Search Results (332)

There has been a great need for replacing combustion-powered vehicles with electric vehicles (EV), and fully electric cars are meant to replace combustion engine cars. This has led to considerable research into improving the performance of EVs, especially via electric motor voltage control. A wide range of optimization algorithms have been used as traditional approaches, but the dynamic parameters of electric motors, impacted by temperature and different driving cycles, continue to be a problem. This study introduces an improved version of the Parrot Optimizer (IPO) aimed at enhancing voltage regulation in EVs. The algorithm can intelligently adjust certain motor parameters for adaptive management to maintain performance based on different situations. To ensure a stable and sustainable power supply for the powertrain of the EV, a photovoltaic (PV) system is used with energy storage batteries. Such an arrangement seeks to deliver permanent electric energy, a solution to traditional grid electricity reliance. This demonstrates the effectiveness of IPO, with the resultant motor performance remaining optimal despite parameter changes. It is also illustrated that energy production, by integrating PV systems, prevents excessive voltage line drops and thus voltage imbalances. The proposed intelligent controller is verified based on multiple simulations, demonstrating and ensuring significant improvements in EV efficiency and reliability. Full article

Autonomous valet parking technology refers to a vehicle’s use of onboard sensors and line-controlled chassis to carry out a fully automatic valet parking function, which can greatly improve the driver’s experience. This study focuses on autonomous parking, employing environmental modeling and vehicle kinematics models. Innovatively applying the PSBi-RRT algorithm to path planning in autonomous parking systems constitutes this research’s contribution to this field. Firstly, the environment is modelled by the raster method; then, the PSBi-RRT algorithm is used for path planning, and a B-spline curve is used for path optimization. Speed and acceleration are smoothed at the same time, and finally, a smooth and obstacle-avoiding path planning scheme is obtained. The results show that an autonomous parking system based on the PSBi-RRT algorithm performs path planning from the vehicle to the parking space. Compared to RRT and Bi-RRT, PSBi-RRT generates shorter planning paths, smoother heading angle changes, shorter planning times, fewer nodes, and higher success rates. This research provides theoretical support for the development of autonomous parking technology. Full article

Metropolitan city express line tunnels are fully enclosed and often span long distances between stations, allowing multiple trains within a single interval. Traditional segmented ventilation ensures only one train per section, but ultra-long tunnels with shaftless designs introduce new challenges under fire conditions. This study investigates smoke behavior in an ultra-long inter-district tunnel during multi-train blockage scenarios. A numerical model evaluates the effects of train spacing, fire source location, and receding spacing on smoke back-layering, temperature distribution, and flow velocity. Results indicate that when train spacing exceeds 200 m and longitudinal wind speed is above 1.2 m/s, the impact of train spacing on smoke back-layering becomes negligible. Larger train spacing increases back-layering under constant wind speed, while higher wind speeds reduce it. Fire source location and evacuation spacing affect the extent and pattern of smoke spread and high-temperature zones, especially under reverse ventilation conditions. These findings provide quantitative insights into fire-induced smoke dynamics in ultra-long tunnels, offering theoretical support for optimizing ventilation control and evacuation strategies in urban express systems. Full article

Glioblastoma (GBM, isocitrate dehydrogenase wild-type) is the most common primary malignant brain tumor in adults and is associated with a severely low survival rate. Treatments offer mere palliation and are ineffective, due, in part, to a lack of understanding of the intricate mechanisms underlying the disease, including the contribution of the tumor microenvironment (TME). Current GBM models continue to face challenges as they lack the critical components and properties required. To address this limitation, we developed innovative and practical three-dimensional (3D) GBM models with structural and mechanical biomimicry and tunability. These models allowed for more accurate emulation of the extracellular matrix (ECM) and vasculature characteristics of the native GBM TME. Additionally, 3D bioprinting was utilized to integrate these complexities, employing a hydrogel composite to mimic the native environment that is known to contribute to tumor cell growth. First, we examined the changes in physical properties that resulted from adjoining hydrogels at diverse concentrations using Fourier-Transform Infrared Spectroscopy (FTIR), compression testing, scanning electron microscopy (SEM), rheological analysis, and degradation analysis. Subsequently, we refined and optimized the embedded bioprinting processes. The resulting 3D GBM models were structurally reliable and reproducible, featuring integrated inner channels and possessing tunable properties to emulate the characteristics of the GBM ECM. Biocompatibility testing was performed via live/dead and AlamarBlue analyses using GBM cells (both commercial cell lines and patient-derived cell lines) encapsulated in the constructs, along with immunohistochemistry staining to understand how ECM properties altered the functions of GBM cells. The observed behavior of GBM cells indicated greater functionality in softer matrices, while the incorporation of hyaluronic acid (HA) into the gelatin methacryloyl (gelMA) matrix enhanced its biomimicry of the native GBM TME. The findings underscore the critical role of TME components, particularly ECM properties, in influencing GBM survival, proliferation, and molecular expression, laying the groundwork for further mechanistic studies. Additionally, the outcomes validate the potential of leveraging 3D bioprinting for GBM modeling, providing a fully controllable environment to explore specific pathways and therapeutic targets that are challenging to study in conventional model systems. Full article

