Search Results (1,094)

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

Caenorhabditis elegans is one of the most extensively studied model organisms in biology. Its advantageous features, including genetic homology with humans, conservation of disease pathways, transparency, short lifespan, small size and ease of maintenance have established it as a powerful system for research in aging, genetics, molecular biology, disease modeling and drug discovery. However, traditional methods for worm handling, culturing, scoring and imaging are labor-intensive, low throughput, time consuming, susceptible to operator variability and environmental influences. Addressing these challenges, recent years have seen rapid innovation spanning microfluidics, robotics, imaging platforms and AI-driven analysis in C. elegans-based research. Advances include micromanipulation devices, robotic microinjection systems, automated worm assays and high-throughput screening platforms. In this review, we first summarize foundational developments prior to 2020 that shaped the field, then highlight breakthroughs from the past five years that address key limitations in throughput, reproducibility and scalability. Finally, we discuss ongoing challenges and future directions for integrating these technologies into next-generation automated C. elegans research. Full article

Excessive energy consumption of communication protocols in IoT/IIoT systems constitutes one of the key constraints for the operational longevity of remote sensor nodes, where radio transmission often incurs higher energy costs than data acquisition or local computation. Previous studies have remained fragmented, typically focusing on selected technologies or specific layers of the communication stack, which has hindered the development of comparable quantitative metrics across protocols. The aim of this study is to design and validate a unified evaluation framework enabling consistent assessment of both wired and wireless protocols in terms of energy efficiency, reliability, and maintenance costs. The proposed approach employs three complementary research methods: laboratory measurements on physical hardware, profiling of SBC devices, and simulations conducted in the COOJA/Powertrace environment. A Unified Comparative Method was developed, incorporating bilinear interpolation and weighted normalization, with its robustness confirmed by a Spearman rank correlation coefficient exceeding 0.9. The analysis demonstrates that MQTT-SN and CoAP (non-confirmable mode) exhibit the highest energy efficiency, whereas HTTP/3 and AMQP incur the greatest energy overhead. Results are consolidated in the ICoPEP matrix, which links protocol characteristics to four representative RS-IoT scenarios: unmanned aerial vehicles (UAVs), ocean buoys, meteorological stations, and urban sensor networks. The framework provides well-grounded engineering guidelines that may extend node lifetime by up to 35% through the adoption of lightweight protocol stacks and optimized sampling intervals. The principal contribution of this work is the development of a reproducible, technology-agnostic tool for comparative assessment of IoT/IIoT communication protocols. The proposed framework addresses a significant research gap in the literature and establishes a foundation for further research into the design of highly energy-efficient and reliable IoT/IIoT infrastructures, supporting scalable and long-term deployments in diverse application environments. Full article

A significant portion of home energy consumption is due to concealed faults and the inefficient usage of home appliances, usually because of user ignorance and a lack of proactive maintenance strategies. In this paper, ENACT, a digital-twin-based system, is proposed as the solution that facilitates better user understanding, encourages sustainable maintenance practices for appliances, and provides prescriptive maintenance recommendations. With the integration of smart plugs, behavioral analysis, and a 3D spatial interface, ENACT offers real-time device monitoring while providing context-aware suggestions. The system was installed in 20 households over a 12-month period, with users engaging with both 2D and 3D models of their surroundings. The quantitative results, including an average System Usability Scale score of 80.5, and qualitative feedback demonstrated intense user engagement, with strong evidence of mindset shifts towards proactive maintenance behavior. The findings confirm that digital twin technologies, when combined with targeted guidance, can significantly improve appliance lifespans, energy efficiency, and user empowerment within homes. Full article

Wellbore washing technology is a basic operation in wellbore maintenance. Problems such as low automation levels, long processing times, the fact that it is easy to cause downhole falling, and cleaning blind areas greatly affect the use and maintenance of traditional cleaning equipment. These problems usually come from design defects such as a complicated installation process, a lack of an anti-impact structure, and a fixed jet direction. To address the aforementioned issues, this paper proposes an efficient and integrated rapid-disassembly and -assembly automatic filtration rotary jet cleaning device. The device is divided into two main units and further subdivided into four modules. The quick-assembly unit comprises an elastic connection module and a downstroke quick-assembly module, which can automatically compensate for deviations in equipment position during the installation process, ensuring the reliability of the installation process and the sealing of the equipment and facilitating the rapid connection and separation of the tool string. The wellbore cleaning unit includes a hydraulic rotary washing module and a rotary filtration storage module. The wellbore is jet-flushed by hydraulic drive, and the solid particles are separated and filtered during the cleaning fluid circulation process to realize the purification and reuse of the cleaning fluid. The device reduces the installation operation time and labor cost, improves the reliability of equipment in the well, improves the flushing coverage area and the cleaning efficiency, realizes the reuse of the cleaning liquid in the wellbore, reduces the energy consumption of the flowback treatment, and comprehensively improves the cleaning efficiency and the energy utilization efficiency. Full article

