Search Results (4,918)

Fragmented, sector-by-sector governance is poorly suited to cascading risks that couple climate, food, water, health, biodiversity, soils, energy, and environmental quality. This paper addresses the translation gap between integrative security–sustainability paradigms and the routine machinery of government, including planning, budgeting, procurement, and accountability. We develop the Spheres of Security (SOS) model as a conceptual–operational method organised around four overlapping spheres (biophysical, economic, social, and governance) and a repeatable cycle—diagnose → co-design → deliver → demonstrate → adapt—illustrated through two stylised vignettes (urban heat and health; watershed food–water–energy). SOS introduces an auditable overlap rule and an Overlap Score, supported by lean assurance, to make verified multi-sphere co-benefits commissionable and to surface trade-offs transparently within normal, accountable institutions (consistent with weak securitisation). We provide implementation guidance, including minimum institutional preconditions and staged entry-point options for jurisdictions where pooled budgets and full administrative integration are not immediately feasible. Full article

Metal Injection Molding (MIM) enables complex orthodontic-bracket geometries but can introduce surface and geometric discontinuities that act as initiation sites for crevice and pitting corrosion. The effect of acidic, kombucha-like exposure on corrosion and repassivation was assessed for MIM-316L brackets relative to a commercial comparator, and the coupling between surface quality (roughness and wettability) and localized damage at scanning electron microscopy (SEM)-identified hot-spots was examined. Kombucha was characterized by pH and titratable acidity. Surfaces were characterized by SEM, areal roughness metrics (R_a, S_a, S_z, and A2), and wettability by sessile-drop goniometry. Electrochemical behavior in artificial saliva was measured using open-circuit potential and cyclic potentiodynamic polarization (ASTM F2129/G59), and a qualitative magnetic check was included as a pragmatic quality-assurance screen. Exposure in kombucha reduced breakdown and repassivation potentials and increased passive current density, with the strongest effects co-localizing geometric discontinuities. Commercial brackets exhibited markedly poorer surface quality (notably higher S_z), amplifying acidity-driven susceptibility. These findings indicate that, under acidic challenges, surface/geometry quality dominates corrosion behavior; non-magnetic-phase compliance and simple chairside screening (e.g., magnet test), alongside tighter manufacturing controls on roughness and edge finish, should be incorporated into clinical and industrial quality assurance (QA). Full article

This review explores the transformative role of three-dimensional (3D) printing in radiation therapy for cancer treatment, emphasizing its potential to deliver patient-specific, cost-effective, and sustainable medical devices. The integration of 3D printing enables rapid fabrication of customized boluses, compensators, immobilization devices, and GRID collimators tailored to individual anatomical and clinical requirements. Comparative analysis reveals that additive manufacturing surpasses conventional machining in design flexibility, lead time reduction, and material efficiency, while offering significant cost savings and recyclability benefits. Case studies demonstrate that 3D-printed GRID collimators achieve comparable dosimetric performance to traditional devices, with peak-to-valley dose ratios optimized for spatially fractionated radiation therapy. Furthermore, emerging applications of artificial intelligence (AI) in conjunction with 3D printing promise automated treatment planning, generative device design, and real-time quality assurance, and are paving the way for adaptive and intelligent radiotherapy solutions. Regulatory considerations, including FDA guidelines for additive manufacturing, are discussed to ensure compliance and patient safety. Despite challenges such as material variability, workflow standardization, and large-scale clinical validation, evidence indicates that 3D printing significantly enhances therapeutic precision, reduces toxicity, and improves patient outcomes. This review underscores the synergy between 3D printing and AI-driven innovations as a cornerstone for next-generation radiation oncology, offering a roadmap for clinical adoption and future research. Full article

