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The rousing phenomenon of the ageing population is becoming a vital issue and demanding fulminant actions. Population ageing is a resultant of the enhanced health-care system, groovy antibiotics, medications and economic well-being. Old age leads to copious amounts of ailments. Aged people, owing to their reduced mobility and enervating disabilities, tend to rely upon caretakers and/or nursing personnel. With the increasing vogue of nuclear families in the society, the elderly are at the risk of being unveiled to emotional, physical and fiscal insecurities in the years to come. Caring for those seniors will be an enormous undertaking.

There is a dire need for an intelligent assistive system to meet out the requirements of continuous holistic care and monitoring. Assistive robots and systems used for elderly care are studied. The design motivation for the robots, elderly–robot interaction capabilities and technology incorporated in the systems are examined meticulously.

From the survey, it is suggested that the subsystems of an assistive robot revamped for better human–machine interactions will be a potential alternative to the human counterpart. Affirmable advancements in the robot design and interaction methodologies that would increase the holistic care and assistance for aged people are analyzed and listed.

This paper reviews the available assistive technologies and suggests a synergistic model that can be adopted for the caring of the elderly.

Corruption in India reached a crescendo between 2011 and 2013, with the exposure of the 2G Spectrum scandal and the “Coalgate” report fiasco at the top of all recent events. The largest working democracy is under the scanner. As the third largest economy in Asia, a nuclear power, and an information technology powerhouse, India has a lot to clean up. Current experience shows the failure of the top investigative agencies and the lack of political will to tackle corruption. The spate of high-level corruption scandals has also led to a popular movement in 2011, which also fizzled out, including the newly introduced “Anti-Corruption, Grievance Redressal and Whistleblower Protection Act, 2011.” This chapter examines the several issues involved.

Machining of curved channels using electrical discharge machining (EDM) is a novel approach. In this study, an experimental setup was designed, developed and mounted on die-sinking EDM to manufacture curve channels in AISI P20 mold steel.

The effect of specific machining parameters such as peak current, pulse on time, duty factor and lift over material removal rate (MRR) and tool wear rate (TWR) were studied. Multi-objective optimization was performed using Taguchi technique and Jaya algorithm.

The experimental results revealed current and pulse on time to have the predominant effect over material removal and tool wear diagnostic parameters with contributions of 39.67, 32.04% and 43.05, 36.52%, respectively. The improvements in material removal and tool wear as per the various optimization techniques were 35.48 and 10.91%, respectively.

Thus, Taguchi method was used for effective optimization of the machining parameters. Further, nature-based Jaya algorithm was implemented for obtaining the optimum values of TWR and MRR.

In this research, electro discharge machining (EDM) of Ti-5Al-2.5Sn titanium alloy is performed taking gap voltage, pulse on time, peak current and duty cycle as process parameters. The purpose of this paper is to find out the optimal process parameters setting for getting higher machining efficiency.

For experimental design, a face-centered central composite design (FCCCD)-based response surface methodology (RSM) is used. Multi-objective optimization like grey relational analysis (GRA) is adopted to achieve the higher machining efficiency by means of lower radial overcut (ROC), surface roughness (Ra), tool wear rate (TWR) and higher material removal rate (MRR). For the statistical study, analysis of variance (ANOVA) has been carried out.

The result shows that gap voltage, peak current and pulse on time are the most efficient parameters for the responses. An optimal parameter setting has been obtained for achieving higher machining efficiency. For validation of the study, confirmation experiment has been performed at optimal parameters setting.

Optimum parameter level for higher machining performance of Ti-5Al-2.5Sn Titanium alloy has been achieved machined by copper electrode during EDM operation.

The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring.

As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created.

Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment.

Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.

The COVID-19 outbreak has severely impacted the health tourism industry. This study aims to examine the research undertaken on health tourism during COVID-19.

A bibliometric analysis was performed using 211 documents written by 645 authors between 2020 and 2024. The databases used for this analysis are SCOPUS, Web of Science and PubMed. The study was conducted using the Biblioshiny application for R software. Various analyses, including citation analysis, keyword analysis, thematic mapping and evolution and collaboration analysis, were conducted to get a comprehensive picture of the impact of COVID-19 on health tourism.

The findings suggest that medical tourism was the major area of focus for researchers during COVID-19, whereas wellness tourism was studied in the later stages of the pandemic and is considered important for coping with the negative effects of COVID-19. The thematic review indicates that pre-pandemic research focused on strategies that accelerate the growth of the health tourism sector, whereas during and post-pandemic, the focus was on studying the impact of COVID-19 on the survival and recovery of the sector. Overall, health tourism was considered instrumental in the post-pandemic revival of the tourism industry.

The bibliometric analysis is limited to only four years, as the focus was on studying health tourism during COVID-19. Hence, the study offers more precise insights into the research focus on health tourism during a pandemic. The current study offers limited insights into the post-pandemic scenario as the research is still emerging.

There is a significant opportunity for future research on post-COVID-19 recovery strategies, wherein this research will be helpful for researchers, particularly from emerging countries and countries with high dependence on health, medical and wellness tourism. The thematic analysis provides a valuable comparison of pre-, during and post-pandemic research.

