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This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

(This article belongs to the Special Issue Optimization-Simulation Modeling of Sustainable Water Resource)

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41 pages, 2762 KiB

Open AccessReview

Memristor Emulator Circuits: Recent Advances in Design Methodologies, Healthcare Applications, and Future Prospects

by Amel Neifar, Imen Barraj, Hassen Mestiri and Mohamed Masmoudi

Micromachines 2025, 16(7), 818; https://doi.org/10.3390/mi16070818 (registering DOI) - 17 Jul 2025

Abstract

Memristors, as the fourth fundamental circuit element, have attracted significant interest for their potential in analog signal processing, computing, and memory storage technologies. However, physical memristor implementations still face challenges in reproducibility, scalability, and integration with standard CMOS processes. Memristor emulator circuits, implemented using analog, digital, and mixed components, have emerged as practical alternatives, offering tunability, cost effectiveness, and compatibility with existing fabrication technologies for research and prototyping. This review paper provides a comprehensive analysis of recent advancements in memristor emulator design methodologies, including active and passive analog circuits, digital implementations, and hybrid approaches. A critical evaluation of these emulation techniques is conducted based on several performance metrics, including maximum operational frequency range, power consumption, and circuit topology. Additional parameters are also taken into account to ensure a comprehensive assessment. Furthermore, the paper examines promising healthcare applications of memristor and memristor emulators, focusing on their integration into biomedical systems. Finally, key challenges and promising directions for future research in memristor emulator development are outlined. Overall, the research presented highlights the promising future of memristor emulator technology in bridging the gap between theoretical memristor models and practical circuit implementations. Full article

(This article belongs to the Section E：Engineering and Technology)

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21 pages, 1186 KiB

Open AccessArticle

How Digital Technology and Business Innovation Enhance Economic–Environmental Sustainability in Legal Organizations

by Linhua Xia, Zhen Cao and Muhammad Bilawal Khaskheli

Sustainability 2025, 17(14), 6532; https://doi.org/10.3390/su17146532 (registering DOI) - 17 Jul 2025

Abstract

This study discusses the role of organizational pro-environmental behavior in driving sustainable development. Studies of green practices highlight their capacity to achieve ecological goals while delivering economic sustainability with business strategies for sustainable businesses and advancing environmental sustainability law. It also considers how the development of artificial intelligence, resource management, big data analysis, blockchain, and the Internet of Things enables companies to maximize supply efficiency and address evolving environmental regulations and sustainable decision-making. Through digital technology, businesses can facilitate supply chain transparency, adopt circular economy practices, and produce in an equitable and environmentally friendly manner. Additionally, intelligent business management practices, such as effective decision-making and sustainability reporting, enhance compliance with authorities while ensuring long-term profitability from a legal perspective. Integrating business innovation and digital technology within legal entities enhances economic efficiency, reduces operational costs, improves environmental sustainability, reduces paper usage, and lowers the carbon footprint, creating a double-benefit model of long-term resilience. The policymakers’ role in formulating policy structures that lead to green digital innovation is also to ensure that economic development worldwide is harmonized with environmental protection and international governance. Using example studies and empirical research raises awareness about best practices in technology-based sustainability initiatives across industries and nations, aligning with the United Nations Sustainable Development Goals. Full article

(This article belongs to the Special Issue Pro-Environmental Behavior and Sustainable Development in Organizations)

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17 pages, 4432 KiB

Open AccessArticle

Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates

by Kaiming Lv, Zhengjun Liu, Hao Zhang, Honggang Jia, Yuanping Mao, Yi Zhang and Guijun Bi

Appl. Sci. 2025, 15(14), 7948; https://doi.org/10.3390/app15147948 (registering DOI) - 17 Jul 2025

