Search Results (211)

Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method. Full article

Extreme damage poses a serious challenge to the safe operation of power grids. Optimizing the allocation of defense resources to improve the grid’s disaster resistance capabilities is the main concern of the power system. In this paper, a distributed robust optimal defense resource allocation method based on the defender–attacker–defender model is proposed to improve the disaster resilience of power grids. This method takes into account the uncertainty of restoration time due to different damage intensities and improves the efficiency of restoration resource scheduling in the restoration process. Meanwhile, a set covering-column and constraint generation (SC-C&CG) algorithm is proposed for the case that the mixed integer model does not satisfy the Karush–Kuhn–Tucker (KKT) condition. A case study based on the IEEE 24-bus system is conducted, and the results verify that the proposed method can minimize the system dumping load under the uncertainty of the maintenance time involved. Full article

Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks. In recent years, denoising methods based on low-rank subspaces have garnered widespread attention. In the low-rank matrix factorization framework, the restoration of HSI can be formulated as a task of recovering two subspace factors. However, hyperspectral images are inherently three-dimensional tensors, and transforming the tensor into a matrix for operations inevitably disrupts the spatial structure of the data. To address this issue and better capture the spatial-spectral priors of HSI, this paper proposes a modeling approach named low-rank Tucker decomposition with subspace implicit neural representation (LRTSINR). This data-driven and model-driven joint modeling mechanism has the following two advantages: (1) Tucker decomposition allows for the characterization of the low-rank properties across multiple dimensions of the HSI, leading to a more accurate representation of spectral priors; (2) Implicit neural representation enables the adaptive and precise characterization of the subspace factor continuity under Tucker decomposition. Extensive experiments demonstrate that our method outperforms a series of competing methods. Full article

Virtual power plants (VPPs) coordinate distributed energy resources (DERs) to collectively meet grid dispatch instructions. When a dispatch command is issued to a VPP, it must be disaggregated optimally among the individual DERs to minimize overall operational costs. However, existing methods for VPP dispatch instruction disaggregation often require solving complex optimization problems for each instruction, posing challenges for real-time applications. To address this issue, we propose a multi-parametric programming-based method that yields an explicit mapping from any given dispatch instruction to an optimal DER-level deployment strategy. In our approach, a parametric optimization model is formulated to minimize the dispatch cost subject to DER operational constraints. By applying Karush–Kuhn–Tucker (KKT) conditions and recursively partitioning the DERs’ adjustable capacity space into critical regions, we derive analytical expressions that directly map dispatch instructions to their corresponding resource allocation strategies and optimal scheduling costs. This explicit solution eliminates the need to repeatedly solve the optimization problem for each new instruction, enabling fast real-time dispatch decisions. Case study results verify that the proposed method effectively achieves the cost-efficient and computationally efficient disaggregation of dispatch signals in a VPP, thereby improving its operational performance. Full article

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

Objectives/Background: With increasing awareness of the association between physical activity and mental health, promoting youth health has gained prominence. For this, education and support are needed. As psychological school-based factors could be key to affecting this behavior, this study investigates middle school students’ trust in their physical education teachers and their grit, analyzing their effects on health-promoting behaviors that could follow these adolescents through adulthood. Methods: Middle school students, aged 12–14, were recruited from three schools in Sejong City, Korea, in May 2025; 420 survey questionnaires were distributed and 390 were returned (response rate: 92.86%). After eliminating those with insincere responses, 369 valid questionnaires (boys = 186, girls = 183) were analyzed. The analysis covered the descriptive statistics, Pearson’s correlation, and structural equation modeling, with grit, trust in physical education teachers, and health-promoting behaviors as variables. Results: The correlation analysis verified multicollinearity between trust in physical education teachers (closeness, fairness, teaching method, and physical ability), grit (effort, perseverance, and interest consistency), and health-promoting behaviors (self-actualization, health management, and stress management). A positive significant correlation was found between all subfactors (p < 0.05). The research model’s fit was confirmed through several fit indices; specifically, normed χ² = 4.138, goodness-of-fit-index = 0.942, root mean square residual = 0.033, root mean square error of approximation = 0.092, incremental fit index = 0.965, Tucker–Lewis index = 0.947, and comparative fit index = 0.965, and all values were judged acceptable. The standardized coefficients of each latent variable explaining the measurement variables were 0.707 or higher. Therefore, the explanatory power of the measurement variables was also satisfactory; thus, the research model was appropriate and could be used for analysis. The model findings revealed that trust in physical education teachers had a positive effect on student grit (β = 0.505, p < 0.001) and that grit had a positive effect on health-promoting behaviors (β = 0.743, p < 0.001); however, trust in physical education teachers did not have a direct effect on health-promoting behaviors (statistically insignificant [β = 0.103, p > 0.05]). Thus, grit had a mediating effect between trust in physical education teachers and health-promoting behaviors (β = 0.375, p < 0.01). Conclusions: This study highlights the educational implications for physical education teachers of building trust and strengthening student grit as key factors in achieving sustainable health-promoting behaviors among adolescents. Full article

