Search Results (1,569)

The diagnosis of missing seedlings in high-density drill-seeded crops is often hindered by the strong coupling between visual perception and diagnostic rules, which leads to an irreversible cascade amplification of underlying missed detection errors. To address this dilemma, this paper proposes a “Perception–Topology” collaborative decoupling framework oriented toward row structure perception. In the perception phase, a row-structure-enhanced detection model (RS-YOLO) is constructed. It integrates Space-to-Depth (SPD) conversion, a Selective Frequency-domain Aggregation Module (SFAM), and a Row-Structure Attention Mechanism (RSM) to effectively suppress tire rut interference and explicitly reinforce the spatial topological priors of crops. In the diagnostic phase, an Adaptive Intra-row Gap Analysis (AIGA) algorithm is proposed. By utilizing a dynamic median intra-plant spacing scale and core canopy geometric pruning, this algorithm fundamentally reformulates missing seedling diagnosis into a physical interruption metric of one-dimensional graph connectivity. Evaluated on a finely reconstructed UAV-based sorghum imagery dataset, RS-YOLO achieved a significant improvement of 2.7% in precision and 3.2% in recall over the baseline model, providing a structure-aligned, high-confidence input for the diagnostic process. Based on this perceptual foundation, the AIGA algorithm ultimately achieved a diagnostic precision of 96.11% and a recall of 91.48% without the need for negative sample annotations. This framework effectively severs the propagation chain of perceptual errors, providing a noise-robust and highly physically interpretable new paradigm for the automated inspection of field population structures. Full article

The Colonel Blotto game is a classical model of competitive resource allocation, but equilibrium computation becomes difficult in heterogeneous and asymmetric instances. In this research, we study a finite-horizon dynamic reformulation in which players allocate resources sequentially over publicly observed stages, while the payoff depends only on terminal cumulative allocations. The purpose of the reformulation is not to change the primitive objective, but to represent the same terminal-payoff problem as a zero-sum Markov game. We first show that the dynamic formulation admits a pathwise payoff-equivalent Markov representation through telescoping rewards. Under a known finite horizon, costless carryover, and terminal-only payoff evaluation, the dynamic game and the corresponding static Blotto game have the same minimax value at every reachable continuation state. This is a value-equivalence result; it does not imply a one-to-one correspondence between static and dynamic equilibrium strategy sets. The proof is based on terminal-deferral upper and lower bounds for the two players. We also study action-independent geometric termination, for which the discounted telescoping return coincides exactly with the expected stopped terminal payoff, and we provide a probability-controlled mismatch bound for truncated stopping rules. Numerical finite-grid experiments illustrate the value identity and report residual diagnostics. The results clarify when sequential Markov representations preserve the original Blotto objective and when additional primitives, such as carryover depreciation or primitive flow payoffs, require separate analysis. Full article

Despite the remarkable progress being made in the study of human facial age estimation, it is still a challenging problem. The main problem lies in the large intra-age appearance variations among different individuals. Sometimes, these variations can even exceed the inter-age appearance variations of the same individual. To address this problem, we construct a cross-identity image sequence for each query image and reformulate age estimation as a multi-image learning task. This provides a basis for learning common age-related cues across identities. Based on this formulation, we propose the Cross-Identity Interaction Transformer (CIIT) for age estimation. The CIIT first extracts multi-scale aging cues through a cross-scale embedding (CSE) module to preserve age evidence from fine textures to coarse structural changes. Secondly, to progressively enhance facial features and capture shared facial characteristics from cross-identity references, intra-image feature attention (IFA) and prior-guided axial cross-image attention (PG-ACIA) operate alternately within each Transformer block. IFA refines local age-discriminative representations within each image, while PG-ACIA uses multi-scale edge priors to guide cross-image interaction toward age-sensitive regions such as wrinkles. Finally, an anchored regression network (ARN) predicts age through a soft-weighted combination of multiple linear regressors for robust age estimation under diverse facial aging patterns. Experiments on four benchmark datasets, namely MORPH Album II, MegaAge-Asian, FG-NET and Adience, demonstrate that the proposed method achieves superior performance across multiple evaluation metrics, validating the effectiveness of the CIIT in capturing shared facial characteristics. Full article

