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Reimagining community resilience and engagement through the digital innovation
In an increasingly interconnected world, reimagining community resilience demands the integration of digital innovation. Digital platforms facilitate real-time information sharing, enabling members to respond effectively to challenges, whether they stem from natural disasters or social issues. Innovative tools such as mobile apps and social media can gather diverse voices and build inclusive environments, promoting empowerment through participatory decision-m aking. Virtual town halls, community forums, and online workshops encourage dialogue, bridging gaps between local government and citizens. Digital innovation also enhances access to resources, providing residents with essential information on health services, educational opportunities, and local initiatives. By utilizing data analytics, communities can better understand their needs and priorities, ensuring tailored solutions for improved resilience. As communities continue to evolve, embracing digital innovation will be pivotal in nurturing resilience and active participation. 2026, IGI Global Scientific Publishing. All rights reserved. -
Reimagining Consumer Experience in the Digital Age: A Strategic and Technological Perspective
In the contemporary digital landscape, consumer experience has emerged as a strategic differentiator and a critical determinant of organizational success. This chapter is intended to elaborate the changing face of customer experience in the digital era, especially with emerging technologies, data- driven strategies, and changing consumer expectations, radically changing traditional business to consumer interactions. Advanced tools like artificial intelligence, machine learning, big data analytics, and augmented reality are changing methods of designing, delivering, and managing customer interactions across digital and physical environments is, therefore, pertinent. In emphasizing aspects of personalization, immediacy, and engagement in real time, all these have dramatically changed consumer expectations and call for a rethink for omnichannel consistency and seamless integration. This chapter presents a realistic and future oriented picture of how an organization can strategically reinvent consumer experience in the digital age. 2026 by IGI Global Scientific Publishing. All rights reserved.. -
Reimagining Future of Future by redesigning Talent Strategy in the Age of Distraction and Disruption
The coronavirus 2019 (COVID-19) pandemic promoted the development of Industry 4.0 leading to the fifth industrial revolution (Industry 5.0). It brought in new ways of working and the role of the office in the future. It redesigned the workplace to support organizational priorities and resize the footprint creatively. Digitalization and globalization have sparked radical shifts in how employees live and work. In an age of digital disruption, companies and HR leaders are forced to revise organizational on how they organize, recruit, develop, manage and engage the 21st-century workforce. The big questions are: how can HR help business leaders reconstruct the workforce of the future? What effort has the company take to change future work and their workforce today so that it looks different 15 years later? Organizational agility, careers and learning disruption, talent disruption, rethinking performance management and people analytics in addition to creating the right structure, analysis, and standardized people metrics are the key to success and critical drivers to design talent strategy. This study aims to identify the magic ingredient (or strategies) behind managing an organization's talent in creating business success. We further examined and mathematically modelled these strategies in attracting and retaining high-quality employees, developing their skills, and continuously motivating them to improve their performance in the age of distraction and disruption. 354 employees from IT companies participated in the survey. The findings of the study show, as expected, that a compelling employer brand is the most effective talent management strategy of all when it combines three key drivers: organizational culture, organization goodwill and competition for talent. Gender was statistically, significantly and positively associated with the imperatives to reset the future of work agenda. 2021. All Rights Reserved. -
Reimagining Healthcare: The South African PPP Revolution
Publicprivate partnerships (PPPs) are rapidly being recognized as transformative methods for improving healthcare delivery and funding, particularly in resource-constrained environments. Global and national policy frameworks, supported by organizations such as the African Development Bank (AfDB) and the World Health Organization (WHO), emphasize the potential for PPPs to fill significant gaps in healthcare infrastructure and service delivery. Countries across Africa, notably South Africa, Kenya, Nigeria, and Rwanda, have put in place national PPP frameworks that formalize partnerships in healthcare, focusing on risk sharing, accountability, and sustainability. South Africas National Treasury PPP Unit is a regional pioneer in promoting PPP development that balances private-sector innovation with governmental control. Such frameworks allow PPPs to mobilize private resources, enhance public spending efficiency, and provide access to high-quality healthcare, particularly in marginalized communities. Despite positive developments, PPPs in African healthcare confront hurdles due to fragmented legal frameworks and low institutional capacity to manage complicated contracts. The AfDBs 20212031 PPP strategic framework seeks to fill these gaps by providing African States with resources to establish enabling environments and prepare viable healthcare projects for the market. Diverse models in South Africa and other countries, such as Kenyas Managed Equipment Services (MES) and Ghanas BuildOperateTransfer (BOT) programs, show how adaptable PPPs can improve healthcare finance and delivery. However, current regulatory frameworks are complicated and often disconnected, emphasizing the need for unifying legal standards to assure transparency and accountability. This chapter highlights the insights that present a strong PPP model adapted to healthcare financing. It highlights the necessity of transparent systems, good risk management, and combining publicprivate expertise to handle current healthcare concerns. PPPs can improve healthcare accessibility and quality, increase patient satisfaction, and strengthen healthcare systems by promoting improved governance, policy consistency, and capacity building. Strategically honed, PPPs can drive long-term breakthroughs, positioning healthcare systems better to address the changing demands of African communities and beyond. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. -
