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Search Results (3,354)

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22 pages, 6187 KiB  
Article
Device Modeling Method for the Entire Process of Energy-Saving Retrofit of a Refrigeration Plant
by Xuanru Xu, Lun Zhang, Jun Chen, Qingbin Lin and Junjie Chen
Energies 2025, 18(15), 4147; https://doi.org/10.3390/en18154147 - 5 Aug 2025
Abstract
With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the [...] Read more.
With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article
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19 pages, 3110 KiB  
Article
Integrated Environmental–Economic Assessment of Small-Scale Natural Gas Sweetening Processes
by Qing Wen, Xin Chen, Xingrui Peng, Yanhua Qiu, Kunyi Wu, Yu Lin, Ping Liang and Di Xu
Processes 2025, 13(8), 2473; https://doi.org/10.3390/pr13082473 - 5 Aug 2025
Abstract
Effective in situ H2S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT® and triazine-based absorption, using a scenario-based [...] Read more.
Effective in situ H2S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT® demonstrates better economic viability at higher gas flow rates and H2S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article
(This article belongs to the Section Chemical Processes and Systems)
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16 pages, 3373 KiB  
Article
Knowledge-Augmented Zero-Shot Method for Power Equipment Defect Grading with Chain-of-Thought LLMs
by Jianguang Du, Bo Li, Zhenyu Chen, Lian Shen, Pufan Liu and Zhongyang Ran
Electronics 2025, 14(15), 3101; https://doi.org/10.3390/electronics14153101 - 4 Aug 2025
Abstract
As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our [...] Read more.
As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article
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21 pages, 1682 KiB  
Article
Profiling External Load in U14 Basketball: Cluster Analysis of Competition Performance Using Inertial Devices
by João Rocha, João Serrano, Pablo López-Sierra and Sergio J. Ibáñez
Appl. Sci. 2025, 15(15), 8616; https://doi.org/10.3390/app15158616 (registering DOI) - 4 Aug 2025
Abstract
Physical performance data is essential for planning youth training effectively; however, there is a lack of scientific information regarding performance in youth competitions. To address this gap, an innovative study was conducted with Portuguese U14 regional selections. Each player was equipped with a [...] Read more.
Physical performance data is essential for planning youth training effectively; however, there is a lack of scientific information regarding performance in youth competitions. To address this gap, an innovative study was conducted with Portuguese U14 regional selections. Each player was equipped with a WimuPro™ inertial device. Six variables were considered: accelerations, decelerations, speed, player load, impacts, and high impacts. The objective of this study, based on data from official competitions, was to statistically analyze the distribution and intensity thresholds of six physical performance variables across five defined zones. A cluster k-means analysis was performed for a significance value of p < 0.05. Five zones were identified for all variables: acceleration [<0.37; 0.37 to 0.81; 0.81 to 1.54; 1.54 to 3.49; >3.49 m/s2], deceleration [<−0.26; −0.27 to −0.63; −0.63 to −1.22; −1.22 to −2.545; >−2.54 m/s2], speed [<5.42; 5.42 to 10.19; 10.20 to 14.63; 14.64 to 18.59; >18.59 km/h2], player load [<1.07; 1.07 to 1.36; 1.37 to 1.63; 1.64 to 1.95; >1.95 u.a./min], impacts [<133.45; 133.45 to 158.75; 158.76 to 181.45; 181.46 to 206.59; >206.59 cont/min], and high impacts [<1.13; 1.14 to 2.11; 2.12 to 3.13; 3.14 to 4.42; >4.42 cont/min]. These intensity zones should be taken into account to optimize training and enhance the understanding of competition in U14 basketball. Full article
(This article belongs to the Special Issue Science and Basketball: Recent Advances and Practical Applications)
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24 pages, 6757 KiB  
Article
Design and Testing of a Pneumatic Jujube Harvester
by Huaming Hou, Wei Niu, Qixian Wen, Hairui Yang, Jianming Zhang, Rui Zhang, Bing Xv and Qingliang Cui
Agronomy 2025, 15(8), 1881; https://doi.org/10.3390/agronomy15081881 - 3 Aug 2025
Viewed by 53
Abstract
Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our [...] Read more.
Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our research group conducted a study on mechanical harvesting technology for fresh jujubes. A pneumatic jujube harvester was designed. This harvester is composed of a self-regulating picking mechanism, a telescopic conveying pipe, a negative pressure generator, a cleaning mechanism, a double-chamber collection box, a single-door shell, a control assembly, a generator, a towing mobile chassis, etc. During the harvest, the fresh jujubes on the branches are picked under the combined effect of the flexible squeezing of the picking roller and the suction force of the negative pressure air flow. They then enter the cleaning mechanism through the telescopic conveying pipe. Under the combined effect of the upper and lower baffles of the cleaning mechanism and the negative-pressure air flow, the fresh jujubes are separated from impurities such as jujube leaves and branches. The clean fresh jujubes fall into the collection box. We considered the damage rate of fresh jujubes, impurity rate, leakage rate, and harvesting efficiency as the indexes, and the negative-pressure suction wind speed, picking roller rotational speed, and the inclination angle of the upper and lower baffles of the cleaning and selection machinery as the test factors, and carried out the harvesting test of fresh jujubes. The test results show that when the negative-pressure suction wind speed was 25 m/s, the picking roller rotational speed was 31 r/min, and the inclination angles of the upper and lower baffle plates for cleaning and selecting were −19° and 19.5°, respectively, the breakage rate of fresh jujube harvesting was 0.90%, the rate of impurity was 1.54%, the rate of leakage was 2.59%, and the efficiency of harvesting was 73.37 kg/h, realizing the high-efficiency and low-loss harvesting of fresh jujubes. This study provides a reference for the research and development of fresh jujube mechanical harvesting technology and equipment. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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20 pages, 4961 KiB  
Article
Optimization of Thermal Conductivity of Bismaleimide/h-BN Composite Materials Based on Molecular Structure Design
by Weizhuo Li, Run Gu, Xuan Wang, Chenglong Wang, Mingzhe Qu, Xiaoming Wang and Jiahao Shi
Polymers 2025, 17(15), 2133; https://doi.org/10.3390/polym17152133 - 3 Aug 2025
Viewed by 65
Abstract
With the rapid development of information technology and semiconductor technology, the iteration speed of electronic devices has accelerated in an unprecedented manner, and the market demand for miniaturized, highly integrated, and highly intelligent devices continues to rise. But when these electronic devices operate [...] Read more.
With the rapid development of information technology and semiconductor technology, the iteration speed of electronic devices has accelerated in an unprecedented manner, and the market demand for miniaturized, highly integrated, and highly intelligent devices continues to rise. But when these electronic devices operate at high power, the electronic components generate a large amount of integrated heat. Due to the limitations of existing heat dissipation channels, the current heat dissipation performance of electronic packaging materials is struggling to meet practical needs, resulting in heat accumulation and high temperatures inside the equipment, seriously affecting operational stability. For electronic devices that require high energy density and fast signal transmission, improving the heat dissipation capability of electronic packaging materials can significantly enhance their application prospects. In order to improve the thermal conductivity of composite materials, hexagonal boron nitride (h-BN) was selected as the thermal filling material in this paper. The BMI resin was structurally modified through molecular structure design. The results showed that the micro-branched structure and h-BN synergistically improved the thermal conductivity and insulation performance of the composite material, with a thermal conductivity coefficient of 1.51 W/(m·K) and a significant improvement in insulation performance. The core mechanism is the optimization of the dispersion state of h-BN filler in the matrix resin through the free volume in the micro-branched structure, which improves the thermal conductivity of the composite material while maintaining high insulation. Full article
(This article belongs to the Special Issue Electrical Properties of Polymer Composites)
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33 pages, 3561 KiB  
Article
A Robust Analytical Network Process for Biocomposites Supply Chain Design: Integrating Sustainability Dimensions into Feedstock Pre-Processing Decisions
by Niloofar Akbarian-Saravi, Taraneh Sowlati and Abbas S. Milani
Sustainability 2025, 17(15), 7004; https://doi.org/10.3390/su17157004 - 1 Aug 2025
Viewed by 208
Abstract
Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria [...] Read more.
Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article
(This article belongs to the Special Issue Sustainable Enterprise Operation and Supply Chain Management)
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15 pages, 1635 KiB  
Article
Modeling the Abrasive Index from Mineralogical and Calorific Properties Using Tree-Based Machine Learning: A Case Study on the KwaZulu-Natal Coalfield
by Mohammad Afrazi, Chia Yu Huat, Moshood Onifade, Manoj Khandelwal, Deji Olatunji Shonuga, Hadi Fattahi and Danial Jahed Armaghani
Mining 2025, 5(3), 48; https://doi.org/10.3390/mining5030048 - 1 Aug 2025
Viewed by 107
Abstract
Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict [...] Read more.
Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R2: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R2 = 0.92), outperforming RF (R2 = 0.85) and GBT (R2 = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article
(This article belongs to the Special Issue Mine Automation and New Technologies)
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29 pages, 1858 KiB  
Article
Securing a Renewable Energy Supply for a Single-Family House Using a Photovoltaic Micro-Installation and a Pellet Boiler
by Jakub Stolarski, Ewelina Olba-Zięty, Michał Krzyżaniak and Mariusz Jerzy Stolarski
Energies 2025, 18(15), 4072; https://doi.org/10.3390/en18154072 - 31 Jul 2025
Viewed by 200
Abstract
Photovoltaic (PV) micro-installations producing renewable electricity and automatic pellet boilers producing renewable heat energy are promising solutions for single-family houses. A single-family house equipped with a prosumer 7.56 kWp PV micro-installation and a 26 kW pellet boiler was analyzed. This study aimed to [...] Read more.
Photovoltaic (PV) micro-installations producing renewable electricity and automatic pellet boilers producing renewable heat energy are promising solutions for single-family houses. A single-family house equipped with a prosumer 7.56 kWp PV micro-installation and a 26 kW pellet boiler was analyzed. This study aimed to analyze the production and use of electricity and heat over three successive years (from 1 January 2021 to 31 December 2023) and to identify opportunities for securing renewable energy supply for the house. Electricity production by the PV was, on average, 6481 kWh year−1; the amount of energy fed into the grid was 4907 kWh year−1; and the electricity consumption by the house was 4606 kWh year−1. The electricity supply for the house was secured by drawing an average of 34.2% of energy directly from the PV and 85.2% from the grid. Based on mathematical modeling, it was determined that if the PV installation had been located to the south (azimuth 180°) in the analyzed period, the maximum average production would have been 6897 kWh. Total annual heat and electricity consumption by the house over three years amounted, on average, to 39,059 kWh year−1. Heat energy accounted for a dominant proportion of 88.2%. From a year-round perspective, a properly selected small multi-energy installation can ensure energy self-sufficiency and provide renewable energy to a single-family house. Full article
(This article belongs to the Section B: Energy and Environment)
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18 pages, 1910 KiB  
Article
Hierarchical Learning for Closed-Loop Robotic Manipulation in Cluttered Scenes via Depth Vision, Reinforcement Learning, and Behaviour Cloning
by Hoi Fai Yu and Abdulrahman Altahhan
Electronics 2025, 14(15), 3074; https://doi.org/10.3390/electronics14153074 - 31 Jul 2025
Viewed by 229
Abstract
Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central [...] Read more.
Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article
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14 pages, 8280 KiB  
Article
Mechanical Characteristics of Glass-Fiber-Reinforced Polyester Composite Materials
by Ioan Milosan, Tibor Bedo, Camelia Gabor and Mihai Alin Pop
Materials 2025, 18(15), 3595; https://doi.org/10.3390/ma18153595 - 31 Jul 2025
Viewed by 147
Abstract
Fiber-reinforced composites are gaining more importance across different fields such as aeronautics, automotives, high-performance sporting equipment, etc., where decreasing weight while improving mechanical properties of polymers is fundamental. This article explores the mechanical behavior of fiber-reinforced polyester composite materials, highlighting their advantages and [...] Read more.
