Search Results (280)

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

Large renewable energy bases are increasingly planned under delivery-hour targets, corridor-capacity constraints, and high shares of wind and photovoltaic generation. Chronological planning samples used in expansion and adequacy studies therefore need to retain not only average renewable-load patterns but also low-probability days with high residual balancing demand, large ramps, curtailment pressure, and sustained renewable scarcity. This paper proposes a risk-anchored order-preserving scenario-chain construction method for renewable energy base planning. The proposed method transforms aligned hourly load, wind, photovoltaic, delivery-demand, and loss-adjusted demand trajectories into distinct operational stress indicators, normalizes them into a joint extreme score, anchors the highest-risk natural days together with their adjacent transition days, and applies clustering only to the remaining regular days. Observed medoid days are then inserted back into chronological order to form a compact scenario chain with explicit weights and adjacency information. A representative 8760 h renewable-base case with 4000 MW wind, 5500 MW photovoltaic, 5400 MW coal support, 1200 MWh storage-energy capacity, a 7600 MW delivery corridor, and a 5600 h delivery target is used to run weight-sensitivity tests and same-budget comparisons against monthly typical days, k-means, k-medoids, hierarchical clustering, Carpe Diem, and seasonal time-series aggregation baselines. Under the equal-weight base case, the proposed chain gives an active-metric mean capture ratio of 1.0660, compared with 0.8377 for monthly typical days. Across four non-equal priority-weight vectors, the active-metric mean remains between 1.0041 and 1.0662. Under the same 151-day budget, conventional k-means, k-medoids, hierarchical clustering, Carpe Diem, and seasonal time-series aggregation baselines obtain active-metric means of 0.8643, 0.9025, 0.8732, 0.9239, and 0.9189, respectively. The results indicate that chronological risk anchoring provides a compact yet physically interpretable sampling layer for planning models that must balance renewable utilization, delivery reliability, and storage adequacy. Full article

Tungsten trioxide (WO₃) is the most widely studied cathodic electrochromic (EC) material, serving as the core component of energy-efficient smart windows, displays, and optical modulation devices. Post-deposition annealing, as a critical post-processing technique, precisely regulates the microstructure, crystallinity, oxygen vacancy concentration, and electronic structure of WO₃ thin films, thereby directly determining their EC performance. This review summarizes the research progress of annealing effects on WO₃ films, focusing on the synergistic regulation of annealing temperature, atmosphere, and dwell time. It elaborates on the fundamental EC mechanisms of amorphous and crystalline WO₃, including polaron hopping and free-electron Drude behavior, and analyzes the influence of different deposition methods (magnetron sputtering, sol–gel, electrodeposition, etc.) on the annealing response of films. The optimal annealing windows for balancing optical modulation, coloration efficiency, switching speed, and cycling stability are clarified: moderate temperatures (200–350 °C) and inert/air atmospheres yield mixed amorphous–nanocrystalline structures with optimal oxygen vacancy content. Current challenges such as the inherent contrast–stability trade-off and thermal budget limitations of flexible substrates are discussed, and future directions including spatially resolved annealing, interface co-design, and machine learning-assisted optimization are prospected. This work provides a theoretical reference and process guidance for the development of high-performance WO₃-based EC devices. Full article

This study introduces the ’Black-Backed Jackal Optimization’ (BBJO), a nature-inspired meta-heuristic algorithm designed for complex, non-linear, and high-dimensional search spaces. The fundamental mathematical model of BBJO relies on the opportunistic hunting behavior and survivability strategies of the black-backed jackal (Lupulella mesomelas). We use non-linear energy decrease and adaptive Lévy flight to maintain the equilibrium of the search. This allows the algorithm to scan large areas first, then zoom in with a high degree of precision once it has identified a suitable location. This configuration prevents the algorithm from getting stuck on a suboptimal local solution, which is a frequent danger during searches in complex spaces. BBJO has been validated against 23 standard benchmark functions, demonstrating significantly greater accuracy than Particle Swarm Optimization (PSO) on complex and large-scale search spaces. On fixed-size domains ($F_{21}$–$F_{23}$), the BBJO algorithm achieved a 100% success rate with zero standard deviation, surpassing the Grey Wolf Optimizer (GWO) and Differential Evolution (DE), which frequently suffered from structural stagnation. Visual convergence study shows that BBJO efficiently identifies optimal search regions early in the iteration budget, saving time compared to traditional linear decay models. BBJO optimizes fuzzy inference systems (FISs) for two practical applications: autonomous car speed control and industrial furnace regulation. Experimental results indicate that BBJO significantly decreased cumulative penalties and improved steady-state error reduction compared to baseline configurations and established meta-heuristic methods. The results show that BBJO is a reliable and useful technique for engineering optimization. Full article

