Search Results (31,807)

The progressive phase-out of coal-fired power plants in Portugal has significantly reduced the availability of fly ash (FA) as a supplementary cementitious material (SCM), reinforcing the need for sustainable alternatives. Waste glass powder (WGP), characterized by its high amorphous silica content, has emerged as a promising candidate; however, most studies focus on ultrafine particles or isolated performance indicators, lacking an integrated technical, environmental, and economic assessment. This study evaluates cement pastes incorporating 25% WGP (by volume) with different particle size distributions, including fineness levels comparable to cement and FA. Mechanical performance, grinding energy demand, carbon footprint, and cost were systematically analyzed. The results indicate that WGP is technically viable as an SCM, with a median particle size (D₅₀) of approximately 48 µm providing the most balanced performance. Although finer particles enhance pozzolanic reactivity, the associated increase in grinding energy and economic cost offsets these gains. The findings demonstrate that optimizing particle size, rather than maximizing fineness, enables a technically robust and industrially realistic use of WGP. This approach supports circular economic strategies and contributes to the decarbonization of the construction sector by identifying an efficient replacement pathway for FA under resource-scarcity conditions. Full article

Soil stabilizers using conventional cement and lime binders incur high environmental costs owing to CO₂ emissions associated with their excavation, production, and processing. This has motivated research on low-carbon, waste-derived alternatives. The review shows that: biochar increases unconfined compressive strength (UCS) by 15–40% with a 2–5% dosage through pore filling and particle binding; nanocellulose promotes soil cohesion by 25–60% through fibrous network development and tensile bridging; recycled PET powder at 5–10% increases shear strength by 20–35% promoting mechanical interlocking, increasing stiffness, crack resistance and durability. Biochar provides direct carbon sequestration with a carbon transfer capacity of up to 2.5 tons CO₂-eq/ton. Recycled PET introduces waste valorization, with the potential to divert millions of tons of annual PET waste, while nanocellulose provides indirect carbon savings by avoiding emissions from cement and lime replacement. This review’s objectives are as follows: providing a comprehensive comparison of biochar, nanocellulose, and PET powder as promising non-cement composite stabilizers; identifying optimal dosage ranges and stabilization mechanisms for each material across different soil types; and outlining knowledge gaps and future research directions in sustainable geotechnical practices. The review assessed the individual and synergistic effects of the additives on critical geotechnical properties, including unconfined compressive strength (UCS), California bearing ratio (CBR), resilient resistance, swelling resistance, and the durability of the treated soil. Findings provide actionable guidance for practitioners seeking to reduce construction carbon footprints while maintaining geotechnical performance standards. Research gaps were identified, and future directions for integrating high-performance, low-carbon soil composites into sustainable construction solutions are proposed. Full article

This study develops a high-resolution techno-economic optimization framework to assess the feasibility of green hydrogen production in 100% renewable, off-grid systems. Utilizing 5-minute interval meteorological data aggregated to hourly resolution spanning 5 years across seven geographically diverse sites, this study co-optimizes the integration of hybrid wind–solar power generation, flexible electrolyzer operation, and a multi-timescale energy storage portfolio, incorporating short-duration, long-duration, and seasonal storage. On the generation side, a hybrid wind–solar configuration achieves the lowest levelized cost of hydrogen (LCOH). For energy storage, no single storage technology can economically address demand fluctuations across short-term, medium-term, long-term, and seasonal timescales. Instead, a coordinated multi-timescale storage strategy incorporating energy-to-energy mechanisms reduces the LCOH by up to 40%. Increasing hydrogen tank capacity and enabling flexible electrolyzer operation further lowers the LCOH. Significant regional resource variability leads to substantial cost disparities, with the most favorable region achieving a low LCOH of $2.45/kg. Several regions are projected to reach the $3/kg target by 2030, while areas with limited resources require large-scale hydrogen storage to ensure supply reliability. These results represent deterministic lower-bound estimates under perfect foresight; accounting for forecast uncertainty and real-world operational constraints would likely increase actual costs by approximately 5–15%. Full article

