The growing depletion of high-grade limestone resources has necessitated the exploration and utilization of low-grade limestone deposits for industrial applications. This study investigates the geochemical and petrographic characteristics of low-grade limestone from the Majhgawan and Hinauti Extension areas of the Vindhyan Basin, Central India. Field-based stratigraphic analysis, borehole litholog interpretation, petrographic examination, and geochemical investigations (XRD and XRF) were conducted. The results reveal significant compositional variability across stratigraphic horizons, influenced by shale intercalations and dolomitization processes. Petrographic analysis indicates dominance of micritic calcite with minor dolomite and quartz, while geochemical results show variations in CaO (38–48%), MgO (3–12%), SiO₂ (5–20%), and Al₂O₃ (1–6%). These variations directly affect industrial suitability, particularly for cement manufacturing. The study highlights that selective mining, blending, and beneficiation strategies can enhance the usability of low-grade limestone.
With the rapid development of technology, artificial intelligence (AI) has become a key force driving the new round of technological revolution and industrial transformation, profoundly impacting the global economic landscape. This study aims to reveal the dynamic relationship among China's Gross Domestic Product (LNGDP), effective invention patents in high-tech industries (LNNVI), and R&D expenditure in high-tech industries (LNRDE) through empirical analysis, in order to provide valuable references for policymakers to promote high-quality economic development in China. The study adopts the Vector Autoregression (VAR) model to systematically analyze the public data released by the National Bureau of Statistics of China. LNGDP is selected as the indicator of economic growth, LNNVI and LNRDE as the indicators reflecting the development of the AI industry. Statistical methods such as variance decomposition and impulse response function are used to explore the interactions among these variables. The study finds that China's R&D expenditure in high-tech industries increased from 29.213 billion yuan in 2004 to 650.77 billion yuan in 2022, an increase of about 22 times. The number of effective invention patents increased from 4,535 in 2004 to 809,824 in 2022, an increase of about 178 times. There is a significant positive correlation among LNGDP, LNNVI, and LNRDE, indicating that economic growth and the development of high-tech industries are mutually reinforcing. There is a strong positive correlation between R&D investment and the number of effective invention patents, and the increase in R&D investment directly drives the output of innovation results. The impulse response of LNGDP to LNNVI and LNRDE changes over time, showing the long-term cumulative effect of R&D investment and patent output on economic growth. This study reveals the close link between economic growth and high-tech industries, especially the AI industry. Specifically, economic growth promotes innovation activities and R&D investment in high-tech industries, and the increase in R&D investment further drives patent output, which ultimately has a positive impact on economic growth. Furthermore, the study also finds the long-term cumulative effect of R&D investment and patent output on economic growth. Based on these findings, the study proposes policy recommendations, including increasing support for high-tech industries, strengthening intellectual property protection, improving the efficiency of R&D investment conversion, optimizing the industrial structure layout, enhancing talent cultivation and introduction, promoting international science and technology cooperation and exchange, and focusing on long-term effects, to jointly promote China's economic development towards high-quality growth.
Along with information technology iterative update, and the rapid penetration into various fields. Improving rural teachers 'digital literacy has become the core element to realize urban-rural education equity. The digital literacy of rural teachers has been paid more and more attention by the academic circle and society. With the help of visualization analysis software Cite Space6.3.1, bibliometric analysis was carried out on the research results of rural teachers 'digital literacy in the past ten years (2014 to 2023). Based on Popper's three world theory and the "second-order science" paradigm, The evolution and progress of the theme of rural teachers 'digital literacy research were deeply analyzed by using Price's formula. The number of published papers shows the characteristics of stages, the distribution of research authors and research institutions is wide, most of them are normal universities, and cooperative research has gradually become a development trend. The research focuses on the topics of data mining, technical support and ascension path of rural teachers' digital literacy. It also reflects the research trends of rural teachers' digital professional development, digital training, and enhancement of digital resource integration ability. Future research should further strengthen cooperation, focus on digital literacy goals, improve rural teachers' ability of data thinking and data mining, improve the training system, pay attention to professional development, and build an overall improvement framework for rural teachers 'digital literacy.
Introduction: The present research seeks to develop new cybersecurity approaches for utilizing management information systems with the aim of safeguarding pertinent data and infrastructure against cyber threats. The increasing development in the internet. Management information systems have a proper security system in the present digital world. There is a necessity to increase the protection and reliability of IT systems that are used in many sectors in the world, which contribute to the stability of the nation’s economy and security. Methodology: The study employed a range of methods to determine the weaknesses inherent in today’s MIS. It utilizes literature reviews, case studies, and testing new security solutions in cybersecurity. It involves industry professionals using the said solutions and how effective they would be in different scenarios. In the context of the application of machine-learning algorithms and artificial intelligence in this study, the ultimate goal is to develop the security model for combating current and future cyber threats. Conclusion: The expected benefits of this research are that it facilitates enhanced security arrangements that safeguard data both in the public and private domains. The protection of personal data and the stability of IT systems these solutions contribute to the strengthening of national security and economic stability. This research contributes to the existing body of knowledge in the field of cybersecurity and offers relevant policy implications for organizations and policymakers who want to strengthen their cybersecurity capacities in the context of growing cybersecurity threats.
We study the supplier's value chain climbing behavior in a two-level supply chain consisting of a buyer and a supplier, where the buyer outsources its production to the supplier in the first stage, and the supplier may enter the market and compete with the buyer in the second stage. We model the problem as a two-stage game, assuming the supplier is relatively powerful and sets the outsourcing price. We show how the buyer decides on the outsourcing strategy and/or in-house production quantities, and obtain three equilibria, depending on the attitudes of the buyer to the supplier's value chain climbing behavior: (1) complete outsourcing in Stage 1; (2) complete in-house production in Stage 1; and (3) partial outsourcing in Stage 1. We identify a few properties of each equilibrium and examine if outsourcing reduces production costs under various conditions. Our computational study analyzes the optimal outsourcing strategies, expected profits, and the impact of demand uncertainty. The computational results indicate that a buyer may switch from outsourcing to in-house production if product prices are high or production costs are low. Additionally, the demand distribution affects both the speed and direction of this shift.
