Search Results (1,138)

An overview of the Common Rail (CR) diesel engine challenges and of the promising state-of-the-art solutions for addressing them is provided. The different CR injector driving technologies have been compared, based on hydraulic, spray and engine performance for conventional diesel combustion. Various injection patterns, high injection pressures and nozzle design features are analyzed with reference to their advantages and disadvantages in addressing engine issues. The benefits of the statistically optimized engine calibrations have also been examined. With regard to the combustion strategy, the role of a CR engine in the implementation of low-temperature combustion (LTC) is reviewed, and the effect of the ECU calibration parameters of the injection on LTC steady-state and transition modes, as well as on an LTC domain, is illustrated. Moreover, the exploitation of LTC in the last generation of CR engines is discussed. The CR apparatus offers flexibility to optimize the engine calibration even for biofuels and e-fuels, which has gained interest in the last decade. The impact of the injection strategy on spray, ignition and combustion is discussed with reference to fuel consumption and emissions for both biodiesel and green diesel. Finally, the electrification of CR diesel engines is reviewed: the effects of electrically heated catalysts, electric supercharging, start and stop functionality and electrical auxiliaries on NO_x, CO₂, consumption and torque are analyzed. The feasibility of mild hybrid, strong hybrid and plug-in CR diesel powertrains is discussed. For the future, based on life cycle and manufacturing cost analyses, a roadmap for the automotive sector is outlined, highlighting the perspectives of the CR diesel engine for different applications. Full article

The passenger car market has experienced a radical shift: the rise of SUV, crossover vehicles, but also Battery Electric Vehicle (BEV) and Plug-In Hybrid Vehicle (PHEV), has blurred the borders between traditional vehicle segments as well as body types, resulting in reduced applicability of conventional taxonomies of vehicle types. This study aims to provide an overview of the vehicle market by proposing a new, machine-learning-based segmentation of the entire German vehicle fleet covering the past years. We merge over 40 million registered vehicles with a technical specifications database and apply data-mining techniques to derive an improved market segmentation. We demonstrate that unsupervised learning techniques, specifically Ward and k-means clustering, yield clusters with enhanced separation, clarity, and practical usability. Clustering was applied to both raw technical features and engineered features designed to capture aspects of economy, ecology, usability, and performance. The silhouette scores can reach 0.19, a significant increase over the +0.05/−0.05 scores of the existing vehicle segments or chassis types. Full article

Gas monitoring and dust control in coal mine goafs are critical for ensuring safe and efficient production. To address the challenges posed by dust accumulation from mechanized mining and ventilation systems, this study designs a spiral-driven gas purification pipeline robot integrating a wet dust removal mechanism. The robot features a modular structure, including a spiral drive, a plugging and extraction system, and a wet dust removal unit, to enhance pipeline adaptability and dust removal performance. Dynamic modeling reveals that the robot’s speed increases with the deflection angle of the driving wheel, with optimal performance observed at a 45° angle. The analysis of the rolling friction, medium resistance, and deflection angle indicates that reducing the angle improves the obstacle-crossing ability. Numerical simulations of gas migration in the goaf identify a high dust concentration at the air outlet and show that flow velocity significantly affects dust removal efficiency. Simulation and prototype testing confirm stable robot operation at deflection angles of between 30° and 90° and effective crossing of 5 mm barriers. Optimal dust removal is achieved with a 5 m/s flow velocity, 0.6 MPa water mist pressure, and 400 mm chord grid spacing, providing both theoretical and practical guidance for gas monitoring and dust control in coal mine goafs. Full article

Gel foam exhibits excellent applicability in fractured-vuggy reservoirs, effectively plugging flow channels and enhancing oil recovery. However, due to the harsh high-temperature environment and the complex and variable fracture-vuggy structure in reservoirs, gel foam may undergo structural changes during its migration, which can affect its flow properties and plugging efficiency. Therefore, investigating the migration characteristics of gel foam in fractured reservoirs through visual experiments is of significant practical importance. In this study, migration experiments with different foam systems were conducted using the visualized vuggy model. The migration stability of foam was characterized by combining the sweep range and liquid drainage rate, and the impact of temperature on the migration characteristics of gel foam was explored. Additionally, a profile control experiment was performed using the fractured-vuggy network model, analyzing and summarizing its mechanisms for enhancing oil recovery in fractured-vuggy reservoirs. The results showed that, in the vuggy model, compared with ordinary foam and polymer foam, gel foam showed a lower drainage rate, higher foam retention rate and wider sweep range, and could form stable plugging in fractured-vuggy reservoirs. An increased temperature accelerated the thermal expansion of gas and changes in liquid film characteristics, which led to the expansion of foam migration speed and sweep range. Although a high temperature increased the liquid drainage rate of foam, it was still lower than 3%, and the corresponding foam retention rate was higher than 97%. In addition, the gel foam had a strong profile control ability, which effectively regulated the gas migration path and improved the utilization degree of remaining oil. Compared with the first gas flooding, the recovery of subsequent gas flooding was increased by 18.85%, and the final recovery of the model reached 81.51%. Comprehensive analysis revealed that the mechanism of enhanced oil recovery by gel foam mainly included density control, foam regeneration, flow redirection, stable plugging, and deep displacement by stable gel foam. These mechanisms worked synergistically to contribute to increased recovery. The research results fully demonstrate the application advantages of gel foam in fractured-vuggy reservoirs. Full article

