Search Results (17,468)

Folding-wing Unmanned Aerial Vehicles (UAVs) have become a key platform in modern aerial applications, owing to their superior portability and rapid deployment capabilities. While the tandem-wing configuration offers a compact solution for strict folding constraints, the resulting high wing loading necessitates a maximized lift coefficient (C_L) to ensure efficient low-speed loitering. This study presents an aerodynamic optimization framework aiming to maximize the C_L of a folding tandem-wing UAV. A combined optimization strategy integrating Optimal Latin Hypercube Sampling (OLHS), orthogonal polynomial surrogate models, and the Multi-Island Genetic Algorithm (MIGA) is established. With aft wing parameters determined, global sensitivity analysis identifies the fore wing span as the dominant factor, contributing 47.40% to lift performance. Crucially, although vertical separation contributes only 6.53% to C_L and sweep angle just −1.22% to drag coefficient, their strong interaction effects with wing span confirm their non-negligible role. Finally, the flow field characteristics at the wing root of the optimized configuration undergo significant changes, resulting in a 4.28% increase in the C_L. This work validates the important role of parameter interaction effects in aerodynamic optimization and provides a theoretical basis for the design of geometrically constrained aerial vehicles requiring high lift coefficients. Full article

The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work. Full article

This study employs a comprehensive bibliometric analysis to map the global scientific landscape of laser wakefield acceleration (LWFA) from 1990 to 2025. Using data extracted from the Web of Science (WoS) and analyzed with Bibliometrix, VOSviewer, and CiteSpace, the study identifies key publication trends, influential authors, leading countries, prominent journals, and thematic evolution within the field. The findings reveal exponential growth in LWFA-related research, driven by advances in high-power laser technology and controlled injection techniques. Network analyses demonstrate extensive international collaboration and a strong interdisciplinary structure linking plasma physics, optics, and accelerator science. Keyword co-occurrence and burst analyses highlight emerging topics such as ionization injection, dual-stage acceleration, betatron radiation, and machine learning-assisted optimization. These insights delineate both the historical progression and the dynamic frontiers of LWFA, providing a systematic understanding of its development and guiding future research toward the realization of compact, high-quality electron sources and next-generation plasma-based accelerators. Full article

The global clean energy transition is essential for limiting the global temperature rise to 1.5 °C and achieving net-zero greenhouse gas (GHG) emissions by 2050. This review synthesizes evidence from peer-reviewed studies, policy reports and industry benchmarks, addressing the three interrelated pillars of the clean energy transition: clean energy technologies, critical material scarcity, and operational challenges. This study highlights that although clean energy technologies, particularly solar photovoltaics and wind power, have achieved cost parity with fossil fuels, their widespread deployment is still hindered by technical, material, and system-level challenges. The demand for critical minerals, essential for renewable energy technologies, is growing faster than mining supply chains can respond, exacerbated by high geographical concentration, price volatility, and low recycling rates. Furthermore, lifecycle and operational challenges, including premature asset retirement and grid integration issues, continue to hinder progress. To address these challenges, this review identifies four priority research areas: reducing material intensity through low-scarcity technologies, improving recycling and reuse systems for critical materials, optimizing smart grid frameworks, and promoting coordinated policy frameworks for fair cost allocation and mineral supply chain governance. This review offers a unified analytical framework to inform technology selection, infrastructure investment, and policy design, contributing to a resource-secure, sustainable clean energy transition. Full article

To meet the global climate change challenge and the International Maritime Organization’s (IMO) greenhouse gas emission reduction strategy, and promote the shipping industry’s transition to clean energy, this study focuses on the 6S35 2-stroke marine low-speed engine to explore hydrogen fuel combustion and emissions in the cylinder. A detailed chemical reaction kinetics model is constructed on the CONVERGE platform, coupling 42 components and 168 elementary reactions, integrating the SAGE combustion model with the extended Zeldovich NOx mechanism for refined numerical simulation of hydrogen combustion. Model validation shows the cylinder pressure peak simulation error is within 5%. Research results indicate hydrogen fuel has significant premixed combustion characteristics with a violent and concentrated heat release. Under simulation, the cylinder explosion pressure reaches about 28 MPa, and the max combustion temperature nears 3000 K, far exceeding traditional diesel engines. In terms of emissions, hydrogen’s carbon-free characteristic keeps CO₂ and CO emissions at extremely low levels (concentrations of approximately 0.02 and 0.085, respectively); whereas NOx emissions exhibit strong “high temperature dependence” and “expansion cooling effect,” with peak concentrations approaching 0.00042. This numerical model can effectively predict the combustion performance of hydrogen fuel, potentially providing a reference for optimizing fuel injection strategies and combustion chamber design to achieve efficient and clean combustion, and offering a theoretical basis for the development and commercial application of marine hydrogen fuel engines. Full article

