Search Results (804)

This study examines how sustainability is discursively constructed, prioritized, and selectively institutionalized in the Turkish textile and apparel sector, conceptualizing sustainability reporting not only as an operational practice but also as a form of strategic communication. Drawing on the Triple Bottom Line framework, stakeholder theory, and institutional theory, it employs a theory-informed thematic content analysis of publicly available sustainability-related disclosures from 52 firms operating between 2020 and 2024. Rather than treating these documents as direct representations of organizational practices, the study approaches them as institutionalized communicative artifacts through which firms signal legitimacy, position themselves in relation to key external audiences, and justify particular sustainability orientations. The findings indicate a pronounced institutionalization of environmental narratives, while social and economic dimensions remain comparatively weakly embedded, particularly among small and domestically oriented firms. This imbalance appears to be structurally reproduced through reporting standards, market pressures, and certification regimes that selectively reward environmental compliance. The analysis further suggests that firms cluster into distinct sustainability profiles, reflecting differentiated pathways of institutional alignment rather than a uniform transition process. Theoretically, the study help explains why certain sustainability dimensions become prioritized over others. Empirically, it provides a comparative mapping of firm-level sustainability orientations in an emerging economy context. By conceptualizing sustainability as a signaling and positioning mechanism, the study contributes to marketing and sustainability literature by highlighting how corporate disclosures function as tools of legitimacy construction rather than neutral representations of practice. Full article

The increasing frequency of interior renovation and fit-out in office buildings raises concerns about indoor environmental quality, occupant health, and sustainability performance, yet existing certification systems remain largely design-stage or whole-building oriented and provide limited guidance for recurring renovation cycles. This study develops a health-oriented assessment framework for office interior renovation as a structured decision-support tool for practitioners and policymakers. We adopted an integrated approach combining a targeted literature review, expert consultation, the Fuzzy Delphi Method (FDM) for indicator screening, and the Analytic Hierarchy Process (AHP) for hierarchical weighting, based on an expert panel of 20 professionals spanning green building certification, architecture/interior design, MEP engineering, property/facility management, and energy/environmental consulting. Through consensus screening and weighting, four assessment dimensions and eighteen key indicators were identified and prioritized. Environmental quality was ranked highest (39.2%), followed by safety management (23.0%), functional usability (21.1%), and resource efficiency and circularity (16.7%). At the indicator level, indoor air quality management, Heating, Ventilation and Air Conditioning (HVAC) energy efficiency, space-friendly layout, preliminary assessment and planning, and thermal comfort emerged as the top priorities. Overall, the framework bridges the gap between certification-oriented evaluation and the operational realities of office renovation, enabling more consistent integration of health and sustainability considerations across renovation decision-making. Full article

Information security auditing plays an important role in information security management because it assesses the status of security mechanisms, risk management, and regulatory compliance. Most information security auditing methodologies have been based on binary assessments or checklists, an approach that is limited in the constant evolution of cyber threats. This paper presents a comparative analysis of the most recognized maturity level structures, such as the Capability Maturity Model Integration (CMMI), the Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Maturity Model Certification (CMMC), in order to identify the most suitable one for an innovative change in the auditing process to obtain a deeper and more detailed evaluation of security controls and, consequently, better decision-making. Full article

In this paper, we examine the existing artificial intelligence policy documents in aviation for the following three regions: the United States, the European Union, and China. These global economic leaders were selected for their dominance in economic activity; as a result, their influence on aviation policy direction is a logical assumption. Historically, the aviation industry has always been a first mover in adopting technological advancements. This early adoption offers valuable insights because of its stringent regulations and safety-critical procedures. Consequently, the aviation industry provides an optimal platform to address AI vulnerabilities through its stringent regulations, standardized processes, and certification of new technologies. Our research aims to compare AI regulations across these regions to guide other sectors in shaping effective policies. The findings of our comparative analysis show that there are vastly differing approaches to the application of AI regulations in the aviation sector, thus weakening desired prospects for global cooperation and worsening existing geopolitical tensions. Therefore, we propose a hybrid model approach as a way forward. Under this model, regions maintain their distinctive AI policies but collaborate on high-risk aviation applications through joint working groups, shared safety intelligence, or mutual recognition agreements. This would preserve incentives for innovation but also reduce regulatory friction. Full article

