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1
Abowaly, Mohamed E., Abdel-Aziz A. Belal, Enas E. Abd Abd Elkhalek, et al. "Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling." Sustainability 13, no. 14 (2021): 8027. http://dx.doi.org/10.3390/su13148027.
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The proper assessment of trace element concentrations in the north Nile Delta of Egypt is needed in order to reduce the high levels of toxic elements in contaminated soils. The objectives of this study were to assess the risks of contamination for four trace elements (nickel (Ni), cobalt (Co), chromium (Cr), and boron (B)) in three different layers of the soil using the geoaccumulation index (I-geo) and pollution load index (PLI) supported by GIS, as well as to evaluate the performance of partial least-square regression (PLSR) and multiple linear regression (MLR) in estimating the PLI based on data for the four trace elements in the three different soil layers. The results show a widespread contamination of I-geo Ni, Co, Cr, and B in the three different layers of the soil. The I-geo values varied from 0 to 4.74 for Ni, 0 to 6.56 for Co, 0 to 4.11 for Cr, and 0 to 4.57 for B. According to I-geo classification, the status of Ni, Cr, and B ranged from uncontaminated/moderately contaminated to strongly/extremely contaminated. Co ranged from uncontaminated/moderately contaminated to extremely contaminated. There were no significant differences in the values of I-geo for Ni, Co, Cr, and B in the three different layers of the soil. According to the PLI classification, the majority of the samples were very highly polluted. For example, 4.76% and 95.24% of the samples were unpolluted and very highly polluted, respectively, in the surface layer of the soil profiles. Additionally, 14.29% and 85.71% of the samples were unpolluted and very highly polluted, respectively, in the subsurface layer of the soil profiles. Both calibration (Cal.) and validation (Val.) models of the PLSR and MLR showed the highest performance in predicting the PLI based on data for the four studied trace elements, as an alternative method. The validation (Val.) models performed the best in predicting the PLI, with R2 = 0.89–0.93 in the surface layer, 0.91–0.96 in the subsurface layer, 0.89–0.94 in the lowest layers, and 0.92–0.94 across the three different layers. In conclusion, the integration of the I-geo, PLI, GIS technique, and multivariate models is a valuable and applicable approach for the assessment of the risk of contamination for trace elements, and the PLSR and MLR models could be used through applying chemometric techniques to evaluate the PLI in different layers of the soil.
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Zhang, Penghao, Li Zhang, Zhongyu Wang, Shuang Chen, and Zhendong Shang. "A Strain-Transfer Model of Surface-Bonded Sapphire-Derived Fiber Bragg Grating Sensors." Applied Sciences 10, no. 12 (2020): 4399. http://dx.doi.org/10.3390/app10124399.
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An improved strain-transfer model was developed for surface-bonded sapphire-derived fiber Bragg grating sensors. In the model, the core and cladding of the fiber are separated into individual layers, unlike in conventional treatment that regards the fiber as a unitive structure. The separation is because large shear deformation occurs in the cladding when the core of the sapphire-derived fiber is heavily doped with alumina, a material with a high Young’s modulus. Thus, the model was established to have four layers, namely, a core, a cladding, an adhesive, and a host material. A three-layer model could also be obtained from the regressed four-layer model when the core’s radius increased to that of the cladding, which treated the fiber as if it were still homogeneous material. The accuracy of both the four- and three-layer models was verified using a finite-element model and a tensile-strain experiment. Experiment results indicated that a larger core diameter and a higher alumina content resulted in a lower average strain-transfer rate. Error percentages were less than 1.8% when the four- and three-layer models were used to predict the transfer rates of sensors with high and low alumina content, respectively.
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Alhassan, Seiba, Gaddafi Abdul-Salaam, Michael Asante, Yaw Missah, and Ernest Ganaa. "Analyzing Autoencoder-Based Intrusion Detection System Performance." Journal of Information Security and Cybercrimes Research 6, no. 2 (2023): 105–15. http://dx.doi.org/10.26735/ylxb6430.
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The rise in cyberattacks targeting critical network infrastructure has spurred an increased emphasis on the development of robust cybersecurity measures. In this context, there is a growing exploration of effective Intrusion Detection Systems (IDS) that leverage Machine Learning (ML) and Deep Learning (DL), with a particular emphasis on autoencoders. Recognizing the pressing need to mitigate cyber threats, our study underscores the crucial importance of advancing these methodologies. Our study aims to identify the optimal architecture for an Intrusion Detection System (IDS) based on autoencoders, with a specific focus on configuring the number of hidden layers. To achieve this objective, we designed four distinct sub-models, each featuring a different number of hidden layers: Test 1 (one hidden layer), Test 2 (two hidden layers), Test 3 (three hidden layers), and Test 4 (four hidden layers).We subjected our models to rigorous training and testing, maintaining consistent neuron counts of 30 and 60. The outcomes of our experimental study reveal that the model with a single hidden layer consistently outperformed its counterparts, achieving an accuracy of 95.11% for NSL-KDD and an impressive 98.6% for CIC-IDS2017. The findings of our study indicate that our proposed system is viable for implementation on critical network infrastructure as a proactive measure against cyber-attacks.
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Schubert, Wayne, Richard Taft, and Christopher Slocum. "A Simple Family of Tropical Cyclone Models." Meteorology 2, no. 2 (2023): 149–70. http://dx.doi.org/10.3390/meteorology2020011.
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This review discusses a simple family of models capable of simulating tropical cyclone life cycles, including intensification, the formation of the axisymmetric version of boundary layer shocks, and the development of an eyewall. Four models are discussed, all of which are axisymmetric, f-plane, three-layer models. All four models have the same parameterizations of convective mass flux and air–sea interaction, but differ in their formulations of the radial and tangential equations of motion, i.e., they have different dry dynamical cores. The most complete model is the primitive equation (PE) model, which uses the unapproximated momentum equations for each of the three layers. The simplest is the gradient balanced (GB) model, which replaces the three radial momentum equations with gradient balance relations and replaces the boundary layer tangential wind equation with a diagnostic equation that is essentially a high Rossby number version of the local Ekman balance. Numerical integrations of the boundary layer equations confirm that the PE model can produce boundary layer shocks, while the GB model cannot. To better understand these differences in GB and PE dynamics, we also consider two hybrid balanced models (HB1 and HB2), which differ from GB only in their treatment of the boundary layer momentum equations. Because their boundary layer dynamics is more accurate than GB, both HB1 and HB2 can produce results more similar to the PE model, if they are solved in an appropriate manner.
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Rowe, Avery. "Effect of drainage layers on water retention of potting media in containers." PLOS ONE 20, no. 2 (2025): e0318716. https://doi.org/10.1371/journal.pone.0318716.
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Excess water retention in the potting medium can be a significant problem for plants grown in containers due to the volume of saturated medium which forms above the drainage hole. Adding a layer of coarse material like gravel or sand at the bottom is a common practise among gardeners with the aim of improving drainage, but some researchers have argued that such layers will raise the saturated area and in fact increase water retention. Two different depths and four different materials of drainage layer were tested with three different potting media to determine the water retention in the container after saturating and draining freely. For loamless organic media, almost all types of drainage layer reduced overall water retention in the container compared to controls. For loam-based media, most drainage layers had no effect on the overall water retention. Two simple models were also used to estimate the water retention in the media alone, excluding the drainage layer itself. All drainage layers reduced water retention of loamless organic media, according to both models. There was disagreement between the two models applied to loam-based media, and further study is required to determine the most accurate. Both models showed that some drainage layers with smaller particle sizes reduced water retention in loam-based media, but disagreed on the effect of drainage layers with larger particle sizes. Overall, any drainage layer was likely to reduce water retention of any medium, and almost never increased it. Thicker drainage layers were more effective than thinner layers, with the most effective substrate depending on the potting media used. A 60 mm layer of coarse sand was the most universally-effective drainage layer with all potting media tested.
