Sudden challenges have arisen in manufacturing from the last few years. These changes destroyed the traditional manufacturing system. High automation was introduced in the industry by connecting physical and virtual world, known as Industry 4.0. Since Industry 4.0 was unable to meet the emergent need for customization, the term Industry 5.0 was developed to address the customised manufacturing. It seeks to achieve social goals beyond employment and growth by centring human well-being in industrial systems, thus providing robust prosperity for a sustainable future for all humanity. This industry revaluation enhanced the customer satisfaction by creating their customised products. The primary goal of industrial transformation is to take into account the social and environmental advantages of the sector in order to become a true source of success. In order to achieve maximum efficiency with maximum profit, this innovative method will improve the wealth of all stakeholders, including investors, employers, consumers, society, and the environment. This paper aims to categorise and summarise the research results from some papers on Industry 5.0 and key challenges of Industry 4.0 in order to provide guidance and insight to other researchers and professionals. In order to achieve SDG 9: Industry, Innovation, and Infrastructure, new cutting-edge technologies must be adopted in a sustainable manner. The flexibility and efficiency of industry production were enhanced by advanced technologies. The study demonstrates how incorporating cutting-edge digital technologies like artificial intelligence (AI), the Internet of Things (IoT), collaborative robotics (cobots), and cyber-physical systems (CPS) into agile approaches improves manufacturing’s flexibility, responsiveness, and customisation. The results show that agile manufacturing in Industry 5.0 promotes productivity, sustainability, and mass customisation while improving worker-machine cooperation.

This study aims to fill the research gap by extensively evaluating the oxidative stability of Jatropha biodiesel and its blends with conventional diesel over a prolonged storage period of 180 days. Initially, Jatropha oil was extracted using mechanical and chemical methods and then converted into biodiesel via an alkaline-catalysed transesterification process with ethanol. We subjected the resultant ethyl esters to a 180-day storage test in pure form and mixed them with 5 %, 10 %, and 20 % diesel. A comprehensive array of analytical techniques was employed to monitor oxidation effects, including Fourier transform infrared spectroscopy, UV-visible spectroscopy, thermogravimetric analysis, rheological assessment, and pressurised differential scanning calorimetry. The results indicated a gradual increase in oxidation markers suggesting biodiesel degradation over the storage period. UV-visible spectroscopy detected the formation of conjugated dienes, trienes, and ketones. Oxidative induction time, as measured by P-DSC, significantly decreased from 41.3 minutes to 7.72 minutes at the end of the 180 days, indicating diminished oxidation stability. Infrared spectra revealed new peaks corresponding to peroxides and dimers, confirming oxidation.The study also observed that diesel-biodiesel blends exhibited a relative increase in oxidation proportional to the concentration of Jatropha biodiesel in the blend. However, these blends displayed longer induction times and greater thermal stability than pure B100 biodiesel. By the study's conclusion, Jatropha biodiesel showed a reduction in oxidation stability by up to 55 %, yet its fuel properties remained within the acceptable range for diesel blend usage, underlining its potential viability as a diesel blend component even after long-term storage.

This work focused on the energy-efficient fabrication of Al7178 based hybrid nanocomposites reinforced with TiB2 & SiO2 by using the stir-casting technique. The investigation aimed to optimize process factors including stirring speed, stirring time, reinforcement weight percentage, and preheat temperature, to improve the tensile strength of the Al7178/TiB2/SiO2 hybrid composite. The Taguchi method and ANOVA were employed to analyze the influence of these parameters on tensile strength. SN ratio analysis helped to identify the optimal parameter settings. Results confirmed that preheat temperature, stir time, and reinforcement weight percentage had a statistically significant impact on tensile strength. Optimal conditions were identified as 800 rpm, 740°C of preheat temperature, 8 wt% of reinforcement, and 9 minutes of stir time, lead to a substantial improvement in tensile strength. These conclusions contributed to the development of energy-efficient and high-performance Al-based hybrid composites.

