Search Results (1,063)

This study presents an asymmetric cooperative perception-and-control architecture in which a sensor-rich mobile master vehicle performs external perception, state estimation, motion planning, and path-tracking control for sensor-light slave vehicles. The proposed vehicle-model-aided LiDAR tracking (VMALT) method combines ego-motion-compensated LiDAR measurements with transmitted speed and steering commands through a kinematic bicycle model. In physical-vehicle experiments, VMALT reduced the slave-position RMSE from 0.36 m with a LiDAR-only constant-velocity Kalman filter to 0.32 m, corresponding to an 11.1% improvement. The estimated state was subsequently used for closed-loop speed and path-tracking control of the physical slave vehicle. Scalability was evaluated using a one-master–two-slave configuration over three separate runs comprising circular and linear paths and geometrically identified line-of-sight-overlap candidates. Concurrent two-target LiDAR availability ranged from 96.465% to 99.934%, with a maximum interior observation gap of 0.10 s. The VMALT trajectories expressed in the GPS coordinate frame followed the position and direction of both slave vehicles, with heading RMSEs ranging from $1.{835}^{\circ }$ to $4.{609}^{\circ }$. In an LTE-tethering communication test, all 2400 UDP packets were returned, with median and 99th-percentile round-trip times of 4.654 and 8.220 ms, respectively. These results demonstrate the feasibility of centralized perception and control for multiple sensor-light vehicles under the evaluated low-speed operating conditions. Full article

Background: Healthcare university students face disruptive sleep schedules and irregular meals caused by busy schedules, multiple activities, and stressful internships. This study aimed to investigate the complex relationships between sleep quality, psychological eating dimensions (cognitive restraint, uncontrolled eating, and emotional eating), accommodation, history of diets and late-night food intake, while evaluating the impact of demographic factors such as age, sex, and residential background. Methods: A cross-sectional survey was conducted among a sample of Romanian healthcare university students (n = 265; 74.3% women). Psychometric evaluation included the Pittsburgh Sleep Quality Index (PSQI) and the Three-Factor Eating Questionnaire (TFEQ-R21). Sociodemographic data (age, gender, environment and accommodation) and information about diets were collected. Late-night food intake was defined as eating after 21:00. Data processing and analysis were performed using IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 26 (SPSS Inc., Chicago, IL, USA). Results: Students exhibit poor global sleep quality, which positively correlated with all TFEQ-R21 subscales. Students with a prior history of following weight-loss diets demonstrated significantly elevated levels of intentional food monitoring and psychological eating disinhibition compared to non-dieters. Urban students feel greater psychological pressure to constantly and consciously control what they eat. Students living in dormitories (Mdn = 2.50, IQR = 1.83) and those living in rented apartments (Mdn = 2.17, IQR = 1.50) reported significantly higher Emotional Eating scores compared to students residing with their parents. Male students demonstrated a substantially higher prevalence of late-night food consumption compared to female students (77.9% vs. 59.4%). Cognitive restraint and female gender protect against nighttime snacking, while uncontrolled eating and poor-quality sleep significantly increase this risk. After adjusting for age, sleep disturbances were positively associated with emotional eating (Path a: B = 0.0967, β = 0.320, p < 0.001, R² = 0.109), which in turn was strongly associated with uncontrolled eating (Path b: B = 0.4621, β = 0.709, p < 0.001). While the model explained 52.16% of the variance in uncontrolled eating, this was driven almost entirely by path b, as the initial contribution of sleep was considerably smaller (Path a: R² = 0.109), yielding a significant standardized indirect association (β = 0.227, 95% BootCI [0.148, 0.309]). Conclusions: Sleep disturbances are linked to uncontrolled eating patterns primarily through their shared indirect association with emotion-driven eating. Prioritizing weight loss over health promotion increases students’ psychological vulnerability. Full article

