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24 pages, 1258 KiB  
Article
Physics-Informed Neural Network Enhanced CFD Simulation of Two-Dimensional Green Ammonia Synthesis Reactor
by Ran Xu, Shibin Zhang, Fengwei Rong, Wei Fan, Xiaomeng Zhang, Yunlong Wang, Liang Zan, Xu Ji and Ge He
Processes 2025, 13(8), 2457; https://doi.org/10.3390/pr13082457 (registering DOI) - 3 Aug 2025
Abstract
The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was [...] Read more.
The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k0 and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
25 pages, 4241 KiB  
Article
Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions
by Mohammad Mahdi Aghabeigi Alooghareh, Mohammad Mohsen Sheikhey, Ali Sahafi, Habibollah Pirnejad and Amin Naemi
Bioengineering 2025, 12(8), 840; https://doi.org/10.3390/bioengineering12080840 (registering DOI) - 3 Aug 2025
Abstract
The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and [...] Read more.
The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. Full article
(This article belongs to the Special Issue Machine Learning in Chronic Diseases)
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23 pages, 3283 KiB  
Article
Light-Driven Optimization of Exopolysaccharide and Indole-3-Acetic Acid Production in Thermotolerant Cyanobacteria
by Antonio Zuorro, Roberto Lavecchia, Karen A. Moncada-Jacome, Janet B. García-Martínez and Andrés F. Barajas-Solano
Sci 2025, 7(3), 108; https://doi.org/10.3390/sci7030108 (registering DOI) - 3 Aug 2025
Abstract
Cyanobacteria are a prolific source of bioactive metabolites with expanding applications in sustainable agriculture and biotechnology. This work explores, for the first time in thermotolerant Colombian isolates, the impact of light spectrum, photoperiod, and irradiance on the co-production of exopolysaccharides (EPS) and indole-3-acetic [...] Read more.
Cyanobacteria are a prolific source of bioactive metabolites with expanding applications in sustainable agriculture and biotechnology. This work explores, for the first time in thermotolerant Colombian isolates, the impact of light spectrum, photoperiod, and irradiance on the co-production of exopolysaccharides (EPS) and indole-3-acetic acid (IAA). Six strains from hot-spring environments were screened under varying blue:red (B:R) LED ratios and full-spectrum illumination. Hapalosiphon sp. UFPS_002 outperformed all others, reaching ~290 mg L−1 EPS and 28 µg mL−1 IAA in the initial screen. Response-surface methodology was then used to optimize light intensity and photoperiod. EPS peaked at 281.4 mg L−1 under a B:R ratio of 1:5 LED, 85 µmol m−2 s−1, and a 14.5 h light cycle, whereas IAA was maximized at 34.4 µg mL−1 under cool-white LEDs at a similar irradiance. The quadratic models exhibited excellent predictive power (R2 > 0.98) and a non-significant lack of fit, confirming the light regime as the dominant driver of metabolite yield. These results demonstrate that precise photonic tuning can selectively steer carbon flux toward either EPS or IAA, providing an energy-efficient strategy to upscale thermotolerant cyanobacteria for climate-resilient biofertilizers, bioplastics precursors, and other high-value bioproducts. Full article
(This article belongs to the Section Biology Research and Life Sciences)
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28 pages, 1874 KiB  
Article
Lexicon-Based Random Substitute and Word-Variant Voting Models for Detecting Textual Adversarial Attacks
by Tarik El Lel, Mominul Ahsan and Majid Latifi
Computers 2025, 14(8), 315; https://doi.org/10.3390/computers14080315 (registering DOI) - 2 Aug 2025
Abstract
Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense [...] Read more.
Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense mechanisms: the Lexicon-Based Random Substitute Model (LRSM) and the Word-Variant Voting Model (WVVM). LRSM employs randomized substitutions from a dataset-specific lexicon to generate diverse input variations, disrupting adversarial strategies by introducing unpredictability. Unlike traditional defenses requiring synonym dictionaries or precomputed semantic relationships, LRSM directly substitutes words with random lexicon alternatives, reducing overhead while maintaining robustness. Notably, LRSM not only neutralizes adversarial perturbations but occasionally surpasses the original accuracy by correcting inherent model misclassifications. Building on LRSM, WVVM integrates LRSM, Frequency-Guided Word Substitution (FGWS), and Synonym Random Substitution and Voting (RS&V) in an ensemble framework that adaptively combines their outputs. Logistic Regression (LR) emerged as the optimal ensemble configuration, leveraging its regularization parameters to balance the contributions of individual defenses. WVVM consistently outperformed standalone defenses, demonstrating superior restored accuracy and F1 scores across adversarial scenarios. The proposed defenses were evaluated on two well-known sentiment analysis benchmarks: the IMDB Sentiment Dataset and the Yelp Polarity Dataset. The IMDB dataset, comprising 50,000 labeled movie reviews, and the Yelp Polarity dataset, containing labeled business reviews, provided diverse linguistic challenges for assessing adversarial robustness. Both datasets were tested using 4000 adversarial examples generated by established attacks, including Probability Weighted Word Saliency, TextFooler, and BERT-based Adversarial Examples. WVVM and LRSM demonstrated superior performance in restoring accuracy and F1 scores across both datasets, with WVVM excelling through its ensemble learning framework. LRSM improved restored accuracy from 75.66% to 83.7% when compared to the second-best individual model, RS&V, while the Support Vector Classifier WVVM variation further improved restored accuracy to 93.17%. Logistic Regression WVVM achieved an F1 score of 86.26% compared to 76.80% for RS&V. These findings establish LRSM and WVVM as robust frameworks for defending against adversarial text attacks in sentiment analysis. Full article
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36 pages, 699 KiB  
Article
A Framework of Indicators for Assessing Team Performance of Human–Robot Collaboration in Construction Projects
by Guodong Zhang, Xiaowei Luo, Lei Zhang, Wei Li, Wen Wang and Qiming Li
Buildings 2025, 15(15), 2734; https://doi.org/10.3390/buildings15152734 (registering DOI) - 2 Aug 2025
Abstract
The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. [...] Read more.
The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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18 pages, 7062 KiB  
Article
Multimodal Feature Inputs Enable Improved Automated Textile Identification
by Magken George Enow Gnoupa, Andy T. Augousti, Olga Duran, Olena Lanets and Solomiia Liaskovska
Textiles 2025, 5(3), 31; https://doi.org/10.3390/textiles5030031 (registering DOI) - 2 Aug 2025
Abstract
This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A [...] Read more.
This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article
30 pages, 4011 KiB  
Article
Multitarget Design of Steroidal Inhibitors Against Hormone-Dependent Breast Cancer: An Integrated In Silico Approach
by Juan Rodríguez-Macías, Oscar Saurith-Coronell, Carlos Vargas-Echeverria, Daniel Insuasty Delgado, Edgar A. Márquez Brazón, Ricardo Gutiérrez De Aguas, José R. Mora, José L. Paz and Yovanni Marrero-Ponce
Int. J. Mol. Sci. 2025, 26(15), 7477; https://doi.org/10.3390/ijms26157477 (registering DOI) - 2 Aug 2025
Abstract
Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha [...] Read more.
Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R2 = 0.86; Q2_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC50 > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article
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36 pages, 12384 KiB  
Article
A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data
by Ali Farahani and Majid Ghayoomi
Remote Sens. 2025, 17(15), 2671; https://doi.org/10.3390/rs17152671 (registering DOI) - 1 Aug 2025
Abstract
Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating [...] Read more.
Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating satellite-based soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission into the assessment of seismic landslide occurrence. Using landslide inventories from five major earthquakes (Nepal 2015, New Zealand 2016, Papua New Guinea 2018, Indonesia 2018, and Haiti 2021), a balanced global dataset of landslide and non-landslide cases was compiled. Exploratory analysis revealed a strong association between elevated pre-event soil moisture and increased landslide occurrence, supporting its relevance in seismic slope failure. Moreover, a Random Forest model was trained and tested on the dataset and demonstrated excellent predictive performance. To assess the generalizability of the model, a leave-one-earthquake-out cross-validation approach was also implemented, in which the model trained on four events was tested on the fifth. This approach outperformed comparable models that did not consider soil moisture, such as the United States Geological Survey (USGS) seismic landslide model, confirming the added value of satellite-based soil moisture data in improving seismic landslide susceptibility assessments. Full article
(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications)
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21 pages, 3744 KiB  
Article
A First-Principles Modeling of the Elastic Properties and Generalized Stacking Fault Energy of Ir-W Solid Solution Alloys
by Pengwei Shi, Jianbo Ma, Fenggang Bian and Guolu Li
Materials 2025, 18(15), 3629; https://doi.org/10.3390/ma18153629 (registering DOI) - 1 Aug 2025
Viewed by 27
Abstract
Iridium, with its excellent high-temperature chemical inertness, is a preferred cladding material for radioisotope batteries. However, its inherent room-temperature brittleness severely restricts its application. In this research, pure Ir and six Ir-W solid solutions (Ir31W1 to Ir26W6 [...] Read more.
Iridium, with its excellent high-temperature chemical inertness, is a preferred cladding material for radioisotope batteries. However, its inherent room-temperature brittleness severely restricts its application. In this research, pure Ir and six Ir-W solid solutions (Ir31W1 to Ir26W6) were modeled. The effects of W on the elastic properties, generalized stacking fault energy, and bonding properties of Ir solid solution alloys were investigated by first-principles simulation, aiming to find a way to overcome the intrinsic brittleness of Ir. With the W concentration increasing from 0 to 18.75 at %, the calculated Cauchy pressure (C12C44) increases from −22 to 5 GPa, Pugh’s ratio (B/G) increases from 1.60 to 1.72, the intrinsic stacking fault energy reduces from 337.80 to 21.16 mJ/m2, and the unstable stacking fault energy reduces from 636.90 to 547.39 mJ/m2. According to these results, it is predicted that the addition of W improves the toughness of iridium alloys. The alloying of W weakens the covalency properties of the Ir-Ir bond (the ICOHP value increases from −0.8512 to −0.7923 eV). These phenomena result in a decrease in the energy barrier for grain slip. Full article
(This article belongs to the Section Metals and Alloys)
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29 pages, 5505 KiB  
Article
Triaxial Response and Elastoplastic Constitutive Model for Artificially Cemented Granular Materials
by Xiaochun Yu, Yuchen Ye, Anyu Yang and Jie Yang
Buildings 2025, 15(15), 2721; https://doi.org/10.3390/buildings15152721 (registering DOI) - 1 Aug 2025
Viewed by 29
Abstract
Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m3 and the sand–gravel skeleton [...] Read more.
Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m3 and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R2 between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article
(This article belongs to the Special Issue Low Carbon and Green Materials in Construction—3rd Edition)
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24 pages, 6260 KiB  
Article
Transforming Product Discovery and Interpretation Using Vision–Language Models
by Simona-Vasilica Oprea and Adela Bâra
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 191; https://doi.org/10.3390/jtaer20030191 (registering DOI) - 1 Aug 2025
Viewed by 38
Abstract
In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various [...] Read more.
In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various product interpretation tasks, including image-grounded question answering, brand recognition and visual retrieval based on natural language prompts. ColQwen2, built on the Qwen2-VL backbone with LoRA-based adapter hot-swapping, demonstrates strong performance, allowing end-to-end image querying and text response synthesis. It excels at identifying attributes such as brand, color or usage based solely on product images and responds fluently to user questions. In contrast, ColPali, which utilizes the PaliGemma backbone, is optimized for explainability. It delivers detailed visual-token alignment maps that reveal how specific regions of an image contribute to retrieval decisions, offering transparency ideal for diagnostics or educational applications. Through comparative experiments using footwear imagery, it is demonstrated that ColQwen2 is highly effective in generating accurate responses to product-related questions, while ColPali provides fine-grained visual explanations that reinforce trust and model accountability. Full article
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8 pages, 3048 KiB  
Communication
Layer-by-Layer Nanoassembly of Cu(OH)2 Multilayer Membranes for Nanofiltration
by Wenbo Sun, Yanpeng Xue and Guozhi Liu
Coatings 2025, 15(8), 895; https://doi.org/10.3390/coatings15080895 (registering DOI) - 1 Aug 2025
Viewed by 34
Abstract
A facile way to prepare Cu(OH)2 inorganic nanofiltration membranes with neatly arranged multilayers has been developed based on the reaction of a sodium hydroxide solution and a copper ammonia solution at the liquid–liquid interfaces. The effects of the concentration, temperature, and time [...] Read more.