Abstract

In response to the challenges associated with weld treatment during the on-site corrosion protection of hydraulic steel gates, this paper proposes a method utilizing a magnetic adsorption climbing robot to perform corrosion protection operations. Firstly, a magnetic adsorption climbing robot with a multi-wheel independent drive configuration is proposed as a mobile platform. The robot body consists of six joint modules, with the two middle joints featuring adjustable suspension. The joints are connected in series via an EtherCAT bus communication system. Secondly, the kinematic model of the climbing robot is analyzed and a PID trajectory tracking control method is designed, based on the kinematic model and trajectory deviation information collected by the vision system. Subsequently, the proposed kinematic model and trajectory tracking control method are validated through Python3 simulation and actual operation tests on a curved trajectory, demonstrating the rationality of the designed PID controller and control parameters. Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model. Full article

(This article belongs to the Section Applied Physics General)

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22 pages, 15962 KiB

Open AccessArticle

Audible Noise-Based Hardware System for Acoustic Monitoring in Wind Turbines

by Gabriel Miguel Castro Martins, Murillo Ferreira dos Santos, Mathaus Ferreira da Silva, Juliano Emir Nunes Masson, Vinícius Barbosa Schettino, Iuri Wladimir Molina and William Rodrigues Silva

Inventions 2025, 10(4), 58; https://doi.org/10.3390/inventions10040058 (registering DOI) - 17 Jul 2025

Abstract

This paper presents a robust hardware system designed for future detection of faults in wind turbines by analyzing audible noise signals. Predictive maintenance strategies have increasingly relied on acoustic monitoring as a non-invasive method for identifying anomalies that may indicate component wear, misalignment, or impending mechanical failures. The proposed device captures and processes sound signals in real-time using strategically positioned microphones, ensuring high-fidelity data acquisition without interfering with turbine operation. Signal processing techniques are applied to extract relevant acoustic features, facilitating future identification of abnormal sound patterns that may indicate mechanical issues. The system’s effectiveness was validated through rigorous field tests, demonstrating its capability to enhance the reliability and efficiency of wind turbine maintenance. Experimental results showed an average transmission latency of 131.8 milliseconds, validating the system’s applicability for near real-time audible noise monitoring in wind turbines operating under limited connectivity conditions. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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33 pages, 534 KiB

Open AccessReview

Local AI Governance: Addressing Model Safety and Policy Challenges Posed by Decentralized AI

by Bahrad A. Sokhansanj

AI 2025, 6(7), 159; https://doi.org/10.3390/ai6070159 (registering DOI) - 17 Jul 2025

Abstract

Policies and technical safeguards for artificial intelligence (AI) governance have implicitly assumed that AI systems will continue to operate via massive power-hungry data centers operated by large companies like Google and OpenAI. However, the present cloud-based AI paradigm is being challenged by rapidly advancing software and hardware technologies. Open-source AI models now run on personal computers and devices, invisible to regulators and stripped of safety constraints. The capabilities of local-scale AI models now lag just months behind those of state-of-the-art proprietary models. Wider adoption of local AI promises significant benefits, such as ensuring privacy and autonomy. However, adopting local AI also threatens to undermine the current approach to AI safety. In this paper, we review how technical safeguards fail when users control the code, and regulatory frameworks cannot address decentralized systems as deployment becomes invisible. We further propose ways to harness local AI’s democratizing potential while managing its risks, aimed at guiding responsible technical development and informing community-led policy: (1) adapting technical safeguards for local AI, including content provenance tracking, configurable safe computing environments, and distributed open-source oversight; and (2) shaping AI policy for a decentralized ecosystem, including polycentric governance mechanisms, integrating community participation, and tailored safe harbors for liability. Full article

(This article belongs to the Section AI Systems: Theory and Applications)

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20 pages, 6173 KiB

Open AccessArticle

Research on an Energy-Harvesting System Based on the Energy Field of the Environment Surrounding a Photovoltaic Power Plant

by Bin Zhang, Binbin Wang, Hongxi Zhang, Abdelkader Outzourhit, Fouad Belhora, Zoubir El Felsoufi, Jia-Wei Zhang and Jun Gao

