Search Results (101)

Typhoon-induced extreme weather poses a severe threat to power systems with high offshore wind penetration. Source-side wind turbine tripping and grid-side transmission line failures are likely to occur simultaneously, which may trigger cascading outages and large-scale load shedding. A multi-level source-grid-load-storage preventive resilience dispatch strategy is proposed. A typhoon spatiotemporal evolution model is first established based on the Batts gradient wind model. Failure probability models for offshore wind turbines and overhead transmission lines are developed while considering strong wind and lightning strike effects. The most probable and severe fault scenario is identified using an entropy-based quantification method. A two-stage robust preventive dispatch model is subsequently formulated. In the day-ahead stage, unit commitment, multi-type reserve allocation, and pumped storage scheduling are optimized at a 1 h resolution. In the real-time stage, combined wind-storage systems are coordinated at a 10 min resolution to accommodate rapid wind power ramps caused by high-wind shutdown events. The model is reformulated through Lagrangian duality and solved by the Benders decomposition algorithm. Case studies on a modified IEEE-RTS 24-bus system with three offshore wind farms demonstrate that the proposed strategy reduces wind curtailment by 66.3%, load shedding by 74.6%, and total cost by 14.8% compared with the case without energy storage. The combined operation cost of storage resources accounts for only 3.1% of the total cost, confirming its favorable cost-effectiveness for resilience enhancement. The proposed strategy contributes to the sustainable integration of offshore wind energy by ensuring a reliable power supply during extreme weather events. Full article

Planning capacity for wind–photovoltaic (PV)–thermal–storage systems with high renewable penetration requires models that address investment costs, operational feasibility, and strict carbon limits under uncertainty. This paper presents a two-stage robust optimization model for integrated wind–PV–thermal–storage capacity expansion that guarantees carbon compliance under worst-case renewable realizations. Unlike conventional approaches that relax carbon constraints through price penalties, we enforce the annual carbon emission cap as a hard operational constraint, ensuring candidate portfolios remain feasible even under adverse renewable conditions. To reflect practical storage design, a fixed energy-to-power (E/P) ratio couples storage energy capacity with power converter ratings, preventing unrealistic storage expansions. Renewable uncertainty is captured through a Bertsimas–Sim budgeted polyhedral set defined over representative days, balancing robustness with computational tractability. A tailored decomposition framework integrates economic dispatch and carbon-compliance verification within an outer column-and-constraint generation (C&CG) loop, simultaneously certifying worst-case operating cost and minimum achievable emissions. By exploiting strong duality, we generate two families of valid inequalities iteratively: economic cuts from the Economic subproblem (Economic-SP) and carbon-feasibility cuts from the Carbon subproblem (Carbon-SP). This dual-certification approach ensures capacity plans remain both economically optimal and carbon-compliant across all uncertainty realizations. Case studies on a realistic wind–PV–thermal–storage system demonstrate that the method produces carbon-compliant, robust capacity plans with manageable computational effort, converging in 10–15 iterations. The model explicitly captures operational coupling among renewables, thermal generation, and storage, providing a decision-support tool for low-carbon power systems under deep decarbonization targets. Full article

