Search Results (4,691)

The focus of this work was to investigate the mechanical properties of additively manufactured (AM) discontinuous carbon fibre-reinforced polymer (DCFRP) composites. Towards the specimen’s fabrication, the Fused Filament Fabrication (FFF) additive manufacturing technique was employed. A number of input printing parameters were varied, such as the infill pattern, infill density, layer height, shell configuration, and raster orientation, in a systematic way. The role of these paraments on the mechanical properties, such as tensile, flexural, and impact strength were investigated. The data was analysed in-depth and the “main effect method” was employed for their comparative ranking. The results of this study showed that tensile and bending strengths were strongly correlated with material content and structural reinforcement. The specimens attained up to 76.7 MPa of tensile strength, while the flexural strength was up to 159.4 MPa, with a deflection of up to 8 mm and 16 mm, respectively. Solid infills, higher densities, finer layer heights, and added shells significantly improved the strength and stiffness. Grid-patterned and low-density specimens caused poor load-bearing capacities, while hexagonal and gyroid infills offered a more balanced performance. Full article

This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation. Full article

The rapid expansion of wireless communication in urban environments requires antenna systems that balance high electromagnetic performance with stringent aesthetic and security constraints. This review examines recent advances in concealed antenna technologies integrated into building structures, with a focus on performance variation, material-induced attenuation, and emerging concealment strategies. Techniques such as transparent conductors on glass, structural embedding within walls, and camouflage-based designs are shown to significantly influence resonance behavior, radiation efficiency, and pattern characteristics compared to free-space operation. Despite these challenges, optimized solutions including transparent conductive oxide arrays, wideband embedded antenna geometries, and metasurface-enhanced window structures can partially recover performance while maintaining optical transparency above 70%. Material loading effects are found to induce resonant frequency shifts of approximately 10–44%, depending on dielectric properties and environmental conditions. Transparent antenna arrays achieve gains ranging from 0.34 to 13.2 dBi, while signal-transmissive wall systems demonstrate transmission improvements of up to 22 dB relative to untreated building materials. These technologies enable a wide range of applications, including 5G and beyond-5G cellular networks across sub-6 GHz and millimeter-wave bands, as well as Internet of Things systems and smart city infrastructure. However, key challenges remain, including the need for comprehensive characterization of building material electromagnetic properties, optimization of multilayer structural environments, and the development of standardized design and evaluation methodologies. This review provides a unified framework for understanding the tradeoffs associated with antenna concealment and identifies critical research directions for the development of building-integrated wireless systems in next-generation communication networks. Full article

Proton exchange membrane fuel cell vehicles (PEMFCVs) have attracted widespread attention in recent years. However, there are many challenges existing in the development, such as the durability and economy of the fuel cell system (FCS). In this investigation, a fuzzy energy management strategy based on Grey Bernoulli Prediction (FEMS-GBP) is proposed to mitigate these two issues. Grey Bernoulli Prediction (GBP) is used to predict the FCS short-term future power demand with a low calculation amount, which is suitable for real-time on-board applications in PEMFCVs. Therefore, FEMS-GBP can proactively adjust FCS output power to reduce large load change times during PEMFCV operation, thereby improving FCS durability. Fuzzy control is employed to accomplish the energy management task between the FCS and the battery for better fuel economy. Numerical simulations and experiments under different vehicle driving cycles are carried out to evaluate the performance of FEMS-GBP. By comparing it with two other conventional energy management strategies, FEMS-GBP is demonstrated to be feasible and effective, as it achieves favorable performance in balancing durability and economy, especially under practical driving conditions. Full article

