Modern industry is undergoing rapid transformation driven by various factors and challenges like digitalization, climate change and systems interdependencies, necessitating a paradigm shift in risk assessment. Traditional methods, often static and siloed, struggle to address dynamic risks such as aging infrastructures, cyber-physical interdependencies and climate-induced hazards. This paper explores cutting-edge approaches to dynamic, data-driven risk assessment, integrating advanced simulation, artificial intelligence and real-time monitoring to enhance reliability and resilience in industrial systems, and shares some reflections and studies on them with the purpose of stimulating further development. Through case studies spanning manufacturing, energy and critical infrastructures, we demonstrate how modern methodologies, from digital twins to probabilistic climate impact models, enable proactive risk management. Key themes include: i ) the role of real-time adaptability of safety-critical systems, ii ) computational advances for rare-event and cascading-risk analysis, and iii ) the integration of environmental stressors into long-term risk analysis frameworks. We conclude with an analysis of possible research directions to bridge gaps between technology, regulation and scalability, offering a roadmap for next-generation risk assessment methods.

Research progress on the formation, regulation, and failure mechanisms of the γ′ phase in nickel-based superalloy

Energy infrastructure constitutes not merely technical hardware but a fundamental determinant of socioeconomic transformation and industrial evolution. While the engineering attributes of Ultra-High Voltage (UHV) transmission have been extensively documented, its role in shaping corporate behavior and catalyzing industrial upgrading remains underexplored. UHV transmission, operating at direct-current voltages of 800 kV and above or alternating-current voltages of 1000 kV and above, has been systematically deployed across China since 2007 to bridge the structural spatial mismatch between inland energy production centers and coastal consumption hubs. This study investigates the sociotechnical nexus between energy systems and firm-level innovation, employing China's large-scale UHV expansion (2007–2024) as an empirical setting with implications for global energy transition strategies. Integrating energy economics with organizational behavior theory, we adopt a staggered difference-in-differences approach to examine how enhanced energy accessibility influences automation innovation. The findings demonstrate that reliable energy infrastructure functions as a catalyst for intelligent manufacturing, operating primarily through the restructuring of business environments via cost reduction, risk mitigation, and the realization of economies of scale. Importantly, the analysis reveals substantial heterogeneity: policy effects are amplified among state-owned enterprises and regulated industries, reflecting the interplay between institutional arrangements and technology adoption. Furthermore, this study uncovers a significant resource redistribution effect whereby firms in net electricity-importing regions derive disproportionately greater benefits, suggesting that strategic infrastructure deployment can compensate for regional resource disadvantages. By situating these findings within a broader analytical framework, this research offers actionable insights for policymakers seeking to leverage energy infrastructure investments as instruments for advancing industrial modernization and fostering sustainable business practices. • Ultra-High Voltage (UHV) expansion significantly drives corporate automation. • Cost reduction and economies of scale mediate the impact. • Electricity-importing regions benefit more from the grid. • Effects are amplified in SOEs and regulated industries. • Energy infrastructure serves as a tool for industrial upgrading.

ABSTRACT Lean manufacturing has emerged as a transformative operational philosophy aimed at maximizing value while minimizing waste across production systems. Rooted in the principles of continuous improvement, waste elimination, and respect for people, lean practices have significantly influenced operational efficiency in modern industries. This study explores the impact of lean manufacturing techniques such as Just-In-Time (JIT), Kaizen, Six Sigma integration, value stream mapping, and standardized work processes on improving productivity, reducing lead time, enhancing quality, and optimizing resource utilization. The research highlights how lean practices contribute to cost reduction, process stability, and improved customer satisfaction. By analyzing operational performance metrics, the study demonstrates that organizations implementing lean principles experience enhanced efficiency, reduced variability, and better adaptability to dynamic market demands. Furthermore, the study emphasizes the importance of organizational culture, employee involvement, and leadership commitment in ensuring successful lean implementation. The findings provide valuable insights for manufacturing firms aiming to improve competitiveness through operational excellence. Keywords: Lean Manufacturing, Operational Efficiency, Waste Reduction, Con