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Tsepav, Matthew Tersoo, Azeh Yakubu, Kumar Niranjan, et al. "Geophysical Characterisation of Native Clay Deposits in Some Parts of Niger State, Nigeria." Journal of Physics: Theories and Applications 6, no. 1 (2022): 43. http://dx.doi.org/10.20961/jphystheor-appl.v6i1.56457.
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<p class="Abstract">Clay minerals are among the world’s most important and useful industrial minerals. Conductance, transmissivity and corrosity are some physical parameters for determining quality clay. Four (4) clay deposit sites in Kaffin-Koro, Dutse, Dogon-Ruwa and Kushikoko were investigated to evaluate corrosivity, the longitudinal conductance and transmissivity to determine the clay quality. Electrical resistivity method employing Schlumberger electrode array was used to determine the thicknesses and depths of the subsurface strata while Interpex 1xD software was used to interpret the data. Three (3) to four (4) layer earth models were delineated. Kaffin-Koro and Dutse showed three layer models while Dogon-Ruwa and Kushikoko revealed four layers. Moderate clay content was found in Kaffin-Koro in the second layer with longitudinal conductance value of 0.4780 siemens and thickness 0.770m at depth of 1.17m Dogon-Ruwa also had moderate clay content in the third layer with conductance value of 0.237 siemens, depth of 2.43m and thickness 1.76m. Kushikoko had low clay deposit in the second layer with conductance 0.1810 siemens and thickness 2.73 m at 4.37 m while the clay deposit in Dutse appeared to be generally poor as the longitudinal conductance values of the top two layers were less than 0.1 siemens.</p>
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Simms, J. E., and F. D. Morgan. "Comparison of four least‐squares inversion schemes for studying equivalence in one‐dimensional resistivity interpretation." GEOPHYSICS 57, no. 10 (1992): 1282–93. http://dx.doi.org/10.1190/1.1443196.
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The problem of equivalence in dc resistivity inversion is well known. The ability to invert resistivity data successfully depends on the uniqueness of the model as well as the robustness of the inversion algorithm. To study the problems of model uniqueness and resolution, theoretical data are inverted using variations of a nonlinear least‐squares inversion. It is only through model studies such as this one, where the true solutions are known, that realistic and meaningful comparisons of inversion methods can be undertaken. The data are inverted using three schemes of fixed‐layer thickness where only the resistivity varies, and the results are compared to the variable parameter inversion where both the layer resistivities and thicknesses are allowed to vary. The purpose of fixing the layer thicknesses is to reduce the number of parameters solved for during the inversion process. By doing this, nonuniqueness may be reduced. The fixed‐layer thickness schemes are uniform thickness, geometrical progression of thickness, and logarithmic progression of thickness. By applying each inversion scheme to different models, the layer thickness that minimizes the data rms error for various numbers of layers is determined. The curve of data rms error versus model rms error consists of three general regions: unique, nonunique, and no resolution. A good inversion routine simultaneously minimizes the data rms and model rms errors. The variable parameter scheme is best at simultaneously minimizing the data rms and model rms errors for models that can be resolved through the inversion process. The optimum number of layers in the model can be determined by using a modified F‐test.
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Lawrence, E., E. J. Garba, Y. M. Malgwi, and M. A. Hambali. "An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports." European Journal of Electrical Engineering and Computer Science 6, no. 1 (2022): 53–59. http://dx.doi.org/10.24018/ejece.2022.6.1.407.
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Wind speed patterns are highly dynamic and non-linear and thus cannot be accurately forecasted using conventional linear regression models. In this work, Artificial Neural Network (ANN) technique was applied to forecast wind speeds and directions in airports. Monthly data of maximum temperature, minimum temperature, wind speed, wind direction, relative humidity and wind run for Yola International Airport were collected from 1995 to 2021 from Nigerian Meteorological Agency (NIMET) Abuja-Nigeria. Six Neural Network models were built. ANN with no hidden layers, ANN model with one hidden layer and two dropout layers, ANN model with four hidden layers and three dropout layers, ANN model with eight hidden layers, ANN model with nine hidden layers and finally, ANN model with ten hidden layers. Back Propagation training algorithm was implemented using the PYTHON toolbox. Each of the models was trained using the training dataset and validated using the validation dataset. To test the forecasting ability of each of the models we tested it using unknown data that is the test dataset. The results from each of the models were organized and assessed in terms of the magnitude of the statistical error between the measured result and the real data. This was achieved by measuring the average of the Mean Square Errors (MSE) and Mean Absolute Error (MAE) for each of the models used for forecasting both wind speeds and directions. The results show that Multilayer perceptron with ten hidden layers with (MSE) = 0.92 and (MAE) = 0.73 emerged as the most preferred model for wind speeds forecast while the multilayer perceptron with four hidden layers with (MSE) = 1,858 and (MAE) = 35 emerged the most preferred model for wind directions forecast. Future research can be carried out to improve the accuracy of the model for wind direction forecasts.
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Mehmood, Maryam, Farhan Hussain, Ahsan Shahzad, and Nouman Ali. "Classification of Remote Sensing Datasets with Different Deep Learning Architectures." Earth Sciences Research Journal 28, no. 4 (2025): 409–19. https://doi.org/10.15446/esrj.v28n4.113518.
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Remote sensing image classification has great advantages in the areas of environmental monitoring, urban planning, disaster management and many others. Unmanned Aerial Vehicles (UAVs) have revolutionized remote sensing by providing high-resolution imagery. In this context, effective image classification is crucial for extracting meaningful information from UAV-captured images. This study presents a comparison of different deep learning-based approach for supervised image classification of UAV images. We have experimented on four different CNN models like VGG 16, Alex net, Resnet50 and a deep neural network Efficient-Net-B0 on different remote sensing datasets; AID and AIDER. Multiple combinations were tried to find out which model performs better on which type of datasets. We have used pre-trained initial layers of four CNN models (AlexNet, VGG 16, Resnet50 and Efficient-Net-Bo) then last three layers of each of the selected models are removed and new layers have been added with better tuned parameters. Two different schemes were analyzed. In Scheme-1 the original AlexNet, VGG 16, Resnet50 and Efficient-Net-B0 were experimented without changing and tuning their number of parameters, while in Scheme-2 transfer learning was applied on the pre-trained models and after removing last three layers new layers were added with better tuned hyper-parameters. The evaluation of above schemes was ensured through comprehensive metrics across diverse land cover classes, four different performance evaluation matrices namely; F1 score, precision, accuracy and recall. The main focus of this research is towards transfer learning and adding new layers into pre-trained models to get better classification accuracy.
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Deng, Qi. "Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud." Journal of Risk and Financial Management 12, no. 4 (2019): 177. http://dx.doi.org/10.3390/jrfm12040177.
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The Artificial Intelligence BlockCloud (AIBC) is an artificial intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer (the latter three are the collective “upper-layers”). The AIBC layers have distinguished responsibilities and thus performance and robustness requirements. The upper layers need to follow a set of economic policies strictly and run on a deterministic and robust protocol. While the fundamental layer needs to follow a protocol with high throughput without sacrificing robustness. As such, the AIBC implements a two-consensus scheme to enforce economic policies and achieve performance and robustness: Delegated Proof of Economic Value (DPoEV) incentive consensus on the upper layers, and Delegated Adaptive Byzantine Fault Tolerance (DABFT) distributed consensus on the fundamental layer. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to enforce the DPoEV, as well as to achieve the best balance of performance, robustness, and security. The DPoEV-DABFT dual-consensus architecture, by design, makes the AIBC attack-proof against risks such as double-spending, short-range and 51% attacks; it has a built-in dynamic sharding feature that allows scalability and eliminates the single-shard takeover. Our contribution is four-fold: that we develop a set of innovative economic models governing the monetary, trading and supply-demand policies in the AIBC; that we establish an upper-layer DPoEV incentive consensus algorithm that implements the economic policies; that we provide a fundamental layer DABFT distributed consensus algorithm that executes the DPoEV with adaptability; and that we prove the economic models can be effectively enforced by AIBC’s DPoEV-DABFT dual-consensus architecture.
