Search Results (801)

The cold recycling (CR) technique is gaining traction, with an increasing demand for sustainable pavement construction practices. Cold in-place recycling (CIR) and cold central plant recycling (CCPR) are two strategies under the umbrella of cold recycling. These techniques use reclaimed asphalt pavement (RAP) to rehabilitate pavement, and CCPR offers the added advantage of utilizing stockpiled RAP. While many agencies have expertise in cold recycling techniques including CCPR, the lack of pavement performance data prevented the largescale implementation of these technologies. Recent studies in high-traffic volume applications demonstrate that CCPR technology can be implemented on the entire road network across all traffic levels. This reignited interest in the widespread implementation of CCPR. Therefore, the purpose of this study is to provide agencies with the most up-to-date information on CCPR to help them make informed decisions. To this end, this paper comprehensively reviews the mix-design for CCPR, the structural design of pavements containing CCPR layers, best construction practices, and the agency experience in using this technology on high-traffic volume roads to provide in-depth information on the steps to follow from project selection to field implementation. The findings specify the suitable laboratory curing conditions to achieve the optimum mix design and specimen preparation procedures to accurately capture the material properties. Additionally, this review synthesizes existing quantitative data from previous studies, providing context for the comparison of findings, where applicable. The empirical and mechanistic–empirical design inputs, along with the limitations of AASHTOWare Pavement ME software for analyzing this non-conventional material, are also presented. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

Organic solvent nanofiltration (OSN) has emerged as a transformative platform for molecular separation, offering energy-efficient and high-performance alternatives to conventional separation techniques across the food, petrochemical, and pharmaceutical industries. At the core of this advancement lie polyamide membranes, whose exceptional chemical resilience, tunable architecture, and compatibility with a wide range of organic solvents have positioned them as the material of choice for industrial OSN applications. Recent progress encompassing nanostructured additives, controlled interfacial polymerization, and advanced crosslinking strategies has led to significant improvements in membrane selectivity, permeability, and operational stability. As OSN continues to gain traction in sustainable chemical processing, enabling reductions in both energy consumption and environmental waste, ongoing challenges such as membrane fouling, structural degradation, and limited solvent resistance remain critical barriers to broader adoption. This review critically examines the role of polyamide membranes in OSN, emphasizing their structural versatility, physicochemical attributes, and capacity to meet the growing demands of sustainable separation technologies. Full article

In recent years, Vision Transformers (ViTs) have gained significant traction in the field of hyperspectral image classification due to their advantages in modeling long-range dependency relationships between spectral bands and spatial pixels. However, after stacking multiple Transformer encoders, challenges pertaining to information degradation may emerge during the forward propagation. That is to say, existing Transformer-based methods exhibit certain limitations in retaining and effectively utilizing information throughout their forward transmission. To tackle these challenges, this paper proposes a novel dual-branch spatial–spectral Transformer model that incorporates similarity propagation (DBSSFormer-SP). Specifically, this model first employs a Hybrid Pooling Spatial Channel Attention (HPSCA) module to integrate global information by pooling across different dimensional directions, thereby enhancing its ability to extract salient features. Secondly, we introduce a mechanism for transferring similarity attention that aims to retain and strengthen key semantic features, thus mitigating issues associated with information degradation. Additionally, the Spectral Transformer (SpecFormer) module is employed to capture long-range dependencies among spectral bands. Finally, the extracted spatial and spectral features are fed into a multilayer perceptron (MLP) module for classification. The proposed method is evaluated against several mainstream approaches on four public datasets. Experimental results demonstrate that DBSSFormer-SP exhibits excellent classification performance. Full article

