Search Results (25,878)

Many kinds of panel structures are proposed in aircraft design. This study presents a topology optimization method to improve the buckling resistance of panel structures. It should be noted that a popular configuration of the present panel structure is that with ribs and frames. Stiffening characteristics (i.e., effects of increasing structural stiffness of a panel structure with ribs and frames) are thus included during analysis of panel structures. After studying the coupling relationship between the dynamic characteristics and buckling behavior of the panel, a developed MMC (moving morphable component) method is proposed for topology optimization to improve the buckling resistance of the panel. It is seen that the coupling relationship between the dynamic characteristics and buckling behavior of the panel is mainly reflected when the compression force acts on the panel, corresponding that dynamic characteristics will vary with the load. If the load acts on the structure, the first-order natural frequency of the panel with ribs and frames in this study decreases with the increase in the load, with the optimization objective of maximizing the first-order natural frequency. Based on the coupling relationship between dynamic characteristics and buckling behavior, the critical buckling load of the panel increases as the first-order natural frequency increases. The present optimization method can reduce computational complexity without changing the accuracy of the calculation. At the same time, the coupling relationship between dynamic characteristics and buckling behavior is applied in topology optimization, which is of great significance to improve the comprehensive performance of panel structures in the engineering design process. This paper improves the dynamic characteristics and buckling resistance of panels with ribs and frames based on the improved MMC method. The proposed method effectively meets the design requirements of flight vehicle design in complex environments. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

The Gliomas account for 81% of all malignant central nervous system tumors and are classified by WHO into four grades of malignancy. Glioblastoma multiforme (GBM), the most common grade IV glioma, exhibits an extremely aggressive phenotype and a dismal five-year survival rate of only 6%, underscoring the urgent need for novel therapeutic approaches. Immunotherapy has emerged as a promising strategy, and photodynamic therapy (PDT) in particular has attracted attention for its dual cytotoxic and immunostimulatory effects. In GBM models, PDT induces immunogenic cell death characterized by the release of damage-associated molecular patterns (DAMPs), which promote antigen presentation and activate T cell responses. Additionally, PDT transiently increases blood–brain barrier permeability, facilitating immune cell infiltration into the tumor microenvironment, and enhances clearance of waste products via stimulation of meningeal lymphatic vessels. Importantly, PDT can reprogram or inactivate immunosuppressive tumor-associated macrophages, thereby counteracting the pro-tumoral microenvironment. Despite these encouraging findings, further preclinical and clinical studies are required to elucidate PDT’s underlying immunological mechanisms fully and to optimize treatment regimens that maximize its efficacy as part of integrated immunotherapeutic strategies against GBM. Full article

Background: Resistance training (RT) promotes muscle hypertrophy and strength gains in both men and women. However, sex differences in neuromuscular performance, muscle fiber composition, and the hormonal environment influence strength and power adaptations. While men generally exhibit greater absolute and relative strength, it remains unclear to what extent these differences persist across various load intensities. A better understanding of sex-specific strength and power profiles may help optimize training strategies. The aim of this study was to compare strength and power performance during the bench press exercise in physically active males and females, relative to body mass and fat-free mass (FFM). Methods: Twenty-nine physically active individuals (16 men: 21.3 ± 4.1 years, 13 women: 22.6 ± 4.9 years) performed a one-repetition maximum (1RM) test and an incremental velocity-based assessment at 45%, 55%, 65%, 75%, and 85% of the 1RM using a Smith machine. The barbell velocity was measured via a linear transducer, with the mean propulsive velocity (MPV) recorded for each load. Power-related variables (e.g., peak force [F0], maximal velocity [V0], and maximal power [Pmax]) were analyzed. To account for differences in body composition, data were adjusted for body mass and FFM. Results: Men exhibited significantly greater strength and power than women across most loads when adjusted for both body mass and fat-free mass (FFM) (p < 0.05). These differences were particularly pronounced when normalized to FFM (45–75%1RM; p = 0.001–0.031), with large effect sizes observed (ηp² = 0.185–0.383). Notably, sex differences in mean propulsive velocity (MPV) disappeared at 85%1RM (p = 0.208; ηp² = 0.06), suggesting that maximal neuromuscular recruitment may minimize sex-related disparities at higher intensities. Furthermore, men demonstrated significantly higher values in six of the seven power-related variables, with no significant differences in the %1RM required to achieve an optimal power output. Conclusions: These findings confirm that men exhibit greater strength and power than women, even after adjusting for body composition. However, at high relative loads (≥85%1RM), sex differences in movement velocity appear to diminish, likely due to similar recruitment patterns of high-threshold motor units. These results highlight the importance of sex-specific resistance training programs, particularly in relation to load prescription and the application of velocity-based training methods. Full article

