Peter Kokol

The software industry is confronting a critical crisis driven by systemic vulnerabilities within the software supply chain and the ubiquitous reliance on third-party dependencies. While individual exploits and localized compliance frameworks have been heavily studied, this highly fragmented academic discourse creates a critical barrier to organizational readiness and proactive incident preparedness. To address this gap and enhance digital resilience, this study maps the conceptual, knowlegde and structural landscape of software supply chain literature using Synthetic Knowledge Synthesis. Analyzing publications from 2021 to 2026, the research reveals an exponential growth in output, heavily dominated by conference proceedings and driven primarily by institutional and state-funded efforts in the United States and China. Thematic mapping identifies five core clusters defining the current ecosystem: Software Composition Analysis, Advanced Vulnerability Detection, AI-Driven Threat Intelligence, Deep Learning, and DevSecOps. The study highlights critical unresolved gaps, notably the prevalence of false positives in static Software Bill of Materials tools due to a lack of execution context, and a pronounced disconnect between theoretical models and practical enterprise application. Future research directions emphasize the integration of Large Language Models for automated patch generation, the development of execution-aware vulnerability triage, and the evolution of dynamic, AI-driven artifacts to secure complex dependency networks.

Class imbalance remains a persistent challenge in supervised classification, particularly when the minority class represents rare but important observations. Common approaches address this problem through undersampling, synthetic oversampling, cost-sensitive learning, or decomposition of complex class distributions. This study introduces Hierarchical Majority Decomposition (HMD), an architecture in which the majority class is recursively partitioned into a binary tree of increasingly localized subclasses while the complete minority class remains intact. A supervised local classifier is associated with each internal majority node and distinguishes the minority class from the node’s two child majority subclasses. During top-down inference, a minority prediction terminates traversal, whereas a majority-subclass prediction simultaneously identifies the current observation as not yet recognized as minority and determines the branch in which the next minority comparison is performed. HMD was evaluated using six binary benchmark datasets with imbalance ratios ranging from approximately 3.2:1 to 42.0:1. An unrestricted decision tree was used as a controlled base learner, and all methods were evaluated using 5-fold stratified cross-validation repeated 10 times. HMD was compared with an untreated decision tree, a class-weighted decision tree, random undersampling, SMOTE, and a root-only flat majority decomposition. Increasing HMD inference depth increased minority recall relative to the untreated decision tree on all six datasets. The strongest effects were observed on Mammography, Climate Model Simulation Crashes, Oil Spill, and Ozone Level Detection. Importantly, the root-only decomposition generally remained close to the untreated decision tree, whereas deeper HMD inference produced substantially larger sensitivity increases, indicating that recursive minority comparison rather than majority decomposition alone is responsible for much of the observed effect. Random undersampling frequently achieved equal or greater minority recall and balanced accuracy, but often at a substantially greater loss of specificity and minority precision. The results demonstrate that a recursively decomposed majority class can be used as an active inference structure in which an unchanged minority class is repeatedly reconsidered against progressively localized majority contexts.

Background/Objectives: Children now spend a substantial part of daily life in digital environments, and their digital, media, and information literacies shape their online safety, social-emotional development, and mental health, making these competencies a concern for child public health and preventive paediatric care. This study applies a Synthetic Knowledge Synthesis (SKS) to map how digital, media, and information literacies (hereafter digital literacies) among children have evolved, comparing neurotypical and neurodivergent children. SKS is a semi-automated approach that combines descriptive bibliometrics, keyword co-occurrence mapping, and qualitative content analysis to map an entire research field or topic. Methods: We treat digital literacies as relevant to children’s health, well-being, and safe online participation, and we analyse Scopus-indexed literature from 1996 to 2025 using descriptive bibliometrics, keyword co-occurrence mapping, and qualitative content analysis. Results: The mapping shows strong growth in general digital-literacy scholarship alongside a small but persistent body of work on a “double digital divide” affecting children with autism spectrum disorder (ASD), ADHD, and dyslexia. The two bodies of work differ in emphasis: the general literature foregrounds social integration and critical agency, while research on neurodivergent children foregrounds inclusive pedagogy, implicit learning, and multimodal expression. It also shifts from general digital-safety awareness toward protective mediations for vulnerabilities such as social-emotional decoding differences and impulsivity, which bear on children’s online safety and mental health. Because bibliometric mapping reveals where research is concentrated rather than what works in practice, the synthesis points to evidence gaps rather than proven methods. Conclusions: On this basis, we argue for an integrated public-health and socio-educational framework that complements universal literacy standards with adaptive, assistive safety nets, so that all children can take part in digital life safely and on equal terms.