This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an indexed framework based on the correspondence between a finite set and its associated index set. Within this framework, subsets are represented as ordered index sequences, allowing subset construction to be reformulated as a constraint-guided search process over index space. Candidate subsets are characterized by numerical descriptors derived from their indices (referred to as index certificates), which guide and filter the construction process. Subset generation is further organized through admissible index intervals that restrict feasible transitions and reduce the effective search space. The framework is based on an index-based representation and structured traversal of pairwise index combinations. Computational experiments on representative instances illustrate the behavior of the indexed construction procedure and indicate its efficiency relative to classical enumeration-based methods for small and medium-sized instances. The proposed approach provides a structured perspective on combinatorial search and offers a basis for further development of algorithms based on constrained exploration of subset structures. Full article

High-resolution images of non-cooperative spacecraft are essential for on-board autonomous operations. Hardware bandwidth limits and continuously changing observation distances mean that a practical super-resolution (SR) system must handle arbitrary, non-integer magnification factors without retraining, a setting known as arbitrary-scale SR (ASSR). Recent 2D Gaussian splatting (2DGS) methods represent image content with explicit anisotropic Gaussian primitives and render at any continuous coordinate, offering substantially faster inference than implicit neural representation (INR) approaches. Yet spacecraft imagery presents a structural mismatch for uniform 2DGS regression: the target occupies a small, densely structured region within a vast, featureless deep-space background, so a network that minimizes average reconstruction loss inevitably over-invests capacity in the irrelevant background and smears the fine edges of antennas and solar panels. We propose S³R-GS, a saliency-guided framework that embeds semantic spatial priors into the 2DGS pipeline at three levels: an encoder-level module that suppresses background noise before it reaches the splatting stage; a discrete Gaussian routing mechanism that assigns each spatial location to a semantically appropriate kernel group and reformulates Gaussian modeling as semantic prototype selection; and a saliency-weighted training strategy that concentrates the optimization gradient on the spacecraft target. Experiments on the SPEED and SPEED+ benchmarks show that S³R-GS achieves strong PSNR performance, competitive SSIM, and improved perceptual quality across scale factors from $\times 2$ to $\times 12$; additional ablation, extreme-lighting, and efficiency analyses further support the robustness and practicality of the proposed design. Full article

This study investigated the reformulation of traditional Anatolian flatbread (bazlama), a staple food of the Mediterranean diet, into a functional product with enhanced nutritional quality. High-amylose refined (white) flour obtained from high-amylose Svevo (Svevo-HA) wheat and resistant starch produced via repeated autoclaving–cooling cycles were incorporated to increase resistant starch content and antioxidant capacity, reduce the predicted glycemic response, and evaluate the resulting changes in textural attributes. Six bazlama formulations were produced using white flours of normal Svevo, Svevo-HA, and recombined Svevo-HA flour containing resistant starch and gluten, with and without vital gluten supplementation. Color, texture profile, phenolic content, antioxidant capacity (DPPH, ABTS, FRAP), resistant starch content, and in vitro glycemic index (GI) were evaluated. Bazlama samples enriched with resistant starch exhibited significantly higher total antioxidant activity (113.7–174.7 mg Trolox equivalent/100 g dw) and resistant starch (9.1–10.3%) levels, along with reduced GI values (53.8–54 < 55), classifying them as low-GI foods. The results demonstrate that incorporating high-amylose wheat–derived resistant starch can successfully convert bazlama into a functional flatbread with improved health-promoting properties. Full article

This paper proposes an outer space branch-and-search method for a class of linear fractional programming problems over a polytope. First, the original problem is reformulated in an equivalent problem by applying the equivalent transformation. Second, by using a new linearization technique, a linear programming relaxation problem of the equivalent problem is constructed. Third, lower bounds are obtained by solving a sequence of linear programming relaxation problems. Fourth, the convergence of the proposed algorithm is proved, and its worst-case computational complexity is estimated. Finally, numerical experimental results are reported to demonstrate the effectiveness of the algorithm. Additionally, an investment decision-making problem was solved to validate the applicability of the method proposed in this paper. Full article