Side-channel analysis (SCA) remains a significant threat to embedded cryptographic implementations, yet hardware security evaluation often lacks a systematic workflow that links leakage detection, exploitability validation, trace-efficiency estimation, and robustness assessment. Rather than proposing a new leakage distinguisher, this work presents an evidence-based framework that integrates established SCA techniques into a reproducible hardware security evaluation methodology for embedded AES implementations. The proposed framework follows a structured screen–validate–quantify–stress-test pipeline. Power traces are acquired from an AES-128 implementation on a CW312/SAM4S target using a shunt-based measurement path and firmware-triggered ChipWhisperer–Husky acquisition. Fixed-versus-random test vector leakage assessment (TVLA) using 5000 fixed and 5000 random traces reveals strong first-order leakage, with a dominant peak of |t| = 210.61 at sample index 1916, defining a leakage window of [1666:2167]. Exploitability is validated through timing-aware per-byte correlation power analysis (CPA) under a Hamming-weight leakage model, successfully recovering the full AES-128 key and revealing staggered byte-wise leakage timing for point-of-interest selection. Operational feasibility is quantified through 100 independent subsampling trials over trace budgets M ∈ {25, 50, 75, 100, 150, 200, 300, 400, 700, 1000}, achieving 97% full-key recovery with 25 attack traces and 100% recovery from 50 attack traces onward when using pre-established byte-specific POIs. Independent split-data point-of-interest validation further confirms the stability and reproducibility of the selected leakage locations. Finally, controlled synthetic timing-misalignment experiments evaluate robustness by measuring recovery degradation, weakest-byte confidence behaviour, and the effect of local peak-search compensation under non-ideal analysis conditions. Collectively, this case study demonstrates a reproducible SCA evaluation workflow on the investigated CW312/SAM4S AES-128 implementation, integrating leakage screening, exploitability validation, trace-efficiency analysis, independent POI validation, and controlled robustness testing. The present results establish the workflow on this experimental platform, but evaluation across additional devices, firmware implementations, keys, acquisition sessions, and protected implementations is required before broader generalisation can be claimed. Full article

Cooperative local navigation of multiple mecanum robots requires efficient coordination around robot–robot conflicts, pedestrians, and static obstacles while preserving direct waypoint following on clear path segments. This paper presents Conflict-Aware Graph-Attention Multi-Agent Proximal Policy Optimization (CA-GAT-MAPPO), a learning-based residual control and coordination framework. Predicted closest-approach events construct a conflict-conditioned robot-interaction graph, so actor message passing is restricted to the local robot and its predicted conflict neighbors. A residual graph encoder preserves waypoint-conditioned state, while a bounded right-of-way coordinator and an interaction gate regulate longitudinal, lateral, and angular residual authority. State-dependent adaptive scalarization combines multiple reward components into a single training objective. The framework is evaluated within a common A*-based waypoint guide, optimal reciprocal collision avoidance (ORCA)-style prior, command-limiting, and safety-envelope interface shared by the compared controllers. Across three four-robot Robot Operating System 2 (ROS 2)/Gazebo scenarios, eight independently trained checkpoints per method–scenario pair were each evaluated in ten randomized episodes. Across the four learned controllers, all 960 main-comparison episodes were completed without a geometric collision or a recorded robot–robot or robot–pedestrian near-miss at the 0.1 s sampled poses under the shared execution boundary. CA-GAT-MAPPO obtained the lowest reported mean completion time, makespan, and waiting time in all three scenarios. The results support a descriptive efficiency advantage for the integrated intelligent-control stack under the evaluated conditions, without establishing universal superiority or a formal safety guarantee. Full article

