Search Results (1,247)

Quadrotor motion planning in cluttered environments presents significant challenges in achieving both computational efficiency and trajectory smoothness, particularly in low-altitude economy and intelligent energy system applications where autonomous aerial vehicles perform infrastructure inspection and power line monitoring. Many existing methods either rely on sampling-based algorithms that suffer from long computation times and suboptimal paths, or employ trajectory representations that produce high-order derivative discontinuities unsuitable for agile flight. In this work, we propose an efficient hierarchical motion planning framework that integrates a JPS–Bresenham-based path search with safe flight corridor construction and Bézier curve optimization. Our approach addresses trajectory generation through a two-stage process: a front-end path search that efficiently identifies collision-free paths with reduced waypoints, followed by a back-end optimization that leverages convex safe corridors with overlapping regions to expand the solution space. Through comprehensive benchmark experiments across six different map scenarios, we demonstrate that our method outperforms RRT* and PRM in both path quality and computational efficiency. Monte Carlo experiments across varying map sizes and obstacle densities confirm robustness and scalability advantages. Comparative studies with state-of-the-art planners demonstrate superior success rates and cost efficiency while maintaining strict kinodynamic feasibility. The Bézier-based optimization reduces snap integral by up to 55% compared to ordinary polynomial approaches, demonstrating its superiority for fast quadrotor trajectory planning in complex environments. Full article

The use of renewable energy sources instead of conventional ones, together with the development of efficient electricity storage solutions, represents a central objective of the transition to sustainable and resilient energy systems. In this context, two main development directions are the integration of hydrogen in the energy chain (Power-to-Gas) and the use of batteries, each with specific advantages and disadvantages, compared to internal combustion engines. The purpose of this work was to evaluate the dynamic response time of a hydrogen fuel cell model powered by green hydrogen, under conditions of sudden and instantaneous power demand, for its integration into wind-based renewable energy systems. Experimental research was carried out on an autonomous installation designed to operate continuously for an unlimited duration, simulating the integration of hydrogen produced from wind sources. The novelty consists of the application of an instrumental method for automatic measurement of the response time of a proton exchange membrane hydrogen fuel cell, based on the automatic acquisition and processing of measured electrical signals. The response time of the fuel cell was compared with that of an internal combustion engine based on the classic Carnot cycle, using a dedicated oscilloscope. The load connection time, the current and voltage variation as a function of time were recorded simultaneously. The results show that the response time of the fuel cell is relatively short (approximately 0.3 ms), much lower than that of the internal combustion engine (0.7 s), being of the order of about 2333 times smaller. In conclusion, the hydrogen fuel cell can be effectively integrated into renewable energy systems for the role of an uninterruptible power supply, with an exceptionally fast dynamic response, suitable for applications in regulating and supporting wind-powered networks. Full article

This study investigates the development of high-strength biofiber cement boards with enhanced thermal insulation properties by utilizing recycled biofibers derived from cement packaging bags, combined with a water-based adhesive to enhance the curing efficiency of Portland cement through a cementation–curing process. This approach reduces waste from cement packaging and other biofiber residues through recycling, thereby promoting environmental sustainability. Moreover, it does not require the use of additional chemicals for the disposal or treatment of fiber waste, nor does it require the incineration of biofiber waste. Recycled biofiber from cement bags, composed primarily of cellulose (60 wt%), lignin (15 wt%), and hemicellulose (10 wt%), serves as a reinforcing phase, while the cement and adhesive mixture functions as a strong binding matrix. The fabrication of composite materials using undamaged cement bag fibers preserves fiber integrity and enables a well-ordered one-dimensional (1D) fiber alignment, which promotes more effective reinforcement than two-dimensional (2D) or three-dimensional (3D) orientations, in accordance with the rule of mixtures. In addition, the incorporation of a water-based PVAc adhesive accelerates the curing rate of the cement phase, promoting the formation of a strong interconnected network structure, and facilitates a more complete curing process. The physical, mechanical, chemical, and thermal properties of the biofiber cement boards were evaluated in accordance with relevant industrial standards, including TISI 878:2023, BS 874, ASTM C1185, ASTM D570, ASTM C518, ISO 8301, and JIS A1412. The results indicate that an optimal cement mortar to water-based adhesive ratio of 1:2, combined with an increased number of biofiber sheet layers, significantly enhances material performance, particularly in Formulas (7)–(9). Among these, Formula (9) exhibits the lowest water absorption (0.0835 ± 0.0102%), the highest tensile strength (19.489 ± 0.670 MPa), the highest flexural strength (20.867 ± 2.505 MPa), the highest Young’s modulus (5735.068 ± 387.032 MPa), and low thermal conductivity (0.152 W/m.K). The resulting boards demonstrate strong bonding ability, enhanced resistance to fire, moisture, and weathering, and a longer service life compared to lower cement-to-adhesive ratios (1:1 and 1:0). These findings demonstrate the potential of recycled biofiber composites, combined with water-based adhesives, as sustainable alternative materials for thermal insulation and structural applications, including ceilings and walls in building construction. Full article