ABSTRACT Lean manufacturing has emerged as a transformative operational philosophy aimed at maximizing value while minimizing waste across production systems. Rooted in the principles of continuous improvement, waste elimination, and respect for people, lean practices have significantly influenced operational efficiency in modern industries. This study explores the impact of lean manufacturing techniques such as Just-In-Time (JIT), Kaizen, Six Sigma integration, value stream mapping, and standardized work processes on improving productivity, reducing lead time, enhancing quality, and optimizing resource utilization. The research highlights how lean practices contribute to cost reduction, process stability, and improved customer satisfaction. By analyzing operational performance metrics, the study demonstrates that organizations implementing lean principles experience enhanced efficiency, reduced variability, and better adaptability to dynamic market demands. Furthermore, the study emphasizes the importance of organizational culture, employee involvement, and leadership commitment in ensuring successful lean implementation. The findings provide valuable insights for manufacturing firms aiming to improve competitiveness through operational excellence. Keywords: Lean Manufacturing, Operational Efficiency, Waste Reduction, Continuous Improvement, Just-In-Time, Kaizen, Productivity, Process Optimization

The modern banking industry functions as the backbone of the global economy, yet it is deeply divided by distinct ownership models. This paper presents a theoretical comparative study between public sector banks and private sector banks, focusing primarily on their capital structures and financial performance metrics. By examining how differing regulatory environments and organizational objectives influence financial behavior, we highlight the critical structural disparities between these two sectors. A multi-stage analytical framework is proposed to evaluate these entities, aiming to provide a deeper understanding of how debt and equity utilization shapes institutional resilience and long-term economic stability.

Abstract: The increasing complexity of industrial processes, coupled with the need for uninterrupted production, has made early fault detection and continuous condition monitoring essential requirements for modern industries. Traditional maintenance strategies—such as scheduled inspections or manual supervision—are often inefficient, labor-intensive, and prone to human error, leading to unexpected equipment failures and extended downtime. To address these challenges, this project presents an integrated Automatic Industrial Fault Detection and IoT-based Remote Monitoring System that leverages smart sensing technologies, embedded computing, and cloud connectivity to provide real-time insights into machine health and operational performance. The system employs a distributed network of sensors to capture critical machine parameters, including temperature, vibration, current, pressure, and rotational speed. These data streams are processed using microcontrollers and edge-computing techniques to detect anomalies through threshold-based evaluation and machine-learning-driven fault classification. When abnormal patterns are identified, the system triggers immediate alerts and transmits diagnostic information to a cloud based IoT platform using secure communication protocols such as MQTT or HTTP. The cloud platform stores, analyzes, and visualizes operational data, enabling remote monitoring from any location through a user-friendly dashboard. Operators and maintenance teams can access live sensor readings, view historical trends, receive predictive maintenance insights, and generate automated reports. This continuous flow of actionable information supports proactive decision making, reduces maintenance overhead, and enhances the reliability and safety of industrial equipment. By integrating real-time fault detection with IoT-based monitoring capabilities, the proposed system provides a scalable and cost-effective solution suitable for a wide range of industrial environments. It minimizes unplanned downtime, extends machinery lifespan, and fosters the development of intelligent, Industry 4.0-compliant maintenance strategies. Overall, the project demonstrates how combining sensing, analytics, and cloud connectivity can transform traditional industrial maintenance practices into a fully automated, data-driven process.

The efficiency of the production process of advanced magnetic materials is key to competitiveness and sustainability in modern industry. Advanced magnetic materials are modern materials with enhanced or unique magnetic properties that find applications in cutting‐edge technologies, such as renewable energy sources, electromobility, sensors and electronics. In this way, using the Tecnomatix Plant Simulation software reduces costs, increases product quality, and brings products to market faster while also helping to mitigate the environmental impact of production. The use of this software in the development and optimisation of production processes is currently an increasingly important task. The presented article focused on optimising the production process for 320 compacted 3D samples from advanced magnetic materials, where specific production locations (bottlenecks) were identified and the use of machines and labour was optimised. The research focused on adding value by reducing handling movement and handling times, as well as increasing productionflow.

