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Search Results (1,627)

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Keywords = multilayered wells

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31 pages, 3643 KB  
Article
Machine Learning for Basketball Game Outcomes: NBA and WNBA Leagues
by João M. Alves and Ramiro S. Barbosa
Computation 2025, 13(10), 230; https://doi.org/10.3390/computation13100230 - 1 Oct 2025
Abstract
Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides [...] Read more.
Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides in-depth insights into individual and team performance, enabling precise evaluation of strategies and tactics. Consequently, the detailed analysis of every aspect of a team’s routine can significantly elevate the level of competition in the sport. This study investigates a range of machine learning models, including Logistic Regression (LR), Ridge Regression Classifier (RR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Stacking Classifier (STACK), Bagging Classifier (BAG), Multi-Layer Perceptron (MLP), AdaBoost (AB), and XGBoost (XGB), as well as deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to compare their effectiveness in predicting game outcomes in the NBA and WNBA leagues. The results show highly acceptable prediction accuracies of 65.50% for the NBA and 67.48% for the WNBA. This study allows us to understand the impact that artificial intelligence can have on the world of basketball and its current state in relation to previous studies. It can provide valuable insights for coaches, performance analysts, team managers, and sports strategists by using machine learning and deep learning models to predict NBA and WNBA outcomes, enabling informed decisions and enhancing competitive performance. Full article
(This article belongs to the Section Computational Engineering)
15 pages, 2103 KB  
Article
Patient Diagnosis Alzheimer’s Disease with Multi-Stage Features Fusion Network and Structural MRI
by Thi My Tien Nguyen and Ngoc Thang Bui
J. Dement. Alzheimer's Dis. 2025, 2(4), 35; https://doi.org/10.3390/jdad2040035 - 1 Oct 2025
Abstract
Background: Timely intervention and effective control of Alzheimer’s disease (AD) have been shown to limit memory loss and preserve cognitive function and the ability to perform simple activities in older adults. In addition, magnetic resonance imaging (MRI) scans are one of the most [...] Read more.
Background: Timely intervention and effective control of Alzheimer’s disease (AD) have been shown to limit memory loss and preserve cognitive function and the ability to perform simple activities in older adults. In addition, magnetic resonance imaging (MRI) scans are one of the most common and effective methods for early detection of AD. With the rapid development of deep learning (DL) algorithms, AD detection based on deep learning has wide applications. Methods: In this research, we have developed an AD detection method based on three-dimensional (3D) convolutional neural networks (CNNs) for 3D MRI images, which can achieve strong accuracy when compared with traditional 3D CNN models. The proposed model has four main blocks, and the multi-layer fusion functionality of each block was used to improve the efficiency of the proposed model. The performance of the proposed model was compared with three different pre-trained 3D CNN architectures (i.e., 3D ResNet-18, 3D InceptionResNet-v2, and 3D Efficientnet-b2) in both tasks of multi-/binary-class classification of AD. Results: Our model achieved impressive classification results of 91.4% for binary-class as well as 80.6% for multi-class classification on the Open Access Series of Imaging Studies (OASIS) database. Conclusions: Such results serve to demonstrate that multi-stage feature fusion of 3D CNN is an effective solution to improve the accuracy of diagnosis of AD with 3D MRI, thus enabling earlier and more accurate diagnosis. Full article
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18 pages, 3872 KB  
Article
Predicting the Bandgap of Graphene Based on Machine Learning
by Qinze Yu, Lingtao Zhan, Xiongbai Cao, Tingting Wang, Haolong Fan, Zhenru Zhou, Huixia Yang, Teng Zhang, Quanzhen Zhang and Yeliang Wang
Physchem 2025, 5(4), 41; https://doi.org/10.3390/physchem5040041 - 1 Oct 2025
Abstract
Over the past decade, two-dimensional materials have become a research hotspot in chemistry, physics, materials science, and electrical and optical engineering due to their excellent properties. Graphene is one of the earliest discovered 2D materials. The absence of a bandgap in pure graphene [...] Read more.