Fiber-reinforced composites are gaining more importance across different fields such as aeronautics, automotives, high-performance sporting equipment, etc., where decreasing weight while improving mechanical properties of polymers is fundamental. This article explores the mechanical behavior of fiber-reinforced polyester composite materials, highlighting their advantages and applications in various industrial fields. Usually, composite materials consist of a polyester matrix reinforced with different types of fibers, such as glass, carbon, or Kevlar, which provide superior mechanical characteristics. This study analyzed the tensile strength, bending resistance, and resilience of glass fiber composites, emphasizing the importance of proper fiber selection and manufacturing processes. These materials stand out for their excellent strength-to-weight ratio and are widely used in the fabrication of tanks in various industries. Experimental results demonstrated tensile strength (Rm) around 115 MPa, Shore D hardness values of 88 units, and impact toughness (resilience) of 2.7 J/cm2. Based on the composite materials’ behavior in testing, the article further offers practical recommendations for the effective deployment of these composites in the fabrication of various types of industrial reservoirs. Full article
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20 pages, 4809 KiB  
Article
Design of a Bidirectional Veneer Defect Repair Method Based on Parametric Modeling and Multi-Objective Optimization
by Xingchen Ding, Jiuqing Liu, Xin Sun, Hao Chang, Jie Yan, Chengwen Sun and Chunmei Yang
Technologies 2025, 13(8), 324; https://doi.org/10.3390/technologies13080324 - 31 Jul 2025
Viewed by 205
Abstract
Repairing veneer defects is the key to ensuring the quality of plywood. In order to improve the maintenance quality and material utilization efficiency during the maintenance process, this paper proposes a bidirectional maintenance method based on gear rack transmission and its related equipment. [...] Read more.
Repairing veneer defects is the key to ensuring the quality of plywood. In order to improve the maintenance quality and material utilization efficiency during the maintenance process, this paper proposes a bidirectional maintenance method based on gear rack transmission and its related equipment. Based on the working principle, a geometric relationship model was established, which combines the structural parameters of the mold, punch, and gear system. Simultaneously, it solves the problem of motion attitude analysis of conjugate tooth profiles under non-standard meshing conditions, aiming to establish a constraint relationship between stamping motion and structural design parameters. On this basis, a constrained optimization model was developed by integrating multi-objective optimization theory to maximize maintenance efficiency. The NSGA-III algorithm is used to solve the model and obtain the Pareto front solution set. Subsequently, three optimal parameter configurations were selected for simulation analysis and experimental platform construction. The simulation and experimental results indicate that the veneer repair time ranges from 0.6 to 1.8 seconds, depending on the stamping speed. A reduction of 28 mm in die height decreases the repair time by approximately 0.1 seconds, resulting in an efficiency improvement of about 14%. The experimental results confirm the effectiveness of the proposed method in repairing veneer defects. Vibration measurements further verify the system’s stable operation under parametric modeling and optimization design. The main vibration response occurs during the meshing and disengagement phases between the gear and rack. Full article
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35 pages, 12322 KiB  
Article
Research on the Evaluation Method of Electrical Stress Limit Capability Based on Reliability Enhancement Theory
by Shuai Zhou, Kaixue Ma, Zhihua Cai, Shoufu Liu, Jian Xiang and Chi Ma
Electronics 2025, 14(15), 3056; https://doi.org/10.3390/electronics14153056 - 30 Jul 2025
Viewed by 128
Abstract
This study focuses on the evaluation of electrical stress limit capability for 3D-packaged memory (256 M × 72-bit DDR3 SDRAM) (Shanghai Fudan Microelectronics Group Co., Ltd., Shanghai, China). Guided by Reliability Enhancement Theory, this study presents a meticulously designed comprehensive test profile that [...] Read more.
This study focuses on the evaluation of electrical stress limit capability for 3D-packaged memory (256 M × 72-bit DDR3 SDRAM) (Shanghai Fudan Microelectronics Group Co., Ltd., Shanghai, China). Guided by Reliability Enhancement Theory, this study presents a meticulously designed comprehensive test profile that incorporates critical stress parameters, including supply voltage, input clock frequency, electrostatic discharge (ESD) sensitivity, and electrical endurance. Explicit criteria for stress selection, upper/lower bounds, step increments, and duration are established. A dedicated test platform is constructed, integrating automated test equipment (ATE) and ESD sensitivity analyzers with detailed specifications on device selection criteria and operational principles. The functional performance testing methodology is systematically investigated, covering test platform configuration, initialization protocols, parametric testing procedures, functional verification, and acceptance criteria. Extreme-condition experiments—including supply voltage margining, input clock frequency tolerance, ESD sensitivity characterization, and accelerated electrical endurance testing—are conducted to quantify operational and destructive limits. The findings provide critical theoretical insights and practical guidelines for the design optimization, quality control, and reliability enhancement of 3D-packaged memory devices. Full article
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5 pages, 628 KiB  
Interesting Images
Infrared Photography: A Novel Diagnostic Approach for Ocular Surface Abnormalities Due to Vitamin A Deficiency
by Hideki Fukuoka and Chie Sotozono
Diagnostics 2025, 15(15), 1910; https://doi.org/10.3390/diagnostics15151910 - 30 Jul 2025
Viewed by 242
Abstract
Vitamin A deficiency (VAD) remains a significant cause of preventable blindness worldwide, with ocular surface changes representing early manifestations that require prompt recognition and treatment. Conventional examination methods are capable of detecting advanced changes; however, subtle conjunctival abnormalities may be overlooked, potentially delaying [...] Read more.
