Search Results (6,095)

Multi-rotor unmanned aircraft systems (UAS) are now pervasive, yet quantitative evidence on how and why they fail remains fragmented across heterogeneous national reporting systems. This study analyses 319 multi-rotor UAS occurrences (2016–2022) coded from three official sources: the U.S. SAFECOM system (122), the Australian Transport Safety Bureau database (159) and the U.K. Air Accidents Investigation Branch reports (38). Each occurrence was assigned a primary causal factor from a twelve-factor taxonomy and a flight phase (take-off, en route, landing). Analyses comprised distributional estimation with Wilson confidence intervals, chi-squared association tests with permutation p-values for sparse tables, Cochran–Armitage trend tests, and correspondence analysis. Human factors (23.2%, 95% CI 18.9–28.1) and data-link problems (21.0%, CI 16.9–25.8) dominated, and 74.6% of occurrences arose en route—a phase profile opposite to that of manned aviation. Cause and phase were significantly associated (permutation p < 0.001, Cramér’s V = 0.305): all take-off occurrences were technological, none human-related, and battery failures clustered in landing (42%). Causal profiles differed markedly between reporting systems (p < 0.0001, V = 0.338), cautioning against naive pooling, and data-link problems nearly tripled from 11.9% (2016–17) to 32.9% (2021–22). Findings inform operator training, link redundancy, battery management and reporting standardisation. Full article

In molten salt reactors (MSRs), helium bubbling systems can be employed for the continuous removal of gaseous fission products. However, helium injection generates local gas–liquid two-phase flow in the fuel salt and may induce corresponding local perturbations in the calculated neutronic field. To investigate these bubble-scale interactions, this study developed a coupled multi-physics framework integrating the volume of fluid (VOF) method with a multigroup neutron diffusion model. The framework was applied to a localized 20 mm × 40 mm fuel-salt domain containing a single 1 mm helium injection nozzle. Planar 2D and axisymmetric calculations were performed to examine the influence of geometrical representation on bubble growth, detachment, transport, and the corresponding local power response. The axisymmetric formulation was further used to evaluate the sensitivity of the calculated response to the helium mass flow rate. Within this restricted numerical test problem, the coupled framework resolved the evolution of helium bubbles and the associated local changes in the power field under the prescribed boundary conditions. For the centered circular nozzle and symmetry-preserving near-inlet conditions considered, the axisymmetric formulation provided a more geometrically consistent representation of rotational volume weighting and interfacial curvature than the planar 2D formulation. Variations in helium mass flow rate also modified the calculated local bubble behavior and power-response metrics. These results constitute a numerical demonstration of local bubble-resolved multi-physics coupling and should not be interpreted as reactor-scale power predictions, core-wide safety metrics, or design criteria for an MSR helium bubbling system. Full article

Robot mobile fulfillment systems (RMFSs), as human–robot collaborative smart warehouses, transform the traditional person-to-goods order picking mode into a goods-to-person mode. Order picking optimization is a core decision-making challenge in RMFSs to improve the efficiency of the system, which needs to jointly optimize pod selection, robot scheduling, station assignment, and manual picking. Although recent studies have widely investigated integrated operational optimization in RMFSs, most of them rely on deterministic transportation and processing time and ignore uncertainties in practical human–robot collaborative operations. It remains challenging to jointly optimize these coupled decisions under uncertain operation times. To address this challenge, we model the concerned problem with the objective of minimizing fuzzy makespan and design an adaptive large-neighborhood-based variable neighborhood descent algorithm to efficiently solve it. The algorithm adopts three-dimensional coupling encoding and multi-stage heuristic decoding mechanisms. It further integrates a learning-based adaptive destroy operator selection method and a variable neighborhood descent search strategy to enhance its exploration and exploitation abilities. In a large number of systematic experiments, ALVND achieved great performance in solving the concerned problem. The objective function value obtained by it was 5.6–25.1% lower than its competitors, demonstrating its effectiveness in uncertain human–robot collaborative warehouse scenarios. Full article