Reverse path tracking of multi-trailer systems is challenging because tractor steering affects the following bodies only through passive hitch joints, causing tracking-error propagation and increasing the risk of jackknifing. This paper proposes a backstepping-based recursive virtual-heading (BS-RVH) controller that converts the path-tracking error of the final trailer into a desired heading rate and recursively generates the desired hitch angles, preceding-body heading rates, and tractor steering command. Desired hitch-angle saturation is included, and local boundedness and the effect of saturation residuals are analyzed. For a tractor–two-trailer system, BS-RVH achieves lateral root-mean-square errors of 0.015 m, 0.021 m, and 0.013 m on the S-curve, circular, and figure-eight paths, respectively. These values represent reductions of 93.0–93.4% compared with the curvature-based controller. The maximum absolute hitch angles remain below 12°, and all paths reach the predefined 99.5% completion threshold without jackknifing. An additional tractor–three-trailer evaluation completes the same paths without jackknifing, with lateral root-mean-square errors ranging from 0.073 m to 0.112 m. These results demonstrate the effectiveness and stage-wise extensibility of BS-RVH under low-speed kinematic conditions. Full article

The Jimsar shale reservoir is characterized by low porosity and low permeability. Multistage fracturing of horizontal wells is an effective development method. However, interference between multiple fractures and the presence of well-developed bedding planes limit the effective extension of fractures. To elucidate the initiation and propagation mechanisms of inter-bed fractures in horizontal multi-stage fracturing within shale reservoirs, this study focuses on the Jimusar Shale outcrop and employs large-scale true triaxial hydraulic fracturing physical simulation experiments to construct an experimental model of horizontal multi-stage fracturing. By analyzing fracture morphology, propagation paths, and bedding plane activation characteristics, the study elucidates the fracture propagation patterns under various conditions. Results indicate the following: (1) Increasing the fluid injection rate enhances the net pressure at the fracture tip, effectively overcoming the resistance of bedding planes and promoting the continued propagation of hydraulic fractures across bedding interfaces. (2) Stage spacing affects the coordinated propagation of hydraulic fractures by modifying the stress shadow effect between adjacent fractures. A smaller stage spacing intensifies stress interference, leading to the suppression of local fracture propagation and an increased likelihood of fracture communication. (3) The horizontal stress difference is the primary controlling factor governing fracture propagation direction and bedding penetration capability. A larger horizontal stress difference promotes stable fracture propagation along the direction of the maximum horizontal principal stress and facilitates penetration through bedding planes, whereas a lower horizontal stress difference favors bedding plane activation and fracture deflection, resulting in the formation of a more complex fracture network. The study has established an understanding of the fracture expansion laws under different parameter conditions, which can provide theoretical basis and technical support for the optimization design of multi-stage fracturing parameters in horizontal shale oil wells and the efficient modification of reservoirs. Full article

To address the difficulty that cross-domain morphing vehicles are prone to infeasibility in complex environments with dense hard-constraint no-fly zones, a three-dimensional no-fly zone modeling approach based on a soft-constraint normalized threat measure is proposed and integrated into sequential convex programming for numerical solution. Based on the normalized distance from the vehicle to the center of spherical/ellipsoidal no-fly zones, an instantaneous normalized threat measure model is constructed, and the overall threat exposure time (OTET) is defined through time integration. This relaxes the traditional hard no-fly zone constraints into soft constraints expressed by the normalized threat measure, which can be embedded into the optimization objective. Then, following the idea of alternating optimality–feasibility iteration, the soft-constraint model is embedded into the sequential convex programming framework. In the optimality iteration, a convex subproblem without relaxation is solved to obtain a descent direction of the objective; in the feasibility iteration, slack variables are introduced and the penalty coefficients are updated adaptively using dual variables. This achieves integrated optimization of the morphing vehicle’s trajectory and configuration. Taking a variable-sweep vehicle as the research object, two operating conditions—adaptive morphing and fixed high-lift-to-drag ratio configuration—are set and compared in scenarios with multiple ellipsoidal/elliptical–cylindrical no-fly zones. Under the baseline scenario, the adaptive morphing strategy achieves an OTET of 1.56 s, which is 96.2% lower than the 40.85 s of the fixed configuration. A sensitivity analysis with elliptical–cylindrical no-fly zones further shows that the adaptive morphing strategy maintains a significant advantage, with an OTET of 10.02 s compared with 74.75 s for the fixed configuration. All state and control variables, as well as path constraints such as heat flux, dynamic pressure, and load factor, strictly satisfy the prescribed bounds. Checked by high-accuracy dynamic integration, the terminal state errors of the optimized solution remain within the allowable range, indicating the continuous feasibility of the optimization solution. The proposed method significantly enhances the mission adaptability of cross-domain morphing vehicles in no-fly zone environments while ensuring solution accuracy, and the proposed threat assessment method can provide a theoretical reference for morphing intelligent decision-making. Full article