ABSTRACT Climate change poses critical threats to environmental and economic stability, driven primarily by fossil fuel dependency and outdated industrial technologies. This study investigates the determinants of carbon emissions in ASEAN, a rapidly industrializing region with acute energy demands and environmental pressures, focusing on the efficacy of climate technologies, environmental regulations, green energy, and industrialization in mitigating CO 2 emissions from 1995 to 2022. Employing second‐generation econometric techniques, specifically the Common Correlated Effects Mean Group (CCEMG) estimator with CuP‐FM and CuP‐BC robustness tests, we examine how these factors interact with economic growth and urbanization. Our findings reveal that advancements in climate technology, stringent environmental regulations, industrial modernization, and green energy expansion significantly reduce carbon emissions, whereas economic growth and urbanization remain emission‐intensive. These results provide targeted policy insights for balancing industrial development with environmental sustainability, offering a region‐specific roadmap for achieving post‐COP28 Sustainable Development Goals in emerging economies.

Digital transformation has injected core momentum into industrial integration. Based on four dimensions including digital technology innovation, this paper constructs a comprehensive evaluation index system covering five dimensions, uses the coupling coordination model to measure the integration level of the two industries in 29 provinces in China from 2009 to 2024, and analyzes the heterogeneity from three dimensions: time, space, and industry. The research finds that: the integration of the two industries has steadily climbed, with the coordination level advancing from barely coordinated to moderately coordinated; spatially, it presents a pattern of "southeastern leadership and inland catch-up", forming three radiation centers; industries such as education are leading in integration with advanced manufacturing, while industries such as finance are slower in integration. Based on this, four suggestions are put forward, including optimizing the regional layout and promoting the differentiated development of industries, to provide reference for the in-depth integration of the two industries and empowering high-quality economic development.

The desire of India to become a developed nation by the centenary year of its independence in 2047, also known as Viksit Bharat 2047 is a radical and holistic national program. This study discusses the opportunities and issues that come with the India development into a developed nation as it relates to the major economic, social, technological, environmental, and governance aspects. The paper will examine the growth prospects of India which are brought about by demographic dividend, digital transformation, entrepreneurship based on innovation, expansion of infrastructure, and policy measures like inclusion based financial system and modernization of industry. The paper also on a critical basis examines the structural and institutional issues that might hinder such a transition, such as income inequality, job creation, skills mismatches, quality education and health, environmental sustainability, urban-rural inequalities, and effective governance. The research employs descriptive and analytical research approach and relies on secondary sources of data in the form of government reports, global development indices and policy reports to determine the current situation of India against that of the developed economies. The results indicate that though India has good macroeconomic fundamentals and strategic advantages, high growth, improvement of human capital, technological improvement, green development channels, and reactive governance reforms would still be needed to help India become a developed nation by 2047. The paper ends with a conclusion of the strategic policy measures, and integrated development framework that can help India fill the existing gaps and speed up the process of inclusive and sustainable development.

Modern industries operate in environments where factors such as hazardous gases, high temperatures, humidity variations, and machine vibrations can directly affect worker safety and equipment performance. Continuous monitoring of these parameters is essential to prevent accidents, reduce equipment damage, and ensure smooth industrial operation. However, traditional monitoring methods mainly depend onmanual inspection or basic control systems, which are time- consuming, less accurate, and unable to provide real-time remote access. These limitations can delay fault detection and increase the risk of industrial hazards. To overcome these issues, this project proposes a human-centered and cost effective IoT-based Industrial SCADA (Supervisory Control and Data Acquisition) system for real-time monitoring andautomated alerts. The system uses multiple sensors such as the MQ7 sensor to detect carbon monoxide, the MQ6 sensor toidentifycombustible gases, the DHT11 sensor to measure temperature and humidity, and a vibration sensor to monitor abnormalmachine movements. These sensors continuously collect datafrom the industrial environment and send it to the NodeMCUmicrocontroller, which acts as the main processing and communication unit. The NodeMCU transmits the collecteddata to the ThingSpeak cloud platform through Wi-Fi,allowing users to monitor the system remotely using graphsand dashboards. This helps operators understandenvironmental conditions clearly and take timely action whenrequired.Keywords: Io T , SCADA, Node MCU, IndustrialMonitoring, Thing Speak c l oud, GSM Alerts, GasSensor, DHT 11 , MQ 6 & 7 , Flamesensor , VibrationSensor.