Background/Objectives: Deceleration can cause liver ruptures via ligament traction, with a specific, little-known transverse rupture in segment II of the left lobe being a concern. This study aimed to provide a detailed morphological characterization of these segment II ruptures, analyse their formation mechanisms using autopsy material, and propose a systematic intraoperative assessment method to improve their detection. Methods: This study analysed the autopsy cases of 132 victims of sudden, violent deceleration (falls from height, traffic accidents) performed between 2011 and 2014. Liver injuries were meticulously described, focusing on the morphological characteristics of ruptures (course, shape, depth) and their location relative to hepatic ligaments. Cases with prior liver resection due to injuries were excluded. Results: Liver ruptures were found in 61 of the 132 analysed cases (46.2%). A “new location” for ruptures was identified on the diaphragmatic surface of the left lobe’s segment II, near and along the left coronary and triangular ligaments. This specific type of rupture was found in 14 cases. Overall, 40 cadavers had liver ruptures near ligaments, totalling 55 such distinct ruptures, indicating that some had multiple ligament-associated tears. The incidence of liver rupture at this newly described site was statistically significant. Conclusions: Transverse rupture of the left hepatic lobe’s segment II, in its subdiaphragmatic area, results from ligament “pulling” forces during deceleration and is a characteristic injury. Its presence should be considered following blunt abdominal trauma involving deceleration, and the subdiaphragmatic area of the left lateral lobe requires intraoperative inspection, especially if other ligament-associated liver ruptures are found. Full article

The scenario adaptability of agricultural machinery chassis in hilly and mountainous regions has become a key area of innovation in modern agricultural equipment development in China. Due to the fragmented nature of farmland, steep terrain (often exceeding 15°), complex topography, and limited suitability for mechanization, traditional agricultural machinery experiences significantly reduced operational efficiency—typically by 30% to 50%—along with poor mobility. These limitations impose serious constraints on grain yield stability and the advancement of agricultural modernization. Therefore, enhancing the scenario-adaptive performance of chassis systems (e.g., slope adaptability ≥ 25°, lateral tilt stability > 30°) is a major research priority for China’s agricultural equipment industry. This paper presents a systematic review of the global development status of agricultural machinery chassis tailored for hilly and mountainous environments. It focuses on three core subsystems—power systems, traveling systems, and leveling systems—and analyzes their technical characteristics, working principles, and scenario-specific adaptability. In alignment with China’s “Dual Carbon” strategy and the unique operational requirements of hilly–mountainous areas (such as high gradients, uneven terrain, and small field sizes), this study proposes three key technological directions for the development of intelligent agricultural machinery chassis: (1) Multi-mode traveling mechanism design: Aimed at improving terrain traversability (ground clearance ≥400 mm, obstacle-crossing height ≥ 250 mm) and traction stability (slip ratio < 15%) across diverse landscapes. (2) Coordinated control algorithm optimization: Designed to ensure stable torque output (fluctuation rate < ±10%) and maintain gradient operation efficiency (e.g., less than 15% efficiency loss on 25° slopes) through power–drive synergy while also optimizing energy management strategies. (3) Intelligent perception system integration: Facilitating high-precision adaptive leveling (accuracy ± 0.5°, response time < 3 s) and enabling terrain-adaptive mechanism optimization to enhance platform stability and operational safety. By establishing these performance benchmarks and focusing on critical technical priorities—including terrain-adaptive mechanism upgrades, energy-drive coordination, and precision leveling—this study provides a clear roadmap for the development of modular and intelligent chassis systems specifically designed for China’s hilly and mountainous regions, thereby addressing current bottlenecks in agricultural mechanization. Full article

Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. Concurrently, deep learning-based pose control algorithms suffer from inherent training instability, slow convergence rates, and inefficient learning processes in complex environments characterized by high-density fruit clusters and occluded picking scenarios. To address these challenges, this paper proposes an enhanced reinforcement learning framework DRFW-TQC that integrates Dynamic L2 Regularization for adaptive model stabilization and a Group-Wise Feature Weighting Network for discriminative feature representation. The methodology further incorporates a picking posture traction mechanism to optimize end-effector orientation control. The experimental results demonstrate the superior performance of DRFW-TQC compared to the baseline. The proposed approach achieves a 16.0% higher picking success rate and a 20.3% reduction in angular error with four target strawberries. Most notably, the framework’s transfer strategy effectively addresses the efficiency challenge in complex environments, maintaining an 89.1% success rate in eight-strawberry while reducing the timeout count by 60.2% compared to non-adaptive methods. These results confirm that DRFW-TQC successfully resolves the tripartite challenge of operational precision, training stability, and environmental adaptability in robotic fruit harvesting systems. Full article