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Al-Khazzar, Ahmed, Zainab Altaweel, and Jabbar Salman Hussain. "Using deep neural networks in classifying electromyography signals for hand gestures." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 217. http://dx.doi.org/10.11591/ijai.v13.i1.pp217-227.
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<span lang="EN-US">Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model.</span>
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Obidallah, Waeal J., Bijan Raahemi, and Waleed Rashideh. "Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques." Data 7, no. 5 (2022): 57. http://dx.doi.org/10.3390/data7050057.
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We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services.
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Baines, Peter G., and Fiona Guest. "The nature of upstream blocking in uniformly stratified flow over long obstacles." Journal of Fluid Mechanics 188 (March 1988): 23–45. http://dx.doi.org/10.1017/s002211208800062x.
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The general method described in Baines 1988 has been applied to stratified flows of finite depth over long obstacles where the flow initially has uniform horizontal velocity. The fluid consists of a finite number of homogeneous layers of equal thickness and with equal density increments. This represents the state of continuous stratification with constant density gradient as closely as possible, for a given number of layers. Two-, three-, four- and sixty-four-layered models are studied in detail. The results are expressed in terms of the initial Froude number F0 (F0 = U/ĉ1 where U is the fluid speed and ĉ1 is the speed of the fastest long internal wave mode in the fluid at rest) and the obstacle height. In general, introduction of an obstacle into the flow causes disturbances to propagate upstream (columnar disturbance modes) which alter the velocity and density profiles there. These may accumulate to cause upstream blocking of some of the fluid layers if F0 is sufficiently small. As the number of fluid layers increases, so does the range of F0 for which this upstream blocked flow occurs. There are no upstream disturbances for F0 > 1, and for F0 < 1 the upstream disturbances are of the rarefaction type if upstream blocking does not occur. The results for three and four layers show how several coexisting modes may interact to affect the upstream profiles. The results for sixty-four-layers provide theoretical support for the observational criterion (Baines 1979b) that blocking in initially uniformly stratified flow occurs when Nhm/U > 2 (N is the Brunt—Väisälä frequency and hm the obstacle height), provided that more than two modes are present. In some situations, layered models are found to be inadequate as a representation of continuous stratification when one or more layers thicken to the extent that their discreteness is significant.
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Zhang, Y., Z. Gao, D. Li, et al. "On the computation of planetary boundary-layer height using the bulk Richardson number method." Geoscientific Model Development 7, no. 6 (2014): 2599–611. http://dx.doi.org/10.5194/gmd-7-2599-2014.
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Abstract. Experimental data from four field campaigns are used to explore the variability of the bulk Richardson number of the entire planetary boundary layer (PBL), Ribc, which is a key parameter for calculating the PBL height (PBLH) in numerical weather and climate models with the bulk Richardson number method. First, the PBLHs of three different thermally stratified boundary layers (i.e., strongly stable boundary layers, weakly stable boundary layers, and unstable boundary layers) from the four field campaigns are determined using the turbulence method, the potential temperature gradient method, the low-level jet method, and the modified parcel method. Then for each type of boundary layer, an optimal Ribc is obtained through linear fitting and statistical error minimization methods so that the bulk Richardson method with this optimal Ribc yields similar estimates of PBLHs as the methods mentioned above. We find that the optimal Ribc increases as the PBL becomes more unstable: 0.24 for strongly stable boundary layers, 0.31 for weakly stable boundary layers, and 0.39 for unstable boundary layers. Compared with previous schemes that use a single value of Ribc in calculating the PBLH for all types of boundary layers, the new values of Ribc proposed by this study yield more accurate estimates of PBLHs.
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Adedotun, Adeyemo I., Akande V. Oluwatimilehin, Akinlalu A. Adewale, and Sanusi S. Olumide. "Comparative Studies of Subsurface Layers’ Competence Evaluation using TOPSIS and AHP Models at Ilaramokin, Southwestern Nigeria." Physics Access 03, no. 02 (2023): 78–98. http://dx.doi.org/10.47514/phyaccess.2023.3.2.012.
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The incessant collapse of buildings associated with geotechnical incompetence in different parts of Nigeria and the rapid growth of Ilaramokin town near Akure Southwestern Nigeria motivated this work. Two multi-criteria decision analysis approaches were used in integrating geoelectric parameters (topsoil resistivity, weathered layer resistivity and bedrock resistivity), static water level measurements and geology in evaluating the subsurface geotechnical competence of Ilaramokin. Thirty (30) vertical electrical sounding, eighty-six (86) static water level measurements and geological maps were used. The vertical electrical sounding (VES) results delineated three to five geoelectric layers which correspond to four geologic layers namely; the topsoil, weathered layer, partially weathered/fractured basement and presumed bedrock. The resistivity of the geologic layers varies respectively from 48 - 701, 31 - 1065, 14 - 139, 132 - 6582 Ωm, while their thickness varies from 0.4 - 4.1, 1.3 - 11.6 and 4.0 - 20.1 m in the three upper layers respectively. The VES results were presented as topsoil, weathered layer and bedrock resistivity maps. The VES results, static water level measurement and geology were integrated using both the Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) models to produce geotechnical competence maps. Consistency test and grain size analysis were carried out on 10 soil samples obtained across the area to validate the geotechnical competence model maps produced using both TOPSIS and AHP models. The validation showed that the geotechnical model map produced from the TOPSIS model has a higher percentage (90%) of correlation with consistency tests and grain size analysis compared to that of the AHP model (70%).
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Qdroo, Awf, and Muhammet Baykara. "A New Approach to Detect Fake News Related to Covid-19 Pandemic Using Deep Neural Network." Journal of Applied Science and Technology Trends 3, no. 02 (2022): 20–27. http://dx.doi.org/10.38094/jastt302124.
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The fake news that accompanied the COVID-19 pandemic on social media platforms negatively affected people and led to a state of panic and fear of the unknown. This study aims to build a model for classifying textual news for four datasets related to COVID-19, binary classification (fake and real) with high performance. Two hybrid deep learning models were built. The first model consists of three layers of a one-dimension convolutional neural network (1D-CNN), followed by two layers of a long-short-term memory neural network (LSTM). The second model consists of three layers of a 1D-CNN followed by two layers of a bidirectional LSTM neural network (BiLSTM). Finally, the results obtained using hybrid models were compared with the results obtained by applying three machine learning classifiers (naïve Bayes, logistic regression, and k-nearest neighbor) on the same data sets. This study achieved promising results with an accuracy of (96.98%, 94.52%, 99.60%, and 99.90%) for the first model with all data sets and (97.15%, 95.32%, 99.40%, and 99.82%) for the second model with the same four data sets.
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Mazurkiewicz, Łukasz, Jerzy Małachowski, Krzysztof Damaziak, Paweł Baranowski, and Paweł Gotowicki. "IDENTIFICATION OF LAYERS DISTRIBUTION IN THE COMPOSITE COUPON USING FINITE ELEMENT METHOD AND THREE POINT BENDING TEST." Acta Mechanica et Automatica 7, no. 3 (2013): 160–65. http://dx.doi.org/10.2478/ama-2013-0027.
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Abstract The main objective of the study is to develop experimentally validated FE model and perform numerical analysis of layered composites made by hand lay-up techniques during tension and bending test. The research object is glass - polyester laminate made of four unidirectional layers. In order to validate the numerical models experimental test were performed. Due to the very different stiffness modulus in tension and bending loading the material properties obtained from standard test are not suitable to apply in numerical model. Significantly different behaviour compared to experimental test was obtained for tree point bending where the numerical model becomes too stiff. Simple coupons, relatively easy to manufacture presented in the paper have very low quality. The differences in actual and theoretical bending stiffness (obtained from tension stiffness) exceed 70%. In order to represent the actual structure the layers of the composite were divided by resin layers and also additional resin layer at the top and bottom of the model were defined. Single stage optimization process was used to adjust the material layout. After layer set-up modification very significant improvement can be seen for flexural behaviour
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Deokar, A. H., and A.A. Korake. "Experimental Investigation of Ferrocement Slab Panels Using Square Metal Mesh." Journal of Structural Engineering, its Applications and Analysis 7, no. 3 (2024): 7–12. https://doi.org/10.5281/zenodo.13770899.