Additive manufacturing (AM) can support more sustainable polymer production, but the benefit is conditional on process energy, material chemistry, build yield, post-processing, service life, repair, and end-of-life recovery. This critical integrative review compares three polymer AM routes: stereolithography (SLA), digital light processing (DLP), and fused deposition modelling (FDM) through two deliberately contrasting application scales: pallet-scale load-bearing structures and membrane feed spacers. The review distinguishes direct application evidence from design opportunities inferred from adjacent AM literature. For pallet-scale structures, the literature currently supports large-format thermoplastic extrusion, zoned cellular architectures, modular repair, and controlled recycled feedstock as plausible translation routes, but direct peer-reviewed evidence for fully additively manufactured transportation pallets remains very limited. For membrane feed spacers, direct studies provide stronger quantitative evidence: published 3D-printed designs have reported approximately threefold pressure-drop reduction with doubled specific water flux, pressure-drop gradients as low as 0.091 bar m⁻¹ under reported test conditions, and a 16% increase in permeate flux with a thinner fouling layer for a honeycomb geometry. Process-energy evidence also shows that results depend strongly on the functional unit and machine state; reported desktop values span 24.8–85.7 kJ cm⁻³ for FFF and 10.8–21.5 kJ cm⁻³ for SLA, while post-processing and machine utilization can materially change the lifecycle result. Recycled polymers likewise involve a performance–circularity trade-off: some post-consumer PLA studies report strength losses of about one-third or more, whereas controlled blends and recycling strategies can retain a much larger fraction of virgin-material performance. The synthesis therefore treats geometry, process parameters, material state, operational performance, lifecycle impact, and cost as one coupled design problem rather than assuming that AM, recycled content, or bio-based chemistry is inherently sustainable. Full article

This study investigates a sustainable vehicle-routing problem in which a heterogeneous fleet serves geographically dispersed customer demands from a central distribution facility. The problem simultaneously minimizes transportation costs and CO₂ emissions, with deliveries performed by either in-house or externally rented vehicles. A bi-objective mixed-integer programming (MIP) model is formulated, and two lexicographic anchor solutions are generated using opposite objective-priority orderings. A tailored Problem Space Search (PSS) metaheuristic is evaluated on five application-informed simulated datasets containing 10–50 nodes. Six parameter configurations combining m ∈ {10, 20} and β ∈ {0.15, 0.20, 0.25} are evaluated using 30 random seeds. For cases where CPLEX certifies primary-objective optimality, the mean PSS deviation ranges from 0.00% to 7.81%, while the best PSS run remains within 3.20% of the optimum in every case. On the 50-node instance, each PSS run improves the time-limited CPLEX primary incumbent under both priority orderings, although unresolved CPLEX gaps preclude near-optimality claims. PSS also improves the embedded heuristic in most cases, while increasing m generally improves solution quality at additional computational cost. The results demonstrate the computational effectiveness of PSS for the sustainable fleet-assignment and routing instances examined. Full article

With the continuous improvement in the high-speed railway (HSR) network, passengers’ holiday travel demand and the scale of tourism passenger flow have grown rapidly, placing higher requirements on HSR transportation services. Current train line plans are formulated according to regular daily passenger flow and fail to adapt to the travel demand generated by various holiday tourism products, resulting in poor adaptation to holiday passenger flow characteristics. Different from existing line-planning studies that consider only regular daily passenger flow, this paper is among the first to embed hierarchical holiday tourism products as a structural input of the HSR line planning problem and to explicitly handle asymmetric passenger demand in holiday periods. To address this gap, this paper optimizes HSR line plans based on the travel characteristics of tourism products. An integer programming model is established that allocates asymmetric passenger flows to train flows while minimizing the total operating cost. The model incorporates both conventional HSR passenger demand and the differentiated travel demand corresponding to different tourism products. A real-world experiment based on the HSR network in Jiangxi Province, China demonstrates that the proposed model effectively improves the tourism transportation efficiency and passenger flow distribution of HSR with reliable practicability. Sensitivity analyses further reveal how the tourism demand scale, the tourism sub-product diversity, and the benchmark line requirements affect the feasibility and cost of the line plan. This study provides a valid reference for the optimization of holiday train line plans and the coordinated development of tourism and transportation. Full article