This research examines environmental impact data, sustainable packaging qualities, consumer feedback surveys, and price comparisons to draw important findings. Research focuses on “Packaging Sustainability Revolution: Life Cycle Thinking Reveals Eco-Friendly Innovations.” This research examines sustainable packaging design evolution. Life cycle analysis (LCA) showed that packaging materials had an average carbon footprint of 120 grams of CO2 per unit and a 60% recycling rate. This shows the diverse environmental impacts of packing options. A study of sustainable package design shows that individuals have preferences. The favorability of biodegradable, recyclable, and minimalist packaging increased significantly. In subjective consumer feedback surveys, Packaging A and Packaging B scored 8.3 and 8.7 total satisfaction. In contrast, Packaging C and D do well. The cost increases among models in Cost comparisons expenditures show the economic effects of sustainable design. This emphasizes the tight balance between consumer satisfaction and sustainable practices' economic sustainability. The empirical findings improve scholarly discourse on life cycle thinking in Cost comparisons by revealing the sustainability variables driving the Packaging Sustainability Revolution.

This paper presents a pioneering routing protocol, particularly in urban infrastructures, by the utilization of a combination of SDN and machine learning technologies, mostly Naïve Bayes, to the programmer. The emerging Internet of Things and 5 G will offer new improvements in connectivity and infrastructure management in smart cities. This paper therefore targets transportation systems of smart cities in order to reduce end-to-end delays and reduce air congestion through predictive routing optimization. The proposed framework is conducted through several steps, among them the generation of a complete dataset derived from the real-world traffic simulations, and realized through the utilization of a mechanism that is commonly known as the Simulation of Urban Mobility (SUMO). This will help the proposed protocol to know the good approaches based on predictive routing with the help of defined Naïve Bayes algorithms in the SDN controller. This architecture introduces a central controller responsible for coordinating communication in the network. The evaluation of the performance of existing routing methods, including multipath routing, optimized link state routing (OLSR), and along with Q learning, shows the importance of the proposed SDN-Naïve Bayes approach. The metrics are packet delivery ratio, throughput, packet loss ratio, end-to-end delay, packet jitter, etc., which will show the effectiveness of the new protocol when enjoying the high quality of the network. The proposed protocol has great potentials in improving the efficiency and reliability of communications in smart city environments using the capacities of SDN and ML. Introducing changes in the route in a predictive manner will not only optimize the use of resources, but also help in the achievement of reliable and reliable connectivity, which holds importance for the development of urban infrastructure.

Empirical insights into the significant effects of IoT-Enhanced Healthcare on patient care and health outcomes are provided by this study. The transformational potential of IoT technology is shown by data generated from a varied patient group, which includes continuous monitoring of blood pressure, body temperature, heart rate, and blood glucose levels via IoT devices. The usage of IoT devices is directly correlated with greater cardiovascular stability, as shown by consistently normal vital signs, according to statistical assessments. Additionally, the data highlights how patients using IoT devices have better control over their blood glucose levels, as seen by fewer cases of increased glucose levels. Evaluations of the quality of patient care show improved levels of satisfaction, efficacy of therapy, and communication, highlighting the benefits of IoT-Enhanced Healthcare. The evaluation of the outcomes of the IoT Healthcare Test confirms the precision and dependability of IoT devices in medical diagnosis, highlighting the significance of IoT-Enhanced Healthcare in transforming patient care. Together, these results provide strong evidence of IoT's ability to improve patient outcomes, treatment quality, and patient health.

Business 4.0 emphasizes mass personalization and customisation. Even though additive manufacturing (AM) technologies are capable of producing single items, they are not suitable for 3D printing in large quantities. They are at a disadvantage because they can’t finish the industrial process in big volumes. As a result, all activity utilizing additive manufacturing techniques in industrial manufacturing is cautious. Thus, that is the basis of this study. In order to increase the dependability of additive manufacturing procedures and large-scale 3D printing of smart products for global businesses, the research attempts to identify and take advantage of Industry 4.0 technologies. Our study focuses on the requirements of Industry 4.0 technology in data science and additive manufacturing applications. Technologies (ITs) are used in additive manufacturing. Business 4.0 emphasizes mass personalization and customisation. Even though additive manufacturing (AM) technologies are capable of producing single items, they are not suitable for 3D printing in large quantities. They are at a disadvantage because they can’t finish the industrial process in big volumes. As a result, all activity utilizing additive manufacturing techniques in industrial manufacturing is cautious. Thus, that is the basis of this study. In order to increase the dependability of additive manufacturing procedures and large-scale 3D printing of smart products for global businesses, the research attempts to identify and take advantage of Industry 4.0 technologies. Our study focuses on the requirements of Industry 4.0 technology in data science and additive manufacturing applications. Technologies (ITs) are used in additive manufacturing.

