Search Results (3,567)

After the completion of temporary repair of naval ship equipment, cost settlement has long relied on an ex post auditing model, which results in long cycles and a lack of immediate pricing references for the military. To address this issue, a comprehensive unit price estimation method based on Proximal Policy Optimization (PPO) is proposed, which rapidly generates reasonable unit prices for each process after the repair is completed, thereby providing a quantitative benchmark for negotiation. The unit price estimation problem is formulated as a Markov decision process, and a multi-objective reward function combining range reward, compliance penalty, and final accuracy reward is designed. To alleviate the sparse reward problem, potential-based reward shaping using the Critic network is introduced, which decomposes the final accuracy signal into each pricing step. The clipping mechanism of PPO is adopted to limit the policy update amplitude, thereby improving training stability. Experimental results on 12,000 desensitized real repair records show that the proposed method achieves a mean absolute percentage error (MAPE) of 11.3%, a coefficient of determination (R²) of 0.913, and an abnormal estimation rate (AER) of 3.5%. Compared with standard PPO, the AER is reduced by 59%. The proposed method can sequentially output reasonable unit prices after repair completion, exploring a technical pathway for transforming temporary repair funding from ex post auditing to immediate verification. Full article

Recognizing the limitations of traditional vehicle routing models in urban environments, this work presents the Time-Dependent Truck-Drone Green Vehicle Routing Problem with Pickup and Delivery (TDTDGVRPPD) to simultaneously optimize environmental impact and operational efficiency. We first develop a truck fuel consumption and carbon emission model that accounts for the effects of time-varying speeds and real-time loads during delivery. A nonlinear energy consumption model is then proposed for drones, considering payload weight. Based on these models, a mathematical formulation is developed to minimize the total operational cost, including truck and drone usage costs, truck fuel and carbon emission costs, drone energy consumption costs, truck–drone coordination time costs, and time-window violation penalties. The model also incorporates truck no-entry zones, time-varying speeds, and customers’ simultaneous pickup and delivery demands. An Improved Whale Optimization Algorithm (IWOA) hybridized with Variable Neighborhood Search (VNS) is developed to solve the problem. Simulation results show that the proposed model and algorithm effectively optimize truck departure times to avoid traffic congestion, reduce truck–drone coordination time, and lower total logistics costs and energy consumption, thereby contributing to energy conservation and emission reduction in logistics operations. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

The karmavācanā are ritual texts found in the Vinaya Piṭaka’s Khandhaka sections and standalone translations, regulating the conduct of monks and nuns. They include procedures for legitimate religious practices and penalties for disciplinary violations. The Taishō Tripitaka compiles five karmavācanā texts, structured into two systems. This study focuses on the Tanwude lübu zajiemo (曇無德律部雜羯磨, No. 1432), attributed to Kang Sengkai (康僧鎧), who is from Tianzhu (India) during the Cao Wei period, and the Jiemo yijuan (羯磨一卷, No. 1433) by Tan di (曇諦), an Anxi (Parthian) śramaṇa of the Cao Wei period. This study addresses three key research questions: (1) the relationship between these two karmavācanā texts and the Dharmaguptaka Vinaya (四分律, No. 1428); (2) whether the Tanwude lübu zajiemo and Jiemo yijuan are the earliest karmavācanā texts to have been translated; and (3) how to explain the process by which their textual structures were formed. By synthesizing extant karmavācanā versions in other languages and Dunhuang-Turpan manuscript fragments, the author argues that No. 1432 and No. 1433 cannot be directly identified as mechanically excerpted copies of the Dharmaguptaka Vinaya. In the formation of their respective structures, the influence of earlier karmavācanā translations—now no longer extant—cannot be ruled out. Judging from the karmavācanā manuscripts preserved at Dunhuang-Turfan, the two texts in question were most likely formed toward the end of the sixth century CE. Full article

Rice production is under increasing threat from adverse climatic trends that exacerbate water scarcity and compromise food safety. The need to transition toward water-saving irrigation is urgent, as is the requirement of addressing the dual burden of selenium (Se) deficiency and arsenic (As) toxicity. This 3-year field study (2020–2022) is the first to evaluate the effects of integrated water-saving irrigation. Permanent flood irrigation (Flood) or alternate wetting and drying was used, in which fields were reflooded when the soil matric potential reached −20 kPa (Reflood-20) and −70 kPa (Reflood-70); the effects of foliar Se biofortification at 15 g Se ha⁻¹ with sodium selenate (15-Se) or no Se (No-Se) on rice production and Se and As accumulation were also investigated. The results identified the Reflood-20 regime as the optimal strategy, achieving 36% water savings without significant grain yield penalties while enhancing grain quality. Foliar Se application successfully increased the dehulled grain Se content by 10.7-fold, effectively meeting human dietary requirements. The As contents were decreased by 27.6% due to water restriction, and an additional 10% loss was observed because of Se supplementation. Analysis of the straw also showed a 23.5% decrease in As and a 5.7-fold increase in Se. Consequently, the synergy between moderate deficit irrigation and Se biofortification provides a robust, cost-effective framework for the large-scale production of safer, nutrient-dense rice, reconciling resource efficiency with food security. Full article