In highway aerial imagery, complex weather conditions such as rain, fog, snow, and low illumination often lead to severe appearance degradation and feature loss of vehicle targets, posing significant challenges for vehicle detection. Existing research faces two major challenges: first, the lack of large-scale, high-quality annotated datasets tailored for complex weather scenarios; second, the difficulty traditional detectors encounter in effectively extracting feature information and performing multi-scale feature fusion under conditions of severe feature degradation and dense distribution of small objects. To address these issues, this paper investigates both data construction and algorithm design. Firstly, a Complex Weather Highway Vehicle Dataset (CWHVD) is established to provide a benchmark for related research. Secondly, a Feature-Enhanced Grid-Based Feature Fusion Complex-Weather Vehicle Detection Network (FGCV-Det) is proposed. A wavelet transform-based Feature Enhancement Module (FEWT) is introduced at the input stage to strengthen edge and texture representation. In the backbone, Adaptive Pinwheel Convolution (APConv) and a C3K2-HD module based on Hidden State Mixer-Based State Space Duality (HSM-SSD) are employed to enhance semantic modeling. Furthermore, a Complex Weather Grid Feature Pyramid Network (CWG-FPN) is designed to achieve weighted cross-scale fusion. The FGCV-Det significantly outperforms YOLO11s on CWHVD, achieving 63.4% precision, 48.6% recall, 51.7% mAP50, and 28.2% mAP50:95. It also generalizes well, reaching 47.1% and 49.6% mAP50 on VisDrone2019 and UAVDT, respectively, surpassing baseline and mainstream detectors, demonstrating strong robustness and generalization capability. Full article

Unraveling the coupling mechanisms of the Water–Energy–Food (WEF) nexus is critical for regional synergistic security and high-quality development. Using an integrated “relationship identification, equation construction, and scenario prediction” framework, this study characterized the spatiotemporal evolution of WEF interactions in the Yangtze River Economic Belt. Under this framework, a Granger causality test coupled with a SHAP interpretability model was first employed to quantify the causal strength among nexus elements, followed by a Bayesian Vector Autoregression model integrated with a hybrid Recurrent Neural Network (RNN) and System Dynamics (SD) approach to simulate evolutionary trajectories from 2024 to 2035. Results showed that: (1) The nexus mechanisms exhibited significant spatial duality. Upstream egg production drove a high virtual water footprint, while inland seafood consumption imposed a non-linear energy premium due to cold-chain dependency. In Shanghai, a strong diesel–groundwater coupling revealed a trade-off between energy input and underground safety. (2) Localized feed cultivation was the core driver for upstream water pressure, whereas logistics intensity was the dominant factor for energy–water interactions in urbanized regions. (3) From 2024 to 2035, the nexus structure will undergo bidirectional divergence. Ecological water demand in the midstream is projected to surge by over 130%, and Anhui’s milk production is forecast to more than double from 107.77 to 225.7 million tons. The findings provide scientific support for coordinating ecological conservation and high-quality development. Full article

This paper proposes a robust bilevel mixed-integer profit maximization model for an independent distributed energy resource (DER) aggregator participating in the wholesale electricity market, considering the uncertain aggregation of electric vehicles (EVs) to the grid, as well as the discrete security check of the distribution system conducted by the non-market-participating distribution company. Regarding the uncertainty in EV–grid connectivity caused by stochastic transportation behavior, we characterize the robust connectivity at the lower level to ensure that the energy required for their daily transportation can be met. Solving the proposed bilevel mixed-integer profit maximization model is challenging due to the integer variables involved in the lower-level security check and robust connectivity problem, which makes the traditional strong duality and KKT method inapplicable. Thus, we propose using the total unimodularity property, multi-value-function approach, and strong duality method to transform the original bilevel model into an equivalent single-level model. Moreover, a sampling-based accelerated optimization algorithm is proposed to solve the equivalent single-level model efficiently. Case studies on a real-world transmission–distribution system verify that: (1) the proposed robust model outperforms deterministic models in profit by accommodating EV aggregation uncertainty; (2) the algorithm significantly reduces computational time compared to stochastic modeling approaches, while ensuring compliance with distribution network discrete security constraints. Full article