The modern software ecosystems employ application programming interface (API) to allow interoperability and integration across various platforms. Non-functional requirements (NFRs) as quality attributes are as vital as functional requirements (FRs) in assuring software quality and user satisfaction. API quality management is a challenge to software industry owing to poor NFRs handling during API development, resulting in cost increase, project failure and dissatisfied customers. The existing standards failed in producing a comprehensive quality framework for APIs. OpenAPI Specification (OAS) focuses on functional aspects, whereas ISO/IEC 25010:2023 emphases on traditional software quality. Hence, this study proposes a novel non-functional requirement quality framework for APIs (NFRQF-API). In compliance with ISO/IEC 25,010, the framework includes key NFRs and their influencing factors to facilitate the development of high-quality APIs. Explanatory sequential mixed-methods design was applied in this study, involving a survey tool. Data were collected using a questionnaire comprising eight Likert-scale items and two open-ended items. Descriptive statistics and reliability testing were applied for quantitative data analysis, while thematic analysis was applied to evaluate the qualitative data, which supports in achieving a comprehensive expert-based justification for the conceptual feasibility and applicability of framework. Based on the experts' survey, analyses results demonstrated the potential effectiveness of the proposed framework in managing the Core, Critical, and Contextual NFRs in the entire API development lifecycle. This study bridges the gap between existing quality models and API quality assurance challenges. The structured expert-refined framework facilitates the enhancement of API quality management and offers KPI-based conceptual quality standards for the industry to design high-quality APIs by effectively handling critical NFR issues.

Background – Improving vocational high school students’ creative thinking skills and learning independence is important to meet modern industry demands, requiring innovative approaches such as STEM-integrated Project-Based Learning (PjBL) that engage students in problem-solving and real-world projects. Purpose - This study aims to analyze the application of the STEM-integrated PjBL model in improving students’ creative thinking skills and learning independence. Method/approach - This study used a quantitative approach with a quasi-experimental non-equivalent control group design involving pretest and posttest. The population consisted of grade XI vocational high school students in Purbalingga, Central Java. The sample was selected purposively from grade XI TKJ students at SMK N 1 Purbalingga and SMK N 1 Kemangkon. Data were collected through creative thinking tests, learning independence questionnaires, and observation sheets. Instrument validity was tested by comparing the calculated r-value with the r-table value (0.3809), and reliability was measured using Cronbach’s alpha. Findings - Several test items were declared valid, and the learning independence questionnaire showed factor loadings between 0.720–0.756. Reliability results showed Cronbach’s alpha values of 0.74 for the creative thinking instrument and 0.773 for the learning independence questionnaire. Conclusions - The N-Gain results showed that students’ creative thinking skills improved more in the experimental class than in the control class. However, the increase in learning independence was minimal and showed no significant difference between the two groups.

Introduction Chlorosilane is widely used in modern industry. The health effect in humans after chlorosilane exposure is still unclear. Case summary A 57-year-old male developed severe lung damage after accidental inhalation of chlorosilane. He received mechanical ventilation and experienced refractory CO2 retention with difficulty weaning. Bronchoscopy showed diffuse endobronchial polyposis two months after exposure. Images Serial radiographs, bronchoscopy images demonstrated the course of severe respiratory injury and an uncommon sequalae of diffuse inflammatory endobronchial polyps. Discussion The pathogenesis of polyp formation may be associated with prolonged inflammation. Interventional bronchoscopy was used to remove the polyps. Corticosteroids may be considered, but there is the risk of superimposed infection. Conclusion Chlorosilane inhalation could induce severe lung damage. Multiple endobronchial polyps may be a severe sequela contributing to difficulty weaning.

