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Research on digital twin diagnosis model for the thermal-electric field of high-voltage switchgears
Abstract High-voltage switchgear is a critical component in modern power systems, yet it remains vulnerable to insulation degradation and other faults under complex operating conditions. To address these challenges, a digital twin-based online fault diagnosis method is proposed for high-voltage switchgear, integrating thermal and electric field analysis. A three-dimensional model of the KYN28-12(Z) switchgear is first established, incorporating multi-physics simulations to identify key monitoring regions. Building on this, a digital twin surrogate and information model are developed to enable real-time reconstruction and online characterization of coupled thermal-electric fields. For fault feature extraction, optimized classification tree (OCT) and random forest algorithms are employed, while an enhanced adaptive neural-fuzzy inference system (ANFIS) is constructed for intelligent fault diagnosis. Ultimately, the diagnosis model is trained using a combination of finite element simulation data, experimental acquisition data, and on-site operational historical data, ensuring comprehensive learning of switchgear behaviors under various conditions. And the diagnosis relies on data from the digital twin model to achieve accurate virtual-real mapping of switchgear states, providing theoretical support for intelligent operation and maintenance. Experimental results demonstrate a fault recognition rate of 93.4%, with only a 2.3% accuracy drop under 30% noise, verifying the robustness and reliability of the proposed method.
Design and analysis of a multi-layer circuit absorber with ultra-wideband and polarization insensitivity
Hydrogen Activation by a σσ*-Carbene Through Quantum Tunneling
Synthesis and antimicrobial activity of thiophene-functionalized Ag(I) and Au(I) N-heterocyclic carbene complexes against ampicillin-resistant Staphylococcus aureus
Visions of the future of molecular cell biology
The mediating role of coping styles between physical activity and sense of meaning in life among Chinese college students
A systematic investigation of endothelial cell behavior under hydrostatic pressure
Predictive mapping of deep soil organic carbon stocks across land use systems in Southern assam, India
Fortification of non-dairy milk with date fruit, mustard seed, and turmeric: nutritional quality, probiotics viability, antimicrobial and antioxidant potentials
On the analyses of graphene oxide/polypyrrole/zinc oxide nanocomposites
Abstract Graphene oxide/Polypyrrole/Zinc oxide GrO/PPy/ZnO nanocomposite was investigated for possible interaction with alanine using B3LYP/LANL2DZ model. Results indicated that GrO/PPy/ZnO exhibited notable electronic accessibility with a total dipole moment (TDM) of 5.62 Debye and HOMO-LUMO energy gap of 1.64 eV, which was significantly modulated upon alanine binding. COOH functionalization induced the greatest reduction in ionization potential (from 3.03 eV to 2.56 eV) alongside increased electron affinity (4.68 to 4.77 eV), while NH₂ functionalization showed moderate improvements (ionization potential to 2.67 eV, electron affinity to 4.75 eV). Quantum Theory of Atoms in Molecules (QTAIM) analysis revealed distinct binding characteristics: NH₂-bound systems formed multiple Zn–N and Zn–O coordination bonds with flexible interaction networks, while COOH-bound systems exhibited fewer but stronger, more localized coordination and hydrogen bonds. Molecular electrostatic potential (MESP) demonstrated enhanced positive potential near NH₂ binding sites and pronounced dipolar features around COOH regions. Non-covalent interaction (NCI) and reduced density gradient (RDG) analyses revealed that COOH functionalization produced more concentrated blue domains, indicating stronger interactions and enhanced selectivity. Density of states (DOS) showed notable band gap reduction after composite formation and functionalization, with GrO/PPy/ZnO exhibiting the most favorable electronic structure for charge transport. Alanine binding lowered system polarity (TDM: 2.81 Debye for COOH and 2.77 D for NH₂) while preserving structural stability, as shown by slight changes in chemical hardness. Overall, COOH-functionalized GrO/PPy/ZnO shows the best balance of reactivity, stability, and selective binding, with favorable electrostatics and strong interactions, highlighting its promise as an efficient amino acid sensor.
The double life of a transcription factor in the cytoplasm
Inverse design of periodic cavities in anechoic coatings with gradient changes of radii and distances via a conditional generative adversarial network
Distal enhancers loop to proximal enhancers, not to promoters
Correction: Exchange-bias and magnetic anisotropy fields in core–shell ferrite nanoparticles
Redox-driven cell death by disulfidptosis and its therapeutic potential
Water storage paradox of reservoir expansion and evaporative losses in the MENA region
Abstract Prolonged droughts and population growth have increased the demand for efficient water storage globally. Small agricultural reservoirs support local water demands, but high evaporation rates particularly in dry regions undermine their storage effectiveness. Integrating fine-resolution Sentinel-2 imagery and physical modeling, we created an annual dataset of small agricultural reservoirs (< 0.1 km2) in the Middle East and North Africa (MENA) and quantified their associated evaporative losses from 2016 to 2023. We identified over 133,700 reservoirs, peaking in 2020, providing a combined surface area of 1,408 km2. The largest cumulative areas are located in Türkiye (309 km2), Pakistan (234 km2), Iran (168 km2), Iraq (108 km2), and Egypt (64 km2). Small agricultural reservoirs offer a storage capacity of 1,243 million cubic meters, accounting for up to 16% of irrigation and livestock water use in most MENA countries. Annual evaporative losses from these reservoirs may potentially exceed 2,400 million cubic meters with hotspots of cumulative evaporation corresponding to regions with the highest reservoir surface area, including southern Pakistan, southwestern Iran, and southeastern Iraq. Our analysis suggests strong climatic and anthropogenic influences on the expansion of reservoirs and their storage efficiency emphasizing the need for mitigation strategies to improve agricultural water security in water-stressed regions.
Snoozing APC/C for a sweet cell cycle entry
Design and simulation of a PLC-controlled omni wheel conveyor sorting system for high-speed material handling
Abstract This study proposes a PLC-controlled omni wheel conveyor sorting system designed to address limitations in traditional sorting mechanisms by integrating barcode-based classification with high-speed and adaptable sorting capabilities. The system utilizes a Siemens S7-1200 PLC, omni wheels, a barcode scanner, and a conveyor motor to achieve precise, flexible, and efficient material handling. Mathematical analysis validated the system’s structural integrity, with deflections under $$0.009 \, \text {mm}$$ , and operational efficiency, including a roller speed of $$35.46 \, \text {RPM}$$ to support a throughput of 2000 objects per hour at a conveyor speed of $$0.167 \, \text {m/s}$$ . Simulations conducted in Factory IO achieved a sorting accuracy of $$98\%$$ , demonstrating the seamless synchronization of barcode scanning and sorting operations through deterministic ladder logic. The omni wheels provided multidirectional flexibility, reducing energy losses and enabling rapid redirection of objects. Compared to reinforcement learning-based approaches, the proposed system offers simplicity, cost-effectiveness, and ease of implementation without compromising accuracy or scalability. However, the simulations assumed ideal conditions, and limitations such as environmental factors, dynamic loading, and real-world scalability remain unaddressed. Future research could explore integrating IoT-enabled monitoring, hybrid control strategies, and dynamic adaptability to enhance performance in complex industrial environments. The results highlight the potential of this system to revolutionize material handling across manufacturing, logistics, and e-commerce sectors.