Over the past decade, two-dimensional materials have become a research hotspot in chemistry, physics, materials science, and electrical and optical engineering due to their excellent properties. Graphene is one of the earliest discovered 2D materials. The absence of a bandgap in pure graphene limits its application in digital electronics where switching behavior is essential. In the present study, researchers have proposed a variety of methods for tuning the graphene bandgap. Machine learning methodologies have demonstrated the capability to enhance the efficiency of materials research by automating the recording of characteristic parameters from the discovery and preparation of 2D materials, property calculations, and simulations, as well as by facilitating the extraction and summarization of governing principles. In this work, we use first principle calculations to build a dataset of graphene band gaps under various conditions, including the application of a perpendicular external electric field, nitrogen doping, and hydrogen atom adsorption. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) Regression were utilized to successfully predict the graphene bandgap, and the accuracy of the models was verified using first principles. Finally, the advantages and limitations of the three models were compared. Full article
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19 pages, 830 KB  
Article
Analysis and Simulation of Dynamic Heat Transfer and Thermal Distribution in Burns with Multilayer Models Using Finite Volumes
by Adriana Sofia Rodríguez-Pérez, Héctor Eduardo Gilardi-Velázquez and Stephanie Esmeralda Velázquez-Pérez
Dynamics 2025, 5(4), 41; https://doi.org/10.3390/dynamics5040041 - 1 Oct 2025
Abstract
Burns represent a significant medical challenge, and the development of theoretical models has the potential to contribute to the advancement of new diagnostic tools. This study aimed to perform numerical simulations of the Pennes bioheat transfer equation, incorporating heat generation terms due to [...] Read more.
Burns represent a significant medical challenge, and the development of theoretical models has the potential to contribute to the advancement of new diagnostic tools. This study aimed to perform numerical simulations of the Pennes bioheat transfer equation, incorporating heat generation terms due to the body’s immunological response to thermal injury, as well as changes in skin thermal parameters and blood perfusion for each burn type. We propose the incorporation of specific parameters and boundary conditions related to multilayer perfusion into the Pennes bioheat model. Using the proposed layered skin model, we evaluate temperature differences to establish correlations for determining burn depth. In this investigation, 1D and 3D algorithms based on the finite volume method were applied to capture transient and spatial thermal variations, with the resulting temperature distributions demonstrating the ability of the proposed models to describe the expected thermal variations in healthy and burned tissue. This work demonstrates the potential of the finite volume method to approximate the solution of the Pennes biothermal equation. Overall, this study provides a computational framework for analyzing heat transfer in burn injuries and highlights the relevance of mathematical simulations as a tool for future research on infrared thermography in medicine. Full article
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22 pages, 2558 KB  
Article
Spectral Derivatives Improve FTIR-Based Machine Learning Classification of Plastic Polymers
by Octavio Rosales-Martínez, Everardo Efrén Granda-Gutiérrez, René Arnulfo García-Hernández, Roberto Alejo-Eleuterio and Allan Antonio Flores-Fuentes
Modelling 2025, 6(4), 115; https://doi.org/10.3390/modelling6040115 - 29 Sep 2025
Abstract
Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- [...] Read more.
Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 cm1. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification. Full article
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14 pages, 3556 KB  
Article
Multi-Layer Molecular Quantum-Dot Cellular Automata Multiplexing Structure with Physical Verification for Secure Quantum RAM
by Jun-Cheol Jeon
Int. J. Mol. Sci. 2025, 26(19), 9480; https://doi.org/10.3390/ijms26199480 - 27 Sep 2025
Abstract
Molecular quantum-dot cellular automata (QCA) are attracting much attention as an alternative that can improve the problems of digital circuit design technology represented by existing CMOS technology. In particular, they are well suited to the upcoming nanoquantum environment era with their small size, [...] Read more.