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<em>The ongoing assessment depicts the outcomes of testing level ferrocement sheets upheld with different quantities of wire network layer and assortment in board thickness. The essential objective of these exploratory tests is to consider the effect of using different amounts of wire network Layers and thickness minor takeoff from the flexural strength of stage ferrocement discussions and to concentrate on the impact of fluctuating the amount of twine local area layers on the malleability and a conclusive force of this type of Ferrocement structure. In this assessment, all of the models were parceled into four social events to explore the strength and lead of ferrocement level sheets presented to two-point stacking. 24 ferrocement added substances have been built and endeavored. 24 ferrocement components were built and tried. The pre-owned number of wire network layers is single, two, three and four layers; additionally, thicknesses of boards are 30mm and 40mm.</em>
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Ikeda, Ryosaku, and Hiroyuki Kusaka. "Proposing the Simplification of the Multilayer Urban Canopy Model: Intercomparison Study of Four Models." Journal of Applied Meteorology and Climatology 49, no. 5 (2010): 902–19. http://dx.doi.org/10.1175/2009jamc2336.1.
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Abstract The study proposes the simplification of the multilayer urban canopy model. Four types of multilayer urban canopy models—level 4, level 3, level 2, and level 1—are developed to reduce the computational load of the heat budget calculations at the wall surface. The level 4 model, which accounts for the wall directions and the vertical layer, is simplified in three ways: the level 3 model only accounts for the vertical layers, the level 2 model accounts for the wall directions, and the level 1 model accounts for neither the wall directions nor the vertical layer. From the simplification, compared to the level 4 model, the memory is reduced by 57%, 65%, and 72% for the level 3–level 1 models, respectively, when the vertical canopy layer is seven. At the same time, the CPU time is reduced by 67%, 70%, and 78% for the level 3–level 1 models. Then, each canopy model is compared with observations in Tokyo. The results show that the simulations from the four models are close to the observed ones, and the differences among the four models are very small. An additional model intercomparison study based on idealized simulations indicates that the level 3 model can be used instead of the level 4 model in any condition, whereas the level 2 and level 1 models are proposed to be used under conditions with a large sky view factor.
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Chiper Titire, Larisa, and Cristian Muntenita. "Experimental and Numerical Analysis of the Damage Mechanism of an Aramid Fabric Panel Engaged in a Medium-Velocity Impact." Polymers 16, no. 13 (2024): 1920. http://dx.doi.org/10.3390/polym16131920.
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The aim of this study is to analyze the ballistic impact behavior of a panel made of Twaron CT736 fabric with a 9 mm Full Metal Jacket (FMJ) projectile. Three shots are fired at different velocities at this panel. The ballistic impact test procedure was carried out in accordance with NIJ 010106. The NIJ-010106 standard is a document that specifies the minimum performance requirements that protection systems must meet to ensure performance. The 9 mm FMJ projectile is, according to NIJ 010106, in threat level II, but the impact velocity is in threat level IIIA. Analysis of macro-photographs of the impact of the Twaron CT736 laminated fabric panel with a 9 mm FMJ projectile involves a detailed examination of the images to gather information about the material performance and failure mechanisms at the macro- or even meso-level (fabric/layer, thread). In this paper, we analyze numerically and experimentally a panel consisting of 32 layers, made of a single material, on impact with a 9 mm FMJ projectile. The experimental results show that following impact of the panel with three projectiles, with velocities between 414 m/s and 428 m/s, partial penetration occurs, with a different number of layers destroyed, i.e., 15 layers in the case of the projectile velocity of 414 m/s, 20 layers of material in the case of the panel velocity of 422 m/s and 22 layers destroyed in the case of the projectile velocity of 428 m/s. Validation of the simulated model is achieved by two important criteria: the number of broken layers and the qualitative appearance. Four numerical models were simulated, of which three models validated the impact results of the three projectiles that impacted the panel. Partial penetration occurs in all four models, breaking the panel in the impact area, with only one exception, i.e., the number of layers destroyed, in which case the simulation did not validate the validation criterion. The performance of Twaron CT736 fabric is also given by the indentation depth values by two methods: according to NIJ 0101.06 and by 3D scanning. The NIJ 010106 standard specifies that a panel provides protection when the indentation depth values are less than 0.44 mm.
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Ruckley, C. V., J. J. Dale, B. Gibson, D. Brown, A. J. Lee, and R. J. Prescott. "Multi-layer compression: comparison of four different four-layer bandage systems applied to the leg." Phlebology: The Journal of Venous Disease 18, no. 3 (2003): 123–29. http://dx.doi.org/10.1258/026835503322381324.
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Objective: To compare on standardized laboratory models the performance of four commercially available four-layer bandage systems. Methods: Four experienced bandagers applied each of the four systems [Profore® Regular (Smith & Nephew, Hull, UK), Ultra Four (Robinsons, Chesterfield, UK), System 4 (SSL International, Knutsford, UK) and K-Four® (Parema, Loughborough, UK)] to two models: a 12.5 cm diameter padded cylinder and a 9.5-14.5 cm padded cone. Bandages were applied individually in single layers and as a completed system using standard application techniques. Pressures were measured by the Borgnis Medical Stocking Tester at positions corresponding to ankle, gaiter and mid-calf areas as determined by the pressure sensor. Results: A total of 768 observations were made: 384 for each model, 192 for each bandaging system, 192 for each bandager and 128 for each measuring point. The increase in pressure produced by each additional layer was in the range of 50-60% of the pressure achieved by the same bandage when used as a single layer. Each bandage system and each bandager produced a gradient of final mean pressure irrespective of whether the bandage was applied to a cylinder or a cone. However, there were no significant differences in the gradients between the four bandage systems or between the four bandagers. There were significant differences in the final pressures achieved among the bandage systems when applied as completed systems (mean: Profore® = 42 mmHg; System 4 = 45 mmHg; K-Four® = 48 mmHg; and Ultra Four = 51 mmHg; P<0.001). Conclusions: These results challenge a commonly-held assumption concerning the additive effect of pressures generated by successive bandage layers. When applied as part of a multi-layered system each bandage adds just over half the pressure achieved by the same bandage when applied alone. The four completed systems produced pressures within a range appropriate for ulcer therapy, although there were significant differences in mean pressures. This capability of the systems to produce different pressures could be clinically important in the hands of inexperienced bandagers or with patients at risk of pressure damage..
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Dobeš, Pavel, Antonín Lokaj, and Kristýna Vavrušová. "Stiffness and Deformation Analysis of Cross-Laminated Timber (CLT) Panels Made of Nordic Spruce Based on Experimental Testing, Analytical Calculation and Numerical Modeling." Buildings 13, no. 1 (2023): 200. http://dx.doi.org/10.3390/buildings13010200.
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Timber structures are currently more important for solving tasks in construction practice. For this reason, there is an opportunity for research in the area of physical tests and numerical models. This paper deals with the determination and comparison of the deformation properties of cross-laminated timber (CLT) panels based on laboratory tests, analytical calculation and numerical modeling. CLT panels are structural building components consisting of cross-oriented solid timber layers. Three types of panels with different geometry and number of layers (three, five and seven) were experimentally tested using a four-point bending test, where load–deformation curves were recorded. The results of the experimental testing of the three-layer panels were subsequently compared with a numerical model in SCIA Engineer, a numerical model in ANSYS Workbench and an analytical calculation. The research shows a good agreement in bending behavior between the laboratory tests, the analytical calculation according to the standard and two different approaches in numerical analysis.
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Sedighian-Fard, Mohammad, Nader Solatifar, and Henrikas Sivilevičius. "CALIBRATION OF REGRESSION-BASED MODELS FOR PREDICTION OF TEMPERATURE PROFILE OF ASPHALT LAYERS USING LTPP DATA." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 29, no. 4 (2023): 329–41. http://dx.doi.org/10.3846/jcem.2023.18611.
