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The growing environmental awareness all through the world has motivated a standard change toward planning and designing better materials having good performance, which are very much suited to the environmental factors. The purpose of this study is to investigate the impact on mechanical, thermal and water absorption properties of sawdust-based composites reinforced by epoxy, and the amount of sawdust in each form.

Manufacturing of the sawdust reinforced epoxy composites is the main area of the research for promoting the green composite by having good mechanical properties, biodegradability or many applications. Throughout this research work, the authors emphasize the importance of explaining the methodology for the evaluation of the mechanical and water absorption properties of the sawdust reinforced epoxy composites used by researchers.

In this paper, a comprehensive review of the mechanical properties of sawdust reinforced epoxy composite is presented. This study is reported about the use of different Wt.% of sawdust composites prepared by different processes and their mechanical, thermal and water absorption properties. It is studied that after optimum filler percentage, mechanical, thermal properties gradually decrease, but water absorption property increases with Wt.% of sawdust. The changes in the microstructure are studied by using scanning electron microscopy.

The novelty of this study lies in its use of a systematic approach that offers a perspective on choosing suitable processing parameters for the fabrication of composite materials for persons from both industry and academia. A study of sawdust reinforced epoxy composites guides new researchers in the fabrication and characterization of the materials.

This study aims to examine the hedonism attributes, memorable tourism experience (MTE), revisit intention and their relationships. This study explores the antecedents of hedonism as physical environment, shopping at the destination, service quality, personalisation and exclusivity that influence MTE. The relationship of hedonism factors with revisit intention is also investigated in light of the mediation of MTE between them.

For this study, a sample of 600 tourists is collected by using the convenience sampling technique. The collected data is analysed by using the confirmatory factor analysis-structural equation modelling approach.

The empirically validated model recommends the significant relationships between the hedonism elements and revisits intention with the mediating effect of MTE. The findings suggest that tourists who positively perceive hedonism attributes are more likely to have positive MTEs, and they revisit the destination.

This research study examines the relationship of hedonism determinants with MTE of the tourists leading to their revisit intention for a tourism destination. It helps to understand MTE as the main component to affect tourists’ revisit intention for a destination and make sustainable tourism.

The complications caused by metallic orthopaedic bone screws like stress-shielding effect, screw loosening, screw migration, higher density difference, painful reoperation and revision surgery for screw extraction can be overcome with the bioabsorbable bone screws. This study aims to use additive manufacturing (AM) technology to fabricate orthopaedic biodegradable cortical screws to reduce the bone-screw-related-complications.

The fused filament fabrication technology (FFFT)-based AM technique is used to fabricate orthopaedic cortical screws. The influence of various process parameters like infill pattern, infill percentage, layer height, wall thickness and different biological solutions were observed on the compressive strength and degradation behaviour of cortical screws.

The porous lattice structures in cortical screws using the rapid prototyping technique were found to be better as porous screws can enhance bone growth and accelerate the osseointegration process with sufficient mechanical strength. The compressive strength and degradation rate of the screw is highly dependent on process parameters used during the fabrication of the screw. The compressive strength of screw is inversely proportional to the degradation rate of the cortical screw.

The present study is focused on cortical screws. Further different orthopaedic screws can be modified with the use of different rapid prototyping techniques.

The use of rapid prototyping techniques for patient-specific bone screw designs is scantly reported. This study uses FFFT-based AM technique to fabricate various infill patterns and porosity of cortical screws to enhance the design of orthopaedic cortical screws.

History teaches us that the glorious victory of mankind across the centuries was accomplished through the successful use of information. The gigantic progressions and rapid transformation of human societies have endorsed legitimacy of abundant data, multiple dynamic variables & critical complexities which reinforce the academia and researchers for understanding and pioneering into ‘Big Data Analytics (BDA)’. Health is one of the vibrant socio-economic variables which have correlations with other aspects of life, that is, education, poverty, income, etc. In fact, there are unending debates whether health can be a basic input for a holistic developmental process or it is the outcome of various developmental factors. BDAs are being used across various sectors of the economy. The developed nations have been yielding most feasible solutions using various forms of analysis of big data. Astronomical research has been using a large quantum of data for accomplishing various satellite projects, space technology, and numerous space missions for the astronaut. With the advent of fourth industrial revolution, the world community has been thriving toward a new age technological innovations that include artificial intelligence, machine learning, block chain technology, etc., which act a pivotal tool for BDAs. In the health sector, application of BDAs has been attempted and experimented in the developed nations which have resulted prolific and sustainable solutions to the most typical cumbersome problems. This chapter has demonstrated how BDAs can make progressive reforms in the Indian Health sector outlining the present status and emerging challenges.

Degraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.

The research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.

The proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.

The models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.

These models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.

A unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

The existing literature on venture capitalists’ (VCs’) exits provides insufficient evidence regarding factors affecting the exit decision. This study aims to identify these factors and examine how VC firms do ranking or prioritize these factors.

The study is based on primary data. The qualitative analysis was done to develop the survey instrument. Fuzzy analytical hierarchical process, which is a popular method of multi-criteria decision modeling, is used to identify or rank the determinants of exit strategy by venture capital firms in India.

Broadly, eight determinants of exit strategy are ranked by VCs. A total of 33 statements describe these eight determinants. The results are analyzed on the basis of four measures of VCs’ profile, i.e. age of VC firm, number of start-ups in portfolios, type of investment and amount of investment.

The survey instrument needs to be validated with a larger sample size and other financial backers than VCs.

The study has direct managerial implication for VC firms as it provides useful information regarding the determinants of exit strategy by VC firms in India. These findings can provide necessary information to other financial backers too, viz., angel investors, banks, non-banking financial institutions and other individual and syndicated set-ups providing funding to start-ups.

The current research is unique as no prior study has explored the determinants of VCs exit strategy and prioritizing these determinants.

The implications of metallic biomaterials involve stress shielding, bone osteoporosis, release of toxic ions, poor wear and corrosion resistance and patient discomfort due to the need of second operation. This study aims to use additive manufacturing (AM) process for fabrication of biodegradable orthopedic small locking bone plates to overcome complications related to metallic biomaterials.

Fused deposition modeling technique has been used for fabrication of bone plates. The effect of varying printing parameters such as infill density, layer height, wall thickness and print speed has been studied on tensile and flexural properties of bone plates using response surface methodology-based design of experiments.

The maximum tensile and flexural strengths are mainly dependent on printing parameters used during the fabrication of bone plates. Tensile and flexural strengths increase with increase in infill density and wall thickness and decrease with increase in layer height and wall thickness.

The present work is focused on bone plates. In addition, different AM techniques can be used for fabrication of other biomedical implants.

Studies on application of AM techniques on distal ulna small locking bone plates have been hardly reported. This work involves optimization of printing parameters for development of distal ulna-based bone plate with high mechanical strength. Characterization of microscopic fractures has also been performed for understanding the fracture behavior of bone plates.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

The purpose of this study is to explore the machining characteristics of electrical discharge machining (EDM) when a tool is fabricated using powder metallurgy. Because pure Cu tools obtained using conventional machining pose problems of high tool wear rate, tool oxidation causes loss of characteristics in tool shape.

The research investigation carried out experiments planned through Taguchi’s robust design of experiments and used analysis of variance (ANOVA) to carry out statistical analysis.

It has been found that copper and chromium electrodes give less metal removal rate as compared to the pure Cu tool. Analytical outcomes of ANOVA demonstrated that MRR is notably affected by the variable’s polarity, peak current, pulse on time and electrode type in the machining of EN9 steel with EDM, whereas the variables pulse on time, gap voltage and electrode type have a significant influence on EWR. Furthermore, the process also showed that the use of powder metallurgy tool effectively reduces the value of SR of the machined surface as well as the tool wear rate. The investigation exhibited the possibility of the use of powder metallurgy electrodes to upgrade the machining efficiency of EDM process.

There is no major limitation or implication of this study. However, the composition of the powders used in powder metallurgy for the fabrication of tools needs to be precisely controlled with careful control of process variables during subsequent fabrication of electrodes.

To the best of the authors’ knowledge, this is the first study that investigates the effectiveness of copper and chromium electrodes/tools fabricated by means of powder metallurgy in EDM of EN9 steel. The effectiveness of the tool is assessed in terms of productivity, as well as accuracy measures of MRR and surface roughness of the components in EDM machining.

This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.

The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.

This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.

The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.