Molecular quantum-dot cellular automata (QCA) are attracting much attention as an alternative that can improve the problems of digital circuit design technology represented by existing CMOS technology. In particular, they are well suited to the upcoming nanoquantum environment era with their small size, fast switching speed, and low power consumption. In this study, we propose a 5 × 5 × 1 ultra-slim vertical panel type multi-layer 2-to-1 multiplexer (Mux) using molecular QCA, departing from conventional multi-layer formats, and show its expansion to 4-to-1 Mux and application to vertical panel type D-latch and RAM cells. In addition, the polarization phenomenon of cells is physically proven using the potential energy, distance among electrons, and the relative positions of cells, and the secure RAM design takes noise elimination and polarization of the output signal into consideration. The circuits are simulated in terms of operation and performance using QCADesigner 2.0.3 and QCADesignerE, and the proposed multi-layer 2-to-1 Mux shows a significant improvement of at least 1473% and 277% in two representative standard design costs compared to the state-of-the-art multi-layer Muxes. Full article
(This article belongs to the Section Molecular Biophysics)
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16 pages, 3417 KB  
Article
Optical Fiber TFBG Glucose Biosensor via pH-Sensitive Polyelectrolyte Membrane
by Fang Wang, Xinyuan Zhou, Jianzhong Zhang and Shenhang Cheng
Biosensors 2025, 15(10), 642; https://doi.org/10.3390/bios15100642 - 25 Sep 2025
Abstract
A novel glucose biosensor is developed based on a tilted fiber Bragg grating (TFBG) functionalized with a pH-responsive polyelectrolyte multilayer membrane, onto which glucose oxidase (GOD) is immobilized. The sensing film is constructed via layer-by-layer self-assembly of poly(ethylenimine) (PEI) and poly(acrylic acid) (PAA), [...] Read more.
A novel glucose biosensor is developed based on a tilted fiber Bragg grating (TFBG) functionalized with a pH-responsive polyelectrolyte multilayer membrane, onto which glucose oxidase (GOD) is immobilized. The sensing film is constructed via layer-by-layer self-assembly of poly(ethylenimine) (PEI) and poly(acrylic acid) (PAA), which undergoes reversible swelling and refractive index (RI) changes in response to local pH variations. These changes are transduced into measurable shifts in the resonance wavelengths of TFBG cladding modes. The catalytic action of GOD oxidizes glucose to gluconic acid, thereby modulating the interfacial pH and actuating the polyelectrolyte membrane. With an optimized (PEI/PAA)4(PEI/GOD)1 structure, the biosensor achieves highly sensitive glucose detection, featuring a wide measurement range (10−8 to 10−2 M), a low detection limit of 27.7 nM, and a fast response time of ~60 s. It also demonstrates excellent specificity and robust performance in complex biological matrices such as rabbit serum and artificial urine, with recovery rates of 93–102%, highlighting its strong potential for point-of-care testing applications. This platform offers significant advantages in stability, temperature insensitivity, and miniaturization, making it well-suited for clinical glucose monitoring and disease management. Full article
(This article belongs to the Section Biosensors and Healthcare)
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22 pages, 5342 KB  
Article
Effect of Protonated Media on Dye Diffusion in Chitosan–Cellulose-Based Cryogel Beads
by Alfredo García-González, Rosa Elvira Zavala-Arce, Pedro Avila-Pérez, Jacob Josafat Salazar-Rábago, Jose Luis Garcia-Rivas and Carlos Eduardo Barrera-Díaz
Gels 2025, 11(10), 770; https://doi.org/10.3390/gels11100770 - 25 Sep 2025
Abstract
Synthetic dyes are increasingly relevant pollutants due to their widespread use and discharge into water bodies. This study examines how the solution pH affects the morphology of chitosan–cellulose cryogel (Ch-C-EGDE) and its impact on dye transport to adsorption sites. Adsorption tests with dyes [...] Read more.