To address the deteriorated control performance of permanent magnet synchronous motor (PMSM) drive systems for new energy vehicles (NEVs) under complex conditions caused by multi-source disturbances (internal parameter perturbations and external load mutations), this paper proposes an improved terminal integral sliding mode control (ITISMC-ADERL) strategy integrating a piecewise adaptive terminal integral sliding mode surface and an ADERL. The proposed sliding mode surface adopts interval-adaptive switching between high- and low-order power terms, completely eliminating singularity and integral saturation defects of traditional terminal sliding mode control while ensuring fast convergence, and achieving an optimal structural balance between convergence speed and chattering suppression. The state-dependent ADERL leverages the synergy of error-sliding variable coupled dynamic gain adjustment and variable exponential power compensation, realizing dual-mode adaptive switching of “strong driving for fast approaching far from the sliding surface, weak gain for smooth regulation near the sliding surface”, which significantly improves control accuracy and anti-disturbance robustness. The finite-time convergence of the closed-loop system is rigorously proved via Lyapunov stability theory. Full-operating-condition comparative tests on a TMS320F28379D DSP platform show that the proposed strategy outperforms SMC-ERL, ISMC-ERL and ITISMC-ERL in all test scenarios (no-load startup, acceleration/deceleration, sudden load changes, flux linkage perturbation), meeting the requirements of high-performance NEV drive systems and possessing important engineering application potential. Full article

Renewable energy sources, such as solar and biomass, represent sustainable alternatives to meet the growing energy demands of the residential sector. This study evaluated the energy, exergy, and environmental performance of two Brayton configurations using supercritical carbon dioxide: a recompression cycle (SRC) and a recompression cycle with intercooling in the main compression (SMC), both coupled to a dual-loop organic Rankine cycle (DORC) and powered by a hybrid solar-biomass thermal system. Mass, energy, and exergy balances were developed, and a life cycle assessment was performed to quantify the environmental impact. The systems were designed to cover a cooling load of 130 kW corresponding to 200 dwellings constructed with Asbestos cement in the Colombian Caribbean region. The results show that both configurations meet the required demand; the SMC-DORC cycle operates at 650 °C, while the SRC-DORC requires 750 °C. The SRC-DORC exhibits higher thermal efficiency (53.24%), while the SMC-DORC achieves a slightly higher exergy efficiency (28.15%). Environmental analysis shows that the construction phase accounts for the majority of the total impact, exceeding 95% of emissions. Overall, both configurations are technically feasible, with the SRC-DORC standing out for its balance between efficiency and environmental impact. Full article

Power systems with high renewable penetration impose multi-dimensional demands on energy storage (ES) regulation. Short-duration ES is required for power balance and frequency support, while medium- and long-duration ES is essential for daily, weekly, and seasonal peak shaving and energy time-shifting. Aiming at the challenge of multi-timescale configuration of hybrid energy storage (HES) in the initial planning stage of carbon-neutral transition, this paper proposes an optimal configuration strategy combining STL-PDM-VMD. First, the seasonal and trend decomposition using Loess (STL) is used to extract quarterly trends of annual net power for seasonal ES configuration. Then, the Past Decomposable Mixing (PDM) module in the time-mixer model is applied to decouple and mix multi-scale features of the detrended power curve for monthly and weekly configurations. Finally, an improved Variational Mode Decomposition (VMD) is adopted to decompose daily net power fluctuations and optimize intra-day energy storage schemes. Based on actual data from a carbon-neutral transition region, simulations are carried out and compared with the VMD method with decomposition layers optimized by Gurobi. The results show that the proposed STL-PDM-VMD multi-timescale hybrid energy storage configuration strategy can effectively capture the multi-timescale fluctuation characteristics of net load, significantly improve the Renewable Energy (RE) penetration rate, and ensure the power and energy balance of the new power system at multiple timescales. penetration, and maintain power and energy balance in the new-type power system. Full article

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

Fibroin-based films represent a promising platform for sustainable and bio-derived materials. Existing literature has mainly focused on isolated molecules, plasticizers, or chemical cross-linkers, and the function of complex, multi-component natural extracts as structure-modulating agents in fibroin films remains poorly understood. In this study, edible films containing mulberry leaf extract (MLE; 2–8 wt%) and fibroin (8 wt%) were prepared by solution casting, and their structures were investigated using spectroscopic, morphological, thermal, mechanical, and barrier property analyses. The results reveal that MLE induces concentration-dependent changes in film performance through multicomponent, non-covalent interactions with the fibroin. An approximately 187% increase in tensile strength was achieved at high MLE concentration, confirming effective physical reinforcement. The water vapor transmission rate decreased markedly from 0.888 to 0.170 g·h⁻¹·m⁻², indicating an enhanced moisture barrier, whereas oxygen permeability increased at higher extract loadings, suggesting localized chain rearrangements. High optical transparency in the visible region was maintained (79.95–83.77%), while UV response was selectively altered with extract concentration. Overall, the 8MLE formulation exhibited the most balanced performance. This study demonstrates that plant-derived extracts can serve as effective natural modifiers for tailoring fibroin film properties without inducing crystallization, offering a sustainable strategy for designing bio-based and edible protein film systems. Full article

