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The COVID-19 pandemic is certain to have an unprecedented impact on the global population, but marginalized and vulnerable groups in low-income countries (LICs) are predicted to carry the largest burden. This study focuses on the implications of COVID-19-related measures on three population groups in India, including (1) migrant laborers (of which a majority come from Scheduled Castes (SCs) and Scheduled Tribes (STs), as well as Other Backward Classes (OBCs)), (2) children from low-income families and, (3) refugees and internally displaced persons (IDPs).

This study adopts a sequential mixed-method research design. A desk-based study of a selection of government reports was undertaken on the COVID-19-related mitigation measures. The desk study was followed by in-depth interviews with purposively recruited high-ranking experts in specific sectors of policy implementation and service delivery across the country.

The outcomes of this study shed light on (1) the most urgent needs that need to be addressed per population group, (2) the variety of state-level responses as well as best practices observed to deal with mitigation issues and (3) opportunities for quick relief as well as more long-term solutions.

The COVID-19 pandemic has not only reduced people's means of maintaining a livelihood but has simultaneously revealed some of India's long-standing problems with infrastructure and resource distribution in a range of sectors, including nutrition and health, education, etc. There is an urgent need to construct effective pathways to trace and respond to those people who are desolate, and to learn from – and support – good practices at the grassroot level.

The current study contributes to the discussion on how inclusive public health might be reached.

This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the computational difficulties and approximations involved with the analytical methods. The results obtained from the ML algorithms and the Denavit–Hartenberg (DH) approach are compared with the experimental results to evaluate their performances. The study is performed on a novel 6-degree of freedom (DoF) PM that offers precise motions with a large workspace for the end effector.

The kinematic model for the proposed 3-PPSS PM is obtained using the modified DH approach and its inverse kinematic solutions are determined using the Levenberg–Marquardt algorithm. Various prediction algorithms such as the multiple linear regression, multi-variate polynomial regression, support vector, decision tree, random forest regression and multi-layer perceptron networks are applied to predict the inverse kinematic solutions for the manipulator. The data set required to train the network is generated experimentally by recording the poses of the end effector for different instantaneous positions of the slider using the concept of ArUco markers.

This paper fully demonstrates the possibility to use artificial intelligence for the prediction of inverse kinematic solutions especially for complex geometries.

As the analytical models derived from the geometrical method, Screw theory or numerical techniques involve approximations and needs more computational power, it is not advisable for real-time control of the manipulator. In addition, the data set obtained from the derived inverse kinematic equations to train the network may lead to inaccuracies in the predicted results. This error may generate significant deviations in the end-effector position from the desired position. The present work attempts to resolve this issue by proposing a camera-based approach that uses ArUco library and ML algorithms to create the data set experimentally and predict the inverse kinematic solutions accurately.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

In a feast of differentiation, Tyson Foods slices up more than 57 varieties of chicken products for four different major markets. Instead of selling broilers at 69 cents a pound, Tyson is a Fortune star selling marinated fillets at more than six times as much.

This is a commentary on educating leaders within the constraints of a highly technical curriculum. The U.S. Naval Academy’s mission is to produce leaders for the nation. Many things compete for the time and attention of Midshipmen (the Academy’s students). Greater attention must be paid to interpersonal communications skills in the formal and informal curriculum.

This study examines the connections between subculture theory, symbolic interaction and the work of David Matza with a special focus on exploring alcohol consumption by young adults in the UK. We apply Matza ideas of the “techniques of neutralization,” “subterranean values,” and “drift” within an ethnographic study on alcohol to suggest that young people's “calculated hedonism” can be understood as a strategy of agency in the context of a subcultural setting. This article adds to the literature of symbolic interaction, subculture and the discipline of sociology by critically focusing on the work of David Matza from its reception in the 1960s to today as a central element of the new paradigm of cultural criminology. For us the sociological imagination is “alive and well” through Matza's advocacy of naturalism whereby he sought to integrate the work Chicago School under Park and Burgess with his assessment of the so-called Neo-Chicago School. In the literature Matza's work is often defined as symbolic interactionist we see his ambition in a wider sense of wanting sociology to recover human struggle and the active creation of meaning. Our approach is to understand the calculated hedonism of young adult use of alcohol through their humanity.