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For analysis, design, and rehabilitation purposes of flexible pavements, the temperature profile of asphalt layers should be determined. The predictive models as an alternative to in-situ measurements, are rapid and easy methods to determine the temperature of asphalt layer at various depths. These models are developed based on limited field data. Hence, there is a need for developing new models for prediction of temperature profile of asphalt layers in various climatic regions. In this study, climatic data was retrieved from the Long-Term Pavement Performance (LTPP) database. The information of 33 asphalt pavement test sections in 16 states in the United States was employed for calibrating the predictive models. Using the prepared data, the temperature profile of asphalt layers was predicted utilizing four regression-based models, including Ramadhan and Wahhab, Hassan et al., Albayati and Alani, and Park et al. models. Existing prediction models were calibrated, and to predict the temperature profile of asphalt layer, new models were developed. Performance evaluation and validation of newly developed models showed an excellent correlation between predicted and measured values. Results show the ability of the developed models in predicting the temperature profile of asphalt layers with very good prediction precision (R2 = 0.94) and low bias.
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Mustak Un Nobi, Md, Md Rifat, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, and Dunren Che. "GLD-Det: Guava Leaf Disease Detection in Real-Time Using Lightweight Deep Learning Approach Based on MobileNet." Agronomy 13, no. 9 (2023): 2240. http://dx.doi.org/10.3390/agronomy13092240.
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The guava plant is widely cultivated in various regions of the Sub-Continent and Asian countries, including Bangladesh, due to its adaptability to different soil conditions and climate environments. The fruit plays a crucial role in providing food security and nutrition for the human body. However, guava plants are susceptible to various infectious leaf diseases, leading to significant crop losses. To address this issue, several heavyweight deep learning models have been developed in precision agriculture. This research proposes a transfer learning-based model named GLD-Det, which is designed to be both lightweight and robust, enabling real-time detection of guava leaf disease using two benchmark datasets. GLD-Det is a modified version of MobileNet, featuring additional components with two pooling layers such as max and global average, three batch normalisation layers, three dropout layers, ReLU as an activation function with four dense layers, and SoftMax as a classification layer with the last lighter dense layer. The proposed GLD-Det model outperforms all existing models with impressive accuracy, precision, recall, and AUC score with values of 0.98, 0.98, 0.97, and 0.99 on one dataset, and with values of 0.97, 0.97, 0.96, and 0.99 for the other dataset, respectively. Furthermore, to enhance trust and transparency, the proposed model has been explained using the Grad-CAM technique, a class-discriminative localisation approach.
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Al-Khazzar, Ahmed M., Zainab Altaweel, and Jabbar S. Hussain. "Using deep neural networks in classifying electromyography signals for hand gestures." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 217–27. https://doi.org/10.11591/ijai.v13.i1.pp217-227.
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Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural networkmodels are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model.
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Nuñez Ponasso, Guillermo, William A. Wartman, Ryan C. McSweeney, et al. "Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy." Bioengineering 11, no. 11 (2024): 1071. http://dx.doi.org/10.3390/bioengineering11111071.
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Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10–20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.
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Aradea, Rianto, and Husni Mubarok. "Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models." Scientific Journal of Informatics 11, no. 3 (2024): 595–608. http://dx.doi.org/10.15294/sji.v11i3.6494.
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Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.
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Vallaghe, Sylvain, and Maureen Clerc. "A Global Sensitivity Analysis of Three- and Four-Layer EEG Conductivity Models." IEEE Transactions on Biomedical Engineering 56, no. 4 (2009): 988–95. http://dx.doi.org/10.1109/tbme.2008.2009315.
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Hekal, Ghada Mousa, Ayman Magdy Moawad Elshaboury, and Yousry B. I. Shaheen. "The impact of openings on ferrocement I-beams: a study on metallic and non-metallic mesh reinforcement." Challenge Journal of Concrete Research Letters 15, no. 2 (2024): 30. http://dx.doi.org/10.20528/cjcrl.2024.02.001.
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The primary objective of this investigation is to assess the influence of openings on the structural performance of ferrocement I-beams, incorporating diverse metallic and non-metallic mesh reinforcements. Sixteen beams underwent testing utilizing a four-point loading system until failure, categorized into four groups based on the type of mesh reinforcement. Each group comprised a control I-beam without openings and three additional beams featuring one, two, and three openings, respectively. To ensure consistent reinforcement weight, the four groups were reinforced with three layers of welded steel meshes, two layers of expanded metal meshes, two layers of Tensar meshes, and eight layers of Gavazzi meshes. Comparative analysis of the experimental outcomes was conducted with finite element models utilizing Abaqus. Therefore, there was good agreement between the experimental and numerical results. The findings showed that beams with no openings, one, and two openings reinforced with Gavazzi meshes had the highest ultimate load compared to other tested beams, while beams with three openings, those reinforced with expanded metal meshes had the greatest ultimate loads. Placing three openings in beams, with dimensions of 100×50 mm (two of these openings are approximately 10 cm apart from each edge while the third opening is located at mid-span), reduced the load-to-weight ratio by about 20.7%, 12.9%, 8.2%, and 23.8% for welded beams, expanded beams, Tensar beams, and Gavazzi beams, respectively, compared to the beams with no openings.
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Raiche, A. P., D. L. B. Jupp, H. Rutter, and K. Vozoff. "The joint use of coincident loop transient electromagnetic and Schlumberger sounding to resolve layered structures." GEOPHYSICS 50, no. 10 (1985): 1618–27. http://dx.doi.org/10.1190/1.1441851.
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One‐dimensional earth models consisting of uniform horizontal layers are useful both as actual representations of earth structures and as host models for more complex structures. However, there are often inherent difficulties in establishing layer thicknesses and resistivities from one type of measurement alone. For example, the dc resistivity method is sensitive to both conductive and resistive layers, but as these layers become thin, nonuniqueness becomes a severe problem. Electromagnetic (EM) methods are good for establishing the parameters of conductive layers, but they are quite insensitive to resistive layers. The use of both coincident loop transient EM (TEM) and Schlumberger methods, together with a joint inverse computer program, can vastly improve interpretation of layered‐earth parameters. The final model is less dependent upon starting guesses, error bounds are much improved, and nonuniqueness is much less of a problem. These advantages are illustrated by interpretation of real field data as well as by a theoretical study of four different types of earth models.
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Abubakar, Aminuddeen, and Ahmed Baita Garko. "A Predictive Model for Network Intrusion Detection System Using Deep Neural Network." Dutse Journal of Pure and Applied Sciences 7, no. 3a (2022): 113–28. http://dx.doi.org/10.4314/dujopas.v7i3a.12.
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Network Intrusion Detection System (NIDS) is an important part of Cyber safety and security. It plays a key role in all networked ICT systems in detecting rampant attacks such as Denial of Service (DoS) and ransom ware attacks. Existing methods are inadequate in terms of accuracy detection of attacks. However, the requirement for high accuracy detection of attacks using Deep Neural Network requires expensive computing resources which in turn make most organisations, and individuals shy away from it. This study therefore aims at designing a predictive model for network intrusion detection using deep neural networks with very limited computing resources. The study adopted Cross Industry Standard Process for Data Mining (CRISP-DM) as one of the formal methodologies and python was used for both testing and training, using crucial parameters such as the learning rate, number of epochs, neurons and hidden layers which greatly determined the accuracy level of the DNN algorithm. These parameters were experimented with values that are lesser compared to previous studies, training and evaluation were also done on the KDD99 data-set. The varying values of accuracy obtained from this study on four models with different numbers of layers of 50-epochs and learning rate of 0.01 achieved competitive results in comparison with the previous research of 100-1000 epochs and learning rate of 0.1. Therefore, the model with two layers attained same accuracy of 0.955 as the model with three layers from the previous study out of the four models tested in this study.
 Also, the models with three and four layers in this study attained an accuracy of 0.956, which is 0.001greater than the previous study's models.
