Search Results (7,403)

The growing demand for sustainable and affordable housing requires construction systems that reduce material consumption, embodied environmental impacts, construction waste, and life-cycle costs while retaining the value of building components across multiple use cycles. This study presents a systematic scoping review and evidence map of circular economy strategies and reuse readiness in modular sustainable and affordable housing. A structured literature search covering publications from 2016 to 2026 identified 283 records, of which 49 studies were included following screening and eligibility assessment. The included studies were coded according to circular economy strategies within the 0R–10R framework, building life-cycle stages, modular technology typologies, sustainability and affordability indicators, and reuse-readiness attributes. The evidence map shows that the literature remains predominantly focused on life-cycle assessment, carbon emissions, waste reduction, reuse, and recycling. In contrast, early-stage circular strategies, including refuse, rethink, reduce, and redesign, as well as repair, refurbishment, remanufacturing, social value, life-cycle affordability, and operational reuse readiness, remain insufficiently examined. The findings further demonstrate that prefabrication or modularity alone does not ensure circularity. High reuse readiness depends on demountable connections, non-destructive disassembly, component standardization, durability, repairability, material traceability, reverse logistics, and credible second-life scenarios. Based on the mapped evidence, this study develops an evidence-informed Integrated Digital Circular Modular Ecosystem pathway that connects circular design, material passports, reuse-readiness assessment, BIM and digital twins, component grading, reverse logistics, circular marketplaces, and life-cycle performance evaluation. By integrating reuse readiness, life-cycle affordability, and digital traceability within a single evidence-informed pathway, the study provides a structured basis for researchers, policymakers, and industry stakeholders to evaluate and advance modular housing beyond construction efficiency toward long-term circularity and component value retention. As a conceptual synthesis, the pathway requires further validation through expert assessment and application to actual modular housing projects. Full article

Memory-safety vulnerabilities remain the dominant class of security defects in C/C++ software underpinning Internet infrastructure. Rust offers a structural solution through its ownership system, yet migrating existing codebases remains costly. This paper defines a structured methodology for LLM-assisted porting of security-critical C libraries to idiomatic Rust and applies it to cJSON (∼3200 LOC, 14 CVEs). A manual expert porting serves as the baseline; five LLMs (Claude Opus 4.6, Gemini 3 Pro, GPT-5.4, Kimi K2.7-Code, and Qwen3.5-27B) produce independent portings in agentic mode. Verification uses an end-to-end pipeline: CVE-specific tests, coverage-guided and differential fuzzing (>1.7 billion executions), Miri analysis, and comparative benchmarking. All six portings eliminate all in-scope CVE classes by construction, with zero unsafe blocks and zero memory-safety crashes. In this case study, structural safety holds consistently across all five evaluated models and across all five Kimi repetitions, whereas code quality varies widely (0–9 residual bugs). Because only Kimi was repeated ($N=5$), its variance bounds run-to-run noise at the 95% confidence level, against which some but not all between-model differences are distinguishable from chance; a single porting attempt costs approximately $3 in API usage. Differential fuzzing reveals complementary bugs in the manual and LLM portings, supporting a hybrid workflow, and we translate these findings into concrete practical guidance for teams planning a similar migration. These results are scoped to one compact, single-threaded C library. The entire codebase and evaluation pipeline are publicly released. Full article

The Future of Jobs Report 2025 identifies technological change, geoeconomic fragmentation, demographic shifts, and the green transition as compounding forces restructuring global labour markets. Rather than merely adding new risks, these pressures erode the foundational assumptions upon which labour rights rest, widening the gap between workers’ lived conditions and the reach of existing legal frameworks across international, regional, national, and platform levels. Algorithmic management serves as the focal case through which these pressures converge. In platform and conventional workplaces alike, technical systems now allocate tasks, price labour, rank workers, enforce sanctions, and determine classification, cutting across traditional boundaries separating labour law, private law, data protection, and AI regulation. Two analytical concepts organise the argument. Mediated autonomy denotes a condition in which workers retain nominal choice, yet that choice is structured in advance by systems governing incentives, visibility, risk, and legal categorisation. Algorithmic subordination captures the resulting form of control: indirect, informationally asymmetric, and legally dispersed dependence on data flows, automated rankings, and code-driven decisions. Drawing on the philosophy of technology and classificatory looping theory, the article demonstrates that legal and technical categories do not merely describe workers; they actively constitute the field within which workers bear risk and assert entitlements. Biometric monitoring and affective computing intensify this dynamic by extending managerial authority to bodily signals and inferred emotional states. China’s regulatory approach to non-standard employment is examined as a case study and compared with the EU’s status-and-algorithm model, situating both within broader patterns of global regulatory divergence. The article concludes that effective labour protection must move beyond employment-status classification toward control-sensitive and technology-sensitive frameworks, encompassing transparency obligations, contestation rights, human review mechanisms, fair remuneration, data minimisation for biometric and affective information, and meaningful worker participation in platform governance. Full article

