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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. -
REIT: A Regulation Framework to Educate and Engage Investors, Improves Trust Ability, Creative Thinking and Transparency
Purpose: This study focuses to understand the Investor?s perception about REITs, REIT provides all the solution to the worries of investors and provide a frame work to educate and engage investors, improve their trust and build transparency. So that they can change their perception that how this underutilised instrument can gain popularity in country as it does in other leading economies. Design/Methodology/Approach: This study's sample design is convenience sampling, and the research design is descriptive. The sample size for this study is 41 and the sample frame is all types of investors in Country. A systematic questionnaire will be used to gather the main source of data. Utilising statistical methods, the data gathered by the questionnaire will be examined. Findings: The results of the study indicate that there is a positive perception amongst the investors in Country regarding REITs, however the study found that the performance of REITs in Country has been promising, with consistent growth in the past few years, and it is expected to continue in the future. Practical Implications: The study's conclusions can help investors, financial institutions, and governments better understand REITs and their expansion in Country. This study may also aid in educating Countryn investors about REITs. Social Implications: This study can have a significant impact on the Countryn economy as with more awareness in the field of REITs, small scale investors or people with low capital can invest in Countryn Real estate through REITs and help in growth of real estate sector and can be a key role in the growth of the real estate sector. Originality/Value: This study is original and valuable as it sheds light on the Countryn investors? perception about REITs. By highlighting the elements that affect investors' perceptions of Countryn REITs, it also adds to the body of current material. 2023, Journal for ReAttach Therapy and Developmental Diversities. All Rights Reserved. -
Rejuvenating human resource accounting research: a review using bibliometric analysis
The current study attempts to map the intellectual structure of Human Resource Accounting to understand the research gaps and future trajectories. The study employs systematic literature review technique to extract relevant literature, bibliometric analysis to map the intellectual structure of research in human resource accounting, to identify underlying research themes and content analysis to identify avenues for future research. Based on 2438publications, author keyword co-occurrences extracted four themes namely, Human Resource Management, Intellectual Capital, Human Capital, and Voluntary Disclosure. The study also summarizes significant findings of papers under each cluster through content analysis identifying areas for future research. The study provides a birds eye view of the intellectual structure of academic research efforts in the field of human resource accounting. The study is one of the first attempt to comprehensively review the academic literature from Scopus database employing systematic literature review, bibliometric methods, and content analysis in the field of human resource accounting. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Relating the role of green self-concepts and identity on green purchasing behaviour: An empirical analysis
At present, consumers in emerging economies are becoming more conscious about environmental well-being. Therefore, organizations compete to make their products and practices more eco-friendly. Several studies have tried to explain the relationship between green consumerism and an individual's buying behaviour using traditional theories. However, there is quite a challenge in understanding the influence of green self-concept (GSC) and green self-identity (GSI) in predicting the green purchase intention (GPI) of consumers. Therefore, the authors developed six hypotheses to assess the relation between self-concept and the GPI. The survey was conducted, and the responses were evaluated through the partial least square (PLS) method. The authors analysed the measurement model results (n = 717) and the direct and indirect mediating effect of the latent variable contributing to GPI. The measurement model results show that a significant relationship exists in the proposed model, namely, GSCs ? green purchasing intentions, product self-concept (PSC) ? green purchasing intentions and GSI ? green purchasing intentions. Further, the GSI acted as a mediator for the measurement model. The implications of the study can be used to understand the green consumer behavior in developing new strategies and policies for the organizational practice in emerging economies. 2020 ERP Environment and John Wiley & Sons Ltd. -
Relation between electricity consumption and economic growth in Karnataka, India: An aggregate and sector-wise analysis
Karnataka is a highly progressive and rapidly growing state in India, with huge potential for industrial growth, however, it grapples with power deficits and other problems in electricity sector, which make it a good case study for Indian electricity sector. Given the importance of electricity in the urbanisation and growth process, the paper analyses the electricity consumption trend in Karnataka, examine its causality with economic growth at aggregate and sectoral levels using Granger causality test, and forecast the future electricity consumption applying Holt-Winters smoothening (no seasonality) technique. The general trend reflects higher consumption by the agricultural consumers, compared to the revenue-generating 'Industries' and 'Commercial' categories, mainly due to the policy of de-metering and providing 'free' power to agricultural consumers since late 1980s. The Granger causality tests reveal that there is no causality relation (neutrality hypothesis) between electricity consumption and economic growth in Karnataka, for total, agricultural and industrial consumption. This basically stems from the inaccurate measurements of agricultural consumption, higher dependence on captive generation, and poor quality grid supply. Finally, electricity consumption is predicted to be around 69,347 GW h by 2019?20. Future policies should focus on universal metering, reducing cross-subsidization, supplying good quality and reliable power to all sectors, and economical planning of resource-mix to achieve adequate, productive and efficient electricity consumption. 2020 Elsevier Inc. -
Relational Time as a Stochastic Variable in ADM Gravity