This paper proposes a novel sparse twin parametric insensitive support vector regression (STPISVR) model, designed to enhance sparsity and improve generalization performance. Similar to twin parametric insensitive support vector regression (TPISVR), STPISVR constructs a pair of nonparallel parametric insensitive bound functions to indirectly determine the regression function. The optimization problems are reformulated as two sparse linear programming problems (LPPs), rather than traditional quadratic programming problems (QPPs). The two LPPs are originally derived from initial L1-norm regularization terms imposed on their respective dual variables, which are simplified to constants via the Karush–Kuhn–Tucker (KKT) conditions and consequently disappear. This simplification reduces model complexity, while the constraints constructed through the KKT conditions— particularly their geometric properties—effectively ensure sparsity. Moreover, a two-stage hybrid tuning strategy—combining grid search for coarse parameter space exploration and Bayesian optimization for fine-grained convergence—is proposed to precisely select the optimal parameters, reducing tuning time and improving accuracy compared to a singlemethod strategy. Experimental results on synthetic and benchmark datasets demonstrate that STPISVR significantly reduces the number of support vectors (SVs), thereby improving prediction speed and achieving a favorable trade-off among prediction accuracy, sparsity, and computational efficiency. Overall, STPISVR enhances generalization ability, promotes sparsity, and improves prediction efficiency, making it a competitive tool for regression tasks, especially in handling complex data structures. Full article

Background: The rising consumption of ultra-processed foods, especially those high in added sugars, poses a growing public health concern. Although several tools exist to assess food addiction, there is a lack of validated instruments specifically designed to measure addiction-like behaviors related to sweet food intake. Objectives: This study evaluates the psychometric properties of the FitMIND Foundation Sweets Addiction Scale (FFSAS), adapted from the Yale Food Addiction Scale 2.0 (YFAS 2.0), using data from Polish adults recruited through the FitMIND Foundation. Methods: The FFSAS was evaluated by 11 expert judges on four criteria: clarity, content validity, linguistic appropriateness, and construct representativeness. Afterwards, 344 adult volunteers (mean age 40.6 ± 10.7 years, 78% female, mean body mass index (BMI) 27.86 kg/m²) completed online FFSAS and provided demographic data, BMI, and self-reported sweets consumption. Internal consistency was assessed with Cronbach’s alpha and external validity was examined through Spearman’s correlations. Moreover, we conducted Exploratory and Confirmatory Factor Analyses (EFA and CFA). Results: Content validity of the FFSAS was supported by expert validation. The scale demonstrated good overall internal consistency (α = 0.85), with specific criteria such as tolerance (α = 0.916) and withdrawal (α = 0.914) showing particularly high reliability. The FFSAS total score was moderately correlated with sweets consumption frequency (ρ = 0.39, p < 0.05) and feelings of guilt (ρ = 0.35, p < 0.05). Exploratory factor analysis (EFA) revealed a robust three-factor structure, explaining 68.6% of the variance; the individual factors (subscales) derived from this structure demonstrated excellent internal consistency (Cronbach’s α ranging from 0.951 to 0.962). Sampling adequacy was high based on Kaiser–Meyer–Olkin measure (KMO = 0.956). Confirmatory factor analysis (CFA) indicated suboptimal model fit (Comparative Fit Index (CFI) = 0.74, Tucker–Lewis Index (TLI) = 0.69, Root Mean Square Error of Approximation (RMSEA) = 0.14), with a significant chi-square test (χ² = 3761.76, p < 0.001). Conclusions: This pilot study demonstrated that the FFSAS may be a promising tool for assessing sweet food addiction in adults. Future research should focus on assessing the FFSAS’ suitability on more diverse populations in other countries for further validation. Full article

