Search Results (2,932)

Short fiber-reinforced polymer (SFRP) has been extensively applied in structural engineering due to its exceptional specific strength and superior mechanical properties. Its mechanical behavior under medium strain rate conditions has become a key focus of ongoing research. A comprehensive understanding of the response characteristics and underlying mechanisms under such conditions is of critical importance for both theoretical development and practical engineering applications. This study proposes an innovative three-dimensional (3D) multiscale constitutive model that comprehensively integrates mesoscopic fiber–matrix interface effects and pore characteristics. To systematically investigate the dynamic response and damage evolution of SFRP under medium strain rate conditions, 3D-printed SFRP porous structures with volume fractions of 25%, 35%, and 45% are designed and subjected to drop hammer impact experiments combined with multiscale numerical simulations. The experimental and simulation results demonstrate that, for specimens with a 25% volume fraction, the strain rate strengthening effect is the primary contributor to the increase in peak stress. In contrast, for specimens with a 45% volume fraction, the interaction between damage evolution and strain rate strengthening leads to a more complex stress–strain response. The specific energy absorption (SEA) of 25% volume fraction specimens increases markedly with increasing strain rate. However, for specimens with 35% and 45% volume fractions, the competition between these two mechanisms results in non-monotonic variations in energy absorption efficiency (EAE). The dominant failure mode under impact loading is shear-dominated compression, with damage evolution becoming increasingly complex as the fiber volume fraction increases. Furthermore, the damage characteristics transition from fiber pullout and matrix folding at lower volume fractions to the coexistence of brittle and ductile behaviors at higher volume fractions. The numerical simulations exhibit strong agreement with the experimental data. Multi-directional cross-sectional analysis further indicates that the initiation and propagation of shear bands are the principal drivers of structural instability. This study offers a robust theoretical foundation for the impact-resistant design and dynamic performance optimization of 3D-printed short fiber-reinforced polymer (SFRP) porous structures. Full article

The presence of ciprofloxacin antibiotic in water is a threat to humans and aquatic life since antibiotics are currently regarded as emerging contaminants of major concern. This work reported the use of Nexar^TM film, a sulfonated pentablock copolymer, to effectively remove ciprofloxacin antibiotic from water in a sustainable approach. The removal efficiency of Nexar film was evaluated in aqueous or salty (NaCl 0.5 M) ciprofloxacin solutions as a function of contact time and the initial ciprofloxacin concentration. In the investigated conditions, the polymeric film totally removed ciprofloxacin in MilliQ solution while its removal efficiency in salty solution was approximately 73%. This lower value is due to the presence of Na⁺ ions that compete with antibiotic molecules for adsorption on active surface sites of the polymeric film. No further release of adsorbed antibiotic molecules occurred. The kinetic studies, conducted for ciprofloxacin adsorption on Nexar film in both MilliQ and salty solutions, revealed that the overall sorption process is controlled by the rate of surface reaction between ciprofloxacin molecules and active sites on Nexar surface. Furthermore, at equilibrium conditions, the isotherm model that best fits experimental parameters was not linear. This indicates that the competition between the solute and the solvent for binding sites on the adsorbent should be considered to describe adsorption processes in both MilliQ and salty solutions. Full article

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article

The O3-type layered oxide materials have the advantage of high specific capacity, which makes them more competitive in the practical application of cathode materials for sodium-ion batteries (SIBs). However, the existing reported O3-type layered oxide materials still have a complex irreversible phase transition phenomenon, and the cycle life of batteries needs, with these materials, to be further improved to meet the requirements. Herein, we performed structural characterization and electrochemical performance tests on O3-NaNi_0.6−xFe_0.25Mn_0.15Mg_xO₂ (x = 0, 0.025, 0.05, and 0.075, denoted as NFM, NFM-2.5Mg, NFM-5.0Mg, and NFM-7.5Mg). The optimized NFM-2.5Mg has the largest sodium layer spacing, which can effectively enhance the transmission rate of sodium ions. Therefore, the reversible specific capacity can reach approximately 148.1 mAh g⁻¹ at 0.2C, and it can even achieve a capacity retention of 85.4% after 100 cycles at 1C, demonstrating excellent cycle stability. Moreover, at a low temperature of 0 °C, it also can keep capacity retention of 86.6% after 150 cycles at 1C. This study provides a view on the cycling performance improvement of sodium-ion layered oxide cathodes with a high theoretical specific capacity. Full article