Gait recognition, as a biometric technology with great potential, has been widely applied in numerous fields due to its unique advantages. Through in-depth research and the creation of in-the-wild gait datasets, gait recognition technology is progressively extending from laboratory settings to complex real-world scenarios, achieving notable advancements. However, the complexity of annotating gait data inevitably leads to labeling errors, known as noisy labels, which are one of the reasons for the suboptimal performance of in-the-wild gait recognition. To address these issues, this paper explores noisy label learning for in-the-wild gait recognition for the first time. We propose a plug-and-play gait recognition framework named Dynamic Noise Label Correction Network (DNLC). Specifically, it consists of two main parts: the dynamic class-center feature library and the label correction module, which can automatically identify and correct noisy labels based on the class-center feature library. In addition, we introduce the two-stage augmentation strategy to increase the diversity of the data and help reduce the impact of noisy labels. We integrated our proposed framework into five existing gait models and conducted extensive experiments on two widely used gait recognition datasets: Gait3D and CCPG. The results show that our framework increased the average Rank-1 accuracy of five methods by 10.03% and 6.45% on the Gait3D and CCPG datasets, respectively. These findings demonstrate the superior performance of our method. Full article

The adaptability of the supervisory control strategy of plug-in hybrid electric buses (PHEBs) to different driving styles determines the energy-saving performance. This paper proposes a driver-oriented adaptive equivalent consumption minimization strategy (ECMS) for PHEBs. The strategy aims to improve the fuel economy of PHEBs as much as possible by adapting to different driving styles while satisfying the physical constraints of the hybrid power system. Firstly, an online driving style recognition algorithm based on the Fuzzy K-means (FKM) algorithm and the random forest (RF) method is devised, in which the FKM algorithm is used to preprocess the feature parameters related to driving styles and the RF method is utilized to identify the driver’s driving style. Secondly, the driving style recognition results are introduced into the ECMS framework to form a driver-oriented energy management strategy. Finally, the proposed control strategy is verified using both Matlab/Simulink and Hardware-in-the-Loop. The verification results demonstrate that the proposed control strategy improves the fuel economy of PHEBs. Full article

This study aims to use Computational Fluid Dynamics (CFD) analysis to improve inclusion removal efficiency in tundishes used in the steelmaking industry, with the broader goal of promoting more sustainable steel production and supporting circular economy objectives by producing cleaner steel. Inclusions are non-metallic particles, such as alumina, that enter the tundish with the molten steel and travel through it; if not removed, they can exit through the nozzles and adversely affect the mechanical properties of the final product and process yield. An existing tundish design is modified using three passive techniques, including adding a vertical dam, adding a horizontal baffle, and inclining the side walls, to assess their influence on fluid flow behavior and inclusion removal. Residence time distribution (RTD) analysis is employed to evaluate flow characteristics via key metrics such as dead zone and plug flow volume fractions, as well as plug-to-dead and plug-to-mixed flow ratios. In parallel, a discrete phase model (DPM) analysis is conducted to track inclusion trajectories for particles ranging from 5 to 80 μm. Results show that temperature gradients due to heat losses significantly influence flow patterns via buoyancy-driven circulation, changing RTD characteristics. Among the tested modifications, inclining the side walls proves most effective, achieving average inclusion removal improvements of 8% (Case B1) and 19% (Case B2), albeit with increased heat loss due to greater top surface exposure. Vertical dam and horizontal baffle, despite showing favorable RTD metrics, generally reduce the inclusion removal rate, highlighting a disconnect between RTD-based predictions and DPM-based outcomes. These findings demonstrate the limitations of relying solely on RTD metrics for evaluating tundish performance and suggest that DPM analysis is essential for a more accurate assessment of inclusion removal capability. Full article

