo University Hospitals, have been enrolled randomly in this study after fulfilling the criteria for CSM surgical intervention. The patient age, complaint duration, number of levels affected, signal intensity on T1-weighted and T2-weighted magnetic resonance (MR) images, Japan Orthopedic Association (JOA) scoring system, and Nurick’s score were evaluated before surgery and correlated with outcome after 1 year follow-up. RESULTS: About 80% of patients improved after operation with average pre- and post-operative JOA and Nurick scores about 11.23 and 3.12; 14.1 and 1.6, respectively. Patient age, sex, number of levels affected, and signal intensity on T1- weighted and T2-weighted MR images were not significantly associated with post-operative improvement, p > 0.05. However, the only significant prognostic factor was the duration of symptoms if less than 1 year with p < 0.05. CONCLUSION: Short complaint duration coupled with close intra-operative monitoring was directly correlated with good CMS operation outcome while age, sex, number of levels affected, presence of cord signal on MR imaging, and surgical approach appear to have no significant effect on outcome.
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Mandarani, Putri, Haula Luthfia Ramadhan, Eva Yulianti, and Anna Syahrani. "Sistem Pendukung Keputusan Penulis Terbaik Menggunakan Metode Rank Order Centroid (ROC) dan Evaluation based on Distance from Average Solution (EDAS)." Journal of Information System Research (JOSH) 3, no. 4 (2022): 686–94. http://dx.doi.org/10.47065/josh.v3i4.1845.
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CV Jejak Publisher is a publishing company who has actively held writing competitions. However, there is a problem that occurs in the assessment process that has been running. The problem is several participants get the same value of the final score which makes it difficult for the jury to determine the order of winners. Therefore, to get a fairer and clearer result, need a calculation method that can support the jury's decision for determining the winners. This study aims to design a decision support system by applying the ROC and EDAS methods. ROC is used to assign a weighted value to each of the criteria set by CV Jejak Publisher, while EDAS is used to assess and rank the data of the participants. This system has output is the rank and final score of the participants from the highest to the lowest score. As a result, the ROC and EDAS methods were successfully applied to the system, as evidenced by 110 input data and all of them were successfully ranked by the system. The highest average score is 88.4 with the highest score based on 5 scoring criteria, namely 422. Thus, this system can be used to assist the jury in making a decision on the winner.
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Saito, Kai, Hitoshi Sugawara, Kiyoshi Ichihara, Tamami Watanabe, Akira Ishii, and Takahiko Fukuchi. "Prediction of 72-hour mortality in patients with extremely high serum C-reactive protein levels using a novel weighted average of risk scores." PLOS ONE 16, no. 2 (2021): e0246259. http://dx.doi.org/10.1371/journal.pone.0246259.
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The risk factors associated with mortality in patients with extremely high serum C-reactive protein (CRP) levels are controversial. In this retrospective single-center cross-sectional study, the clinical and laboratory data of patients with CRP levels ≥40 mg/dL treated in Saitama Medical Center, Japan from 2004 to 2017 were retrieved from medical records. The primary outcome was defined as 72-hour mortality after the final CRP test. Forty-four mortal cases were identified from the 275 enrolled cases. Multivariate logistic regression analysis (MLRA) was performed to explore the parameters relevant for predicting mortality. As an alternative method of prediction, we devised a novel risk predictor, “weighted average of risk scores” (WARS). WARS features the following: (1) selection of candidate risk variables for 72-hour mortality by univariate analyses, (2) determination of C-statistics and cutoff value for each variable in predicting mortality, (3) 0–1 scoring of each risk variable at the cutoff value, and (4) calculation of WARS by weighted addition of the scores with weights assigned according to the C-statistic of each variable. MLRA revealed four risk variables associated with 72-hour mortality—age, albumin, inorganic phosphate, and cardiovascular disease—with a predictability of 0.829 in C-statistics. However, validation by repeated resampling of the 275 records showed that a set of predictive variables selected by MLRA fluctuated occasionally because of the presence of closely associated risk variables and missing data regarding some variables. WARS attained a comparable level of predictability (0.837) by combining the scores for 10 risk variables, including age, albumin, electrolytes, urea, lactate dehydrogenase, and fibrinogen. Several mutually related risk variables are relevant in predicting 72-hour mortality in patients with extremely high CRP levels. Compared to conventional MLRA, WARS exhibited a favorable performance with flexible coverage of many risk variables while allowing for missing data.
