Search Results (122,520)

Efficient UAV logistics in complex urban airspaces requires a synergistic approach to task allocation and path planning. However, traditional methods often decouple these two phases, leading to physically infeasible or sub-optimal delivery schedules. This paper proposes a Dual-Layer Optimization Framework (D-LOF) to address the Multi-UAV delivery problem in 3D urban environments. The upper layer utilizes an improved Genetic Algorithm (GA) with a specialized constraint repair operator to optimize task sequences for a heterogeneous UAV fleet. The lower layer employs an altitude-aware A* algorithm that dynamically balances vertical energy costs and horizontal cruise efficiency across multiple altitude layers. Unlike conventional models, our framework iteratively feeds precise 3D flight costs from the lower layer back to the upper layer to guide evolutionary search. Simulation results demonstrate that the D-LOF consistently achieves global convergence within 20 generations. Compared to single-altitude planning and rule-based strategies, the proposed method can reduce total operational costs and maintains zero time-window violations in high-density obstacle scenarios. This study provides a robust decision-making tool for “last-mile” urban logistics by navigating the trade-offs between 3D spatial constraints and delivery punctuality. Full article

Caprine herpesvirus 1 (CpHV-1) is responsible for significant economic losses in goat farming. The CpHV-1 genital infection in goats has been used as a homologous animal model for the study of human herpes simplex virus type 2 (HSV-2). This study aimed to investigate the in vitro virucidal and antiviral effect of lemon juice (LJ) and its main component, citric acid (CA), against CpHV-1 on Madin-Darby Bovine Kidney (MDBK) cells. Cytotoxicity was assessed using an XTT assay, while viral titers were determined by the Reed–Muench method and viral DNA was quantified via qPCR. Pure LJ (pH 2.3) and its corresponding CA solution demonstrated potent and rapid virucidal activity, reducing the viral titer by over 5.0 log10 TCID₅₀/50 µL within 1 min. When applied after viral entry, a non-cytotoxic dilution of LJ (pH 4.32) significantly inhibited viral replication, causing a 2.5 log10 TCID₅₀/50 µL reduction in viral titer and a corresponding decrease in viral DNA. The antiviral effects were minimal at a near-neutral pH of 6.67, probably interacting with envelope structures. These results suggest that LJ could be a potential low-cost topical agent or disinfectant for controlling CpHV-1 in goat populations and offer a basis for translational research on human herpesviruses. Full article

The performance improvement of mechanical components often relies on heat treatment processes, but these processes inevitably result in oxidation burn-off. The repeated formation and spallation of Fe₂O₃ rich oxide scales lead to substantial iron depletion and surface deterioration. Consequently, environmentally sustainable and economically viable protective coatings are required to suppress oxidation induced burn off. In this work, a TiO₂-MgAl₂O₄ composite coating was synthesized from magnesium slag and applied to Q235 carbon steel to enhance its performance during prolonged high temperature heat treatment. Oxidation tests conducted at 900 °C for 60 min demonstrated that the coating markedly improved the oxidation resistance of carbon steel, with an enhancement of approximately 87% relative to the uncoated specimens. To elucidate the protective mechanism, SEM-EDS, XRD, TG-DSC, and XPS analyses were employed. Based on Wagner Theory, the formation of interfacial phases such as Mg_7.92Al_15.31Fe_0.66O₃₂, which effectively impeded oxygen ion diffusion and thereby enhanced the oxidation resistance during high-temperature exposure. Furthermore, the synergistic effect of aluminum-, magnesium-, and titanium-containing compounds in the coating contributed to suppressing the diffusion of oxygen and iron ions, thus further improving the protective performance. This study provides a systematic theoretical foundation and practical guidance for addressing material loss during high-temperature processing of mechanical components, as well as for promoting the resource utilization of magnesium slag. Full article