ISO 22002-100:2025 introduces stringent and more technically explicit prerequisite programme (PRP) requirements for allergen management, food fraud mitigation, and the control of chemical and packaging-related contaminants across the food, feed, and packaging supply chain. This review examines how advanced chromatographic methods provide the analytical basis required to meet these requirements and to support alignment with GFSI-recognized certification schemes. Recent applications of liquid and gas chromatography coupled with mass spectrometry for allergen quantification, authenticity assessment, and the determination of packaging migrants, auxiliary chemical residues, lubricants, and indoor pest-control pesticides are presented to demonstrate their relevance as verification tools. Across these PRP-related controls, chromatographic methods enable trace-level detection, structural specificity, and reproducible measurement performance, thereby shifting PRP compliance from a documentation-based activity to a process verified through measurable analytical evidence. The review highlights significant progress in method development and simultaneous multi-target analytical approaches while also identifying remaining challenges related to matrix-appropriate validation, harmonization, and analytical coverage for chemical contamination, which is now formally defined as a measurable PRP requirement under ISO 22002-100:2025. Overall, the findings demonstrate that chromatographic analysis has become essential to demonstrating PRP effectiveness under ISO 22002-100:2025, supporting the broader shift toward evidence-based, scientifically robust food safety assurance. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

This paper reviews the fracture mechanics parameters associated with the variability in the crack growth curves associated with forty-two different tests that range from additively manufactured (AM) steels to cold spray additively manufactured (CSAM) 316L steel. As a result of this review, it is found that, to a first approximation, the effects of different building processes and R-ratios on the relationship between ΔK and the crack growth rate (da/dN) can be captured by allowing for changes in the fatigue threshold and the apparent cyclic toughness in the Schwalbe crack driving force (Δκ). Whilst this observation, when taken in conjunction with similar findings for AM Ti-6Al-4V, Inconel 718, Inconel 625, and Boeing Space Intelligence and Weapon Systems (BSI&WS) laser powder bed (LPBF)-built Scalmalloy^®, as well as for a range of CSAM pure metals, go a long way in making a point; it is NOT a mathematical proof. It is merely empirical evidence. As a result, this review highlights that for AM and CSAM materials, it is advisable to plot the crack growth rate (da/dN) against both ΔK and Δκ. The observation that, for the AM and CSAM steels examined in this study, the da/dN versus Δκ curves are similar, when coupled with similar observation for a range of other AM materials, supports a prior study that suggested using fracture toughness measurements in conjunction with the flight load spectrum and the operational life requirement to guide the choice of the building process for AM Ti-6Al-4V parts. The observations outlined in this study, when taken together with related findings given in the open literature for AM Ti-6Al-4V, AM Inconel 718, AM Inconel 625, and BSI&WS LPFB-built Scalmalloy^®, as well as for a range of CSAM-built pure metals, have implications for the implementation and certification of limited-life AM parts. Full article

Quantum Key Distribution (QKD) is a process to establish a symmetric key between two parties using the principles of quantum mechanics. Currently, commercial QKD systems are still expensive, they require specific infrastructure, and they are impractical for deployment in portable or resource-constrained devices. In this article, we introduce the Butterfly Protocol (and its extended version) that enables QKD to be offered as a service to non-QKD-capable (portable or IoT) devices. Our key contributions include (1) resilience to the compromise of any single classical link, (2) protection against malicious QKD users, (3) implicit mutual authentication between users without relying on large post-quantum certificates, and (4) the Double Butterfly extension, which secures communication even when the underlying QKD network cannot be fully trusted. We also demonstrate how to integrate the Butterfly Protocol into TLS 1.3 and provide its initial security analysis. We present preliminary performance results and discuss the main bottlenecks in the Butterfly Protocol implementation. We believe that our solution represents a practical step toward integrating QKD into classical networks and extending its use to portable devices. Full article

