The large-scale integration of wind and solar power is essential for power-sector decarbonization, but their variability creates persistent challenges for forecasting, reserve allocation, storage scheduling, curtailment, and emission-aware operation. This review synthesizes recent advances in statistical, deep-learning, transformer, foundation-model, probabilistic, and physics-informed renewable forecasting, with emphasis on physical consistency, uncertainty, computational requirements, real-time applicability, and system-level operational value. We propose the Physics-Informed Large Model Carbon-Aware (PILM-CA) framework, a conceptual architecture linking physical renewable dynamics, physics-informed large-model forecasting, grid-operational decision support, and carbon-aware optimization. Unlike accuracy-centric forecasting frameworks, PILM-CA explicitly represents the pathway through which forecast uncertainty can affect commitment, reserves, storage, fossil backup, curtailment, and consequent carbon intensity. Published evidence indicates that these effects are system-dependent rather than universal; therefore, the framework distinguishes evidence-supported components from conceptual cross-layer relationships requiring operational validation. Probabilistic forecasting metrics, extreme-event resilience, digital-twin deployment, technology readiness, and climate adaptation are also critically examined. The review identifies research priorities for scalable, physically coherent, uncertainty-aware forecasting that supports both decarbonization and resilience in high-renewable power systems.
Healthcare 4.0 applies innovative information and communication technologies to revolutionize the healthcare sector. Internet of Things (IoT)- based healthcare systems are systems with sensing, communication, and computing capabilities that provide on-demand, at-home healthcare services. The Internet of Healthcare Things (IoHT) system architecture comprises three modules: data collection, data transmission, and data processing, each of which contains several components. The development of technology and prototypes for a technology-centric healthcare system is receiving increasing attention across industry and academia. This survey covers key enabling technologies, applications, and policy approaches needed for implementation. It assesses different research and industrial projects, noting their successes and shortcomings. The study also highlights the importance of Artificial Intelligence (AI) and Big Data analytics for interpreting clinical data to derive actionable insights and facilitate disease diagnosis and treatment. In addition, it addresses privacy & security issues related to patient data. Recent research advances are summarized, and future directions and open challenges are discussed for each system module, thereby advancing understanding of this emerging field.
Converter-dominated power systems with high penetration of inverter-based resources exhibit fast, nonlinear, and multi-timescale dynamics that introduce new stability modes and operational risks beyond the scope of traditional assessment techniques. In recent years, artificial intelligence and machine learning methods have been widely applied to small-signal, transient, voltage, and frequency stability prediction using simulation data and wide-area measurements. Although many studies report high predictive accuracy, the literature remains fragmented across stability categories, data regimes, and model classes, and typically emphasizes point prediction rather than uncertainty, regime transitions, and control-oriented early-warning. This review presents a systematic and literature-rich synthesis of AI-driven stability assessment approaches for converter-dominated power systems, covering learning architectures, feature representations, graph-based methods, and real-time monitoring integration. Key gaps are identified in uncertainty-aware prediction, cross-timescale stability coupling, interpretability for control action, and operational risk quantification. To address these limitations, a novel AI-Based Stability Risk Envelope Framework is proposed, linking predictive models with uncertainty bounds, operating-region tracking, and decision-triggered early-warning mechanisms for dynamics-informed grid operation.
The proliferation of recommender systems (RS) research has attracted researchers to explore new tools and techniques to address several issues in the domain. In this context, text mining has received considerable acceptance for solving cold start and data sparsity problems in recommender systems. Additionally, text mining has been exploited to design modern recommender systems through sentiment analysis of user reviews, social network posts, web data, etc. These sentiment identifications help create user profiles, identify preferences, understand the context in which a review is provided, and extract features to assess item quality, facilitating better recommendations. This paper presents a comprehensive and systematic review of text mining-based recommender systems (TMRS). We analyze how text mining techniques have been integrated into different RS paradigms and propose a novel taxonomy that classifies TMRS based on underlying text mining approaches and recommendation approaches. In addition, we examine commonly used evaluation metrics and discuss how they are applied to assess TMRS performance. Key research challenges and open issues are identified, along with promising future research directions. This review provides a structured overview of the state of the art in TMRS and serves as a useful reference for researchers and practitioners seeking to design, evaluate, and advance next-generation recommender systems.
PurposeThis study aims to investigate how business-to-business (B2B) sales professionals use social media in their roles, focusing on its impact on personal branding, customer relationship management and sales performance. It fills gaps in social selling literature by exploring new variables and relationships. Design/methodology/approachA quantitative method was used, with 276 valid responses from salespeople across sectors, collected through snowball and purposive sampling. The analysis examined how job-related social media usage (SMU) and personal branding SMU affect in-role performance, customer relationship performance and overall sales outcomes. FindingsFindings reveal that job-related SMU significantly boosts in-role performance, with online social capital enhancing this effect. While job-related SMU does not directly influence sales performance, it indirectly affects it via improved in-role performance. Personal branding SMU positively impacts customer relationship performance, further amplified by online social capital. Both in-role and customer relationship performances strongly predict sales success. Practical implicationsThis study uniquely integrates personal branding and job-related SMU in the B2B context, offering new insights into how social media influences sales. It also provides practical recommendations for sales organizations to enhance social media skills, emphasizing relationship-building and online social capital to drive better sales performance. Originality/valueTo the best of the authors’ knowledge, this study is among the first to integrate SMU for personal branding with job-related tasks in the B2B sales context. By highlighting the mediating role of in-role performance and the amplifying effect of online social capital, the research introduces new perspectives on the relationship between social media and sales performance.