China’s deep coalbed methane (CBM) resources hold significant development potential, yet their commercial exploitation remains hindered by four interrelated reservoir challenges: substantial burial depth, elevated in situ stress, ultra-low matrix permeability, and pronounced stress-dependent permeability decline. These conditions collectively induce rapid reservoir energy depletion and flow-path deterioration, leading to characteristic production behavior—namely, high initial gas rates followed by steep decline and persistent instability in long-term output. Accordingly, a rigorously optimized production system is indispensable to suppress formation damage, sustain desorption-driven gas release, and maximize ultimate recovery efficiency. To address this, this study establishes a physics-based numerical model that integrates field-calibrated geological and operational parameters, gas–water two-phase flow dynamics, coupled desorption–diffusion–seepage processes, and quantitatively constrained stress–permeability relationships for coal. Implemented in the CMG-IMEX simulator, the model is validated through robust history matching against production data from ten representative wells. A comparative analysis of two drawdown management strategies—the conventional constant-decline approach and the progressively decreasing drawdown strategy—demonstrates clear performance differentiation in both cumulative production and reserve utilization. Under the conventional scheme, the average ultimate recovery factor at economic abandonment reaches only 36.42%, with adsorbed-gas recovery limited to 25.53%, underscoring substantial untapped resource potential. In contrast, the progressively decreasing drawdown strategy alleviates multiphase flow restrictions, improves pressure maintenance, and elevates the average ultimate recovery factor to 40.43%—a net gain of 4.43 percentage points in adsorbed-gas recovery. Sensitivity analysis further identifies an initial drawdown of 5 MPa as the optimal balance between early productivity and reservoir sustainability. The coupling among reservoir pressure, flowing bottomhole pressure, and casing pressure enables the bottomhole drawdown-control protocol to be converted into a surface-measurable casing-pressure decline-rate criterion. These findings improve the mechanistic understanding of gas–water co-production in deep CBM systems and provide an operational framework for production-system design. Full article

Transferring unmanned surface vehicle (USV) algorithms from simulation to physical vessels is constrained by heterogeneous hardware interfaces, degraded wireless links, and ambiguous boundaries between human and autonomous control. This study designs and implements a ship–shore cooperative experimental platform comprising a shore control station, a portable control terminal, and an onboard system. The platform integrates multimodal sensing, private-radio and 4G/5G communication, an independent short-range remote-control (RC) path, and hardware arbitration. Small, safety-relevant commands are transmitted redundantly over the heterogeneous links using a shared application protocol with sequence-based deduplication. A two-dimensional control-authority model separates the authorized control source (shore, portable terminal, or short-range RC) from the active onboard behavior (path following, local collision avoidance, or safety protection) to organize fail-safe degradation and control transfer. Geometric light detection and ranging (LiDAR) obstacle detection and optimal reciprocal collision avoidance provide a representative onboard avoidance workflow. Full-scale vessel tests closed the loop from mission dispatch and command parsing to actuation and status feedback and demonstrated autonomous navigation, human takeover, and local collision avoidance. Across four water environments, the platform recorded 5.39 h of multimodal data over 18.26 km of valid trajectories. The results establish a physical testbed for ship–shore cooperative control, algorithm transfer, and multimodal data acquisition. Full article

