Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes.

Photovoltaic (PV) cells play an important role in the development of green energy. However, in practical photovoltaic systems, shunting-related defects and hotspot phenomena may originate not only from manufacturing imperfections, but also from mechanical stress and environmental factors during transportation, installation, and long-term field operation. Such hotspots not only reduce the power-generation efficiency and service life of PV cells but may also pose safety risks to grid-connected photovoltaic power stations. To address this problem, a squared even-order derivative (SEOD) method based on surface temperature analysis is introduced to enable the quantitative detection of thermal defects in PV cells. In this study, typical faults in PV cells, including low-resistance defects and silicon-based deep scratches, are analyzed. A simulation model is established to correlate typical faults with their equivalent volumetric heat sources, followed by experimental validation for low-resistance defects. Based on this framework, the SEOD algorithm is developed and applied to achieve high-precision localization and quantitative characterization of thermal defects in both simulation models and experimental samples.

To mitigate the inherent high flow resistance of conventional corrugated tubes, a novel design with alternating clockwise/counterclockwise corrugated segments separated by smooth sections is proposed. A 3D numerical model was developed to systematically evaluate the thermal-hydraulic performance of the novel tube against smooth and conventional corrugated tubes, with simulations conducted at Reynolds number (Re) = 9952–35,827. Results show both corrugated configurations enhanced heat transfer significantly relative to the smooth tube: the conventional tube had the highest Nusselt number (Nu) (1.76–1.79 times that of the smooth tube), while the novel tube achieved Nu = 1.61–1.65 times that of the smooth tube. Notably, the novel tube reduced flow resistance substantially—at Re = 35,827, its friction factor (f) was only 65.2% of the conventional tube’s. Parametric studies revealed that more corrugated segments improved heat transfer but increased pressure drop: the 72-12 configuration exhibited the best heat transfer, while the 72-2 configuration reduced f by 40.7%. The novel tube showed superior overall performance (Performance Evaluation Criterion (PEC) > 1.24 for all Re), as corrugated segments generated periodic vortices to disrupt the thermal boundary layer, while smooth segments enabled flow redevelopment and pressure recovery. This study provides valuable guidance for designing high-efficiency, low-resistance heat exchange elements.

Research shows that individuals with heart failure are 60% more likely to develop dementia because of their shared metabolic risk factors. Developing a classification model to differentiate between these two conditions effectively is crucial for improving diagnostic accuracy, guiding clinical decision-making, and supporting timely interventions in older adults. This study proposes a novel method for dementia classification, distinguishing it from its common comorbidity, heart failure, using blood testing and personal data. A dataset comprising 11,124 imbalanced electronic health records of older adults from hospitals in Chiang Rai, Thailand, was utilized. Conditional tabular generative adversarial networks (CTGANs) were employed to generate synthetic data while preserving key statistical relationships, diversity, and distributions of the original dataset. Two groups of ensemble models were analyzed: the boosting group—extreme gradient boosting, light gradient boosting machine—and the bagging group—random forest and extra trees. Performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver-operating characteristic curve were evaluated. Compared with the synthetic minority oversampling technique, CTGAN-based synthetic data generation significantly enhanced the performance of ensemble learning models in classifying dementia and heart failure.

This research details the development and evaluation of a Modern Ultrasonic Cleaning Tank (MUCT) designed to enhance cleaning efficiency in jewelry manufacturing, particularly for silver jewelry, replacing the traditional method, which was less efficient and had higher operating costs. The MUCT offers capabilities of single- or dual-frequency ultrasonic operation (28 kHz and 40 kHz) and adjustable transducer positioning. An advanced method involving computer simulations, utilizing harmonic response analysis and transient dynamic analysis, was employed to determine the acoustic pressure inside the MUCT, thereby indicating the cavitation intensity required to achieve high cleaning efficiency. Simulation results confirm that this design can distribute acoustic pressure throughout the MUCT, as intended. A prototype MUCT was assembled, and its operation was validated through foil corrosion tests, ultrasonic power concentration (UPC) measurements, and jewelry cleaning tests. The results revealed that the MUCT’s center provided the maximum UPC of 28 W/L and an acoustic pressure of 30.43 MPa, effectively operating at single and dual frequencies, and achieving superior dirt removal. The highest cleaning efficiency of 100% was achieved using dual frequency with a 97% water and 3% dishwashing liquid mixture at 60 °C, exceeding the 23.52% obtained with water at 27 °C without ultrasonic treatment. The MUCT, successfully integrated into the manufacturing process, offers customizable features to meet various cleaning needs, providing flexibility, improved performance, and cost savings.