Transformer-based deformable brain MRI registration achieves high overlap accuracy, but predicted displacement fields can contain voxels with a non-positive Jacobian determinant—local foldings that violate the diffeomorphism assumption required by tensor-based morphometry and atlas-fusion segmentation workflows. We introduce HypEReg, a non-linear hyperelastic regularizer that acts directly on the Jacobian determinant of the predicted displacement field. HypEReg couples a clamped-rational volume-distortion penalty ${(det{J}_{\varphi }-1)}^2/max(det{J}_{\varphi },ϵ)$ with an explicit per-voxel anti-folding hinge ${[max(0,ϵ-det{J}_{\varphi })]}^2$, integrated as a purely loss-side module into a TransMorph backbone with no inference-graph modifications. On the IXI atlas-to-subject benchmark (115 test subjects), HypEReg-TransMorph maintains grouped Dice (0.7537) while reducing the $det\left(J_{\varphi }\right)\le 0$ voxel ratio from $1.502\times {10}^{-2}$ (TransMorph) to $1.5\times {10}^{-5}$, with identical per-case runtime and parameter count to the unregularized baseline. In strict zero-shot transfer to OASIS Learn2Reg test pairs (no fine-tuning), HypEReg-TransMorph achieves Dice 0.7756 with a $det\left(J_{\varphi }\right)\le 0$ ratio of $7.6\times {10}^{-5}$, roughly two orders of magnitude below plain TransMorph zero-shot (Dice 0.7691; ratio $9.6\times {10}^{-3}$); downstream multi-atlas label fusion further confirms the practical benefit of fold suppression (fused Dice 0.8271 vs. 0.8201 for TransMorph). OASIS-2 longitudinal and ROI analyses support deformation plausibility (lower folding/SDlogJ and stronger ventricular ROI agreement), while clinical-covariate associations remain exploratory rather than biomarker-validating. Determinant-level, non-linear hyperelastic regularization substantially suppresses folding in Transformer dense-flow brain MRI registration while preserving alignment accuracy and adding zero inference cost, providing a practical drop-in regularization strategy that improves the reliability of deformation fields for morphometry-oriented deformable registration. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

Online apparel size recommendation remains difficult because consumers cannot physically assess garment fit before purchase. It is a multi-dimensional fit evaluation problem, particularly for complex garments such as jackets, where multiple body areas jointly influence perceived fit. Existing methods often rely on limited anthropometric measures, heuristic rules, or behavioral data, restricting both accuracy and interpretability. To address this issue, this study proposes an interpretable multi-dimensional fit evaluation framework based on garment ease theory. The framework defines ideal ease as the target fit condition and quantifies deviations through a segment-based weighting mechanism. Section-level mappings between body and garment measurements are established, and differentiated penalties are assigned according to the semantic fit interval of each body area. Section-specific evaluations are aggregated into an overall fit score (OFS) for candidate size ranking and Top-K recommendation, while also providing detailed fit feedback. Experiments involving 270 female participants and two jacket styles show high recommendation accuracy, achieving Top-3 accuracies of 99.6% for the regular-fit jacket and 98.9% for the tight-fit jacket. Compared with traditional heuristic methods, the proposed approach demonstrates clear advantages in both performance and interpretability, offering a practical solution that balances accuracy, transparency, and deployability. Full article

Digital policy has become an important instrument for promoting sustainable agricultural modernization. However, its effectiveness depends on the strategic responses of the government, agricultural operators, and farmers. This study develops a theoretical framework to examine how digital policy affects sustainable agricultural modernization through multi-agent interaction. Specifically, it constructs a three-party evolutionary game model and a Stackelberg game model to analyze strategy evolution under different implementation costs, subsidies, and penalties, as well as the government’s first-mover role in subsidy design. The results show that digital policy does not promote sustainable agricultural modernization through a simple linear pathway. Instead, it operates by reshaping the incentive structures of agricultural operators and farmers. Lower government implementation costs increase the likelihood of active policy implementation, while subsidies for agricultural operators and farmers strengthen their willingness to adopt digital tools, engage in standardized production, and participate in digital agricultural activities. However, the marginal effect of subsidies weakens as participation and digitalization increase, indicating that unlimited subsidy expansion may reduce policy efficiency and increase fiscal pressure. This study contributes to the literature by linking digital policy design, multi-agent strategic interaction, and sustainable agricultural modernization within a unified theoretical framework. It highlights that effective digital agricultural policy requires incentive compatibility, fiscal sustainability, inclusive participation, and adaptive governance, rather than reliance solely on digital technology investment or subsidy expansion. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