In the context of the dual-carbon goals, this study proposes a cooperative game-theoretic optimization strategy to enhance the energy utilization efficiency, operational efficiency, and cost-effectiveness of integrated energy systems (IESs) while simultaneously reducing carbon emissions, improving operational flexibility, and mitigating renewable energy variability. To achieve these goals, an IES framework integrating power-to-gas (P2G) technology and carbon capture and storage (CCS) facilities is established to regulate carbon emissions. The system incorporates P2G conversion units and thermal components—specifically, hydrogen fuel cells, electrolyzers, reactors, and electric boilers—aiming to maximize energy conversion efficiency and asset utilization. A cooperative game-theoretic optimization model is developed to facilitate collaboration among multiple stakeholders within the coalition, which employs the Shapley value method to ensure equitable distribution of the cooperative surplus, thereby maximizing collective benefits. The model is solved using an improved gray wolf optimizer (IGWO). The simulation results demonstrate that the proposed strategy effectively coordinates multi-IES scheduling, significantly reduces carbon emissions, facilitates the efficient allocation of cooperation gains, and maximizes overall system utility. Full article

The increasing adoption of electric vehicles (EVs) requires efficient management of charging infrastructure, particularly in optimizing the allocation of limited charging resources. This paper addresses the preemptive electric vehicle charging scheduling problem (EVCSP), where charging sessions can be interrupted to maximize the number of satisfied demands. The existing mathematical formulations often struggle with scalability and computational efficiency for even small problem instances. As a result, we propose an enhanced mathematical programming model, which is further refined to reduce decision variable complexity and improve computational performance. In addition, a constraint programming (CP) approach is explored as an alternative method for solving the EVCSP due to its strength in handling complex scheduling constraints. The experimental results demonstrate that the developed methods significantly outperform the existing models in the literature, providing scalable and efficient solutions for optimizing EV charging infrastructure. Full article

We develop novel extensions in the theory of weighted Lorentz spaces. In particular, we generalize classical results by introducing variable-exponent Lorentz spaces, establish sharp constants and quantitative bounds for maximal operators, and extend the framework to encompass fractional maximal operators. Moreover, we analyze endpoint cases through the study of oscillation operators and reveal new connections with weighted Hardy spaces. These results provide a unifying approach that not only refines existing inequalities but also opens new avenues in harmonic analysis and partial differential equations. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

This study investigates the impact of rural digital economy development on agricultural carbon emission efficiency, aiming to elucidate the intrinsic mechanisms and pathways through which digital technology enables low-carbon transformation in agriculture, thereby contributing to the achievement of agricultural carbon neutrality goals. Based on provincial-level panel data from China spanning 2011 to 2022, this study examines the relationship between the rural digital economy and agricultural carbon emission efficiency, along with its underlying mechanisms, using bidirectional fixed effects models, mediation effect analysis, and Spatial Durbin Models. The results indicate the following: (1) A significant N-shaped-curve relationship exists between rural digital economy development and agricultural carbon emission efficiency. Specifically, agricultural carbon emission efficiency exhibits a three-phase trajectory of “increase, decrease, and renewed increase” as the rural digital economy advances, ultimately driving a sustained improvement in efficiency. (2) Industrial integration acts as a critical mediating mechanism. Rural digital economy development accelerates the formation of the N-shaped curve by promoting the integration between agriculture and other sectors. (3) Spatial spillover effects significantly influence agricultural carbon emission efficiency. Due to geographical proximity, regional diffusion, learning, and demonstration effects, local agricultural carbon emission efficiency fluctuates with changes in neighboring regions’ digital economy development levels. (4) The relationship between rural digital economy development and agricultural carbon emission efficiency exhibits a significant inverted N-shaped pattern in regions with higher marketization levels, planting-dominated areas of southeast China, and digital economy demonstration zones. Further analysis reveals that within rural digital economy development, production digitalization and circulation digitalization demonstrate a more pronounced inverted N-shaped relationship with agricultural carbon emission efficiency. This study proposes strategic recommendations to maximize the positive impact of the rural digital economy on agricultural carbon emission efficiency, unlock its spatially differentiated contribution potential, identify and leverage inflection points of the N-shaped relationship between digital economy development and emission efficiency, and implement tailored policy portfolios—ultimately facilitating agriculture’s green and low-carbon transition. Full article