Using the method of laser ablation in liquid, submicron-sized particles of zero-valent amorphous selenium (Se SMPs) were created. A number of composite polymer materials were manufactured based on polyurea and Se SMPs at concentrations ranging 0.1–2.5 wt.%. The manufactured materials showed no significant surface or internal defects at either the macro or micro level. It was found that the Se SMPs were not uniformly distributed inside the polymer, but formed ordered areas with slightly higher and lower concentrations of the particles. It was demonstrated that the manufactured materials did not generate a significant amount of active oxygen species, which could damage biological objects such as protein molecules and DNA, while also exhibiting pronounced bacteriostatic properties without significantly affecting the growth and reproduction of mammalian cells. Materials containing 0.25 and 1% Se SMPs, when added to soil, improved the morphometric parameters of radish plants (Raphanus sativus var. sativus). These polymer composite materials based on polyurea with the addition of Se SMPs are promising functional materials for agriculture due to their antibacterial activity.

With the implementation of China’s “dual carbon” strategy, the installed capacity of new energy has grown rapidly. Wind power and photovoltaic power have accounted for more than 40%, but the integration of power electronic apparatus into the grid has resulted in the manifestation of a system with “low inertia and weak damping”, which can easily lead to transient overvoltage problems at transmitters when high-voltage direct-current (HVDC) latching faults occur. Although a variety of dynamic reactive power optimization strategies have been proposed in the existing research, most of them are aimed at single equipment, and multi-reactive power source collaborative control schemes are lacking. In this paper, we innovatively establish a transient voltage analysis model for a new energy transmitter, derive the expression of overvoltage amplitude, and propose a method for the construction of a multi-reactive source collaborative optimization model, which can effectively suppress transient overvoltage through capacity and initial output configuration. We provide a new idea for the safe operation of a significant percentage of new energy grids. The case analysis shows that the co-optimization method outlined in this paper is an effective solution to suppress the transient overvoltage triggered by AC faults and has wide application value.

China frequently experiences natural disasters, making emergency language services a key link in information transmission, cross-lingual communication, and resource coordination during disaster relief. Traditional contingency plans rely on manual experience, which results in low efficiency, limited coverage, and insufficient dynamic adaptability. Large language models (LLMs), with their advantages in semantic understanding, multilingual adaptation, and scalability, provide new technical approaches for emergency language services. Our study establishes the country’s first generative evaluation index system for emergency language service contingency plans, covering eight major dimensions. Through an evaluation of 11 mainstream large language models, including Deepseek, we find that these models perform excellently in precise service stratification and resource network stereoscopic coordination but show significant shortcomings in legal/regulatory frameworks and mechanisms for dynamic evolution. It is recommended to construct a more comprehensive emergency language service system by means of targeted data augmentation, multi-model collaboration, and human–machine integration so as to improve cross-linguistic communication efficiency in emergencies and reduce secondary risks caused by information transmission barriers.

The weights and structure determination (WASD) neuronet (or neural network) is a single-hidden-layer feedforward neuronet that exhibits an excellent approximation ability, despite its simple structure. Thanks to its strong generalization, fast speed, and ease of implementation, the WASD neuronet has been the subject of many modifications, including metaheuristics, and applications in a wide range of scientific fields. As it has garnered significant attention in the last decade, the aim of this study is to provide an extensive overview of the WASD framework. Furthermore, the WASD has been effectively used in numerous real-time learning tasks like regression, multiclass classification, and binary classification due to its exceptional performance. In addition, we present WASD’s applications in social science, business, engineering, economics, and medicine. We aim to report these developments and provide some avenues for further research.

Accurate geospatial data are essential for intelligent transportation systems and automated reporting applications, as location precision directly impacts safety analysis and decision-making. GPS devices are now routinely employed by law enforcement officers when filing vehicle crash reports, yet our investigation reveals that significant data quality issues persist. The high apparent precision of GPS coordinates belies their actual accuracy as we find that approximately 20% of crash sites need correction—results consistent with existing research. To address this challenge, we present a novel credibility scoring and correction algorithm that leverages a state-of-the-art multimodal large language model (LLM) capable of integrated visual and textual reasoning. Our framework synthesizes information from structured coordinates, crash diagrams, and narrative text, employing advanced artificial intelligence techniques for comprehensive geospatial validation. In addition to the LLM, our system incorporates open geospatial data from Overture Maps, an emerging collaborative mapping initiative, to enhance the spatial accuracy and robustness of the validation process. This solution was developed as part of research leading to a patent for autonomous vehicle routing systems that require high-precision crash location data. Applied to a dataset of 5000 crash reports, our approach systematically identifies records with location discrepancies requiring correction. By uniting the latest developments in multimodal AI and open geospatial data, our solution establishes a foundation for intelligent data validation in electronic reporting systems, with broad implications for automated infrastructure management and autonomous vehicle applications.