Search Results (2,836)

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

Aircraft taxiing speed control along predefined airport surface routes is a constrained single-aircraft longitudinal control problem involving heterogeneous route geometry, action smoothness, and terminal velocity feasibility. Existing learning-based taxiing controllers commonly use a unified policy for the whole route, which may be insufficient for handling straight-segment propulsion, curved-segment speed regulation, and action discontinuities near straight–curve transitions. This paper proposes SegCoord-Taxi, a geometry-aware segmented deep reinforcement learning framework for taxiing speed control. The route is decomposed into straight segments, curved segments, and transition boundary zones. A Straight-Segment Policy (SSP) and a Curved-Segment Policy (CSP) generate geometry-dependent base acceleration commands, a Switch Residual Adapter (SRA) provides local residual correction near transition regions, and a Route-Level Feasibility Projection (RFP) maps the coordinated action into an executable acceleration satisfying route-level feasibility constraints. Experiments on departure taxiing routes at Chengdu Tianfu International Airport (ZUTF) included baseline comparison, ablation analysis, projection diagnostics, sensitivity analysis, and a trajectory-level case study. On the evaluated ZUTF case-study routes, SegCoord-Taxi achieves the lowest final velocity on the test set, 0.336 ± 0.017 m/s, compared with 0.732 ± 0.061 m/s for the unified Proximal Policy Optimization (PPO) controller and 0.586 m/s for the curvature-aware constrained optimizer. The complete framework also reduces switch action jump from 1.022 ± 0.017 m/s² to 0.429 ± 0.004 m/s² in the ablation study. These results indicate improved terminal feasibility and transition-region smoothness in the evaluated single-airport case-study setting under an explicit efficiency–smoothness–feasibility trade-off. Future work will extend the framework to multi-aircraft and multi-airport settings under operational uncertainty. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Cuba is driving its sustainable energy transition with renewables as the central axis, although the Cuban government estimates that substantial investments will be necessary to achieve this goal. This work presents a proposal for a Management Model of International Cooperation for the Development of Renewable Energies, aimed at mobilizing the required funds. The model was designed through a structured questionnaire with 7 dimensions, 22 activities, and 5 subprocesses to guide the collaborative management of projects. The methodological approach was quantitative, descriptive, and psychometric, ensuring content validity through expert evaluation and statistical analysis. Reliability was established through internal consistency measures, while construct validity was supported by an exploratory factor analysis, confirming its feasibility and coherence. The validated questionnaire confirms its methodological rigor and practical utility, favoring the subsequent implementation of the model for Cuba’s transition toward green energy. Full article

A solar-assisted thermochemical cycle to store concentrated solar energy in solid elemental sulfur via the reversible interconversion of sulfuric acid and sulfur is being developed within the SULPHURREAL project. This process enables long-term, transportable energy storage through internal recycling of sulfur oxides. A central objective is to integrate SO₂ capture and conversion in separation-friendly steps that support closed-loop operation with minimal additives and limited downstream purification, while remaining compatible with industrial sulfuric acid and sulfur feedstocks. The method presented in this paper can also be feasible for SO₂ removal from fossil fuels and industrial emissions. With this purpose, indirect SO₂ conversion via bisulfite disproportionation was investigated using elemental sulfur as a heterogeneous auto-catalyst. Batch tests were performed in a pressurized, Teflon-lined autoclave with concentrated bisulfite solutions (3 M) at 140–180 °C for 3 h. Sodium bisulfite showed no conversions at 140–160 °C, whereas sulfur auto-catalysis was observed at T ≥ 170 °C. Ammonium bisulfite was also evaluated as a separable SO₂-capture intermediate; due to thermal instability, operation was limited to 170 °C, where sulfur formation remained detectable. For loop closure, NH₃ and H₂SO₄ must be recovered from the produced sulfate. This was addressed by reacting (NH₄)₂SO₄ with metal oxides in a tubular furnace at 500 °C. The evolved NH₃ was trapped in acid and quantified by ion chromatography. Near-quantitative NH₃ recovery (≈92–98%) was achieved with MgO and ZnO, and the corresponding metal sulfates were identified by XRD. These results support integrated process development and motivate kinetic and mass-balance studies toward continuous operation and scale-up. Full article