The short half-life of positron emission tomography (PET) radioisotopes makes transport time a critical factor in medical logistics. While drones have demonstrated advantages in short-range medical deliveries, the feasibility and benefits of long-distance drone transport remain largely unexplored. In a comparative simulation-based modelling framework, this study explores whether long-range drone transport (117–376 km) can improve delivery performance of fluorodeoxyglucose-18 ([¹⁸F]FDG) PET isotopes compared with two existing ground-only routes (146 km and 348 km) and two combined car–airplane routes (532 km and 546 km). Simulated transport times, radioactive decay losses, and economic implications were estimated using drone speeds of 150, 200, and 250 km/h. Hourly weather data from 2023–2024 were incorporated to model flight feasibility and weather-related no-fly conditions. Time savings were translated into preserved radioactive activity and analyzed together with break-even transport costs. A drone speed of 150 km/h provided limited benefit, whereas speeds of 200–250 km/h preserved activity corresponding to a reduction from the current total use of 118 GBq to 72 and 65 GBq, respectively. Weather constraints reduced feasible winter flights by up to 30%. Estimated break-even drone costs ranged from EUR 3–18/km and increased to EUR 14–20/km when accounting for preserved isotopes, corresponding to annual economic gains of EUR 1.0–1.7 million. These results suggest that long-range drone transport could reduce isotope losses and improve diagnostic capacity, although feasibility depends on drone costs, weather resilience, and integration into clinical logistics systems. Full article

This study addresses the dual challenges of low copper recovery and persistent arsenic pollution in the bioleaching of low-grade, high-arsenic copper ores containing enargite (Cu₃AsS₄). Through integrated electrochemical, chemical, and biological investigations, a selective and environmentally sustainable two-stage hybrid leaching process was developed. Electrochemical analysis identified a critical oxidation threshold of ~750 mV governing enargite dissolution. Chemical leaching and X-ray Photoelectron Spectroscopy (XPS) analysis revealed a temperature-dependent sulfur transformation pathway, enabling a staged thermal strategy: flotation below 40 °C to maximize hydrophobic elemental sulfur (S⁰) formation, and bioleaching at 40–55 °C to promote complete sulfur oxidation to sulfate. Optimization produced a two-stage process comprising 10-day chemical pre-leaching with FeSO₄ (10.0 g/L Fe²⁺) followed by bioleaching, achieving 78.3% copper extraction while suppressing arsenic dissolution to approximately 10%. The use of FeSO₄ instead of Fe₂(SO₄)₃ reduces reagent costs by ~70%, saving an estimated CNY 47,250 daily at 1000 t/d scale. Leaching toxicity tests confirm residue As < 0.10 mg/L, meeting non-hazardous waste standards (GB5085.3-2007). This work provides the first integrated demonstration of electrochemical threshold control combined with temperature-dependent sulfur speciation for selective copper extraction from arsenic-bearing enargite ores, offering a scalable, reagent-economical, and environmentally sustainable metallurgical route. Full article

To facilitate the next generation of renewable energy devices, it is important to engineer oxygen reduction reaction (ORR) catalysts that balance efficiency and production costs. This work examines oxygen adsorption on the WC (0001) surface as a function of electrode potential, utilizing DFT simulations with an implicit solvent environment. The results demonstrate that electrode potential significantly influences oxygen adsorption energy and electronic structure. Among the adsorption sites examined, the top site exhibits the highest stability across the entire potential range. The observed reduction in adsorption energy at lower potentials is attributed to the d-band center moving further from the Fermi energy, which weakens C–O orbital interactions, as revealed by DOS and COHP analyses. Our results demonstrate the crucial role of electrochemical conditions in modulating catalytic behavior and provide valuable insights for optimizing tungsten carbide (WC)-based electrocatalysts for ORR applications. Full article

Buildings account for a substantial share of global energy use, yet the adoption of advanced optimal control strategies remains limited due to high computational costs and the difficulty of safe deployment. This paper presents a fully Python-based, data-driven deep reinforcement learning (DRL) supervisory control framework that leverages gray box surrogate modeling and Imitation Learning to overcome these barriers. The novelty of this work lies in the integration of an ontology-based Twin4Build surrogate model with Imitation Learning and Deep Reinforcement Learning, enabling efficient training of building control policies in a low-cost environment before transfer to a high-fidelity BOPTEST emulator. Results demonstrate that the trade-off of using a lower-accuracy surrogate accelerates training by a factor of 11 compared to high-fidelity models. Furthermore, the RL agent successfully learned load-shifting and peak-shaving strategies, eliminating start-up power spikes and achieving energy savings of up to 28.9%. Beyond substantial energy reductions, this pipeline yields a calibrated digital twin suitable for ongoing building services like anomaly detection, presenting a scalable path for real-world smart building optimization. Full article