This study investigates the determinants of driver acceptance of “M-Flow”, Thailand’s first Advanced Traffic Management solution utilizing Multi-Lane Free Flow (MLFF) technology. While designed to eliminate toll plaza bottlenecks through AI-driven automated billing, the system’s operational efficiency is hindered by a “trust gap” caused by a stringent ten-fold penalty for late payment compliance. By integrating the Technology Readiness Index (TRI 2.0) with the Technology Acceptance Model (TAM), this research explores how psychological readiness dictates the success of smart traffic infrastructures. Data from 485 drivers were analyzed using Structural Equation Modeling (SEM). The results reveal that while technological optimism and innovativeness act as motivators, Insecurity (β = −0.723) emerges as the dominant psychological barrier, directly suppressing the perceived ease of use and triggering behavioral resistance. The findings demonstrate that technical efficiency and diverse payment options alone are insufficient to ensure mass adoption if the regulatory climate fosters financial anxiety. To maximize system throughput, this study recommends that policymakers shift from punitive enforcement to “trust engineering.” By enhancing financial transparency, simplifying the registration-to-payment workflow, and mitigating the “penalty trap” perception, authorities can achieve the psychological seamlessness that is a strict prerequisite for a fully trusted smart transportation infrastructure in Thailand. Full article

The increasing integration of distributed electricity–heat adjustable resources into distribution networks poses significant challenges for virtual power plant (VPP) dispatch, as conventional aggregation models often neglect network constraints, leading to infeasible or unsafe operation plans. To address this issue, this paper proposes a source-grid-load-storage aggregation optimization method that explicitly incorporates both distribution network power flow constraints and district heating network hydraulic–thermal coupling constraints. The network constraints are integrated into the optimization objective as penalty terms, and the dispatch problem is formulated as a Markov decision process. A deep reinforcement learning framework, combining twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms, is employed to solve the sequential decision-making problem. Simulation results demonstrate that the proposed method effectively ensures distribution network security and heating quality while maintaining economic efficiency, providing a feasible and safe dispatch strategy for VPPs in coupled electricity–heat systems. Full article

Introduction: Restoring riverine connectivity is a cornerstone of ecological restoration for migratory fish populations. When physical barriers like dams lack effective fishways, translocation to more suitable sites becomes an alternative. Objectives: This study aims to present a decision-support methodology based on the Fragmentation Index (FI), designed to prioritize release sites in alternative river stretches that maximize the likelihood of survival of translocated diadromous fish. Methodology: The method integrates field-based obstacle characterization and transposability classification, together with a weighted penalty for restrictive obstacles located closer to the confluence with the main stem. The methodology was applied to six tributaries of the Douro River, targeting the European eel (Anguilla anguilla). Results: The FI successfully distinguished between functional reaches and severely fragmented systems. Results revealed high heterogeneity among the studied tributaries, with the Távora (FI = 1.07) and Ceira (FI = 1.12) Rivers identified as top priorities due to low fragmentation and stable hydrology. In contrast, the Tedo River (FI = 5.18) illustrates index’s sensitivity. Despite a high barrier density, its downstream stretch of ~14 km remains functionally connected because the first restrictive obstacles are located far upstream from the confluence. Conversely, the Torto River (FI = 0) was excluded due to severe drought conditions, underscoring the need to pair connectivity metrics with hydrologic viability. Conclusions: For large-scale translocations, this framework enables distributing fish across multiple systems to safeguard the ecological integrity of recipient communities while ensuring individuals can successfully complete their life cycles. Overall, this approach provides a quantitative and replicable framework for managing endangered species by prioritizing release sites with high longitudinal connectivity. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

Real-time scheduling in large-scale electric vehicle charging stations is challenged by stochastic vehicle arrivals, dynamic departures, limited charging resources, and station-level power constraints. To address this problem, this paper proposes a two-stage Supervised Service Matching and Reinforcement Power Dispatch (SMPD) framework, termed SMPD, which decomposes the original coupled scheduling problem into supervised service matching and reinforcement learning-based power dispatch. In the first stage, a supervised matching network learns EV-charger service suitability from historical charging-session records and determines service access decisions for feasible EV–charger pairs. In the second stage, a Soft Actor-Critic-based controller allocates continuous charging power to connected EVs under EV-side charging limits, charger capacity constraints, and the station-level total power constraint. The proposed framework is evaluated using public charging-session data from the ElaadNL dataset. Experimental results show that SMPD achieves lower average waiting time, higher average revenue, lower composite penalty, and comparable demand satisfaction compared with rule-based, single-stage reinforcement learning, and multi-agent baselines. Sensitivity and robustness analyses further indicate that SMPD maintains favorable scheduling performance and acceptable online decision time under the tested charger-scale settings and operational disturbance scenarios. These results suggest that the proposed two-stage design provides an effective and computationally tractable approach for real-time scheduling in large-scale EV charging stations. Full article