This paper addresses global path planning for a wheeled mobile robot with two different kinematic structures, considering both shortest path and minimum energy consumption criteria. The main research question concerns how the robot’s kinematic structure and the selected planning algorithm influence the resulting path with respect to these criteria. Our review of the state of the art discusses selected path planning methods, including model-based approaches. To determine the energy optimal path, a simplified model of the PIAP GRANITE robot was developed. The robot can be configured as either differentially driven or skid-steered. In the differentially driven configuration, the robot has two driven wheels and two caster wheels, whereas in the skid-steered configuration all wheels are independently driven. The robot’s models are based on previous theoretical and experimental studies and include kinematics, dynamics, drive units, and wheel slip phenomena. For path planning, it was assumed that the robot can move straight or turn. A flat terrain representative of typical urban environments was modeled as a grid of square cells, each characterized by friction and rolling resistance coefficients. Path planning was performed using A*, Theta*, and RRT* algorithms. In order to quantitatively evaluate the results, quality indexes were defined, including path length, energy consumption, computation time, and the number of analyzed nodes. Simulation results are presented for selected terrain maps, both robot configurations, all algorithms, and both optimization criteria. The results show that the differentially driven configuration is consistently more energy-efficient. For the skid-steered robot, minimizing the number of turns is crucial due to high turning energy costs. The A* algorithm consistently finds optimal paths, whereas RRT* is faster but produces non-optimal and non-repeatable results. Theta* does not always achieve optimality due to limitations imposed by the line-of-sight function. Full article

To address the challenges of dense structural layouts, limited path feasibility, and stringent assembly constraints in cable routing within complex compartments of aerospace equipment, this paper proposes a cable path planning method that integrates Bidirectional Crossing Line Pruning (BCLP) with an improved ant colony optimization (IACO) algorithm. First, a hierarchical activation strategy for key obstacles is realized by constructing primary and extended crossing lines. On this basis, the BCLP algorithm is introduced, combining global perspective with local reduction capability to significantly reduce the complexity of the search space. Second, in line with cable assembly process requirements, a composite heuristic function is formulated by integrating obstacle-crossing cost and bending penalty. Additionally, a multi-objective-driven pheromone update model is developed to enhance the routing process’ feasibility and convergence performance. Experimental results across various aerospace cabling simulation scenarios demonstrate that the proposed method achieves an average reduction of 19.6% in multi-objective process cost and a 68.5% improvement in convergence efficiency compared to traditional visual graph methods combined with standard ACO. The approach provides effective support for the automation and intelligent planning of cable layouts in complex environments, offering strong potential for engineering applications. Full article

To address the instability in extracting key parameters such as time-of-flight (ToF) from ultrasonic echoes due to noise and multi-echo superposition, this paper proposes a robust parameter estimation method based on the secretary bird optimization algorithm (SBOA). The proposed approach adheres to the Gaussian convolution-based echo parameterization and cosine-similarity matching framework, while innovatively introducing SBOA to perform global optimization of model parameters. Consequently, the multi-echo ToF estimation is formulated as a nonlinear optimization problem aimed at maximizing waveform shape consistency. To evaluate the method’s performance, simulations are conducted under multi-echo superposition scenarios. Comparisons are made with representative baseline techniques, including wavelet transform (WT), empirical mode decomposition (EMD), and variational mode decomposition (VMD), using mean squared error (MSE), estimated signal-to-noise ratio (ESNR), and normalized cross-correlation (NCC) as performance metrics. Experimental results demonstrate that, in challenging low-SNR and echo-interference environments, the proposed method achieves overall superiority across all quantitative metrics and exhibits a stronger capability to preserve the main-lobe morphology and structural features of echoes. Validation on semi-synthetic signals further confirms its effectiveness, with practical applicability to be verified by measured datasets in future work. This work provides an effective and robust solution for ultrasonic signal processing in complex field conditions. Full article