Energies 2025, 18(14), 3786; https://doi.org/10.3390/en18143786 (registering DOI) - 17 Jul 2025

Abstract

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

(This article belongs to the Section D: Energy Storage and Application)

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12 pages, 2176 KiB

Open AccessArticle

Technical Skill Acquisition in Pediatric Minimally Invasive Surgery: Evaluation of a 3D-Printed Simulator for Thoracoscopic Esophageal Atresia Repair

by Sara Maria Cravano, Annalisa Di Carmine, Chiara De Maio, Marco Di Mitri, Cristian Bisanti, Edoardo Collautti, Michele Libri, Simone D’Antonio, Tommaso Gargano, Enrico Ciardini and Mario Lima

Healthcare 2025, 13(14), 1720; https://doi.org/10.3390/healthcare13141720 (registering DOI) - 17 Jul 2025

Abstract

Background: Minimally invasive surgery (MIS) is increasingly adopted in pediatric surgical practice, yet it demands specific technical skills that require structured training. Simulation-based education offers a safe and effective environment for skill acquisition, especially in complex procedures such as thoracoscopic repair of esophageal atresia with tracheoesophageal fistula (EA-TEF). Objective: This study aimed to evaluate the effectiveness of a 3D-printed simulator for training pediatric surgeons in thoracoscopic EA-TEF repair, assessing improvements in operative time and technical performance. Methods: A high-fidelity, 3D-printed simulator replicating neonatal thoracic anatomy was developed. Six pediatric surgeons at different training levels performed eight simulation sessions, including fistula ligation and esophageal anastomosis. Operative time and technical skill were assessed using the Stanford Microsurgery and Resident Training (SMaRT) Scale. Results: All participants showed significant improvements. The average operative time decreased from 115.6 ± 3.51 to 90 ± 6.55 min for junior trainees and from 100.5 ± 3.55 to 77.5 ± 4.94 min for senior trainees. The mean SMaRT score increased from 23.8 ± 3.18 to 38.3 ± 3.93. These results demonstrate a clear learning curve and enhanced technical performance after repeated sessions. Conclusions: Such 3D-printed simulation models represent an effective tool for pediatric MIS training. Even within a short time frame, repeated practice significantly improves surgical proficiency, supporting their integration into pediatric surgical curricula as an ethical, safe, and efficient educational strategy. Full article

(This article belongs to the Special Issue Contemporary Surgical Trends and Management)

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10 pages, 2486 KiB

Open AccessArticle

Performance of Miniature Carbon Nanotube Field Emission Pressure Sensor for X-Ray Source Applications

by Huizi Zhou, Wenguang Peng, Weijun Huang, Nini Ye and Changkun Dong

Micromachines 2025, 16(7), 817; https://doi.org/10.3390/mi16070817 (registering DOI) - 17 Jul 2025

Abstract

There is a lack of an effective approach to measure vacuum conditions inside sealed vacuum electronic devices (VEDs) and other small-space vacuum instruments. In this study, the application performance of an innovative low-pressure gas sensor based on the emission enhancements of multi-walled carbon nanotube (MWCNT) field emitters was investigated, and the in situ vacuum performance of X-ray tubes was studied for the advantages of miniature dimension and having low power consumption, extremely low outgassing, and low thermal disturbance compared to conventional ionization gauges. The MWCNT emitters with high crystallinity presented good pressure sensing performance for nitrogen, hydrogen, and an air mixture in the range of 10⁻⁷ to 10⁻³ Pa. The miniature MWCNT sensor is able to work and remain stable with high-temperature baking, important for VED applications. The sensor monitored the in situ pressures of the sealed X-ray tubes successfully with high-power operations and a long-term storage of over two years. The investigation showed that the vacuum of the sealed X-ray tube is typical at a low 10⁻⁴ Pa level, and pre-sealing degassing treatments are able to make the X-ray tube work under high vacuum levels with less outgassing and keep a stable high vacuum for a long period of time. Full article