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Yun, Jiyeo. "Relationships among Different Effect-Size Indexes for Inter-Rater Agreement between Human and Automated Essay Scoring." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 18 (2023): 901–19. http://dx.doi.org/10.22251/jlcci.2023.23.18.901.
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Objectives The purpose of this study is to investigate the magnitudes of and relationships among different effect-size indexes for inter-rater agreement between human and machine scoring in writing assessments.
 Methods The procedure of meta-analyses consists of data gathering, including literature search with criteria for inclusion and exclusion, and data analysis, including data cleaning and coding, after tests of heterogeneity for each index, hierarchical weighted models, and moderator and contrast analyses were conducted. Appropriate analyses were conducted using rma, robust, and robu functions in the metafor and robumeta packages in R software Version 3.3.2.
 Results Based on the results, the overall random-effects means for correlations, kappa values, and adjacent proportions of agreement between automated and human scoring of essay writing were .75, .48, and .99, respectively. The results from hierarchical weighed models and heterogeneity tests indicate that the rates of agreement for each index were inconsistent across studies. According to moderator and contrast analyses, correlations and kappa values using 6-point scales significantly differed from those using 3-, 4-, and 5-point scales, respectively, at alpha level .05. On the other hand, the adjacent proportions of agreement on either 3- or 4-point scales significantly differed from the adjacent proportions of agreement on the 5- and 6-point scales, respectively, at alpha level .01. Regarding the exact and adjacent proportions of agreement, the average of discrepancies was 0.34 units, and the variance of discrepancies between exact and adjacent proportions of agreement was 0.004. Similarly, the mean of discrepancies between the correlation and kappa was 0.27, and the variance of discrepancies between the correlation and kappa was 0.003.
 Conclusions According to this finding, machine scoring is similar to human scoring in terms of relative consistency and absolute consensus. Compared to the evaluation criteria suggested by prior studies, the degrees of inter-rater agreement seen in this study were above the thresholds for correlations, moderate agreement for kappa, and in the range of consensus rates for adjacent proportion agreement. The rates of agreement for each index were inconsistent across studies. This means that the all agreement indexes had relatively large between-studies differences compared to the between-effects differences within the studies. It is necessary to investigate if some moderators explain the between-studies differences. The number of score-scale points used for measuring inter-rater agreement between human and machine scoring influenced the agreement rates. The relationships across the four indexes (i.e., , , , and ) from the study appear to be reasonably strong and linear. Regardless of the number of points on the score scales, kappa values are 0.27 points lower than correlations. In addition, the mean exact proportions of agreements is 0.34 points lower than the mean adjacent proportions of agreements. Since each inter-rater agreement index has its own disadvantages, such as scale dependency, not showing the degree of identical matching and matching patterns, and so on, it is advised to report several inter-rater agreement indexes.
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Lee, Jung-Oh, Eun-Ah Park, Daebeom Park, and Whal Lee. "Deep Learning-Based Automated Quantification of Coronary Artery Calcification for Contrast-Enhanced Coronary Computed Tomographic Angiography." Journal of Cardiovascular Development and Disease 10, no. 4 (2023): 143. http://dx.doi.org/10.3390/jcdd10040143.