Sustainable tourism is essential for preserving natural habitats and represents a vital component of sustainable development. This study addresses a business decision-making problem related to natural resource conservation and habitat protection through waste management and IT applications in the Serbian hotel sector. Tourism in Serbia and the Western Balkans represents a sensitive issue concerning the balance between economic development and environmental protection. Therefore, the multi-criteria optimization methods Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are applied to address this problem. To achieve this goal, a hierarchical model was developed that considers nine criteria and four alternatives. The alternatives considered are: service user satisfaction, service cost, waste minimization, and service quality. The developed model was analyzed using a hybrid AHP–TOPSIS approach to identify the optimal alternative. The results indicate that environmental waste prevention ranks highest among all considered alternatives and plays a significant role in the development of sustainable tourism in Serbia. Full article

Forests are important food granaries. The accessibility of a healthy diet is the key factor in food and health equity. However, there is a lack of research focusing on its influence on locals at different levels of development. China’s population comprises various groups of farmers, allowing for the comparison of influence pathways across different economic levels of farmers. This research explores the topic with an empirical study conducted in Jiangxi Province, China, using data from 1939 valid responses collected across 216 villages. The analysis was performed using a mixed-effects ordered logistic model and a mediation effect model. The results of the baseline and mediation effect analyses reveal that there are four influence pathways. First, farmers’ forest resource endowments play a significant role in improving farmers’ perception of healthy diet accessibility (direct access type). Second, farmers’ forest resource endowments increase the accessibility of healthy diets by reducing the perceived costs of healthy diets (cost-relieving type). Third, farmers’ forest resource endowments increase the accessibility of a healthy diet by enhancing the perceived quality of the natural environment (quality scarcity type). Fourth, farmers’ forest resource endowments increase the perceived environmental quality, decrease the perceived costs of healthy diets, and affect the perception of healthy diets’ accessibility (cost-reducing type). The results of heterogeneity analysis based on the independent variables (health-related information, age, education level, disposable income, household size, communication and transportation convenience) reveal that for disadvantaged groups, the effect type tends to be the “direct access type” and “cost-relieving type”, and for advantaged groups, the effect type tends to be the “quality scarcity type”. Through empirical analysis, this study explains how forest resource endowments of different farmer groups influence their access to healthy diets, which lays a foundation for better understanding the association and formulating relevant policies. Decision makers should recognize the distinct influence of forest resource endowments on different farmer groups and develop policies related to forest resource management and healthy diets for farmers. Full article

Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a synergistic platform able to generate and study realistic irrigation scenarios. These scenarios, potentially emulating anomalies such as clogged emitters or pipe leaks with a satisfactory time granularity of a few minutes, provide valuable data that pave the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. The proposed system utilizes widely available, well-documented, low-cost components to form a functioning whole which is optimized for outdoor, low-power, low-maintenance and long-term operation and is accessible remotely via typical end-user devices. Two drip irrigation points were set up, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules sent data in real time, via LoRa, to a central node implemented using a Raspberry Pi for further processing and to make them widely available via common network infrastructures, also provisioning for remote scenario invocation. The system does not claim to achieve specific irrigation water savings, but it contributes to maintaining/increasing the benefits of modern irrigation practices (such as drip irrigation). This goal is served by emulating a wide variety of irrigation events and by gathering and studying the corresponding data. These multimodal data are collected at a frequency of a few minutes, reflecting key irrigation-specific parameters with an accuracy better than or equal to 3%. The exact steps for specific hardware and software component interoperation are clearly explained, allowing other teams of researchers and/or university educators worldwide to be inspired and benefit from platform replication. Full article

Under the global trend of population aging, providing a safe and reliable living environment for the elderly who live at home has become a major social issue. This study reports a monitoring technology for elderly-friendly residential buildings based on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS), which is used to monitor and identify the physical behaviors of residents and temperature changes at different locations in the space. The results show that the distributed acoustic sensing (DAS) system can initially identify typical behavioral states such as walking, squatting, and falling. The fiber DTS technology can not only monitor the temperature distribution at different locations indoors, but also be used for the monitoring and early warning of local fires in different areas of the room. The sensing probes of the monitoring system proposed in this paper are linear optical cables, which have the advantages of easy installation, strong anti-interference ability, intrinsic explosion-proof, less likely to leak residents’ privacy, all-weather operation, precise event location, and low cost for large-scale distributed measurement systems. By integrating the sensing optical cables, fiber signal processing systems, and application software introduced in this paper, an intelligent management and early warning platform for elderly-friendly residential buildings can be established, providing a new solution for remote supervision of the living safety of the elderly. Full article

