With the rapid development of computer networks, digitization has become a new symbol of today's social and economic development. The "digitalization" of mining exploration and mining results data is one of the essential contents of current mining work. This article will use the large-scale mine engineering software Surpac Vision to construct a digital ore deposit model of the Manjiazhai section and realize the three-dimensional visualization of the ore body. It intuitively reflects the spatial continuity, thickness variation, resource reserves, and grade spatial distribution of ore bodies. On this basis, a block model is established. In calculating the valuation of the unit block, a computer is used to dynamically analyze the changes of ore bodies with different boundary grades and the changes of ore bodies' shape and average grade under single elements and combined elements. Comparative analysis shows that the ore bodies with comprehensive grade delimits have good continuity under the same boundary grade. The average grade and metal content are higher than the single-grade circled ore body, indicating that improving the comprehensive utilization of resources will be conducive to improving the service life of the mine and increasing the overall economic benefits of the mine, suggesting that it is adequate to apply the digital deposit model to the comprehensive utilization of mineral resources, which will provide data support for the green and sustainable development of mining enterprises.

The zuozhen wood, a unique and precious Cudrania tricuspidataspecies native to Nantong, is an ecologically and economically versatile species. Historically, it has been used for furniture making, brewing wine, medicinal purposes, and more, playing a key role in the local ecological environment and culture of Nantong. This paper explores the potential for the ecological and social benefits of the Zuozhen and its industry under the new era context, as well as some current issues in its development, and offers suggestions for further development.

As social media platforms have penetrated every aspect of people’s lives, many mental health problems have also arisen along with them. In today’s digital age, analyzing mental health trends through these platforms has become critical. In this study, we present a system designed to identify the mental health trends of Weibo users by extracting and analyzing the content posted by users on Weibo, China’s leading social media platform. The system is mainly composed of two parts: a data acquisition module and an analysis module. The data collection module uses the Python-based web scraping tool Scrapy to scrape comments from popular topics on Weibo. At the heart of the analysis module is a large language model fine-tuned from a psychological database. The module assesses the topic and specific content of the posts, scoring comments based on criteria such as positivity, alignment with mood disorders, and potential signs of psychoactive substance use. This data is stored and mediated using the relational database MySQL, and then analyzed and visualized using advanced data analysis tools. Through this method, we can timely and comprehensively monitor the mental health status of social media platforms, and provide a solid foundation for further academic research on public mental health.

This research examines energy-efficient solutions in industrial manufacturing through a Multi-Criteria Decision Analysis (MCDA) approach to discover optimal methods for improving energy consumption alongside cost-efficiency and environmental sustainability. The production activities within the industrial sector including chemical processing, metalworking and food manufacturing make up substantial portions of world energy usage. This study examines the role of advanced technologies including heat recovery systems and high-efficiency furnaces together with energy-efficient refrigeration systems in reducing energy consumption within industrial sectors. This research employs the MCDA framework which combines the Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW) methods. The research examines how energy-efficient solutions perform during a six-month period by measuring energy savings alongside cost reductions and environmental benefits. The study results reveal significant energy reductions alongside cost savings between 10% to 30% while chemical production achieved a 25% decrease in energy consumption. The observed environmental improvements that include a 30% decrease in carbon emissions from chemical production demonstrate how these technologies can advance sustainable industrial practices.

This paper focuses on the reliability and radiation effects of wide bandgap SiC MOSFET power devices. Based on the failure issues of SiC MOSFET power devices used in space solar power stations, the paper investigates the feasibility of applying SiC MOSFET power devices in space solar power stations. By examining the failure mechanisms of wide bandgap SiC MOSFET power devices, the paper proposes reinforcement methods for radiation resistance and high reliability from both device and circuit application perspectives, providing feasible solutions for the use of these devices in space solar power stations.