Introduction: Modern industries have become deeply concerned by environmental sustainability because of the rising rates of pollution, depletion of resources, and environmental degradation. Through industrial activities, the impacts on the environment are very pronounced, and this is the reason why there is the necessity to have an effective monitoring system and sustainable management practices. Digital technologies like the Internet of Things, artificial intelligence, and big data analytics offer new possibilities to industries to monitor environmental conditions and enhance environmental performance using the help of data-driven decision-making. Aim: This research is intended to analyze how digital technologies can be used to monitor and decrease environmental effects of contemporary industries. The research question explores the potential of the acquisition of digital technologies to enhance environmental monitoring systems and promote environmental sustainability. Methodology: The research design used in this study was quantitative. The structured questionnaire was the means by which primary data were collected through distributing the survey to professionals operating in the industrial sectors, including manufacturing, energy, and logistics. A mean score of 150 valid responses was performed in terms of descriptive statistics, correlation analysis and regression analysis to ascertain the relationship between digital technology adoption and reduction of environmental impact. Results: According to the findings, digital technologies are very applicable in increasing environmental monitoring abilities and add to environmental sustainability. Internet of Things monitoring systems exert the biggest impacts in reduction of the environmental impact, followed by artificial intelligence and big data analytics. Conclusion: The paper comes to a conclusion that digital technologies in the industrial processes can enhance environmental monitoring and lessen the effects on the environment. Development of a sustainable industrial system and the improvement of practical environmental management can be, therefore, supported with the adoption of the newest digital technologies.

Developing surface coatings to enhance material properties is crucial in modern industry. Cladding, where a superior material is applied to a base metal, is one effective approach. This study investigates the macro and microstructural features, as well as corrosion and wear resistance, of super austenitic stainless steel cladding on carbon steel via gas tungsten arc welding. It emphasizes how welding parameters, especially travel speed, affect the process. Carbon steel plates served as the base, with AWS A5.9 filler used for cladding. Three travel speeds were tested to observe their impact on dilution ratio, microstructure, and mechanical properties. The research included welding, metallographic preparation, microhardness testing, electrochemical corrosion testing, and wear testing. Results indicated that lower travel speeds improved the dilution ratio and, consequently, the properties of the clad layer. The heat‐affected zone presented a two‐phase microstructure with dendritic austenite and δ‐ferrite. The cladding layer's hardness exceeded that of the base metal, with minimal variation across different travel speeds. Overall, the clad layers showed significant improvements in wear and corrosion resistance. The clad layer exhibited a strong metallurgical bond in the cladding.

High-selectivity phenol detection in cumene process wastewater via bromination and dynamic optical path.

Abstract: In modern industry, bearings are critical components in mechanical equipment, responsible for supporting and transmitting rotational motion. However, due to prolonged operation and various environmental factors, bearings are prone to wear, cracks, loosening, and other failures. If these faults are not detected and addressed in a timely manner, they can lead to equipment shutdowns, production interruptions, and even severe accidents. Therefore, to monitor and diagnose the operational state of bearings promptly, vibration signals have become a crucial source of information. By collecting and analyzing vibration signals, it is possible to detect abnormal conditions in bearings early on, allowing for appropriate maintenance measures to be taken and providing significant support for the preventive maintenance of equipment. The purpose of this patent paper is to provide a reference for relevant scholars and researchers in rolling bearing field. The various vibration signal processing methods of rolling bearings had been analyzed in detail. The problems of the existing methods had been summarized, and the future research directions had been proposed. This essay reviews the literature on rolling bearing vibration signal processing methods in recent years, explains the principle of rolling bearing vibration signal processing methods, and analyzes the advantages and disadvantages of each technique. By searching the relevant literature and patents of rolling bearing vibration signal processing methods, the problems existing in developing vibration signal processing methods are summarized, and the future development direction is put forward. Rolling bearing vibration signal processing not only improves the accuracy of fault diagnosis but also enhances the prediction ability of rolling bearing performance, so as to provide technical support for fault prevention and predictive maintenance in advance.

Next Generation EU and industrial transformation: Evidence from Spain