Search Results (18,621)

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

World Natural Heritage sites are shared assets of humanity; therefore, assessing and analyzing their ecological environments is crucial for informed decision-making and providing a basis for ecological risk control and security measures. This study utilizes GIS spatial analysis and a comprehensive ecological risk evaluation method, based on LUCC, NDVI, and DEM, to investigate trends in ecological risk changes within the Karst World Natural Heritage site. Additionally, a geographical detector model is introduced to quantitatively analyze influencing factors. Furthermore, simulations are conducted to predict the evolution of ecological sensitivity over the next twenty years. The results indicate that: (1) In the single-factor sensitivity evaluation, NDVI exhibits the highest influence (0.36), whereas the aspect factor demonstrates the lowest (0.08). (2) The ecological sensitivity of the study area is predominantly classified as medium–high across all stages. The proportion of areas with medium sensitivity decreased from 39.57% in 2000 to 35.02% in 2020, while other sensitivity levels remained relatively stable. The spatial distribution of comprehensive ecological sensitivity exhibits a positive correlation, displaying an aggregated pattern with a tendency towards homogeneity over time. High ecological sensitivity areas tend to aggregate in the north, while low-value areas aggregate in the south. (3) NDVI and land use demonstrate significant explanatory power for the spatial differentiation of ecological sensitivity, with land use serving as the dominant factor in the Shibing Karst. The strongest interaction exists between NDVI and land use, with explanatory powers of 0.906 and 0.889, respectively. (4) Finally, the CA–Markov model was employed to simulate ecological sensitivity from 2030 to 2040. It was predicted that overall ecological sensitivity will increase in the future, particularly within the northern buffer zone, which is in urgent need of improvement. Overall, the ecological sensitivity of the Shibing Karst is moderate. Heritage management must prioritize ecological protection to promote the conservation of the World Natural Heritage site. Management authorities should pay greater attention to ecological conservation to foster the sustainable development of natural World Heritage sites, providing scientific references for the preservation of other World Heritage sites. Full article

Contemporary social theory has extensively examined how structural arrangements shape social life, yet the mediating processes through which structural conditions are translated into lived experience remain insufficiently conceptualised. This article addresses this gap by developing an analytical framework that reconceptualises social position as a mediating configuration through which social reality becomes experientially organised. Rather than treating position as a fixed location within social hierarchies or as a subjective standpoint, the article conceptualises it as a historically sedimented relational formation that structures perception, normativity, affect, and practical orientation. On this basis, the article advances an analytical model in which inequality is understood not only as a structural distribution of resources and power, but also as an experiential organisation of social relations, shaping how constraints, opportunities, and recognition are encountered in everyday life. Subjectivity and agency are analysed as emerging within positionally structured relations of power and mediation, rather than as pre-social or purely individual capacities. By articulating social position as a constitutive form of mediation, the article contributes to sociological analysis by clarifying how structure, history, and subjectivity are internally articulated within lived social experience, offering a conceptual framework that moves beyond dualist accounts of structure and agency. Full article

A large number of ontologies have been developed over the past two decades for the education domain. Some of these ontologies are available in public repositories published within the Linked Open Data cloud. However, a significant portion of educational ontologies remain distributed across project-specific websites, making their discovery and access challenging. Ontologies designed for education often have domain- or task-specific characteristics and conceptual structures. To facilitate their discovery, interoperability, actualization and reuse, it is essential to annotate them with rich, standardized metadata, such as domain coverage, pedagogical objectives, target learner groups, and technical specifications, to enable effective search and support integration within educational systems. Other components, such as knowledge graphs, rules, learning analytics, and machine learning-based models also play an important role. In this research, a conceptual model of a heterogeneous educational ontology repository for storing and reusing ontologies, knowledge graphs, and other objects and tools needed for the development of knowledge bases for intelligent education systems is proposed. An OWL ontology modeling the needed metadata for the description of repository objects and supporting semantic search and recommendations to support the development of knowledge bases for intelligent educational systems is also developed. The proposed heterogeneous ontology repository can help in solving many of the challenges related to hallucinations, transparency, personalization, privacy, and pedagogical alignment that arise when integrating large language models into educational systems by proposing or recommending easy-to-use ontologies for the development of intelligent educational systems, integrating generative AI, symbolic AI, machine learning and statistical techniques. It also integrates LLMs to ensure effective and easy search, recommendation of stored objects, and ontology management. The proposed LLM-powered ontology extraction use case demonstrates an encouraging ontology metadata extraction quality (a precision of about 0.7 and a recall of about 0.9) combined with an ontology development strategy that is easy for education professionals to use. Full article

