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The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.

For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.

Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.

The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

This study explores the potential of artificial intelligence (AI) in fostering sustainable entrepreneurship within the tourism industry. The rapid growth of the tourism sector has raised concerns regarding its environmental impact, social equity and economic sustainability. Sustainable entrepreneurship offers a promising approach to address these challenges by integrating environmental, social and economic considerations into business practices. AI technologies, with their ability to process vast amounts of data, analyse patterns and make predictions, have the potential to support sustainable entrepreneurship initiatives in the tourism industry. By analysing the current literature, this study provides insights into the effective utilisation of AI to promote sustainable entrepreneurship in the tourism industry, while acknowledging the need for responsible and ethical AI implementation. The findings contribute to the understanding of how AI can be harnessed as a tool for driving sustainable practices and innovation in the tourism sector, ultimately leading to a more sustainable and responsible tourism industry.

The purpose of this study is to follow up on the structural and fatigue analysis of car wheel rims with carbon fibre composites in order to ensure the vehicular safety. The wheel is an essential element of the vehicle suspension system that supports the static and dynamic loads encountered during its motion. The rim provides a firm base to hold the tire and supports the wheel, and it is also one of the load-bearing elements in the entire automobile as the car's weight and occupants' weight act upon it. The wheel rim should be strong enough to withstand the load with such a background, ensuring vehicle safety, comfort and performance. The dimensions, shape, structure and material of the rim are crucial factors for studying vehicle handling characteristics that demand automobile designers' concern.

In the present study, solid models of three different wheel rims, namely, R-1, R-2 and R-3, designed for three different cars, are modelled in SOLIDWORKS. Different carbon composite materials of polyetheretherketone (PEEK), namely, PEEK 90 HMF 40, PEEK 450 CA 30, PEEK 450 GL 40 and carbon fibre reinforced polymer-unidirectional (CFRP-UD) are used as rim materials for conducting the structural and fatigue analysis using ANSYS Workbench.

The results thus obtained in the analyses are used to identify the better carbon fibre composite material for the wheel rim such that it gives better structural properties and less fatigue. The R-3 model rim has shown better structural properties and less fatigue with PEEK 90 HMF 40 material.

The carbon composite materials used in this study have shown promissory results that can be used as an alternative for aluminium, steel and other regular materials.

Additive manufacturing (AM), a rapidly evolving paradigm, has shown significant advantages over traditional subtractive processing routines by allowing for the custom creation of structural components with enhanced performance. Numerous studies have shown that the technical qualities of AM components are profoundly affected by the discovery of novel metastable substructures in diverse alloys. Therefore, the purpose of this study is to determine the effect of cell structure parameters on its mechanical response.

Initially, a methodology was suggested for testing porous materials, focusing on static tensile testing. For a qualitative evaluation of the cellular structures produced, computed tomography (CT) was used. Then, the CT scanner was used to analyze a sample and determine its actual relative density, as well as perform a detailed geometric analysis.

The experimental research demonstrates that the mechanical properties of a cell’s structure are significantly influenced by its shape during formation. It was also determined that using selective laser melting to produce cell structures with a minimum single-cell size of approximately 2 mm would be the most appropriate method.

Further studies of cellular structures for testing their static tensile strength are planned for the future. The study will be carried out for a larger number of samples, taking into account a wider range of cellular structure parameters. An important step will also be the verification of the results of the static tensile test using numerical analysis for the model obtained by CT scanning.

The fabrication of metallic parts with different cellular structures is very important with a selective laser melted machine. However, the determination of cell size and structure with mechanical properties is quiet novel in this current investigation.

In electrical discharge machining (EDM) process, EDM oil used as a dielectric fluid plays an important role in determining quality of the machining operation, serving as a medium to generate controlled electrical discharges, quenching medium to cool down and solidify the eroded gaseous particles, removal of solidified waste, and lubrication medium to absorb and remove the heat generated at the machining zone. Due to presence of numerous decisive factors, no single dielectric fluid (mainly in the form of EDM oil) meets all the required characteristics during a real-time EDM operation. Thus, this paper proposes application of an integrated methodology to select the most appropriate EDM oil for enhanced machining performance during deep-hole drilling of aluminum bronze alloy.

A good dielectric fluid should possess several characteristics, like low cost, non-toxicity, low viscosity, good wetting property, high flash and fire points to avoid fire hazards, chemically non-corrosive, high electric strength and specific gravity, minimal aromatics and good quenching behavior. In this paper, performance of 10 alternative EDM oils is evaluated based on six selection criteria. Integrated determination of objective criteria weights (IDOCRIW) method is adopted to compute the criteria weights, whereas double normalization-based multiple aggregation (DNMA) approach is applied to identify the best-suited EDM oil from the candidate alternatives.

