Knowledge, Innovation and Technology in Scientific Research Vol. 3

Artificial Intelligence-Enabled Social Media Analytics for Marketing Resilience and Digital Risk Management

Shivani Vats — 2026-09-25

The rapid growth of social media has transformed the marketing environment by enabling organisations to interact with consumers, monitor market trends, and obtain real-time insights from large volumes of user-generated content. At the same time, the digital environment has increased organisational exposure to misinformation, online manipulation, negative electronic word-of-mouth, privacy concerns, algorithmic bias, and rapidly evolving crises. Artificial intelligence (AI) offers significant potential to address these challenges through capabilities such as natural language processing, sentiment analysis, automated text classification, predictive analytics, and social listening. However, the effective use of AI-enabled social media analytics requires organisations to balance technological opportunities with ethical and digital risk considerations. This paper develops a conceptual understanding of the role of AI-enabled social media analytics in strengthening marketing resilience and digital risk management. Drawing upon the literature on artificial intelligence, social media marketing, crisis management, consumer engagement, organisational resilience, and ethical AI, the study proposes a framework in which AI capabilities enhance social media analytics, leading to improved digital intelligence and marketing response capabilities. These capabilities, in turn, contribute to marketing resilience by supporting early risk detection, adaptive decision-making, effective crisis response, and sustained consumer engagement. The framework also recognises algorithmic bias, data privacy, misinformation, and ethical concerns as critical factors influencing the effectiveness of AI-driven marketing practices. The study contributes by integrating previously fragmented research streams and positioning AI-enabled social media analytics as a strategic capability for managing digital uncertainty and building resilient marketing systems.

Predict–Prevent–Protect: An Explainable AI-Driven Patient-Centred Early-Warning and Intervention System to Prevent Leave Against Medical Advice and Improve Continuity of Care

Ajit Pal Singh — 2026-09-25

Background: Leave Against Medical Advice (LAMA), also termed Discharge Against Medical Advice (DAMA), is a clinically important event associated with interrupted treatment, avoidable care gaps, readmission and other adverse outcomes. Existing evidence indicates that LAMA is influenced by interacting clinical, socioeconomic, behavioural, communication and health-system factors. Although recent machine-learning studies demonstrate that LAMA/DAMA can be predicted from routinely collected electronic health record (EHR) and hospital data, prediction alone does not address the modifiable reasons that precipitate premature departure.

Objective: To develop and prospectively evaluate an explainable, patient-centred artificial intelligence (AI) early-warning and intervention system that links risk prediction to human-led preventive action and a structured continuity-of-care pathway.

Methods: We propose a prospective, pragmatic, mixed-methods, two-phase study. Phase I will develop and temporally evaluate candidate prediction models using routinely collected clinical, operational and patient-centred data. Candidate approaches will include regularised logistic regression, random forest and gradient-boosting models. Performance will be assessed using discrimination, calibration, decision-curve analysis, operational utility, temporal stability and subgroup fairness. Phase II will prospectively evaluate a human-in-the-loop intervention using a stepped-wedge or controlled before-and-after design, subject to institutional feasibility and ethical approval. Explainable model outputs will identify potentially modifiable contributors to elevated risk and trigger proportionate clinical, communication, social-support and care-coordination interventions. When a patient continues to choose LAMA, a structured Protect pathway will minimise avoidable harm through medication reconciliation, return precautions, follow-up planning and post-discharge contact.

Expected Results: The study is a research protocol and conceptual implementation framework; therefore, no empirical results are claimed. Expected outputs include a temporally evaluated prediction model, an actionable explanation layer, estimates of intervention effects on LAMA and continuity-of-care outcomes, subgroup fairness analyses and implementation measures.

Conclusion: The Predict–Prevent–Protect framework shifts the role of AI from retrospective risk labelling towards proactive, patient-centred risk reduction while preserving autonomy and clinician accountability. Prospective clinical evaluation is required to determine whether this closed-loop approach reduces avoidable LAMA and downstream harm without increasing inequity, alert burden or loss of trust.

Dairy Supply Chain Sustainability Under Climate Change: A Review of Risk, Adaptation and Resilience