(This article belongs to the Section D：Materials and Processing)

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17 pages, 2823 KiB

Open AccessArticle

Information Reuse Methods for Multi-Dimensional Models in Discrete Workshops

by Ruiping Luo and Jiaxing Zhu

Machines 2025, 13(7), 614; https://doi.org/10.3390/machines13070614 (registering DOI) - 17 Jul 2025

Abstract

With the gradual development of digital twin technology from theory to practice, the importance of the efficient reuse of existing digital twin models has become increasingly prominent in order to reduce the waste of resources and additional costs caused by repeated modeling. To address the difficulty of reusing multi-dimensional model information (MMI) in existing digital twin models during the conversion process from geometric models to digital twin models, this paper proposes a method for reusing MMI in discrete workshops. First, MMI and its representations are defined and constructed. Subsequently, a model-matching approach is introduced to identify appropriate MMIs for geometric models. Following this, a reuse strategy for workshop MMIs is thoroughly explained. Finally, the effectiveness of the proposed method is validated through case studies in the arc-welding workshop. The accuracy of single-model matching remains consistently at 1 across all model tests, and the proposed method reduces the total number of operations by 126 (94.7%) compared to existing methods in multi-device model construction. The results show that this method can effectively organize the workshop digital twin model, compensate for the shortage of digital twin model reuse, and help engineers reuse the existing MMI to build a digital twin model. Full article

(This article belongs to the Section Industrial Systems)

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29 pages, 435 KiB

Open AccessArticle

Possibility and the Impossibility of Reliable Broadcast: A 1-Safe and Reliable Broadcast Algorithm in the Presence of Arbitrary Initialization

by Aisha Dabees and Mehmet Hakan Karaata

Algorithms 2025, 18(7), 437; https://doi.org/10.3390/a18070437 (registering DOI) - 17 Jul 2025

Abstract

In this paper, we first prove that it is impossible to devise an asynchronous reliable broadcast algorithm that can start in an arbitrary initial system configuration where processes execute their actions solely based on local knowledge. We then propose the first asynchronous reliable broadcast algorithm that, starting in any arbitrary initial system configuration, ensures three properties, namely, premature feedback safety, propagation 1-safety, and propagation reliability. Premature feedback refers to the conclusion of a broadcast operation at a process although the process has neighbors that did not receive the most recent broadcast yet. Due to possessing the premature feedback safety property, the proposed algorithm always executes as per its specification without premature feedback and is able to implement propagation reliability with exactly once semantics. In addition, our proposed algorithm works even if the processes that the broadcast did not reach yet are perturbed by transient faults, making it more scalable compared to their counterparts requiring initialization. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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28 pages, 2701 KiB

Open AccessArticle

Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty

by Haihong Bian, Kai Ji, Yifan Zhang, Xin Tang, Yongqing Xie and Cheng Chen

World Electr. Veh. J. 2025, 16(7), 401; https://doi.org/10.3390/wevj16070401 (registering DOI) - 17 Jul 2025

Abstract

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

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18 pages, 3307 KiB

Open AccessArticle

by Shuyi Yang, Ningke Tong and Jianhua Zuo

Coatings 2025, 15(7), 834; https://doi.org/10.3390/coatings15070834 (registering DOI) - 17 Jul 2025

