Search Results (5,677)

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

Storage location assignment in automated warehouses strongly affects order-processing efficiency. Existing co-occurrence-based approaches often rely on pointwise mutual information (PMI) statistics or direct frequency co-occurrence. This paper compares two deliberately chosen representation families for storage assignment in automated warehouses operated under Multi-Agent Pickup and Delivery (MAPD) constraints: Pointwise Positive PMI (PPPMI), representing direct pairwise co-occurrence, and Latent Dirichlet Allocation (LDA), representing latent-topic smoothing. The purpose is not to benchmark every possible representation space, but to make the pairwise-versus-latent contrast interpretable under a fixed execution pipeline consisting of task construction, visit-order selection, path planning, and collision avoidance. The broader research setting is motivated by real warehouse order data in which SKU co-occurrence structure is present, but such logs mix latent-topic effects, explicit family-based co-occurrence, noise, and demand variation. We therefore use two controlled abstractions of order structure: one generator with latent-topic mixtures and one generator with more direct family co-occurrence. We embed the proposed LDA representation and the PPPMI baseline in constrained-clustering and simulated-annealing placement methods and evaluate them against frequency-based, load-balancing, and random baselines. Evaluation is conducted in a fixed extended MAPD simulator that explicitly models orientation-aware motion, turning costs, service times, dynamic task release, and collision avoidance. In the latent-topic regime, LDA-based methods tended to form the leading group in average finite-horizon makespan, computed over completed combinations of random seeds and operating conditions. In the supplementary direct-co-occurrence condition, PPPMI was competitive in the plain representation comparison, while LDA-driven local search on top of a frequency-based initial layout remained strong. These results do not imply that LDA is universally superior; rather, they indicate that the relative suitability of PPPMI and LDA depends on the order structure and on how the representation interacts with the placement optimizer. The controlled generators are useful for isolating those effects, but they do not replace external validation on real warehouse logs. Full article

Ricin and abrin are highly lethal Type II ribosome-inactivating proteins. They depurinate the same site of the 28S rRNA to inhibit protein synthesis. Consequently, standard molecular-level activity assays used to detect the toxic activity of ricin or abrin do not distinguish between the two in mixed samples without prior physical separation or specially designed substrates. This study proposes a novel, cost-effective method to separately and simultaneously quantify the activities of ricin and abrin in mixtures by exploiting their distinct thermal stabilities. Thermal inactivation was used to demonstrate that heating samples at 80 °C for 5 min maximized the difference in their activities; while ricin retained most of its activity, abrin activity dropped to 20% after thermal treatment. This thermal treatment yielded 4 standard curves—ricin or abrin, thermally treated or not treated—in the 0.3 to 50 µg/mL range. By applying Cramer’s rule, the individual concentrations of active ricin and abrin in mixed samples were successfully calculated. However, this method should be used with a method detecting presence of ricin/abrin, to avoid unexpected reactivity due to contaminating RIPs. Full article

Heritage-led urban redevelopment is increasingly adopted to advance cultural continuity and social vitality; however, its long-term sustainability is often compromised due to the absence of user-oriented assessment methods. Conventional Post-Occupancy Evaluation (POE) approaches are limited in their ability to capture experiential, social, and participatory dimensions of architectural and urban spaces. This study examines the potential of Virtual Reality (VR) as a participatory POE tool for sustainable heritage redevelopment through the case study of Souq Al Muharraq in Bahrain. A convergent mixed-method approach is employed, integrating immersive VR 360-degree walkthroughs, structured questionnaires, qualitative semi-structured interviews, and expert evaluation. The findings reveal significant discrepancies between design intentions and lived experience, specifically in thermal comfort, circulation, social usability, and informal spatial practices. The study demonstrates that VR supports a user-centered and experiential approach aligned with Sustainable Development Goals (SDGs) 9, 11, and 16. It further proposes a sustainable and cost-efficient framework for architecture and urban projects’ evaluation by enabling early and post-user-centered evaluation of projects to reduce costly revisions and the creation of inclusive, adaptive, and resilient architecture and urban spaces. Full article