Small gases pose an unusual problem for studies of molecular transport in proteins. O₂, CO, H₂, and NO can cross short-lived internal spaces opened by protein fluctuations, often faster than experiments can follow continuous migration. Time-resolved crystallography can localize sufficiently populated intermediates, whereas spectroscopy, isotope exchange, and kinetic measurements report molecular exchange over their respective timescales without resolving the complete route. Pressurized noble-gas structures expose internal accommodation sites but rely on surrogate molecules whose size and interactions differ from those of physiological gases. Geometry-based tunnel searches identify available space, while molecular dynamics follows explicit movement through a fluctuating protein. Free-energy and enhanced-sampling approaches can access states or transitions that remain undersampled in direct trajectories. These techniques resolve different quantities rather than progressively more accurate estimates of gas transport. In this Perspective, we argue that gas-migration pathways should be evaluated by the physical consistency of independent observables, with each method interpreted according to the quantity it resolves. This distinction explains why a cavity visible crystallographically may not carry substantial flux, why a rapidly crossed route can remain structurally inconspicuous, and why static narrowing can alter diffusion without predicting its magnitude. Agreement among methods can support a transport assignment when the quantities they resolve are physically consistent with the same mechanism; apparent disagreement may instead reflect differences among occupancy, accessibility, residence, energetic preference, and molecular traffic. Full article

Large amounts of dust are generated during stainless steel production; however, the extremely poor fluidity of this dust prevents its transportation by pipeline. The effects of glidants and their particle size, addition amount, and mixing time on the fluidity of stainless steel dust (SSD) were systematically investigated. When nano-silica (particle size of 200 nm) was used as a glidant, and the added amount and mixing time were set to 5% and 10 min, respectively, the SSD exhibited the best fluidity. Generally, the repose angle and Hausner ratio (HR) of the SSD decreased by 16.8% and 14.5% to 40.05° and 1.47, respectively. In addition, the adhesion force reduced from 36 nN to 7 nN. The van der Waals force among the SSD particles also reduced, and the electrostatic repulsion force increased, providing a feasible solution to SSD fluidity problems. However, it should be noted that this study was limited to laboratory-scale measurements, and industrial-scale validation is required in future work. Full article

Inter-hub feeder service is a promising application for electric vertical takeoff and landing aircraft (eVTOL), especially for passengers connecting to subsequent flights under tight time constraints. We develop an optimization framework that accounts for heterogeneous passenger behavior. Using stated-preference survey data, we incorporate delay-risk perception under remaining connection time constraints, identify heterogeneous preference classes, and formulate a bilevel optimization model. The upper level selects eVTOL schedules under given resource and fare configurations. The lower level captures the stochastic user equilibrium of heterogeneous passengers choosing among eVTOL and external transport alternatives. To solve the resulting mixed-integer nonlinear bilevel problem, we propose a Neural Bilevel Optimization and generalized Benders decomposition (Neur2BiLO-GBD) hybrid algorithm. Numerical experiments on the Shanghai Hongqiao–Pudong corridor show that the baseline profit-maximizing plan also generates positive social net utility for the feeder system. Fleet size, charging infrastructure, and fare affect operator profit and social net utility differently, so their high-value regions do not fully coincide. When external transport has larger potential delays and remaining connection time is short, eVTOL is more likely to achieve both high operator profit and high social net utility. Full article

The growing accumulation of microplastics in marine ecosystems, coupled with the presence of emerging contaminants such as pharmaceuticals, represents an environmental problem that has received little attention to date. The present study evaluates the potential of microplastics to act as transport vectors for pharmaceuticals through their adsorption onto two plastic polymers widely used today: polyethylene terephthalate (PET) and medium-density polyethylene (MDPE) in seawater. As a control, the same experiments were conducted in ultrapure water (Milli-Q). Both plastics were exposed to a mixture of 29 pharmaceuticals belonging to different therapeutic classes over a 28-day period, with sampling conducted at various time intervals. The identification, quantification, and characterization of the compounds were performed using ultra-high-performance liquid chromatography coupled with mass spectrometry detection. Statistical analysis was performed using R-Studio software. Outcomes reveal that the type of polymer significantly influences adsorption, with MDPE being more efficient than PET. In contrast, the type of water and the therapeutic class of the drugs were not identified as determining factors. Furthermore, a positive, albeit moderate, correlation was observed between the hydrophobicity of the pharmaceutical and its adsorption efficiency on MDPE. These findings suggest that hydrophobic interactions play a key role in the adsorption of drugs onto microplastics, providing a solid foundation for future research, such as predictive models or mitigation strategies in aquatic ecosystems. Full article