This systematic mapping review and bibliometric analysis examines Scopus-indexed research on computer vision, image processing, and deep learning applied to wood and timber materials and products. A rule-based Scopus search (TITLE-ABS-KEY, 9 December 2025), combining wood and timber terms with imaging and computer vision terminology, followed by duplicate removal and structured exclusions, retained 1019 papers (1983–2026, early access) covering surface inspection, internal imaging, species identification, processing operations (log-yard/sawmill/panels), automation, dimensional metrology, and image-based property/structure characterization. The papers were classified into nine application categories and three methodological classes using improved rule-based classification with weighted scoring and exclusion rules. Paper output continues to accelerate, with 63.7% of papers published since 2016; Wood Surface Quality Control dominates (48.3%), followed by 3D and Internal Wood Imaging (13.6%), Wood Microstructure and Characterization (10.1%), and Wood Species and Origin Identification (10.6%). Methodologically, classical computer vision prevails (73.6%). Deep learning accounts for 26.4% of the corpus overall and 48.8% of papers from 2023–2026 (early access), while classical computer vision remains prevalent (70.1%) across most categories; the dataset totals 11,961 citations (mean: 11.74 per paper). Validation on 97 papers showed 80.41% accuracy for methodological classification and 70.1% for application categories. We quantitatively map method evolution across the nine categories, introducing a tailored taxonomy and tracking the shift from classical vision to deep learning at the category level. The remaining gaps include dimensional measurement automation, warp detection, sawing optimization, and benchmark datasets, with future directions emphasizing Vision Transformers, multi-modal sensing, edge computing, and explainable AI for certification. Full article

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

The search for innovative solutions in the field of construction materials used in aircraft manufacturing has led to the development of composite materials, particularly CFRP polymer composites. Composite airframe components, which are required to have high strength, are joined using mechanical fasteners. Considering that the composite consists of a polymer matrix, which is a material susceptible to rheological phenomena occurring rapidly at elevated temperature, there is a high probability of significant changes in the strength and performance properties. Coupled thermal and mechanical loads on composite material joints occur in everyday aircraft operation. Experimental tests were conducted using a quasi-isotropic CFRP on an epoxy resin matrix with aerospace certification. The assessment of changes in the strength parameters of the material itself showed a decrease of approx. 40% in its short-term strength at 80 °C compared to the ambient temperature and a decrease in the load-bearing capacity of single-lap bolted joints of over 25%. Even more rapid changes were observed when assessing the fatigue life of the joints assessed at ambient and elevated temperature. In addition, the actual glass transition temperature of the resin was determined using the DSC technique. Analysis of the damage mechanisms showed that at 80 °C, the main degradation mechanisms of the material are accelerated creep processes of the CFRP and softening of the matrix, increasing its susceptibility to damage in the joint area. Full article

This study proposes a multi-dimensional, fuzzy logic-based decision-making framework to assess airport sustainability performance under uncertainty, addressing a notable gap in the literature. The proposed model integrates the Fuzzy Analytic Hierarchy Process (FAHP) to determine the weights of sustainability criteria and the Intuitionistic Fuzzy COPRAS (IF-COPRAS) method to evaluate airport alternatives. The assessment considers four main sustainability dimensions: environmental, economic, social, and technical/institutional. A case study involving five major airports in Türkiye reveals that environmental and economic indicators play a pivotal role in shaping sustainability performance. While Istanbul Airport (IST) demonstrated the highest performance across all scenarios, a comparison with Airport Carbon Accreditation (ACA) levels indicates that carbon-focused certification alone is insufficient to reflect the full spectrum of sustainability outcomes. This research presents a novel and robust evaluation framework, contributing to the limited body of fuzzy logic-based MCDM applications for airport sustainability in the Turkish context. The findings offer actionable strategic insights for policymakers and airport managers regarding investment prioritization, operational strategy reinforcement, and the alignment of airport development with long-term sustainability goals. The results are validated through rigorous sensitivity analyses, confirming the robustness of the model despite the focused expert panel. Full article