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article

Conceptual ambiguity in People–Place Relationships (PPR) research limits consistent operationalization and cross-context comparability, particularly in under-represented cultural settings. This study develops an integrated, context-sensitive framework for assessing PPR in residential environments and empirically examines its measurement structure. The framework is applied in Damascus as a pilot context to assess its structural validity, internal consistency, and applicability. The methodological approach comprised two stages: conceptual development and empirical validation. First, two rounds of case-study analysis derived from a prior systematic literature review synthesized environmental (social and urban) and relational (cognitive, affective, attachment) dimensions into a coherent framework. Second, the framework was operationalized and tested using survey data from 1610 residents across Damascus districts. Six first-order indices and one composite PPR index were constructed and evaluated using exploratory factor analysis and Cronbach’s alpha with item–total correlation analysis. Results demonstrate a stable multidimensional structure that integrates evaluative environmental conditions with relational processes, moving beyond emotion-dominant interpretations of attachment. The framework advances existing approaches by linking theoretical constructs to empirically tested measurement dimensions. While further validation in diverse contexts is required, the results indicate that the model provides a coherent and adaptable basis for assessing residential PPR in socio-culturally complex urban environments. Full article

The rapid growth of scientific publications in sport science has intensified competition for publication and increased the importance of methodological rigor, transparent reporting, and effective scientific communication. Despite the availability of general guidance on scientific writing, recommendations specifically tailored to the context of sport science publishing remain fragmented. The aim of this narrative review was to synthesize methodological, conceptual, and editorial perspectives in order to identify the key factors that influence the quality and publishability of sport science research. The review examines major dimensions of research quality, including theoretical grounding, methodological rigor, statistical inference, open science practices, and the structure of scientific manuscripts. In addition, common weaknesses that frequently lead to manuscript rejection, such as limited scientific contribution, methodological flaws, statistical misinterpretation, and inadequate scientific writing, are discussed. Building on this synthesis, the article proposes an integrated conceptual framework that conceptualizes publishable sport science research as a progressive process moving from conceptual foundations to methodological and analytical rigor, research transparency, and effective scientific communication. The framework, presented as a funnel, illustrates how these interconnected dimensions ultimately contribute to two complementary outcomes: the advancement of scientific knowledge and the practical application of research findings in sport contexts. By providing a structured overview of these elements, the proposed framework aims to support researchers in designing more rigorous studies, improving manuscript quality, and strengthening the impact of sport science research. Full article

Accurate prediction of transient melt pool thermal fields in Laser Powder Bed Fusion (LPBF) is essential for understanding melt pool geometry and defect formation mechanisms, yet conventional finite element methods (FEM) impose prohibitive computational costs for parametric process exploration. A variational physics-informed neural network (VPINN) framework is presented for 3D transient thermal modeling of a GH3536 single-track LPBF scan. The framework incorporates a continuously differentiable Goldak double-ellipsoid moving heat source, temperature-dependent thermophysical property surrogates, and an effective heat-capacity treatment of latent heat associated with solid–liquid phase change and vaporization. These components are embedded in a weak-form residual-minimization scheme with octree-adaptive domain decomposition, hierarchical Legendre test functions, and sequential sliding-window time marching. Effective absorptivity is inferred jointly with the network parameters, using sparse experimental melt pool profiles as supervision. Within a parametric study covering laser powers from 100 to 140 W and scan speeds from 1000 to 1500 mm/s, the predicted melt pool width, depth, and aspect ratio agree closely with FEM benchmarks and cross-sectional optical micrograph measurements across both supervised and held-out interpolation conditions, with total relative L₂ nodal temperature errors ranging from 3.23% to 6.75%. Following a one-time offline training investment of 15,323 s that simultaneously resolves the full parametric space, surrogate inference reduces per-condition query time from 3000–4000 s (FEM) to merely 4–5 s, delivering a speedup of two to three orders of magnitude and making the framework increasingly cost-effective for high-throughput parametric studies and digital-twin integration as the number of queried conditions grows. Full article