Continued use of fitness technology is often treated as a prerequisite for health behavior change, yet platform engagement and behavioral regulation may operate through different mechanisms. This four-wave panel study tested whether an engagement pathway in online fitness communities extends across the digital–physical boundary. After a priori quality screening, 506 Chinese adults who had used the community functions of a smart fitness platform, application, or wearable device within the previous three months and had access to a community activity venue were retained at T1, with 417 at T2, 377 at T3, and 394 at the outcome-only T4 wave. A unified full-information maximum-likelihood path system adjusted for prior outcomes and demographics and used 2000 participant-level bootstrap resamples. Perceived interaction at T1 predicted social presence ($\beta$ = 0.297), flow ($\beta$ = 0.355), and belonging ($\beta$ = 0.245) at T2, and all three independently predicted continuance intention at T3 ($\beta$s = 0.203–0.302). Continuance intention is the intention to keep using the community, not observed use. It showed no detectable association with physical activity at T4 once prior activity was controlled ($\beta$ = 0.016, 95% CI [−0.061, 0.082]), and the serial indirect effects were likewise undetectable. This did not follow from conditioning on the strong autoregressive path: the zero-order correlation between continuance intention and later activity was 0.068, while associations along the hypothesized within-platform chain ranged from 0.218 to 0.563. Missing-data, complete-case, log-transformed, and inverse-probability-weighted specifications converged. Exploratory equivalence testing rendered standardized effects of ±0.10 or larger implausible (p = 0.012) but not ±0.05, and a BIC approximation favored the model without the path (BF₀₁ = 17.81). Experiences that sustain digital continuity should not be assumed to produce physical activity spillover. Full article

This study proposes a driver monitoring and active rear-wheel assist steering control scheme integrating Model Predictive Control (MPC) and Recursive Least Squares (RLS) to enhance path-following precision and facilitate seamless control authority distribution in autonomous mobility. Rather than attempting to directly measure internal physiological cognitive states, the proposed approach quantifies physical steering performance degradation by employing a dual RLS algorithm to estimate a Steering Performance Degradation Index, which systematically fuses temporal response delay and spatial tracking deviation. Based on this real-time index, an assist MPC dynamically computes the auxiliary rear-wheel steering angle by adapting its tracking input weights according to three candidate weighting functions: exponential, linear, and threshold-based. High-fidelity co-simulations in IPG CarMaker and MATLAB/Simulink are conducted under various velocities and road curvatures with systematic driver delays. The evaluation results demonstrate that the proposed assist controller effectively enhances path-tracking precision, reducing the maximum lateral error and yaw angle error by up to approximately 82.24% and 73.53%, respectively, compared to the unassisted delayed driver. These findings verify that the proposed steering control architecture successfully mitigates transient trajectory deviation during driver performance degradation, establishing a promising candidate fail-safe strategy for advanced automated driving systems. Full article

This article presents a low-cost, high-voltage excitation circuit (EC) for capacitive ultra-sonic transducers (CUTs) based on a dual-path architecture. The proposed design comprises two independent AC excitation channels (AC-branch) that share a regulated DC-bias voltage (DC-branch). The circuit was developed to satisfy a fundamental operational requirement of CUTs: simultaneous application of a static bias voltage and a time-varying drive voltage. Because the electrostatic force depends nonlinearly on the applied voltage, efficient first-harmonic actuation requires the superposition of DC and AC voltage components. To reach this objective, the circuit and the transducer must be treated as a coupled electrical, electrostatic, mechanical, and acoustic system. In the proposed implementation, the DC-branch uses a TL494-PWM controller, a TIP50 switching transistor, a step-up transformer, and a rectifier-filter stage to generate the high-voltage bias of up to 200 V_DC. Each AC-channel employs an LM3886TF amplifier followed by a 1:15 step-up transformer, enabling the generation of excitation signals of up 180 V_peak. A key feature of the proposed architecture is the electrical independence of the two AC-channels, which allows for distinct excitation frequencies with minimal mutual interference. Experimental validation, performed with and without ultrasonic loads, demonstrates the relation between excitation conditions and the acoustic performance of the CUTs. Full article

