Search Results (9,168)

During liquid drainage from intermediate vessels in various industrial processes such as continuous steel casting, aircraft fuel supply, and chemical separation, free-surface vortices commonly occur. The formation and evolution of these vortices not only entrain surface slag and gas, but also lead to deterioration of downstream product quality and abnormal equipment operation. The vortex evolution process exhibits notable three-dimensional unsteadiness, multi-scale turbulence, and dynamic gas–liquid interfacial changes, accompanied by strong coupling effects between temperature gradients and flow field structures. Traditional macroscopic numerical models show clear limitations in accurately capturing these complex physical mechanisms. To address these challenges, this study developed a mesoscopic numerical model for gas-liquid two-phase vortex flow based on the lattice Boltzmann method. The model systematically reveals the dynamic behavior during vortex evolution and the multi-field coupling mechanism with the temperature field while providing an in-depth analysis of how initial perturbation velocity regulates vortex intensity and stability. The results indicate that vortex evolution begins near the bottom drain outlet, with the tangential velocity distribution conforming to the theoretical Rankine vortex model. The vortex core velocity during the critical penetration stage is significantly higher than that during the initial depression stage. An increase in the initial perturbation velocity not only enhances vortex intensity and induces low-frequency oscillations of the vortex core but also markedly promotes the global convective heat transfer process. With regard to the temperature field, an increase in fluid temperature reduces the viscosity coefficient, thereby weakening viscous dissipation effects, which accelerates vortex development and prolongs drainage time. Meanwhile, the vortex structure—through the induction of Taylor vortices and a spiral pumping effect—drives shear mixing and radial thermal diffusion between fluid regions at different temperatures, leading to dynamic reconstruction and homogenization of the temperature field. The outcomes of this study not only provide a solid theoretical foundation for understanding the generation, evolution, and heat transfer mechanisms of vortices under industrial thermal conditions, but also offer clear engineering guidance for practical production-enabling optimized operational parameters to suppress vortices and enhance drainage efficiency. Full article

The increasing demand for lithium-ion batteries (LIBs) has intensified the need for efficient lithium (Li) recovery from secondary sources. This study focuses on anti-solvent crystallization for the recovery of high-purity lithium hydroxide monohydrate (LiOH·H₂O) from a Li-rich leachate, derived from the flue dust of a pilot-scale pyrometallurgical process for LIB material recycling. To optimize product yield and purity, a series of experiments were performed, focusing on the influence of parameters such as solvent type, organic-to-aqueous (O/A) volumetric ratio, crystallization time, stirring rate, and anti-solvent addition rate. Acetone was identified as the most effective anti-solvent, producing rectangular cuboid crystals with approximately 90% Li recovery and around 95% purity, under optimized conditions (O/A = 4, 3 h, 150 rpm, and solvent flow rate of 5 mL/min). The flow rate influenced crystal morphology and impurity entrapment, with 5 mL/min favoring nucleation-dominated crystallization regime, producing ~20 μm of well-dispersed crystals with reduced impurity incorporation. SEM-EDS, surface washing, and gradual dissolution of obtained LiOH·H₂O crystals revealed that the impurities sodium (Na), potassium (K), aluminum (Al), calcium (Ca) and chromium (Cr) were crystallized as conglomerates. It was found that Na, K, Al, and Ca primarily crystallized as highly soluble conglomerates, while Cr was crystallized as a lowly soluble conglomerate impurity. In contrast Zn was distributed throughout the crystal bulk, suggesting either the entrapment of soluble zincate species within the growing crystals or the formation of mixed Li-Zn phase. Therefore, to achieve battery-grade purity, further purification measures are necessary. Full article

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from $42.3\%$ to $97.1\%$ on MMM2, from $0.0\%$ to $57.5\%$ on 6h vs. 8z, and from $58.0\%$ to $96.6\%$ on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered. Full article