Efficient scheduling of electric bus (EB) photovoltaic-storage charging stations (PSCSs) is essential for ensuring the operational economy of public transit and the security of the power grid. Existing scheduling studies generally simplify charging and storage efficiencies as fixed constants, neglecting their dynamic dependence on power levels. Meanwhile, the stochasticity of photovoltaic (PV) generation further complicates scheduling decisions. To address these issues, this paper proposes a day-ahead robust scheduling method for EB PSCSs that incorporates dynamic charging efficiency. First, the dynamic battery efficiency model is reasonably simplified and reformulated, and the big-M method is employed to transform the nonlinear efficiency model into an equivalent set of linear constraints, thereby effectively integrating dynamic efficiency characteristics into the day-ahead optimization framework. Then, information gap decision theory (IGDT) is adopted to model PV output uncertainty, establishing a risk-averse decision optimization model. On this basis, a two-stage solution algorithm integrated with the bisection method is designed to decompose the IGDT optimization problem into a series of linear programming subproblems, balancing solution accuracy and computational efficiency. Case studies validate the effectiveness of the proposed method. The results demonstrate that the dynamic efficiency model significantly improves scheduling accuracy, and the IGDT framework provides a reliable, robust scheduling strategy for PSCSs under limited information conditions. Full article

This paper develops a unified α-cut optimization framework for modular electric vehicle (EV) fast-charging station design under fuzzy uncertainty. Uncertain peak demand, annual delivered energy, electricity price, ambient temperature, arrival rate, and energy per session are represented by triangular or trapezoidal fuzzy numbers and reformulated through α-cut bounds. The resulting design problem is expressed as a hybrid discrete–continuous model in which the number of modules, the selected catalog module rating, installed power, cooling provision, and a station-volume proxy are jointly optimized. An aggregated representation of interchangeable modules is adopted to remove permutation-equivalent descriptions and preserve a compact search space. Three planning views are examined: minimum CAPEX at a prescribed α-cut level, minimum loss-driven OPEX under a CAPEX budget, and a service-oriented admissibility/coverage analysis that avoids interpreting larger α values as greater robustness. The strengthened numerical study includes a deterministic nominal benchmark, peak demand sensitivity regimes, feasibility threshold and budget sweep results, explicit service stress scenarios, and a queueing sensitivity check against Erlang-C and discrete-event simulation indicators. The results show that baseline CAPEX designs may be dominated by catalog thresholds, whereas OPEX and service-oriented conclusions become informative once budget and traffic regimes are varied. The proposed framework is therefore positioned as a tractable α-cut-based design screening and comparative optimization tool for representative modular EV charging station scenarios, rather than as a universally validated operational design rule. Full article

Effective peak load management enables utilities to mitigate increased electricity demand and optimize the use of available resources during periods of maximum consumption. Accurate forecasting of the peak load is essential for ensuring the reliability, efficiency, and resilience of contemporary power systems. In this study, commercial customer-level data were employed to identify electricity peak demand within the Polish power system, drawing upon historical records of both energy consumption and meteorological variables. Departing from conventional time series forecasting approaches, the problem was intentionally reformulated as a pattern recognition task. Three classification techniques were systematically evaluated to identify individual customers’ peak load events, thereby offering a basis for demand-side management strategies and incentive mechanisms aimed at flattening load profiles and improving grid stability. The proposed approach demonstrates how data-driven analytics can support utilities in extracting actionable knowledge from large-scale energy datasets and enabling proactive demand response programs. Empirical results indicate that the proposed methods are capable of predicting up to 90% of electricity peak occurrences, with a forecasting horizon of 24 h leading to significant shifts in the load curve. Full article

This paper investigates the optimal control problem of opinion dynamics within complex networks. By introducing a state transformation, the original problem is reformulated within a discounted Linear Quadratic Regulator (LQR) framework, establishing a connection between opinion control and classical control theory. Within this unified framework, the optimal control law can be obtained by solving the discrete-time algebraic Riccati equation, thereby circumventing the complexity of dealing with linear terms inherent in traditional dynamic programming approaches. Numerical experiments validate the effectiveness of the algorithm in a benchmark case, a 20-node complete network, and complex topologies. They also reveal the influence mechanisms of network heterogeneity on convergence speed and control energy consumption, providing a theoretical basis for public opinion guidance strategies under different network structures. Full article