To address the challenge of dynamic monitoring during grouting operations in coal mine fault zones under pressurized mining, this study proposes the Borehole–Tunnel Joint Resistivity Method (BTJRM). By integrating three-dimensional (3D) electrode arrays in both tunnels and boreholes with 3D resistivity inversion technology, this approach enables fully automated underground data acquisition and real-time processing, facilitating comprehensive dynamic monitoring of grout propagation. A case study was conducted on a coal mine fault grouting project, where tunnel and borehole survey lines were deployed to construct a 3D cross-monitoring network, overcoming the limitations of traditional 2D data acquisition. Finite volume method and quasi-Gauss–Newton inversion algorithms were employed to analyze dynamic resistivity variations, enhancing spatial resolution for detailed characterization of grout migration. Key findings include: (1) Grout diffusion reduced resistivity by 10%, aligning with electrical response patterns during fracture-filling stages; (2) 3D inversion reveals that grout propagates along the principal stress axis, forming a “Y”-shaped low-resistivity anomaly zone that penetrates the fault structural block and extends into roadway areas. The maximum planar and vertical displacements of grout reach 100 m and 40 m, respectively. Thirty days post-grouting, resistivity recovers by up to 22%, reflecting the electrical signature of grout consolidation; (3) This method enables 3D reconstruction of grout diffusion pathways, extends the time window for early warning of water-conducting channel development, and enhances pre-warning capabilities for grout migration. It provides a robust framework for real-time sealing control of fault strata, offering a novel dynamic monitoring technology for mine water inrush prevention. The technology can provide reliable grouting evaluation for mine disaster control engineering. Full article

We developed a fully automated key production line for smart manufacturing technologies based on the Internet of Things (IoT) and automatic optical inspection (AOI) to enable efficient and consistent production. The production line consists of seven processing stations: raw materials uploading, groove milling, laser marking, key tooth cutting, deburring, defects inspection, and a discharge station. IoT technology enables real-time monitoring and data transmission through a visual panel that displays the operational status of each station and provides immediate alerts in case of abnormalities for quick intervention. The defects inspection station ensures comprehensive quality checks, automatically stops the production line for detected defects, and prevents defective products from proceeding to subsequent stages. Chronological data are used to support predictive maintenance, production parameter optimization, and energy efficiency improvements. Overall, the system effectively integrates automation, real-time monitoring, and quality control to ensure stable production and high product quality. Full article

Background/Objectives: Continuous manufacturing is gaining importance in the nanopharmaceutical field, offering improved process efficiency and product consistency. To fully leverage its potential, the integration of Process Analytical Technology (PAT) tools is essential for real-time quality control and robust process monitoring. Among the critical quality attributes (CQAs) of nanosystems, particle size plays a key role in ensuring product consistency and performance. However, real-time size monitoring remains challenging due to complex process dynamics and nanosystem heterogeneity. Methods: This study evaluates the applicability of conventional Dynamic Light Scattering (DLS) and spatially resolved DLS (SR-DLS) using the NanoFlowSizer (NFS) as PAT tools in a temperature-regulated top-down nano-production line. Various lipid-based nanosystems, including solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLC), and nanoemulsions (NEs), were investigated. To ensure reliable implementation, key factors such as sample dilution, viscosity, focus position, measurement angle and temperature effects were systematically assessed for offline and at-line DLS using the Litesizer 500, as well as for offline, inline, and online SR-DLS using the NFS. Results: Offline screening confirmed that selecting the appropriate dilution medium and rate ensures measurement reliability. At-line methods provided an efficient alternative by enabling rapid final product control with minimal manual intervention. Inline and online monitoring further enhanced process efficiency by enabling real-time tracking of size, reducing waste, and allowing immediate process adjustments. Conclusions: This study demonstrates that integrating offline, at-line, in-line, and online DLS techniques allows for comprehensive product monitoring throughout the entire production line. This approach ensures a streamlined process, enables real-time adjustments, and facilitates reliable quality control after production and during storage. Full article