Objectives: This study aimed to evaluate the available evidence on the use of orthodontic mini-implants (MIs) as temporary anchorage devices (TADs), with particular focus on how insertion angulation may influence clinical outcomes. A clinical case report was also included to complement the review findings. Methods: A systematic review was performed following PRISMA guidelines and a focused PICO question. Searches in PubMed, Web of Science, and Scopus, supplemented by manual screening of reference lists. Duplicates, systematic reviews, and studies outside the PICO scope were excluded. An observational analysis of CBCT and intraoral images, and a clinical case report, were evaluated with a standardized protocol for angulation classification based on anatomical landmarks and angular measurements. Results: Ten studies met the eligibility criteria. Most reported high survival rates, with stability defined by the absence of TAD mobility or loss. CBCT-derived data from two studies, together with one clinical case, demonstrated maintenance of alveolar bone. Improved outcomes were occasionally associated with changes in insertion angulation. Vertical positioning was more frequently linked to complications in shorter TADs, while horizontal placement preserved bone but introduced hygiene-related difficulties. Conclusions: TAD success and bone preservation may depend on insertion angulation, TAD size, and soft tissue conditions. Further standardized prospective studies are needed to validate these findings, particularly regarding intermediate diagonal insertion angles (between vertical and horizontal) extending from palatal to buccal, as observed in our clinical case, which is not yet reported in the literature. Full article

Hydraulic jumps typically exhibit a distinct symmetry under ideal boundary conditions and are characterized by a sudden change in flow depth and velocity. They are commonly employed in a diverse array of water management systems to dissipate excess energy due to their high energy dissipation rate, strong adaptability to geological conditions and tailwater variation, small fluctuation in tailwater, and low cost of maintenance. In this study, a GPU-based Smoothed Particle Hydrodynamics (SPH) model of 3D hydraulic jumps is established. Numerical simulation of three 3D symmetric full hydraulic jumps with large Froude numbers are carried out, and satisfactory agreements are shown with a largest $L_2$ error of 0.442 between the numerical free surface and experimental data. The model can reliably reproduce the free surface, jump the toe position, and jump the skimming flow. The analysis of the model efficiency shows that a maximum GPU acceleration of 12, which is equivalent to the theoretical maximum speedups, against parallel CPU can be achieved with a common GPU device. Furthermore, the energy dissipation in the stilling basin of a real sluice gate is investigated by the model. Therefore, the SPH model is a powerful tool for investigating the complex and large-scale 3D full hydraulic jumps for similar hydraulic engineering with the same boundary condition. Full article

Air compressors are vital across various sectors—automotive, manufacturing, buildings, and healthcare—as they provide pressurized air for air suspension systems in vehicles, supply power pneumatic machines throughout industrial production lines, and support non-clinical infrastructure within hospital environments, including pneumatic control systems, isolation room pressurization, and laboratory equipment operation. Ensuring that such components are reliable is critical, as unexpected failures can disrupt facility functions and compromise patient safety. Predictive maintenance (PdM) has emerged as a key factor in enhancing the reliability and operational efficiency of medical devices by leveraging sensor data and artificial intelligence (AI)-based algorithms to detect component degradation before functional failures occur. In this paper, a predictive maintenance solution for condition monitoring and fault prediction for the exhaust valve, bearings, water pump, and radiator of an air compressor is presented, by comparing a hybrid deep neural network (DNN) as a feature extractor and a support vector machine (SVM) for condition classification: a pure DNN classifier as well as a standalone SVM model. Additionally, each model was trained and validated on three devices—NVIDIA T4 GPU, Raspberry Pi 4 Model B, and NVIDIA Jetson Nano—and performance reports in terms of latency, energy consumption, and CO₂ emissions are presented. Moreover, three model agnostic explainable AI (XAI) methods were employed to increase the transparency of the hybrid model’s final decision: Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDP). The hybrid model achieves on average 98.71%, 99.25%, 98.78%, and 99.01% performance in terms of accuracy, precision, recall, and F1-score across all devices Additionally, the DNN baseline and SVM model achieve on average 93.2%, 88.33%, 90.45%, and 89.37%, as well as 93.34%, 88.11%, 95. 41%, and 91.62% performance in terms of accuracy, precision, recall, and F1-score across all devices. The integration of XAI methods within the PdM pipeline offers enhanced transparency, interpretability, and trustworthiness of predictive outcomes, thereby facilitating informed decision-making among maintenance personnel. Full article

