Search Results (1,290)

This study comprehensively investigates the dynamic vibration behavior of multilayer carbon nanotubes with stepped cross-sectional geometries under various boundary conditions, which is crucial for their advanced engineering applications. The methodology integrates classical molecular dynamics simulations to determine the bending stiffness of single-walled and multi-walled atomistic structures, which are subsequently utilized in the Euler–Bernoulli beam theory based on nonlocal elasticity for vibration analysis. The research focuses on elucidating the influence of the μ/L ratio (a key length parameter) and different support conditions on the natural frequencies and mode shapes of these nanostructures. Key findings reveal that the cross-sectional geometry significantly impacts the vibrational characteristics. A consistent trend observed across all examined boundary conditions is a decrease in natural frequencies as the μ/L ratio increases, indicating that increased free length or reduced fixed length leads to lower stiffness and, consequently, reduced natural frequencies. The study presents Frequency Response Functions (FRFs) and the first four mode shapes, which visually confirm these dynamic characteristics. Graphical representations further reinforce the sensitivity of natural frequencies to both the μ/L ratio and support conditions. The systematic analysis presented in this work provides vital data for predicting resonance phenomena, optimizing structural stability, and enabling precise control over the vibrational response of these advanced nanomaterials in diverse engineering applications. Full article

This paper presents a rigid–flexible coupling dynamic modeling framework and a fixed-time control strategy for a flexible space-tethered satellite (STS) system. A high-fidelity rigid–flexible coupling dynamic model of STS is developed using the finite element method, accurately capturing the coupled attitude dynamics of the satellite platform and flexible tether. Leveraging a simplified representation of the STS model, a nonsingular terminal sliding-mode controller (NTSMC) is synthesized via fixed-time stability theory. Uncertainties and disturbances within the system are compensated for by a radial basis function neural network (RBFNN), ensuring strong robustness. The controller’s fixed-time convergence property—with convergence time independent of initial conditions—is established using Lyapunov stability theory, enabling reliable operation in complex space environments. Numerical simulations implemented on the STS rigid–flexible coupling model validate the controller’s efficacy. Comparative analyses demonstrate superior tracking performance and enhanced practicality over conventional sliding-mode controllers, especially in the aspect of chattering suppression for the satellite thrusters. Full article

This study investigates the influence of heavy-layer thickness on shock-accelerated interfacial instabilities in single-mode stratifications using high-order discontinuous Galerkin simulations at a fixed shock Mach number ($M_s=1.22$). By systematically varying the layer thickness, we quantify how acoustic transit time, shock attenuation, and phase synchronization modulate vorticity deposition, circulation growth, and interface deformation. The results show that thin layers ($d=2.5$–5 mm) generate strong and early baroclinic vorticity due to frequent reverberations, leading to rapid circulation growth, vigorous Kelvin–Helmholtz roll-up, and early jet pairing. In contrast, thick layers ($d=20$–40 mm) attenuate and dephase shock returns, producing weaker baroclinic reinforcement, delayed shear-layer growth, and smoother interfaces with reduced small-scale activity, while the intermediate case ($d=10$ mm) exhibits transitional behavior. Integral diagnostics reveal that thin layers amplify dilatational, baroclinic, and viscous vorticity production; sustain stronger circulation and enstrophy growth; and transfer bulk kinetic energy more efficiently into interface deformation and small-scale mixing. Full article

Early train detection is vital for ensuring the safety of railway personnel, particularly in remote locations where fixed signaling infrastructure is unavailable. Unlike many existing solutions that rely on high-power, high-cost sensors and compute platforms, this work presents a lightweight, low-cost, and portable framework designed to run entirely on resource-constrained microcontrollers with just kilobytes of Random Access Memory (RAM). The proposed system uses vibration data from low-cost accelerometers and employs a simple yet effective Linear Regression (LR) model for almost real-time prediction of train arrival times. To ensure feasibility on low-end hardware, a parallel-processing framework is introduced, enabling continuous data collection, Machine Learning (ML) inference, and wireless communication with strict timing and energy constraints. The decision-making process, including data preprocessing and ML prediction, completes in under 10 ms, and alerts are transmitted via LoRa, enabling kilometer-range communication. Field tests on active railway lines confirm that the system detects approaching trains 15 s in advance with no false negatives and a small number of explainable false positives. Power characterization demonstrates that the system can operate for more than 6 days on a 10 Ah battery, with potential for months of operation using wake-on-vibration modes. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article