Spark SPO-A EDM oil appears as the most suitable dielectric fluid, followed by Fine Spark 110. Contrarily, Exxsol D80 emerges as the worst choice.

The robustness of the adopted methodology is finally validated through sensitivity analysis studies. It can thus be applied to solve any of the decision-making problems with high degree of accuracy and consistency.

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

The purpose of this paper is to study the functionality of additively manufactured (AM) parts, mainly depending on their dimensional accuracy and surface finish. However, the products manufactured using AM usually suffer from defects like roughness or uneven surfaces. This paper discusses the various surface quality improvement techniques, including how to reduce surface defects, surface roughness and dimensional accuracy of AM parts.

There are many different types of popular AM methods. Unfortunately, these AM methods are susceptible to different kinds of surface defects in the product. As a result, pre- and postprocessing efforts and control of various AM process parameters are needed to improve the surface quality and reduce surface roughness.

In this paper, the various surface quality improvement methods are categorized based on the type of materials, working principles of AM and types of finishing processes. They have been divided into chemical, thermal, mechanical and hybrid-based categories.

The review has evaluated the possibility of various surface finishing methods for enhancing the surface quality of AM parts. It has also discussed the research perspective of these methods for surface finishing of AM parts at micro- to nanolevel surface roughness and better dimensional accuracy.

This paper represents a comprehensive review of surface quality improvement methods for both metals and polymer-based AM parts.

This study aims to understand the current production scenario emphasizing the significance of green manufacturing in achieving economic and environmental sustainability goals to fulfil future needs; to determine the viability of particular strategies and actions performed to increase the process efficiency of electrical discharge machining; and to uphold the values of sustainability in the nonconventional manufacturing sector and to identify future works in this regard.

A thorough analysis of numerous experimental studies and findings is conducted. This prominent nontraditional machining process’s potential machinability and sustainability challenges are discussed, along with the current research to alleviate them. The focus is placed on modifications to the dielectric fluid, choosing affordable substitutes and treating consumable tool electrodes.

Trans-esterified vegetable oils, which are biodegradable and can be used as a substitute for conventional dielectric fluids, provide pollution-free machining with enhanced surface finish and material removal rates. Modifying the dielectric fluid with specific nanomaterials could increase the machining rate and demonstrate a decrease in machining flaws such as micropores, globules and microcracks. Tool electrodes subjected to cryogenic treatment have shown reduced tool metal consumption and downtime for the setup.

The findings suggested eco-friendly machining techniques and optimized control settings that reduce energy consumption, lowering operating expenses and carbon footprints. Using eco-friendly dielectrics, including vegetable oils or biodegradable dielectric fluids, might lessen the adverse effects of the electrical discharge machine operations on the environment. Adopting sustainable practices might enhance a business’s reputation with the public, shareholders and clients because sustainability is becoming increasingly significant across various industries.

A detailed general review of green nontraditional electrical discharge machining process is provided, from high-quality indexed journals. The findings and results contemplated in this review paper can lead the research community to collectively apply it in sustainable techniques to enhance machinability and reduce environmental effects.

This study assesses the impact of biogas adoption on household energy use and livelihood improvement. Also, this study aims to clarify the benefits of biogas adoption as a pathway to sustainable household energy.

The study explored the benefits of adopting biogas uptake. Fourteen sustainability indicators were identified, validated and categorized.

Adopting biogas technology provides numerous benefits, including better air quality, reduced deforestation and lower greenhouse gas emissions. Households can replace up to 4.5 tons of wood fuel, cutting CO₂ emissions by around 6.75 tons annually. This shift saves approximately US$25 (Ksh.3223) monthly and frees up 45.5 h per week, enabling families to pursue additional income-generating activities. Biogas also produces digestate, a natural fertilizer that improves soil fertility, water retention and reduces erosion while minimizing the need for chemical fertilizers. Integrating biogas in livestock housing reduces odors, pathogens and methane emissions.

The findings demonstrate numerous social, economic and environmental implications of biogas use.

Health benefits include lower exposure to smoke and particulate matter, particularly benefiting women and children by reducing respiratory issues, improving lighting and enhancing educational opportunities. Biogas further improves hygiene, promotes cleanliness, strengthens energy security and alleviates energy poverty. In addition, the construction, operation and maintenance of biogas systems create jobs, and the use of digestate enhances agricultural productivity.

This study provides a unique and thorough analysis of the benefits of biogas, offering valuable insights and outlining a sustainable approach.