Background/Objectives: Substance use disorders are common in the prison population and are associated with relapse and criminal recidivism, yet qualitative evidence on recovery capital and family support within Eastern European penitentiary systems remains limited. This study explored how incarcerated men with a history of substance use perceive initiation, withdrawal, recovery, and the role of family support as a recovery capital resource. Methods: A qualitative descriptive study was conducted in a Romanian penitentiary with 50 definitively sentenced men with self-reported substance use history, selected through purposive sampling. Data were collected through semi-structured interviews, without audio recording due to institutional security regulations, and reconstructed in detailed written notes validated through member checking, then analyzed using inductive thematic analysis via consensus-based double coding (initially coded by the first researcher and reviewed by a second researcher, with discrepancies resolved through discussion), until thematic saturation was reached. Results: Six themes were identified: initiation of use influenced by peer groups, socioeconomic vulnerability, and criminogenic environments; effects initially perceived as beneficial, followed by progressive deterioration; severe withdrawal and denial of dependence; cognitive mechanisms sustaining use, including externalization of responsibility; the dual role of the family as both a risk and a protective factor; and a recovery process marked by ambivalence toward abstinence, in which family support, employment, and prosocial relationships were central. Abstinence during incarceration was frequently perceived as maintained under institutional constraint rather than as internally motivated recovery. Conclusions: In this single-prison sample, recovery extended beyond abstinence, suggesting the need to strengthen personal, social, and family recovery capital. The findings, which require confirmation in larger samples, support exploring integrated prison–community continuum-of-care interventions centered on addiction treatment, family involvement, and post-release support. Full article

Sexual violence produces enduring and multidimensional harm, yet how legal systems compensate it varies in ways a comparison of remedies alone cannot capture. This article examines compensation for sexual violence under the Iranian Islamic Penal Code, structured around the classical categories of diyya, qisas, arsh, and taʿzir, alongside the reparation frameworks of international human rights, humanitarian, and criminal law. Adopting a doctrinal and comparative method, it treats compensation not as a remedy following a given injury but as a process through which a legal order constitutes the injury as an object it can recognize and value. The comparison is organized around five questions: what each system recognizes as injury, who holds the claim, how it is valued, what its categories exclude, and what separates promised from delivered redress. The two frameworks diverge not incidentally but in their underlying conceptions of harm. Yet neither provides a straightforward measure of the other: the international order is internally plural and marked by its own gap between promise and delivery, while Iran’s gendered valuations are contested within juristic reasoning and their distributive effects are mitigated, if incompletely, through state-funded supplementation. The article relocates access from delivery to the point at which a system defines what the injury of sexual violence is and what it is worth. Full article

Background/Objectives: Obtaining timely access to detailed clinical trial data is not always straightforward. Privacy requirements, governance processes, and study-specific eCRF configurations can delay access, particularly during study start-up, when teams need realistic data to develop and test validation rules, reporting pipelines, and centralized monitoring tools. Methods: We developed SYNDATA, a modular framework that generates synthetic clinical trial datasets conforming to a target electronic case report form (eCRF). The framework combines study metadata from the Medidata Rave Architect Loader Spreadsheet (ALS) with selected empirical patterns learned from a closely matched reference study. It constructs patient-specific timelines from the ALS visit matrix, generates module-specific records using Bayesian networks for selected categorical dependencies and density-based methods for numeric and temporal variables, and applies postprocessing for counters, dictionary coding, and conditional missingness. A configurable Noise Tool injects controlled and reproducible data defects, including timeline inconsistencies, visit-window violations, numeric threshold exceedances, randomization or eligibility conflicts, and structural collisions, to stress-test downstream validation logic. Results: Synthetic and source data were compared descriptively using Jensen–Shannon distance, Cramér’s V, and representative plots across selected domains. Several binary operational fields showed close descriptive agreement, whereas agreement was weaker for some more complex, multi-category safety- and medication-related variables. The evaluation covered a representative subset and does not establish uniform fidelity, formal statistical equivalence, or clinical validity across all generated domains. Conclusions: SYNDATA supports reproducible generation of ALS/eCRF-conformant datasets for operational validation, reporting development, and workflow testing before real trial data are available. Its demonstrated value is limited to the evaluated operational use case; fidelity is variable-specific, and more complex domains require further development and validation. Full article