To address the limitations of existing models that typically treat crater formation and shear plugging as independent processes and only consider angle of attack effects during the initial crater phase, this study proposes a dynamic shear _plugging model for projectile penetration into thin concrete targets. The model is built upon the improved three-stage penetration theory and cavity expansion principles, and introduces a coupled cratering, plugging mechanism that captures the simultaneous interaction between these stages. A differential surface force approach is employed to describe the asymmetric stress distribution on the projectile nose under non-zero angle of attack conditions, while free surface effects are incorporated to refine local stress predictions. A series of validation experiments was performed with 30 mm rigid projectiles penetrating 27 MPa concrete slabs under different impact velocities and initial angles of attack. The results show that the proposed model achieves prediction errors of less than 20% for both residual velocity and exit attitude angle, significantly outperforming classical models such as those of Duan and Liu, which tend to underestimate post-impact deflection by treating cratering and plugging separately. Based on this validated framework, parametric studies were conducted to examine the effects of the initial inclination, impact velocity, and target thickness on the evolution of projectile attitude and angle of attack. The findings demonstrate that the dynamic shear plugging mechanism exerts a critical regulatory influence on projectile deflection during thin target penetration. This work, therefore, not only resolves the directional reversal issue inherent in earlier theories but also provides theoretical support for the engineering design of concrete protective structures subjected to angular impact conditions. Full article

Hydrogen injection timing (HIT) plays a crucial role in the combustion and emission characteristics of a hydrogen/n-butanol dual-fuel engine with hydrogen direct injection. This study employed an integrated approach combining three-dimensional simulation modeling and engine test bench experiments to investigate the effects of HIT on engine performance. In order to have a more intuitive understanding of the physical and chemical change processes, such as the stratification state and combustion status of hydrogen in the cylinder, and to essentially explore the internal mechanism and fundamental reasons for the improvement in performance of n-butanol engines by hydrogen addition, a numerical study was conducted to examine the effects of HIT on hydrogen stratification and combustion behavior. The simulation results demonstrated that within the investigated range, at 100 °CA BTDC hydrogen injection time, hydrogen forms an ideal hydrogen stratification state in the cylinder; that is, a locally enriched hydrogen zone near the spark plug, while there is a certain distribution of hydrogen in the cylinder. Meanwhile, the combustion state also reaches the optimal level at this hydrogen injection moment. Consequently, 100 °CA BTDC is identified as the optimal HIT for a hydrogen/n-butanol dual-fuel engine. At the same time, an experimental study was performed to capture the actual complex processes and comprehensively evaluate combustion and emission characteristics. The experimental results indicate that both dynamic performance (torque) and combustion characteristics (cylinder pressure, flame development period, etc.) achieve optimal values at the HIT of 100 °CA BTDC. Notably, under lean-burn conditions, the combustion parameters exhibit greater sensitivity to HIT. Regarding emissions, the CO and HC emissions initially decreased slightly, then gradually increased with advanced injection timing. The 100 °CA BTDC timing effectively reduced the CO emissions at λ = 0.9 and 1.0. CO emissions at λ = 1.2, and showed minimal sensitivity to the injection timing variations. Therefore, optimized HIT facilitates enhanced combustion efficiency and emission performance in hydrogen-direct-injection n-butanol engines. Full article

Gastrodin is the main active ingredient of Gastrodia elata Blume, known for its prominent neuroprotective effects, especially in Alzheimer’s disease. Meanwhile, aging is a critical risk factor in age-related diseases, including AD. Now, the underlying mechanisms of gastrodin against aging-related genes in Alzheimer’s disease remain unclear. Our study aimed to identify and validate the molecular mechanisms of gastrodin on aging-related genes in Alzheimer’s disease. Firstly, we analyzed gene expression datasets from GEO in NCBI, and weighted gene co-expression network analysis (WGCNA) was used to identify the intersected genes with differentially expressed genes (DEGs) and aging genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses and protein–protein interaction (PPI) network were performed to analyze and evaluate the intersected genes to screen key genes. Subsequently, we used machine learning techniques to screen hub genes and subcellular localization to confirm the reliability of the hub genes. Finally, molecular docking and molecular dynamics simulation were combined to explore the binding interactions between core targets and gastrodin. We identified 29 intersecting genes among DEGs of AD, key module genes of AD, and aging genes. Next, nine common hub genes were identified by four algorithms of the cytoHubba plug-in. Four hub genes, GFAP, NPY, SNAP25, and SST, were found as possibly hub aging-related genes in Alzheimer’s disease from machine algorithms by adopting the random forest, LASSO, SVM, and Boruta models. Among them, GFAP showed marked upregulation, while NPY, SNAP25, and SST exhibited significant downregulation (p < 0.05). Finally, molecular docking and molecular dynamics simulation exhibited that gastrodin has an excellent affinity in docking with SNAP25. This study demonstrates that SNAP25 can be considered a key aging-related gene in AD, and gastrodin could treat AD by targeting specific genes and signaling pathways. These findings provide critical insights for the clinical application of gastrodin. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