This paper revisits assimilation theory—developed to explain immigrant incorporation into U.S. society—and advances a reformulation centered on reciprocal assimilation. Classical models describe a linear convergence toward dominant Anglo-American norms, while segmented assimilation highlights multiple pathways shaped by context, race, and class. Both, however, tend to frame incorporation as a directional process in which minority groups adapt to dominant institutions. Drawing on contemporary autism scholarship, this paper brings assimilation theory into dialogue with neurodiversity to examine how its core assumptions extend beyond immigrant contexts. Using autism as a critical case, we show that social adaptation often occurs through camouflaging (masking, compensation, and behavioral adjustment), producing outward conformity without changing underlying neurological differences and often carrying psychological costs. These dynamics suggest that inclusion is frequently conditional on sustained performance of normative behavior rather than true structural incorporation. We identify an underlying assumption of universal assimilability within assimilation research and show how engaging with disability calls for a broader conception of incorporation. In response, we propose reciprocal assimilation as a framework in which adaptation emerges through dynamic interaction among individuals, institutions, and social structures. Integrating life-course concepts—turning points, cumulative (dis)advantage, agency, and social bonds—we illustrate how participation trajectories are shaped by accessibility, accommodations, stigma, and support over time. We conclude that a reciprocal model shifts emphasis from cultural convergence to meaningful participation, offering a more flexible framework for understanding incorporation across diverse populations, with implications for research, measurement, and policy. Full article

In high-altitude interception, low atmospheric density limits the effectiveness of aerodynamic control, making thruster-based attitude control essential. In systems using single-use impulse-type lateral thrusters, each actuator can be fired only once, generates a fixed thrust magnitude, and is subject to a limit on the number of simultaneously active thrusters. Therefore, selecting an appropriate set of thrusters to track a desired control moment can be formulated as a cardinality-constrained combinatorial optimization problem. This paper proposes a depth-first search (DFS)-based branch-and-bound algorithm for sparse thruster selection. The objective is to minimize the tracking error between the generated and desired control moments while penalizing the number of active thrusters. To improve computational efficiency, thrusters are ordered by moment magnitude, and a problem-specific lower bound is derived from the residual moment and an upper bound on the achievable contribution of the remaining thrusters. This bound enables effective pruning of unpromising branches. The search space is further reduced by reformulating the problem using symmetric thruster pairs that generate opposing moments. Numerical results show that the proposed method achieves accurate moment tracking while significantly reducing computation time compared with the exact mixed-integer quadratic programming (MIQP) benchmark. Mixed-integer linear programming (MILP) is also included as an additional mixed-integer linear surrogate comparison. Full article

Accurate quantification of uncertainty risks is pivotal for enhancing the reliability of generation maintenance scheduling (GMS) in power systems. However, existing risk quantification methods predominantly focus on the aggregate impact of uncertainty on system-wide operational risks, failing to identify critical risk sources. This limitation hinders the secure and efficient operation of power systems with high penetration of renewable energy. To address this issue, we propose a risk-averse GMS approach for power systems based on contagious value-at-risk (CoVaR). Specifically, we first introduce the CoVaR theory to identify dominant risk sources affecting the secure operation of the system and derive a general analytical expression for CoVaR that incorporates integral terms of uncertain variables. Subsequently, a scenario-based linearization reconstruction strategy is developed to discretize these integral terms, and the complex CoVaR model is reformulated into a computationally tractable mixed-integer linear programming (MILP) model. On this basis, a new risk-averse GMS model embedded with CoVaR constraints is constructed. This model achieves precise identification of critical risk sources by quantifying and comparing the impacts of different risk sources on both system operational costs and risk costs. Finally, simulation results on the modified IEEE 24-bus power system and IEEE 118-bus power system demonstrate the effectiveness and superiority of the proposed approach. Full article

This study demonstrates how structured algorithmic optimization can enhance intelligent visual measurement systems in mining engineering. Real-time visual measurement of mining conveyor belts is critical for operational safety, yet achieving high-precision anomaly detection under complex environmental conditions remains a significant challenge. Conventional approaches often struggle to balance detection accuracy with computational efficiency due to inefficient feature representation and optimization strategies. To address this, this study proposes FDSE-DETR, a lightweight end-to-end framework designed for real-time anomaly evaluation. The framework eliminates Non-Maximum Suppression (NMS) to streamline inference. Specifically, this study introduces a deformation-aware sampling mechanism to enhance feature representation of irregular hazards, alongside a cost-effective multi-scale aggregation strategy to preserve fine cues within strict device budgets. Furthermore, a reformulated loss objective is developed to rebalance hard samples under severe class imbalance, improving the detection confidence. Experimental results on mining conveyor belt foreign object datasets show a 4.5% improvement in mean average precision (mAP), a 3.9% improvement in overall recall and a 22.5% reduction in computational cost, achieving 120.7 FPS. This study aims to address the problems of insufficient accuracy and low efficiency in real-time material flow measurements on mining conveyor belts under high-dust and low-illumination conditions. Full article