A facile way to prepare Cu(OH)2 inorganic nanofiltration membranes with neatly arranged multilayers has been developed based on the reaction of a sodium hydroxide solution and a copper ammonia solution at the liquid–liquid interfaces. The effects of the concentration, temperature, and time of the liquid–liquid reaction on membrane structure and pore sizes were studied by SEM, TEM, and X-ray diffraction. The growth mechanism of the membrane was discussed and the formation process model was proposed. It was found that the reaction temperature was a key factor in obtaining a Cu(OH)2 monolayer, and this could be used to adjust the thickness and pore size of the monolayer. The as-prepared Cu(OH)2 membranes exhibited excellent filtration performance with the pure water fluxes of 156.2 L·m−2 h−1 bar−1 and retention rates of 100% for methylene blue (50 ppm) at a pressure of 0.1 MPa. This successfully opens up a new method of synthesizing multilayer nanoarrays’ Cu(OH)2 structure for nanofiltration. Full article
(This article belongs to the Special Issue Deposition-Based Coating Solutions for Enhanced Surface Properties)
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19 pages, 2196 KiB  
Article
User-Centered Design of a Computer Vision System for Monitoring PPE Compliance in Manufacturing
by Luis Alberto Trujillo-Lopez, Rodrigo Alejandro Raymundo-Guevara and Juan Carlos Morales-Arevalo
Computers 2025, 14(8), 312; https://doi.org/10.3390/computers14080312 (registering DOI) - 1 Aug 2025
Viewed by 70
Abstract
In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant errors, and inefficiencies. This article focuses on addressing this deficiency [...] Read more.
In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant errors, and inefficiencies. This article focuses on addressing this deficiency by designing a computer vision desktop application for automated monitoring of PPE use. This system uses lightweight YOLOv8 models, developed to run on the local system and operate even in industrial locations with limited network connectivity. Using a Lean UX approach, the development of the system involved creating empathy maps, assumptions, product backlog, followed by high-fidelity prototype interface components. C4 and physical diagrams helped define the system architecture to facilitate modifiability, scalability, and maintainability. Usability was verified using the System Usability Scale (SUS), with a score of 87.6/100 indicating “excellent” usability. The findings demonstrate that a user-centered design approach, considering user experience and technical flexibility, can significantly advance the utility and adoption of AI-based safety tools, especially in small- and medium-sized manufacturing operations. This article delivers a validated and user-centered design solution for implementing machine vision systems into manufacturing safety processes, simplifying the complexities of utilizing advanced AI technologies and their practical application in resource-limited environments. Full article
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21 pages, 2557 KiB  
Article
Coupling Patterns Between Urbanization and the Water Environment: A Case Study of Neijiang City, Sichuan Province, China
by Xiaofan Min, Jirong Liu, Yanlin Liu, Jie Zhou and Jiangtao Zhao
Sustainability 2025, 17(15), 6993; https://doi.org/10.3390/su17156993 (registering DOI) - 1 Aug 2025
Viewed by 81
Abstract
The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation [...] Read more.
The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management. Full article
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20 pages, 3979 KiB  
Article
Theoretical Study of CO Oxidation on Pt Single-Atom Catalyst Decorated C3N Monolayers with Nitrogen Vacancies
by Suparada Kamchompoo, Yuwanda Injongkol, Nuttapon Yodsin, Rui-Qin Zhang, Manaschai Kunaseth and Siriporn Jungsuttiwong
Sci 2025, 7(3), 101; https://doi.org/10.3390/sci7030101 - 1 Aug 2025
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Abstract
Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO2 is an effective strategy to reduce these emissions. In this [...] Read more.
Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO2 is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C3N monolayers with various vacancy defects, including single carbon (CV) and nitrogen (NV) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@NV-C3N exhibited the most favorable catalytic properties, with the highest O2 adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@NV-C3N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@NV-C3N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article
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