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Background: Quality assurance (QA) is a cornerstone of safe and effective postgraduate training in gynecological laparoscopic surgery, where clinical outcomes depend on procedural complexity, equipment sophistication, and the coordinated competence of an entire surgical team. This article is a narrative review of the conceptual, professional, and regulatory foundations of QA in healthcare education, complemented by a structured synthesis of the collective teaching and clinical experience of the author group. It is not an empirical study, and the sections that follow present a conceptual mapping supported by expert perspective rather than original outcome data. Methods: Sources were identified through targeted searches of PubMed/MEDLINE and Google Scholar, supplemented by hand-searching of policy and curriculum documents issued by professional bodies and by backward citation tracking, without date restriction. Approximately 480 records were screened at title and abstract level, 112 were assessed as full text, and 41 sources were retained against predefined inclusion and exclusion criteria, with five further sources added during peer review. In a second and separate step, the ten co-authors, who hold teaching and supervisory roles in gynecological laparoscopy across academic centers in Romania and Germany, each completed the same six-item written prompt on training provision, trainer development, trainee assessment, and institutional QA practice; responses were coded thematically by two authors independently and consolidated into explicit points of convergence and divergence. Results: We describe how QA principles, originally articulated in general quality-management and health-promotion literature, have been adapted by professional and regulatory bodies, including the European Board and College of Obstetrics and Gynecology (EBCOG), European Society for Gynecological Endoscopy (ESGE), American Society for Gastrointestinal Endoscopy (ASGE), and the UK General Medical Council, into concrete standards for training providers, trainers, and trainees. We outline the structures used to monitor and audit training quality, including Kirkpatrick’s four-level evaluation model and periodic institutional review cycles, alongside documentation and certification pathways such as the Gynecological Endoscopic Surgical Education and Assessment (GESEA) program, with governance and financing as cross-cutting determinants of sustainability. The author synthesis converged on a consistent gap between documented standards and their routine enforcement and diverged on whether external review and formal certification should become mandatory. Conclusions: The conceptual architecture of QA in gynecological laparoscopy is coherent and broadly fit for purpose, but we identified no study linking the implementation of a specific QA standard to measurable gains in trainee competency or patient outcomes in this field. Proposals for harmonized, outcome-oriented, and where appropriate, mandatory external review should therefore be read as reasoned positions awaiting prospective evaluation rather than as evidence-based recommendations; generating that evidence, with the trainee’s own perspective collected rather than inferred, is the principal task for future research. Full article

Scapholunate (SL) instability is the most common form of carpal instability, and if untreated, it may progress through dorsal intercalated segment instability to scapholunate advanced collapse. Contemporary understanding emphasizes a dual stabilizing system in which the SL interosseous ligament (SLIL) acts as the intrinsic primary restraint, reinforced by extrinsic “critical” secondary stabilizers; isolated SLIL division does not by itself produce static malalignment until these secondary restraints are also compromised. Against this background, wrist arthroscopy has become a widely used reference standard for diagnosis and grading, allowing for graded assessment through the Geissler and the more granular European Wrist Arthroscopy Society (EWAS) classifications, while dry arthroscopy has reduced fluid-related morbidity and facilitated combined arthroscopic and mini-open procedures. Treatment has correspondingly shifted from open reconstruction toward arthroscopic and arthroscopic-assisted repair, capsulodesis, and ligamentoplasty, which are intended to preserve native capsuloligamentous structures and may offer potential advantages in limiting capsular disruption and postoperative stiffness. This narrative review synthesizes the literature identified through a selective search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar (final search 13 June 2026). The evidence consists mainly of small observational series and technique reports. The review stages the injury by arthroscopic grade (EWAS/Geissler) and organizing techniques by the target ligament(s), from partial and dynamic injuries through complete, reducible, and advanced deformities. We align each technique with the corresponding pattern of intrinsic and extrinsic ligament injury to clarify indications, summarize reported outcomes, and propose a stage-based conceptual treatment framework rather than a validated, evidence-based guideline. Current evidence derives largely from small, heterogeneous case series with variable classifications and outcome measures, underscoring the need for standardized staging and comparative studies. Full article