To address the challenges of equipment reliability assessment in the context of intelligent power systems, especially the shortcomings of traditional methods in dealing with multi-factor coupling and uncertain fault inference of CVT (capacitive voltage transformer) online monitoring devices, this study proposes a reliability analysis method based on Bayesian networks (BNs). The research aims to evaluate the reliability of CVT online monitoring devices, identify key risk factors, and optimize maintenance strategies. Firstly, a Bayesian network reliability model is constructed for the CVT online monitoring device, defining key influencing factors such as environmental factors and component quality as network nodes, and establishing conditional probability dependency relationships between nodes. Subsequently, the MATLAB R2021b simulation platform was used to simulate the system’s operating status under different combinations and scenarios. The experimental results indicate that the combination of high-temperature and high-humidity environments has the most significant impact on reliability; among the component factors, the failure of the data acquisition and processing unit has the greatest impact on system reliability; wiring process issues pose a greater threat to reliability than mechanical fixing issues; and regular maintenance can significantly improve system reliability. This method validates the effectiveness of Bayesian networks in dynamic reliability analysis of CVT online monitoring devices, which can accurately locate high-risk factors and support maintenance decision optimization. Full article

This work provides the first demonstration of FFTCCV as a dual-purpose method, serving both as a real-time diagnostic tool and as a phase- and morphology-engineering strategy. By adjusting the scan rate, FFTCCV directs the crystallographic evolution of Ni (OH)₂ on Ni foam—stabilizing α-nanoflakes at 0.7 V·s⁻¹ and β-platelets at 0.007 V·s⁻¹—while simultaneously enabling electrode-resolved ΔQ tracking and predictive state-of-health (SoH) monitoring. This approach enabled the precise regulation of electrode morphology and phase composition, yielding high areal capacitance (546.5 mF·cm⁻² at 5 mA·cm⁻²) with ~75% retention after 3000 cycles. These improvements advance the development of high-performance micro-supercapacitors, facilitating their integration into wearable and miniaturized devices where compact and durable energy storage is required. Beyond performance enhancement, FFTCCV also enabled continuous monitoring of capacitance during extended operation (up to 40,000 s). By recording both anodic and cathodic responses, the method provided time-resolved insights into device stability and revealed characteristic signatures of electrode degradation, phase transitions, and morphological changes. Such detection allows recognition of early failure pathways that are not accessible through conventional testing. This monitoring capability functions as an embedded health sensor, offering a pathway for predictive diagnosis of supercapacitor failure. Such functionality is particularly important for energy-driven actuators and smart materials, where uninterrupted operation and preventive maintenance are critical. FFTCCV therefore provides a scalable strategy for developing energy-autonomous microsystems with improved performance and real-time state-of-health monitoring. Full article

Laser- and energy-based device (EBD) treatments in aesthetic medicine pose substantial environmental challenges, including high energy consumption, carbon emissions, and disposable medical waste. Although sustainable healthcare has gained global attention, no competency framework currently exists to guide physicians in integrating environmental sustainability into aesthetic medicine. This study applied McClelland’s (1973) Competency Model Theory (CMT) to develop a sustainability-oriented competency model for physicians specializing in laser and EBD treatments. Using a mixed-methods design, competency-based interviews were conducted to identify the key tasks and sustainability-related competencies, followed by expert panel validation using content validity and Kendall’s coefficient of concordance. The final model consists of 33 competencies across six domains: sustainable operations, regulatory and ethical knowledge, physician patient communication, dermatological science, EBD techniques, and maintenance care. Experts rated most competencies as highly or very highly important and frequently used, and Kendall’s W confirmed significant consensus across domains. The model provides standardized competency benchmarks that can support future curriculum development, professional training, and sustainable healthcare governance. This study extends CMT into the field of environmental sustainability in medicine and offers a structured framework to reduce ecological footprints and promote low-carbon, socially accountable practices. Full article