The societal effects of cybersecurity are widely discussed, but it remains less clear how software security regulations specifically contribute to building a resilient society, particularly in relation to Sustainable Development Goals 5 (Gender Equality), 10 (Reduced Inequalities), and 16 (Peace, Justice and Strong Institutions). This study investigates this connection by examining key EU and U.S. strategies through comparative legal analysis, software development (SDLC) case studies, and a normative–sociological lens. Our findings reveal that major regulations—such as the EU’s Cyber Resilience Act and the U.S. SBOM rules—are not merely reactive, but proactively embed resilience as a fundamental mode of operation. This approach structurally reallocates digital risks from users to manufacturers, reframing software security from a matter of compliance to one of social fairness and institutional trust. We conclude that integrating ‘resilience by design’ into technology rules is more than a technical fix; it is a mechanism that makes digital access fairer and better protects vulnerable populations, enabling technology and society to advance cohesively. Full article

Unmanned Aerial Vehicles (UAVs) have revolutionized various industries due to their adaptability, efficiency, and capability to operate in diverse environments. However, conventional UAV designs face trade-offs between flight endurance and maneuverability. This study explores the design, analysis, and optimization of a biplane quadrotor UAV, integrating the vertical takeoff and landing (VTOL) capabilities of multirotors with the aerodynamic efficiency of fixed-wing aircraft to enhance flight endurance while maintaining high maneuverability. The UAV’s structural design incorporates biplane wings with different NACA airfoil configurations (NACA4415, NACA0015, and NACA0012) to assess their impact on drag reduction, stress distribution, and flight efficiency. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent 2023 R2 (Canonsburg, PA, USA).reveal that the NACA0012 airfoil achieves the highest drag reduction (75.29%), making it the most aerodynamically efficient option. Finite Element Analysis (FEA) further demonstrates that NACA4415 exhibits the lowest structural stress (95.45% reduction), ensuring greater durability and load distribution. Additionally, a hybrid flight control system, combining Backstepping Control (BSC) and Integral Terminal Sliding Mode Control (ITSMC), is implemented to optimize transition stability and trajectory tracking. The results confirm that the biplane quadrotor UAV significantly outperforms conventional quadcopters in terms of aerodynamic efficiency, structural integrity, and energy consumption, making it a promising solution for surveillance, cargo transport, and long-endurance missions. Future research will focus on material enhancements, real-world flight testing, and adaptive control strategies to further refine UAV performance in practical applications. Full article

This study examines the efficiency trade-offs of Controllable Pitch Propeller (CPP) systems by comparing Combination and Fixed operation modes using real ship operational data. The analysis focuses on mechanical efficiency (η_mech), propulsive efficiency expressed through the normalized Relative Propulsive Efficiency Index (RPEI_norm), and fuel consumption. Combination mode consistently maintained higher η_mech across all load conditions, with pronounced advantages at low load and low speed (<50% load, <12 knots), where both propulsive efficiency and fuel economy improved. In contrast, Fixed mode outperformed Combination mode at high load and high speed, exceeding approximately 50% load and 12 knots, as propeller performance approached its optimal operating point despite some sacrifice in engine efficiency. To integrate these effects, a proxy overall efficiency index (η_{overall,proxy} = η_mech × RPEI_norm) was introduced, revealing a crossover point at 0.525 load where the efficiency dominance shifted between modes. These findings demonstrate that neither mode is universally superior, but rather their advantages depend on operating conditions. The results provide practical insights for adaptive operational strategies, enabling real-time switching between modes to optimize fuel consumption and overall propulsion performance while supporting compliance with environmental regulations. Full article