While there is some agreement on estimating construction cost contingency for “known unknowns,” there is little consensus on estimating management reserves for “unknown unknowns.” Definitions of risk and uncertainty also differ between the economics and finance literature and the cost engineering literature. This paper examines how cost engineering guidance on estimating management reserves is applied in government-sponsored project cost estimates. This lack of consensus is evident in a specific program: the management, treatment, and disposal of 212,000 cubic meters of mixed radioactive and hazardous chemical waste generated by plutonium production at the Hanford Nuclear Site. Over $30 billion has been invested in treatment facilities, vitrification plants, and laboratories analyzing gases, liquids, sludges, and salt cake from 177 aging storage tanks. The remaining construction and operating costs are highly uncertain, with estimates ranging from $300 billion to $640 billion. Analyses of alternatives for constructing Hanford waste treatment facilities assume 15% contingencies and 40% management reserves. A method is presented to compute the implicit moments of Extreme Value distributions of cost estimates for different options, helping determine whether one alternative’s cost estimate stochastically dominates others. Adopting industry definitions of contingency and management reserves by federal government agencies could improve construction cost estimation in government-financed programs. Full article

With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward–penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems. Full article

Optimal allocation of distributed generation (DG) and feeder reconfiguration are critical strategies for improving the operational efficiency and voltage stability of modern radial distribution networks under increasing penetration of renewable resources. However, the simultaneous optimization of DG placement, sizing, and network topology constitutes a highly nonlinear multi-objective problem subject to electrical, operational, and radiality constraints. Unlike existing studies that treat DG allocation and feeder reconfiguration as separate or weakly coupled problems, this work introduces a unified mixed-integer nonlinear optimization framework that captures their strong interdependency. In addition, a hybrid Big Bang–Big Crunch (HBB-BC) algorithm is proposed, combining stochastic contraction with adaptive learning mechanisms to improve convergence robustness in highly nonlinear search spaces. This contribution addresses the limitations of conventional metaheuristics in handling coupled topology–generation optimization problems and provides a scalable solution for modern active distribution networks. We propose a coordinated optimization framework for optimal DG placement and feeder reconfiguration aimed at minimizing real power losses while enhancing voltage stability and reducing both operational cost and environmental impact. The problem is formulated as a constrained multi-objective optimization model and solved using an improved hybrid Big Bang–Big Crunch metaheuristic algorithm which integrates exploration and exploitation mechanisms to achieve fast convergence and robust global search performance. The proposed method is validated on both IEEE 33-bus and IEEE 69-bus radial distribution systems under multiple operational scenarios. The results demonstrate that the coordinated optimization consistently achieves significant performance improvements across different network scales, confirming the robustness and scalability of the proposed framework. Full article

Against the backdrop of global climate warming and energy shortages, China proposed the” dual-carbon strategy” in 2020 to address climate change and promote ecological civilization. As a high-carbon emission industry, the iron and steel sector faces an urgent need to accelerate low-carbon transformation. In 2024, China’s crude steel production accounted for over 50% of the total global crude steel production, with the blast furnace–basic oxygen furnace route remaining the dominant process. As a natural iron-bearing raw material, lump ore features high iron grade and low cost, eliminating the requirements of high-temperature processing steps such as sintering or pelletizing. Therefore, increasing the proportion of lump ore in the blast furnace burden represents an effective approach to achieving energy conservation and emission reduction. However, constrained by technical constraints, the current utilization rate of natural lump ore in Chinese steel enterprises remains generally low. Research indicates that despite their higher iron content, lump ores exhibit deficiencies in metallurgical properties such as thermal shock resistance and softening–melting drip characteristics, limiting their large-scale application. Therefore, it is typically necessary to perform pre-treatment such as preheating before charging into the furnace. In actual blast furnace burden design, it is essential to balance metallurgical performance and economic considerations by appropriately combining lump ore with high-basicity sinter and pellets. This approach leverages high-temperature interactions among the burden materials to optimize the overall softening and melting behavior of the mixed charge, thereby ensuring smooth furnace operation while simultaneously advancing the low-carbon transition of the iron and steel industry. Full article

Rapid detection of wilt-affected pine crowns in mountainous forests is hindered by occlusion, self-shadowing, and heterogeneous backgrounds in conventional nadir products. We evaluated whether oblique UAV RGB imagery improves crown-level detection relative to nadir imagery under matched site, season, sensor, and workflow conditions. The workflow was designed for rapid post-flight screening of geotagged UAV photographs. Paired nadir orthophotos and 45–70° oblique photographs were acquired over pine stands in Wenshan Prefecture, Yunnan, China, and organized into D1 (nadir), D2 (oblique), and D3 (simple mixed-view concatenation). Three YOLO11 detectors were trained for crown shoot damage ratio (SDR)-derived operational classes: early-stage (SDR < 50%), severely damaged (SDR ≥ 50%), and withered (needle-free dead crowns). A paired crown-level RGB subset (n = 20 crowns observed in both views) was analyzed as supporting evidence for view-dependent appearance differences. The oblique-image model (D2) achieved the highest validation performance, with precision of 0.994, recall of 0.991, F1-score of 0.989, mAP@0.5 of 0.995, and mAP@0.5:0.95 of 0.880. The paired subset showed a significant multivariate RGB profile difference between views (Hotelling’s T2 = 58.91, F = 3.10, p = 0.044), driven mainly by reduced Excess Green and greater dispersion of blue-related traits under oblique viewing. These results indicate that oblique UAV photographs retain additional crown-edge, lateral-structure, and chromatic context for detecting wilt-affected pine crowns. Oblique RGB imagery therefore provides a practical, low-cost input for rapid forest health surveillance and targeted field verification in rugged pine landscapes. Full article

