This study introduces a new framework, PACBDHTE, designed to evaluate materials for fusion reactor applications. To provide an integrated assessment that encompasses radiation damage, hydrogen behavior, transmutation effects, and material erosion within a unified evaluation scheme. The methodology includes evaluation Displacement per Atom (DPA) calculations, hydrogen retention analysis, transmutation assessments, and erosion rate determinations. The results identified SiC and WC-Be are strong candidates due to their exceptional hydrogen retention capabilities. Tungsten-based materials are competitive, but careful consideration is needed for 316L stainless steel due to lower hydrogen retention. additionally, Cu(I)-functionalized metal–organic frameworks (MOFs), such as Cu(I)-MFU-4l, show promising selectivity for hydrogen isotope separation which can support more efficient fusion fuel-cycle management. Overall, the findings highlight erosion rates are critical for material longevity, emphasizing the need for continuous monitoring. Overall, the study contributes to safe and efficient fusion energy technology.

Large-scale Vision-Language Models (VLMs) like Contrastive Language-Image Pre-training (CLIP) have demonstrated their impressive zero-shot capabilities. However, adapting them to downstream tasks remains challenging, especially under domain shifts where visual features become unreliable. Existing training-free methods, such as Tip-Adapter, rely heavily on visual similarity, which often fails in out-of-distribution (OOD) scenarios. To address this, Decoupled Correction Adapter (DeCo-Adapter), a robust adaptation framework that integrates a Decoupled Knowledge Stream into the visual baseline, is proposed. Specifically, a novel Negative Semantic Suppression mechanism is introduced, leveraging Large Language Models (LLMs) to generate and penalize distractor descriptions. This mechanism effectively corrects visual ambiguities without requiring any training. Extensive experiments on ImageNet-Sketch, ImageNet-V2, and ImageNet-A demonstrate that DeCo-Adapter consistently outperforms state-of-the-art methods. Notably, it achieves a top-1 accuracy of 54.11% on ImageNet-Sketch, surpassing the strong Tip-Adapter baseline by leveraging negative knowledge for error correction.

Mini hydropower plants (MHPs) play a vital role in providing sustainable electricity to off-grid rural communities in Indonesia. This study optimizes the hydraulic performance of the penstock system for the Way Melesom MHP in Pesisir Barat, Lampung. Using a conservative design discharge of 0.822 m³/s, derived from the F.J. Mock rainfall–runoff model and Flow Duration Curve (Q₇₀) analysis, hydraulic modeling was conducted using the Darcy–Weisbach and Hazen–Williams equations for four pipe diameters (DN400–DN700). The results show that increasing the pipe diameter reduces headloss and increases net head and power output, with diminishing efficiency gains beyond DN600. The DN600 configuration achieves an optimal balance—yielding a velocity of 2.91 m/s, headloss of 3.45 m, and a net head of 61.81 m, corresponding to an estimated output of 0.45 MW (2.76 GWh/year). This capacity can supply electricity to approximately 2,300 rural households, or up to 3,000 customers (450 VA each), supporting 10–12 small villages under an off-grid distribution network. The analysis confirms that DN600 provides the best technical–economic trade-off, recovering 95% of the gross head (65.26 m) with 90% hydraulic efficiency. The study highlights the importance of integrating hydrological, hydraulic, and energy modeling for optimizing closed-conduit systems in small-scale hydropower, ensuring both engineering efficiency and sustainable rural electrification.

Efficient coordination of heterogeneous mobile resources is essential for delivering large-scale urban services, such as sanitation, infrastructure inspection, or last-mile delivery. This study addresses the problem of scheduling aerial and ground service vehicles—unmanned aerial vehicles (UAVs) and mobile ground crews—to cover spatially distributed demand points under operational constraints. We formulate the task as a multi‑objective optimization problem that simultaneously maximizes service coverage, minimizes total completion time, and optimizes resource utilization while respecting safety, capacity, and time‑window restrictions. A hierarchical solution framework is proposed: global task allocation first assigns demand zones to vehicle types according to their capabilities, and local path planning then generates efficient routes for each agent. A dynamic re‑optimization mechanism adjusts schedules in real time when disturbances occur, such as resource depletion or environmental changes. The method is evaluated on scenarios of increasing scale (51, 113, and 212 demand points) that emulate urban public spaces. Results from ten repeated experiments show that the cooperative strategy achieves coverage rates (CRs) above 97% across all scales, reduces total operation time (TOT) by up to 33% compared with single‑mode operations, and improves resource efficiency by 21.10% and 47.40% Statistical analysis confirms the robustness of the improvements. The framework offers a scalable, resource‑aware solution for coordinating heterogeneous service fleets, with direct applicability to intelligent transportation systems, particularly in demand‑responsive urban services and multimodal fleet management.

