Search Results (2,942)

With the increasing integration of high-penetration renewable energy and emerging loads, power system operation simulation faces two major challenges, namely strong uncertainty and significant heterogeneity in the output characteristics of multiple generator types. Traditional mathematical programming methods struggle to effectively handle uncertainty while meeting real-time computational requirements. Existing deep learning approaches fail to decouple the heterogeneous output characteristics of different generator types, which limits their ability to achieve coordinated operation. To address these issues, this paper proposes a task-decoupled and multi-task synergistic LLM-MoE method for power system operation simulation. First, a feature encoder based on Residual-Gated Linear Units is constructed to perform deep filtering and efficient representation of multi-source heterogeneous data. Second, a pre-trained large language model is employed as a temporal feature extractor to enhance temporal modeling capability and cross-scenario generalization. Finally, a customized gating-controlled mixture-of-experts decoder is developed. It dynamically coordinates task-specific and shared experts, which enables unified modeling of task decoupling, cross-task information sharing, and system physical constraints. Simulation results based on a provincial-level power grid in China demonstrate that the proposed method achieves high-accuracy and high-efficiency operation simulation while ensuring physical consistency. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

With the increasing integration of photovoltaic (PV) generation, short-term scheduling of hydro–PV–battery energy storage systems (HPBS) faces growing challenges due to the stochastic variability of PV output, the temporal coupling of hydropower operation, and the accumulation of deviations during the real-time execution of day-ahead schedules. This paper proposes a bi-level coordinated scheduling framework that integrates day-ahead mixed-integer quadratic programming (MIQP) with intraday Soft Actor–Critic (SAC)-based correction. In the upper layer, MIQP generates a 24 h baseline schedule subject to unit output limits, mutually exclusive charging/discharging logic, and operational constraints. In the lower layer, SAC performs bounded real-time residual correction for hydropower and battery storage around the MIQP baseline, while a deviation-triggered replanning mechanism forms a closed-loop process of planning, execution, correction, and replanning. Comparative experiments under the tested setting show that SAC achieves better overall performance than Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Typical-day evaluations under dry-, normal-, and wet-season conditions show that, in the selected case studies, the proposed MIQP + SAC framework achieves better performance than standalone MIQP and MIQP-Replan, which refers to a deviation-triggered MIQP re-optimization strategy, in load tracking, PV curtailment reduction, and hydro-storage coordination. These results indicate the effectiveness of the proposed framework for short-term HPBS scheduling under representative operating conditions. Full article

Makerspaces can serve as distributed urban infrastructures for repair, reuse, tool sharing, and peer learning, yet their contributions to circular economy (CE) goals often occur without being explicitly recognized or framed as CE practices. Inspired by practice theory and the literature on quiet sustainability, this study introduces implicit circularity as circular practices enacted without an explicit sustainability/CE framing by participants, and examines how such practices shape bottom-up circular transitions. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we examined three linked questions: which circular practices occur in makerspaces and how they cluster into domains, how these practices vary across makerspace types, and which barriers and governance arrangements shape makerspaces’ consolidation as circular urban infrastructure. A qualitative multi-method design was employed in Czechia, combining field mapping with in-depth qualitative inquiry. Data included 40 semi-structured interviews with makerspace founders and operators, documentary analysis based on websites, social media, event listings, rules, and other documents, and 21 observations. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we analyzed how circular practices cluster into domains, how implicit versus explicit circularity varies across makerspace types, which barriers constrain makerspaces’ consolidation as circular urban infrastructure, and what governance arrangements could mitigate them. Circularity was dominated by implicit, routine practices rather than formal, CE-branded programs. Three practice domains were identified: repair and maintenance, material flows, and learning/education. Explicit programming was comparatively less common and context-dependent. Barriers formed a reinforcing system spanning institutional fragmentation and coordination deficits, capability gaps, infrastructural constraints, and tensions around autonomy and legitimacy, which together kept many circular contributions low-visibility. Makerspaces constitute an under-recognized form of circular micro-infrastructure that couples technical capacity with social learning and can translate CE ambitions into everyday practice. To mobilize these latent capacities, cities need hybrid governance, especially light-touch coordination platforms, long-horizon operational support, and integration of makerspaces into municipal material-flow systems and repair/reuse strategies. The study offers a practice-based framework and a cross-case typology to support comparative research and grounded urban CE policy design. Full article