Creating Ultra-fine grains by severe plastic Deformation has been made by various researchers as demand of lightweight materials increases in various industries. SPD techniques have been utilised by researchers for producing UFGs. But to maintain the overall properties of the material at the same time is very difficult. To enrich the overall properties of the materials as well as alloys, Liquid nitrogen processes are involved. Processes at Cryogenic temperature improved the strengthen of the material. Comparison has been also made by various researchers and it has been noted, the properties of the materials after Liquid nitrogen improved as it compared with room temperature processes. The microstructure Evolution has also been made and it has been noticed the properties enhancement by liquid nitrogen is much more preferable than conventional processes.

The growing interest in the Internet of Things (IoT) has made innovative approaches to safety monitoring and real-time data collection possible. Using real-time location, speed, and pulse rate data, this study presents an Internet of Things (IoT)-based safety monitoring and alert system for 2-wheelers that aims to improve bikers' safety. Utilizing a GPS module, a pulse sensor, and a speed sensor, the device makes use of an ESP8266 microprocessor. The position, speed, and health parameters of a cyclist are to be tracked and sent in real-time to a smartphone application. A designated contact can receive instant alerts from the system in the case of an accident when it detects abrupt movements. This studyprovides a thorough description of the system's architecture, hardware, and software implementation in this document. The method illustrates connecting the ESP8266 to the mobile application, integrating sensors, gathering data, and developing algorithms for accident detection. Experimental results show that the system can precisely track a cyclist's whereabouts, keep an eye on their health indicators, and recognize accident situations. This study observed that the Internet of Things (IoT)-based safety monitoring and alert system for 2-wheelers has a lot of potential to improve riders' security and safety while also offering useful information to athletes and health-conscious people. The system provides chances for extra study and advancement in the field of Internet of Things applications for wellbeing and personal safety.

This article presents a comprehensive review of the studies in the domain of Solid Oxide Fuel Cells (SOFC) and Electrolysers, highlighting their integral role in promoting green hydrogen production. Over the past three decades, SOFC technology has seen profound advancements, transitioning from its initial phases to the cusp of commercialisation. The progression of this technology is illuminated through the systematic evolution of international symposia dedicated to SOFCs. These events have chronicled pivotal breakthroughs in materials innovation, a deepened understanding of electrode reactions, intricate chemical interactions, and the design and efficacy of SOFC configurations, from individual cells to complex multi-cell assemblies and integrated power systems. Concurrently, the studies shed light on the potential of solid oxide electrolyser technology in addressing the challenges posed by the intermittent nature of renewable energy sources, such as solar and wind. Operating at temperatures surpassing 600°C, these electrolysers adeptly decompose water and/or carbon dioxide into chemical fuels, capitalising on both thermodynamic and kinetic advantages. The review concludes with the findings of the exploration of nuclear power’s capability in championing high-temperature steam electrolysis for sustainable hydrogen production, encompassing a thorough analysis of material innovations, degradation mechanisms, and strategies to offset such deteriorations.

Packaging helps to protect and preserves the quality of the goods available for storage and transport purposes. Many businesses have begun to look for new techniques and tactics for better design in order to sell their products, which has boosted the need for change packaging materials. This market segmentation is expected to change significantly by the end of 2018 as Asia is projected to reach for more than 40% of global demand. Many of the popular packaging were plastic materials in form of boxes, cans, bags, bottles, wrappers, envelopes and containers. Those plastic-packaging materials performs well but it becomes non-degradable waste that causes unnecessary landfills and harmful to environment. To overcome such drawbacks, the recent researches reported that the renewable and biodegradable materials have drawn much attention in the packaging domain due to their eco-friendly nature. In this research, the biodegradable composite material has introduced for packaging applications using natural fibres. The different material of natural fibres such as Aloe Vera, Jute and Coir have used as reinforcement to make composite material. The mechanical behavior of Tensile, Compression, Flexural strength and Impact for different composition have studied experimentally and conducted the SEM analysis to determine casting failures. Comparatively, the obtained results show that the Jute material behaves better than others to replace packaging material do.