This study examines the performance implications of CEO duality from a configurational governance perspective, with particular attention given to its relevance within an ESG-oriented framework. While prior research on CEO duality has produced inconsistent findings, much of the literature relies on variable-centered approaches that overlook the systemic and context-dependent nature of governance mechanisms. Drawing on agency theory, stewardship theory, and resource dependence theory, we analyze 59 publicly listed South Korean firms between 2018 and 2022 using fuzzy-set qualitative comparative analysis (fsQCA). Five governance-related conditions—CEO duality, ownership concentration, CEO tenure, institutional ownership, and environmental dynamism—are calibrated into fuzzy sets to identify causal configurations associated with high firm performance, defined as membership in the top 30% of return on assets (ROA). The results reveal six equifinal pathways to high performance, two of which exhibit particularly strong consistency and coverage. These dominant configurations show that CEO duality contributes positively to performance when embedded in either strong internal governance alignment or robust external monitoring under dynamic conditions. By demonstrating that the effectiveness of CEO duality is contingent upon its governance configuration, this study challenges one-size-fits-all prescriptions and contributes to the ESG literature by highlighting the conditional role of leadership structure in sustainable value creation. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

Unsteady flow-induced pressure fluctuations and the consequent dynamic stresses in pump-turbines are critical determinants of their operational reliability and fatigue resistance. This investigation systematically examines the spatiotemporal propagation of Rotor–Stator Interaction (RSI)-induced pressure pulsations and evaluates the corresponding dynamic stress mechanisms based on a phase-resolved fluid–structure interaction strategy. The results reveal a significant hydrodynamic duality: RSI pressure waves manifest as convective traveling waves on the pressure side but as modal standing waves on the suction side. Crucially, a severe spanwise phase mismatch is identified between the hub and shroud streamlines, which induces a periodic hydrodynamic torsional moment on the blade. Due to the rigid constraint at the blade–crown junction, this torsional tendency is restricted, resulting in high-amplitude constrained tensile stresses at the root. This explains why the stress concentration at the crown inlet is significantly higher than in other regions. Additionally, the stress spectrum shows strong load dependence, characterized by low-frequency modulations on the suction side under high-load conditions. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

The reflected–forward–backward splitting (RFBS) method is well-established for solving monotone inclusion problems involving Lipschitz continuous operators in Hilbert spaces, where it converges weakly under mild assumptions. Extending this method to Banach spaces presents significant challenges, primarily due to the nonlinearity of the duality mapping. In this paper, we propose and analyze an RFBS algorithm in the setting of real Banach spaces that are 2-uniformly convex and uniformly smooth. To the best of our knowledge, this work presents the first strong (R-linear) convergence result for the RFBS method in such Banach spaces, achieved under a newly adapted notion of strong monotonicity. Our results thus establish a foundational theoretical guarantee for RFBS in Banach spaces under strengthened monotonicity conditions, while highlighting the open problem of proving weak convergence for the general monotone case. Full article

The aim of this article is to study duality results for nonsmooth mathematical programs with equilibrium constraints in terms of tangential subdifferentials. We study the Wolfe-type dual problem under the convexity assumptions and a Mond–Weir-type dual problem is also formulated under convexity and generalized convexity assumptions for MPEC by using tangential subdifferentials. We establish weak duality and the two dual programs by assuming tangentially convex functions and also obtain strong duality theorems by assuming generalized standard Abadie constraint qualification. Full article

Anyone who starts reading the Qurʾān out of linguistic and literary interest—whether in the original language or in a translation—very quickly becomes aware of the strong rhetorical effect of the text in its forcefulness and intensity. But by what means is this effect achieved? One means is duality, which, in Arabic, is already inherent in thought through the existence of the dual between singular and plural and is therefore of particular importance. The constantly repeated mention of God’s attributes in the Qurʾān—usually two terms of similar meaning, such as ġafūrun raḥīmun “All-forgiving, All-compassionate” (Arberry) or ʿalīmun ḥakīmun “All-knowing, All-wise” (Arberry)—determines the text as caesuras, and a second term is also often added to other terms in order to emphasise and intensify the statement, such as mā la-hū min waliyyin wa-lā naṣīrin “to have neither protector nor helper.” The phenomenon of merism—the totality ‘everything,’ ‘everywhere,’ and ‘always’ expressed by two opposing terms—is also used in the Qurʾān, for example, in ẓāhirun/bāṭinun “inward/outward,” meaning ‘all.’ Full article