The absence of a fundamental time parameter in canonical quantum gravity is motivating the search for internal clocks by which evolution is defined relationally. While formal solutions are provided classical deparametrization schemes they often rely on semiclassical limits or fixed foliations that break general covariance. In this work, a canonical framework is constructed where time emerges dynamically as a stochastic degree of freedom, identified with a massless scalar field whose conserved momentum current defines a relational foliation. Unlike semiclassical stochastic gravity approaches where noise is introduced externally, here the stochasticity arises from coarse-graining of unresolved transverse-traceless graviton modes, leading to an intrinsic, dynamically coupled stochastic clock field. This leads to a diffusion-like broadening of the wavefunctional across neighboring clock slices, offering a novel stochastic phenomenology of the WheelerDeWitt equation. The resulting evolution remains consistent with hypersurface-deformation algebra in an ensemble sense, while introducing an intrinsic probabilistic nature to relational time. This work thus establishes the theoretical foundation for future applications in quantum cosmology and the emergence of classicality from quantum gravitational systems. 2025 Wiley-VCH GmbH. -
Relationship between Digital Leadership and Organizational Culture: Role of Digital Literacy
This study investigates the influence of digital literacy on leadership styles embraced by organizational leaders and its subsequent impact on organizational culture. The objective is to provide insights that can guide strategic decision-making and leadership development initiatives in the digital age. The research focuses on exploring the relationship between Digital Leadership and Organizational Cultural Changes, with a specific emphasis on the role of Digital Literacy. Primarily quantitative, the research relies on primary data for its insights. A meticulously designed questionnaire is administered to collect the necessary data. The results indicate that a Banking and Financial Services (BFS) manager, aged 31 to 40 years, earning a monthly income between INR 50,001 to INR 1,00,000, regardless of gender, marital status, and education, demonstrates a higher level of perceptions of digital leadership. Conversely, a BFS employee aged above 50 years, earning a monthly income up to INR 30,000, irrespective of gender, marital status, and education, exhibits a lower level of perceptions of digital leadership. Moderation analysis outcomes reveal that the primary connection between perceptions of digital leadership and organizational culture is significant. However, the moderation effect of digital literacy in the relationship between perceptions of digital leadership and perceptions of organizational culture is deemed insignificant. These findings contribute valuable insights for organizational decision-makers seeking to understand the intricate dynamics of digital leadership and its impact on shaping organizational culture in the contemporary business landscape. 2024, Iquz Galaxy Publisher. All rights reserved. -
Relationship between Digital Learning, Digital Literacy and Academic Performance of Higher Education Students: Moderated Mediation Role of Critical Thinking
In today's rapidly evolving educational landscape, digital technologies have become increasingly prevalent, transforming how students access and engage with information. This study explores the relationships among digital learning, digital literacy, and academic performance in higher education, focusing on the moderating and mediating role of critical thinking. The adoption of digital learning platforms, such as online courses and virtual classrooms, has expanded educational access and flexibility. However, concerns regarding their effectiveness persist. Digital literacy, encompassing the ability to navigate digital tools and critically evaluate information, is crucial in this context. This research investigates how students' digital literacy levels influence their academic achievement and examines the mediating role of critical thinking in this relationship. Critical thinking is hypothesized to mediate the effects of digital literacy on academic performance and the impact of digital learning on critical thinking skills. Additionally, the study assesses whether critical thinking moderates the prime relationship between digital learning and academic performance. This descriptive, cross-sectional study employs structured questionnaires to gather primary data from 384 students enrolled in undergraduate, postgraduate, professional, and research programs at private universities in Bangalore, India. The findings indicate that the academic program significantly influences students' perceptions of digital literacy, digital learning, critical thinking, and academic performance, while demographic factors do not. Digital learning alone has a slight negative effect on academic performance, but digital literacy acts as a positive mediator, mitigating this impact. However, critical thinking does not significantly moderate the relationship between digital learning and academic performance. 2024, Iquz Galaxy Publisher. All rights reserved. -
Relationship between Emotional Intelligence and Academic Achievement among College Students
Indian Journal of Applied Psychology Vol. 50, pp. 78-81, ISSN No. 0019-5073 -
Relationship Between Family Environment, Objectified Body Consciousness and Appearance Self-Esteem Among Urban Indian Young Adults
Extant research has shown that objectification, especially sexual objectification, can encourage the internalization of others perspective on their own bodies and thereby transforming their own self into object of continuous assessment and judgement. Using the objectification theory and theories of identity formation, the present research examines how family environment (FE) and objectified body consciousness (OBC) may have a relation with appearance self esteem (ASE) among urban Indian young adults. Based on previous literature, it was hypothesized that OBC and FE would have a significant association with ASE. To examine the hypotheses, a survey was conducted on young adults (N = 141) of age range from 18 to 25years. Regression analysis was carried out using statistical tools. Multiple-linear regression showed that the model was found to account for a statistically significant amount of variance in ASE. The results point out how OBC has a negative relationship with ASE. This implies that the level to which one objectifies themselves negatively relates to how they value their appearance or looks. The present research discusses the implication of understanding the different factors which may be associated with low appearance-related self-esteem. The research also explains the findings in line with cultural underpinnings. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Relationship between financial inclusion and financial development in India: Is there any link?
A dynamic chain of financial activities and services can be served from debtors to creditors in the international economy through an efficient and effective financial sector. The motivation behind this study is to investigate the linkages between financial inclusion and financial development in India during the period (19802017). For this, the study employ principal component analysis (PCA) to construct both financial inclusion index and financial development index which measures financial access and financial depth position respectively. Using a set of determinants related to financial inclusion and financial development, the present study estimates there is a unidirectional relationship between financial inclusion and financial development in India. So, it reveals that financial inclusion is an essential element for financial sector development especially in a developing country like India. 2021 John Wiley & Sons Ltd.