Opioids are first-line therapy for cancer pain, yet up to 30% of patients fail to achieve adequate control at standard doses. Opioid response is partly genetically mediated, and understanding these factors may improve symptom management. This project aimed to assess the feasibility of using tumor bank DNA for pharmacogenetic analyses and to validate previously identified genetic variants associated with opioid response using existing genetic and clinical data. In this retrospective cohort study, clinical data (morphine equivalent daily dose, demographics) and genetic data (single-nucleotide polymorphisms) were analyzed across 31 candidate loci. Adult deceased patients with melanoma, colorectal, or lung cancer treated with opioids between 2016 and 2021 and with available tumor bank DNA were included. Patients without sufficient DNA or not deceased were excluded. Of 3503 potential samples, 502 met the inclusion criteria. The median morphine equivalent daily dose was 40 mg (range 1–2140 mg). Eleven loci across six genes may be associated with higher (OPRM1, TAOK3, NFKBIA, COMT, and RHBDF2) and lower (COMT and GCH1) opioid dose requirements (p < 0.05, not significant after Bonferroni correction). Ultimately, tumor bank DNA is a feasible resource for pharmacogenetic research. Identified loci may contribute to variability in opioid response and support future personalized pain management strategies. Full article

This study evaluates the spatial and seasonal feasibility of residential PV integration across 52 Spanish municipalities representing the country’s main urban areas. The assessment is based on the normalized photovoltaic sizing indicator (A_PV,N), defined as the PV area required to offset electricity demand per square metre of conditioned floor area. Simulations were performed under current climate conditions and future projections for 2050 and 2100 using the RCP4.5 scenario. Results reveal strong climatic and seasonal contrasts. Under current conditions, annual PV generation offsets approximately 17–18% of residential electricity demand. Southern and Mediterranean municipalities show the highest feasibility, with annual A_PV,N values of approximately 2–2.5, whereas northern and inland regions present severe winter limitations, with A_PV,N values frequently exceeding 15–20. Summer is the most favourable season, with PV systems covering more than 50% of seasonal demand in several southern municipalities. Future climate projections indicate a progressive improvement in PV feasibility. Under RCP4.5, annual A_PV,N decreases by approximately 5–10% by 2100, while the production-to-consumption (P/C) ratio improves by about 15–20% relative to present conditions, mainly due to reduced heating demand. The results demonstrate that future climate conditions may improve the viability of residential PV systems in Spain, particularly in southern and coastal urban areas, while northern regions will remain constrained during winter. The study provides quantitative benchmarks for climate-sensitive PV planning and long-term urban energy strategies. Full article

To explore the life-cycle carbon emission characteristics of zero-carbon buildings and the economic feasibility of carbon reduction strategies, this study takes the Life Cycle Assessment (LCA) method as the core and constructs a full life-cycle carbon emission accounting system for buildings covering building material production, transportation, construction, operation and demolition in accordance with the standards. Taking the Jinan Zero-Carbon Operation Center Project as a case, this paper systematically calculates its carbon emissions at all stages of the life cycle, identifies the key carbon emission stages and core influencing factors, and comparatively analyzes the economic efficiency of two carbon offset strategies, namely increasing photovoltaic power generation and purchasing green electricity, for the two goals of zero carbon in the operation stage and zero carbon in the full life cycle by using the equivalent annual cost (EAC) method. The results show that the total life-cycle carbon emissions of the case project reach 149,974.04 tCO₂e, with the operation stage and building material production stage being the core carbon emission stages, accounting for 75.50% and 24.19% respectively, while the carbon emissions in the transportation, construction and demolition stages account for a negligible proportion. The economic analysis indicates that although the increase in photovoltaic power generation systems involves a high initial investment, its equivalent annual cost is significantly lower than that of the green electricity purchase strategy. Comparative analysis using equivalent annual costs shows that adding a photovoltaic system achieves equivalent annual costs of $206,589.58 and $273,630.84 for operation stage and life-cycle zero-carbon targets, respectively. In contrast, purchasing green power certificates annually to achieve the same goals incurs equivalent annual costs of $316,223.13 and $317,096.45, representing annual savings of 34.67% and 13.71%. The carbon emission accounting method constructed in this study can provide a reference for the life-cycle carbon quantification of zero-carbon buildings, and the conclusions on the economic efficiency of carbon reduction strategies can serve as an economic decision-making basis for the planning, design and carbon reduction scheme selection of zero-carbon buildings. Full article