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Background: We evaluated the accuracy of a deep learning-based automated quantification algorithm for coronary artery calcium (CAC) based on enhanced ECG-gated coronary CT angiography (CCTA) with dedicated coronary calcium scoring CT (CSCT) as the reference. Methods: This retrospective study included 315 patients who underwent CSCT and CCTA on the same day, with 200 in the internal and 115 in the external validation sets. The calcium volume and Agatston scores were calculated using both the automated algorithm in CCTA and the conventional method in CSCT. The time required for computing calcium scores using the automated algorithm was also evaluated. Results: Our automated algorithm extracted CACs in less than five minutes on average with a failure rate of 1.3%. The volume and Agatston scores by the model showed high agreement with those from CSCT with concordance correlation coefficients of 0.90–0.97 for the internal and 0.76–0.94 for the external. The accuracy for classification was 92% with a 0.94 weighted kappa for the internal and 86% with a 0.91 weighted kappa for the external set. Conclusions: The deep learning-based and fully automated algorithm efficiently extracted CACs from CCTA and reliably assigned categorical classification for Agatston scores without additional radiation exposure.
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Albayrak, Umit, Adem Golcuk, Sinan Aktas, Ugur Coruh, Sakir Tasdemir, and Omer Kaan Baykan. "Classification and Analysis of Agaricus bisporus Diseases with Pre-Trained Deep Learning Models." Agronomy 15, no. 1 (2025): 226. https://doi.org/10.3390/agronomy15010226.
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This research evaluates 20 advanced convolutional neural network (CNN) architectures for classifying mushroom diseases in Agaricus bisporus, utilizing a custom dataset of 3195 images (2464 infected and 731 healthy mushrooms) captured under uniform white-light conditions. The consistent illumination in the dataset enhances the robustness and practical usability of the assessed models. Using a weighted scoring system that incorporates precision, recall, F1-score, area under the ROC curve (AUC), and average precision (AP), ResNet-50 achieved the highest overall score of 99.70%, demonstrating outstanding performance across all disease categories. DenseNet-201 and DarkNet-53 followed closely, confirming their reliability in classification tasks with high recall and precision values. Confusion matrices and ROC curves further validated the classification capabilities of the models. These findings underscore the potential of CNN-based approaches for accurate and efficient early detection of mushroom diseases, contributing to more sustainable and data-driven agricultural practices.
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Ahmad, Faiz U., Charif Sidani, Roberto Fourzali, and Michael Y. Wang. "Postoperative magnetic resonance imaging artifact with cobalt-chromium versus titanium spinal instrumentation." Journal of Neurosurgery: Spine 19, no. 5 (2013): 629–36. http://dx.doi.org/10.3171/2013.7.spine1359.
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Object Cobalt-chromium alloy (CoCr) rods haves some preferred biomechanical properties over titanium rods for spinal fixation. The use of CoCr rods in spinal fusion is relatively new, and there is no study in the existing world literature assessing the artifact caused by these rods in patients undergoing postoperative MRI. The purpose of this study is to compare the amount of imaging artifact caused by these implants and to assess its impact on the visualization of neighboring neural structures. Methods This study investigated MR images in patients who underwent implantation of thoracolumbar instrumentation using 5.5-mm-diameter CoCr rods between November 2009 and March 2011 and images obtained in a comparison group of patients who had 5.5-mm titanium rods implanted during the same time period. Axial measurements of the artifact created by the rods between the screw heads were compared between the groups. Two blinded board-certified radiologists performed the measurements independently. They scored the visualization of the spinal canal using a subjective scoring system of 1–3, with 1 representing very good visualization and 2 and 3 representing reduced (good or suboptimal, respectively) visualization as a result of rod-related artifact. All measurements and scores were independently provided for T1-weighted and T2-weighted fast spin echo sequences (1.5-T magnet, 5-mm slice thickness). Results A total of 40 levels from the CoCr group (6 patients) and 30 levels from the titanium group (9 patients) were included in the analysis. Visualization of the canal at all levels was rated a score of 1 (very good) by both evaluators for both the CoCr and titanium groups. The average artifact on T1-weighted images measured 11.8 ± 1.8 mm for the CoCr group and 8.5 ± 1.2 mm for the titanium group (p < 0.01). The corresponding measurements on T2-weighted images were 11.0 ± 2.3 mm and 8.3 ± 1.7 mm (p < 0.01), respectively. In a mixed regression model, the mean artifact measurement for the CoCr group was, on average, 3.5 mm larger than for the control group. There was no significant difference between the measurements of the 2 evaluators (p = 0.99). Conclusions The artifact caused by CoCr rods is approximately 3.5 mm larger than that caused by titanium rods on axial T1- and T2-weighted MRI. However, artifact from either CoCr or titanium was not found to interfere with the evaluation of the spinal canal and surrounding neural elements.