This research has successfully addressed the challenges attributed with SMS, including the fragmented data, heavy reliance on experience, and lack of life cycle integration. This study presents the development and validation of a novel sustainable material selection (SMS) model using Artificial Intelligence (AI). The proposed model structures the process around four core life cycle phases—design, construction, operation and maintenance, and end of life—and incorporates a dual-interface system. This includes a main credits interface for high-level tracking of 100 total credits to trace the dynamics of SMS in relation to energy efficiency, indoor air quality, site selection, and efficient use of water. Further, it includes a detailed credit interface for granular assessment of specific material properties. A key innovation is the formalization of closed-loop feedback mechanisms between phases, ensuring that practical insights from construction and operation inform earlier design choices. The model’s functionality is demonstrated through a proof of concept for SMS considering thermal properties, showcasing its ability to contextualize benchmarks by climate, map properties to building components via a weighted networking system, and rank materials using a comprehensive database sourced from the academic literature. Automated scoring aligns with green building certification tiers, with an integrated alert system flagging suboptimal performance. The proposed model was validated through a structured practitioner survey, and the collected responses were analysed using descriptive and inferential statistical analysis. The result presents a scalable quantitative AI-assisted decision-making support model for optimizing material selection across different project phases. This work paves the way for further research with additional assessment criteria and better integration of AI and Machine Learning for SMS. Full article

The textile sector provides essential goods, yet it remains environmentally and socially intensive, driven by high water use, pesticide dependent monocropping, chemical pollution during processing, and growing waste streams. This review examines credible pathways to sustainability by integrating emerging plant-based fibres from hemp, abaca, stinging nettle, and pineapple leaf fibre. These underutilised crops combine favourable agronomic profiles with competitive mechanical performance and are gaining momentum as the demand for demonstrably sustainable textiles increases. However, conventional fibre identification methods, including microscopy and spectroscopy, often lose reliability after wet processing and in blended fabrics, creating opportunities for mislabelling, greenwashing, and weak certification. We synthesise how advanced molecular approaches, including DNA fingerprinting, species-specific assays, and metagenomic tools, can support the authentication of fibre identity and provenance and enable linkage to Digital Product Passports. We also critically assess environmental Life Cycle Assessment (LCA) and social assessment frameworks, including S-LCA and SO-LCA, as complementary methodologies to quantify climate burden, water use, labour conditions, and supply chain risks. We argue that aligning fibre innovation with molecular traceability and harmonised life cycle evidence is essential to replace generic sustainability claims with verifiable metrics, strengthen policy and certification, and accelerate transparent, circular, and socially responsible textile value chains. Key research priorities include validated marker panels and reference libraries for non-cotton fibres, expanded region-specific LCA inventories and end-of-life scenarios, scalable fibre-to-fibre recycling routes, and practical operationalisation of SO-LCA across diverse enterprises. Full article

The rising usage of IoT devices in everyday life has formed smart cities that require the adoption of decentralized systems for a secure and transparent mechanism to manage asset exchange across automotive supply chains. Several existing Blockchain-based models built on public chains focus on traceability while overlooking scalability limits, transaction fees, conditional payment trust, or real-time delivery validation. We introduce DeFiTrustChain, a DeFi-enabled framework that combines free NFTs, escrow-based automation, and IoT verification within a Hyperledger Fabric network. It represents each vehicle using a unique NFT to capture the details of manufacturing and ownership, along with immutable asset verification. The payment release between stakeholders is governed by a dedicated escrow contract responsible for IoT-based delivery confirmation. The proposed framework ensures authenticated access and prevents identity misuse through integration of the Fabric Certificate Authority. The experimental results demonstrate the coherent and dependable execution of NFT creation, escrow enforcement, and IoT-triggered validation, with low local transaction processing time and consistent behavior across peers. Full article