Rural areas often lack convenient delivery logistics services, which has become a major obstacle to their economic development. Network design initiatives that synergize passenger and freight transport have been identified as effective solutions to address this challenge. Building upon this initiative, this study investigates a novel three-echelon location-routing problem that synergizes trucks and fixed-route buses (3E-LRP-TF). The model is designed with an innovative operational mode that enables fixed-route buses and trucks to travel in a parallel manner, representing a valuable extension to traditional integrated passenger–freight distribution network design. A mixed-integer nonlinear programming model with the objective of minimizing the total network cost is constructed to formulate the problem. Furthermore, a bottom-up three-phase adaptive large neighborhood search (ALNS) algorithm is designed to solve the problem. A final empirical study was conducted, with Qingchuan County in China serving as a case study, with the aim of validating the effectiveness of the proposed model and algorithm. The results show that, compared with using trucks alone, the synergistic network system has the potential to reduce costs by more than 5% for parcel pickup and delivery services. The proposed algorithm can address larger-scale problems and exhibits better performance with regard to solution quality and efficiency. Sensitivity analysis indicates that the parcel transport capacity of bus routes exerts a nonlinear effect on total costs, and changes in service radius result in trade-offs between cost and accessibility. These findings provide actionable insights for policymakers and logistics operators. Full article

This paper addresses the path tracking problem of underactuated unmanned surface vessels (USVs) in the presence of unknown external disturbances. A fixed-time adaptive integral sliding mode control (AISMC) method, incorporating a nonlinear disturbance observer (NDO), is proposed. Initially, a three-degree-of-freedom dynamic model of the USV is developed, accounting for external disturbances and model uncertainties. Based on the vessel’s longitudinal and transverse dynamic position errors, a virtual control law is designed to ensure fixed-time convergence, thereby enhancing the position error convergence speed. Next, a fixed-time NDO is introduced to estimate real-time external perturbations, such as wind, waves, and currents. The observed disturbances are fed back into the control system for compensation, thereby improving the system’s disturbance rejection capability. Furthermore, a sliding mode surface is designed using a symbolic function to address the issue of sliding mode surface parameter selection, leading to the development of the adaptive integral sliding mode control strategy. Finally, compared with traditional SMC and PID, the proposed AISMC-NDO offers higher accuracy, faster convergence, and improved robustness in complex marine environments. Full article

Green innovation (GI) is a key measure for enterprises to realize green transformation and sustainable development. Top management teams’ environmental attention (TMTEA) plays a critical role in shaping organizational strategic direction, value orientation, management mode, and behavioral patterns, serving as a micro-foundation for GI. Based on exploring the relationship between TMTEA and GI, this study adopts the ambidexterity theory to categorize dual green innovation (Dual_GI) into breakthrough green innovation (BGI) and progressive green innovation (PGI), and examines the impact of TMTEA on Dual_GI from the perspectives of external government environmental regulation (GER) and internal absorptive capacity (AC). Drawing on the attention-based view (ABV), this study uses data samples of Chinese A-share listed companies from 2010 to 2022 and establishes a fixed-effect model to empirically test this relationship. The results show the following: (1) TMTEA has a positive impact on corporate Dual_GI, and the promotion effect on PGI is more significant. (2) Both GER and AC can positively moderate the impact of TMTEA on Dual_GI, and both have a stronger moderating effect on TMTEA on PGI. (3) Further analysis shows that this driving effect is more obvious in state-owned enterprises, non-heavy polluting enterprises and enterprise maturity, and TMTEA can also drive Dual_GI to improve sustainable development performance. This study deepens the research scope and boundary conditions of TMT’s micro-psychological cognition and GI. It provides new insights for managers in emerging economies to rebalance their companies’ economic benefits and environmental transformation. Full article

The ground taxiing phase is a crucial stage for the autonomous takeoff and landing of fixed-wing unmanned aerial vehicles (UAVs), and its trajectory tracking accuracy and stability directly determine the success of the UAV’s autonomous takeoff and landing. Therefore, researching the adaptive robust optimal control technology for UAV taxiing is of great significance for enhancing the autonomy and environmental adaptability of UAVs. This study integrates the linear quadratic regulator (LQR) with sliding mode control (SMC). A compensation control signal is generated by the SMC to mitigate the potential effects of uncertain parameters and random external disturbances, which is then added onto the LQR output to achieve a robust optimal controller. On this basis, through ANFIS (Adaptive Neuro-Fuzzy Inference System), the nonlinear mapping relationship between multiple state parameters such as speed, lateral/heading deviation and the weight matrix of the LQR controller is learned, realizing a data-driven adaptive adjustment mechanism for controller parameters to improve the tracking accuracy and anti-interference stability of the UAV’s taxiing trajectory. Full article