Background/Objectives: Suboptimal dietary patterns are among the leading modifiable contributors to global morbidity and mortality, particularly in cardiovascular disease, type 2 diabetes mellitus (T2DM), obesity, metabolic syndrome, and hypertension. Digital nutrition platforms have emerged to improve adherence to evidence-based dietary strategies; however, many systems lack structured optimization, processing-aware nutrient profiling, and explainable artificial intelligence (AI) mechanisms. The integration of large language models (LLMs) into digital health introduces conversational personalization but also risks hallucination and unsafe outputs without constraint enforcement. This study aimed to describe the system development, architecture, database infrastructure, optimization algorithms, explainability enforcement, and digital health implications of NutriSteppe-AI, a chatbot-first LLM-driven system for personalized health menu generation constrained by deterministic nutrient logic and processing-aware scoring. Methods: NutriSteppe-AI integrates: (1) a multi-source structured nutrient database of 20,000 food products with up to 130 tracked nutrients; (2) energy requirement estimation using the revised Harris-Benedict equation; (3) linear programming-based multi-objective optimization; (4) a Healthy Food Index (HFI; 0.5–5.0 scale) incorporating NOVA processing classification penalties; (5) traffic-light nutrient gating; and (6) a constrained LLM orchestration layer governed by structured API contracts. Algorithmic validation was performed using 10,000 simulated user profiles spanning diverse age, anthropometric, activity, dietary exclusion, and budget parameters. Results: The system achieved 96.8% full constraint satisfaction with macronutrient mean absolute errors of 11.60% (energy), 18.86% (protein), 16.26% (fat), and 20.91% (carbohydrates). Incorporating NOVA processing penalties reduced ultra-processed food HFI scores by 0.73 points (p < 0.001). Median optimized menu HFI improved from 3.6 to 4.3. Median system latency was 1.8 s. Explainability validation confirmed 100% deterministic alignment with zero hallucinated numeric claims. Conclusions: NutriSteppe-AI demonstrates that LLM-driven nutrition chatbots can achieve deterministic, explainable, and clinically aligned performance when governed by structured optimization, processing-aware scoring, and explainability enforcement. This architecture provides scalable digital health infrastructure for cardiometabolic disease prevention in diverse populations. Full article

Low-Power Wide-Area Networks based on LoRa are widely deployed in smart city, agricultural, and environmental monitoring, where their constrained energy budget makes them vulnerable to radio-frequency jamming. (1) Background: a node that keeps transmitting into a jammed channel wastes energy on undeliverable packets, yet detection and energy management are usually treated separately. (2) Methods: we present J-DEAS, a Jamming-Driven Exponential Adaptive Sleeping technique that couples a lightweight, threshold-based detector with an exponential sleep back-off scheduler. The detector uses only the RSSI and SNR reported by commodity transceivers, and a single exponentially weighted confidence variable drives the sleep interval; we analyze the decision boundary, confidence dynamics, steady-state duty cycle, and latency–energy trade-off in closed form. (3) Results: on a measurement dataset the detector reaches an AUC of 0.985 and an F1 of 0.969; under sustained jamming, J-DEAS cuts the duty cycle from 100% to 5.5% and wasted transmissions from 94.3% to 8.3%, with a single-slot median latency and a sub-2% false-sleep rate on clean channels. (4) Conclusions: the technique needs no training and no extra hardware, making it suitable for resource-constrained end devices. Full article

Oysters, as sessile organisms, are highly influenced by environmental conditions affecting their growth and reproduction. Therefore, unfavorable conditions can directly compromise the effectiveness of oyster reef restoration efforts. The study applied the dynamic energy budget (DEB) theory to model the growth of the Suminoe oyster (Crassostrea ariakensis) and estimated the model’s main parameters through linear and nonlinear regression methods. Meanwhile, the parameters of the existing DEB models for the Pacific oyster (Crassostrea gigas) and the Portuguese oyster (Crassostrea angulata) were optimized to improve the model performance. Model accuracy was confirmed by comparing the simulated and observed values of shell height and wet weight for three oyster species. The validated models were subsequently applied to three distinct habitats: Qianhu Bay, the Yangtze River Estuary, and Jiaozhou Bay, to simulate the individual dynamic growth and survival processes of the three oyster species. The simulations of shell height and wet weight for the three oyster species showed strong agreement with observations, with a mean coefficient of determination (R²) of 0.98. The mean NRMSE value of the three models was 0.16, indicating low model-prediction error. The simulation of various habitat scenarios suggested that C. ariakensis exhibited faster growth in the Yangtze River Estuary and Qianhu Bay, and C. gigas in the Yangtze River Estuary. Additionally, C. angulata experienced mortality in the Yangtze River Estuary (February) and Jiaozhou Bay (January). The findings of this study can provide scientific guidance on selecting species for oyster reefs across various marine environments to effectively enhance their ecological restoration outcomes. Full article