Concrete bricks have been used in construction for over a century, often used for foundations and retaining walls. Its production contributes significantly to carbon emissions and the depletion of natural resources. Thus, this study aims to develop an outdoor brick using crushed bivalve shell (Vivipara angularis), locally known as “Ige”, as an aggregate to reduce the need for natural aggregates, which are a finite resource, and molasses as an admixture to improve the brick’s workability and strength. A series of experiments was conducted to test the bricks’ fire resistance, water absorption, and compression, aiming to determine the feasibility of making bricks from crushed bivalve shells, shredded plastic bottles, cement, sand, water, and molasses. The project used an experimental and developmental research approach. The study was conducted at the Caraga State University, Cabadbaran Campus. Results showed that (a.) Sample 2, which contains 400 g of cement, 800 g of sand, and 1200 g of bivalve freshwater shell with the ratio of 1:2:3, has good fire resistance characteristics (b.) Sample 5, which contains more plastic bottles rather than freshwater shells, performed well in water absorption and (c.) Sample 2, a mixture of 400 g of cement, 800 g of sand, and 1200 g of bivalve freshwater shell with a ratio of 1:2:3, exhibits good comprehensive strength. The study revealed that using bivalve freshwater shells can improve the durability of concrete bricks and, when combined with plastic bottles, reduce water absorption; however, it can also compromise brick durability. Full article

To address fault-induced boundary variations and conflicting commands among heterogeneous controllers in microgrid clusters with high distributed generation penetration, this paper proposes a multilayer multiagent control strategy based on cross-coordinated multiagent control and conflict coordination. The method uses a hierarchical distributed hybrid architecture. Local grid-forming (GFM) energy storage and photovoltaic (PV) converters provide autonomous voltage source support, microgrid coordination controllers generate distributed candidate commands, and the system-level coordination controller performs event-triggered arbitration. Unlike consensus-based cooperative control with fixed exchanged variables, the proposed method enables overlapping supervisory authority, weighted command fusion, explicit conflict classification, and feasible command projection under resource, state-of-charge (SOC), ramping, and load priority constraints. Direction, capacity, and objective conflicts are resolved through system-level arbitration, which converts multiple candidate commands into a single executable command. Comparative simulations show that the proposed method reduces frequency and voltage deviations, shortens power recovery time, improves SOC balancing among energy storage units, and enhances constrained hydropower coordination compared with conventional droop control and one-to-one hierarchical control. These results verify its effectiveness in improving dynamic stability and coordinated support capability in microgrid clusters. Full article

Mass concrete design presents a significant challenge due to the inherent conflicts among key performance metrics: high compressive strength, low heat of hydration, and low water absorption (a key durability indicator). Traditional trial-and-error methods are inefficient and fail to systematically navigate these complex trade-offs. To address this, this study proposes a data-driven multi-objective optimization framework for mass concrete mix design. A comprehensive experimental dataset of 64 mixtures was established by varying the water-to-binder ratio (0.40–0.55), fly ash content (0–120 kg/m³), and slag content (0–120 kg/m³), with cement content fixed at 400 kg/m³. Kriging surrogate models were developed to accurately map the nonlinear relationships between these design variables and the three performance responses. These models were then integrated with the NSGA-II algorithm to generate a Pareto-optimal front of solutions. The framework’s predictive accuracy and generalization capability were rigorously validated through out-of-sample experiments, demonstrating prediction errors consistently below 10%. The results provide a quantified map of feasible engineering compromises, enabling engineers to select tailored mixtures for specific project priorities, such as low-heat mixes for dams or high-strength mixes for foundations. Full article