The research indicates an opportunity to study the policies and strategies adopted by various countries regarding the health tourism sector in the post-COVID-19 era. The research also highlights the interwoven, intricate, interdisciplinary nature of health tourism in relation to public and mental health and the need for collaborative efforts.

The study provides critical insights and new directions for future research by highlighting the crucial role of health tourism in mitigating the impact of COVID-19.

The electric discharge machining (EDM) involves electrons discharged from the electrode and machining progresses due to the removal of the material from the component. This a thermal-based machining process primarily used for hard to machine components with conventional methods. This process is used to make intricate cavities and contours. The fabricated part is the replica of the tool material with high surface finish and good dimensional accuracy. This study aims to evaluate the comprehensive effect of process parameters on electric discharge machining of maraging steel.

Multiple criteria Decision making (MCDM) techniques are used to select the best parameters by comparing several responses to achieve the desired goal. There are different MCDM techniques available for optimization of machining parameters. In the current investigation, multi-objective optimization by data envelopment analysis based ranking (DEAR) approach was used for machining Maraging C300 grade steel.

The Taguchi L9 runs were planned with process parameters such as current (Amp), Tool diameter (mm) and Dielectric pressure (MPa). The effect of process parameters on the responses, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were evaluated. High MRR is found at 15 A current, 14 mm tool diameter and dielectric pressure of 0.2 MPa. Optimum process parameters experiment showed reduced crack density.

An effort was made successfully to enhance the responses using the DEAR method and establish the decision making of selecting the optimal parameters by comparing the results obtained by machining maraging steel C300 grade.

This paper aims to understand the effect of extrusion conditions on the degree of foaming of polylactic acid (PLA) during three-dimensional (3D) printing. It was also targeted to optimize the slicing parameters for 3D printing and to study how the properties of printed parts are influenced by the extrusion conditions.

This study used a commercially available PLA filament that undergoes chemical foaming. An extrusion 3D printer was used to produce individual extrudates and print samples that were characterized using an optical microscope, scanning electron microscope and custom in-house apparatuses.

The degree of foaming of the extrudates was found to strongly depend on the extrusion temperature and the material feed speed. Higher temperatures significantly increased the number of nucleation sites for the blowing agent as well as the growth rate of micropores. Also, as the material feed speed increased, the micropores were allowed to grow bigger which resulted in higher degrees of foaming. It was also found that, as the degree of foaming increased, the porous parts printed with optimized slicing parameters were lightweight and thermally less conductive.

This study fills the gap in literature where it examines the foaming behavior of individual extrudates as they are extruded. By doing so, this work distinguishes the effect of extrusion conditions from the effect of slicing parameters on the foaming behavior which enhances the understanding of extrusion of chemically foamed PLA.

The purpose of this research is to identify the most impactful delay risks in Australian construction projects, including the associations amongst those risks as well as the project phases in which they are most likely present. The correlation between project and organisational characteristics with the impact of delay risks was also studied.

A questionnaire survey was used to collect data from 118 delayed construction projects in Australia. Data were analysed to rank the most impactful delay risks, their correlation to project and organisational characteristics and project phases where those risks are likely to emerge. Association rule learning was used to capture associations between the delay risks.

The top five most impactful delay risks in Australia were changes by the owner, slow decisions by the owner, preparation and approval of design drawings, underestimation of project complexity and unrealistic duration imposed to the project, respectively. There is a set of delay risks that are mutually associated with project complexity. In addition, while delay risks associated with resources most likely arise in the execution phase, stakeholder and process-related risks are more smoothly distributed along all the project phases.

This research for the first time investigated the impact of delay risks, associations amongst them and project phases in which they are likely to happen in the Australian context. Also, this research for the first time sheds light on the project phases for the individual project delay risks which aids the project managers to understand where to focus on during each phase of the project.

This study examines the impact of response time on user experience for mobile applications and considers the moderating influence of gender and network environment on this relationship.

An experiment was conducted with 50 young adults to evaluate their user experience of a mobile application that simulates variations in network environment and response time. User experience was evaluated based on the three constituent dimensions of tolerance, acceptance, and satisfaction.

Analytical results demonstrate that response time not only adversely affects user experience of mobile applications, but that this effect is not homogeneous across the three dimensions of tolerance, acceptance and satisfaction. The findings also illustrate that gender moderates the effect of response time on user experience, however, the negative influence is more salient for males than females, which is opposite to our hypothesis. The joint moderating influence of gender and network environment turned out to be partly significant.

By illuminating users' tolerance, acceptance, and satisfaction with varied response times, findings from this study can inform the design of mobile applications such that desired levels of user experience can be assured with minimum resources.

Although response time has been hailed as a key determinant of user experience for desktop applications, there is a paucity of studies that have investigated the impact of response time on user experience for mobile applications. Furthermore, prior research on response time neglects the multi-dimensional nature of user experience. This study bridges the above mentioned knowledge gaps by delineating user experience into its constituent dimensions and clarifying the effects of response time on each of these dimensions.