According to the World Health Organization (WHO), a healthy lifestyle is defined as a way of living that lowers the risk of becoming seriously ill or dying prematurely. Physical activity, as a well-known contributor to overall health, plays a vital role in supporting such a lifestyle. Exercise induces complex molecular responses that mediate both acute metabolic stress and long-term physiological adaptations. FGF21 (fibroblast growth factor 21) and GDF15 (growth differentiation factor 15) are recognized as metabolic stress markers, while their receptors play critical roles in cellular signaling. However, the differential gene expression patterns of these molecules in trained and untrained individuals following exhaustive exercise remain poorly understood. This study aimed to examine the transcriptional and protein-level responses in trained and untrained individuals performed a treadmill maximal exercise test to voluntary exhaustion. Blood samples were collected at six time points (pre-exercise, immediately post-exercise, and 0.5 h, 6 h, 24 h, and 48 h post-exercise). Gene expression of FGF21, GDF15, FGFR1 (fibroblast growth factor receptors), FGFR3, FGFR4, KLB (β-klotho), and GFRAL (glial cell line-derived neurotrophic factor receptor alpha-like) was analyzed using RT-qPCR, while plasma protein levels of FGF21 and GDF15 were quantified via ELISA. The results obtained were statistically analyzed by using Shapiro–Wilk, Mann–Whitney U, and Wilcoxon tests in Statistica 13 software. Untrained individuals demonstrated significant post-exercise upregulation of FGFR3, FGFR4, KLB, and GFRAL. FGF21 and GDF15 protein levels were consistently lower in trained individuals (p < 0.01), with no significant correlations between gene and protein expression. Trained individuals showed more stable expression of genes, while untrained individuals exhibited transient upregulation of genes after exercise. Full article

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

Inspired by real-world logistics scenarios, in this paper, we introduce a new variant of the Orienteering Problem known as the Multi-zone Orienteering Problem with Time Windows (MzOPTW). In the MzOPTW, customers are situated in distinct zones, each with multiple entrances and exits. Each customer has specific time window requirements; access to them will generate certain profits. This problem is to simultaneously determine which zones and customers to visit, select the zonal entrances and exits, and generate the routes for visiting each zone and its customers, all while maximizing total profits within a limited time frame. To tackle the MzOPTW, this paper develops an integer programming model. There are significant computational challenges in the strong interdependencies among zone selection, customer selection within zones, entrance and exit selection for each zone, the sequence of visits to zones and customers, and arrival and stay times. To address these challenges, this paper proposes a learning-enhanced metaheuristic algorithm called the Hybrid Ant Colony Optimization (HACO) algorithm, which incorporates Pointer Network learning. The HACO algorithm combines the global search capabilities of a population-based algorithm with the parallel decision-making abilities of the Pointer Network learning model. Additionally, a method to optimize zonal stay time limits is proposed to further enhance the solution. Experimental results demonstrate that the HACO algorithm outperforms comparative algorithms, achieving better solutions in 73% of the instances within the same time frame. Furthermore, the proposed optimization method for zonal stay time limits results in improvements in 78% of instances. Full article