Reimagining North Korea in a new frame: review of the series Crash Landing on You (2019)
[No abstract available] -
Reimagining occupational psychiatry in Asia: the case for mental health digital twins
[No abstract available] -
Reimagining the Digital Twin: Powerful Use Cases for Industry 4.0
Novel cohorts of information technologies are transformation and upgrading the global manufacturing sector. The analysis of product procedure in discrete globe might furnish significant perceptions resting on scheme routine which may change manufacturing product design. Digital twin predictive analysis on both historical and future performances of an organizations physical resources leading to proficient industry functioning. In digital twin, cloud-based virtual image of industrial asset is maintained throughout the lifecycle which can be accessed at any time. Digital twin enhances the degree and functions of manufacturing world by integrating with the physical world. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reiner-Rivlin nanomaterial heat transfer over a rotating disk with distinct heat source and multiple slip effects
The thermodynamic features of the Reiner-Rivlin nanoliquid flow induced by a spinning disk are analyzed numerically. The non-homogeneous two-phase nanofluid model is considered to analyze the effect of nanoparticles on the thermodynamics of the Reiner-Rivlin nanomaterial, which also includes a temperature-dependent heat source (THS) and an exponential space-dependent heat source (ESHS). Further, the transfer of heat and mass is analyzed with velocity slip, volume fraction jump, and temperature jump boundary conditions. The finite difference method-based routine is used to solve the complicated differential equations formed after using the von-Karman similarity technique. Limiting cases of the present problem are found to be in good agreement with benchmarking studies. The relationship of the pertinent parameters with the heat and mass transport is scrutinized using correlation, which is further evaluated based on the probable error estimates. Multivariable models are fitted for the friction factor at the disk and heat transport, which accurately predict the dependent variables. The Reiner-Rivlin nanoliquid temperature is influenced comparatively more by the ESHS than by the THS. The Nusselt number is decreased by the ESHS and THS, whereas the friction factor at the disk is predominantly decremented by the wall roughness aspect. The increment in the non-Newtonian characteristic of the liquid leads more fluid to drain away in the radial direction far from the disk compared with the fluid nearby the disk in the presence of the centrifugal force during rotation. The increased thermal and volume fraction slip lowers the nanoliquid temperature and nanoparticle volume fraction profiles. 2021, Shanghai University. -
Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes
Microservices and Kafka have become a perfect match for enabling the Event-driven Architecture and this encourages microservices integration with various opensource platforms in the world of Cloud Native applications. Kubernetes is an opensource container orchestration platform, that can enable high availability, and scalability for Kafkacentric microservices. Kubernetes supports diverse autoscaling mechanisms like Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA) and Cluster Autoscaler (CA). Among others, HPA automatically scales the number of pods based on the default Resource Metrics, which includes CPU and memory usage. With Prometheus integration, custom metrics for an application can be monitored. In a Kafkacentric microservices, processing time and speed depends on the number of messages published. There is a need for auto scaling policy which can be based on the number of messages processed. This paper proposes a new autoscaling policy, which scales Kafka-centric microservices deployed in an eventdriven deployment architecture, using a Reinforcement Learning model. 2022 IEEE. -
Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
Early detection and treatment of sepsis, a condition that can become fatal through the bodys response to infection, can enhance patient life. This paper explores how reinforcement learning can be applied to the early detection and treatment of sepsis, along with its novel features, which include personalized treatment recommendations and graph-based representations using Graph Neural Networks (GNNs). Moreover, domain adaptation and transfer learning strategies make the model applicable in a wide range of clinical contexts. The RL model is therefore designed to identify early warning signs and give prompt, individualized answers to avoid major repercussions. To ensure wide application, the RL model was trained using an enormous dataset of patient vitals, lab results, and clinical notes from numerous centers. It is already proven in real-life clinical situations that this model can improve patient outcomes and the quality of clinical decisions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
Early detection and treatment of sepsis, a condition that can become fatal through the bodys response to infection, can enhance patient life. This paper explores how reinforcement learning can be applied to the early detection and treatment of sepsis, along with its novel features, which include personalized treatment recommendations and graph-based representations using Graph Neural Networks (GNNs). Moreover, domain adaptation and transfer learning strategies make the model applicable in a wide range of clinical contexts. The RL model is therefore designed to identify early warning signs and give prompt, individualized answers to avoid major repercussions. To ensure wide application, the RL model was trained using an enormous dataset of patient vitals, lab results, and clinical notes from numerous centers. It is already proven in real-life clinical situations that this model can improve patient outcomes and the quality of clinical decisions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. 2024 IEEE. -