Edge server placement (ESP) is a critical determinant of service quality in edge–cloud computing systems, yet existing solutions often neglect the inherent collaboration between edge and cloud, leading to suboptimal performance under dynamic workloads. To address this gap, this paper proposes a novel meta-heuristic edge server placement algorithm based on the Coati Optimization Algorithm (COA). We first formulate the ESP problem as a constrained binary nonlinear programming model that explicitly incorporates edge–cloud collaboration, aiming to minimize the average request processing delay. The proposed COA-based solver features a compact one-dimensional encoding scheme that simultaneously represents server placement and request offloading decisions, a tailored boundary correction mechanism to enforce coverage and atomicity constraints, and a balanced exploration–exploitation strategy inspired by coatis’ natural hunting and escape behaviors. Extensive simulations are conducted, comparing the proposed algorithm against ten representative heuristic and meta-heuristic algorithms, including GA, PSO, DE, GWO, and their variants. The experimental results demonstrate that our algorithm significantly outperforms all compared methods in terms of the mean, minimum, and standard deviation of the overall average processing delay. Specifically, it achieves a 98.2% reduction in the mean delay relative to suboptimal algorithms while maintaining near-zero variance, confirming its effectiveness, efficiency, and robustness. The proposed algorithm provides a promising solution for service providers to enhance quality of service through optimal edge server deployment and request offloading under edge–cloud collaboration. Full article

Autonomous vehicles must enforce safety constraints even when their state estimates are corrupted by sensor faults and disturbances. This paper develops a separation-based robust safety-control framework that couples a fault-tolerant observer with a control barrier function (CBF) safety filter through an explicit estimation-error envelope. First, a uniformly ultimately bounded observer-error estimate is derived. This bound is then injected into an estimated-state robust CBF condition, yielding safety margins that account for both observation error and bounded disturbances. The construction is further extended to time-varying safe sets induced by moving obstacles. For implementation, the resulting condition is realized as a quadratic-program safety filter with high-order obstacle and lane constraints. Simulations on a nonlinear 3-DOF bicycle model evaluate bias faults, gust-like disturbances, dense traffic, and tightened stress tests. Compared with a standard CBF baseline and observer/safety-filter ablations, the proposed method preserves nonnegative safety margins while keeping slack activation negligible. Additional sensitivity experiments quantify the trade-off among safety margin, slack usage, observer accuracy, control conservatism, and QP computation time. The results support the proposed architecture as a practical bridge between bounded state estimation and fault-aware safety filtering. Full article

Urban farming microinterventions are small, place-based cultivation projects that operate under severe spatial and resource constraints yet can generate social learning and locally embedded resilience. The present paper examines how design decisions shape the effectiveness of such interventions through three design-led case studies: Blooming Structure (2018, Warsaw), a temporary hydroponic “laboratory” installation; Micro-cultivation (2018, Warsaw), a shopfront vertical demonstration farm; and Micro-cultivation 2 (2019), modular “cultivation furniture” for interiors and exhibition deployment. The analysis combines project documentation with practice-based observations and applies five interpretive dimensions: spatial fit, technical feasibility, communicative legibility, replicability, and social programming. Findings highlight that successful microinterventions align legible cultivation infrastructure with high visibility, accessibility and participatory formats that support skills transfer and copying-based scaling. Rather than offering universal claims about urban agriculture outcomes, the paper provides a reference set of design principles that may inform similar micro-scale interventions in other contexts, subject to local constraints. Limitations include the small sample size and the concentration on projects from Poland. Practically, the findings can support designers, municipalities, and civic organisations in structuring microinterventions as replicable, low-threshold prototypes and in aligning technical systems with maintenance capacity and public engagement. Full article

DTDL (Digital Twins Definition Language) provides no mechanism for logical reasoning or constraint checking over digital twin models. We integrate DTDL with ASP Chef, a web-based Answer Set Programming (ASP) platform, via a structured DTDL-to-ASP mapping and three dedicated operations: @DTDL/Parse for fact generation, @DTDL/Analysis for structural metrics, and @DTDL/Debug for symbolic validation with LLM-guided repair. The key design decision is that error detection is symbolic and deterministic within the implemented set of constraint classes; a language model is invoked only after the ASP layer has produced a concrete, grounded diagnostic, keeping the correctness boundary with the symbolic layer. Soundness and completeness guarantees are scoped to these constraint classes; a formal proof is left as future work. We illustrate the framework on two agricultural use cases and report a proof-of-concept assessment on 99 diagnostics spanning 21 error classes across four domains. Three binary metrics are used: json_valid and entity_recall are computed mechanically; fix quality (judge_correct) is assessed by an independent LLM judge (Claude Sonnet 4.6). The complete grounded workflow achieves 90% judge_correct and 86% json_valid; a fair ablation baseline—same LLM and system message, but error type and entity name in natural language without structured diagnostics—achieves 77% and 75%, respectively. The gap is consistent across three independent judges and statistically significant (McNemar $p<0.01$), but the inter-judge reliability of judge_correct is limited ($\kappa$ ranging from 0.00 to 0.44), so results should be read as directional evidence rather than precise effect estimates. Excluding the dominant isolated_interface class ($n=28$, ceiling score), the conservative estimate is 87% vs. 79% on the remaining 71 diagnostics. These results constitute a preliminary proof-of-concept limited to a small number of models, a few application domains, and a single LLM configuration; results do not generalize beyond this specific setting. The judge_correct metric is assessed by LLM-as-judge and does not carry a perfect inter-annotator agreement. Full article

