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This research examines the amalgamation of Circular Economy concepts with Lean Six Sigma approaches to enhance manufacturing operations, emphasizing waste minimization, resource efficiency, and economic performance. The study assesses the influence of Lean Six Sigma on production efficiency and environmental sustainability via empirical analysis and process optimization methodologies. Conventional manufacturing procedures, including machining, milling, and grinding, were assessed for resource utilization and waste production, revealing that Lean Six Sigma methodologies markedly decreased material waste, especially in milling operations, where waste was curtailed by 20%. Advanced precision techniques, including CNC and laser cutting, were evaluated for their efficiency, with CNC cutting realizing a 15% decrease in energy usage and a 30% enhancement in material utilization. Laser cutting exhibited improved accuracy, resulting in a 35% decrease in rework and scrap. The economic study indicated that the application of Lean Six Sigma in CNC operations resulted in a 25% reduction in operating expenses over time, albeit greater initial expenditures. These results provide significant insights into the enhancement of industrial efficiency and the promotion of sustainable production practices via the integration of Circular Economy concepts with Lean Six Sigma methodologies. Keywords: Circular Economy, Precision Manufacturing, Comparative Analysis, Tool Life, Economic Implications.

The rapid expansion of green energy resources (RER) into existing electrical networks necessitates an evolved approach to voltage distribution. This study explores the challenges and solutions associated with integrating green energy into high and low voltage distribution systems (HVDS and LVDS). The research evaluates various protection schemes for dynamic fault currents, voltage control systems for mitigating power quality issues, and optimal planning strategies for distributed generation. Innovative methodologies for integrating solar and wind energy, such as centralized-decentralized control approaches and demand response mechanisms, are proposed. The study demonstrates, through MATLAB simulations, that HVDS configurations significantly improve system efficiency and reduce technical losses compared to LVDS, particularly when interfacing with green energy sources.

Internet of Things isn’t always approximately about the things themselves; it’s approximately being clever. IoT is real and helpful because of its ability to apply intelligence to sense facts, especially in the context of construction engineering. A smart building’s architecture is a great place to start as IoT is reshaping every aspect of a building, from design to occupancy to maintenance. The experience of workers, control, and tenants is being optimized through the use of IoT data to inform decision-making. Better facilities may simplify corporate processes and increase revenue in smarter homes. The goal of intelligent houses is sustainability. There are several methods for automating tasks with the Internet of Things. It is necessary to address every single facet of the building architecture. This article discusses the problems and technologies of IoT-based smart building architecture. The Internet of Things (IoT) and embedded systems provide the foundation of the “Smart Building” idea. Together with smart lighting in smart buildings and seismic detection, the model that is being shown has several features. When the smart lighting system turns on and off, it is determined by the amount of natural light available and the presence of people within the building. In order to reduce the amount of maintenance needed, smart dustbins that open up when they sense a person are available. Watering systems that are designed to measure the moisture content of the soil are extremely useful for the maintenance of lawns. A seismic activity detection module allows for early warnings of earthquakes and other seismic activity that may occur in the future. It has been successfully developed a smart building concept that uses Arduino and a cloud server to analyze the data gathered from the smart building.

Traditional synthesis techniques that use perilous solvents and poisonous precursors give rise to environmental and health apprehensions. This research explores sustainable methods of synthesizing materials utilizing environmentally friendly substances to tackle these difficulties. The synthesis of perovskite films was carried out utilizing environmentally friendly solvents, namely ethanol and water, and lead-free or recycled precursors, under optimum circumstances. An assessment was conducted on the efficiency of solar cell devices made from environmentally friendly perovskite materials. The results showed that these devices achieved efficiencies ranging from 11.8% to 13.1%, open-circuit voltages between 0.72 V and 0.78 V, short-circuit current densities from 17.8 mA/cm² to 18.5 mA/cm², and fill factors ranging from 76% to 80%. Material characterization demonstrated that the green perovskite films exhibited similar properties to traditionally produced films, including comparable bandgaps, crystallinity percentages, surface areas, and absorption peaks. The use of life cycle assessment (LCA) measures revealed significant decreases in energy consumption, waste production, and CO2 emissions linked to green synthesis techniques, hence highlighting the positive environmental effects. By using sustainable synthesis procedures, there was a significant improvement in environmental performance. Specifically, energy consumption decreased by 25%, trash production reduced by 20%, and CO2 emissions decreased by 30% compared to traditional methods. The results emphasize the capacity of green synthesis techniques to increase the sustainability of perovskite solar cells, without compromising or even enhancing device performance. In summary, this study makes a significant contribution to the progress of perovskite technology that is ecologically sustainable and offers vital insights for the advancement of renewable energy solutions.