Nidhi H. Patel — 2026-09-25

Climate change challenges dairy sustainability through interacting biological, infrastructural and socioeconomic pathways rather than through heat stress alone. This critical narrative review evaluates how climatic hazards propagate from feed and water resources through dairy animals, milk collection, processing, refrigeration, distribution and waste, and assesses the evidence for adaptation and resilience strategies across these connected stages. Literature published primarily from 1 January 2008 to 10 July 2026 was identified through multidisciplinary scholarly sources, supplemented by citation chaining and authoritative bibliographic verification. Evidence was appraised for design strength, exposure measurement, external validity, treatment of uncertainty and capacity to address cross-stage consequences. The strongest and most consistent evidence concerns heat stress in dairy cattle, including reduced milk yield, altered metabolism, impaired reproduction and welfare, and effects that may extend from late gestation into offspring performance. Evidence for climate effects on forage quality, water demand, microbial safety, logistics and cold-chain performance is important but more heterogeneous and frequently model-based. Farm-level cooling can substantially reduce thermal load, yet its effectiveness and sustainability depend on water, electricity, housing, capital and local climate. Nutritional and genetic strategies can complement environmental modification, although genetic definitions of thermotolerance must avoid selecting narrowly for production persistence at the expense of survival, fertility or recovery. Beyond the farm gate, resilience depends on reliable cooling, energy and water flexibility, route and supplier diversification, monitoring, coordination, finance and governance, but these measures have been evaluated less rigorously in dairy-specific climate studies. Mitigation and adaptation can be synergistic, but cooling energy, water use, resource redundancy and emissions-intensity objectives can also create trade-offs. The review argues for whole-chain, multi-objective resilience assessment that combines biological tolerance with infrastructure robustness, recovery capacity and equitable adaptation. Priority research should connect farm and post-farm datasets, evaluate interventions under real climate extremes, incorporate uncertainty and distributional effects, and measure continuity of safe milk supply alongside conventional environmental metrics.

Immersive Virtual Reality as a Psychoanalytic Setting: Epistemic Safeguards, Clinical Technique, and Human-in-the-Loop Governance

Vincenzo Maria Romeo — 2026-09-25

Immersive virtual reality (IVR) is becoming a clinically credible medium for mental-health assessment and intervention, but most validated applications remain grounded in exposure, cognitive-behavioural, rehabilitation, or skills-training paradigms. A previous article by the author proposed a three-layer psychoanalytic IVR architecture integrating somatic sensing, symbolic-narrative modulation, and relational interaction. The present book chapter addresses a different question: how can an adaptive IVR environment be governed clinically without converting physiological correlates, behavioural traces, or algorithmic classifications into claims about unconscious meaning? To answer this question, the chapter proposes a set of translational safeguards rather than a new biomarker model. Four design principles are developed: an epistemic firewall separating machine state estimation from psychodynamic interpretation; minimum-necessary adaptation, which limits automated environmental changes to containment and safety functions; interpretive latency, which protects time for association and reflective elaboration before meaning is assigned; and dual-loop human-machine governance, in which the technical loop regulates the environment while the clinician-patient loop retains authority over formulation, interpretation, and therapeutic direction. The framework is extended to embodiment, presence, avatar-mediated transference, countertransference, source-memory confusion, suggestibility, clinician dashboards, privacy, and derived psychological data. A staged session protocol and an empirical measurement battery are proposed using established instruments for presence, embodiment, reflective functioning, therapeutic alliance, and simulator sickness, together with system-level safety and override metrics. The framework is conceptual and has not been empirically validated as an integrated package; its feasibility, safety, and clinical utility therefore remain to be established in prospective studies. The chapter does not claim that biosignals reveal unconscious content or that psychoanalytic IVR is an established treatment. Its purpose is to define conditions under which the earlier architecture can be tested without sacrificing uncertainty, subjectivity, relational accountability, or patient autonomy.

Library 3.0 and the Generations of the Library Web: A Critical Narrative Review of Conceptual Boundaries, Evidence and Contested Claims

V. Sheela — 2026-09-25

The vocabulary of numbered web generations has become a routine organising device in library and information science, yet the analytical content of the labels remains disputed. Library 3.0 is the pivotal case. It is invoked as a semantic, machine-assisted and apomediated stage of library service, but it is also used loosely for any service that appears technologically advanced, and its boundaries with Library 2.0 on one side and with artificial intelligence driven models on the other have never been settled. This review examines what Library 3.0 means, what distinguishes it from adjacent generational labels, how far the empirical record supports the claims attached to it, and where the conceptual apparatus breaks down. Peer-reviewed journal literature, authoritative standards documentation and institutional reporting were identified through open scholarly indexes and citation searching, appraised for methodological adequacy and relevance, and synthesised thematically rather than catalogued. Four findings emerge. First, the generational sequence conflates three distinct axes, namely underlying web architecture, library service philosophy and institutional capability, and most disagreement in the literature is traceable to that conflation. Second, apomediation and machine-processable metadata are the only criteria that consistently separate Library 3.0 from Library 2.0 in the original formulations, yet later usage has substituted looser criteria such as participation and adaptability. Third, implementation evidence concentrates on metadata transformation and practitioner perception, while user-level outcome evidence remains sparse, geographically uneven and rarely comparative. Fourth, the arrival of large language models has displaced rather than resolved the generational debate, because the capabilities now marketed as intelligent services do not depend on the semantic infrastructure that defined Library 3.0. The label retains descriptive value as a shorthand for semantically enriched, machine-mediated library service, but it cannot presently support claims about service quality, user benefit or institutional maturity. Priority should shift from taxonomy to the measurement of outcomes that any generational claim would need to demonstrate.