Background/Objectives: Intraoperative aberrometry has gained clinical relevance in correcting aberrations during cataract and corneal refractive surgeries. As wavefront aberrations are typically measured with patients seated, while surgeries are performed supine, this study aimed to compare ocular aberrations between seated and supine positions, using a hand-held Hartmann–Shack aberrometer. Methods: Total ocular wavefront aberrations were measured five times consecutively from cyclopledged eyes for a 6 mm pupil, under three conditions: (1) seated with a holder-fixed mode (reference), (2) seated with a hand-held mode, and (3) supine with a hand-held mode. Condition 2 was included to assess potential measurement errors from the hand-held mode. Repeatability was assessed using the standard deviation of repeated measurements (Srm) and the intraclass correlation coefficient (ICC). Differences among the three conditions were analyzed to evaluate the effects of positional change and hand-held stability on ocular wavefront aberration measurements. Results: Eighteen healthy subjects (36 eyes) were enrolled. The Srms for the sphere were 0.37, 0.38, and 0.40 diopters (D); and for the cylinder, 0.10, 0.10, and 0.11 D, with no significant differences across conditions. ICC values exceeded 0.9 for both lower-order aberrations (LOAs) and higher-order aberrations (HOAs), indicating excellent repeatability. The mean root mean square HOAs (HOA_RMS) were 0.48, 0.49, and 0.45 µm, with no statistically significant differences by position (p = 0.913) or measurement mode (p = 0.966). Conclusions: The hand-held Hartmann–Shack aberrometer demonstrated satisfactory repeatability for LOAs and HOAs. Supine measurements did not differ from seated, supporting the feasibility of extending preoperative results to intraoperative aberrometry, regardless of positional differences. Full article

With the rapid development of deep-sea resource exploration and marine scientific research, wheeled remotely operated vehicles (ROVs) have become crucial for seabed operations. However, under complex seabed conditions, traditional ROV control systems suffer from insufficient trajectory tracking accuracy, poor disturbance rejection capability, and low dynamic torque distribution efficiency. These issues lead to poor motion stability and high energy consumption on sloped terrains and soft substrates, which limits the effectiveness of deep-sea engineering. To address this, we proposed a comprehensive motion control solution for deep-sea wheeled ROVs. To improve modeling accuracy, a coupled kinematic and dynamic model was developed, together with a body-to-terrain coordinate frame transformation. Based on rigid-body kinematics, three-degree-of-freedom kinematic equations incorporating the slip ratio and sideslip angle were derived. By integrating hydrodynamic effects, seabed reaction forces, the Janosi soil model, and the impact of subsidence depth, a dynamic model that reflects nonlinear wheel–seabed interactions was established. For optimizing disturbance rejection and trajectory tracking, a hierarchical control method was designed. At the kinematic level, an improved model predictive control framework with terminal constraints and quadratic programming was adopted. At the dynamic level, non-singular fast terminal sliding mode control combined with a fixed-time nonlinear observer enabled rapid disturbance estimation. Additionally, a dynamic torque distribution algorithm enhanced traction performance and trajectory tracking accuracy. Full article

This paper presents an enhanced operation strategy for a recently proposed converter called Modified Non-Inverting Step-Down/Up (MNI-SDU) DC-DC converter intended for battery voltage regulation. Unlike the conventional approach, where both switching stages share a single duty cycle, the proposed method controls asynchronously the two duty cycles through a fixed time offset to optimize performance. A methodology is developed to define suitable duty cycle ranges that ensure proper converter operation according to input/output voltage specifications, while simultaneously reducing the current and voltage ripples and electrical stress in the capacitor and semiconductors. Furthermore, a model-based control strategy is proposed, taking into account the enhanced operational characteristics. Consequently, a PI-PI current-mode controller is designed using loop shaping techniques to maintain the output voltage regulated at the desired level. The proposed approach is analyzed mathematically and validated through experimental results. The findings demonstrate that optimizing through asynchronous duty-cycle control with a fixed time offset improves ripple, stress values, and overall efficiency, while maintaining robust output voltage regulation, making this method well-suited for applications requiring compact and reliable power conversion. Full article