The purpose of this work was to study purolate (porous carbons from the Kuzbass deposits, Russia). Thermograms in the temperature range 150−700 ℃ showed an up to 8.7% mass loss in the purolate samples. It was proven that purolate has a large range of particle size (from 0.1 to 3 mm) and pH (8.0−9.0) and a low total pore volume in water (0.5 cm$^3$/g). It was found that in addition to C and O$_2$, Zn (5,346.8 mg/kg), Ba (256 mg/kg), Sr (304 mg/kg), Cu (541 mg/kg), and MnO (119 mg/kg) are present in significant amounts in purolate; it does not contain Al$_2$O$_3$, SiO$_2$, Rb, and Zr. It was established that the service life of the sorbent layer is 380 min at an adsorption temperature of 28−30 ℃ (analysis of the adsorption breakthrough curve). The final degree of purification from the model mixture ranged from 35.4% for manganese ions to 98.1% for iron ions. Analysis of the kinetic curves of ion extraction found that the highest adsorption (0.07 g/g) for 250 min was observed during the extraction of manganese ions, the lowest (0.045 g/g) for 300 min, during the extraction of nitrite ions. The development of a new technology using anthracite-based adsorbents for treating water from coal mining operations would help address environmental concerns in resource-dependent areas and contribute to the rehabilitation and revitalization of aquatic ecosystems.

Industrialization and population growth pose significant environmental issues, particularly in water quality, making many sources unsuitable for domestic use. Natural organic compounds and metal nanoparticles (NPs) are used as wastewater adsorbents. The current research investigated the adsorption kinetics, isotherms, and reusability of the manganese oxide NPs synthesized from star anise (SA) (Illicium verum) extract (MnO@SE) to aid in the creation of environmentally friendly water purification solutions. MnO@SE was prepared with SA extract and manganese acetate (II) tetrahydrate solution. The green-synthesized biosorbent was characterized employing methods including Fourier transform infrared spectroscopy, X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). These evaluations offered good information on surface shape and surface-available functional groups. The influences of pH, adsorbent dosage, ion concentration, and contact time on metal ion adsorption were all examined. The results revealed that model solutions with a pH of 2.0, a biosorbent dosage of 0.8 g/L, an initial concentration of 25 mg/L, and a contact time of 50 minutes produced the best removal efficiency (96.34% for Cr(VI) and 87.01% for Pb(II)). The adsorption processes of both metal ions occurred in a multilayer fashion on the heterogeneous surface of the biosorbent through diffusion kinetics, according to the isotherm and kinetic findings. The adsorption process is endothermic and spontaneous, according to thermodynamic analysis. The study revealed that the green-synthesized MnO@SE effectively removed 96.34% Cr(VI) and 87.01% Pb(II) under optimal conditions, promoting eco-friendly water purification through multilayer, endothermic, spontaneous, and diffusion-driven adsorption.

Energy-efficient path planning for multi-Unmanned Aerial Vehicle (UAV) data-collection missions requires balancing trajectory efficiency, energy consumption, and workload distribution among UAVs. This study presents a controlled computational evaluation of three routing paradigms: random assignment, Greedy nearest-neighbor routing, and Greedy + K-means clustering. The evaluation is conducted using a mission-level energy model that incorporates propulsion energy and mission-phase components, including take-off, hovering, sensing, communication, and landing. Simulation experiments were performed using fleets of 1–10 UAVs serving 100 Points-of-Interest (PoIs) under two spatial deployment scenarios: a structured grid layout and a spatially heterogeneous random layout. Each configuration was executed over 20 independent episodes to ensure statistical robustness. The results demonstrate that routing structure significantly influences geometric mission efficiency. In the propulsion-dominated regime (U $\geq$ 5 under random PoI layouts), Greedy + K-means clustering reduces mission travel distance by approximately 11.6–24.5% compared with Greedy routing, corresponding to an energy reduction of approximately 4.6–10.5%. In contrast, under the phase-dominated regime, where fixed mission-phase energy dominates the total energy budget, performance differences between routing strategies remain below 5%. Statistical analysis further confirms large practical differences in geometric performance across algorithms ($\eta^2$ $>$ 0.86). These findings indicate that routing strategy selection should depend on mission scale and spatial characteristics rather than assuming universal optimality. Greedy routing performs effectively in small or spatially structured deployments, whereas Greedy + K-means clustering provides greater robustness and scalability in larger or spatially heterogeneous missions.