This study investigates the operational performance of fruit-picking robots under varying terrain slopes and soil moisture conditions, with a focus on comparing wheeled and tracked locomotion systems. A modular robot platform was designed and tested in both controlled environments and actual mountainous orchards in Shandong, China. The experiments assessed key performance metrics—average speed, slip rate, and path deviation—under combinations of four slope levels (0°, 8°, 18°, 28°) and three soil moisture levels (dry 10%, moderate 20%, wet 35%). Results reveal that wheeled robots perform optimally on dry and flat terrain but experience significant slippage and path deviation under steep and wet conditions. In contrast, tracked robots maintain better stability and terrain adaptability, demonstrating lower slip rates and more consistent trajectories across a wide range of conditions. A synergistic deterioration effect was observed when high slope and high soil moisture co-occur, significantly degrading the performance of wheeled systems, while tracked systems mitigated these effects. Complementary semi-structured interviews with 20 orchard stakeholders—including farmers, growers, and hired pickers—highlighted key user expectations: robust traction, terrain adaptability, reduced physical labor, and operational safety. The findings suggest that future agricultural robots should adopt adaptive hybrid mobility systems and integrate environmental perception capabilities to enhance performance in complex agricultural scenarios. These insights contribute practical and theoretical guidance for the design and deployment of intelligent fruit-picking robots in diverse field environments. Full article

This research presents a method for improving the regenerative efficiency of interior permanent magnet synchronous motors (IPMSMs) used in traction applications such as electric vehicles. In conventional powertrain control, the maximum torque per ampere (MTPA) strategy is commonly applied in the constant-torque region. However, this approach does not account for the combined losses of both the motor and inverter. In this study, overall system efficiency is investigated, and an improved current combination is proposed to minimize total losses. The single switching method is employed in the inverter due to its simplicity and its ability to reduce inverter losses. Simulations incorporating both motor and inverter losses were performed for two driving conditions around the MTPA current point. The results show that the optimal current combination slightly deviates from the MTPA point and leads to a slight improvement in efficiency. Experimental results under the two steady-state driving torque and angular velocity conditions confirm that the optimized current combination enhances system efficiency. Furthermore, simulations based on the Urban Dynamometer Driving Schedule predict an increase in recovered energy of approximately 1%. The proposed control strategy is simple, easy to implement, and enables the powertrain to operate with highly efficient current references. Full article

To address the challenges of inadequate adaptability, insufficient power, high ground clearance, and limited functionality in existing pineapple field machinery, this study proposes a height-adjustable pineapple field management platform based on previously established cultivation patterns and agronomic requirements. The structural configuration and operational principles of the platform’s power chassis are elucidated, with specific emphasis on the development of the traction power system and modular operational systems. Theoretical and experimental analyses of steering parameters, stability, and field performance were conducted. Finite element simulation analysis of the frame revealed that under full-load conditions, the equivalent elastic strains during descent and ascent phases were 0.000317 and 0.00125, respectively. Maximum equivalent stresses (48.27 MPa and 231.6 MPa for descent and ascent, respectively) were localized at the beam–plate junctions, while peak deformations of 1.14 mm (descent) and 4.31 mm (ascent) occurred at mid-frame and posterior–mid regions, respectively. Field validation demonstrated operational velocities of 0.16–1.77 m/s (forward) and 0.11–0.28 m/s (reverse), with a maximum gradability of 20°. The platform exhibited multifunctional capabilities including weeding, spraying, fertilization, flower induction, harvesting, and transportation, demonstrating its potential to fulfill the operational requirements for pineapple field management. Simultaneously, the overall work efficiency is increased by more than 50%, compared to manual labor. Full article