False data injection (FDI) cyberattacks pose a growing threat to modern power distribution systems in smart cities by manipulating state-estimation processes and provoking covert voltage violations that traditional defense mechanisms fail to detect. Recent industry data indicate that coordinated FDI attacks can distort measurement sets by as little as 3–7%, yet trigger voltage deviations exceeding 10% in vulnerable feeders, resulting in operational instability, unnecessary load curtailments, and elevated outage risk. To address these challenges, this paper proposes a quantum-accelerated digital twin (QDT) framework that integrates quantum optimization algorithms with a high-fidelity digital twin (DT) of the distribution system to detect, localize, and remediate FDI-induced cyberattacks in real time. The rationale behind the approach lies in the superior combinatorial search capability of quantum solvers, which accelerates the identification of falsified measurement vectors and optimal corrective control actions compared with classical methods. In addition, the framework introduces an innovative Bitcoin-mining-oriented virtual energy storage (BMOVES) mechanism that treats mining facilities as dynamically controllable, fast-response electrical loads within smart city demand–response programs. By modulating mining power consumption with sub-second granularity, the proposed BMOVES resource provides up to 18–45% flexible capacity during attack scenarios, enabling voltage restoration without relying on conventional energy storage assets. The unified QDT + BMOVES architecture is validated using the 136-bus Brazilian distribution system, a realistic benchmark for cyber–physical resilience studies. Simulation results demonstrate over 99% FDI detection accuracy, up to an 82% reduction in peak voltage violations, and restoration of operational limits 11 times faster than state-of-the-art classical methods. These findings highlight the transformative potential of integrating quantum computing, digital twins, and nontraditional flexible assets to enhance cyber-resilient power infrastructure in future smart cities. Full article

To address the issue of traditional bistable vibration energy harvesters (BVEHs) being prone to becoming trapped in a single potential well—which results in a narrowed energy harvesting bandwidth and reduced efficiency—this paper proposes a method that utilizes the nonlinear electromagnetic force generated during the induction process to modulate the kinematic behavior of the oscillator. The characteristics and influencing factors of the nonlinear force produced during electromagnetic induction are analyzed. A dual-cantilever beam structure is designed, with an iron-core coil and a magnet placed at the respective free ends. A mathematical model of a piezoelectric–electromagnetic coupled bimodal broadband vibration energy harvester is established and numerically simulated. Furthermore, a vertical vibration experimental platform is constructed to conduct frequency sweep tests. The experimental results demonstrate that the proposed piezoelectric–electromagnetic coupled bimodal broadband vibration energy harvester effectively improves energy harvesting efficiency. Within the frequency range of 5–20 Hz, the system exhibits two vibration modes, with resonant frequencies of approximately 7.7 Hz and 15.7 Hz. For a single-layer PVDF piezoelectric film, the maximum output power at the first and second resonance points is 8.9 μW and 9.7 μW, respectively. The electromagnetic module achieves maximum output powers of 0.39 W and 0.71 W. Moreover, within the frequency ranges of 6.3–9.8 Hz and 14–17.7 Hz (a total bandwidth of 7.2 Hz), the device maintains a stable power output. The effective bandwidth is broadened by approximately 80%, demonstrating excellent broadband performance. Full article

Producing high-conductivity aluminum conductors for power transmission involves 23 trace elements and multiple interconnected thermo-mechanical stages. The ultra-low alloying levels required to preserve high electrical conductivity create a narrow compositional window and highly imbalanced distributions, which hinder traditional data-driven learning. Here, we developed a physics-guided machine-learning framework based on 4458 valid industrial production records to predict tensile strength and electrical resistivity. In addition to raw composition and process parameters, we introduce ratio descriptors (e.g., Fe/Si and Al/Si) and propose a physics-informed metric termed the Equivalent Solute–Heat Index (ESHI) to couple key solute chemistry (Si, Fe, B) with normalized thermal-history intensity. Fe and Si primarily influence resistivity through impurity/solute scattering, while B mainly affects microstructural uniformity via grain refinement. Incorporating ESHI as an augmented signal into the best-performing XGB surrogate markedly improves generalizability, increasing the tensile strength R² from 0.75 to ~0.92. SHAP analysis reveals that ESHI dominates the decision logic by modulating both targets with metallurgically interpretable mechanisms: solute-controlled scattering and thermal history-traced second-phase evolution that stabilizes the microstructure. NSGA-III was further employed to map the Pareto front and identify composition–process combinations that optimize the strength–conductivity trade-off, enabling improved mechanical reliability while minimizing resistive losses in practical power-transmission applications. Experimental validation on industrial wires confirms this reliability. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control. Full article