The widespread presence of stable and hazardous organic contaminants, such as synthetic dyes, in industrial effluents necessitates the development of resilient treatment strategies capable of achieving efficient degradation and decolorization of dye pollutants. Conventional treatment processes often fail to remove such recalcitrant compounds, prompting growing interest in integrated advanced systems. Photocatalytic membranes represent a promising solution due to the synergistic combination of physical separation and catalytic degradation. In this study, zinc oxide (ZnO) thin films were deposited by spin coating onto smectite–zeolite ceramic membranes (MS10/Z90), applying one (M1), two (M2), and three (M3) successive coating layers to control catalyst thickness. SEM analysis confirmed that increasing the number of layers resulted in a thicker and more homogeneous ZnO coating, while XRD revealed enhanced crystallinity and larger crystallite size. Water permeability decreased progressively from 623 L·h⁻¹·m⁻²·bar⁻¹ for the uncoated membrane to 506, 439, and 350 L·h⁻¹·m⁻²·bar⁻¹ for M1, M2, and M3, respectively. Photocatalytic performance was evaluated using Rhodamine B (RhB) (10 mg·L⁻¹) under UV irradiation (365 nm, 18 W) for 180 min, achieving degradation efficiencies of 83.0%, 94.6%, and 99.1% for M1, M2, and M3, respectively. The degradation kinetics followed a pseudo-first-order model, with rate constants increasing with catalyst layer thickness. Free radical scavenging assays confirmed that hydroxyl radicals (•OH) were the primary reactive species responsible for RhB degradation. These findings highlight the critical influence of ZnO layer thickness and mass transfer on photocatalytic performance, demonstrating the potential of ZnO-coated ceramic membranes for efficient pollutant degradation and in situ photocatalytic regeneration. Permeability measurements after photocatalytic treatment confirmed effective flux recovery, supporting the operational durability of the developed membranes. Full article

Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet, a convolutional model optimized for feature-centric detection, and SigNet, a gated recurrent model designed for sequence-oriented modeling of ordered flow-feature representations. Both models are trained with Cost-Robust Focal Loss (CRF-Loss), a cost-aware objective that penalizes false positives and false negatives according to deployment-specific risk preferences. We evaluate the models on the UNSW-NB15 and CIC-IDS2017 benchmarks using six standard metrics (accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC)), complemented by an analysis of false-positive behavior. On CIC-IDS2017, ForNet achieves precision up to 0.95 and MCC up to 0.93 with AUROC above 0.94, while SigNet shows a stronger recall-oriented profile on UNSW-NB15. In an ablation study, replacing Binary Cross-Entropy with CRF-Loss reduces the false-positive rate by approximately 15–20% and improves robustness-oriented metrics such as MCC by up to 12% on CIC-IDS2017. Rather than claiming universal state-of-the-art performance, the study focuses on performance–risk trade-offs under realistic operational constraints. The results highlight how architectural bias and cost-aware optimisation jointly shape IDS behaviour and offer benchmark-based guidance for interpreting performance–risk trade-offs in lightweight intrusion detection. Full article

Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the disordered microstructure of a composite due to processing and manufacturing is critical to developing the material digital twin in the multiscale hierarchy. Automating microstructure characterization is typically done by either training convolutional neural network models using a pretrained encoder or using prompt-based segmentation tools. In this work, a toolset for developing segmentation models is presented, combining these two methods to enable rapid annotation, training, and deployment of microscopy segmentation models for automated material digital twin development without user knowledge of machine learning. Additionally, a Bayesian optimization framework is developed for generating statistically equivalent representative volume elements (SRVE) to a segmented microstructure using a random microstructure generator that implements soft body dynamics. Progressive failure analysis of random, statistically equivalent, and ordered microstructures is compared to the segmented microstructure subject to transverse loading to demonstrate the importance of accurately representing the driving material length scale of a composite digital twin. Ordered microstructures over-predicted crack initiation and ultimate strength and strain. Random and optimized RVE microstructures better agreed with the segmented simulation results, with no significant difference observed between the two methodologies. The improvement in predicted macroscale behavior for models that capture disordered microstructures due to manufacturing processes demonstrates the importance of capturing microstructure features in composites modeling and indicates that SRVEs that capture microstructural features of the physical material can be used in material digital twin development. Further, the toolsets provided in this work allow for rapid development of composite material digital twins without user expertise in machine learning. This has enabled the development of an integrated workflow to automatically characterize and idealize composite microstructures and generate representative geometric models for efficient micromechanics analysis. Full article