This research focuses on developing a deep learning-based framework to forecast maintenance expenditures within the residential sector. To maintain building value, resident safety, and energy efficiency, consistent facility maintenance is indispensable. This necessity is especially heightened given the recent increase in the construction of supertall and high-performance buildings. However, estimating repair costs for residential facilities is challenging due to the diverse building types, ownership structures, and occupancy patterns compared to other property uses. Therefore, this research proposes a deep-learning model to establish a highly reliable and scientific method for estimating repair costs using empirical data gathered from actual residential facilities. Among the deep learning algorithms, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) were adopted to develop models and optimize them through a fixed split. The framework and results of this paper facilitate the prediction of maintenance costs for residential facilities, which can contribute to budget planning, long-term facility management, preventive maintenance, resource management, and advanced decision-making. Moreover, it will contribute to the advancement of facility management of residential facilities. Full article

Accurate estimation of daily net radiation (R_{n_daily}) at high spatial resolution (1 km) over the Tibetan Plateau (TP) is crucial for understanding land surface energy budgets and climate dynamics. This study proposes a densely connected multilayer perceptron (DenseMLP)-based transfer learning framework, with a two-stage strategy (coarse pre-training on GLASS R_{n_daily}, followed by fine-tuning on limited TP ground observations) using MODIS land surface parameters and auxiliary data to generate 1 km R_{n_daily}. When evaluated on the training set, the proposed model achieves an overall R² of 0.87, MAE of 16.06 W m⁻², RMSE of 21.94 W m⁻², and a near-zero bias of 0.07 W m⁻². On an independent test set, the model maintains robust performance with R² = 0.83, MAE = 17.43 W m⁻², RMSE = 22.55 W m⁻², and bias = −1.12 W m⁻². The method exhibits consistently low bias across individual sites (mostly within ±3.7 W m⁻²) and accurately captures seasonal variability. When applied to the entire TP for 2018, the 1 km R_{n_daily} product reveals clear aspect-related terrain effects and a distinct annual cycle. This framework effectively mitigates site-dependent errors, providing a useful reference for long-term R_n product development over the TP. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

Background/Objectives: Important gaps remain in understanding the association between ultra-processed food (UPF) consumption and same-day energy intake. This study aimed to expand the understanding of how within-person variation in UPF consumption is associated with total energy intake. Methods: Nationwide, representative dietary survey data from 38,854 participants (≥10 years old) in the 2017–2018 Brazilian Household Budget Survey food intake module were analyzed. These cross-sectional repeated-measures data were used to estimate within-person differences in energy intake between two days of food consumption. Mixed models were applied to examine the associations between the presence and energy share of UPFs in the diet and total daily energy intake. Results: Among participants who consumed UPFs on only one of the two days (n = 8055), daily energy intake was higher on the day when UPFs were consumed than on the day when UPFs were not consumed (1699 kcal vs. 1530 kcal; p < 0.001). On average, among all participants (n = 38,854), a 10 percentage-point increase in UPF energy share on a given day was associated with an increase of 39 kcal in energy intake over the day. We found statistically significant effect modification by sex (p = 0.024) and age group (p < 0.001). Supplementary analyses suggest that the association between UPF energy share and total energy intake is consistent with partial replacement of non-UPF by UPF and is unlikely to reflect differences in food quantity or energy density. Conclusions: Among participants who consumed UPFs on only one of the two days, the day with UPF consumption was associated with higher total daily energy intake than the day without UPF consumption. A higher within-person UPF energy share was also associated with higher total daily energy intake. Understanding the association between UPF consumption and same-day energy intake can inform public health strategies aimed at reducing excessive energy intake. Full article