Existing force-measuring devices lack versatility in studying the dynamic coupling process between the seedling-picking device and the plug seedling pot during automatic transplanting. This research developed a universal force-measuring device featuring a centrally symmetrical clamping needle layout and a simultaneous insertion and clamping mechanism. The force-measuring device enables the flexible adjustment of the number of clamping needles (2/3/4 needles) via a modular structure. It can also modify the insertion depth and angle of the clamping needles to accommodate three specifications of plug seedlings, namely 50-hole, 72-hole, and 128-hole plug seedlings. A real-time monitoring system with dual pull-pressure sensors is integrated to precisely acquire the dynamic response curves of the clamping force (F_J) and the disengaging force (F_N) of the plug seedling pot during the seedling-picking process. Taking water spinach plug seedlings as the research object and combining with EDEM-RecurDyn coupling simulation, the interaction mechanism between the clamping needle and the plug seedling pot was elucidated. The performance of the force-measuring device was verified through systematic force-measuring experiments. The main research findings are as follows: The force-measuring device designed in this study can successfully obtain the mechanical characteristic curve of the relevant seedling plug pot throughout the automatic seedling-picking process. The simulation results show high consistency with the experimental results, indicating that the force-measuring device can effectively reveal the dynamic coupling process between the seedling-picking device and the plug seedling pot. The verification experiment demonstrates that the force-measuring device can effectively quantify the mechanical properties of the of plug seedling pots under different plug seedlings specifications and different clamping needles configurations. Reducing the hole size and increasing the number of clamping needles can effectively decrease the peak value of the disengaging force (F_Nmax). The peak clamping force (F_Jmax) is approximately inversely proportional to the needle number, with the four-needle layout providing the most uniform force distribution. The force-measuring device developed in this study is functional, applicable, and versatile, offering a general force-measuring tool and a theoretical foundation for optimal seedling-picking device design. Full article

Pure copper joints (PCJs) were fabricated using pinless friction stir spot welding (P-FSSW), a solid-state welding technique, to investigate the influence of plunge depth, rotational speed, and dwell time on PCJ performance. Thermal cycles under different welding parameters were recorded, while the microstructure at various locations within the welded zone was characterized using electron backscatter diffraction (EBSD). The microhardness and tensile–shear force (T-SF) of the PCJs were evaluated, and the fracture types together with fracture evolution were analyzed. The experimental results reveal that, under the combined effect of thermal cycles and mechanical stirring, subgrains in the welded zone transformed into recrystallized grains, whereas intense material flow contributed to an increased fraction of deformed grains. At the Hook region and the interface between the upper and lower sheets, grains were tightly bonded, resulting in effective metallurgical joining. Higher microhardness values were observed in the stir zone (SZ), whereas lower values appeared in the heat-affected zone beneath the interface. With increasing plunge depth, rotational speed, and dwell time, the T-SF of the PCJs first increased and then decreased, achieving a relatively high value at a plunge depth of 0.4 mm, a rotational speed of 1500 rpm, and a dwell time of 9 s. The fracture types of the PCJs were shear fracture and plug fracture, with the Hook region identified as the weakest zone. Full article

To improve the ballistic resistance of hydrogen storage tank-grade 316L austenitic stainless steel (ASS) plates that are prone to shear plugging failure under blunt projectile impact, this study proposes a non-bonded bilayer protective configuration: covering the 316L ASS substrate with a thin front layer of 2024-T351 aluminum alloy (AA) plate. Ballistic impact tests were performed on monolithic 5 mm thick 316L ASS plates and bilayer targets composed of a 2.05 mm thick 2024-T351 AA plate and a 5 mm thick 316L ASS substrate (total thickness: 7.05 mm), using a single-stage light gas gun combined with high-speed photography. Parallel explicit dynamics models were established using ABAQUS/Explicit, incorporating a modified Johnson–Cook constitutive model and a Lode-dependent Modified Mohr–Coulomb (MMC) fracture criterion, thereby enabling rigorous mutual validation between experimental results and numerical simulations. Results demonstrate that the addition of a mere 2.05 mm thick aluminum alloy front layer significantly enhances the ballistic limit velocity (BLV) of the 5 mm thick 316L stainless steel target plate, increasing it from 167.5 m/s to 250.7 m/s. The enhancement mechanism is closely related to the transition in the failure mode from localized shear plugging to a combination of bulging, dishing, and plugging. This shift substantially improves the structure’s overall plastic deformation capacity and energy dissipation efficiency. This research provides an effective solution and establishes a reliable experimental–numerical benchmark for the lightweight, impact-resistant design of hydrogen storage tanks. Full article