In pico-hydropower, the use of pumps as turbines is a cost-effective solution, especially for remote areas. The abundant seawater makes it a good fluid for pumped storage. The operation of centrifugal pumps in normal and reverse modes involves thickness loss because of solid particle concentration and vapor cavitation. Some research has been performed to predict cavitation in centrifugal pumps, but this issue still exists in several pico-hydropower plants. Therefore, to analyse the synergy between erosion and cavitation in a seawater centrifugal pump, we performed a CFD analysis to compute the effect of material mass loss due to erosion on cavitation risk. The Euler–Lagrangian method was used to track the released particles combined with the RNG k-ε turbulence model. The first part studied the effect of the surface mean roughness height (R_a) on the performance of the centrifugal pump. Increasing R_a from 0 to 15 μm decreases the pump hydraulic efficiency from 93% to 91%, respectively. The second analysis focused on the distribution of erosion thickness and its temporal evolution for 40 μm and 50 μm particles. For both the pump mode and the turbine mode, the erosion thickness is a polynomial function of power 2 with time. The most eroded regions are the blade leading edge (LE) and the blade trailing edge in pump and turbine mode, respectively. The last section focuses on analysing the effect of erosion thickness on cavitation damage. As the surface roughness increases, the cavitation damage power increases. The cavitation power risk increases from 111 kW to 156 kW in pump mode. In turbine mode, when the erosion thickness is between 0.0011 μm and 0.0022 μm, the cavitation damage is the same, approximately 170 kW, whereas the total gas distribution is uniformly distributed in the blade channel. With respect to seawater, the NPSHr increased compared with that with freshwater, from 3.35 m to 3.67 m. Full article

The rapid increase in memory power density has made memory thermal management a critical challenge in high-density servers, where extremely limited DIMM spacing significantly reduces the effectiveness of air cooling. Compared with CPUs and GPUs, memory-level liquid cooling has received less systematic study, particularly regarding the influence of cold plate structural design on thermal and hydraulic performance under realistic server conditions. In this paper, three engineering-feasible memory liquid cooling solutions (water-flowing cold plate, clamp-type cold plate and heat-pipe-based cold plate) are experimentally compared on a high-density server system. Experiments are conducted at coolant inlet temperatures of 37–50 °C with a fixed flow rate of 0.8–1.5 L/min. Memory, CPU, and voltage regulator temperatures, as well as system pressure drop, are measured. Results show that memory temperature increases with coolant inlet temperature for all configurations, while their relative performance remains unchanged. Memory temperatures range from 62.04 to 71.13 °C, 57.65 to 66.98 °C, and 66.22 to 76.07 °C, with corresponding pressure drops of 24.19–26.69 kPa, 32.73–35.98 kPa, and 27.00–29.96 kPa. These results provide insight into the role of coolant distribution and flow-path topology in memory thermal performance. Full article

Receiver nonlinear distortion severely impacts modern wireless systems. Traditional power series polynomial models suffer from numerical instability in parameter estimation, especially at high orders or with memory. This paper investigates orthogonal memory polynomial models from the perspectives of memory depth, nonlinear order, input signal distribution, and temporal correlation of the input signal, focusing on effective methods for improving the condition number. Comprehensive analysis reveals that the condition number of the Gram matrix grows rapidly with polynomial order and memory depth for the conventional polynomial, while orthogonal polynomials remain well-conditioned due to their inherent orthogonality and normalization. Notably, orthogonal polynomials maintain robust performance even when the input distribution does not perfectly match the basis weight function. Experiments using OFDM and 3-carrier WCDMA signals confirm that orthogonal polynomials achieve condition numbers orders of magnitude lower than those of power series, along with superior fitting accuracy. Full article

In this paper, an intelligent localization framework based on deep learning is proposed to address the limitations of insufficient accuracy and robustness in defect identification and localization during the ultrasonic guided-wave non-destructive testing (NDT) of receiver tubes in tower-type molten salt Concentrated Solar Power (CSP) stations. In the proposed method, a 1D convolutional neural network (1D-CNN) initially processes raw time-series-guided wave signals, achieving coarse identification and preliminary localization of defective segments. Then, Mel spectrograms are employed to exploit multi-dimensional features in the time–frequency domain and transform 1D signals into 2D representations, thereby enriching feature diversity. A regression-based 2D-CNN was designed to predict the start and end points of defect segments, enabling precise interval localization. Furthermore, the Tree Seed Algorithm (TSA) was integrated to jointly optimize key hyperparameters, enhancing training efficiency and prediction accuracy. Experimental validation on a dataset of ultrasonic guided-wave signals from molten salt receiver tubes demonstrates that the TSA-optimized Mel+2D-CNN model achieves superior performance, with a Mean Absolute Error (MAE) of 75.11 sampling points and a Coefficient of Determination (R²) of 0.90. At an Intersection over Union (IoU) threshold of 0.3, the model achieves a hit rate of 89.21%, exhibiting significantly higher localization accuracy and stability compared to the 1D-CNN baseline model. These findings indicate that the proposed method effectively enhances the accuracy and robustness of guided wave-based defect localization in slender structures. While promising, the model’s generalization capability remains dependent on the data distribution and operating conditions; future work will focus on validating its engineering applicability across diverse, multi-scenario industrial environments. Full article