This study delves into the transformative role of mobile applications in shaping sustainable tourism from 2013 to 2023. Analysing 56 articles from Scopus, it reveals the pervasive influence of these apps beyond altering traveller behaviour, encompassing destination management, and fostering sustainability. Mobile apps contribute to environmental conservation, cultural heritage preservation, and responsible tourism practices. The research emphasizes integrating sustainability principles into app development, including reducing ecological footprints and promoting environmental awareness among travellers. Leading studies highlight mobile apps not just as enhancers of user experiences but also as drivers for sustainable travel behaviours, emphasizing local cultural engagement and environmental stewardship. This study offers a comprehensive view, urging ongoing research leveraging mobile technology to propel sustainable practices within the tourism industry.

The escalating use of plastic products alongside rising ambient temperatures has intensified concerns regarding their performance under diverse thermal conditions. This paper delves into the intricate relationship between plastic materials, ambient temperature fluctuations, and resulting stresses. Various grades of plastic materials are subjected to distinct ambient temperatures to elucidate stress generation—a pivotal aspect in plastic product design. Leveraging the finite element method, a comprehensive analysis is conducted to design and assess a plastic product under specified loads and ambient temperatures. The study encompasses the evaluation of equivalent stresses, normal and shear stresses, and deformations. Ultimately, the research culminates in the development and analysis of a thermally stable plastic product, offering valuable insights for robust design practices amidst evolving environmental conditions.

The Internet of Things (IoT) has enabled automation in every other field and is one of the emerging technologies. It facilitates communication between devices and the cloud and between the devices themselves. In the current world, many modern techniques are evolved in promoting effective and smart agriculture. Even though early and traditional farming techniques are being used in most parts of the nation, these techniques can’t guarantee the efficient yield of crops. Smart agriculture is helpful in many ways to obtain a high crop yield. The proposed work utilizes sensors and data analytics to monitor crop growth and provide recommendations on optimal farming practices. The system collects data on factors such as soil moisture, temperature, and nutrient levels to provide real-time updates on the health of the crops. The application made by Kodular can be used for monitoring the real-time conditions of the crops. It also recommends the crop to be grown based on conditions like soil type and farming season. This system can help farmers improve crop yields and reduce the need for manual labour, leading to increased efficiency and profitability in the agricultural domain.

Noise pollution from aircraft is a critical environmental issue, affecting not only wildlife but also human health and comfort, especially for populations living near airports. The recent, researchers have been primarily focused on reducing noise pollution in aircraft systems. Among the major contributors to noise pollution are the aircraft engine and nozzle. To address noise in the nozzle, the chevron nozzle, characterized by a sawtooth pattern at its edge, has been commercialized. Thus, there is a need for dependable Computational Fluid Dynamics (CFD) capabilities to swiftly assess initial designs for noise reduction and analyze problems that involve fluid flows. For noise reduction in aircraft systems, CFD capabilities are essential. This study conducts Finite Element Method (FEM) analysis of the chevron nozzle. Additionally, design optimization is performed utilizing the Taguchi method.

Transportation gets more intricate when cities get busier. Increased motor vehicles are moving into towns, which means increased traffic jams. The impacts of urbanisation are pervasive and include, but are not limited to, traffic performance, road safety, pollution, and traffic congestion. The rising number of people in the nation is driving up demand for personal vehicles including cars and motorcycles. Adding additional lanes is not enough to solve traffic congestion effectively. So, studying passenger car units (PCU) in heterogeneous traffic becomes necessary. Efforts to derive PCU values for cities roads with various traffic situations are made on this article. Using the density technique, we got somewhat different PCU values for the remaining three cars, but Chandra's method gave us PCU values of 1.99, 3.37, and 1.33, respectively. The data shows that buses make up a significant portion of the traffic in this region, constituting 23% of the total. Of the entire traffic volume, cars account for 16%. There are 19% fewer cars and trucks on two wheels in the research region. Of the total vehicles in the research region, buses constitute 21%. Nineteen percent of all traffic is caused by cars. It has been found that according to site 1 in the research region, three-wheeled vehicles constitute 18% of total traffic.