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

Passive micromixers can be used in a wide range of chemical applications for reagent preparation as well as chemical analysis. To investigate a micromixer with high performance under various Re conditions, based on the research of previous scientists, we hereby parameterize three influential factors on the structural design of the micromixer. In this study, we chose five distinct level values from a set of three influential factors of the micromixer, based on the results of a one-way impact analysis to ascertain their degree of influence. For five different Re cases, the Taguchi orthogonal test was performed using the selected five-level values, the fluid mixing efficiency was examined numerically, and we used the orthogonal table L₂₅5⁶. Finally, a set of optimization parameters was selected. An optimized micromixer structural model with high mixing efficiency under different Re conditions has been achieved. The degree of stirring of the optimized micromixer and the comparison curves before and after the optimization were also analyzed. We have also manufactured and tested the micromixer of this design. The simulation results show that the mixing efficiency of “the After Optimized Y-shaped channel Koch fractal Cesàro construction” micromixer (AOYKC) designs in this paper is increased by 15.99%, 21.19%, 19.34%, 11.41%, and 0.04% at Re = 0.1, 1, 10, 20, and 100. Full article

Fish passes are essential hydraulic structures that maintain longitudinal connectivity in regulated rivers, but their hydraulic performance may be affected by debris accumulation at chamber openings. This study investigates the influence of partial and total inlet blockage by plant debris on flow conditions within a semi-natural fish pass under field conditions. Hydraulic measurements were conducted at multiple locations along the fish pass, and the effects of debris covering were evaluated using statistical and mixed-effects modeling approaches. Field measurements demonstrated that the Froude number decreases systematically with increasing distance from the inlet, indicating progressive longitudinal dissipation of flow energy along the chamber sequence. Partial debris accumulation caused only marginal changes in the Froude number, remaining close to the threshold of statistical significance. In contrast, mean flow velocity decreased markedly with increasing inlet blockage, by approximately 17% at 50% covering and by about 36% under full blockage, indicating that debris primarily acts as a hydraulic damper rather than inducing a change in flow regime. The highest variability in hydraulic conditions was observed in chambers associated with changes in flow direction and local geometry. These results highlight the dominant role of longitudinal layout and chamber geometry in shaping hydraulic conditions in semi-natural fish passes, while moderate debris accumulation affects local velocities without fundamentally compromising hydraulic functionality. From an ecological perspective, transition zones with elevated hydraulic variability may represent critical locations influencing the swimming effort and passage efficiency of migrating fish. Full article

This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km², the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article

The global maritime and logistics sectors are undergoing rapid digital transformation driven by emerging technologies such as automation, the Internet of Things (IoT), and blockchain. Artificial Intelligence (AI), with its ability to analyze complex datasets, predict operational patterns, and optimize resource allocation, offers a transformative potential beyond the capabilities of conventional technologies. However, mixed results are shown in its implementation. This study examines the current state of AI applications to unlock higher levels of efficiency and competitiveness in logistics firms. A mixed-methods approach was employed, combining surveys from logistics companies with in-depth interviews from key stakeholders in ports and logistics firms to triangulate insights and enhance the validity of the findings. Our results reveal that while technologies such as automation and digital tracking are increasingly utilized to improve operational transparency and cargo management, AI applications remain limited and largely experimental. Where implemented, AI contributes to strategic decision-making, predictive maintenance, customer service enhancement, and cargo flow optimization. Nonetheless, financial conditions, data integration challenges, and a shortage of AI-skilled professionals continue to impede its wider adoption. To overcome these challenges, this study recommends targeted investments in AI infrastructure, the establishment of collaborative frameworks between public authorities, financial institutions, and technology-driven Higher Education Institutions (HEIs), and the development of human capital capable of sustaining AI-enabled transformation. By strategically leveraging AI, Oman can position its ports and logistics sector as a regional leader in efficiency, innovation, and sustainable growth. Full article