The remote prognostic, diagnosis, and maintenance of electrolytic capacitors are research topics of interest due to their presence in numerous electronic devices and their increased susceptibility to degradation over time. The authors’ focus in this article is on the proposal of a new diagram for monitoring the parameters of the capacitors that compose the filter bank of a DC–DC buck converter by connecting them in parallel. Each capacitor is modeled by an equivalent series R–C circuit composed of an equivalent capacitance and an equivalent series resistance (ESR). The method used allows successive investigation of the three capacitors that compose the bank by triggering discharge/charge sequences, acquiring the voltages at the capacitor terminals, and estimating the time constants of each capacitor using a parameter observer. During the estimation of the parameters of a capacitor, the converter uses the other two capacitors maintained in operation. The monitoring cycle of all capacitors of the bank lasts less than $40 \mathrm{m}\mathrm{s}$, not significantly affecting the operation of the converter. The study undertaken is correlated with the thermal map of the board on which the converter is made. The dispersion of the measured values of the equivalent capacitances is below $0.25\%$, and of the ESR below $2.6\%$. The major advantage of the method is that the monitoring is performed online and in real time. Full article

Detecting and alerting for falls is a crucial component of both healthcare and assistive technologies. Wearable devices are vulnerable to damage and require regular inspection and maintenance. Manned video surveillance avoids these problems, but it involves constant labor-intensive attention and, in most cases, may interfere with the privacy of the observed individuals. To address this issue, in this work we introduce and evaluate a novel approach for fully automated fall detection. The presented technique uses direct reconstruction of principal motion parameters, avoiding the computationally expensive full optical flow reconstruction and still providing relevant descriptors for accurate detections. Our method is systematically compared with state-of-the-art techniques. Comparisons of detection accuracy, computational efficiency, and suitability for real-time applications are presented. Experimental results demonstrate notable improvements in accuracy while maintaining a lower computational cost compared to traditional methods, making our approach highly adaptable for real-world deployment. The findings highlight the robustness and universality of our model, suggesting its potential for integration into broader surveillance technologies. Future directions for development will include optimization for resource-constrained environments and deep learning enhancements to refine detection precision. Full article

With advancements in artificial intelligence (AI), deep learning (DL) has become crucial for real-time data analytics in areas like autonomous driving, healthcare, and predictive maintenance; however, its computational and memory demands often exceed the capabilities of low-end devices. This paper explores optimizing deep learning architectures for memory efficiency to enable real-time computation in low-power designs. Strategies include model compression, quantization, and efficient network designs. Techniques such as eliminating unnecessary parameters, sparse representations, and optimized data handling significantly enhance system performance. The design addresses cache utilization, memory hierarchies, and data movement, reducing latency and energy use. By comparing memory management methods, this study highlights dynamic pruning and adaptive compression as effective solutions for improving efficiency and performance. These findings guide the development of accurate, power-efficient deep learning systems for real-time applications, unlocking new possibilities for edge and embedded AI. Full article

To address the challenge of predicting lubrication states and reliability caused by the uncertainty of gear materials and structural parameters, a lubrication reliability analysis method considering the randomness of gear parameters is proposed. Firstly, a nonlinear dynamic model of a gear pair is established to derive the dynamic meshing force. The geometric and kinematic analyses are then performed to determine time-varying equivalent curvature radius and entrainment velocity. The minimum film thickness during meshing is further calculated. Considering gear parameters as random variables, a gear lubrication reliability model is formulated. Monte Carlo Simulation method is employed to accurately analyze the dynamic response, dynamic meshing force, equivalent curvature radius, entrainment velocity, probability distribution of minimum film thickness, and gear lubrication failure probability. Additionally, a specialized wear test device is designed to investigate the evolution of tooth surface roughness with wear and to forecast trends in gear lubrication failure probability as wear progresses. The results indicate that the uncertainty in gear parameters have minimal impact on the equivalent curvature radius and entrainment velocity, but significantly affect the dynamic meshing force. The gear speed and root mean square roughness are critical factors affecting lubrication reliability, and the early wear of the teeth enhances the lubrication reliability. The present work provides valuable insights for the design, maintenance, and optimization of high-performance gear systems in practical engineering applications. Full article