Greenhouse cultivation plays a pivotal role in modern agriculture by providing a controlled environment for plant growth. The significance of greenhouse insulation lies in its ability to create optimal conditions for plant development, ensuring increased crop productivity and quality. This paper emphasizes the criticality of greenhouse insulation and the necessity for effective predictive models to anticipate plant growth and yield accurately. This research proposes the utilization of Machine Learning (ML)and Deep Learning (DL) techniques to forecast plant growth and yield within controlled greenhouse settings. To achieve this, a novel deep recurrent neural network (RNN) architecture employing the Long Short-Term Memory (LSTM) neuron model is deployed in the prediction process.The study offers a comparative analysis involving various ML methodologies such as support vector regression and random forest regression. The performance evaluation of these diverse methods is conducted using the mean square error criterion to assess their effectivenessin predicting plant growth and yield. The model's sophisticated architecture enables it to produce accurate and timely predictions regarding specific growth parameters by leveraging an advanced neural network. This holistic approach introduces a novel perspective in greenhouse tomato cultivation, providing growers with valuable insights to facilitate informed decision-making, streamline resource distribution, and promote heightened agricultural sustainability.

In the dynamic landscape of our hyper-connected digital world, social media platforms play a dual role as facilitators of global interaction and breeding grounds for harmful behaviors. Cyberbullying, an insidious online menace, inflicts emotional distress and psychological trauma on numerous individuals, underscoring the urgent need for advanced tools to detect and prevent such malevolent actions. This innovative project harnesses the power of artificial intelligence and text analysis to illuminate the dark corners of social media where cyberbullying thrives, offering hope to countless victims. At its core, this endeavor utilizes cutting-edge ensemble techniques, a fusion of diverse machine learning algorithms, to analyze textual content across social media platforms. This approach ensures unparalleled accuracy in identifying and flagging cyberbullying instances, enhancing the efficiency of the detection process while minimizing false positives. The project adopts a multifaceted approach to text analysis, examining explicit language, sentiments, context, and behavioral patterns in online interactions. By delving into the intricacies of human communication, the system distinguishes between genuine expressions and malicious intent, providing a nuanced and accurate assessment.

The emergence of artificial intelligence (AI) with synergistic integration is currently a paradigm-shifting strategy for the direction of biomaterials development and design. This paper analyzes the connection between AI and biomaterials, explaining the significant influence of predictive modelling on the path of the area. By carefully investigating state-of-the-art studies and unique applications, it illustrates how AI-driven predictive modelling redefined biomaterial design and entered a new era of unusual accuracy and productivity. This research covers a wide variety of AI technologies, from deep neural networks to machine learning, that facilitates the development of prediction models that use large datasets to anticipate the behaviour, characteristics, and interactions of biomaterials. It examines how artificial intelligence (AI) may speed up the method of screening for viable materials, improve their qualities, and forecast there in vivo reactions. This can help translate beachside discoveries into clinical applications more quickly. This paper further explains the future prospects and problems in the field of biomaterials and AI integration, underlining the significance of interdisciplinary working together, standardization of data, and ethical concerns.

Bioinspired composites have become an increasingly popular area of research in materials science, as they offer a promising approach to developing high-performance materials. By drawing inspiration from the structures and properties of natural materials, researchers can design composites with enhanced mechanical, thermal, and other properties. This review article discusses the lessons that can be learned from nature for materials design and performance, with a focus on the structures and properties of biological materials such as bone, spider silk, and nacre. We explore the key mechanisms that give these materials their unique properties, including hierarchical structures, nanoscale building blocks, and interfacial interactions. By understanding these mechanisms, researchers can develop new materials with improved strength, toughness, and other desirable properties. We also discuss the potential applications of bioinspired composites in fields such as aerospace, engineering, and biomedical science. Overall, this review highlights the importance of nature as a source of inspiration for materials design and provides insights into the development of high-performance composites.

There is a category of materials known as self-healing materials, which are distinguished by their inherent capacity to mend themselves in the event of internal damage or fractures. Because it possesses a built-in healing mechanism, it possesses this one-of-a-kind power. This system can react to injury in methods that range encompassing chemical reactions, physical alterations, and biological processes. The need to extend the endurance and longevity of materials used in a variety of industries, such as building, transportation, and electronics, has been a driving force behind the creation of self-healing materials. The mechanisms that are used to research self-healing materials as well as the approaches that are used to characterise them are discussed in this article. The many methods of self-healing, such as microcapsule-based healing, intrinsic healing, and extrinsic healing, are explored in this article. Intrinsic healing is also covered. In addition, the characterization methods that were utilised in order to evaluate the efficacy of the healing process, such as mechanical assessment, thermal evaluation, and microscopy, are discussed here. In addition, the prospective usages for self-healing materials in several industries, such as coatings, adhesives and related products composites, and biomedical devices, are addressed in this article. In this article, the advantages of using self-healing materials in certain applications are described such as an improvement in the materials' longevity, reliability, and sustainability.