 Keywords: Network-Based IDS, Host-Based IDS, Deep Neural Network, Denial of Service, Knowledge Discovery Dataset
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ZHANG, SHANHUI, QI GAO, and CHAOYING YANG. "A NEW METHOD FOR DESIGN PROCESS KNOWLEDGE MANAGEMENT." Journal of Advanced Manufacturing Systems 07, no. 01 (2008): 107–10. http://dx.doi.org/10.1142/s0219686708001188.
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According to the state-of-the-art of design process management, a new management method is presented in this paper. Firstly, a design process is divided into three layers: harmony layer, purpose layer and action layer. Secondly, considering the operation features in every design process layer, tasks are divided into four categories, and the relevant models are proposed subsequently. Finally, the framework of a design guide management system is presented, and an example of engine design process is used to illustrate the efficiency of this method. It is approved that the design guide can accumulate design process knowledge and improve design efficiency.
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van Till, Sietske A. L., Mariia V. Maksimova, Ghislaine J. M. W. van Thiel, and Eline M. Bunnik. "An assessment of the moral value of neuronal cell models and brain organoids." Molecular Psychology: Brain, Behavior, and Society 2 (July 10, 2023): 15. http://dx.doi.org/10.12688/molpsychol.17557.1.
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Advances in stem cell technology enable neuroscientists to develop induced pluripotent stem cell (iPSC)-based neuronal models of varying complexity, ranging from single human brain cells to two-dimensional neuronal cell models and three-dimensional brain organoids. While the discussion on the moral status of brain organoids is taking center stage in the bioethical literature and is invariably linked to the presumed capacity of future brain organoids to develop some form of consciousness, analyses of the moral status of other – less complex – iPSC-based neuronal models are lacking. In this paper we aim to clarify the moral value of various types of existing neuronal models, including brain organoids. We show how it is made up of several layers that may encompass various sorts of considerations, including moral values, the results of empirical research, and biological characteristics. We identify four such layers – instrumental, intrinsic, symbolic, and relational – that are relevant for the assessment of the moral value of neuronal models. We demonstrate that it lies not in a capacity to develop some form of consciousness (which is absent in current iPSC-based neuronal models, including brain organoids), but in other considerations, including the genetic links between models and donors, the ability of models to mimic brain (dys)function, and their symbolic value, all of which are often overlooked in the bioethical literature. Also, we demonstrate that the 'thickness' of the layers (i.e., their moral weight) increases when the neuronal model is more complex. Finally, we discuss the practical-ethical implications of our analysis for the use of neuronal models in research settings, for instance in relation to informed consent and biobank governance. Our four-layer framework can be applied also in moral assessments of other iPSC-based models, including emerging and future cell models.
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Lin, Yaoqiang, Mengfei Zhou, Nan Wang, et al. "Optimization of the Target Layer for Three-Dimensional Shale Gas Development in Weiyuan Block." E3S Web of Conferences 598 (2024): 01008. http://dx.doi.org/10.1051/e3sconf/202459801008.
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In order to explore whether the Weiyuan block can effectively carry out three-dimensional development well network deployment, we should first explore whether there is a target layer to meet the three-dimensional development. Based on three-dimensional geological, fracture and rock mechanics models, this paper finds that the absolute stress difference between Longyi14a minor layer and Longyi14b minor layer in the study area is less than 5 MPa, and there is no obvious stress isolation section, and the longitudinal distance of the upper part of Longyi14a minor layer from the top of Longyi11 layer distributes in the range of 18~28 m, which is able to effectively avoid strong inter-well interference with the horizontal wells of Longyi11 layer. So, according to the distribution characteristics of the remaining reserves and reservoir characteristics of Weiyuan block, Longyi14a minor layer is initially preferred as the target for three-dimensional development. In order to further explore the specific layers, based on the original three wells of H3 platform, four threedimensional development schemes with different target layers were designed, deploying L4 and L5 wells in the Longyi13 layer~Longyi14c minor layer, and the Longyi14a minor layer was confirmed as a three-dimensional development target layer system based on numerical simulation results. Finally, in the lower half branch of H3 platform, an “M” type three-dimensional well network pattern with five wells was adopted (three 4a wells of the lower layer Longyi11 layer L1, L2 and L3, and two wells of the upper Longyi14a minor layer - L4 and L5). The three-dimensional well network can increase production for the platform by 1.76×108 m3 and recovery increased from 14.3% to 29.3%, an overall increase of 15%. The relevant understanding can provide a reference for the three-dimensional development strategy of Weiyuan shale gas block.
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Al-Bashaireh, Khaled. "Radiocarbon Age Determinations of Mosaic Mortar Layers of Churches from North Jordan." Radiocarbon 57, no. 5 (2015): 851–63. http://dx.doi.org/10.2458/azu_rc.57.18197.
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This research is aimed at radiocarbon dating organic inclusions and lime-binder powders of mortar layers of mosaic pavements in four churches of arguable archaeological date located in northern Jordan. One mortar sample from each mosaic pavement of each church was collected, examined by thin section microscopy, and then physically pretreated by gentle crushing and dry sieving to collect lime-binder powders of different grain sizes. Charcoal samples uncovered from three samples and the CO2 gases, collected by hydrochloric acid (HCl) hydrolysis of the powders, were 14C dated using accelerator mass spectrometry (AMS). Four powders of 63–45 μm from the four samples and two powders of 45–38 μm from two samples were analyzed in order to get more precise dates and examine previous proposed models for the interpretation of the results. 14C determinations showed agreement between charcoal ages and archaeological data, while the fine lime-binder's powders, especially from the mosaic's bedding layer, produced more precise dates. Results suggest that 14C date profiles produced by HCl hydrolysis of the lime-binder powders can be clearly interpreted by the existing models.
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Gómez Franco, Maribel, Antonio Ramírez Treviño, Eduard Figueras, and Angel Sauceda Carvajal. "Thermoelectrical characterization and comparative analysis of three finite element models of a MEMS thermal sensor." Superficies y Vacío 31, no. 2 (2018): 33–38. http://dx.doi.org/10.47566/2018_syv31_1-020033.
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This document presents the finite element modeling using ANSYS to obtain the thermal resistance of a MEMS thermal sensor. Additionally, the document describes a thermoelectrical characterization to find the sensor performance parameters. For modeling purposes, we divided the thermal sensor into four different thickness zones. We analyzed three different models, the first includes all materials layers, the second involves an equivalent thermal conductivity and an equivalent thickness for each zone, and the proposed model besides using an equivalent thermal conductivity by zone also considers the same thickness for all zones to reduce simulation time and to optimize thermal sensor design parameters. The first model evaluates three different boundary conditions, while the second and third models consider two different thermopile wide strips. The thermal resistance of the proposed model has a relative error of 11% in relation to the experimental value. The model, considering all layers and heat power applied to the surface as boundary conditions, has the lowest error (9%), while models considering the thermopile strips width have shown a higher error, 67%. As a result, the proposed model for heat transfer analysis simplifies complex geometries and reduces simulation time.
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Yun, Jong-Hwan, Gun-Woong Yoon, Yu-Jae Jeon, and Min-Soo Kang. "Evaluation of the Properties of 3D-Printed Onyx–Fiberglass Composites." Materials 17, no. 16 (2024): 4140. http://dx.doi.org/10.3390/ma17164140.
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This study evaluated the properties of 3D-printed Onyx–fiberglass composites. These composites were 3D-printed with zero, one, two, three, and four layers of fiberglass. Ten samples of each configuration were printed for the tensile and flexural tests. The average tensile strength of the Onyx specimens was calculated to be 44.79 MPa, which increased linearly by approximately 20–25 MPa with each additional fiberglass layer. The elastic moduli calculated from the micromechanics models were compared with the experimental values obtained from the tensile tests. The experimental elastic modulus increased more significantly than the model prediction when more fiberglass layers were added. The flexural modulus of Onyx was 17.6 GPa, which increased with each additional fiberglass layer. This quantitative analysis of composites fabricated using 3D printing highlights their potential for commercialization and industrial applications.