Optimizing the parameters of the manufacturing process for products in terms of metalworking equipment is one of the key tasks in technological preparation for production. This process is structurally complex, characterized by an ordered set of actions of various types. The basis for improving the efficiency of the technological process is the comprehensive optimization of the parameters of individual elements that form its structure. To solve this problem, an integrated model for comprehensive multi-criteria optimization of a structurally complex process has been developed, establishing a clear hierarchical relationship between its elements. The model is based on the structural decomposition of two processes: the process of forming individual design elements and the technological process of manufacturing a product. Structural hierarchical models have been developed for each process. The structure of the integrated model contains six levels of control. For each level of control, a set of target indicators and control parameters has been formed. The article presents the results of testing the proposed model using the example of optimizing the technological process of mechanical processing for the “Housing” product. As part of the study, structural and parametric optimization of the manufacturing process for this part was carried out. During the study, the structure of the technological processing route was optimized, as well as individual technological operations and technological transitions. Over the course of the work, the technological equipment and processing methods used for shaping a number of surfaces were replaced. As a result of the optimization, the overall labor intensity of the technological process for manufacturing the “Housing” product was reduced by 19.8%, and the manufacturing accuracy of the most critical surfaces was increased by 16.4%. The results confirm the effectiveness of the proposed model for comprehensive optimization of the mechanical processing technological process. Full article

Within a waste biorefinery framework, integrating agro-industrial by-products into the circular economy requires a detailed understanding of the thermochemical conversion behaviour of low-grade carbonaceous materials. This study evaluates the co-pyrolysis characteristics of Soma lignite (SL) and pectin-rich sugar beet pulp (SBP) as a sustainable route for upgrading these resources into clean energy carriers. Interactions between the two feedstocks were analysed by thermogravimetric measurements, triple-region kinetic modelling, and quantitative synergy indices at six mixing ratios, including the pure samples (100:0, 80:20, 60:40, 40:60, 20:80, and 0:100 wt% SL:SBP). The Reactivity Index ($R_{\mathrm{m}}$) increased from 0.97 × 10⁻⁴ s⁻¹K⁻¹ for pure SL to 8.65 × 10⁻⁴ s⁻¹K⁻¹ for the 20:80 blend, showing that SBP acts as a highly reactive biomass component that accelerates devolatilisation in the main pyrolysis region. Synergy analysis indicated a shift from inhibitory behaviour in coal-rich blends to slightly positive synergy in SBP-rich mixtures, with the onset of positive $\Delta TC$ around 60 wt% SBP under the present single-heating-rate, non-replicated TGA conditions. This tentative threshold-like behaviour suggests that a critical level of literature-supported, hypothesised hydrogen-donating biomass radicals may be required to overcome the structural resistance of the coal matrix. Within these experimental limitations, the apparent macro-kinetic deviations and first-order Arrhenius parameters suggest that SL/SBP co-pyrolysis follows a complex, non-additive pathway that should be further validated by multi-heating-rate and product characterisation studies in future work. The primary contribution of this work lies in proposing this distinct threshold-like biomass fraction at the macro-kinetic level that governs the transition from heat-transfer-limited antagonism to radical-influenced synergy in low-rank coal and pectin-rich biomass blends. Overall, the combined $\Delta TC$, $\Delta E$ and $R_{\mathrm{m}}$ descriptors provide useful macro-kinetic benchmarks for guiding the optimisation of thermochemical processes for low-grade carbonaceous resources. Full article