Pig detection is a fundamental yet challenging task in intelligent livestock farming, primarily due to difficulties in capturing both global contextual information and multi-scale features within complex environments. To address this, we propose VM-RTDETR, a novel detection model based on an enhanced RT-DETR architecture. The model incorporates a Vision State-Space Duality (VSSD) backbone, leveraging a novel Non-Causal State-Space Duality (NC-SSD) mechanism to overcome the limitations of traditional SSMs by enabling efficient modeling of long-range dependencies and global context. Furthermore, we design a Multi-Scale Efficient Hybrid Encoder (M-Encoder) that employs parallel convolutional kernels to capture both local details and global contours, effectively addressing scale variations. The synergistic design of the VSSD backbone and the M-Encoder enables our model to achieve more comprehensive feature representation. Experimental results on a custom dataset of 8070 images from a pig farm (with 6955 images for training and 1115 for testing) demonstrate that VM-RTDETR significantly outperforms existing mainstream detectors, improving $AP$, $A{P}_{50}$, and $A{P}_{75}$ by up to 2.35%, 0.63%, and 2.76%, respectively, over the strong R50-RTDETR baseline. Our model significantly enhances detection robustness in complex scenarios, offering an efficient and accurate solution for intelligent livestock farming. Full article

The corporate finance field is inherently engaging, with a strong focus on factors influencing various performance indicators. This study analyzes 66 companies from the Information and Technology sector, all part of the Standard and Poor’s 500 index, over a 22-year period from 2003 to 2024. I applied linear, nonlinear, and interaction-variable models to identify the causal relationship between profitability and key influencing factors. The results reveal that firm size, sales growth rate, current ratio, long-term debt to total capital, free cash flow, asset turnover, receivable turnover, number of board meetings, percentage of women on the board, CEO age, audit committee independence, the presence of compensation and nomination committees, and a pandemic dummy variable all had positive effects on performance. In contrast, firm age, dividend payout ratio, effective tax rate, board size, CEO duality, and the presence of a corporate social responsibility committee negatively impacted firm performance. This research also explores corporate governance by evaluating the role of regulations and internal policies designed to promote financial transparency and protect shareholders’ interests. Additionally, it highlights the importance of board independence, the effectiveness of specialized committees, and the role of ethical leadership in driving long-term corporate success. Full article

In view of the remarkable progress in microrheology to monitor the random motion of Brownian particles with a size as small as a few nanometers, and given that de Broglie matter waves have been experimentally observed for large molecules of comparable nanometer size, we examine whether Brownian particles can manifest a particle-wave duality without employing a priori arguments from quantum decoherence. First, we examine the case where Brownian particles are immersed in a memoryless viscous fluid with a time-independent diffusion coefficient, and the requirement for the Brownian particles to manifest a particle-wave duality leads to the untenable result that the diffusion coefficient has to be proportional to the inverse time, therefore, diverging at early times. This finding agrees with past conclusions published in the literature, that quantum mechanics is not equivalent to a Markovian diffusion process. Next, we examine the case where the Brownian particle is trapped in a harmonic potential well with and without dissipation. Both solutions of the Fokker–Planck equation for the case with dissipation, and of the Schrödinger equation for the case without dissipation, lead to the same physically acceptable result—that for the Brownian particle to manifest a particle-wave duality, its mean kinetic energy $k_{B}T/2$ needs to be ½ the ground-state energy, $E_0={\displaystyle \frac{1}{2}}\hslash \omega$ of the quantum harmonic oscillator. Our one-dimensional calculations show that for this to happen, the trapping needs to be very strong so that a Brownian particle with mass m and radius R needs to be embedded in an extremely stiff solid with shear modulus, G proportional to $\left(m/R\right){\left(k_{B}T/\hslash \right)}^2$. Full article