Generative Artificial Intelligence (GenAI) systems have introduced new classes of security incidents that traditional response frameworks were not designed to manage, ranging from model manipulation and data exfiltration to misinformation cascades and prompt-based privilege escalation. This study proposes a Practical Incident-Response Framework for Generative AI Systems (GenAI-IRF) that bridges established cybersecurity standards with emerging AI assurance principles. Using a Design Science Research (DSR) approach, this study identifies six recurrent incident archetypes and formalises a structured playbook aligned with NIST SP 800-61r3, NIST AI 600-1, MITRE ATLAS, and OWASP LLM Top-10. The artefact was evaluated in controlled scenarios using scenario-based simulations and expert reviews involving AI-security practitioners from academia, finance, and technology sectors. The results suggest high inter-rater reliability (κ = 0.88), strong usability (SUS = 86.4), and improved incident resolution times compared to baseline procedures. The findings demonstrate how traditional response models can be adapted to GenAI contexts using taxonomy-driven analysis, artefact-centred validation, and practitioner feedback. This framework provides a practical foundation for security teams seeking to operationalise AI incident response and contributes to the emerging body of work on trustworthy and resilient AI systems. Full article

In the era of ever-greater data production and collection, public health research is often limited by the scarcity of data. To improve this, we propose data sharing in the form of Data Spaces, which provide technical, business, and legal conditions for an easier and trustworthy data exchange for all the participants. The data must be described in a commonly understandable way, which can be assured by machine-readable ontologies. We compared the semantic interoperability technologies used in the European Data Spaces initiatives and adopted them in our use case of physical development in children and youth. We propose an ontology describing data from the Analysis of Children’s Development in Slovenia (ACDSi) study in the Resource Description Framework (RDF) format and a corresponding Next Generation Systems Interface-Linked Data (NGSI-LD) data model. For this purpose, we have developed a tool to generate an NGSI-LD data model using information from an ontology in RDF format. The tool builds on the declaration from the standard that the NGSI-LD information model follows the graph structure of RDF, so that such translation is feasible. The source RDF ontology is analyzed using the standardized SPARQL Protocol and RDF Query Language (SPARQL), specifically using Property Path queries. The NGSI-LD data model is generated from the definitions collected in the analysis. The translation has been verified on Smart Applications REFerence (SAREF) ontology SAREF4BLDG and its corresponding Smart Data Models (52 models at the time). The generated artifacts have been tested on a Context Broker reference implementation. The tool supports basic ontology structures, and for it to translate more complex structures, further development is needed. Full article

This study reviews the current applications of generative artificial intelligence (GenAI) in architectural design education using the PRISMA framework. It compares these applications across the different design stages, namely the pre-design, concept generation, design development, and design production, to identify the current state of evidence and conceptual discussions reported in the literature. The study also discusses the associated opportunities and challenges in this regard. The findings indicate that there is a growing interest in integrating GenAI into architectural design education, especially in the early design stages. However, one of the most significant gaps in this regard lies in the lack of empirical evidence on the long-term impacts of GenAI on students’ critical thinking and problem-solving skills. Future research is needed to explore the integration of GenAI throughout the entire design process, including design development and refinement. There is also a need to incorporate the relevant ethical guidelines for AI-generated content into academic quality assurance systems and to strengthen institutional preparedness through targeted training and policy development. Full article

By analyzing Food and Drug Administration (FDA) enforcement reports from 2004 to 2025, we can determine the incidence of microbial contamination in non-sterile and sterile drugs in the United States of America and, at the same time, compare the trends and patterns over a period of 21 years to determine the distribution and frequency of microbial contaminants. The most common microorganisms detected from 2019 to 2025 were the mold Aspergillus penicilloides, with 17 citations for sterile products, followed by 16 citations for non-sterile products of Burkholderia cepacia complex (BCC) bacteria. Analysis from the last 21 years revealed the dominant microbial contaminants belong to the BCC, reaching a maximum level between 2012 and 2019. Some of the previous microbial contaminants, such as Salmonella and Clostridium, decline in the 2019–2025 period, with no notifications issued. S. aureus and Pseudomonas contamination persisted through the years but at very low levels. Gram-negative bacteria contaminated non-sterile drugs more frequently than Gram-positive. A worrisome trend continued with unacceptable levels of enforcement reports not providing any information on the identity of the microbial contaminant. New species of Bacillus and Acetobacter nitrogenifigens were responsible for a significant increase in non-sterile drug recalls. The main driver for sterile product recalls over a 21-year period is the lack of assurance of sterility (LAS) where major failures in process design, control, and operational execution were not conducive to the control of microbial proliferation and destruction. Enforcement data analysis identified the problematic trends and patterns regarding microbial contamination of drugs, providing important information to optimize process control and provide a framework for optimizing risk mitigation. Although the 21-year landscape demonstrated that some microbial contaminants have been successfully mitigated, others remain resilient. The emergence of new contaminants highlights the evolving nature of microbial risk. The consistent problem with LAS is not only a major regulatory violation but also a potential catalyst for the next major healthcare-associated outbreak. Full article