The volume of academic and scientific publications grows rapidly, increasing the need for efficient mechanisms for accessing, obtaining and managing large collections of Open Access (OA) journal articles. For the purposes of an ongoing project requiring the analysis of thousands of OA Journal articles, a fast and reliable way to automatically download and rename PDF files was essential. To address this need, ChatGPT was employed to generate Python scripts from scratch, with the task deliberately assigned to a user with no Python programming experience, relying partially on his familiarity with HTML and CSS structures. Excluding one manually processed journal, which was used as a descriptive baseline, the study achieved a workflow-level success rate of 90.32% across the 31 AI-assisted journal workflows that were evaluated. Of these, 25 workflows were completed through fully functional downloader/renamer scripts, while three additional journals were processed through successful renaming workflows after automated downloading proved unsuccessful. Four MDPI journals were handled through a shared semi-automated workflow. The paper also presentsdescriptive observations from the documented workflow, indicating a gradual reduction in development time, prompts, and debugging iterations across later stages of the project, as the interaction process became more refined. Furthermore, within this feasibility case, the observed average operational time corresponded to approximately 15.8 s per file for the fully manual procedure, 13.8 s for the complete automated workflow corpus, and 10.8 s after excluding one highly time-consuming outlier case. Statistical analyses of the generated scripts, including imported modules, libraries, functions, constants, control structures, and total lines of code, are also presented. Overall, the study demonstrates the feasibility of AI-assisted scripting in one documented case involving a user without Python programming experience to accomplish tasks that were previously associated with programming expertise. Full article

Decision-making during shield tunneling remains challenging due to complex shield–ground interactions, and experience-driven adjustments to shield operating parameters often fail to balance tunneling efficiency and energy consumption. For this purpose, a rule-constrained multi-objective optimization framework for shield operating parameters is proposed and validated using field data from a slurry shield tunneling project in Changsha, China. Here, a comprehensive field dataset is established by integrating ground conditions, operating parameters, and energy consumption indicators. Association rule mining is employed to identify typical combination patterns of operating parameters under different ground conditions, which are included as feasibility constraints in the parameter optimization. The relationship between operating parameters, tunneling efficiency, and energy consumption is captured by a random forest model, which serves as a surrogate model for rapid evaluation of operating parameters. Therefore, the NSGA-II algorithm is employed to obtain Pareto-optimal parameter combinations under feasibility constraints. The results indicate that the proposed framework can provide adaptive optimization strategies under different ground conditions. The resulting Pareto solutions can be classified into three tunneling modes, including robust, balanced, and high-speed, facilitating practical decision-making for shield operators. Full article

This paper is concerned with cooperative multi-UAV navigation in a planar obstacle environment. A hierarchical embodied swarm framework with leader, subleader, and follower roles is proposed. At the high level, a passable-corridor-driven decision layer is developed to perform split–merge reconfiguration and navigate/encircle mode switching. At the low level, a multi-term force synthesis controller is constructed for formation maintenance, inter-agent collision avoidance, obstacle avoidance, and sub-swarm cohesion. To accommodate both rule-based and local large language model (LLM) decisions, a feasibility projection operator is introduced so that only kinematically admissible structural actions are executed. In addition, a LiDAR-based obstacle-repulsion term and an occlusion-attenuated attraction mechanism are incorporated to improve navigation safety in cluttered environments. A Lyapunov analysis of the smooth controller core further certifies that, for a known (possibly time-varying) cruise velocity compensated by feedforward, the formation tracking error is uniformly bounded by the initial energy. Finally, multi-seed numerical simulations verify the proposed framework in standard, ablated, and complex scenarios. In the hardest alternating-gate scenario, the LLM-assisted variant raises mission success from $0.000$ to $0.100$, increases the goal-reaching ratio from $0.025$ to $0.125$, and reduces the mean terminal error from $44.738\mathrm{m}$ to $39.851\mathrm{m}$, showing the value of semantic high-level reconfiguration under tight passage constraints. Full article