Abstract

Aero-engine casings with excellent impact resistance are a practical requirement for ensuring the safe operation of aero-engines. In this paper, we report on numerical simulations of broken rotating blades impacting aluminum honeycomb sandwich casings under different temperatures and optimization of structural parameters. Firstly, an impact test system with adjustable temperature was established. Restricted by the temperature range of the strain gauge, ballistic impact tests were carried out at 25 °C, 100 °C, and 200 °C. Secondly, a finite element (FE) model including a pointed bullet and an aluminum honeycomb sandwich plate was built using LS-DYNA. The corresponding simulations of the strain–time curve and damage conditions showed good agreement with the test results. Then, the containment capability of the aluminum honeycomb sandwich aero-engine casing at different temperatures was analyzed based on the kinetic energy loss of the blade, the internal energy increment of the casing, and the containment state of the blade. Finally, with the design objectives of minimizing the casing mass and maximizing the blade kinetic energy loss, the structural parameters of the casing were optimized using the multi-objective genetic algorithm (MOGA). Full article

(This article belongs to the Special Issue Surface Modification, Repairing and Forming of Metallic Materials by Additive Manufacturing)

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15 pages, 1142 KiB

Open AccessTechnical Note

Terrain and Atmosphere Classification Framework on Satellite Data Through Attentional Feature Fusion Network

by Antoni Jaszcz and Dawid Połap

Remote Sens. 2025, 17(14), 2477; https://doi.org/10.3390/rs17142477 (registering DOI) - 17 Jul 2025

Abstract

Surface, terrain, or even atmosphere analysis using images or their fragments is important due to the possibilities of further processing. In particular, attention is necessary for satellite and/or drone images. Analyzing image elements by classifying the given classes is important for obtaining information about space for autonomous systems, identifying landscape elements, or monitoring and maintaining the infrastructure and environment. Hence, in this paper, we propose a neural classifier architecture that analyzes different features by the parallel processing of information in the network and combines them with a feature fusion mechanism. The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. In addition, the classifier is guided by an attention mechanism for focusing more on different channels, spatial information, and even feature pyramid mechanisms. Atrous convolutional operators were also used in such an architecture as better context feature extractors. The proposed classifier architecture is the main element of the modeled framework for satellite data analysis, which is based on the possibility of training depending on the client’s desire. The proposed methodology was evaluated on three publicly available classification datasets for remote sensing: satellite images, Visual Terrain Recognition, and USTC SmokeRS, where the proposed model achieved accuracy scores of 97.8%, 100.0%, and 92.4%, respectively. The obtained results indicate the effectiveness of the proposed attention mechanisms across different remote sensing challenges. Full article

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19 pages, 2720 KiB

Open AccessArticle

Application of Ice Slurry as a Phase Change Material in Mine Air Cooling System—A Case Study

by Łukasz Mika, Karol Sztekler and Ewelina Radomska

Energies 2025, 18(14), 3782; https://doi.org/10.3390/en18143782 (registering DOI) - 17 Jul 2025

Abstract

Fossil fuels, including coal, are a basis of energy systems in many countries worldwide. However, coal mining is associated with several difficulties, which include high temperatures within the coal mining area. It causes a need for cooling for safety reasons and also for the comfort of miners’ work. Typical cooling systems in mines are based on central systems, in which chilled water is generated in the compressor or absorption coolers on the ground and transported via pipelines to the air coolers in the areas of mining. The progressive mining operation causes a gradual increase in the distance between chilled water generators and air coolers, causing a decrease in the efficiency of the entire system and insufficient cooling capacity. As a result, it is necessary to increase the diameter of the chilled water pipelines and increase the cooling capacity of the chillers, which is associated with additional investment and technical problems. One solution to this problem may be the use of so-called ice slurry instead of chilled water in the existing mine cooling system. This article presents the cooling system, located in the mine LW Bogdanka S.A., based on ice slurry. The structure of the system and its key parameters are presented. The results show that switching from cooling water to ice slurry allowed the cooling capacity of the entire system to increase by 50% while maintaining the existing piping. This demonstrates the very high potential for the use of ice slurry, not only in mines, but wherever further increases in piping diameters to maintain the required cooling capacity are not possible or cost-effective. Full article

(This article belongs to the Special Issue Advances in Research on Phase Change Materials for Thermal Energy Storage Applications)

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