Various factors influence the formation and adjustment of network freight prices, including transportation costs, cargo characteristics, and policies and regulations. The interaction of these factors increases the difficulty of accurately predicting network freight prices through regressions or other machine learning models, especially when the amount and quality of training data are limited. This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. Different data sorting methods and serialization strategies are employed to construct the corpora of LLMs, which are then tested on multiple base models. A few-shot sample dataset is constructed to test the performance of models under insufficient information. The Chain of Thought (CoT) is employed to construct a corpus that demonstrates the reasoning process in freight price prediction. Cross entropy loss with LoRA fine-tuning and cosine annealing learning rate adjustment, and Mean Absolute Error (MAE) loss with full fine-tuning and OneCycle learning rate adjustment to train the models, respectively, are used. The experimental results demonstrate that LLMs are better than or competitive with the best comparison model. Tests on a few-shot dataset demonstrate that LLMs outperform most comparison models in performance. This method provides a new reference for predicting network freight prices. Full article

This study proposes a novel metaheuristic algorithm, Cosmic Evolution Optimization (CEO), for numerical optimization and engineering design. Inspired by the cosmic evolution process, CEO simulates physical phenomena including cosmic expansion, universal gravitation, stellar system interactions, and celestial orbital resonance.The algorithm introduces a multi-stellar framework system, which incorporates search agents into distinct subsystems to perform simultaneous exploration or exploitation behaviors, thereby enhancing diversity and parallel exploration capabilities. Specifically, the CEO algorithm was compared against ten state-of-the-art metaheuristic algorithms on 29 typical unconstrained benchmark problems from CEC2017 across different dimensions and 13 constrained real-world optimization problems from CEC2020. Statistical validations through the Friedman test, the Wilcoxon rank-sum test, and other statistical methods have confirmed the competitiveness and effectiveness of the CEO algorithm. Notably, it achieved a comprehensive Friedman rank of 1.28/11, and the winning rate in the Wilcoxon rank-sum tests exceeded 80% in CEC2017. Furthermore, CEO demonstrated outstanding performance in practical engineering applications such as robot path planning and photovoltaic system parameter extraction, further verifying its efficiency and broad application potential in solving real-world engineering challenges. Full article

Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction data now incorporates multiattribute information, including timestamps, images, and textual content. The information of multiple modalities is difficult to effectively utilize due to their different representation structures and spaces. The existing methods attempt to utilize the above information through simple embedding representation and aggregation, but ignore targeted representation learning for information with different attributes and learning effective weights for aggregation. In addition, existing methods are not sufficient for effectively modeling temporal information. In this article, we propose MTR, a knowledge graph recommendation framework based on mixture of experts network. To achieve this goal, we use a mixture-of-experts network to learn targeted representations and weights of different product attributes for effective modeling and utilization. In addition, we effectively model the temporal information during the user shopping process. A thorough experimental study on popular benchmarks validates that MTR can achieve competitive results. Full article

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This work aims to theoretically describe how the monomer mixture composition in the butadiene–isoprene copolymerization affects the activity of the TiCl₄-Al(i-C₄H₉)₃ catalytic system (expressed by active sites concentration) via kinetic modeling. This enables development of a reliable kinetic model for divinylisoprene rubber synthesis, predicting reaction rate, molecular weight, and composition, applicable to reactor design and process intensification. Active sites concentrations were calculated from experimental copolymerization rates and known chain propagation constants for various monomer compositions. Kinetic equations for active sites formation were based on mass-action law and Langmuir monomolecular adsorption theory. An analytical equation relating active sites concentration to monomer composition was derived, analyzed, and optimized with experimental data. The results show that monomer composition’s influence on active sites concentration is well described by a two-step kinetic model (physical adsorption followed by Ti–C bond formation), accounting for competitive adsorption: isoprene adsorbs more readily, while butadiene forms more stable active sites. Full article