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Gutierrez Becker, B., S. Fraessle, H. Yao, et al. "P098 The Endoscopic Severity Score Map (ESSM): An Artificial Intelligence scoring system providing accurate, objective and localised measurements of endoscopic disease severity in ulcerative colitis." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i377—i378. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0228.
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Abstract Background Commonly used scoring schemes as the Mayo Endoscopic Subscore (MES) account for disease severity only at specific (i.e., the worst) segments and do not capture disease extent. However, for an accurate assessment of disease severity in patients with ulcerative colitis (UC), the measure of the precise extent of disease activity is necessary. Alternative systems that include disease extent have been proposed (Balint 2018), but their implementation is prohibited by the time and cost constraints of comprehensively scoring each location along the entire colon. Here, we present the Endoscopic Severity Score Map (ESSM), a scoring system based on Artificial Intelligence, capable of providing an assessment of disease severity and extent in UC in a fully automated manner. Methods The ESSM consists of 3 main elements (Fig. 1): 1) a quality algorithm which selects readable frames from a colonoscopy video, 2) a scoring system which assigns an MES to each readable frame (Gutierrez Becker 2020) and 3) a camera localisation algorithm that assigns each frame to an anatomical location within the colon (Yao 2022). The ESSM was trained and tested using 4,306 sigmoidoscopy videos from phase III Etrolizumab clinical trials (Hickory NCT02100696, Laurel NCT02165215, Hibiscus I NCT02163759, Hibiscus II NCT02171429 and Gardenia NCT02136069). Results We evaluate the performance of the ESSM by first assessing the agreement of scoring as compared to centrally read MES. The agreement between central reading and the ESSM at the colon section level was high (quadratic-weighted kappa k=0.81; Tab. 1). This was comparable to the agreement between central and local reading (k=0.84), suggesting that the ESSM shows levels of inter-rater variability comparable to experienced readers. Finally, we found correlations between the average ESSM at all anatomical locations and other disease activity markers to be moderate to high: faecal calprotectin rs=0.24, CRP rs=0.29, stool frequency rs=0.49, rectal bleeding rs=0.43 and physician global assessment rs=0.47 (Tab. 1). Conclusion Here, we introduced the ESSM, a fully-automated AI-based scoring system that enables accurate, objective and localised assessment of disease severity in UC. In brief, we show that the ESSM compares well with central reading (at the colon section level) and has clinical relevance when compared to other markers of disease activity. This tentatively suggests that the ESSM has the potential to augment the current way of assessing disease severity, both in clinical trials and everyday clinical practice.
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Yu-Dong, Cao, Liao Xin-Lin, and Liu Hai-Yan. "Image Quality Predictor with Highly Efficient Fully Convolutional Neural Network." Advances in Multimedia 2022 (February 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/1686298.
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A highly efficient deep fully convolutional neural network (DFCN) for image quality assessment (IQA) is designed in this paper. The DFCN consists of two branches, one scoring local patches and the other estimating the weights of local patches to enhance quality prediction. Then, the DFCN outputs quality score of the whole image with aggregate weighted average pooling. There are no fully connected layers in the DFCN, resulting in far fewer parameters. In addition, the network model utilizes multiscale images as inputs to enrich the extracted distortion information. Furthermore, the parameters of the model are optimized in two steps to reduce the requirement for computing power and the risk of overfitting. The parameters of the shared layers and the quality module are optimized firstly, and then, the parameters of the weight module are optimized with the designed loss function. The extensive experimental results show that the proposed DFCN outperforms other competing IQA methods and has strong generalization ability.