Synthetic dyes are increasingly relevant pollutants due to their widespread use and discharge into water bodies. This study examines how the solution pH affects the morphology of chitosan–cellulose cryogel (Ch-C-EGDE) and its impact on dye transport to adsorption sites. Adsorption tests with dyes Y5, R2, and B1 over a pH range of 2–12 revealed optimal performance at pH 2.5. High hydronium ion concentrations significantly improved adsorption capacities (945–1605 mg/g), with a hierarchy B1 > R2 > Y5 at 250 mg/L initial concentration. The dependence of the dye adsorption on the acidic pH of the solution suggests that there is a mechanism of adsorption by electrostatic forces due mainly to the protonation of the amino group (NH3+). During the dye adsorption studies, a decrease in the diameter of the cryogel beads was observed, as well as a possible “zipper effect” in the pores of the Ch-C-EGDE cryogel beads, which depends on the pH at which the anionic molecules of the dyes attract the positively charged chitosan-based adsorbent walls, which physically closes the pores and results in a decrease in pore size as well as a geometric and/or load-bearing impediment. The experimental data fitted well with the pseudo-second-order kinetic models and the Sips isotherm model, indicating multilayer and heterogeneous adsorption behavior. In the Sips model, a value of n > 1 was obtained, which confirms favorable adsorption conditions and suggests strong dye-adsorbent material interactions, especially at higher dye concentrations. Full article
(This article belongs to the Special Issue Cellulose Gels: Properties and Prospective Applications)
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23 pages, 3914 KB  
Article
Machine Learning-Driven Early Productivity Forecasting for Post-Fracturing Multilayered Wells
by Ruibin Zhu, Ning Li, Guohua Liu, Fengjiao Qu, Changjun Long, Xin Wang, Shuzhi Xiu, Fei Ling, Qinzhuo Liao and Gensheng Li
Water 2025, 17(19), 2804; https://doi.org/10.3390/w17192804 - 24 Sep 2025
Viewed by 125
Abstract
Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current [...] Read more.
Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current limitations in understanding post-fracturing production dynamics and the lack of efficient prediction methods severely constrain the evaluation of fracturing effectiveness and the adjustment of development plans. This study proposes a machine learning-based method for predicting post-fracturing productivity in multi-layer commingled production wells and validates its effectiveness using a key block from the PetroChina North China Huabei Oilfield Company. During the data preprocessing stage, the three-sigma rule, median absolute deviation, and density-based spatial clustering of applications with noise were employed to detect outliers, while missing values were imputed using the K-nearest neighbors method. Feature selection was performed using Pearson correlation coefficient and variance inflation factor, resulting in the identification of twelve key parameters as input features. The coefficient of determination served as the evaluation metric, and model hyperparameters were optimized using grid search combined with cross-validation. To address the multi-layer commingled production challenge, seven distinct datasets incorporating production parameters were constructed based on four geological parameter partitioning methods: thickness ratio, porosity–thickness product ratio, permeability–thickness product ratio, and porosity–permeability–thickness product ratio. Twelve machine learning models were then applied for training. Through comparative analysis, the most suitable productivity prediction model for the block was selected, and the block’s productivity patterns were revealed. The results show that after training with block-partitioned data, the accuracy of all models has improved; further stratigraphic subdivision based on block partitioning has led the models to reach peak performance. However, data volume is a critical limiting factor—for blocks with insufficient data, stratigraphic subdivision instead results in a decline in prediction performance. Full article
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16 pages, 1339 KB  
Article
Cyclic di-GMP Modulation of Quorum Sensing and Its Impact on Type VI Secretion System Function in Sinorhizobium fredii
by Juan Aranda-Pérez, María del Carmen Sánchez-Aguilar, Ana María Cutiño-Gobea, Francisco Pérez-Montaño and Carlos Medina
Microorganisms 2025, 13(10), 2232; https://doi.org/10.3390/microorganisms13102232 - 24 Sep 2025
Viewed by 90
Abstract
Effective rhizobium–legume symbiosis depends on multiple molecular signaling pathways, integrating not only classical nodulation factors and surface polysaccharides but also diverse protein secretion systems. Among them, the Type VI Secretion System (T6SS) has emerged as a key player, due to its dual roles [...] Read more.