As the energy and power sector transitions toward clean and low-carbon development, the installed capacity of renewable energy sources such as wind and photovoltaic power has been rapidly increasing. Wind–solar hydrogen production via water electrolysis can enhance renewable energy utilization and enable the supply of green hydrogen. Meanwhile, the $H_2/C{O}_2$ molar ratio in the syngas produced by conventional biomass gasification generally cannot directly meet the 2:1 stoichiometric requirement for methanol synthesis. To address this issue, this paper proposes an off-grid coordinated system integrating wind–solar hydrogen production and biomass gasification for methanol synthesis. The system incorporates multi-operating-condition constraints of electrolyzers, coordinated regulation between electrochemical energy storage and hydrogen storage, and coordinated matching between biomass gasification and the water–gas shift reaction. Based on the system energy and material balance, a mixed-integer linear programming (MILP) model is formulated with the objective of minimizing the annualized total cost and is solved using the Gurobi solver in the MATLAB environment. To highlight the roles of HES and the WGS reaction, four comparative scenarios are designed for validation. The results show that the system with an annual methanol production capacity of 100,000 tons achieves an annualized total cost of 318 million CNY, with a wind–solar utilization rate of 98.86%. The system is configured with 12 electrolyzers of 5 MW each. The biomass consumption per ton of methanol is 3.06, and the $C{O}_2$ emissions per ton of methanol are 2.37. Finally, a sensitivity analysis of the levelized methanol cost (LCOM) was conducted, providing guidance for cost reduction in green methanol production. Full article

This paper investigates the approximation of robust control invariant (RCI) sets for linear discrete-time systems subject to p-norm ball constraints ($p\ge 1$). Unlike classical results focusing on specific cases like polytope or ellipsoid constraints, we propose a unified framework for arbitrary p-norm ball constraints. Sufficient conditions for a non-empty set to be contained within a p-norm ball are established, revealing the geometric insight that the set is essentially contained within the inscribed 2-norm ball. Utilizing these results, the approximation problem of the RCI set is formulated as a standard linear programming problem that requires verifying constraints at only n standard basis vectors, significantly reducing computational complexity. Specific optimization models are derived, and numerical experiments demonstrate the effectiveness and competitive accuracy of the proposed method. Full article

To address the challenge of stable tracking of moving maritime targets by unmanned aerial vehicle(UAV) swarm in environments with threat zones and platform failure risks, this paper proposes a cooperative tracking and guidance strategy integrating Distributed Model Predictive Control (DMPC) with Sequential Quadratic Programming (SQP). A cooperative tracking model is developed incorporating UAV kinematics, environmental threats, stereo-vision positioning, and field-of-view constraints. Two original strategies are introduced within the DMPC framework: an altitude-cooperative target recapture strategy reduces target total loss duration by approximately 7 s compared to fixed-altitude baselines, while a distributed formation reconfiguration strategy restores stable tracking within 10 s after member failure and ensures safe inter-UAV separation. A multi-constraint trajectory tracking controller based on DMPC-SQP achieves real-time co-optimization of threat avoidance, formation maintenance, and tracking accuracy. Simulation results in dense threat environments demonstrate a 93.4% Quadratic Programming feasibility rate, with mean tracking error reduced by 25.4% over fixed-altitude DMPC and 48.7% over methods based on the Linear Quadratic Regulator (LQR), while maintaining robust performance under 300 ms communication delay, sensor noise, and moderate wind disturbance. Full article

This study aimed to identify the level of awareness and application of learning principles among agricultural extension service providers when communicating extension recommendations to farmers. It also sought to determine the major constraints that may hinder the effective application of these principles in extension practice. The study adopted a descriptive analytical approach. Data were collected using a structured questionnaire designed to achieve the objectives of the research. The study was conducted on all agricultural extension service providers in Kafr El-Sheikh Governorate, totaling 55 respondents. The study focused on nine learning principles relevant to extension education: motivation, clarity of objectives, self-activity, transfer of learning, learner individuality, readiness, reinforcement, modification or relearning, and repetition. The findings revealed variation in the levels of knowledge and application of these principles among the respondents. The results indicated that 65.4% of the respondents had a moderate level of knowledge of the motivation principle, while 67.2% applied it at a moderate level. In contrast, 81.8% of the respondents had a low level of knowledge of the principle of clarity of objectives, and 85.4% applied it at a low level. The results also revealed several constraints that limit the effective application of learning principles in extension work, most notably the limited effectiveness of communication with farmers and the need to strengthen the educational competencies of extension service providers. Accordingly, the study recommends developing the instructional capacities of extension service providers through specialized training programs on learning principles and extension education methods in order to improve the effectiveness of communicating agricultural recommendations and enhance the adoption of agricultural innovations. Full article