Background: The integration of artificial intelligence (AI) and machine learning in nursing practice has transitioned from theoretical exploration to active clinical implementation. However, early adoption relied heavily on opaque "black-box" models, raising significant concerns regarding algorithmic accountability, clinician trust, and patient safety. Explainable Artificial Intelligence (XAI) addresses these socio-technical challenges.Purpose: This study aims to structurally map the conceptual landscape and current applications of XAI within nursing research.Methods: A Synthetic Knowledge Synthesis framework was employed to systematically map the fragmented evidence base of XAI in nursing. Following the synthesis, an inductive thematic analysis was conducted to categorize the literature, identifying the primary clinical, administrative, and socio-technical domains where interpretable machine learning is currently deployed.Findings: The analysis identified five distinct thematic clusters characterizing the XAI ecosystem in nursing: Acute Clinical Decision Support, Predictive Explainable Machine Learning in Geriatric and Long-Term Care, Critical Care Informatics and Human-Computer Interaction, Clinical Risk Factors and Administrative Support Systems, and Nursing Workforce Wellbeing and Psychometric Evaluation. The literature reveals a bifurcation in XAI deployment based on clinical acuity, ranging from natural language processing for rapid emergency triage to random forest models for longitudinal geriatric forecasting.Conclusions: The application of XAI in nursing extends far beyond technical algorithm development; it deeply intersects with clinical workflows, human-computer interaction, and workforce sustainability. Transitioning away from opaque predictive models is essential for fostering clinician trust and ensuring safe, ethical care.

Background: The convergence of Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) has established the field of Intelligent Evolutionary Games (IEGs). While IEG applications have flourished in general systems and social sciences, their operationalization within healthcare (IEG Health) remains significantly underdeveloped. This study identifies a “knowledge void” in the literature, where the bottleneck is not a lack of clinical data but a scarcity of frameworks that integrate intelligent strategic modelling into clinical practice. Methods: We employ the Synthetic Near-Empty Review (SNER) framework, utilizing Synthetic Knowledge Synthesis (SKS) and bibliometric triangulation via VOSviewer. Three distinct corpora—IEG Health, EG Health, and IEG All (IEG)—were harvested from Scopus and mapped to identify thematic clusters and translation pathways. Results: The analysis reveals that IEG Health is a nascent domain currently focused on service regulation in elderly care and chronic disease management. We demonstrate a “Translation Framework” to bridge the research void, mapping concepts like Social Trust and Reputation Management from the broader IEG literature into clinical-specific models, such as Doctor-AI Adoption and Adaptive Coordination Games. Conclusions: By shifting from static Replicator Dynamics to Adaptive Learning Strategies (e.g., MARL and Bayesian updating), IEG Health can address critical challenges like algorithm aversion and clinical deskilling. Furthermore, transitioning these models into clinical environments requires the incorporation of structured ethical guidelines, such as ALTAI, to ensure algorithmic accountability. This study provides a structured foundation for future research to transition from theoretical modelling to AI-augmented clinical decision-making.

Background: The integration of Artificial Intelligence (AI) into the management of pediatric metabolic diseases offers unprecedented opportunities for precision medicine. However, the explosive growth of research literature production has led to a fragmented research landscape, often skewed by the indexing biases of major academic databases. Objective: This study aims to conduct a comparative thematic analysis of the Web of Science and Scopus databases to uncover the distinct research paradigms governing AI in pediatric metabolic diseases. Methods: We employed the Synthetic Knowledge Synthesis methodology, integrating automated bibliometric mapping (co-word analysis via VOSviewer) with qualitative content analysis. Metadata was extracted from both databases and author keywords were clustered to evaluate underlying thematic structures. Results: The comparative analysis revealed a significant thematic divergence. Literature indexed in WoS predominantly emphasizes algorithmic novelty and methodological advancement, highlighting the use of Deep Learning, Large Language Models (LLMs), and complex metabolomic integrations. Conversely, Scopus encapsulates a distinctly clinical and translational paradigm, prioritizing Explainable AI (XAI), the integration of Natural Language Processing (NLP) with Electronic Health Records (EHR), and the application of clinical decision support systems like Continuous Glucose Monitoring (CGM). Conclusion: Relying on a singular bibliographic database provides an incomplete view of the field, creating a disconnect between algorithmic development and clinical implementation. To successfully bridge the "algorithm-to-clinic" gap in pediatric endocrinology, researchers must adopt a holistic approach that synthesizes the predictive power emphasized in WoS with the clinical transparency and applicability highlighted in Scopus.