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response measurement, verification, and incentive settlement. To address this issue, this paper proposes a constrained reinforcement learning method with customer baseline credibility for dynamic resource allocation in incentive-based demand response. Based on user-side load measurements and demand response event records, the proposed framework evaluates user resources using flexible capacity, response reliability, response cost, and CBL credibility. The CBL credibility score reflects the measurement quality of the delivered response and is used as a pre-event allocation factor. Users are then grouped into different resource levels, and a group-level reinforcement learning agent dynamically determines incentive multipliers and response task allocation ratios. To improve feasibility, an action correction module revises raw policy outputs under budget, price, response capacity, and CBL risk constraints before implementation. Case studies are conducted using public industrial demand response measurements and open electricity-system time-series data. The results show that the proposed CBL-CRL method reduces the normalized total operating cost to 0.897, reduces the response tracking error to 0.108, and lowers CBL risk exposure to 0.087 under the normal scenario. Relative to the No-DR reference, CBL-CRL reduces the normalized total operating cost by 10.3 percent. Compared with MAPPO, the strongest learning-based baseline, CBL-CRL reduces the response tracking error by 10.7 percent and the CBL risk exposure by 40.8 percent, while maintaining the same renewable accommodation rate of 0.970. Compared with rule-based and learning-based baselines, CBL-CRL achieves a better balance between operational performance, incentive efficiency, action feasibility, and baseline-related settlement reliability. The results demonstrate that CBL credibility should not only be used for post-event settlement, but can also serve as an effective pre-event resource allocation factor for measurement-driven demand response programs. Full article

Renovations aimed at improving energy conservation in older urban residential areas are essential for sustainable urban development; however, they encounter obstacles such as energy inefficiency and issues in sustaining long-term sustainability following renovation. Based on resource-based theory and collaborative governance theory, this study investigates how organisational resilience affects the efficacy of energy-saving renovations and confirms the mediating role of resource allocation efficiency. A mixed-methods approach was used in this investigation. Grounded theory was first used to establish the components of organisational resilience. A questionnaire survey was then used to gather information from those participating in energy-efficient renovation of old urban residential complexes. System dynamics (SD) was applied for empirical validation and simulation analysis across many intervention scenarios after structural equation modelling (SEM) was used to develop and evaluate study hypotheses. The results show that rather than the support of any particular strategy, the crucial elements in improving the efficacy of energy-saving renovations are efficient interdepartmental coordination and rational budget allocation. Notably, all energy-saving renovation outcome measures in this study are based primarily on stakeholder perceptions and survey responses rather than objectively measured energy consumption data. Full article

Mechanistic interpretability seeks to reverse-engineer the computational circuits within large language models, but current methods rely on exhaustive or heuristic search over exponentially many feature interactions. This paper introduces Active Circuit Discovery (ACD), a framework that combines attribution-graph analysis with active inference to select interventions efficiently. ACD uses Anthropic’s circuit-tracer library as its attributiongraph backend, applying Edge Attribution Patching with transcoders to identify the active transcoder features for each prompt. A partially observable Markov decision process (POMDP) agent, implemented with pymdp, maintains a multi-factor generative model of feature importance, layer role, and causal influence. At each step, the agent selects both a target feature and an intervention type (ablation, activation patching, or feature steering) by minimising Expected Free Energy over the joint feature–action space, and it learns its observation model online through Dirichlet parameter updates. ACD is an interventionselection layer over existing attribution-graph tools; it is not a whole-circuit discovery method, and no claim of state-of-the-art circuit discovery is made. The framework is evaluated on Gemma-2-2B (26 layers) and Llama-3.2-1B (16 layers) across four settings: Indirect Object Identification (IOI), multi-step reasoning, feature steering, and a multidomain benchmark spanning geography, mathematics, science, logic, and history. With a budget of 20 interventions per prompt, an ablation-only agent scored by bounded oracle efficiency against the ablation oracle reaches 82.0% efficiency on Gemma IOI and 73.0% on Gemma multi-step. It exceeds random selection by 43.5% (relative) on Gemma IOI (paired permutation p = 0.031) and is competitive with greedy ranking, a heuristic UCB bandit, and a plain UCB baseline. A direct Edge-Attribution-Patching ranking is itself a strong baseline that the agent does not consistently surpass, and on Llama multi-step the agent reaches 9.3% efficiency (37.8% with finer layer-role bins). All comparisons report bootstrap 95% confidence intervals. The full multi-action agent is characterised separately by a Relative Cumulative KL, a steering-driven amplification factor reported apart from the bounded efficiency. Feature steering changes the top-1 prediction in a dose-dependent manner, but a matched random-feature control shows that circuit-selected features are only marginally, and not significantly, more steerable than random active features at large multipliers, indicating that part of the effect is generic activation scaling. Multi-domain analysis shows task-dependent circuit structure, with IOI circuits concentrated in late layers and reasoning and scientific knowledge recruiting early and middle layers. Code, notebooks (free T4), AMD64/aarch64 Docker images, and raw results are publicly available. Full article

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article