There are a number of applications for underwater wireless sensor networks (UWSNs), including disaster management, underwater navigation, and environmental monitoring. However, the limited battery capacity of UWSNs necessitates the optimization of energy efficiency to prolong network operation. The purpose of this paper is to propose the use of Glowworm Swarm Optimization (GSO) for the purpose of addressing the energy efficiency challenges in UWSNs. Traditional clustering and routing approaches designed for terrestrial wireless networks are not suitable for UWSNs due to factors like low bandwidth, long spread stays, underwater currents, and high error rates. GSO is employed as a solution to these unique challenges. The proposed model utilizes energy matrix, network lifetime matrix, cluster time matrix, and cluster head count matrix to compute simulation results. Based on a comparison with existing techniques, the proposed model outperforms them all. It enhances overall routing efficiency and extends network lifetime, particularly when clusters are established, surpassing competing systems. ACO, MFO, and LEACH algorithms are also outperformed by the proposed model in terms of the number of rounds required, indicating its effectiveness for long-term network operations. The integration of Glowworm Swarm Optimization in UWSNs presents a promising approach to maximize energy efficiency and extend network lifetime. By addressing the unique challenges posed by underwater environments, the proposed model offers potential benefits for various applications, ensuring sustained and reliable operation of UWSNs over extended periods.

This investigation investigates the domain of energy-efficient steering calculations inside Remote Sensor Systems (WSNs) for accuracy agribusiness. Four conspicuous algorithms—LEACH, EEUC, PSO-based Steering, and ACO-based Routing—were rigorously assessed, considering key measurements such as arrange lifetime, vitality utilization, and information conveyance rate. In comparison to related work, our examination adjusts with patterns in leveraging bio-inspired optimization and machine learning integration to address the interesting challenges posed by energetic agrarian situations. The results illustrate that EEUC, through distance-based clustering, essentially moves forward vitality adjust, advertising a 25% increment in organized lifetime compared to Filter. PSO-based Routing and ACO-based Directing exhibit flexibility to changing conditions, with a 20% and 18% improvement in organize lifetime, individually, compared to Filter. Besides, the computational overhead presented by EEUC is relieved by a parallel preparing approach, driving a 15% decrease in communication delays. These discoveries emphasize the significance of custom-fitted directing arrangements in exactness horticulture, giving an establishment for the improvement of brilliantly, energy-efficient WSNs pivotal for feasible and optimized crop administration.

It has been corroborated that thermoelastic damping (TED) is one of incontrovertible sources of energy dissipation and limiting the quality factor ( Q -factor) in micro/nanostructures. On the other hand, it has been clarified that the fitting description of heat transfer process in structures with such small dimensions should be carried out through non-Fourier models of heat conduction. This article strives for providing a size-dependent analytical framework for estimating the value of TED in circular cross-sectional micro/nanorings with the help of Moore–Gibson–Thompson (MGT) generalized thermoelasticity theory. To reach this objective, after deriving the equation of heat conduction according to MGT model, the fluctuation temperature in the ring is obtained. Then, by applying the existing definition of TED in the purview of entropy generation (EG) method, an analytical relationship in the form of infinite series is rendered to evaluate the amount of TED. In the results section, first, the precision of the developed formulation is examined by way of a validation study. Graphical data are then presented to illuminate how many terms of the extracted infinite series yield convergent results. The final stage is to conduct an all-embracing parametric analysis to make clear the role of various crucial factors in the alterations of TED. According to the obtained results, the impact of MGT model on TED sorely relies on the vibrational mode number of the ring.

With an emphasis on machine learning and artificial intelligence (AI), the Internet of Things (IoT), robotics, and data analytics, this research offers a methodical empirical evaluation of cutting-edge technologies in the field of smart manufacturing. The findings indicate notable progress in the abilities of the employees. Employee 2 had an astounding 30% gain in machine learning competence, while Employee 3 demonstrated a 50% growth in robotics proficiency. Production Line Efficiency showed scope for development; Line B showed a 0.7% gain in efficiency, indicating that there is still opportunity for process improvements. Analyzing sensor data highlights the need of ongoing maintenance and monitoring to guarantee optimum machine functioning. Data from quality control indicated that stricter guidelines were required to lower product faults. With implications for increased productivity and quality, this study advances our knowledge of the revolutionary potential of smart manufacturing technologies, including workforce development, technology adoption, and process optimization.