This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles. Full article

This paper analyzes the electromagnetic characteristics of an interior permanent magnet synchronous motor (IPMSM) for electric vehicle traction under various control imbalance conditions in a twin inverter system, assuming that one of the inverters fails to operate properly. The imbalance conditions are first investigated through dynamometer experiments and then applied to finite element method (FEM) simulations to evaluate their electromagnetic effects. Since the focus is on scenarios where a single inverter malfunctions, a stator winding configuration is first redefined to ensure stable operation in a single inverter system by preventing voltage and current imbalances within the circuit. When the stator winding is configured with eight parallel paths, the dynamometer test results show a phase voltage imbalance. However, when the number of parallel circuits is reduced to four, this voltage imbalance disappears. Using this configuration, a twin inverter system is constructed, and various imbalance conditions are applied to intuitively examine the electromagnetic characteristics when one inverter fails to accurately control current magnitude or phase angle. The simulation results showed that applying unbalanced conditions to the current and current phase angle led to a decrease in torque and an increase in torque ripple. In addition, when one of the inverters was completely disconnected, the motor performance analysis showed that it operated with approximately half of its original performance. Based on dynamometer experiments and finite element method (FEM) simulations, the electromagnetic characteristics under inverter fault conditions and appropriate stator winding configurations were analyzed. When an optimal number of parallel circuits is applied to the stator winding and a twin inverter system is employed, the load on each individual inverter is reduced, enabling accurate control. This makes the application to high-voltage and high-current systems feasible, allowing higher performance. Moreover, even if one inverter fails, the system can still operate at approximately half its capacity, ensuring high operational reliability. Full article

Autonomous race driving has emerged as a challenging domain for reinforcement learning (RL) applications, requiring high-speed control while adhering to strict safety constraints. Existing RL-based racing methods often struggle to balance performance and safety, with limited adaptability in dynamic racing scenarios with multiple opponent vehicles. The high-dimensional state space and strict safety constraints pose significant challenges for efficient learning. To address these challenges, this paper proposes an integrated RL framework that combines three novel components: (1) a state mapping mechanism that dynamically transforms raw track observations into a consistent representation space; (2) an action mapping technique that rigorously enforces physical traction constraints; and (3) a safe exploration guidance method that combines conservative controllers with RL policies, significantly reducing off-track incidents during training. Extensive experiments conducted in our simulation environment with four test tracks demonstrate the effectiveness of our approach. In time trial scenarios, our method improves lap times by 12–26% and increases the training completion rate from 33.1% to 78.7%. In competitive racing, it achieves a 46–51% higher average speed compared to baseline methods. These results validate the framework’s ability to achieve both high performance and safety in autonomous racing tasks. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

In reactive power compensators applied in drives with asynchronous motors, a control strategy focusing on the compensation of higher-order current harmonics is implemented. Control schemes of such compensators typically employ low-pass Butterworth filters with fixed cut-off frequencies to isolate the reactive power component. However, the impact of alternative filter types on compensator performance remains insufficiently explored. Furthermore, in the control systems under consideration, stator phase current signals of the asynchronous motor are used as reference inputs. This approach proves effective under the steady-state operating conditions of the drive. Under non-steady-state operating conditions—typical for traction drive systems—this approach becomes ineffective due to the increased complexity in obtaining accurate reference current signals. As a result, the performance of the filters also deteriorates. It is therefore proposed to investigate the impact of alternative filter types on the efficiency of compensator operation. To address this challenge, the following strategies are suggested: implement higher-order harmonic compensation in the system of stator phase supply voltages of the asynchronous motor; use the control signals from the Field-Oriented Control (FOC) algorithm as reference inputs; and adapt the cut-off frequencies of the filters dynamically to match the frequency of the supply voltage. The simulation results indicate that the use of an elliptic filter in compensator control systems yielded the highest effectiveness. Moreover, the results confirmed the efficiency of the proposed solutions under both steady-state and non-steady-state operating conditions of the traction drive. These approaches support the development of reactive power compensators integrated into traction drive systems for railway rolling stock. Full article