Under fault conditions, Photovoltaic-Storage-Charging Integrated Stations (PSCISs) are regarded as a key resource for enhancing distribution network resilience. However, traditional centralized optimization fails to account for conflicts of interest between the distribution network and PSCISs and neglects the actual response behavior of EV users. To address these issues, a coordinated emergency operation strategy for distribution networks and PSCISs based on the master–slave game is proposed. Firstly, a bilevel optimization framework based on the master–slave game is constructed, where the upper level performs system-level coordination and the lower level handles autonomous decision-making. For the upper level, the minimization of distribution network operation cost is set as the optimization objective by the dispatching center to determine power purchase prices and load shedding rates, which serve as guidance signals for lower-level PSCISs. In terms of the lower level, a dual-factor S-shaped response curve is introduced into the lower-level model to precisely characterize EV users’ nonlinear response behavior to price incentives. Furthermore, based on the signals received from the upper level, the maximization of each PSCIS’s profit is set as the optimization objective to determine the PV output, storage dispatch, and V2G incentive prices. Subsequently, Model Predictive Control (MPC) is employed to implement rolling optimization during the fault period, addressing the source-load uncertainties. Finally, an improved IEEE 33-node distribution network is used for case analysis and validation of the proposed operation strategy. The results indicate that the proposed strategy can effectively coordinate the interests of multiple parties, achieving synergistic improvements in both the economy and reliability of the distribution network. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

This article integrates sustainability principles into a three-echelon supply chain for deteriorating items with imperfect quality, consisting of a single vendor, a third-party logistics enterprise (3PL), and a single buyer, with a focus on balancing economic efficiency with environmental responsibility. The vendor is assumed to operate an imperfect production system, resulting in products of imperfect quality. The 3PL undertakes all transportation activities, while the buyer conducts a quality inspection process to detect defective items, which is subject to Type-I and Type-II errors. Aside from that, the inventory model also assesses carbon emissions arising from various operational activities including energy usage during production, warehousing, and disposal processes, and fuel consumption in transportation, for which the above members of the supply chain are accountable. Afterward, carbon management policies such as a carbon tax and carbon cap-and-trade are considered to regulate total supply chain emissions. The objective is to minimize the joint expected total cost by simultaneously optimizing shipment frequencies and the replenishment cycle for the buyer within carbon emission constraints. An iterative solution procedure is developed to address the problem. A numerical example and sensitivity analysis are provided to demonstrate the model’s applicability and to explore the influence of critical parameters. Finally, the study presents managerial insights, along with conclusions and recommendations for future research directions. Full article

Background: Accurate prognostic stratification remains essential for optimizing outcomes in hepatocellular carcinoma (HCC) patients undergoing liver transplantation (LT). The hemoglobin–albumin–lymphocyte–platelet (HALP) score is a composite biomarker reflecting systemic inflammation, nutritional status, and immune competence, and has demonstrated prognostic value in several malignancies. This study aimed to evaluate the predictive utility of the HALP score for survivals and recurrence in HCC patients undergoing LT. Methods: A total of 476 consecutive patients who underwent LT for HCC between 2006 and 2024 were retrospectively analyzed. Pretransplant HALP scores were calculated for all patients. Receiver operating characteristic (ROC) analysis identified an optimal cut-off value of 29 for recurrence prediction. Patients were stratified into HALP ≥ 29 and HALP < 29 groups. DFS and recurrence rates were compared. Prognostic performance was assessed using the concordance index (C-index) and area under the ROC curve (AUC). Outcomes were further compared with the Milan and Expanded Malatya criteria. Results: Of the 476 patients, 335 (70.4%) had HALP ≥ 29 and 141 (29.6%) had HALP < 29. The HALP ≥ 29 group demonstrated significantly higher 5- and 10-year DFS rates compared with the HALP < 29 group (67.1% vs. 58.5% and 49.5% vs. 33.5%, respectively; p < 0.001). Recurrence rates were significantly lower in the HALP ≥ 29 group (14.0% vs. 31.9%; p < 0.001). However, patients within the Milan and Expanded Malatya criteria showed superior long-term DFS and lower recurrence rates in the HALP ≥ 29 compared to the HALP < 29 group (p ≤ 0.037). HALP ≥ 29 was associated with lower tumor burden parameters and improved hepatic functional reserve. Despite its significance, HALP demonstrated inferior discriminative performance (C-index: 0.565) compared with the Milan (0.621) and Expanded Malatya (0.648) criteria. Patients beyond the Milan criteria (n = 233) with HALP ≥ 29 achieved a 5-year overall survival of 54.2%, compared with 37.8% with HALP < 29. Conclusions: Low HALP score is associated with poor DFS and a high post-transplant recurrence rate. Although it represents a non-invasive and cost-effective biomarker, its prognostic accuracy remains inferior to established transplant selection criteria, limiting its use as a standalone selection tool. However, individuals beyond Milan with HALP ≥ 29 achieved survival outcomes exceeding internationally accepted post-transplant benchmarks. Incorporating HALP into pre-transplant evaluation may help identify a biologically favorable subgroup among patients traditionally considered high risk based solely on tumor burden. Full article