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions. Full article

Cemented backfill material is an important technical means for improving the safety, efficiency, and environmental sustainability of underground mining. In tailings-free mining conditions, however, suitable aggregates for cemented backfill are often limited, making it necessary to identify alternative aggregates and optimize their mix proportions. To address this issue, clay-bearing crushed stone was selected as the primary aggregate for a tailings-free bauxite mine, and its effects on the mechanical properties, slurry stability, and rheological properties of cemented backfill material were systematically investigated. Crushed stone ratio, mass concentration, and fly ash ratio were used as experimental factors, and 24 experimental mixes were designed to determine the 3-day compressive strength, bleeding rate, and yield stress. Based on the experimental results, response surface regression models were established, and multi-objective optimization was performed using cost analysis, NSGA-II, and entropy-weighted TOPSIS. The results showed that the system containing clay-bearing crushed stone exhibited better stability than the clay-free crushed stone system. The response surface models for 3-day compressive strength, bleeding rate, and yield stress were all significant, with p-values below 0.0001 and R² values of 0.9658, 0.9306, and 0.8704, respectively. Comprehensive optimization gave the optimal mix proportions as a crushed stone ratio of 6.9721, a mass concentration of 0.82, and a fly ash ratio of 1, corresponding to a predicted 3-day compressive strength of 0.9629 MPa, a bleeding rate of 3.73%, and a cost of 68.225 RMB/t. For engineering application, the recommended mix proportions were adjusted to X₁ = 7, X₂ = 0.82, and X₃ = 1. Parallel tests gave a 3-day compressive strength of 0.99 MPa and a bleeding rate of 3.52%, both within the 95% prediction interval. These results demonstrate that clay-bearing crushed stone can serve as a feasible alternative aggregate for cemented backfill material under tailings-free conditions and that the proposed method combining response surface modeling with multi-objective optimization can effectively balance early strength, slurry stability, and material cost. Full article

In complex obstacle environments, the RRT* algorithm, an asymptotically optimal variant of the Rapidly exploring Random Tree (RRT), and its related variants often suffer from slow generation of the initial feasible solution, unstable sampling efficiency, and high computational costs associated with nearest-neighbor search and collision checking. To address these issues, this paper proposes a coordinated lightweight improved RRT* algorithm. First, a bidirectional growth mechanism combined with goal-biased sampling is introduced to enhance search directionality and improve the efficiency of initial feasible path generation. After an initial path is obtained, informed elliptical sampling is adopted, and the sampling weights are adaptively allocated among the elliptical region, the global space, and goal-biased sampling, thereby balancing local convergence and global exploration. Furthermore, a spatial-hash structure with a dynamic neighborhood radius is employed to accelerate nearest-neighbor search, while lazy collision checking and a two-stage collision-detection mechanism are incorporated into parent selection to reduce redundant expansions and unnecessary exact collision checks. Simulation results in mixed-type and single-type obstacle environments show that the proposed algorithm improves planning efficiency while maintaining competitive path quality. These results demonstrate that the proposed method has good engineering applicability for global path planning in complex environments. Full article