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Pérez-Castillo, José Luis, Angel Mora, Rogelio Perez-Santiago, Armando Roman-Flores, Rafiq Ahmad, and Enrique Cuan-Urquizo. "Flexural Properties of Lattices Fabricated with Planar and Curved Layered Fused Filament Fabrication." Materials 16, no. 9 (2023): 3451. http://dx.doi.org/10.3390/ma16093451.
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The use of curved layers in fused filament fabrication could lead to various advantages in surface finishing and mechanical properties. Here, the influence of three different structural and manufacturing parameters (volume fraction, raster arrangement, and the use of curved or planar layers) on the mechanical properties of lattice structures under three-point bending is studied. Two different raster arrangements were considered, i.e., those with rasters at planes parallel to the principal axes of the samples and those diagonally arranged, all at four different volume fractions. All different samples were additively manufactured using planar and curved layers. Samples were further dimensionally inspected to refine the computational models before their analysis via finite element simulations. The linear elastic region of the load-displacement curves was further analyzed numerically via finite element models. Predictions with finite element models resulted in good agreement with errors below 10%. Samples with diagonal rasters were 70% softer than those parallel to the principal axes.
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Chen, Peng. "Investigating the Impact of Parameter Variations of Transformer Models on Sentiment Classification." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 176–82. https://doi.org/10.54097/qah1rn94.
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With the development of the Internet, an increasing number of applications and websites appearing on the Internet allow movie viewers to add comments for movies. If people want to know what per cent of comments are positive or negative, it will cost a large amount human resources and time to check each comment. However, with the help of the transformer model, it will save a large number of human resources and time to finish sentiment classification for long movie comments. The dataset ‘IMDb’ used to train the transformer model is a large Movie Review Dataset for binary sentiment classification of movie reviews. Furthermore, since sentiment classification for movie comments does not require the decoder in the transformer model to predict the next token, the transformer model only need to preserve the part of Positional Encoding, Encoder and Multi-head self-attention mechanism. This paper will investigate how three parameters (the number of layers in encoder, the amount of heads in multi-head self-attention, expansion factor in position-wise fully connected feed-forward network) affect the performance of the transformer model and which set of parameters could allow the transformer to have the best performance. After researching on three parameters, the transformer model used to do sentiment classification for movie comments has the best performance when there are sixteen heads in multi-head self-attention mechanism, four layers in Encoder, and expansion factor in feed-forward network which is position-wise fully connected is four.
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Faried, A. S., Kh Osman, and M. G. Mohamed. "The Effect of Diffrenet Types of Strengthening RC Columns Subjected to Eccentric Load." International Journal of Membrane Science and Technology 10, no. 3 (2023): 3303–15. http://dx.doi.org/10.15379/ijmst.v10i3.3281.
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In many constructions, columns are subjected to eccentric loads. So, they are unable to withstand higher loading requirements. Ferrocement jacketing and fiber-reinforced polymer (FRP) have demonstrated exceptionally high efficacy in comparison to other strengthening techniques. This paper presents the results of experimental studies on reinforced concrete columns strengthened with carbon fiber reinforced polymer (CFRP) and ferrocement layer composites under an eccentric load. The new techniques depended on the combination of (CFRP) and Ferrocement to strengthen RC columns, which are subjected to eccentric loads. To get the average result for the model, ten tested models are patterned using two of each specimen, totaling twenty tested specimens. Models are divided into four groups in accordance with the method of strengthening. The specimen in the first group has not been strengthened. The second group consists of six specimens strengthened with one, two, or three successive layers of the wire mesh with an outer 15 mm thickened coating. The third group consists of four specimens strengthened with one or two successively wrapped layers of the (CFRP). The fifth group consists of six specimens strengthened with one or two layers of CFRP and one or two layers of concrete wire mesh. The purpose of the current study is to compare the use of CFRP, ferrocement layers, or a combination of carbon fibers and ferrocement to strengthen concrete columns that are subjected to eccentric loads (e/t = 0.5). The study has also shown that mixing GFRP with ferrocement is more effective than using ferrocement or carbon, but more expensive.
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Sharma, Divya. "Quantifying Fraction of Total Power Vs Wavelength of Ultra-Nanoscale Plasmonic Biosensor Device using Metal-Insulator-Metal-Metal Stack, Nano wells and Biotin Layer." International Journal of Recent Technology and Engineering (IJRTE) 8, no. 2 (2019): 339–43. http://dx.doi.org/10.35940/ijrte.b1464.078219.
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An ultra-thin three-dimensional nanostructured biosensor device based on the Plasmonic principle is custom designed and analyzed for the Plasmonic properties. Here the FDTD (Finite Difference Time Domain) method is adopted as mathematical model using MEEP (MIT Electromagnetic Equation Propagation) open-source simulation tool. The four models are investigated and analyzed in the following order for respective Plasmonic properties of fraction of total power with respect to the wavelength for model-I MIMM layers (Metal-Insulator-Metal-Metal) with no nanostructure (AlAl2O3 -Cr-Au), model-II MIMM layers with no nanostructure (AlAl2O3 -Cr-Au) and Biotin layer, model-III MIMM layers (AlAl2O3 -Cr-Au) with 11 x 11 Nano well structures and model-IV MIMM layers with Nano well structures and Biotin layer (AlAl2O3 -Cr-Au-Biotin). Here the structural and functional behavior of model I Vs Model II Vs Model III vs Model IV is simulated and the fraction of power is measured across the biosensor stack layer of MIMM for the wave length range quantified. In model II there is an approximate 5% power loss at all layers when compared to model I due to addition of the Biotin layer. In model IV there is an approximate 50 % power loss when compared to model III at Au layer, 60% power loss when compared to model III at Al layer and 67% of power loss at Cr + Al2O3 due to Biotin layer. These quantifications can be used to understand the model and the behavior of the biosensor under various conditions well before the fabrication, thereby reducing the cost and to comprehend the behavior of each material in terms of power dissipation so different material can be experimented.
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Elshamy, M. M. M., A. N. Tiraturyan, and E. V. Uglova. "Evaluation of the elastic modulus of pavement layers using different types of neural networks models." Advanced Engineering Research 21, no. 4 (2022): 364–75. http://dx.doi.org/10.23947/2687-1653-2021-21-4-364-375.
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Introduction. This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. The falling weight deflectometer (FWD) was selected to simulate the dynamic traffic loads and measure the flexural bowls on the road surface to obtain the database of ANN models.Materials and Methods. Artificial networks types (the feedforward backpropagation, layer-recurrent, cascade back- propagation, and Elman backpropagation) are developed to define the optimal ANN model using Matlab software. To appreciate the efficiency of every model, we used the constructed ANN models for predicting the elastic modulus values for 25 new pavement sections that were not used in the process of training, validation, or testing to ensure its suitability. The efficiency measures such as mean absolute error (MAE), the coefficient of multiple determinations R2, Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) values were obtained for all models results.Results. Based on the performance parameters, it was concluded that among these algorithms, the feed-forward model has a better performance compared to the other three ANN types. The results of the best four models were compared to each other and to the actual data obtained to determine the best method.Discussion and Conclusions. The differences between the results of the four best models for the four types of algorithms used were very small, as they showed the closeness between them and the actual values. The research results confirm the possibility of ANN-based models to evaluate the elastic modulus of pavement layers speedily and reliably for using it in the structural assessment of (NDT) flexible pavement data at the appropriate time.
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Shariat, Mehrdad, Ömer Bulakci, Antonio De Domenico, et al. "A Flexible Network Architecture for 5G Systems." Wireless Communications and Mobile Computing 2019 (February 11, 2019): 1–19. http://dx.doi.org/10.1155/2019/5264012.
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In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.
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Jahantigh, Moslem, and Hamidreza Ramazi. "KNOWLEDGE DRIVEN METHODS FOR CU-AU PORPHYRY POTENTIAL MODELLING; A CASE STUDY OF THE MOKHTARAN AREA, EASTERN IRAN." Rudarsko-geološko-naftni zbornik 39, no. 3 (2024): 131–44. http://dx.doi.org/10.17794/rgn.2024.3.10.