Generative AI has advanced architectural and landscape layout generation, but regional gardens still lack formal spatial representations, controllable generation of intrinsic spatial prototypes, and domain-aligned evaluation methods. This study presents a theory-informed proof of concept for structured translation, conditional generation, and evaluation of Lingnan courtyard garden layouts. Based on classical Lingnan garden theory, 63 plans are encoded through five semantic layers: period style, spatial structure, organization mode, courtyard type, and constituent elements. The resulting multimodal dataset pairs normalized color-coded plans with structured prompts. A latent diffusion model fine-tuned using low-rank adaptation supports boundary-constrained generation. A multi-level evaluation framework combines spatial-prototype label ablation, expert relevance assessment, and element-proportion analysis. Empirical results show that structured spatial labels improve generation controllability: the structural-label hit rate reaches 78.8% (vs. 19.4% for element-only controls), and the organizational-label hit rate reaches 52.1% (vs. 13.6%). Structural control is stronger, whereas precise organizational relationships remain difficult to generate. This study establishes a traceable pathway from regional garden knowledge encoding to generative control and evaluation, provides an open and reusable dataset, and offers a methodological reference for the digital preservation of regional architectural and landscape heritage. Full article

Fraxinus mandshurica (Manchurian ash) is an ecologically and economically valuable hardwood tree native to Northeast Asia, yet its genomic resources remain limited. We assembled its complete chloroplast (cp) genome (155,559 bp) using hybrid PacBio and Illumina sequencing and performed comparative, phylogenetic, and evolutionary analyses. The cp genome exhibits a typical quadripartite structure encoding 132 gene copies, comprising 114 unique genes (80 protein-coding, 30 tRNA, and 4 rRNA genes), with 18 genes duplicated in the inverted repeat (IR) regions. Simple sequence repeat analysis revealed dominance of mononucleotide A/T repeats. Phylogenetic analysis of 53 complete cp genomes strongly supported the monophyly of Oleaceae and resolved F. mandshurica as sister to the North American F. nigra, consistent with previously proposed Miocene intercontinental dispersal scenarios between East Asia and North America. Most protein-coding genes were under strong purifying selection (Ka/Ks << 1), whereas petB, rpl2, and several ndh genes showed elevated Ka/Ks values that are suggestive of altered selective constraint but are based on very few substitutions and are therefore not, on their own, evidence of positive selection. Nucleotide diversity (Pi) analysis identified 15 hypervariable intergenic spacers (mean Pi = 0.067), among which trnM-CAU-rps14, ndhJ-ndhK, and petL-petG represent promising candidate barcode regions requiring further validation. This study provides a high-quality, fully annotated cp genome of F. mandshurica and a valuable genomic resource for future phylogenetic, population genetic, and conservation studies of this important genus. Full article

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments. Full article

Integrated management systems (IMSs) are currently experiencing a radical revolution triggered by digital technologies, sustainability issues, and human-centric management philosophies of Industry X.0. Even though there have been many advances in research about IMSs, our understanding of the evolution process and digital revolution of IMSs is still far from being comprehensive. In this research, a combination of bibliometric analysis and qualitative content analysis is used to explore the evolution of IMSs published in the scientific literature from 2015 to 2026. A set of 103 peer-reviewed publications, obtained through searches in the Scopus, Web of Science, and ScienceDirect databases, was analyzed following the PRISMA 2020 guidelines; of these, 59 full-text papers were selected for qualitative analyses. This bibliometric study discloses the ongoing advancement of IMSs, with major characteristics highlighting the increasing relevance of digital transformation, sustainability, artificial intelligence, and human-oriented management. According to the results of the bibliometric analysis, two theoretical contributions are made in this paper. First, a Digital Enabler Classification (DEC) taxonomy is proposed to classify IMS configurations according to their dominant digital enablers and associated management characteristics, comprising six analytical categories (DEC-0 to DEC-5). Second, a conceptual framework is proposed to provide a possible evolutionary interpretation of IMS transformation from standards-driven integration toward connected, intelligent, sustainable, and human-centric governance systems. The DEC taxonomy and its quantitative distribution across the six classes are derived directly from criterion-based coding of the 59 full-text articles; the evolutionary reading of the framework is a conceptual interpretation of this cross-sectional evidence rather than a longitudinally validated developmental sequence. Together, the contributions presented offer a structured view of the digital transformation process of IMSs and provide a foundation of theory and practice for the development of further research. Full article