The rapid growth of e-commerce has intensified operational and sustainability challenges in urban last-mile delivery, necessitating routing methods that perform reliably under realistic traffic and spatial conditions. This study evaluates three routing algorithms, Nearest Neighbor (NN), Clarke–WrightSavings (CWS), and Ant Colony Optimization (ACO), using 1764 real-world Amazon delivery stops grouped into ten operational clusters in the Nashville metropolitan area. Travel distances and times were obtained through the Google Maps Distance Matrix API in driving mode to reflect actual road network structure and typical traffic conditions. Substantial performance differences were observed across algorithms and cluster configurations. NN achieved a strong performance in compact clusters (18.43 miles and 58.48 min in Cluster 4) but performed poorly in dispersed clusters (82.44 miles and 196.48 min in Cluster 9), reflecting high sensitivity to spatial dispersion. In contrast, CWS consistently reduced travel distance and time across clusters, achieving the shortest observed route (18.50 miles and 47.82 min in Cluster 10). Relative to ACO, CWS reduced travel distance by up to 42% (Cluster 9) and reduced travel time by over 45% in high-dispersion clusters. ACO exhibited the highest variability, with distances reaching 98.77 miles and travel times exceeding 218 min. Multi-criteria evaluation using efficiency ratios, distributional analysis, performance quadrant visualization, and a Composite Performance Index (CPI) confirmed the dominance of CWS. CPI scores of 1.00 (CWS), 0.78 (NN), and 0.00 (ACO) reflected balanced spatial and temporal efficiency under identical traffic-informed inputs. The results demonstrate that deterministic savings-based routing provides superior stability, efficiency, and scalability in semi-static urban delivery systems. However, the present study did not benchmark the evaluated algorithms against state-of-the-art exact TSP solvers (e.g., Concorde, LKH) or more recent metaheuristics such as Genetic Algorithms or Variable Neighborhood Search. The objective was to provide a controlled empirical comparison under consistent traffic-informed cost matrices rather than to establish global optimality bounds. Consequently, while the findings strongly support the relative superiority of the Clarke–Wright Savings approach within the evaluated framework, future research incorporating advanced exact and hybrid optimization methods would further contextualize algorithmic performance. Full article

Chronic kidney disease (CKD) represents a major global health burden, with growing evidence indicating that its origins extend back to early developmental stages. This narrative review integrates epidemiological, clinical, and mechanistic experimental evidence to position inflammation as a life-course driver of kidney vulnerability rather than a late-stage consequence. Inflammation has emerged as a central mechanistic link connecting adverse prenatal and postnatal exposures to lifelong kidney vulnerability. We highlight the translational potential by identifying pathways amenable to early-life interventions that could modify disease trajectory. During fetal development, maternal nutritional status, metabolic stress, and inflammatory exposures influence nephron endowment, immune maturation, and epigenetic regulation, thereby shaping long-term CKD risk. In childhood, early immune dysregulation and low-grade inflammation contribute to disease initiation, defining critical windows for preventive and renoprotective interventions that can be implemented in at-risk populations. In adulthood and aging, persistent activation of cytokine signaling, inflammasomes, oxidative stress pathways, autophagy–mitophagy imbalance, and cellular senescence drives progressive kidney injury, further amplified by gut microbiota dysbiosis and renin–angiotensin system interactions. Emerging life-course strategies include maternal nutrition optimization, early-life risk stratification, targeted anti-inflammatory and immunomodulatory therapies, and microbiota-directed interventions tailored to developmental stage and individual risk profile. By emphasizing inflammation as a developmentally programmed and preventable process, this review underscores opportunities for early-life and transgenerational CKD prevention, translating mechanistic insights into actionable strategies for preventive medicine and public health. Full article