Vitamin A deficiency (VAD) remains a significant cause of preventable blindness worldwide, with ocular surface changes representing early manifestations that require prompt recognition and treatment. Conventional examination methods are capable of detecting advanced changes; however, subtle conjunctival abnormalities may be overlooked, potentially delaying the administration of appropriate interventions. We herein present the case of a 5-year-old Japanese boy with severe VAD due to selective eating patterns. This case demonstrates the utility of infrared photography as a novel diagnostic approach for detecting and monitoring conjunctival surface abnormalities. The patient exhibited symptoms including corneal ulcers, night blindness, and reduced visual acuity. Furthermore, blood tests revealed undetectable levels of vitamin A (5 IU/dL), despite relatively normal physical growth parameters. Conventional slit-lamp examination revealed characteristic sandpaper-like conjunctival changes. However, infrared photography (700–900 nm wavelength) revealed distinct abnormal patterns of conjunctival surface folds and keratinization that were not fully appreciated on a routine examination. Following high-dose vitamin A supplementation (4000 IU/day), complete resolution of ocular abnormalities was achieved within 2 months, with infrared imaging objectively documenting treatment response and normalization of conjunctival surface patterns. This case underscores the potential for severe VAD in developed countries, particularly in the context of dietary restrictions, thereby underscoring the significance of a comprehensive dietary history and a meticulous ocular examination. Infrared photography provides a number of advantages, including the capacity for non-invasive assessment, enhanced visualization of subtle changes, objective monitoring of treatment response, and cost-effectiveness due to the use of readily available equipment. This technique represents an underutilized diagnostic modality with particular promise for screening programs and clinical monitoring of VAD-related ocular manifestations, potentially preventing irreversible visual loss through early detection and intervention. Full article
(This article belongs to the Collection Interesting Images)
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20 pages, 5568 KiB  
Article
Dynamic Wear Modeling and Experimental Verification of Guide Cone in Passive Compliant Connectors Based on the Archard Model
by Yuanping He, Bowen Wang, Feifei Zhao, Xingfu Hong, Liang Fang, Weihao Xu, Ming Liao and Fujing Tian
Polymers 2025, 17(15), 2091; https://doi.org/10.3390/polym17152091 - 30 Jul 2025
Viewed by 230
Abstract
To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element [...] Read more.
To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element simulation, and experimental validation, we establish a bidirectional coupling framework analyzing dynamic contact mechanics and wear evolution. By developing phased contact state identification criteria and geometric constraints, a transient load calculation model is established, revealing dynamic load characteristics with peak contact forces reaching 206.34 N. A dynamic contact stress integration algorithm is proposed by combining Archard’s theory with ABAQUS finite element simulation and ALE adaptive meshing technology, enabling real-time iterative updates of wear morphology and contact stress. This approach constructs an exponential model correlating cumulative wear depth with docking cycles (R2 = 0.997). Prototype experiments demonstrate a mean absolute percentage error (MAPE) of 14.6% between simulated and measured wear depths, confirming model validity. With a critical wear threshold of 0.8 mm, the predicted service life reaches 45,270 cycles, meeting 50-year operational requirements (safety margin: 50.9%). This research provides theoretical frameworks and engineering guidelines for wear-resistant design, material selection, and life evaluation in high-reliability automatic docking systems. Full article
(This article belongs to the Section Polymer Processing and Engineering)
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