Effective rhizobium–legume symbiosis depends on multiple molecular signaling pathways, integrating not only classical nodulation factors and surface polysaccharides but also diverse protein secretion systems. Among them, the Type VI Secretion System (T6SS) has emerged as a key player, due to its dual roles in interbacterial competition and interactions with eukaryotic hosts, though its contribution to symbiosis remains unclear. Key regulatory messengers, including the main autoinducer of the quorum sensing (QS) systems, the N-acyl homoserine lactones (AHLs), and the second messenger cyclic di-GMP (c-di-GMP), modulate the transition between motility and biofilm formation, especially in the context of bacteria interacting with eukaryotes, including rhizobia. While c-di-GMP’s impact on exopolysaccharide production in these organisms is well established, its influence on protein secretion systems, particularly in conjunction with QS, is largely unexplored. To contribute to the study of such interplay, we artificially increased intracellular c-di-GMP levels by overexpressing a heterologous diguanylate cyclase in three Sinorhizobium fredii strains of agronomic relevance. This engineering revealed strain-specific outcomes, since elevated c-di-GMP enhanced biofilm development in two strains, but reduced it in another. Furthermore, using β-galactosidase expression assays, we confirmed that both high c-di-GMP and/or AHL concentrations contribute to the transcriptional activation of T6SS. These results demonstrate a direct regulatory link between c-di-GMP, QS signals, and T6SS expression, shedding light on the multilayered control mechanisms that structure beneficial rhizobia–plant interactions. Full article
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15 pages, 2412 KB  
Article
A Physics-Informed Neural Network Integration Framework for Efficient Dynamic Fracture Simulation in an Explicit Algorithm
by Mingyang Wan, Yue Pan and Zhennan Zhang
Appl. Sci. 2025, 15(19), 10336; https://doi.org/10.3390/app151910336 - 23 Sep 2025
Viewed by 117
Abstract
The conventional dynamic fracture simulation by using the explicit algorithm often involves a large number of iteration computation due to the extremely small time interval. Thus, the most time-consuming process is the integration of constitutive relation. To improve the efficiency of the dynamic [...] Read more.
The conventional dynamic fracture simulation by using the explicit algorithm often involves a large number of iteration computation due to the extremely small time interval. Thus, the most time-consuming process is the integration of constitutive relation. To improve the efficiency of the dynamic fracture simulation, a physics-informed neural network integration (PINNI) model is developed to calculate the integration of constitutive relation. PINNI employs a shallow multilayer perceptron with integrable activations to approximate constitutive integrand. To train PINNI, a large number of strains in a reasonable range are generated at first, and then the corresponding stresses are calculated by the mechanical constitutive relation. With the generated strains as input data and the calculated stresses as output data, the PINNI can be trained to reach a very high precision, whose relative error is about 7.8×105%. Next, the mechanical integration of constitutive relation is replaced by the well-trained PINNI to perform the dynamic fracture simulation. It is found that the simulation results by the mechanical and PINNI approach are almost the same. This suggests that it is feasible to use PINNI to replace the rigorous mechanical integration of constitutive relation. The computational efficiency is significantly enhanced, especially for the complicated constitutive relation. It provides a new AI-combined approach to dynamic fracture simulation. Full article
(This article belongs to the Section Mechanical Engineering)
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16 pages, 5890 KB  
Article
Wideband Multi-Layered Dielectric Resonator Antenna with Small Form Factor for 5G Millimeter-Wave Mobile Applications
by Sung Yong An and Boumseock Kim
Electronics 2025, 14(19), 3756; https://doi.org/10.3390/electronics14193756 - 23 Sep 2025
Viewed by 88
Abstract
A ceramic-based wideband capacitive-fed patch-loaded multi-layered rectangular dielectric resonator antenna (CFPL-ML-RDRA) with a compact form factor is proposed in this paper. The proposed antenna is composed of two ceramic substrates and a polymer as an adhesive. A capacitive-fed metallic patch structure is located [...] Read more.