Background: The convergence of Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) has established the field of Intelligent Evolutionary Games (IEG). While IEG applications have flourished in general systems and social sciences, their operationalization within healthcare (IEG Health) remains significantly underdeveloped. This study identifies a "knowledge void" in the literature, where the bottleneck is not a lack of clinical data but a scarcity of frameworks that integrate intelligent strategic modeling into clinical practice. Methods: We employ the Synthetic Near-Empty Review (SNER) framework, utilizing Synthetic Knowledge Synthesis (SKS) and bibliometric triangulation via VOSviewer. Three distinct corpora—IEG Health (), EG Health (), and IEG All ()—were harvested from Scopus and mapped to identify thematic clusters and translation pathways. Results: The analysis reveals that IEG Health is a nascent domain currently focused on service regulation in elderly care and chronic disease management. We demonstrate a "Translation Framework" to bridge the research void, mapping concepts like Social Trust and Reputation Management from the broader IEG literature into clinical-specific models, such as Doctor-AI Adoption and Adaptive Coordination Games. Conclusions: By shifting from static replicator dynamics to Adaptive Learning Strategies (e.g., MARL and Bayesian updating), IEG Health can address critical challenges like algorithm aversion and clinical deskilling. This study provides a structured foundation for future research to transition from theoretical modeling to AI-augmented clinical decision-making.

Background: This longitudinal study examined the clinical outcomes of physiotherapy interventions in patients with chronic low back pain, specifically observing the interactions between demographic characteristics, physical metrics, and psychological variables. Methods: A cohort of n = 150 patients, Final n = 123 (18% attrition rate), was assessed using a one-group pre-test/post-test design, with primary outcome measures including Health-Related Quality of Life, the Perceived Stress Scale, and the Numerical Pain Rating Scale. Participants received eight standardized sessions over 4 weeks, including electro-physical agents combined with individualized kinesiotherapy. Data analysis/synthesis was performed via Multiple Correspondence Analysis (MCA) to map associations between categorical variables and treatment responses. Results: The predominant clinical profile found was a middle-aged female with moderate educational attainment, presenting with a Body Mass Index in the overweight range and moderate-to-high baseline pain intensity. MCA revealed distinct phenotypic trends: longer Work Experience was associated with lower baseline Quality of Life (QoL) and heightened stress/pain levels. In contrast, patients characterized by higher education and significant Work Experience demonstrated notable post-intervention QoL gains. High baseline QoL served as a predictor for sustained improvement and pain attenuation, while elevated pre-intervention pain scores were consistently linked to perceived unmet clinical needs and exacerbated stress. Conclusions: MCA successfully mapped non-linear clusters—such as the “Socio-Psychological Barrier” profile—that traditional univariate methods fail to visualize, suggesting that “individualized care” must prioritize health literacy among patients experiencing extensive work-related strain.

The rapid expansion of real-world medical data is driving a transformative shift toward integrating artificial intelligence (AI) with propensity score matching (PSM) to enhance clinical research. While AI provides advanced capabilities in diagnostics and prediction, PSM serves as a critical statistical tool for mitigating confounding bias in quasi-experimental studies, thereby approximating the reliability of randomized controlled trials. This study utilized synthetic thematic analysis (STA) and bibliometric mapping via VOSviewer and Bibliometrix to analyze 433 documents retrieved from the Scopus database. The findings reveal an exponential growth in this field between 2020 and 2024, with the United States and China emerging as the primary contributors to global research output. Four central thematic clusters were identified: prediction, cancer management, diagnostics, and deep learning. The integration is bidirectional, characterized by AI algorithms optimizing propensity score estimation and PSM frameworks being used to enhance AI-driven models. This methodological convergence is significantly improving the rigour of observational studies, particularly in complex clinical domains such as cardiovascular disease and chronic illness management. Ultimately, the AI-PSM symbiosis represents a critical trend in medical informatics, refining the accuracy of predictive modelling and strengthening the evidentiary value of real-world data in global health research.

Background: The rapid expansion of real-world data in medicine is driving the adoption of advanced methods like Artificial Intelligence (AI) and Propensity Score Matching (PSM). AI is widely applied across diagnostics, prediction, and treatment planning, while PSM is a crucial statistical technique used in quasi-experimental studies to mitigate confounding bias and approximate the reliability of randomized controlled trials. There is a growing research interest in combining these two methods to leverage their symbiotic strengths, but this association has not been holistically explored. Methodology: This study employed Synthetic Thematic Analysis (STA), derived from synthetic knowledge synthesis, to systematically review the existing literature on AI and PSM in medicine. Publications were harvested from the Scopus database using a comprehensive search string limited to the Medical subject area. The resulting corpus (N=433 documents) was analyzed using bibliometric tools (Bibliometrix and VOSViewer) to map the research landscape, identify thematic clusters based on author keywords, analyze collaboration patterns, and synthesize findings from highly prolific publications. Results: The field is young and rapidly accelerating, showing an exponential increase from 2020 to 2024. China and the USA dominate research production and citation impact. The symbiotic relationship is published in high-impact medical journals and health informatics journals. STA identified four main thematic clusters: Prediction, Cancer Management, Diagnosing, and Deep Learning. AI and PSM are combined in two primary ways: AI used in PSM and PSM used in AI. Conclusion: The symbiotic association between AI and PSM is a global and rapidly developing trend in medical research, driven by major international contributors. This convergence is enhancing methodological rigor in observational studies, primarily by improving prediction models and refining causal inference in complex areas like cardiovascular disease, cancer, and diagnostics.