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Bieniek-Majka, Maryla. "USING THE EFE MATRIX TO ASSESS THE FACTORS DETERMINING THE DEVELOPMENT OF ENERGY COOPERATIVES IN POLAND." Annals of the Polish Association of Agricultural and Agribusiness Economists XXVII, no. 1 (2025): 9–20. https://doi.org/10.5604/01.3001.0054.9795.
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This study aimed to identify the key factors influencing the development of energy cooperatives in Poland and assess their impact within the framework of the PESTEL analysis criteria. The research was conducted through a literature review and expert opinions gathered during the National Forum of Energy Cooperatives (2023-2024). A PESTEL analysis combined with a weighted scoring method using the EFE (External Factor Evaluation) matrix was applied to evaluate the driving forces and barriers affecting the growth of energy cooperatives. The average score obtained in the EFE matrix (2.95) suggests that the analyzed factors significantly influence the development of energy cooperatives in Poland. Legal and economic factors play the most substantial roles, highlighting the necessity for legislative changes and financial support for energy cooperatives and renewable energy initiatives. Additionally, modernizing energy infrastructure is crucial for fully harnessing the potential of renewable energy sources (RES). The relatively low importance of social and environmental factors underscores the need for enhanced education and public engagement in environmental protection.
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Liu, Jianan, Chen Wang, Lingyan Zhang, and Yichen Wu. "Insights into the Demand for Children's Education and Entertainment Products in Family Settings." Philosophy and Social Science 1, no. 11 (2024): 50–58. https://doi.org/10.62381/p243b07.
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Children's education and entertainment products play a crucial role in children's growth by integrating knowledge transfer, skill development, and enjoyable experiences. As family education concepts continue to evolve, parents' demands for these products have become increasingly diverse. This study focuses on children under 12 and their parents, aiming to explore the demands for education and entertainment products in family settings and understand the core needs and expectations of both parents and children when using these products. The research employs qualitative and quantitative methods, including user interviews, the KJ method, literature review, analytic hierarchy process (AHP), and surveys, to comprehensively explore the hierarchical structure of user needs. Additionally, the weighted average and scoring methods are used to rank the priority of these needs. The findings reveal the key factors parents consider most important when selecting education and entertainment products and provide clear guidance on the priority of these demands for product innovation and development.
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Recker, Rebecca, Arash Adami, Beatriz Tone, et al. "Rodent Neonatal Bilateral Carotid Artery Occlusion with Hypoxia Mimics Human Hypoxic-Ischemic Injury." Journal of Cerebral Blood Flow & Metabolism 29, no. 7 (2009): 1305–16. http://dx.doi.org/10.1038/jcbfm.2009.56.
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We report a new clinically relevant model of neonatal hypoxic-ischemic injury in a 10-day-old rat pup. Bilateral carotid artery occlusion and 8% hypoxia (1 to 15 mins, BCAO-H) was induced with varying degrees of injury (mild, moderate, severe), which was quantified using magnetic resonance imaging including diffusion-weighted and T2-weighted imaging at 24 h and 21/28 days. We developed a magnetic resonance imaging-based rat pup severity score and compared 3D ischemic injury volumes/rat pup severity score with histology and behavioral testing. At 24 h, hypoxic-ischemic injury was observed in 17/27 animals; long-term survival was 81%. Magnetic resonance imaging lesion volumes did not correlate with hypoxia duration but correlated with rat pup severity score, which was used to classify animals into mild ( n = 21), moderate ( n = 6), and severe ( n = 10) groups with average brain lesion volumes of 0.9%, 33.2%, and 56.3%, respectively. Histology confirmed lesion location and histologic scoring correlated with the rat pup severity score. We also found excellent correlation between injury severity and multiple behavioral tasks. Bilateral carotid artery occlusion and hypoxia in the P10 rat pup is an excellent model of neonatal hypoxic-ischemic injury because it induces diffuse global injury similar to the term infant. This model can produce graded injury severity, similar to that seen in human neonates, but manipulation with hypoxia duration is unpredictable.