A ceramic-based wideband capacitive-fed patch-loaded multi-layered rectangular dielectric resonator antenna (CFPL-ML-RDRA) with a compact form factor is proposed in this paper. The proposed antenna is composed of two ceramic substrates and a polymer as an adhesive. A capacitive-fed metallic patch structure is located on the top side of the bottom ceramic substrate. This novel structure generates two distinct resonant modes: the fundamental resonant mode of the RDRA and a hybrid resonant mode, which was confirmed through electric field (E-field) analysis and parametric studies. By merging these two resonant modes, the proposed antenna achieves a wide impedance bandwidth of 5.5 GHz, sufficient to cover the fifth-generation (5G) millimeter-wave (mmWave) frequency bands n257, n258, and n261 (5.25 GHz), while reducing the height of the DRA by 38.5% compared to the conventional probe-fed RDRA (PF-RDRA). Additionally, the 4 dBi realized gain bandwidth of the proposed CFPL-ML-RDRA is 5.4 GHz, which is 28.6% broader than that of the conventional PF-RDRA. To experimentally verify the antenna’s performance, the CFPL-ML-RDRA mounted on a test printed circuit board with a small ground size of 3.2 × 3.2 mm2 was fabricated and characterized. The measured data align well with the simulated data. Furthermore, excellent antenna array performance was achieved based on array simulations. Therefore, the proposed antenna structure is well-suited for 5G mmWave mobile applications. Full article
(This article belongs to the Section Electronic Materials, Devices and Applications)
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20 pages, 7508 KB  
Article
Design and Assessment of Flexible Capacitive Electrodes for Reusable ECG Monitoring: Effects of Sweat and Adapted Front-End Configuration
by Ivo Iliev, Georgi T. Nikolov, Nikolay Tomchev, Bozhidar I. Stefanov and Boriana Tzaneva
Sensors 2025, 25(18), 5856; https://doi.org/10.3390/s25185856 - 19 Sep 2025
Viewed by 258
Abstract
This work presents the development and characterization of a flexible capacitive electrode for non-contact ECG acquisition, fabricated using a simple and cost-effective method from readily available materials. The electrode consists of a multilayer structure with a copper conductor laminated by a polyimide (Kapton [...] Read more.
This work presents the development and characterization of a flexible capacitive electrode for non-contact ECG acquisition, fabricated using a simple and cost-effective method from readily available materials. The electrode consists of a multilayer structure with a copper conductor laminated by a polyimide (Kapton®) dielectric layer on a polyurethane support. The impedance and capacitance of the electrode were evaluated under varying textile moisture levels with artificial sweat, as well as after exposure to common disinfectants including ethyl alcohol and iodine tincture. Electrochemical impedance spectroscopy (EIS) and broadband impedance measurements (10−1–105 Hz) confirmed stable capacitive behavior, moderate sensitivity to moisture, and chemical stability of the Kapton–copper interface under conditions simulating repeated use. A custom front-end readout circuit was implemented to demonstrate through-textile ECG signal acquisition. Simulator tests reproduced characteristic waveform patterns, and preliminary volunteer recordings confirmed the feasibility of through-textile acquisition. These results highlight the promise of the electrode as a low-cost platform for future wearable biosignal monitoring technical research. Full article
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20 pages, 2451 KB  
Article
Development of an Early Lung Cancer Diagnosis Method Based on a Neural Network
by Indira Karymsakova, Dinara Kozhakhmetova, Dariga Bekenova, Danila Ostroukh, Roza Bekbayeva, Lazat Kydyralina, Alina Bugubayeva and Dinara Kurushbayeva
Computers 2025, 14(9), 397; https://doi.org/10.3390/computers14090397 - 18 Sep 2025
Viewed by 276
Abstract
Cancer is one of the most lethal diseases in the modern world. Early diagnosis significantly contributes to prolonging the life expectancy of patients. The application of intelligent systems and AI methods is crucial for diagnosing oncological diseases. Primarily, expert systems or decision support [...] Read more.