Programs aimed at reducing the CO₂e footprint associated with the residential building stock should be informed by several key elements, including the expected evolution of the occupied housing stock, projected population dynamics driven by socio-economic and cultural factors, available implementation budgets, and the specific costs of intervention measures. However, in regions characterized by high seismic hazard, the occurrence of a major earthquake may substantially alter the projected outcomes of emission-reduction programs, as seismically vulnerable buildings may experience severe structural damage. This paper presents the results obtained by applying an integrated methodology for assessing the CO₂e footprint associated with residential buildings. The methodology accounts for emissions related to building operation (space heating), energy-renovation interventions, and seismic retrofitting works. While the proposed approach is applicable to other seismically exposed regions, the results presented herein refer specifically to the residential building stock in Romania and its local seismic conditions. The methodology integrates information on the existing building stock, the projected evolution of population and the built environment, energy consumption associated with building operation, changes in the energy fuel mix, construction practices across different historical periods with respect to energy efficiency and seismic protection, and the CO₂e footprint associated with energy renovation and seismic retrofitting. In addition, the analysis explicitly considers the potentially negative effects of a major earthquake, particularly the disruption of greenhouse-gas emission-reduction programs. The assessment is conducted at the building stock level and is based on combining building stock evolution with average, representative CO₂e intensity values for heating, energy renovation, and seismic retrofitting. The results demonstrate that when the sole objective is to reduce the CO₂e footprint associated with space heating, renovation of the energy fuel mix represents the most effective measure. At the same time, the analysis shows that the CO₂e footprint generated by construction works for energy renovation and/or seismic retrofitting represents only a small fraction of the emissions associated with building operation. The occurrence of a major earthquake is likely to jeopardize overall environmental objectives by increasing emissions related to building operation, energy renovation, reactive seismic retrofitting, and replacement of severely damaged buildings. Conversely, systematic preventive seismic retrofitting of the building stock does not lead to an increase in cumulative CO₂e emissions over the program implementation period. Full article

Rift Valley fever (RVF) is a viral zoonosis endemic in Madagascar, threatening human and animal health as well as the economy. Trade-related livestock movements are a major factor in the spread of RVF virus. While previous RVF research in Madagascar has focused on farmers or general ecology, this study is the first to specifically target livestock traders, the primary drivers for long-distance viral spread, in the Alaotra Mangoro endemic hotspot. This study aimed to assess the level of knowledge, prevailing attitudes and current practices regarding RVF among people engaged in livestock trade in the Alaotra Mangoro region, as well as the factors associated with these KAPs. A descriptive and analytical cross-sectional survey was conducted among 406 livestock traders in five districts of the Alaotra Mangoro region, using a structured questionnaire. A multi-stage sampling approach was employed, utilising purposive selection of markets followed by snowball sampling to reach informal traders often missed by traditional surveys. Generalised linear mixed models were used to analyse factors associated with KAPs regarding RVF. Awareness of RVF was very low (only 18.5% respondents had heard of it), with significant regional disparities (0% in Anosibe An’Ala versus 51.6% in Moramanga). Veterinarians (15.5%), family (12.8%), radio (9.6%) and neighbours (9.6%) were the main sources of information. Understanding of symptoms and modes of transmission (particularly mosquito bites) was limited. Higher levels of education (OR = 181.6; 95% CI: 29.9–1123.7; p < 0.001) and older age (50–60 years) were associated with better knowledge. Proactive attitudes were scarce (21.4%), although more than half (53.4%) believed that RVF is a real disease. Perception of personal risk and the contribution of livestock trade to the spread of the disease was low. However, confidence in animal vaccination was relatively high (60.3%). Preventive practices were highly inadequate. The majority did not wear protective equipment when handling sick animals (94.6%) and rarely avoided touching aborted foetuses (12.6%). Less than half (48.3%) expressed a willingness to report sick or dead animals, and nearly half admitted to having sold or purchased sick livestock (49.5%). Cooking meat (95.1%) and using mosquito nets (74.1%) were the only well-established practices. More than half of respondents (57.9%) lived more than 5 km from veterinary services, and cost was the most frequently cited barrier to consultation. Participation in awareness campaigns was virtually non-existent (5.4%). Results revealed critical gaps in KAP that may contribute to the persistence of RVF. A “One Health” approach is imperative, integrating human, animal and environmental health. Full article

With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous and discrete variables, resulting in high computational complexity that renders them unsuitable for daily real-time scheduling in large-scale power systems. This paper develops a flexible real-time rolling optimization scheduling model that incorporates OTS and proposes a two-stage fast solution framework based on deep neural networks (DNN). In the offline training phase, a multilayer perceptron-based DNN is trained using load and renewable generation data to rapidly and accurately predict the optimal line switching scheme. In the online application phase, the network topology predicted by the DNN transforms the original mixed-integer linear programming problem into a standard linear programming problem, substantially reducing computational complexity and solution time. Case studies on the modified IEEE 118-bus and IEEE 300-bus systems show that the proposed method achieves high prediction accuracy, reduces solution time by up to 117 times, and maintains nearly identical system operating costs to the physics-driven approach in the majority of cases. The results demonstrate that the proposed approach effectively balances computational efficiency and economic performance, verifying the practical value of optimal transmission switching in enhancing large-scale renewable energy accommodation and overall power system flexibility. Full article