DBSCAN is widely used to identify structured regions in unlabeled data, but its performance depends critically on the selection of the neighborhood parameter $\epsilon$. Traditional heuristics for estimating $\epsilon$ often become unreliable in high-dimensional or varying-density settings because they rely heavily on local geometric criteria and may fail under smooth transitions or topological ambiguity. This work presents a three-level perspective on DBSCAN hyperparameter selection. At the algorithmic level, $\epsilon$ controls neighborhood connectivity and structural transitions in clustering. At the modeling level, the ordered k-distance signal is approximated through a surrogate dynamical estimation framework inspired by a mass–spring–damper system. At the causal level, the resulting estimator is interpreted through interventions on its internal threshold-selection mechanism. The proposed method models the variation of $\epsilon$ using ordinary differential equations defined on the ordered k-distance signal, enabling analysis of structural transitions in density organization via a surrogate dynamical representation. System identification is performed using L-BFGS-B optimization on the smoothed k-distance curve, while the system dynamics are solved with the fourth-order Runge–Kutta method. The resulting estimator identifies transition regions that are structurally informative for $\epsilon$ selection in DBSCAN. To analyze the estimator at the intervention level, Pearl’s do-calculus is used to compute the Average Causal Effect (ACE). The method was evaluated on synthetic benchmarks and on the Covtype dataset, including scenarios with multi-density overlap and dimensionality up to ${\mathbb{R}}^{10}$. The resulting ACE values, $+0.9352$, $+0.5148$, and $+0.9246$, indicate that the proposed estimator improves intervention-based $\epsilon$ selection relative to the geometric baseline across the evaluated datasets. Its practical computational cost is dominated by nearest-neighbor search, behaving approximately as $O(NlogN)$ under favorable indexing conditions and degrading toward $O\left(N^2\right)$ in high-dimensional or weak-pruning regimes. Full article

This study investigates the construction of a topological network for resilient low-altitude vertiports. Addressing the issue of excessive network redundancy often caused by maximizing algebraic connectivity in traditional topology optimization problems, we employ algebraic connectivity—a key spectral metric—as a measure of network topology resilience. The objective function employs normalized algebraic connectivity that simultaneously considers total network distance, achieving an effective trade-off between global fault tolerance and construction costs at the model level. To address this challenging combinatorial optimization problem, the Gray Wolf Optimizer (GWO) algorithm is mechanistically enhanced. Experiments demonstrate that the proposed method achieves superior performance in key metrics such as objective function value and total network distance, significantly enhancing network resilience while controlling construction costs. For the optimized network topology solutions, simulations of six failure modes for nodes and edges analyze the response characteristics of the vertiport network’s maximum connected subgraph proportion and global efficiency during the gradual removal of nodes and edges. Results demonstrate that the designed vertiport topology network exhibits robust resilience. It maintains high connectivity and global efficiency under both random attacks and degree-based targeted node attacks, showcasing strong engineering applicability. Full article

Drone logistics is emerging as a key trend in future delivery systems due to its efficiency. However, current benefit assessments are often one-dimensional, focusing on single-node modes and overlooking load variations and charging processes in continuous multi-node delivery. To address this gap, this paper develops an integrated assessment framework across three dimensions: environment, economy, and time. Based on lifecycle emissions and total cost of ownership, a structured time-performance indicator, time value, is introduced. By incorporating an energy consumption model that accounts for dynamic loads and a charging model that considers charging behavior, an improved genetic algorithm is designed to optimize large-scale urban drone dispatch. Furthermore, a comparative sensitivity analysis with electric trucks quantifies the effects of market demand, charging strategy and technological progress. Results show that, under the modeled scenarios and parameter assumptions, electric trucks remain preferable in the short term, while drones demonstrate stronger long-term potential. Enterprises should align drone and truck deployment with demand and manage charging dynamically, while governments should combine initial subsidies with long-term guidance and systemic support to enable large-scale drone logistics adoption. Full article