Cancer is one of the most lethal diseases in the modern world. Early diagnosis significantly contributes to prolonging the life expectancy of patients. The application of intelligent systems and AI methods is crucial for diagnosing oncological diseases. Primarily, expert systems or decision support systems are utilized in such cases. This research explores early lung cancer diagnosis through protocol-based questioning, considering the impact of nuclear testing factors. Nuclear tests conducted historically continue to affect citizens’ health. A classification of regions into five groups was proposed based on their proximity to nuclear test sites. The weighting coefficient was assigned accordingly, in proportion to the distance from the test zones. In this study, existing expert systems were analyzed and classified. Approaches used to build diagnostic expert systems for oncological diseases were grouped by how well they apply to different tumor localizations. An online questionnaire based on the lung cancer diagnostic protocol was created to gather input data for the neural network. To support this diagnostic method, a functional block diagram of the intelligent system “Oncology” was developed. The following methods were used to create the mathematical model: gradient boosting, multilayer perceptron, and Hamming network. Finally, a web application architecture for early lung cancer detection was proposed. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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19 pages, 5589 KB  
Article
Finite Element Simulation and Experiment for Electromagnetic Flanging Forming of Aluminum Alloy Sheet
by Zhengrong Zhang, Jingchao Yao, Fei Wu, Jun Zhang, Chaojun Chen and Chun Huang
Materials 2025, 18(18), 4345; https://doi.org/10.3390/ma18184345 - 17 Sep 2025
Viewed by 277
Abstract
In order to address the problem of the large gap in the film on the straight edge of the electromagnetic flanging forming by the flat coil affecting the quality of the flanging part, a multi-layer variable-turn stepped coil is proposed. Numerical simulation analysis [...] Read more.
In order to address the problem of the large gap in the film on the straight edge of the electromagnetic flanging forming by the flat coil affecting the quality of the flanging part, a multi-layer variable-turn stepped coil is proposed. Numerical simulation analysis and experimental research were conducted on the electromagnetic flanging forming process of flat coil and stepped coil. Research shows that in the early stage of forming, the electromagnetic force of the flat coil is uniformly distributed at the edge of the hole and the middle of the deformation zone of the sheet metal, causing the upper surface of the middle of the deformation zone of the sheet metal to present radial compressive stress and tangential compressive stress, and the upper surface of the sheet metal at the fillet of the die to present radial tensile strain, tangential compressive strain and thickness direction compressive strain. The electromagnetic force of the step coil is mainly concentrated at the hole edge of the sheet metal, causing the upper surface in the middle of the deformation zone of the sheet metal to present radial tensile stress and tangential tensile stress, as well as radial tensile strain, tangential and thickness direction compressive strain. Under the flat coil, the sheet material mainly undergoes plastic deformation under the action of axial electromagnetic force and can only be bent into a curved edge. Under the stepped coil, the sheet metal undergoes plastic deformation simultaneously under the combined action of axial and radial electromagnetic forces and can be flipped into a vertical edge. The feasibility of the electromagnetic flanging forming of the stepped coil was verified through experiments, and the experimental results were basically consistent with the simulation results. Full article
(This article belongs to the Section Materials Simulation and Design)
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