Accurate remaining useful life (RUL) prediction is essential for the safe and cost-effective operation of safety-critical systems such as electronic components and engines. While data-driven machine learning approaches have demonstrated strong performance for RUL estimation, their effectiveness is limited by the lack of full run-to-failure data and by strict privacy and intellectual property constraints in industrial settings. Federated learning (FL) enables collaborative model training across multiple data owners without direct data sharing, but it does not, by itself, provide formal privacy guarantees and remains vulnerable to information leakage. This paper presents a privacy-preserving DP-enhanced FL setup for RUL prediction that combines federated learning with differential privacy (DP). We describe an end-to-end implementation based on the Opacus DP library, highlight practical challenges arising from the integration of DP into recurrent neural network architectures, and propose solutions to address them. Using two representative RUL datasets (CMAPSS and SiC MOSFET), we analyze the effect of DP noise on prediction performance and on the functional dependence between the predicted RUL and the already lived life feature. The results demonstrate that differential privacy can be integrated into federated RUL prediction with limited degradation in predictive performance, providing practical insights for deploying privacy-aware collaborative models in industrial environments. Full article

Snow accumulation and ice formation can significantly reduce pavement friction, posing a serious threat to traffic safety during winter. Traditional snow-removal methods, including mechanical removal, chemical de-icing agents, and heated pavement systems, suffer from several limitations such as low efficiency, environmental impacts, and high operational costs. Electrically conductive asphalt concrete (ECAC) has therefore emerged as a promising active snow-melting technology. When an electric current passes through the conductive network formed within the asphalt mixture, heat is generated through the Joule heating effect. After incorporating conductive fillers, the electrical resistivity of ECAC mixtures can be reduced from approximately 10⁶–10⁸ Ω·cm for conventional asphalt mixtures to about 10⁻¹–10² Ω·cm. Under an applied voltage typically ranging from 30 to 60 V, ECAC pavements can increase the surface temperature by 10–30 °C within 10–30 min, thereby enabling rapid snow melting and ice removal. Meanwhile, an optimized conductive network can maintain sufficient mechanical performance, with dynamic stability generally exceeding 3000 cycles/mm. When the conductive filler content is reasonably controlled, only a limited reduction in fatigue resistance is observed. This paper presents a comprehensive review of electrically conductive asphalt concrete technologies for snow-melting pavements. The background, underlying mechanisms, material development, system configuration, and field applications of ECAC are systematically summarized. Finally, the current challenges are discussed, including the stability of conductive networks, the trade-off between electrical conductivity and pavement performance, and electrical safety. Future research directions focusing on material optimization, intelligent power control, and long-term field performance evaluation are proposed to support the practical application of ECAC pavements in sustainable winter road maintenance. Full article

Escalating requirements for smart energy management are driving advances in functional electrochromic devices (ECDs), which are pivotal for the regulation of light, heat, and reduction in energy consumption in buildings, transportation, and smart devices. However, the commercialization of ECDs is hindered by com plex designs, high fabrication costs, and slow switching speeds. Additive manufacturing (AM, 3D-printing) emerges as a promising approach to overcome these limitations, as it enables the creation of complex structures, enhances design flexibility, and can reduce production costs. For such printed devices, materials combining poly(ionic liquids) (PILs) with ionogels—an emerging and promising class of materials known for their high ionic conductivity, stability, and tunable properties—are particularly suitable for integration with 3D printing. Comparing previous reviews that address PILs, ionogels, or AM modalities in isolation, this work uniquely combines the structure–property–processing relationships specific to the synergistic integration of these fields. Current work highlights recent progress in PIL/ionogel-based ECDs and distills specific design guidelines for optimizing ink rheology, balancing ionic conductivity with mechanical integrity, and selecting appropriate printing modalities. These insights provide a roadmap for overcoming current fabrication challenges and scaling up next-generation smart devices. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article