This work focuses on studying the magnetohydrodynamic flow and heat transfer mechanism of a Casson–Maxwell nanofluid due to a stretching surface through a porous medium, using a physics-informed neural network (PINNs) approach as the main tool for the solution of the physical problem. The mathematical model describes the phenomena of viscosity variation with temperature, viscous dissipation, thermal slip, Brownian motion, thermophoresis, and drag force due to a porous medium, which give a realistic physical scenario of the coupled transport phenomena of momentum, heat, and nanoparticles. First, the nonlinear partial differential equations are converted into a dimensionless boundary layer model using similarity transformations. Then, the yielded system is solved via the PINNs approach, which integrates physical law within the optimization procedure. The proposed technique does not require a significant number of labeled datasets and provides accurate and stable predictions of the strongly nonlinear flow. A comprehensive parametric analysis was performed to explore the impact of the dimensionless controlling factors on the velocity, temperature, and nanoparticle concentration distributions. It is found that the interaction of magnetic field effects, porous media resistivity, thermal and concentration slip, viscosity variation, and viscous heating significantly modifies the transport features for the studied model of the Casson–Maxwell nanofluid, which can be used effectively to control the rate of heat and mass transfer. This study proves the efficiency of the PINN technique in solving this type of model, and it also provides useful insights for designing thermal systems, energy conversion devices, and electrically conducting viscoelastic nanofluid transport problems. The close concordance between the present findings and established data from the literature validates the precision and dependability of the developed PINN-based framework. Full article

This paper develops and rigorously analyzes a fully discrete upwind interior penalty discontinuous Galerkin (IPDG) scheme for simulating solute transport in two-dimensional variable-order fractional mobile–immobile media. The temporal variable-order Caputo derivative is discretized via a Grünwald–Letnikov approximation in conjunction with a first-order backward difference, while the spatial discretization employs an IPDG method featuring an upwind numerical flux for the convection term and a penalty formulation for the diffusion operator. Under the physically relevant assumption of a divergence-free velocity field, we establish the unconditional stability of the proposed scheme. A comprehensive error analysis in the $L^2$ norm yields a convergence rate of $O(\Delta t+h^{\min (k+1,s)-\chi -1/2})$, explicitly linking the polynomial degree k, solution regularity s, and the penalty variant $\chi$. Numerical experiments in two dimensions are conducted to verify the accuracy and robustness of the proposed scheme in simulating anomalous transport phenomena in subsurface environments. Full article

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments. Full article

This study formulates panel furniture production as a flexible job shop scheduling problem (FJSP) with constraints on transport resources under varying production conditions. An adaptive large neighborhood search (ALNS) method guided by a heterogeneous graph neural network (HeteroGNN), termed HeteroGNN-ALNS, is developed to balance completion time, waiting time, and workload during the production process. The current scheduling state in ALNS is represented as a heterogeneous graph, where panel jobs and production resources are modeled as different node types, and assignment, transport, and sequence information is represented by different edge types. A search state vector is also introduced to describe the current search process. The HeteroGNN is trained using an actor–critic method to guide neighborhood operator selection and destroy set construction in ALNS. Experiments are conducted under four production conditions and five job scales. The results show that HeteroGNN-ALNS achieves better overall scheduling performance than dispatching rules and representative search methods. Statistical and ablation analyses further verify the effectiveness of the proposed method. Full article

High-resolution remote sensing semantic segmentation is essential for land-cover mapping, urban monitoring, and object-level geospatial analysis, but accurate prediction remains difficult because remote sensing images often contain complex backgrounds, shadows, weak object contrast, and complex texture variations. Moreover, spatial details lost during feature downsampling cannot be fully recovered by subsequent decoding. As a result, coarse predictions usually contain two coupled residual problems: spatial boundary displacement and local semantic inconsistency. To address these problems, we propose DGSRef (Decoupled Geometric-Semantic Refinement Network), a lightweight attachable refiner for improving coarse predictions from existing segmentation models. DGSRef treats coarse logits as semantic priors and refines them through two decoupled stages. In the geometric alignment stage, a displacement field is predicted to warp coarse logits in the output space, modeling boundary correction as spatial transport rather than direct reclassification. A Multi-Scale Semantic-Guided Structural Difference (MSGSD) module further provides semantic-guided structural cues for displacement estimation. In the semantic residual stage, gated residual logits are predicted to correct remaining local semantic inconsistencies without globally overwriting the aligned prediction. Experiments on ISPRS Vaihingen, ISPRS Potsdam, and LoveDA show that DGSRef improves diverse segmentation architectures with limited additional computation and parameters, confirming its effectiveness as a lightweight decoupled refinement framework. Full article