The geomorphological characterization of coastal–marine environments is essential for environmental management and biodiversity conservation. The objective of this study was to model the geomorphological diversity of the Punta de Coles National Reserve, located in Puerto de Ilo, Moquegua, Peru, using GNSS geodetic receivers, integrating topographic and bathymetric data to continuously represent both the emerged and submerged relief. The methodology involved establishing two “C”-order geodetic control points, implementing a closed polygon with 13 vertices, conducting a topographic survey, and recording bathymetric data along coastal transects extending 1 km offshore using an echo sounder and GNSS positioning. The data were processed in a GIS environment to generate a Coastal–Marine Digital Terrain Model (CM-DTM) with metric resolution. The results showed a total area of 171.451 ha, with elevation variations ranging from sea level to 71.617 m above sea level. Distinct geomorphological units were identified, such as coastal plains (0–5% slope), hills (15–35%), and cliffs (>45%), in addition to 16 rocky islets covering 1.537 ha. In the underwater environment, the model made it possible to identify submerged terraces, slopes, and local depressions down to a depth of −115 m, revealing a continuous transition between the land and sea topography; additionally, areas with a higher susceptibility to erosion and areas of high ecological importance were identified. This study’s contribution lies in the integration of GNSS geodetic data with topobathymetric surveys, which enabled the generation of a high-precision continuous model in an area with limited prior information, establishing a scientific baseline for coastal and marine management and conservation. Full article

Long-term safety assessment of deep geological repositories for spent nuclear fuel requires explicit evaluation of thermo-mechanical (TM) processes induced by decay heat and their influence on fractured host rock. A safety-relevant, though low-probability, scenario concerns shear reactivation of fractures intersecting deposition holes, which could compromise canister integrity if displacement exceeds design limits. This study presents a three-dimensional discrete element modelling approach to analyze the thermal evolution of the Forsmark repository (Sweden) and the associated long-term response of a discrete fracture network (DFN) during the post-closure phase. The model explicitly represents repository panel, deterministic deformation zones, and a stochastically generated fracture network embedded in a bonded particle assembly representing the rock for Particle Flow Code (PFC) numerical simulations. Time-dependent heat release from spent nuclear fuel canisters is implemented using a physically based decay power function. A deposition panel-scale heat-loading formulation accounts for deposition-hole and tunnel spacing. Two emplacement scenarios are analyzed: (a) a simultaneous all-panel heating scenario, used as a conservative bounding case, and (b) a sequential panel heating scenario representing staged emplacement and closure. The simulations show that temperature and thermally induced stress evolution are sensitive to the emplacement and closure sequence. Sequential heating produces a more gradual thermal build-up and lower peak temperatures than simultaneous heating, indicating that thermal and stress perturbations in the host rock can be influenced not only through repository design, but also by operational strategy. Thermally induced fracture shear displacement displays a systematic temporal response. Fractures located within the deposition panel footprint develop shear displacement rapidly during the early post-closure period, reaching peak values at approximately 200 years, followed by gradual relaxation as temperatures decline. The average peak shear displacement on fractures is on the order of 2–3 mm, while fractures outside the panel footprint show smaller early-time displacements and a more prolonged long-term response. All simulated shear displacements remain more than one order of magnitude below the commonly cited canister damage threshold for Forsmark of approximately 50 mm, even for the conservative simultaneous heating case. These results indicate that thermally induced fracture shear is unlikely to cause direct mechanical damage to canisters. At the same time, the persistence of residual shear displacement after heating implies permanent fracture dilation, which may influence long-term hydraulic properties and indirectly affect processes such as groundwater flow and canister corrosion. The modelling framework and results presented here were conducted for review purposes independently from the Swedish safety case, and provide a mechanistic basis for evaluating thermally induced fracture deformation in crystalline rock repositories and contribute to bounding the role of thermo-mechanical processes in the safety assessment of spent nuclear fuel disposal at Forsmark. Full article

Decision-making under time pressure has been associated with reduced deliberation and increased sensitivity to contextual cues such as framing. This study investigates how time pressure reshapes information processing in risky decision-making and which types of information receive greater attention when cognitive resources are constrained. Two experiments examined the combined effects of time pressure, spatial position, and presentation order on framing effects, integrating behavioral risk-choice measures with gaze-based indices of attention allocation. The results show that time pressure significantly reduces fixation counts and fixation durations, suggesting more restricted information search. Moreover, time pressure enhances frame-consistent risk preferences, with contextual presentation factors further shaping decision outcomes. Specifically, under time pressure and loss framing, stronger risk seeking emerged when the certain option was presented second. Overall, these findings suggest that time pressure not only amplifies framing effects in risky decision-making but also is associated with changes in attentional allocation patterns and increased reliance on contextual cues underlying framed choices. This study highlights how the temporal and spatial characteristics of information presentation shape decision processes under temporal constraint and provides theoretical and practical implications for decision-making under pressure. Full article