This exploratory study examines the implementation of a GenAI-supported curriculum redesign in a project-based multimedia computing course by comparing students’ experiences in Python-based and GenAI-supported production pathways in relation to creativity, applied digital competence, and perceived professional readiness. A mixed-methods, quasi-experimental design was employed to compare two naturally occurring undergraduate cohorts at a Gulf-region university. Data was collected from 29 students through post-course surveys, open-ended reflections, project reports, student-generated artefacts, and instructional observations. Quantitative data were analyzed using nonparametric statistical tests and effect size reporting, while qualitative evidence was examined through inductive thematic analysis supported by human-in-the-loop AI-assisted coding. The findings indicate that the GenAI-supported cohort reported significantly higher project satisfaction, perceived ease of use, and job relevance, with additional positive trends in creativity and professional output. Qualitative evidence and illustrative project examples further suggest that students experienced fewer technical barriers and greater opportunities for multimodal production, particularly in community-facing projects. Based on these findings, the study presents the INSPIRE framework, a seven-stage, practice-informed conceptual model for integrating GenAI into project-based computing education. Although limited by a small sample and single-institution context, the study provides exploratory comparative evidence and a practice-informed framework that offers actionable guidance for responsible and structured GenAI integration in higher education. Full article

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

The deployment of real-time three-dimensional (3D) visual perception, continuous spatial sensing, and graphics-intensive multimedia applications on embedded edge devices introduces substantial runtime memory pressure. During execution, these workloads continuously generate small-granularity memory pages, including image buffers, depth maps, rendering caches, intermediate feature tensors, and media buffers. To reduce memory footprint while preserving low processing overhead, this paper proposes a sequence-aware cascaded compression framework for runtime memory pages generated by visual and multimedia workloads. Instead of following the traditional restore-then-recompress path, the proposed framework directly reuses the intermediate sequence representation produced by LZ4. It parses LZ4 sequences into literal and structured stream sequences and applies stream-specific entropy coding using Huffman and Finite State Entropy (FSE) coding. By avoiding full-page restoration and repeated redundancy analysis, the framework preserves lossless reconstruction semantics while reducing compression overhead. Experimental results on real-world runtime memory pages show that the proposed framework reduces compression processing time by 42.10% on average and by up to 52.22%. It also reduces Central Processing Unit (CPU) time by 20.28% on average and peak memory usage by 29.92% on average. Additional validation on a RISC-V edge platform shows that the compression processing time is reduced by up to 64.36%. A 16 MB point-cloud memory sample derived from 3D scan data further shows a 33.16% reduction in compression time. Functional tests on more than 200K mixed-entropy memory pages demonstrate 100% lossless reconstruction and successful detection of corrupted compressed streams. Full article

Education on natural hazards is a non-structural component of Disaster Risk Reduction (DRR), but the evidence base spans curriculum studies, risk-perception research, educational interventions, geospatial approaches and analyses of education-system continuity. This systematic narrative review synthesizes 27 outcome-bearing studies published between 2013 and the partial year 2026. PRISMA 2020 was used as a reporting framework, while PRISMA-S informed a retrospective audit of the search documentation. A structured design-sensitive appraisal recorded evidence family, comparison or temporal structure, outcome directness, permitted inference and principal limitation. The studies were coded into six mutually exclusive primary axes: reviews and frameworks; curriculum and policy; knowledge and risk perception; educational interventions and active methodologies; GIS and geospatial technologies; and educational continuity and system resilience. The included literature suggests that locally situated problems, maps, simulations and inquiry can support knowledge, risk appraisal and preparedness intentions, although demonstrated effects on sustained performance or actual preparedness behavior remain limited. Cross-cutting gaps include weak longitudinal assessment, sparse attention to teacher professional development, limited treatment of indigenous or local knowledge, and no core study centered on learners with disabilities or special educational needs. The review defines critical territorial risk literacy as the capacity to interpret hazard, exposure, vulnerability, capacity and uncertainty through spatial evidence; evaluate their unequal territorial distribution; and translate that understanding into inclusive, proportionate preparedness and collective action. Full article

Energy management in battery-powered embedded Artificial Intelligence of Things (AIoT) nodes requires coordinated control of sensing, computation, and wireless communication. This structured review combines a documented search of Scopus, Web of Science Core Collection, and IEEE Xplore with screening of 548 unique records and full-text source-to-construct coding of 56 database-derived sources. The synthesis shows that acquisition, Tiny Machine Learning (TinyML) inference, communication, protocol-state overhead, adaptive sensing, and energy harvesting are commonly optimized separately, while field-validated joint control remains limited. The review develops a full-cycle energy model and cross-layer framework linking sensing, processing, payload representation, radio policy, and energy availability. Dimensionless break-even analysis shows that the admissible local-processing budget is bounded by fixed communication overhead and non-machine-learning edge costs. A targeted Long-Term Evolution for Machines (LTE-M) experiment comprising 40 complete transactions showed that payload length alone was insufficient to characterize communication energy; a state-aware model incorporating measured signaling duration achieved $R^2=0.980$ and predicted a temporally separated second session with 1.66% relative root mean square error (RMSE). Precision agriculture and precision apiculture illustrate modality- and network-dependent trade-offs. The findings support full-cycle benchmarking and context-aware orchestration rather than isolated optimization of inference or communication. Full article