The determination of principal dimensions in the early ship design stage requires iterative calculations based on the basis ship particulars and ship owner’s requirements, demanding considerable time and engineering effort. In modern shipbuilding practice, errors introduced at the early design stage carry a high risk of necessitating a complete redesign, particularly under the mandatory EEDI Phase 3 requirements. To address these challenges, this study presents an automated optimization system for the determination of principal dimensions, adopting $L_{BP}$ (Length Between Perpendiculars), B (Breadth), D (Depth), and $C_B$ (Block Coefficient) as design variables. The NSGA-II (Non-Dominated Sorting Genetic Algorithm) is employed to minimize total resistance ($R_T$), specific fuel oil consumption (SFOC), and lightweight (LWT) as objective functions, with EEDI Phase 3 compliance and minimum freeboard requirements imposed as design constraints. The developed program was applied to a 114K Aframax Tanker with VLSFO/LNG dual-fuel capability, yielding a reduction in total resistance of approximately 65 kN relative to the basis ship with improved propulsive efficiency and economic feasibility. The proposed methodology is expected to enhance the efficiency of the early ship design process and provide a systematic framework for meeting stringent environmental regulations. Full article

Multi-building, multi-story prefabricated construction projects are notably characterized by high complexity and repetitiveness, which necessitate efficient resource scheduling. Traditional resource-constrained project scheduling problems primarily address global resources, whereas existing studies on repetitive scheduling emphasize crew allocation and often neglect constraints associated with spatially localized resources, such as tower cranes. To address the challenges posed by repetitive prefabricated construction, this study systematically analyzes scheduling characteristics and classifies renewable resources into three categories: local, crew, and global resources. This study also introduces a novel spatial precedence relationship to capture dependencies between activities on adjacent floors. A linear programming model is formulated to minimize both project duration and total resource idle time. The model is developed under several explicit simplifying assumptions to ensure computational tractability while preserving the core-resource interdependencies. The proposed model’s effectiveness is validated through an empirical case study and additional numerical experiments. In the case study, utilization rates for local resources and crews increased by 20% and 8%, respectively. Furthermore, sensitivity analysis of local-resource allocation indicates that increasing the number of tower cranes yields diminishing marginal reductions in project duration, while total resource idle time first decreases and then increases. Consequently, resource over-allocation should be avoided to prevent degradation in utilization. Full article

Intraoperative fluorescence-guided surgery is an important adjunct to brain tumor resection. However, fluorescent probe performance varies across molecularly and histopathologically distinct entities, including IDH-wildtype glioblastoma, metastatic brain tumors (MBTs), and primary central nervous system lymphoma (PCNSL), and the mechanisms underlying this variability remain poorly understood. We propose a mechanistic framework integrating biomechanical constraints, molecular barrier heterogeneity, and probe-specific pharmacokinetics to explain cross-tumor differences in fluorescence signal. Probe performance is conceptualized through three sequential bottlenecks: extravasation (blood–brain barrier/blood–tumor barrier permeability and transcytosis), interstitial penetration (extracellular matrix density and hydraulic resistance), and retention/clearance (efflux transporters and metabolic processing). An overlying optical layer, including tissue absorption, scattering, and autofluorescence, further modulates the detected signal. Tumor-specific molecular heterogeneity critically shapes these processes. In IDH-wildtype glioblastoma and legacy high-grade glioma cohorts, heterogeneous expression of ATP-binding cassette transporters has been associated with reduced intracellular accumulation of protoporphyrin IX after 5-aminolevulinic acid administration and may contribute to false-negative fluorescence in selected tumor regions. In MBTs, stage-dependent blood–tumor barrier integrity and vascular programs influence probe delivery, whereas in PCNSL, corticosteroid-sensitive restoration of endothelial barrier function may compromise the performance of leakage-dependent tracers. Together, this framework highlights how tumor biology, barrier function, and probe pharmacology jointly shape fluorescence contrast. Rational probe selection informed by tumor-specific transport and barrier constraints may improve intraoperative visualization of brain tumors and optimize surgical decision-making. Full article