Generative artificial intelligence (GenAI) is increasingly embedded in higher education, but the association between students’ efficacy beliefs for AI-supported learning and their self-perceived digital creative functioning remains under-specified. This cross-sectional study tested a domain-specific efficacy account and examined a broad, study-specific AI learning risk-awareness measure as an exploratory boundary condition. Survey data from 920 Chinese higher education students were analyzed using confirmatory factor analysis and regression-based conditional process analysis with 5000 bootstrap resamples. The four focal measures showed a statistically distinguishable four-factor structure, although digital creative self-efficacy and self-perceived digital creative functioning remained conceptually close. AI learning self-efficacy was positively associated with self-perceived digital creative functioning, with a smaller statistical indirect association through digital creative self-efficacy. At mean risk awareness, the model-implied indirect point estimate was 0.177, compared with a direct association of 0.612. Conditional indirect point estimates decreased modestly from 0.196 to 0.157 as risk awareness increased. The conventional index of moderated mediation was negative and small and is interpreted here only as a statistical index of change in the conditional indirect association. These findings are consistent with a domain-specific efficacy account while indicating that the indirect component was meaningful but non-dominant. Single-wave self-reports preclude causal inference and do not constitute evidence of objectively rated creativity. Full article

Journalism and media studies increasingly describe a communication environment in which news, explanation and public orientation are accessed through search engines, social feeds, notifications, short-form video, platform summaries, podcasts and conversational artificial intelligence. Existing accounts identify attention scarcity, information overload, platform power, news avoidance and AI disruption, yet they do not fully explain why some communicative practices become less sustainable while faster forms of orientation expand under the same conditions. This conceptual paper develops an integrative theoretical synthesis across media practice theory, deep mediatization, platform studies, digital journalism, digital reading research, cognitive load theory, acceleration studies and AI-mediated communication. It proposes latency mismatch as a middle-range mechanism of temporal selection. Latency mismatch occurs when the time a practice requires to generate stable understanding exceeds the continuity intervals that a media environment makes available, rewards or treats as reasonable, especially when lower-latency alternatives provide sufficient orientation. The concept clarifies how platformized journalism and media environments govern not only visibility and attention but also the durations through which information becomes meaningful, trustworthy and actionable. It reframes long-form news reading, investigative engagement and other high-latency practices as structurally vulnerable but socially necessary conditions of public knowledge. Full article

Construction supply chains are pivotal to circular economy transitions but remain structurally fragmented, limiting the scalability of resource-efficient solutions. At the same time, digital technologies and life cycle assessment are often deployed in isolation, constraining their ability to enable system-level circularity. Using a theory-building literature synthesis of 141 publications across circular economy, sustainable supply chain management, digitalization, and life cycle sustainability assessment, this study develops an integrated conceptual framework that explains how circular performance may be shaped by AI-enabled decision capabilities, lifecycle sustainability constraints operationalized through PESI-LCA, and system-level alignment conceptualized through DCAM. AI is conceptualized as a dynamic capability for prediction and optimization, while PESI-LCA is positioned as an operationalized LCSA-based constraint system that embeds environmental, social, and economic criteria into decision architectures. DCAM defines the alignment conditions required across digital infrastructure, circular strategies, business models, and institutional enablers. The framework advances a non-additive logic: circular outcomes depend on how sustainability constraints shape AI-driven decision-making and how alignment enables coordinated implementation across supply chains. A key theoretical contribution is the identification of structural distortion as a failure mode in which digital optimization reinforces linear resource flows. The study advances sustainable supply chain theory and offers testable propositions and governance implications for scaling circular construction systems. Full article