New manufacturing expertise, along with user expectations for gradually modified products and facilities, is creating changes in manufacturing scale and distribution. Standardization is essential for every industrial manufactured sector that delivers goods to consumers. Digital manufacturing (DM) is a vital component in the scheduling of all knowledge-based manufacturing. Additive Manufacturing (AM) is recognized as a useful technique in the area of sustainable development goals (SDGs). Modern Development techniques are inspected as a tool for the practices that are being adopted. Additive Manufacturing (AM) was introduced as an advanced technology that includes a new era of complicated machinery and operating systems. Cloud manufacturing framework makes it much easier to gain access to a variety of AM resources while investing as little as possible. This paper contributes an overview of used technologies advancement in the era of Additive manufacturing such as IoT, Big Data, ML, Digital twins, and Blockchain, and their contribution to Industry 4.0 for better and effective design, development, and production while at the same time providing a richer and ethical environment.

The advancement in low power wide area (LPWA) mechanism has revolutionized this existing landscape of Internet of Things (IoT) use cases, particularly through emergence from Long Range (LoRa) - proposed framework networks. Operating on absence of license industrial, scientific and medical (ISM) band, proposed mechanism server provide longer range connection for users operating from power saving mode and enhance this protocol established from the proposed mechanism network. This article provides an overview of proposed mechanism server architecture, focusing on proposed mechanism nodal point, gateways, network servers, and application servers. The challenges of global deployment and integration with current cloud loT platforms are addressed by the proposed solution, which emphasizes flexibility, scalability, and performance evaluation. Proposed mechanism pathway hardware design and implementation and proposed mechanism server architecture optimization are detailed. By analyzing test results that evaluate coverage performance and network server capacity, this can understand the deployment and performance of proposed mechanism server in urban environments.

This study focuses on the production of stir-squeeze cast hybrid composites made of aluminium with reinforcements. Base metal AA6105 is combined with SiC/TiC. TiC is changed from 3 to 9 wt% while SiC is kept constant (3wt%).Nine specimens are created using the deviation of the stir casting parameters of Stirrer speed (SP) 350-550rpm, Stir-time (TI) 15-25 min, and Reinforcement (RE) 3-19 w.t% in accordance with L9 Taguchi's design. By applying pressure of 60 MPa during squeeze casting, reinforced composites are created. Both stir casting and squeeze cast specimens are examined for their mechanical characteristics, such as tensile strength (TS) and hardness (BHN). Based on elongation, tension, and strain, TS was assessed for both processes. The outcomes showed that SQC sample L6 obtained the maximum TS of 303.19 MPa while STC specimen only managed to reach 269.88 MPa. The STC method produced a hardness value of roughly 83.56 Hv, whereas the greatest hardness value of 94.73 Hv was obtained with SQC sample L9 (SP-550 rpm, TI-25 min & RE-6%).

Sustainable manufacturing aims to boost output while utilising fewer resources, cutting costs, and having a smaller negative impact on the environment. Regarding the expenditure of materials, power, and resources required for creation as well as the prices of manufacturing processes, the longevity of tools used in woodworking operations is one of the most crucial variables in this context. The current research focuses on the using the rice bran oil as a cooling agent to produce minimum friction and reducing the cutting forces by using TiAln tipped insert for producing low BUE and longevity of the tool. The Taguchi’s robust design was used to minimise the number of experiments using L-9 orthogonal array. Speed (m/min) Feed (mm/rev), Depth of Cut (mm), Rice Bran oil(ml/min), were chosen as controllable process factors in the turning process, and surface roughness were engaged into consideration as performance evaluation features. The Taguchi analysis that revealed the appropriate process parameters markedly increased the turning performance when turned 17-4 PH, according to the results of the conformation tests. The rice bran oil used as a cutting fluid had significantly reduced the frictional forces and the tool insert could managed to cut all the nine samples with improved surface.