An analog joint source-channel coding (AJSCC) based on Shannon–Kotel’nikov (S-K) mapping transmitting discrete-time encoded samples in orthogonal frequency division multiplexing (OFDM) systems over wireless channel has exhibited excellent performance. However, the phenomenon of carrier frequency offset (CFO) caused by the frequency mismatch between the transmitter’s and receiver’s local oscillators often exists in actual scenarios; thus, in this paper the performance of AJSCC-OFDM with CFO is analyzed and the S-K mapping is optimized. A joint optimization strategy is developed to maximize the signal-to-distortion ratio (SDR) subject to CFO constraints. Considering that the optimized AJSCC-OFDM strategies will change the amplitude distribution of encoded symbol, the peak-to-average power ratio (PAPR) characteristics under different AJSCC parameters are also analyzed. Full article

Background: Physical exercise during pregnancy is strongly recommended due to its well-established benefits for both mother and child. However, its impact on the pelvic floor remains insufficiently studied. This study aimed to evaluate pelvic floor adaptations to a structured prenatal exercise program using transperineal ultrasound, and to assess associations with the duration of the second stage of labor and mode of delivery. Methods: This is a planned secondary analysis of a randomized controlled clinical trial (RCT) (NCT04563065) including women with singleton pregnancies at 12–14 weeks of gestation. Participants were randomized to either an exercise group, which followed a supervised physical exercise program three times per week, or a control group, which received standard antenatal care. Transperineal ultrasound was used at the second trimester of pregnancy and six months postpartum to measure urogenital hiatus dimensions at rest, during maximal pelvic floor contraction, and during the Valsalva maneuver, to calculate hiatal contractility and distensibility and to evaluate levator ani muscle insertion. Regression analyses were performed to assess the relationship between urogenital hiatus measurements and both duration of the second stage of labor and mode of delivery. Results: A total of 78 participants were included in the final analysis: 41 in the control group and 37 in the exercise group. The anteroposterior diameter of the urogenital hiatus at rest was significantly smaller in the exercise group compared to controls (4.60 mm [SD 0.62] vs. 4.91 mm [SD 0.76]; p = 0.049). No other statistically significant differences were observed in static measurements. However, contractility was significantly reduced in the exercise group for both the latero-lateral diameter (8.54% vs. 4.04%; p = 0.012) and hiatus area (20.15% vs. 12.55%; p = 0.020). Distensibility was similar between groups. There were no significant differences in the duration of the second stage of labor or mode of delivery. Six months after delivery, there was an absolute risk reduction of 32.5% of levator ani muscle avulsion in the exercise group compared to the control group (53.3% and 20.8%, respectively; p = 0.009). Conclusions: A supervised exercise program during pregnancy appears to modify pelvic floor morphology and function, reducing the incidence of levator ani muscle avulsion without affecting the type or duration of delivery. These findings support the safety and potential protective role of prenatal exercise in maintaining pelvic floor integrity. Full article

This paper presents a large-scale empirical study aimed at identifying the optimal local deep learning model and data volume for deploying intrusion detection systems (IDS) on resource-constrained IoT devices using federated learning (FL). While previous studies on FL-based IDS for IoT have primarily focused on maximizing accuracy, they often overlook the computational limitations of IoT hardware and the feasibility of local model deployment. In this work, three deep learning architectures—a deep neural network (DNN), a convolutional neural network (CNN), and a hybrid CNN+BiLSTM—are trained using the CICIoT2023 dataset within a federated learning environment simulating up to 150 IoT devices. The study evaluates how detection accuracy, convergence speed, and inference costs (latency and model size) vary across different local data scales and model complexities. Results demonstrate that CNN achieves the best trade-off between detection performance and computational efficiency, reaching ~98% accuracy with low latency and a compact model footprint. The more complex CNN+BiLSTM architecture yields slightly higher accuracy (~99%) at a significantly greater computational cost. Deployment tests on Raspberry Pi 5 devices confirm that all three models can be effectively implemented on real-world IoT edge hardware. These findings offer practical guidance for researchers and practitioners in selecting scalable and lightweight IDS models suitable for real-world federated IoT deployments, supporting secure and efficient anomaly detection in urban IoT networks. Full article