In this exploratory review and conceptual analysis, we examine cases and debates in the theoretical literature that highlight biodiversity and conservation decision-making contexts in which knowledge systems, and their interactions or lack thereof, are central concerns. A review of scientific articles indexed in the Web of Science reveals a notable trend in the conservation literature: the growing prominence of knowledge systems, particularly Indigenous knowledge systems. Framing knowledge as systems, we suggest, draws attention to both differences and similarities among scientific, Indigenous, and local forms of knowledge. Such discussions are increasingly common in the literature, including recent IPCC and IPBES reports. Against this backdrop, the present paper explores the potential role of multiple knowledge systems in identifying and, where possible, addressing knowledge gaps relevant to biodiversity conservation and environmental decision-making. The paper distinguishes between epistemic and non-epistemic motivations for including different knowledge systems and offers conceptual tools for clarifying both their internal structure and the relationships through which knowledge exchange occurs. Rather than advocating full integration, we argue that selective overlap and dialogical interaction may provide more practical pathways towards inclusive and evidence-informed decision-making. By focusing on how different knowledge systems can contribute to filling specific knowledge gaps, the paper seeks to advance a more nuanced understanding of their role in conservation policy and practice. Full article

Commercialisation of research outcomes depends not only on technological quality but also on the competences through which research organisations identify market opportunities, coordinate stakeholders and translate knowledge into sustainable value. This study identifies and classifies commercialisation competences in research network organisations and develops a time-structured framework for operationalising their contribution to sustainability-oriented innovation (SOI). A structured review of Scopus and Web of Science publications, using predefined relevance criteria, yielded 129 competence formulations. Competence extraction was conducted by one author, while two authors independently synthesised the formulations into 25 conceptually distinct competences: five cognitive, eight functional, four personal and eight meta-competences. The 25 competences were then mapped against the triple bottom line, dynamic capabilities and ESG perspectives through a consensus-based expert assessment. Ten competences were classified as core sustainability-oriented, twelve as enabling and three as neutral. Because this classification is interpretative rather than empirically validated, it is intended as a transparent analytical proposition for subsequent testing. Each competence was linked to an Activity–Process Performance–Outcome (A–S–W) structure based on leading, diagnostic and lagging indicators, producing 75 illustrative indicators. The proposed numerical thresholds are heuristic calibration examples rather than universal benchmarks. The framework connects competence activation with process quality and commercialisation outcomes while incorporating economic, environmental and social dimensions and provides a basis for competence development, performance monitoring and future empirical validation. Full article

Urban services increasingly depend on interconnected sensing, communication, computing, decision-support, and response functions, yet technical performance alone does not establish service effectiveness. This structured integrative review examines WSNs, WBANs, V2X systems, 5G-enabled edge–cloud infrastructures, and prospective 6G capabilities across health, mobility, environmental monitoring, energy, water, infrastructure safety, and climate resilience. A five-database search covering January 2018–March 2025 was supplemented by citation tracing and a documented gap-directed update with a final cutoff of 1 July 2026. The analytical corpus comprised 40 peer-reviewed studies, five deployment cases, and one scope-boundary case. An outcome-mediated perception–network–edge/cloud–decision–response framework enabled categorical comparison of heterogeneous evidence without pooling non-comparable measures. Attribution was classified as T1 (comparatively evaluated downstream outcome), T2 (technical or bounded operational outcome), or T3 (conceptual linkage): three studies were T1, 34 T2, and three T3. Ten studies supported target-level SDG alignment, whereas none reached indicator-level correspondence. Evidence remained concentrated at communication, processing, decision-support, and bounded operational endpoints, with limited assessment of response availability and disruption–recovery conditions. Among the deployment cases, only SFpark supported T1 interpretation. These findings characterize the selected corpus and identify a persistent gap between technical performance and comparative, longitudinal, and distributionally assessed urban-service outcomes. Full article