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Current research investigates multi-criteria decision methods, consisting of AHP TOPSIS, AHP VIKOR and AHP MOORA, to model porphyry copper potential in the Mokhtaran area in Eastern Iran. Evidential layers in this study include intrusive rocks, volcanic rocks, faults, Geochemical mineralization probability index (GMPI), reduction to the magnetic pole of the total magnetic intensity map, argillic and phyllic alterations. The importance of these evidential layers was calculated using the AHP method. Then, a fuzzy method was applied to the same scale the evidential layers. The threshold values of these layers were discretized with the Fractal method. Then, a weight was assigned to each evidential layer. After weighing all of the evidential layers, different MCDM methods, including AHP TOPSIS, AHP VIKOR, and AHP MOORA, were implemented to combine these layers and outline the Porphyry Copper Prospectivity Models. The predicted models show the same promising areas. The appropriate coincidence can be seen between high potential areas and mine indications. Then the success curve rate was implemented to compare the three predicted models. Based on this method, the AHP TOPSIS has a better performance. Since the success rate curve belongs to AHP TOPSIS, it is placed above the other two methods. Next, AHP VIKOR has a better performance than AHP MOORA. The three MCDM methods produced the same Cu porphyry mineralization areasd along fault zones.
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Soon, Hwai Ing, Azian Azamimi Abdullah, Hiromitsu Nishizaki, et al. "Optimizing hybrid neural networks for precise COVID-19 mRNA vaccine degradation prediction." International Journal of ADVANCED AND APPLIED SCIENCES 11, no. 7 (2024): 87–100. http://dx.doi.org/10.21833/ijaas.2024.07.011.
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Conventional hybrid models often miss an essential factor that can lead to less effective performance: intrinsic sequence dependence when combining various neural network (NN) architectures. This study addresses this issue by highlighting the importance of sequence hybridization in NN architecture integration, aiming to improve model effectiveness. It combines NN layers—dense, long short-term memory (LSTM), and gated recurrent unit (GRU)—using the Keras Sequential API for defining the architecture. To provide better context, bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) replace their unidirectional counterparts, enhancing the models through bidirectional structures. Out of 25 NN models tested, 18 four-layer hybrid NN models consist of one-quarter dense layer and the rest BiLSTM and BiGRU layers. These hybrid NN models undergo supervised learning regression analysis, with mean column-wise root mean square error (MCRMSE) as the performance metric. The results show that each hybrid NN model produces unique outcomes based on its specific hybrid sequence. The Hybrid_LGSS model performs better than existing three-layer BiLSTM networks in predictive accuracy and shows lower overfitting (MCRMSEs of 0.0749 and 0.0767 for training and validation, respectively). This indicates that the optimal hybridization sequence is crucial for achieving a balance between performance and simplicity. In summary, this research could help vaccinologists develop better mRNA vaccines and provide data analysts with new insights for improvement.
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Xia, Youlong, Michael B. Ek, Yihua Wu, Trent Ford, and Steven M. Quiring. "Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I: Comparison and Analysis." Journal of Hydrometeorology 16, no. 5 (2015): 1962–80. http://dx.doi.org/10.1175/jhm-d-14-0096.1.
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Abstract Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.
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Al-Jawad, Mohammed Saleh, and Khalid Ahmed Kareem. "Geological Model of Khasib Reservoir- Central Area/East Baghdad Field." Iraqi Journal of Chemical and Petroleum Engineering 17, no. 3 (2016): 1–10. http://dx.doi.org/10.31699/ijcpe.2016.3.1.
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The Geological modeling has been constructed by using Petrel E&P software to incorporate data, for improved Three-dimensional models of porosity model, water saturation, permeability estimated from core data, well log interpretation, and fault analysis modeling.
 Three-dimensional geological models attributed with physical properties constructed from primary geological data. The reservoir contains a huge hydrocarbon accumulation, a unique geological model characterization with faults, high heterogeneity, and a very complex field in nature.
 The results of this study show that the Three-dimensional geological model of Khasib reservoir, to build the reservoir model starting with evaluation of reservoir to interpretation of well log by using IP software for 14 wells, defining and divided the layers based on the GR Log and Resistivity log to nine layers and then maintained the fault model for a divided central area to four regions. Compared porosity log with porosity core to estimate correction porosity and enter this value to predict the permeability value for each layer by using FZI, and RQI method. The model Containing faults, horizons, zones, and layers depending on this data to make gridding by using pillar gridding.
 This paper presents a geological modeling and an uncertainty analysis for stock-tank original oil in place. The distribution of the faults is also discussed.
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Yang, Yang, Siqin Liu, Fangbo Chen, et al. "Exploring multiple electrical layers overlying coal seams using the transient electromagnetic method." PLOS ONE 17, no. 10 (2022): e0273423. http://dx.doi.org/10.1371/journal.pone.0273423.
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The transient electromagnetic method (TEM) is widely applied in coal hydrogeological exploration owing to its sensitivity to low-resistivity bodies. However, when a coal seam is buried deep, particularly if there are multiple electrical layers in the vertical direction of the overlying stratum, the results of the calculation using the late-channel empirical formula of the TEM may no longer reflect the actual situation. In this study, we evaluated the principle of the one-dimensional (1D) Occam algorithm and the steps for performing an inversion. We proposed various two-, three-, and four-layer electrical models for inversion using the 1D Occam algorithm. Our results are consistent with the electrical distribution of the models, thus indicating the effectiveness of the algorithm. A test project of large-depth transient electromagnetic exploration in the Datong Coalfield in Shanxi, China, was selected for experimental verification. The 1D Occam inversion was used to successfully identify various electrical strata overlying the coal seam.
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Sato, Hédison Kiuity, and José Humberto de Souza Prates. "The magnetometric resistivity method in a stratified medium having resistivities varying exponentially with depth." GEOPHYSICS 82, no. 3 (2017): E121—E127. http://dx.doi.org/10.1190/geo2016-0479.1.
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Using a known solution for the electric potential and Ampère’s law, the azimuthal component of the magnetic field is deduced in a horizontally layered medium with a current point source placed anywhere, considering that the resistivity in each layer varies exponentially with depth. This theoretical result contributes to model the magnetometric resistivity method, which had been applied onshore, e.g., for mineral exploration, offshore to investigate the permafrost layer at bottom sea, hydrothermal flux, and natural resources. We have numerically tested the obtained formulation against previous results found in the literature that use distinct electrode and sensor dispositions, with models having three and four layers. Introducing the exponential variation, it verified the sensitivity to physical and geometric parameters comparing the exponential and homogeneous models.
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Abdelhakim, Maizia, Abdelkader Hocine, Ghania Habbar, et al. "Stochastic sensitivity analysis of micromechanical properties of unidirectional layer with natural fibers - hydrogen pipeline application." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e12319. https://doi.org/10.54021/seesv5n2-821.
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The primary objective of this study is to predict the sensitivity of the mechanical properties in unidirectional layers of bio-composites. Four bio-sourced fibers were investigated: PALF (Pineapple leaf fiber), Date palm, Alfa, and Hemp fiber. In order to conduct this sensitivity analysis, three models were implemented. The first model utilizes analytical approaches based on four theoretical methods: Halpin-Tsai, Chamis, Hashin-Rosen, and the Rule of Mixtures (ROM). The second model is based on the finite element method (FEM) applied to the unit cell of bio-layer composites. After validating the analytical and numerical results with experimental data, a coupled methodology combining probabilistic stochastic analysis and FEM was employed to determine the sensitivity of all mechanical properties of the natural fiber-based layers. These bio-layers are designed for use in manufacturing the layers and skins of sandwich composite hydrogen transportation pipelines. The probabilistic stochastic approach, utilizing the Monte Carlo method, enables the execution of the sensitivity analysis. The results indicate that uncertainties in the micromechanical characteristics of fibers, the fiber volume fraction, and the fibers' radius are the most significant factors affecting the stiffness of the layer or the multi-layered structure in longitudinal and transversal directions. These findings provide valuable insights for designers to estimate the volume ratio and select the types of components or fibers to manufacture reliable and economically viable Bio-Composites layers with high resistance and quality.