In this paper we have calculated the thermal partition function of the thermo- dynamical system of Quark Gluon Plasma and also we have calculated the entropy of quark gluon plasma for the zero chemical potential at the thermal equilibrium condition.

This study explores the use of Internet of Things (IoT) based predictive maintenance techniques for sustainable transportation fleets. It utilizes various datasets to enhance operational efficiency and reduce environmental consequences. An examination of the fleet data uncovers interesting findings: the average mileage of the fleet is about 28,400 miles, indicating that different vehicles have been used to different extents. Notably, vehicle 002 stands out with the greatest mileage of 32,000 miles. Varying sensor measurements reveal discrepancies in tire pressure, brake pad thickness, and oil levels, suggesting different patterns of wear across the fleet. The historical maintenance data highlight the differences in maintenance intervals among automobiles. Based on predictive maintenance analysis, it is projected that vehicle 001 will need its next oil change after covering 27,000 miles, which is an increase of 2,000 miles compared to its last service. Percentage change study demonstrates the ever-changing nature of maintenance needs, highlighting the need of customized maintenance interventions that are specifically designed for each vehicle's unique characteristics. The combination of these discoveries clarifies the potential of IoT-enabled predictive maintenance in customizing tailored maintenance plans, increasing fleet efficiency, and reducing environmental impact. This research offers practical insights for adopting proactive maintenance techniques, promoting sustainability, and improving operational efficiency in transportation fleets.

With the growing number of automobiles, traffic accidents are increasing daily. The World Health Organization (WHO) study reports that annually, 1.4 million individuals have died, and 50 million have been wounded globally. An advanced accident detection technique using cognitive agents will reduce rescue operational delays, perhaps saving several lives. Intelligent Transportation Systems (ITS) are gaining significant attention in academia and industry because of the increasing popularity of smart cities. They are seen to enhance road safety in these urban areas. Internet of Things (IoT) and Artificial Intelligence (AI) systems have been widely used to decrease the time needed for rescue operations after an accident. This study introduces an IoT-enabled Automotive Accident Detecting and Categorization (IoT-AADC) method that combines a smartphone's internal and external sensors to identify and categorize the kind of accident. This innovative method enhances the effectiveness of emergency support like fire departments, towing agencies, etc., by providing crucial information regarding the accident category for better planning and execution of rescuing and relief activities. Emergency support providers enhance their preparedness by assessing the injuries experienced by those injured and the damage to the automobiles.

This research examined the effectiveness of data-intensive traffic management in urban settings using real-time insights from traffic management simulation experiments. The examination of data on traffic flow revealed a noteworthy decrease in congestion, with a 25% increase in traffic velocity during peak hours. Real-time information led to a 40% drop in the severity of traffic accidents and a 50% reduction in reaction times. Improved road safety was aided by a 30% decrease in accidents during inclement weather thanks to real-time weather data. To further optimize urban traffic flow, dynamic traffic management operations based on real-time information also resulted in a 20% reduction in congestion. These results highlight the revolutionary potential of data-intensive traffic management, offering safer and more effective urban transportation solutions by incorporating real-time information into traffic control plans.

For a better detection in Network information security monitoring system, the author proposes a method based on adaptive depth detection. A deep belief network (DBN) was designed and implemented, and the intrusion detection system model was combined with a support vector machine (SVM). The data set adopts the NSL-KDD network communication data set, and this data set is authoritative in the security field. Redundant cleaning, data type conversion, normalization, and other processing operations are performed on the data set. Using the data conversion method based on the probability mass function probability mass function coding, a standard data set with low redundancy and low dimensionality can be obtained. Research indicates that when the batch size reaches 64, the accuracy of the test set reaches its maximum value. As the batch size increases, the accuracy first increases and then decreases. When the batch size continues to increase, the model will inevitably fall into the local optimal state, resulting in the degradation of the detection performance of the system. In terms of the false alarm rate, the DBN-SVM model is also the highest; however, it is only 10.73%. Under the premise of increasing the detection rate, the false alarm rate is improved; for the overall detection performance of the model, it is within an acceptable range. In terms of accuracy, the DBN-SVM model also scored the highest. The accuracy rate is the ratio of normal and correct classification for intrusion detection. It can explain the detection ability of the model. In summary, the overall detection ability of the DBN-SVM model is the best. The good classification ability to use SVM is proved, and the classification of low-dimensional features is expected to increase the detection rate of the system.