Conceptual metaphors structure abstract domains through concrete, perceptual, affective, or bodily experience, but they are accessed through metaphorical expressions. Controlled experimental work requires normed materials characterizing expressions, not only the conceptual mappings they instantiate. This is particularly relevant for European Portuguese, where research on metaphors lacks multidimensional normative resources for everyday metaphorical expressions. We introduce ME6D-PT, a database of 213 European Portuguese metaphorical expressions instantiating 20 conceptual metaphors and accompanied by English counterparts. Portuguese native speakers rated the expressions on six dimensions relevant to language processing: familiarity, concreteness, valence, arousal, embodiment, and transparency. Items were presented individually without context, organized in seven lists in a within-subject design. Aggregated item-level reliability ranged from moderate to excellent across dimensions. Descriptive results showed that the expressions were generally familiar and transparent while varying substantially in concreteness, valence, arousal, and embodiment. Further analyses showed strong positive associations among familiarity, transparency, and concreteness, whereas embodiment was weakly related to experiential and semantic dimensions. Exploratory quadratic models revealed nonlinear patterns involving valence, including the expected U-shaped association with arousal, and a decelerating positive association between transparency and familiarity. ME6D-PT provides a controlled resource for stimulus selection and research on metaphors, abstract language, embodiment, and future cross-linguistic research. Full article

Libraries are increasingly recognized as strategic institutions that support innovation, creativity, and knowledge-based economic development. This study presents a narrative and conceptual review of scholarship on the role of libraries in supporting innovation and creative industries. Using a purposive rather than exhaustive search, the review examines a corpus of 26 peer-reviewed studies published between 2010 and 2025. Across the reviewed studies, libraries are represented as increasingly active knowledge institutions supporting innovation through technological infrastructure, digital services, makerspaces, collaborative environments, entrepreneurship support, and community engagement. Within this purposively selected corpus, public-library contexts and qualitative or case-based approaches are substantially represented, while fewer included studies address academic-library contributions to entrepreneurship, research commercialization, and knowledge transfer. These patterns describe the reviewed set and are not intended as prevalence estimates for the wider literature. The principal contribution is a structured synthesis and organization of existing knowledge: the review identifies recurring themes, clarifies relationships among previously separated strands of scholarship, highlights unresolved questions, and develops a future research agenda. An integrative library-specific framework is offered as a conceptual organizing device that brings these strands into a common input–process–outcome structure; it is not presented as a novel or empirically validated theory. Full article

As sports content platforms increasingly occupy a central role in consumers’ media consumption and fitness-related activities, understanding the factors that encourage sustained user engagement has become an important research concern. Although corporate social responsibility (CSR) has been widely recognized as a strategic tool for enhancing consumer perceptions and organizational reputation, prior research has focused primarily on traditional sports organizations and general commercial settings, with limited attention given to its influence within digitally mediated sports content platform environments. Therefore, this study investigates the relationships among CSR, empathy, attitude, and usage intention in the context of sports content platforms. Data were collected in Republic of Korea from 225 university students who regularly use sports content platforms. Structural equation modeling was employed to test the proposed conceptual framework. The findings reveal that CSR positively influences empathy and attitude, while empathy positively influences attitude. In addition, attitude is positively associated with usage intention. However, CSR does not directly influence usage intention, and empathy does not exhibit a direct relationship with usage intention. These results suggest that users’ continued engagement with sports content platforms is shaped more by evaluative attitudes than by direct emotional responses or perceptions of social responsibility alone. The study contributes to the literature by extending CSR research beyond traditional sports organizations into digitally transformed sports content platform environments and by identifying the psychological mechanisms through which CSR shapes user behavior in digital sports media environments. It also offers practical guidance for platform operators seeking to strengthen long-term user relationships through socially responsible initiatives. Full article

Big Data Analytics (BDA) has evolved from a predominantly technical batch function into a socio-technical capability integrating cloud-native platforms, stream processing, Lakehouse architecture, machine learning operations (MLOps), visualization, governance, and managerial judgment. This paper proposes an integrated BDA decision-making framework developed through a structured conceptual synthesis of research on data platforms, analytical capabilities, decision processes, organizational readiness, technology adoption, governance, and responsible artificial intelligence. The framework comprises seven interconnected stages: data sources, ingestion and integration, storage and platform, processing, analytics and artificial intelligence, visualization and interpretation, and decision, action, and learning. Governance, human oversight, organizational readiness, task characteristics, and continuous feedback influence all stages. Key implementation requirements include data quality, interoperability, security, privacy, scalability, cost, explainability, bias, skills, and sustainability. The proposed configurable reference architecture links technical integration, task–analytics fit, governance assurance, human judgment, and organizational readiness with decision quality and organizational outcomes. Organizational size and maturity, sectoral risk, decision criticality, technological context, and regulatory environment are defined as boundary conditions for future empirical validation. Full article

