Safe and intuitive human robot interaction (HRI) requires precise regulation of contact forces and torques while adapting to dynamic and uncertain human behavior. Traditional impedance and admittance control strategies rely on fixed parameters and accurate system modeling, which often limit their performance in unstructured or collaborative environments. This paper presents an AI-enabled force and torque control framework that integrates machine learning techniques with conventional control methods to enhance adaptability, compliance, and safety in physical human robot interaction. The proposed approach employs deep neural networks and reinforcement learning to learn human intent and interaction dynamics directly from multi-modal sensor data, including force torque sensors, joint encoders, and inertial measurements. By continuously adjusting control gains in real time, the system achieves stable interaction while minimizing excessive contact forces and undesired torques. Experimental evaluations conducted on a collaborative robotic platform demonstrate significant improvements over classical control schemes, including reduced interaction force peaks, smoother torque profiles, and improved task execution efficiency during cooperative manipulation tasks. The results indicate that AI-driven force and torque control can substantially improve robustness, adaptability, and user comfort in human robot collaboration, making it a promising solution for applications in rehabilitation robotics, assistive devices, and industrial cobots.
Wildlife is facing unprecedented threats from human activities, including habitat destruction, climate change, pollution, illegal wildlife trade, overexploitation, and human–wildlife conflict. These pressures are accelerating biodiversity loss, disrupting ecosystem functions, and reducing the resilience of natural environments. This chapter examines the major anthropogenic drivers of wildlife decline and evaluates contemporary conservation strategies for safeguarding biodiversity. It highlights the role of protected areas, habitat restoration, community-based conservation, ecological corridors, and stronger policy frameworks in promoting sustainable wildlife management. The chapter also explores the application of emerging technologies such as Artificial Intelligence (AI), Geographic Information Systems (GIS), remote sensing, drones, camera traps, and environmental DNA (eDNA) for wildlife monitoring, anti-poaching initiatives, and evidence-based conservation planning.
The aim of this chapter is to explore collective socio-emotional well-being from an organisational framework. Defining well-being as a phenomenon occurring at a higher than individual level, this chapter approaches well-being as a collective outcome of interpersonal dynamics, emotional atmosphere and organizational structure. Leveraging theories involving emotional contagion, psychological safety, social exchange theory, and Stimulus Organism Response (S-O-R) theory, organizational climates affect collective psychological responses which predict employee productivity, resilience, and engagement. By combining well-being theory at the individual-level with an organizational framework, this chapter provides a conceptual model where leadership and organizational culture are established as possible predictors of collective well-being. Lastly, suggestions for organizational application of these concepts are provided for corporations within high emotionally stimulating environments.
Health workforce crisis is a significant challenge to universal health coverage (UHC) and health-related Sustainable Development Goals (SDGs). World Health Organization estimates that by 2030, there will be 10 million health worker shortages, mostly in low and lower-income countries in sub-Saharan, South and Southeast Asia. The problem is not limited to figures but is full of profound injustices, urban labour concentration and rural under-service. In this chapter, major drivers such as poor training capacity, wage constraint, poor working conditions, migration, and governance gaps are analyzed. It examines policy interventions like reforms in education, task redistribution, retention policies, technological advances, and international collaboration, and how they have affected, scaled, and how they have impacted equity.
In the present work, the machine learning methods were used to develop the predictive models for the Compressive Strength (CS) of the graphene oxide-enhanced self-compacting concrete. The models were built and analysed using a comprehensive database of 76 samples extracted from the literature. The model uses water content, ground granulated blast furnace slag, fly ash, fine aggregates, coarse aggregates, superplasticizer and cement quantity as input parameters and concrete CS as output. The predictive modelling methods used are linear regression, genetic algorithm, particle swarm optimization, support vector regression, grey wolf optimization, differential evolution and manta rays foraging optimization. The model efficiency was assessed on the basis of seven parameters, namely, coefficient of correlation (CC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), normalized root mean square error, Willmott’s index, Nash Sutcliffe efficiency and coefficient of determination. The results showed that the support vector regression differential equation had the highest accuracy and lowest error to predict CS. The maximum CC obtained were 0.990 and 0.960 for training and testing datasets respectively. The error values for training and testing datasets are SVR DE achieved CC values of 0.995 and 0.958, RMSE values of 1.785 and 3.947 MPa, MAE values of 0.874 and 2.899 MPa, NRMSE values of 0.023 and 0.088, WI values of 0.997 and 0.978, and NSE values of 0.990 and 0.911 respectively.