Weakly nonlinear oscillators display complex behavior that perturbation methods struggle to analyze, particularly near critical thresholds. The non-perturbation approach (NPA) offers a unified, parameter-agnostic approach that is effective in strongly resonant situations, accurately capturing global phase space structures, and directly addressing chaotic transitions, providing predictive insights where traditional methods fail. The NPA as a novel technique successfully converts the nonlinear weakly oscillator of the ordinary differential equation (ODE) into a linear issue. Theoretical findings are confirmed through a numerical comparison using Mathematica Software (MS). The results of the numerical solution (NS) show excellent agreement. It is commonly acknowledged that all conventional perturbation methods utilize Taylor expansion to augment restoring forces, hence optimizing the usual conditions. A comprehensive analysis of the issue’s stability is easily achievable via NPA. Accordingly, when evaluating NS estimates of weakly nonlinear oscillators, NPA occupations serve as a more useful form of responsibility. Additionally, the stability analysis is easily accomplished via NPA. The system’s dynamics are examined by chaotic analyses, incorporating bifurcation diagrams (BDs), Poincaré maps (PMs), and Lyapunov exponents (LEs). This analysis identifies transitions between regular and complicated behavior and thoroughly examines the system’s stability features. The results provide a comprehensive understanding of the fundamental nonlinear dynamics and offer significant insights for future research on analogous systems. Full article

Targetless LiDAR–camera extrinsic calibration remains challenging due to unreliable cross-modal correspondences and sensitivity to initialization. We present a targetless extrinsic calibration framework based on class-agnostic boundary mask alignment in a shared image-plane representation. This scheme first constructs consistent LiDAR–camera mask pairs from image-plane depth and intensity projections of LiDAR data and camera images. It then obtains robust initial pose candidates through bounded rotation-only global initialization and refines them using a computationally efficient stochastic gradient approximation to estimate the optimal extrinsic parameters. Experiments on the KITTI benchmark demonstrate a superior accuracy–runtime trade-off compared with a segmentation-based global optimization baseline, while real-world driving tests confirm stable cross-modal alignment under vibration and inter-modal timing jitter. Full article

Federated Learning (FL) represents a promising paradigm for collaborative model training across numerous devices, preserving data locality and offering potential privacy benefits for industries such as finance, healthcare, and Internet of Things (IoT). Nonetheless, real-world deployments of FL encounter challenges arising from dynamic and diverse environments, which adversely affect training speed and model convergence. To address these issues, this paper introduces FedDCS, an adaptive federated learning framework that effectively manages resources during training through two primary innovations. First, it establishes a reliable method for predicting client training durations, estimating completion times while filtering noise and detecting performance variations. Second, it implements a two-stage adaptive waiting strategy that dynamically determines the optimal timing and selection of client batches for aggregation, thereby balancing collection efficiency with model accuracy. This approach optimizes the trade-off between efficiency and accuracy in heterogeneous settings. Extensive evaluations on datasets such as Fashion-MNIST and CIFAR-10/100, incorporating simulated device and data heterogeneity, demonstrate that FedDCS consistently achieves superior time efficiency and higher global model accuracy compared to state-of-the-art (e.g., synchronous, asynchronous, and semi-asynchronous) baselines. Its robustness and versatility render it effective across various complex and heterogeneous environments. Full article

Background: Regional citrate anticoagulation (RCA) is the recommended first-line strategy for continuous renal replacement therapy (CRRT), yet global implementation remains inconsistent. We reviewed our extensive single-center experience to provide a real-life evaluation of RCA feasibility, limitations, and environmental sustainability. Methods: We retrospectively analyzed RCA treatments performed in our center from 2012 to 2022. Technical performance was assessed by circuit patency and failure timing. Environmental impact was evaluated by simulating filter consumption and hazardous waste production compared to a projected strategy using unfractionated heparin (UFH). Results: RCA utilization grew progressively, reaching 98% of all treatments. The technique showed an 80.2% success rate and a robust safety profile, demonstrating excellent feasibility and efficacy in clinical practice. However, among circuits intended for 72 h survival, over one-third failed prematurely; segmented regression confirmed a critical high-risk period within the first 24 h, with a significantly higher rate of circuit loss compared to the subsequent period (p < 0.05). In our simulation, RCA adoption saved over 1000 filters, preventing the emission of approximately 12 tons of CO₂ equivalent. Conclusions: RCA is a safe, effective, and environmentally appealing method of CRRT. However, significant room for improvement remains; refining the technique to increase circuit success rates is essential to optimize clinical efficiency and further minimize the ecological footprint of acute renal care. Full article

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance. Full article