The city of Skopje faces persistent inefficiencies in waste management, characterised by rising waste generation and limited progress in sustainable treatment solutions. This paper addresses the need for a structured approach to identify barriers to implementing Waste-to-Energy (WtE) technology, focusing on incineration-based energy recovery as a potential option for improving waste management while generating energy. Interpretive Structural Modelling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) were applied to six categories of implementation challenges: operational, technical, financial, regulatory, social, and environmental. ISM identified hierarchical relationships within each category, while MICMAC classified challenges according to their driving and dependence powers. The results revealed distinct structural patterns across categories, with underlying barriers including inadequate waste separation and collection, lack of integrated waste information, competitive renewable energy prices, regulatory preferences for waste reuse, recycling and reduction over WtE, institutional distrust, and combustion residue management. The findings reveal interconnected barriers with distinct structural roles, while conceptual mitigation measures and relevant SDG linkages are proposed based on the identified barriers and literature. The proposed ISM-MICMAC framework provides a structured approach for analysing WtE implementation barriers and can be adapted to other contexts following appropriate contextual and quantitative assessment. Full article

To investigate the potential of generative artificial intelligence (Gen AI) in sustainable engineering education and laboratory instruction, this study used the direct shear test in soil mechanics as a case study. A total of 112 third-year undergraduate students majoring in hydraulic engineering were assigned to traditional and AI-assisted groups. Their performance in experimental operation, data processing, report writing, presentation of results and problem solving was compared using grade statistics, classroom observations and interview data. The results showed that the AI group generally outperformed the traditional group in Experimental operation, data analysis, report completeness, presentation and defense, and overall scores. The analysis showed that Gen AI can function as a form of cognitive scaffolding in conceptual explanation, data processing, report structuring, and error analysis. Specifically, it can provide stage-specific prompts for understanding, procedural support, and feedback during the learning process, thereby helping students complete experimental learning tasks. However, the qualitative corpus included documented cases of overreliance on Gen AI, including uncritical acceptance of generated outputs and alteration of discrepant data without adequate verification; these behaviours were not tabulated at the pair level, so their prevalence cannot be estimated. These findings suggest that the integration of Gen AI into soil mechanics laboratory instruction should be grounded in teacher guidance, disciplinary knowledge support, data verification awareness, and standardized tool-use practices. These findings also offer implications for the responsible use of Gen AI in sustainable engineering education and for the development of students’ AI literacy, awareness of data authenticity, and responsible technology use competencies. Full article

Despite advances in climate modelling, observation systems, and risk assessment, a persistent gap remains between climate knowledge production and its translation into effective adaptation action. Existing approaches often identify physical hazards and vulnerabilities but provide limited mechanisms for integrating socio-cultural values and place-based conditions into adaptation decision-making. This paper proposes a Climate Knowledge Interpretation Framework that conceptualizes adaptation as a structured process connecting climate information, vulnerability contexts, decision systems, and socio-spatial outcomes. The framework draws on established climate datasets, assessment approaches, and socio-spatial analysis to establish a basis for interpreting climate knowledge within local contexts. It argues that adaptation effectiveness depends not only on the availability of climate information but also on the capacity to incorporate intangible values, relational networks, and place-based knowledge into planning processes. The study proposes Value Maps as a conceptual decision-support tool for revealing socio-spatial infrastructures and relational anchors that contribute to adaptive capacity. The proposed framework provides a pathway for developing more inclusive, context-sensitive, and transferable climate adaptation strategies across diverse climatic contexts. Full article