Disitamab Vedotin has shown good therapeutic efficacy against bladder cancer. Although its mechanism is clear, the regulation of gene expression in bladder cancer cells by Disitamab Vedotin is not fully understood. We searched the GEO database and identified the GSE237789 dataset, in which researchers treated the bladder cancer cell line SW780 with Disitamab Vedotin and performed high-throughput transcriptome sequencing. Compared with the control SW780 cells, the expression levels of the vast majority of genes (16,223/16,390, 98.98%) in Disitamab Vedotin-treated SW780 cells remained unchanged. Only one hundred fifty-nine genes (0.97%) were upregulated, and eight genes (0.05%) were downregulated. Enrichment analysis results showed that the related differentially expressed genes (DEGs) were mainly enriched in the TNF signaling pathway, NF-κB signaling pathway, and other pathways. Protein–protein interaction analysis revealed that 10 genes, TNF, IL1B, IL1A, CXCL8, CXCL1, CCL2, MMP9, ICAM1, CXCL10, and CCL20, had the highest connectivity, and all of these genes belong to the TNF signaling pathway. These results suggest that the TNF signaling pathway is the key pathway regulated by Disitamab Vedotin in bladder cancer cells, which may represent a stress response of bladder cancer cells to Disitamab Vedotin. Full article

This research examines the critical issue of greenhouse gas emissions, focusing on carbon dioxide (CO₂) as a significant contributor to climate change and its threats to environmental sustainability. The primary objective of this paper is to highlight the environmental impacts resulting from economic growth, energy consumption, and agricultural development in Saudi Arabia. The purpose of the empirical research is to investigate the dynamic causal relationships between CO₂ emissions, agricultural development, economic growth, energy consumption, and additional control variables in Saudi Arabia from 1990 to 2022. It is hypothesised that increases in agricultural land, economic activity, and energy use contribute to rising CO₂ emissions. This study examines these relationships using the Autoregressive Distributed Lag (ARDL) and Fully Modified Ordinary Least Squares (FMOLS) methodologies, along with unit root tests, the ARDL bounds test, and Vector Error Correction Model (VECM) causality analysis, to assess both short-term and long-term interactions among the variables. The findings reveal that agricultural land expansion, economic growth, and energy consumption significantly contribute to increased CO₂ emissions. Specifically, a 1% increase in agricultural land correlated with a 0.16% rise in CO₂ emissions, while a 1% increase in economic growth and energy use led to 0.28% and 0.85% rises, respectively. These results underscore the environmental challenges posed by economic expansion and energy dependence. This paper emphasises the need for policies that balance economic growth with emissions reduction, in line with Saudi Vision 2030. Transitioning to a low-carbon, circular economy supported by renewable energy and innovation is essential for sustainable development and climate change mitigation. Full article

In the fields of meteorological simulation and computer graphics, precise simulation of clouds has been a recent research hotspot. The existing cloud modeling methods often ignore the differentiated characteristics of cloud layers at different heights, and suffer from high computational costs under long-range conditions, making them unsuitable for large-scale scenes. Therefore, we propose a multi-level particle system 3D cloud modeling algorithm based on the Weather Research and Forecasting Model (WRF), which combines particle weight adjustment with a Proportional Integral Derivative (PID) feedback mechanism to represent cloud features of different heights and types. Based on the multi-scale mean-shift clustering algorithm, Adaptive Kernel Density Estimation (AKDE) is introduced to map density to bandwidth, achieving adaptive adjustment of clustering bandwidth while reducing computational resources and improving cloud hierarchy. Meanwhile, selecting the optimal control points based on the correlation between particle density in the edge region and cloud contour can ensure the integrity of the internal structure of the cloud and the clarity of the external contour. To improve modeling efficiency, cascade Bezier curves are designed at different line-of-sights (LoSs), utilizing the weight information of boundary particles to optimize cloud contours. Experimental results show that, compared with similar algorithms, our algorithm reduces the average running time by 37.5%, indicating enhanced computational efficiency and real-time capability, and the average number of required particles by 30.1%, reducing the cost of long-range computing. Our algorithm can fully demonstrate cloud characteristics and interlayer differences, significantly improve modeling efficiency, and can be used for accurate modeling of large-scale cloud scenes, providing strong support for meteorological and climate prediction. Full article

Recent observations have increasingly challenged the conventional understanding of atmospheric NH₃ and its potential sources in remote environments. Laboratory studies suggest that the microdroplet redox generation of NH₃ could offer an alternative explanation. However, key questions remain: (1) Can microdroplet redox generation of NH₃ occur in ambient air? (2) Is it restricted by the presence of specific catalysts? (3) What factors determine the efficiency of ambient NH₃ generation via microdroplet redox reactions? We investigate these questions based on adiabatic-expansion-induced perturbation observations performed in various atmospheres over the last decade. Our results indicate the adiabatic-expansion-induced generation of NH₃ + HNO₃ at ultrafast formation rates, with campaign-dependent stable stoichiometric ratios of HNO₃ to NH₃, as well as highly variable occurrence frequencies and efficiencies. These findings suggest that microdroplet redox reactions are more likely responsible for the generation of NH₃ + HNO₃ than conventional atmospheric NH₃ chemistry. Moreover, our analysis suggests that the line speed of microdroplets may be one of the key factors in determining the occurrence, stoichiometric ratio and efficiency of the redox reaction. Additionally, the presence of sea salt aerosols and low ambient temperature, rather than the specific catalysts, may significantly influence these processes. However, the current observational data do not allow us to derive a functional relationship between the redox reaction rate and these parameters, nor to fully detail the underlying chemistry. Comprehensive and controlled laboratory experiments, similar to our adiabatic-expansion-induced observations but utilizing state-of-the-art highly sensitive analyzers, would be necessary, though such experiments are beyond our current capabilities. Full article