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

Accurate peak detection in the frequency domain is fundamental to reliable range estimation in Frequency-Modulated Continuous-Wave (FMCW) radar systems, particularly in challenging conditions characterized by a low signal-to-noise ratio (SNR) and phase noise impairments. This paper presents a comprehensive comparative analysis of five peak detection algorithms: FFT thresholding, Cell-Averaging Constant False Alarm Rate (CA-CFAR), a simplified Matrix Pencil Method (MPM), SVD-based detection, and a novel Learned Thresholded Subspace Projection (LTSP) approach. The proposed LTSP method leverages singular value decomposition (SVD) to extract the dominant signal subspace, followed by signal reconstruction and spectral peak analysis, enabling robust detection in noisy and spectrally distorted environments. Each technique was analytically modeled and extensively evaluated through Monte Carlo simulations across a wide range of SNRs and oscillator phase noise levels, from $-100$ dBc/Hz to $-70$ dBc/Hz. Additionally, real-world validation was performed using a custom-built harmonic FMCW radar prototype operating in the 2.4–2.5 GHz transmission band and 4.8–5.0 GHz harmonic reception band. Results show that CA-CFAR offers the highest resilience to phase noise, while the proposed LTSP method delivers competitive detection performance with improved robustness over conventional FFT and MPM techniques. Furthermore, the hardware feasibility of each algorithm is assessed for implementation on a Xilinx FPGA platform, highlighting practical trade-offs between detection performance, computational complexity, and resource utilization. These findings provide valuable guidance for the design of real-time, embedded FMCW radar systems operating under adverse conditions. Full article

Rowing is a cyclic sport that consists of repetitive biomechanical actions, with performance being influenced by the balance between propulsive and resistive forces. The current study aimed to assess the relationships between intracycle velocity variation (IVV) and key biomechanical and performance variables in male and female single scullers. Twenty-three experienced rowers (10 females) completed a 2000 m rowing competition, during which boat position and velocity were measured using a 15 Hz GPS, while cycle rate was derived from the integrated triaxial accelerometer sampling at 100 Hz. From these data, it was possible to calculate distance per cycle, IVV, the coefficient of velocity variation (CVV), and technical index values. Males presented higher mean, maximum and minimum velocity, distance per cycle, CVV, and technical index values than females (15.40 ± 0.81 vs. 13.36 ± 0.88 km/h, d = 0.84; 21.39 ± 1.68 vs. 18.77 ± 1.52 km/h, d = 1.61; 11.15 ± 1.81 vs. 9.03 ± 0.85 km/h, d = 1.45; 7.68 ± 0.32 vs. 6.89 ± 0.97 m, d = 0.69; 14.13 ± 2.02 vs. 11.64 ± 1.93%, d = 2.06; and 34.25 ± 4.82 vs. 26.30 ± 4.23 (m²/s·cycle), d = 4.56, respectively). An association between mean velocity and intracycle IVV, CVV, and cycle rate (r = 0.68, 0.74 and 0.65, respectively) was observed in males but not in female single scullers (which may be attributed to anthropometric specificities). In female single scullers, mean velocity was related with distance per cycle and was associated with technical index in both males and females (r = 0.76 and 0.66, respectively). Despite these differences, male and female single scullers adopted similar pacing strategies and CVV remained constant throughout the 2000 m race (indicating that this variable might not be affected by fatigue). Differences were also observed in the velocity–time profile, with men reaching peak velocity first and having a faster propulsive phase. Data provided new information on how IVV and CVV relate to commonly used biomechanical variables in rowing. Technical index (r = 0.87): distance per cycle was associated with technical index in both males and females (r = 0.76 and 0.66, respectively). Future studies should include other boat classes and other performance variables such as the power output and arc length. Full article

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience. Full article

In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters. Full article

From the perspective of credit ratings, this paper studies the impact of air pollution on corporate credit costs and the impact mechanism. Based on 2007–2022 data on A-share listed companies in the Chinese capital market, this paper uses a two-way fixed effects model to examine the impact of air pollution on corporate credit costs and the impact mechanism. The results show that air pollution increases the credit costs for enterprises because air pollution affects the sentiment of rating analysts, leading them to give more pessimistic credit ratings to enterprises located in areas with severe air pollution. The moderating effect analysis reveals that the effect of air pollution on the increase in corporate credit costs is more pronounced for high-polluting industries, manufacturing industries, and regions with weaker bank competition. Further analysis reveals that in the face of rising credit costs caused by air pollution, enterprises tend to adopt a combination strategy of increasing commercial credit financing and reducing the commercial credit supply to cope. Although this response behavior alleviates corporations’ own financial pressure, it may have a negative effect on supply chain stability. This paper provides new evidence that reveals that air pollution is an implicit cost in the capital market, enriching research in the fields of environmental governance and capital markets. Full article