This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks. Full article

Modular construction (MC) offers significant environmental and efficiency advantages yet maintains low market penetration in China despite substantial government support. This study addresses the critical knowledge gap by systematically analyzing complex barrier interrelationships across project phases and stakeholder groups (university, construction authority, supplier/manufacturer company) to develop a comprehensive MC promotion framework. A four-phase mixed method approach was employed. (1) Grounded theory analysis of MC policy frameworks was performed in Singapore, the United States, and Hong Kong to extract best practice insights. (2) A systematic literature review and multi-round Delphi expert consultations were used to identify 21 core barriers across six project stages (decision-making, procurement, design, production, transportation, and construction acceptance). (3) The DEMATEL analysis reveals causal relationships among barriers based on experts’ perceived influence between factors. (4) Integrated ISM-MICMAC methodology was used to establish hierarchical structures and barrier classifications. Institutional barriers emerged as the primary impediment to MC diffusion, with unclear authority distribution between government administrations and design organizations identified as the most critical factor. The MICMAC analysis categorized the 21 barriers into four distinct groups based on their driving power and dependence characteristics, revealing complex causal relationships among barriers across the six project stages while highlighting the emergent role of higher education institutions in industrial transformation. Successful MC implementation requires market-oriented, context-specific strategies prioritizing institutional framework development, with the findings providing actionable insights for policymakers to address regulatory ambiguities and practical guidance for industry practitioners developing targeted MC promotion strategies in emerging markets. Full article

Early weaning in intensive lamb production improves reproductive efficiency but predisposes lambs to diarrhea, oxidative stress, and intestinal barrier dysfunction, highlighting the need for non-antibiotic strategies to protect gut health. This study evaluated whether a sheep-derived mixed probiotic could alleviate early weaning–induced intestinal injury and clarified its potential molecular mechanisms. Early weaning reduced body weight, average daily gain and feed efficiency, increased diarrhea, decreased plasma and colonic catalase (CAT), glutathione peroxidase (GSH-PX), and superoxide dismutase (SOD) activities, increased malondialdehyde (MDA), elevated interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α), reduced interleukin-10 (IL-10) and transforming growth factor-β (TGF-β), increased plasma and mucosal immunoglobulin A, M, and G (IgA, IgM, IgG), and increased colonic lipopolysaccharide (LPS) with reduced diamine oxidase (DAO). Intestinally, EW induced villus atrophy, deeper crypts, lower villus height-to-crypt depth ratios, goblet cell loss, higher histopathological scores, and decreased colonic mucin 2, zonula occludens-1, claudin-1, and occludin. Probiotic supplementation partially reversed these alterations, restoring antioxidant enzyme activities, improving villus architecture and barrier protein expression, and rebalancing cytokine and immunoglobulin profiles. Transcriptomic and network analyses showed that early weaning activated Cytokine–cytokine receptor, NF-κB, TNF and Th17 pathways, whereas probiotics suppressed a weaning-responsive inflammatory gene module, downregulated key hub genes, and enhanced peroxisome proliferator-activated receptor (PPAR) signaling. These results show that supplementing early-weaned lambs with a mixed probiotic generated from sheep is an efficient nutritional strategy to reduce intestinal oxidative and inflammatory damage associated with weaning and to enhance their health and performance. Full article

Emerging technologies are transforming Facilities Management (FM), enabling more efficient and accurate building inspections and maintenance. Mixed Reality (MR), which integrates virtual content into real-world environments, has shown potential for improving operational performance and technician training. This study presents the development and evaluation of an MR-assisted system designed to support facility operations in academic buildings. The system was tested across three case scenarios, namely plumbing, lighting, and fire sprinkler systems, using Microsoft HoloLens^®. A mixed-methods approach combined a post-use questionnaire and semi-structured interviews with twelve FM professionals, including technicians, inspectors, and managers. Results indicated that 66.67% of participants found the MR interface highly effective in visualizing systems and guiding maintenance steps. 83.33% agreed that checklist integration enhanced accuracy and learning. Technical challenges, including model drift, latency, and occasional software crashes, were also observed. Overall, the study confirms the feasibility of MR for FM training and inspection, offering a foundation for broader implementation and future research. The findings provide valuable insights into how MR-based visualization and interaction tools can enhance efficiency, learning, and communication in facility operations. Full article