In natural agricultural environments, plant disease monitoring faces significant challenges, including a highly uneven (long-tail) distribution of disease species, tiny scales of early-stage lesions, and complex, variable backgrounds. These factors hinder the ability of existing lightweight models to balance detection accuracy and computational efficiency. To address these issues, this paper proposes a detection scheme driven by the synergy of data distribution reshaping and model architecture optimization. At the data level, we propose the CALM-Aug augmentation strategy. Based on the statistical distribution characteristics of disease categories, this strategy utilizes object-level copy-paste logic to specifically compensate for the feature shortcomings of rare disease samples. It introduces a teacher-guided screening mechanism and employs accept–reject sampling to ensure the pathological consistency of the augmented samples, thereby alleviating the model’s inductive bias toward head categories. At the model architecture level, using YOLOv11 as the baseline, the YOLO11-ARL model adapted to agricultural scenarios is constructed. It enhances sensitivity to early point-like disease spots through Efficient Multi-Scale Convolutional Pyramids and lightweight decoupled detection heads. Furthermore, a Layer-wise Adaptive Feature-guided Distillation Pruning (LAFDP) algorithm is utilized to extract a lightweight version, YOLO11-ARL-PD, achieving a significant reduction in parameters and computational cost. Experimental results on the PlantDoc dataset show that the final model achieves a precision of 89.0% and an mAP@0.5 of 85.3%. Compared to the baseline model YOLOv11n, YOLO11-ARL-PD improves precision and average precision by 7.7 and 2.6 percentage points, respectively, while reducing parameters by 51.93% and weights by 46.15%. Cross-dataset tests prove the good generalization performance of the proposed method. This study indicates that, under lightweight constraints, jointly optimizing the training distribution and model architecture is an effective way to improve plant disease monitoring and to support the edge deployment of smart crop-protection systems. All resources for CALM-Aug are available at wyz-2004/CALM-Aug on GitHub. Full article

The connection of large-capacity loads at nodes in a radial distribution network can readily lead to severe voltage sag phenomena, thereby significantly deteriorating power supply quality. To ensure the safe operation of both voltage-sensitive equipment and the power grid, the deployment of Dynamic Voltage Restorers (DVR) and Static Var Compensators (SVC) is recognized as one of the most effective countermeasures for addressing voltage sag issues. Considering the inherent topological characteristics of the radial distribution network, a dimensional collaborative governance strategy is proposed, which takes longitudinal dimension collaborative governance as the primary approach and horizontal dimension collaborative governance as a supplementary measure. Based on sensitivity analysis, the concepts of horizontal sensitivity and longitudinal sensitivity are defined. Furthermore, considering the response time of governance equipment, the voltage sag governance process is divided into two distinct stages: in the first stage, governance is primarily reliant on DVR, and a longitudinal dimension collaborative optimization algorithm is proposed to solve the corresponding optimization model; in the second stage, governance mainly utilizes SVC, where a standard particle swarm optimization (PSO) algorithm is employed to solve its optimization model. A case study conducted on a 42-node radial distribution network validates that the proposed approach effectively governances the voltage sag problem in the distribution network. Through analysis, the number of nodes experiencing voltage sag was reduced from 29 to 0 in both the first and second governance stages. In the first stage, the total compensation voltage of the DVR is 0.581 p.u. With the coordinated participation of SVC in the second stage, the total DVR compensation voltage decreases to 0.100 p.u., corresponding to a significant reduction of 82.79%. Given the higher capital cost of DVR relative to SVC, this substantial decrease in required DVR capacity effectively lowers the overall governance cost. Full article