Background: Three-dimensional skeletal muscle organoids (3D SkMO) are becoming of increasing interest for preclinical studies in Duchenne muscular dystrophy (DMD), provided that the used platform demonstrates the possibility to form functional and reproducible 3D SkMOs, to investigate on potential patient-related phenotypic differences. Methods: In this study, we employed fibrin-based 3D skeletal muscle organoids derived from immortalized myogenic precursors of DMD patients carrying either a stop codon mutation in exon 59 or a 48–50 deletion. We compared dystrophic lines with a healthy wild-type control (HWT) by assessing microtissue formation ability, contractile function at multiple timepoints along with intracellular calcium dynamics via calcium imaging, as well as expression of myogenic markers. Results: We found patient-specific structural and functional differences in the early stages of 3D SkMO development. Contractile force, measured as both single twitch and tetanic responses, was significantly lower in dystrophic 3D SkMOs compared to HWT, with the most pronounced differences observed at day 7 of differentiation. However, these disparities diminished over time under similar culturing conditions and in the absence of continuous nerve-like stimulation, suggesting that the primary deficit lies in delayed myogenic maturation, as also supported by gene expression analysis. Conclusions: Our results underline that, despite the initial maturation delay, DMD muscle precursors retain the capacity to form functional 3D SkMOs once this intrinsic lag is overcome. This suggests a critical role of dystrophin in early myogenic development, while contraction-induced stress and/or an inflammatory microenvironment are essential to fully recapitulate dystrophic phenotypes in 3D SkMOs. Full article

We present the Virtual Beamline (VBL) application, an interactive web-based platform for visualizing high-intensity laser–matter interactions using particle-in-cell (PIC) simulations, with future potential for experimental data visualization. These interactions include ion acceleration, electron acceleration, $\gamma$-flash generation, electron–positron pair production, and attosecond and spiral pulse generation. Developed at the ELI Beamlines facility, VBL integrates a custom-built WebGL engine with WebXR-based Virtual Reality (VR) support, allowing users to explore complex plasma dynamics in non-VR mode on a computer screen or in fully immersive VR mode using a head-mounted display. The application runs directly in a standard web browser, ensuring broad accessibility. VBL enhances the visualization of PIC simulations by efficiently processing and rendering four main data types: point particles, 1D lines, 2D textures, and 3D volumes. By utilizing interactive 3D visualization, it overcomes the limitations of traditional 2D representations, offering enhanced spatial understanding and real-time manipulation of visualization parameters such as time steps, data layers, and colormaps. Users can interactively explore the visualized data by moving their body or using a controller for navigation, zooming, and rotation. These interactive capabilities improve data exploration and interpretation, making VBL a valuable tool for both scientific analysis and educational outreach. The visualizations are hosted online and freely accessible on our server, providing researchers, the general public, and broader audiences with an interactive tool to explore complex plasma physics simulations. By offering an intuitive and dynamic approach to large-scale datasets, VBL enhances both scientific research and knowledge dissemination in high-intensity laser–matter physics. Full article

In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality. Full article

This study investigates the surrounding rock failure caused by the fracture line of the main roof above the gob-side roadway during fully mechanized top-coal caving mining in a 19 m thick coal seam. As mining progresses, stress concentration occurs in the roadway roof. Furthermore, the fracture line of the main roof above the roadway poses a significant threat to the structural stability of the gob-side roadway, leading to the localized failure of the roof structure, which consequently affects the safe and efficient production of the mine. This study investigates the shear failure mechanism of the roadway top coal and analyzes the failure characteristics and stress evolution law of the surrounding rock when the main roof fracture line (MRFL) is located above the roadway through three integrated approaches: theoretical analysis, numerical simulation, and physical similarity modeling. To effectively mitigate damage to the top coal, it is proposed to implement a five-hole tray coupled with high-strength prestressed anchor cables for reinforcing the surrounding rock, while compact wooden piles in combination with single pillars are employed to strengthen the roadway support control measures. It is verified by field tests that these control methods significantly improve the stability of coal above the entry and greatly mitigate the likelihood of surrounding rock failure. Full article