This paper proposes a two-stage stochastic non-cooperative game model to solve relief supplies procurement and distribution optimization of multiple rescue organizations in crisis rescue. Rescue organizations with limited budgets minimize rescue costs through relief supply procurement, storage, and transportation in an uncertain environment. Under a mild assumption, we establish the existence and uniqueness of the equilibrium point and derive the optimality conditions by using the duality theory, characterizing the saddle point in the Lagrange framework. The problem is further reformulated as a constraint system governed by Lagrange multipliers, and its optimality is characterized by the Karush–Kuhn–Tucker condition. The economic interpretation of the multipliers as shadow prices is elucidated. Numerical experiments verify the effectiveness of the model in cost optimization in crisis rescue scenarios. Full article

Within the context of global warming, the frequent occurrence of extreme weather may lead to problems, such as a sharp decrease in new energy output, insufficient system backups, and an increase in the amount of energy consumed by users, resulting in large-scale power shortages within the grid for a short period of time. With the increase in the numbers of electric vehicles (EVs) and microgrids (MGs), which are resilient resources, the ability of a system to participate in demand response (DR) is further improved, which may make up for short-term power shortages. In this paper, we first propose a charging and discharging model for EVs during the onset of a cold wave, and then perform load forecasting for EVs during cold wave weather based on user behavioral characteristics. Secondly, in order to accurately portray the flexible regulation capability of microgrids with massively flexible resource access, this paper adopts the convex packet fitting expression based on MGFOR to characterize the flexible regulation capability of MGs. Then, the Conditional Value at Risk (CVaR) is used to quantify the uncertainty of wind and solar power generation, and a two-layer model with the objective of minimizing the operation cost in the upper layer and maximizing the rate of new energy consumption in the lower layer is proposed and solved using Karush–Kuhn–Tucker (KKT) conditions. Finally, the proposed method is verified through examples to ensure the economic operation of the system and improve the new energy consumption rate of the system. Full article

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. First, the multimodal feature tensor is constructed: the fourier synchro-squeezed transform is used to convert the multisensor time-domain signals into time–frequency images, and then the tensor is reconstructed to retain the three-dimensional structural information of the sensor coupling relationship and time–frequency features. The nonlinear feature mapping strategy combined with Tucker decomposition effectively maintains the high-order correlation of the feature tensor. Second, the adaptive sample-weighting mechanism is developed: an intuitionistic fuzzy membership score assignment scheme with global–local information fusion is proposed. At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by K-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. Finally, a dual hyperplane classifier is constructed in tensor space: a structural risk regularization term is introduced to enhance the model generalization ability and a dynamic penalty factor is set to set adaptive weights for different categories. A linear equation system solving strategy is adopted: the nonparallel hyperplane optimization is converted into matrix operations to improve the computational efficiency. The extensive experimental results on the two rolling bearing datasets have verified that the proposed method outperforms existing solutions in diagnostic accuracy and stability. Full article

In this article, the planning and energy administration of energy hubs in electric, thermal and gas networks are presented, considering the resilience of the system against natural phenomena like floods and earthquakes. Each hub consists of bio-waste, wind and solar renewable units. These include non-renewable units such as boilers and combined heat and power (CHP) units. Compressed air and thermal energy storage are used in each hub. The design is formed as a bi-level optimization framework. In the upper level of the scheme, the energy management of networks bound to system resiliency is provided. This considers the minimization of annual operating and resilience costs based on optimal power flow equations in networks. In the lower-level model, the planning (placement and sizing) of hubs is considered. This minimizes the total building and operation costs of hubs based on the operation-planning equations for power supplies and storages. Scenario-based stochastic optimization models are used to determine the uncertainties of demand, the power of renewable systems, energy price and the accessibility of distribution networks’ elements against natural disasters. In this study, the Karush–Kuhn–Tucker technique is used to extract the single-level formulation. A numerical report for case studies verifies the potential of the plan to enhance the economic, operation and resilience status of networks with energy administration and the optimal planning of hubs in the mentioned networks. By determining the optimal capacity for resources and storage in the hubs located in the optimal places and the optimal energy administration of the hubs, the economic, exploitation and resilience situation of the networks are improved by about 27.1%, 97.7% and 23–50%, respectively, compared to load flow studies. Full article