Cadmium (Cd) contamination in paddy soils poses health risks, yet the role of aggregate-scale Cd binding remains poorly understood under interactive agricultural management. Unlike primary textural separates that are fixed by parent material, aggregates are dynamic and manageable. This study investigates the interactive effects of parent material, water regime shifts in annual tobacco–rice rotation, and chlorine-containing fertilizers on aggregate stability, carbon/nitrogen sequestration, and cadmium (Cd) adsorption in paddy soils from subtropical paddy fields of Hunan Province, South China. Two paddy soils, Masaniutan (granite-derived sandy loam) and Shanniutan (slate/shale-derived clayey soil), were divided into four size classes. Selective removal of soil organic carbon (SOC) and free iron oxides (Fe_d), followed by Cd adsorption experiments. Shanniutan had greater aggregate stability than Masaniutan. Water regime shifts did not significantly alter aggregate structure. Chlorine fertilizers disrupted aggregates, increasing finer fractions. Cd adsorption rose as aggregate size decreased. SOC removal reduced the maximum adsorption capacity (q_m) by 36.8%, while Fe_d played a supporting role. Partial least squares structural equation modeling (PLS-SEM) revealed that parent material had a strong positive direct effect on aggregate stability (path coefficient = 2.346), while chlorine fertilization (−0.750) and water regime shifts (−0.344) had negative effects. Parent material, water regime shifts, and fertilization jointly influence aggregate stability and the distribution of cementing agents, thereby controlling Cd adsorption capacity—with SOC as the dominant factor. These findings provide a basis for sustainable paddy soil management by promoting SOC retention and avoiding excessive chloride inputs. Full article

Autonomous surface vessels (ASVs) operating in narrow and restricted waterways must follow a planned path while avoiding nearby static hazards and maintaining safe clearance from boundaries. This paper presents a LiDAR-based deep reinforcement learning framework for path following and static obstacle avoidance of an underactuated ASV. The vessel receives local pose information from a localization system and surrounding environment through a 2D LiDAR scan, which is converted into compact sector features using feasibility-inspired pooling method. A Soft Actor-Critic (SAC) policy is trained in simulation to output continuous rudder and propulsion commands, based on LiDAR features, estimated motion states, and path-relative errors. The policy is evaluated over 500 randomized simulation episodes ranging from 0–4 obstacles. The trained policy achieved an overall success rate of 95.0%, with an average cross-track error of 0.66 m. Obstacle and border collision rates are 3.80% and 1.20%, respectively; indicating that the policy can perform path tracking and collision avoidance in constrained layouts. A single field trial was then conducted in each of three fixed obstacle layouts using the model-scale Bluefin vessel. In these trials the policy executed on the physical platform and avoided static obstacles, with minimum obstacle clearances of 0.50–1.17 m. However, the field trajectories exhibit larger oscillations and longer path lengths than simulation, with an average RMS cross-track error of 1.14 m compared to 0.69 m in simulation. Full article

The mechanical properties of continuous-fiber-reinforced composites in FDM-3D printing are significantly influenced by process parameters. However, research in this area is constrained by printing equipment, control software, and material preparation. This study examines how the tensile and interlaminar shear properties of CCF/PA and CF/PEEK are affected by continuous-fiber nozzle temperature, platform temperature, and printing speed. Studies indicate that enhancing the nozzle temperature can notably improve mechanical properties by enhancing material flowability, ensuring consistent fiber encapsulation and reducing pore defects. Increasing the platform temperature initially boosts both tensile strength and ILSS, but, beyond a certain point, these properties decline. Inadequate platform temperature can result in uneven infiltration and diffusion among deposited CCF/PA paths, leading to void defects. Conversely, excessive platform temperature can cause semi-molten CCF/PA layers to be vulnerable to nozzle pressure and scraping, resulting in continuous-fiber debonding and interlayer tearing. Furthermore, lower printing speeds extend the melt wetting time between adjacent paths, promoting diffusion and adhesion for enhanced performance. Following an experimental investigation, the optimal parameters are identified as a nozzle temperature of 295 °C, a platform temperature of 240 °C, and a printing speed of 3 mm/s. This research provides valuable guidance for the practical production of continuous-fiber-reinforced composites using FDM-3D printing. Full article