This study evaluated low-cost food waste anaerobic digestion (FWAD) designed for African urban informal settlements, where electricity and process control are limited. Eight small-scale reactors were operated under varying mixing, pH control, and temperature conditions to assess the feasibility of stable operation with minimal input. Results showed no significant difference in methane yield between continuously mixed and minimally mixed (48-hourly) systems, nor between reactors with continuous pH dosing and those adjusted every 48 h (ANOVA p > 0.05 for all comparisons). The highest mean methane yield, 0.267 L CH₄ g VS⁻¹, was achieved by the minimally mixed reactor with 48-hourly pH control at 30 °C, while the controlled reactor at 37 °C produced a comparable 0.247 L CH₄ g VS⁻¹. Total methane production was similar at both temperatures, although gas generation was faster during the first 24 h at 37 °C. Compared to gas recovery achieved by extended batch operation following semi-continuous feeding, 58–73% of total methane was produced within the 48-h cycle, suggesting conversion could increase by 30–40% with extended liquid retention. Microbial analyses showed compositional differences but consistent performance, indicating functional redundancy within the microbial consortia. These results confirm the capacity of FWAD for stable, efficient biogas production without continuous energy input. Full article

In recent years, unmanned aerial vehicles (UAVs) have become increasingly prevalent across diverse application scenarios due to their high maneuverability, compact size, and cost-effectiveness. However, these advantages also introduce significant challenges for UAV detection in complex environments. This paper proposes an efficient feature perception network (EFPNet) for UAV detection, developed on the foundation of the RT-DETR framework. Specifically, a dual-branch HiLo-ConvMix attention (HCM-Attn) mechanism and a pyramid sparse feature transformer network (PSFT-Net) are introduced, along with the integration of a DySample dynamic upsampling module. The HCM-Attn module facilitates interaction between high- and low-frequency information, effectively suppressing background noise interference. The PSFT-Net is designed to leverage deep-level features to guide the encoding and fusion of shallow features, thereby enhancing the model’s capability to perceive UAV texture characteristics. Furthermore, the integrated DySample dynamic upsampling module ensures efficient reconstruction and restoration of feature representations. On the TIB and Drone-vs-Bird datasets, the proposed EFPNet achieves mAP50 scores of 94.1% and 98.1%, representing improvements of 3.2% and 1.9% over the baseline models, respectively. Our experimental results demonstrate the effectiveness of the proposed method for small UAV detection. Full article

Actinium-225 (²²⁵Ac) has emerged as a pivotal alpha-emitter in modern radiopharmaceutical therapy, offering potent cytotoxicity with the potential for precise tumour targeting. Accurate, patient-specific image-based dosimetry for ²²⁵Ac is essential to optimize therapeutic efficacy while minimizing radiation-induced toxicity. Establishing a robust dosimetry workflow is particularly challenging due to the complex decay chain, low administered activity, limited count statistics, and the indirect measurement of daughter gamma emissions. Clinical single-photon emission computed tomography/computed tomography protocols with harmonized acquisition parameters, combined with robust volume-of-interest segmentation, artificial intelligence (AI)-driven image processing, and voxel-level analysis, enable reliable time-activity curve generation and absorbed-dose calculation, while reduced mixed-model approaches improve workflow efficiency, reproducibility, and patient-centred implementation. Cadmium zinc telluride-based gamma cameras further enhance quantitative accuracy, enabling rapid whole-body imaging and precise activity measurement, supporting patient-friendly dosimetry. Complementing these advances, the cerium-134/lanthanum-134 positron emission tomography in vivo generator provides a unique theranostic platform to noninvasively monitor ²²⁵Ac progeny redistribution, evaluate alpha-decay recoil, and study tracer internalization, particularly for internalizing vectors. Together, these technological and methodological innovations establish a mechanistically informed framework for individualized ²²⁵Ac dosimetry in targeted alpha therapy, supporting optimized treatment planning and precise response assessment. Continued standardization and validation of imaging, reconstruction, and dosimetry workflows will be critical to translate these approaches into reproducible, patient-specific clinical care. Full article