Promoting the effective use of abandoned farmland has become a key policy priority for strengthening food security in China. However, disentangling the decision-making processes among diverse participating actors is a foundational prerequisite for addressing the governance challenge of abandoned farmland utilization. Building on this, the present study employs a four-actor evolutionary game model and sensitivity analysis of key parameters to systematically examine the interactions among four key actors—local governments, village collectives, family farms, and farmers—and to identify the corresponding evolutionarily stable strategies (ESSs) across different stages of abandoned farmland utilization. The results show that: (1) Multi-actor strategic interactions in abandoned farmland utilization exhibit a multi-stage evolutionary trajectory, in which all actors gradually shift their strategic choices under changing cost–benefit structures, regulatory intensity, and coordination conditions, leading to different evolutionary stable equilibria across governance stages. (2) The configuration in which local governments adopt loose regulation, the village collective plays an active coordinating role, family farms pursue long-term operations, and farmers choose recultivation is a key condition for achieving a Pareto-optimal equilibrium. (3) Although farmers’ production willingness and behavioral choices form the basis for the utilization of abandoned farmland, spontaneous individual action alone is insufficient to address the structural contradictions currently facing abandoned farmland utilization in China. To effectively promote the evolution of abandoned farmland governance toward a stable collaborative equilibrium and ultimately realize sustainable utilization, it is necessary to further optimize governmental administrative control models and incentive mechanisms, strengthen the organizational and coordinating functions of village collectives, and improve long-term operational support systems for family farms. This study systematically elucidates the underlying logic of China’s abandoned farmland utilization from the perspective of multi-actor behavioral decision-making, providing policy-referential insights for optimizing policy design, reducing coordination costs, and improving the efficiency of abandoned farmland utilization. Full article

Flue gas denitrification represents an environmentally friendly and economically viable strategy for alleviating energy crises and advancing carbon neutrality goals. Although traditional selective catalytic reduction (SCR) catalysts demonstrate excellent denitrification efficiency and catalytic stability, they still face significant challenges, including high cost and ammonia slip. In this study, the high-entropy oxide (HEO) NiCoZnVCrO_x was synthesized via the sol–gel method and evaluated for the reduction of NO to N₂. The effects of varying reaction conditions on the NO reduction performance of this material were systematically investigated alongside the underlying reaction mechanism. The results reveal that the reduced oxygen carrier (OC) achieves optimal performance at an oxidation temperature of 800 °C, oxidizing gas flow rate of 200 mL/min and reduction time of 60 min, yielding the highest NO conversion and N₂ selectivity while simultaneously minimizing NO₂ selectivity. The reaction mechanism was further elucidated through a series of characterization techniques, including DRIFTS. Overall, this HEO demonstrates significant potential as a candidate OC for flue gas denitrification. Full article

Maintenance is crucial for the durability of the existing building stock and should be perceived as an opportunity to improve the built environment. The implementation of thermal retrofitting measures to the building’s envelope enhances global energy performance, which is economically and environmentally beneficial. Building-related energy consumption during the operation phase is key to tackling carbon neutrality and climate change. Introducing thermal retrofitting within the context of maintenance planning can be cost-optimizing, as it reveals the technical–economic synergy between building pathology and energy efficiency. Maintenance activities and energy demand throughout the building’s service life influence life-cycle costs (LCCs). Decision-making based on LCC awareness is an advantage for owners. This study discusses the impact of implementing an optimal retrofitting solution (ORS), according to different maintenance strategies, on the LCC of an existing single-family home. The ORS comprises the following measures: adding an external thermal insulation composite system (ETICS) to external walls, extruded polystyrene (XPS) panels to the roof, and replacing the existing windows with others with improved thermal performance. The three maintenance strategies involve different complexity levels, concerning the type, number and timing of activities. Moving beyond isolated assessments, this study develops an integrated framework that bridges based on two existing background methodologies, involving optimal thermal retrofitting and condition-based maintenance planning, which, combined with new research, enable the assessment of maintenance, energy and global LCC for a time horizon of 100 years. The evaluation of energy-related LCC is based on simulations. The results indicate that these costs represent the majority of the global LCC. The ORS has a considerable positive impact on energy and global LCC. Adopting a maintenance strategy characterized by fewer planned activities and an earlier schedule of replacement interventions, which determines the implementation of the retrofitting measures, is better in terms of LCC savings. Full article