Developmental dysplasia of the hip (DDH) is a common musculoskeletal disorder in infancy, and early detection is essential for optimal clinical outcomes. Radiographic assessment is traditionally based on angular measurements, which may be limited by variability in landmark identification and do not fully capture the complex morphology of the hip joint. In this study, we investigate whether geometric features derived from the hip joint articulation space can be used to differentiate between normal and dysplastic hips in infant radiographs. Pelvic X-ray images from infants (mean age 4.5 ± 0.83 months) were analyzed, and custom segmentation masks were developed to isolate the joint space region. A total of 99 geometric and radiomic features were extracted and evaluated using statistical analysis and supervised machine learning methods. Multiple features demonstrated strong discriminative power between normal and DDH (p < 0.001), with shape and spatial distribution characteristics showing the highest relevance. Classification models achieved an F1-score of approximately 80% on the full dataset. Notably, patient age was identified as a significant confounding factor, and analysis on an age-matched subset improved classification performance to 94% accuracy and 93% recall. These findings suggest that geometric characterization of the hip joint space provides a promising and interpretable framework for DDH detection. The results also highlight the importance of age-stratified analysis in pediatric imaging. Further validation on larger and more diverse datasets is required to assess clinical applicability. Full article

This study applies selective laser melting (SLM) to fabricate stainless steel 316L (SS316L) structures on the distribution plate of a Venturi-type nozzle in a pressurized dissolution microbubble generator. SLM is employed because the fabricated structures are approximately hundreds of micrometers in size, making them difficult to produce using conventional milling or other machining methods. These structures are designed to enhance cavitation and gas–liquid interaction, thereby enhancing microbubble generation. Various conditions of the SLM process are conducted, and the combination of 140 W laser power, 100 mm/s scan speed, 30 µm layer thickness, and 120 µm hatch distance achieves the highest relative density while maintaining the austenite phase of SS316L, thus being selected as the optimal SLM process parameters. Microbubble generation test are conducted under three different dissolution tank pressure conditions (0.20, 0.25, and 0.30 MPa) using nozzles with and without the SLM structures. The generated microbubbles in both nozzles ranges from 1 to 110 µm, satisfying the size conditions for microbubbles. The average microbubble size is smaller in the SLM-assisted nozzle (31.8 µm) compared with the plain nozzle (38.8 µm). Furthermore, under the dissolution tank pressure of 0.30 MPa for 30 s, the SLM-assisted nozzle generates a maximum of 52,368 microbubbles, representing approximately a 102.1% increase compared with the plain nozzle (25,907 microbubbles). These results demonstrate that incorporating SLM structures to Venturi-type nozzle effectively enhances microbubble generation, offering promising potential for applications in water treatment, biomedical processes, and chemical engineering. Full article

Recreational vitality is widely recognized as a core metric for assessing the quality of human settlements. Elucidating the relationship between recreational vitality and landscape characteristics is crucial for guiding the optimization and quality enhancement of urban waterfront spaces. This study takes the micro-scale waterfront space of the Beijing–Hangzhou Grand Canal (Hangzhou section) as its research object, systematically analyzes the correlation between waterfront landscape attributes and recreational vitality, and formulates specific optimization strategies for enhancing recreational vitality. A total of 310 representative sampling sites was established. The study integrates machine learning-driven semantic image segmentation to achieve refined quantification of waterfront landscape metrics and employs anonymized mobile phone signaling data to dynamically characterize the spatiotemporal distribution of recreational vitality. Through correlation analysis and regression modeling, it quantifies the effect size and functional mechanisms of key landscape metrics on recreational vitality, and further proposes adaptive strategies for recreational vitality enhancement tailored to different urban functional zones. The key findings are as follows: (1) Recreational vitality is significantly higher on holidays than on workdays. High-vitality areas are concentrated in commercial functional zones, with an overall spatial gradient of “low in the east and high in the west, low in the north and high in the south”. (2) High-level Green View Factor (HGVF) shows a stable positive correlation with vitality, whereas the Sky View Factor (SVF) and the Enclosure Interface View Factor (EIVF) correlate negatively. (3) The influence of landscape metrics is strongly moderated by functional zone type: in residential functional zones, HGVF has strong explanatory power; in commercial functional zones, it shows complex nonlinearity; in ecological conservation zones, its explanatory power is generally weaker. (4) Tailored enhancement strategies are proposed for each functional zone. This study clarifies the link between core waterfront landscape attributes and micro-scale recreational vitality, and provides a scientific basis for evidence-based design and sustainable enhancement of urban waterfront spaces. Full article