Accurate electric vehicle (EV) charging load forecasting is essential for grid planning and resource allocation, yet existing approaches struggle with the inherent sparsity of charging data—a phenomenon characterized by excessive zeros representing periods of no charging activity. This paper addresses this challenge through a novel framework combining a Zero-Inflated Neural Network (ZINN) architecture with an Evolutionary Neural Architecture Search (ENAS) algorithm. ZINN explicitly decomposes the forecasting problem into binary classification (predicting charging occurrence) and regression (estimating energy magnitude conditioned on occurrence), enabling the model to learn distinct patterns for the absence and presence of charging events. Rather than relying on manually designed architectures, ENAS automatically discovers optimal encoder and decoder configurations from a comprehensive search space encompassing modern architectures (LSTM, GRU, Transformer, and iTransformer), layer configurations, activation functions, and hyperparameters. The evolutionary algorithm balances prediction accuracy with computational efficiency through multi-objective optimization. Extensive experiments on real-world EV charging data from 30 stations in Wuhan demonstrate that the ZINN+ENAS framework achieves the lowest prediction error compared to conventional baselines, with the discovered optimal configuration substantially outperforming hand-crafted designs. Comprehensive ablation studies reveal that the asymmetric dual-head architecture and adaptive regularization strategies are critical for handling data sparsity. These findings highlight the importance of explicit zero-inflation modeling and automated architecture discovery for specialized forecasting tasks, providing practitioners with an open-source framework for practical EV charging load prediction. Full article

This review comprehensively examines the incorporation of nanoparticles (NPs) into biopolymers for 3D printing in biomedical applications, integrating material design, processing strategies, and translational considerations within a unified framework. Different types of NPs are analyzed regarding their effects on mechanical reinforcement, rheological modulation, and structural organization of biopolymeric matrices. The discussion covers principal additive manufacturing technologies, including extrusion-based systems such as fused deposition modeling (FDM) and direct ink writing (DIW), vat photopolymerization, powder-bed fusion (SLS), and emerging in situ nanoparticle formation approaches, emphasizing how nanoparticle loading and surface functionalization govern yield stress, shear-thinning behavior, viscoelastic recovery, and dimensional fidelity while mitigating agglomeration and optimizing interfacial interactions. Comparative evaluation of compressive modulus, strength, toughness, crystallinity, and porosity establishes structure–property–processing relationships directly linked to printability and functional performance. Biomedical applications are addressed in tissue engineering, biosensing, controlled and targeted drug delivery, and bioimaging, highlighting the balance between bioactivity and manufacturability. Finally, critical challenges—including compatibility, reproducibility, biological safety, long-term stability, regulatory adaptation, and environmental impact—are discussed, alongside future perspectives focused on green nanomaterials, AI-driven predictive formulation design, and digital twins for real-time monitoring and quality control in nano-enabled additive manufacturing. Full article

Children’s scooters, as products integrating mobility, safety, and developmental functions, require systematic and reliable design decision-making approaches. However, existing processes often suffer from unsystematic user demand extraction, strong subjectivity in weight determination, and insufficient quantitative support for evaluating alternative schemes. To address these issues, this study proposes an integrated AHP–CRITIC–VIKOR framework for engineering-oriented design optimization. User requirements are identified through field investigation, questionnaires, and affinity diagram analysis, and a multi-level evaluation indicator system is constructed. AHP is applied to determine subjective weights, while CRITIC incorporates objective data characteristics, enabling balanced weighting. VIKOR is then used to evaluate design schemes and obtain compromise solutions under multi-criteria conflicts. The results show that safety-related factors, including material safety, braking performance, and load-bearing capacity, dominate the decision process. The optimal scheme demonstrates the closest proximity to the ideal solution. Sensitivity analysis confirms the robustness of the model, and comparison with TOPSIS shows consistent results and improved compromise decision capability. The proposed framework enhances decision reliability and provides an effective quantitative tool for multi-criteria product design optimization. Full article

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article