The aerospace and automotive sectors are now experiencing a growing need for lightweight and high-performance components. As a result, there is a significant interest in investigating new welding methods that can effectively fuse different materials. The process of welding different materials poses notable difficulties as a result of disparities in physical qualities, metallurgical attributes, and thermal expansion coefficients. The research starts by examining the rationales for employing diverse materials in various sectors, emphasising the benefits they provide in relation to decreased weight, greater fuel economy, and improved mechanical characteristics. Following this, the study explores a range of sophisticated welding techniques that have arisen in response to these issues. This paper examines several fusion welding techniques, including laser welding, electron beam welding, and friction stir welding. Each approach is discussed in detail, with a comprehensive description of its fundamental concepts. This discussion focuses on the advantages of each approach in relation to the reduction of heat-affected zones, the attainment of precise control over the welding process, and the minimization of intermetallic compound formation. The study also emphasises the use of case studies and practical instances to showcase the effective implementation of sophisticated welding techniques on dissimilar materials. The feasibility and efficiency of these approaches in combining incompatible materials, such as aluminium to steel, titanium to composites, and others, are exemplified by instances observed in the aerospace and automotive sectors.

The investigation of innovative materials and intelligent control systems has been motivated by the desire to provide sustainable energy solutions, with the aim of improving the efficiency and adaptability of energy harvesting and storage devices. This study introduces an innovative methodology to tackle this issue by combining SMART (Self-Monitoring, Analysing, and Reporting Technology) materials with sophisticated intelligent control approaches. The system under consideration utilises the intrinsic material characteristics of SMART materials, including piezoelectric, thermoelectric, and shape memory alloys, with the objective of capturing and transforming ambient energy into electrical power that can be effectively utilised. In order to fully harness the capabilities of SMART materials, a novel control framework is proposed that integrates machine learning algorithms, real-time sensor data, and adaptive control procedures. The intelligent control system enhances the effectiveness and durability of energy harvesting and storage devices by effectively adjusting to different operational situations and optimising energy conversion and storage processes. The findings demonstrate significant enhancements in energy conversion efficiency as well as notable advancements in the longevity and dependability of energy systems utilising SMART materials. Furthermore, the capacity of the control system to adjust to various environmental circumstances and energy sources situates this research at the forefront of cutting-edge energy technology.

The use of super alloy coatings is an innovative method for enhancing the durability of materials against the detrimental effects of corrosion and oxidation, especially in areas with severe circumstances. This study explores the forefront of super alloy coatings, examining their significant role in minimising the adverse effects of corrosive and oxidative processes on materials working in harsh conditions. This study highlights the transformative potential of super alloy coatings in extending the lifespan and enhancing the reliability of critical components. It achieves this by presenting a comprehensive overview of the fundamental principles, fabrication methodologies, and practical applications associated with these coatings. As many sectors navigate more difficult environments, the effective incorporation of advanced coatings presents a potential direction, advancing the field of materials science and guaranteeing consistent performance in the presence of significant challenges. The emergence of super alloy coatings represents a significant transformation in the field of corrosion and oxidation control, providing a robust defence against the persistent deteriorating factors seen in highly challenging settings. The combination of complex production procedures and superior material qualities empowers these coatings to strengthen materials, therefore assuring their durability.

The science of materials joining has witnessed the emergence of advanced welding techniques, which have become crucial operations. These techniques provide improved manipulation and regulation of the microstructure and mechanical characteristics of welded joints. The primary objective of this work is to examine the complex correlation between the microstructure and mechanical characteristics of materials that have undergone joining processes utilising sophisticated welding methods. The present research provides insight into the development of many microstructural characteristics, including grain growth, phase transitions, and defect creation, and their influence on the mechanical properties of welded joints. The mechanical characteristics being examined span a broad spectrum of factors, such as tensile strength, toughness, hardness, and fatigue resistance. The evaluation of these qualities is conducted by subjecting specimens obtained from the welded joints to a variety of mechanical tests. Through the correlation of microstructural traits with the observed mechanical behaviour, a more profound comprehension of the structure-property link in advanced welding is attained. In addition, this study investigates the impact of welding parameters, including welding speed, heat input, and shielding gas composition, on the microstructure and mechanical characteristics. The objective of this inquiry is to optimise the welding parameters in order to get the specified material characteristics in the welded connections, while simultaneously minimising the development of faults and ensuring the maintenance of structural integrity.