Three-level photovoltaic grid-connected inverters are widely used in the photovoltaic grid-connected systems because of their high efficiency and low harmonic characteristics. However, the major problem of the three-level inverter has always been its core challenge, significantly affecting its system reliability and performance. This study reviews the causes of neutral-point voltage imbalance, discusses three typical three-level inverter topologies, including neutral-point-clamped inverter, flying capacitor inverter, and cascaded H-bridge inverter, and compares their application effectiveness in neutral-point voltage balancing control. Then, it analyzes several common control methods, such as the modulation method based on space vector, model predictive control, and relatively new techniques for controlling virtual neutral-point voltage. The comparative analysis results show that space vector is easy to implement, but the neutral-point voltage fluctuates markedly; model predictive control exhibits strong dynamic response performance but has a high computational complexity; virtual neutral-point voltage does not rely on the real-time feedback of actual neutral-point voltage. In conclusion, this study makes a prospect for the development trends of neutral-point voltage balance control technologies and proposes possible development directions for further improving the performance of grid-connected photovoltaic inverters.

Extreme Ultraviolet (EUV) lithography is pivotal in semiconductor manufacturing and attosecond metrology. This paper delves into the current research status and future development trends of EUV, aiming to offer a comprehensive understanding and predictions for this technology’s evolution. Considering the challenges in applying EUV in chip manufacture, we will discuss advanced methods such as metalenses, phase-shifting masks, EUV transient grating spectroscopy, and negative tone development (NTD) processes to address issues in photolithography. Additionally, this article employs a methodical literature review and analysis. Presently, EUV lithography machines with a numerical aperture (NA) of 0.33 have achieved mass production of 5nm logic chips. Nevertheless, generating a more stable EUV light source and enhancing the focusing ability and power of the light source remain critical challenges requiring resolution.

In response to the current challenge of design efficiency faced by companies, this study focuses on the design process of the control arms in the five-link rear suspension. By summarizing the common structural types of control arms and their corresponding key design parameters, Visual Basic and CATIA parametric modeling are applied for secondary development. This approach enables the rapid structural design of control arms and facilitates quick model updates during the design iteration process, significantly reducing modeling time, effectively improving design efficiency, and laying the foundation for intelligent design development systems.

In recent years, the development of Bimodal Neural Probes has brought unprecedented breakthroughs to Brain-Computer Interfaces (BCI) and neuroscience research. Traditional neural probes can only record either the brain's electrical signals or the chemical signals of neurotransmitters, but bimodal probes enable the simultaneous acquisition and integration of both, allowing scientists to better understand neuronal interactions. This technology's core innovation lies in integrating Microelectrode Arrays (MEA) and Microfluidic Channels, ensuring high temporal and spatial alignment for more accurate neural signal decoding. This study explores the applications of bimodal neural probes in learning enhancement, motor recovery for paralyzed patients, Parkinson’s disease treatment, and epilepsy prediction, demonstrating their potential in neurological disease diagnosis, BCI optimization, and human-machine interaction through experimental case studies. Additionally, we analyze how key material innovations, such as graphene, polymer PI/PDMS, and PEDOT, enhance the probe's sensitivity, flexibility, and long-term stability while proposing future technological optimizations. Despite their promising prospects, widespread application faces challenges related to ethics, safety, and cost, which we address by proposing key feasibility recommendations, including establishing neural data security regulations to prevent misuse, reducing manufacturing costs through mass production and material optimization, conducting long-term biocompatibility testing to ensure stability, and developing fair-use guidelines to prevent social inequalities. Ultimately, this study highlights the significant contributions of bimodal neural probes to BCI technology and neuroscience while emphasizing the importance of responsible technological advancement. With improvements in manufacturing processes and increasing clinical applications, we believe bimodal neural probes will revolutionize human-brain interactions, profoundly impacting medicine, neurorehabilitation, and artificial intelligence.

During a vehicle accelerated corrosion test, a front coil spring in the MacPherson suspension system of an SUV exhibited fracture failure. The results indicated that the material composition, microstructure, and decarburization layer of the coil spring met the technical requirements; however, the tempering hardness was found to be excessively high. Fracture surface analysis revealed a small amount of intergranular cracking in the crack initiation zone, and the crack propagation zone exhibited ductile tearing characteristics, ultimately leading to fatigue fracture. Through force analysis, it was determined that the coil spring was subjected to lateral loads. Additionally, ABAQUS software simulations revealed that during wheel hop conditions, the coil spring end coil came into contact with the spring seat, leading to coating failure and the formation of surface fatigue defects. Based on these findings, an improvement plan was proposed, followed by failure reproduction and optimization validation in a test rig.