Reinforcement Learning for Quantum Phase Estimation Using Deep Q-Network
Quantum Phase Estimation(QPE) is a fundamental quantum algorithm that is used for the estimation of eigenphases of unitary operators. Its main goal is to determine the phase associated with each eigenstate. Usually, it take steps such as prepare quantum states, apply controlled unitaries, inverse quantum fourier transformation, and measurement. This study uses the OpenAI Gym framework to build a customized QPE environment. Here, the phase of a randomly generated target unitary operator is estimated using a quantum circuit. Through interaction with this environment, the DQN agent learns the best course of action to increase phase estimation accuracy. It exhibits more flexibility in noisy environments and reduces estimating mistakes. With its insights and approaches for further study in this area, this effort represents a significant advancement in the use of Deep Reinforcement Learning in quantum computing. A Comparative analysis between IBM Quantum(ibm kyiv) and the Aer Simulator on the OpenAI Gym environment using RL agents has been done. 2025 IEEE. -
Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Reinforcement Learning-Driven Energy Management for Battery-Supercapacitor Hybrid Storage in Electric Vehicles
The fast growth of the electric vehicles (EVs) market has increased the requirements towards high power transients, efficiency, and reliability on automotive onboard energy management systems by extending battery lifetime. Pure battery storage systems are similarly subject to frequent peak power demands during rapid acceleration and regenerative braking, and thus suffer from rapid aging. Aiming at this issue, in this paper, an AI-based EMS for a battery-supercapacitor HESS in EVs is developed. Dynamic driving conditions are handled by an RL-based power splitting control strategy which dynamically divides power between lithium-ion battery and supercapacitor in this context. The battery stress is to be minimized with the stabilization of the DC-link voltage and traction power demand. System modeling and validation is carried out in MATLAB/Simulink with the use of typical urban drive cycles. Simulation results show that, compared with a rule-based control of the EMS, our proposed AI-enabled EMS can decrease battery peak current by 38.6%, enhance energy efficiency by 11.2%, and increase cycle life by around 27%. The deviation of the DC-link voltage is limited within 1.8% and such control can be used to reduce total system response time in rapid load transition by 22%. Comparison results reveal that the optimal management framework has better adaptability and stability when compared to the corresponding one under different loads and driving conditions, which are promising for next generation EVs energy management issues. 2026 IEEE. -
Reinforcement Learning-Driven Innovation Clusters: Strategic Planning for Sustainable Corporate Growth
This paper explores the role of reinforcement learning (RL) in optimizing innovation clusters to foster sustainable corporate growth. We go on to establish how RL allows organizations to optimize core performance metrics (innovation output, profit growth, sustainability impact and resource allocation efficiency), and show in dynamic datasets how a network of simulated strategic decisions were made in an innovation ecosystem. Moreover, it highlights the ability of RL to adapt to ever changing industries and implement long term strategic plans besides traditional strategic practices. The results demonstrate that RL-based methods contribute to unleashing innovation and profitabilising the companies, but also to more sustainable operations, bringing into proportion the growth and social responsibility. These results demonstrate RL as an implication tool with a strong future for optimizing corporate strategies that serves as an incentive for further innovation, translating into long-term viability and success. 2025 IEEE. -
Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation
Effective lunar navigation is difficult in rough terrain and scarce energy resources. Classical path-planning has difficulty with terrain adaptation and energy optimization. This work introduces a Reinforcement Learning (RL)-based solution for energy-optimal lunar rover navigation based on NavCam data from Chandrayaan-3. A Q-learning framework translates terrain characteristics - elevation, slope, and hazards - into a reward scheme, balancing safe travel, minimal energy consumption, and mission effectiveness. The RL agent learns to respond to varying conditions, punishing dangerous regions such as craters and slopes. Simulations on lunar grids demonstrate better energy efficiency and accuracy than traditional approaches. This research pushes autonomous planetary exploration forward, optimizing rover navigation with actual mission imagery for future lunar missions. 2025 IEEE. -
Reinventing Coffee: Pandemic Lessons from Sleepy Owl Coffee
[No abstract available] -
Reinventing the business model to navigate the evolving business landscape
In today's rapidly evolving business landscape, technological advancements, shifting consumer behaviours, and global economic fluctuations increasingly challenge traditional business models. Organisations must reinvent their business models to remain competitive, embracing innovation, agility, and sustainability. This chapter explores the critical components of reinventing business models, including leveraging digital transformation, adopting customer-centric approaches, and integrating sustainable practices through a comprehensive view of their development. This study thoroughly understands the existing literature on business models, focusing on the features. Integrating sustainability into a business model is also a challenge for many practitioners. The business model innovation agenda is the topic of discussion among most companies. This chapter will explore adopting a business model towards sustainability by integrating environmental, social, and governance factors. The findings underscore the necessity for continuous adaptation and strategic foresight to drive long-term success. 2024, IGI Global. All rights reserved.