Background/Objectives: Parental attitudes play a crucial role in shaping children’s oral health habits and preventing dental diseases. This study aimed to explore the theoretical pathways through which parental behavior and attitudes toward child oral health can influence the dental caries experience as measured by the dmf-t index in preschool children in Lithuania. Methods: A cross-sectional study was conducted involving 302 children aged 4–7 years and their parents (262 mothers). Parental attitudes were assessed using the Parental Attitudes Towards Child Oral Health (PACOH) scale. For the children, the following variables were considered: sex, age, dental caries experience (dmf-t index in the primary dentition), oral hygiene index (Silness–Löe Plaque Index), toothbrushing frequency, and parental assistance with toothbrushing. Structural Equation Modeling (SEM) was applied for the data analysis. Results: The main path through which parental attitudes towards child oral health influenced the dmf-t index was via toothbrushing frequency (β = −0.17) or parental assistance with toothbrushing (β = 0.24). These factors were then linked to the oral hygiene index (β = 0.20 and β = −0.47, respectively), which ultimately influenced dmf-t (β = 0.52). The parents’ attitudes and toothbrushing frequency per se had no significant effect on children’s dmf-t (β = −0.06 and β = −0.04, respectively). The final model met all goodness-of-fit criteria: Chi-square test p = 0.211, Incremental Fit Index IFI = 0.994, Tucker–Lewis Index TLI = 0.982, Comparative Fit Index CFI = 0.994, and Root Mean Square Error of Approximation RMSEA = 0.038. Conclusions: Findings from this study demonstrate that parents play a significant role in determining children’s oral health. Regular toothbrushing, parental assistance with toothbrushing, and good oral hygiene are critical factors linking parents’ oral health-related attitudes to a child’s experience of early caries. Identifying the associations between dental caries risk factors helps plan interventions. Full article

The accurate prediction of concrete temperature during arch dam construction is essential for crack prevention. The internal temperature of the poured blocks is influenced by dynamic factors such as material properties, age, heat dissipation conditions, and temperature control measures, which are highly time-varying. Conventional temperature prediction models, which rely on offline data training, struggle to capture these time-varying dynamics, resulting in insufficient prediction accuracy. To overcome these limitations, this study constructed a sparrow search algorithm–incremental support vector regression (SSA-ISVR) model for online concrete temperature prediction. First, the SSA was employed to optimize the penalty and kernel coefficients of the ISVR algorithm, minimizing errors between predicted and measured temperatures to establish a pretrained initial temperature prediction model. Second, untrained samples were dynamically monitored and incorporated using the Karush–Kuhn–Tucker (KKT) conditions to identify unlearned information, prompting model updates. Additionally, redundant samples were removed based on sample similarity and error-driven criteria to enhance training efficiency. Finally, the model’s accuracy and reliability were validated through actual case studies and compared to the LSTM, BP, and ISVR models. The results indicate that the SSA-ISVR model outperforms the aforementioned models, effectively capturing the temperature changes and accurately predicting the variations, with a mean absolute error of 0.14 °C. Full article

This paper proposes a fault self-healing recovery strategy for passive low-voltage power station areas (LVPSAs). Firstly, being aware of the typical structure and communication conditions of the LVPSAs, a fog computing load forecasting method is proposed based on a dynamic aggregation of incremental learning models. This forecasting method embeds two weighted ultra-short-term load forecasting techniques of complementary characteristics and mines real-time load to learn incrementally, and thanks to this mechanism, the method can efficiently make predictions of low-voltage loads with trivial computational burden and data storage. Secondly, the low-voltage power restoration problem is overall formulated as a three-stage mixed integer program. Specifically, the master problem is essentially a mixed integer linear program, which is mainly intended for determining the reconfiguration of binary switch states, while the slave problem, aiming at minimizing load curtailment constrained by power flow balance along with inevitable load forecast errors, is cast as mixed integer type-1 Wasserstein distributionally robust optimization. The column-and-constraint generation technique is employed to expedite the model-resolving process after the slave problem with integer variables eliminated is equated with the Karush–Kuhn–Tucker conditions. Comparative case studies are conducted to demonstrate the performance of the proposed method. Full article