Defect categorization is essential to product quality assurance during the production of magnetic random access memory (MRAM). Nevertheless, traditional defect detection techniques continue to face difficulties in large-scale deployments, such as a lack of labeled examples with complicated defect shapes, which results in inadequate identification accuracy. In order to overcome these problems, we create the MARMset dataset, which consists of 39,822 photos and covers 14 common defect types for MRAM defect detection and classification. Furthermore, we present a baseline framework (GAGBnet) for MRAM defect classification, including a global attention module (GAM) and an attention-guided block (AGB). Firstly, the GAM is introduced to enhance the model’s feature extraction capability. Secondly, inspired by the feature enhancement strategy, the AGB is designed to incorporate an attention-guided mechanism during feature fusion to remove redundant information and focus on critical features. Finally, the experimental results show that the average accuracy rate of this method on the MARMset reaches 92.90%. In addition, we test on the NEU-CLS dataset to evaluate cross-dataset generalization, achieving an average accuracy of 98.60%. Full article

In the current context of climate change, special attention should be paid to assuring the security of food and fruits. Lemon trees struggle to keep their physiological traits stable in the context of all the cumulated challenges originating from climate stress. Therefore, our aim was to assess two seed priming methods’ long-term effects on some physiological parameters of young lemon trees. The relative chlorophyll content reveals that hydropriming shows 26% increases from E1 to E6, similar to the control, while osmopriming has a 31% higher value at the beginning and after three years. Leaf stomatal density has 80% lower values due to osmopriming compared to the control, while hydropriming show 15% lower values. Leaf area development was slightly similar between treatments, with more leaves being developed after hydropriming treatments. Guard cell width has similar values for priming, with both being with 40% higher than that of the control. Lemon trees grown after osmotic stress have the highest mass percentages of magnesium and potassium in the leaves. Hydropriming promotes calcium oxalate accumulation and a high mass percentage of phosphorus. The percentage allocation of carbon as dry matter is 32% for osmopriming, significantly higher than for the other treatments. The quantum yield of photosynthetic electron transport is the only significant photosynthetic parameter for osmoprimed lemon young trees. Physiological techniques successfully enhanced the overall growth of three-year-old lemon trees, especially osmopriming treatment. Full article

Automated quality assurance is essential for low-dose computed tomography (LDCT) lung screening, yet manual checks strain clinical workflows. We present a fully automated artificial intelligence tool that quantifies scan coverage and image noise in LDCT without user input. Lungs and the aorta are segmented to measure cranial/caudal over- and underscanning, and noise is computed as the standard deviation of Hounsfield units (HUs) within descending aortic blood, normalized to a 1 ${\mathrm{mm}}^3$ voxel. Performance was verified in a reader study of 98 LDCT scans from the National Lung Screening Trial (NLST), and then applied to 38,834 NLST scans reconstructed with a standard kernel. In the reader study, lung masks were rated ≥“Nearly Perfect” in 90.8% and aorta-blood masks in 96.9% of cases. Across 38,834 scans, mean overscanning distances were 31.21 mm caudally and 14.54 mm cranially; underscanning occurred in 4.36% (caudal) and 0.89% (cranial). The tool enables objective, large-scale monitoring of LDCT quality—reducing routine manual workload through exception-based human oversight, flagging protocol deviations, and supporting cross-center benchmarking—and may facilitate dose optimization by reducing systematic over- and underscanning. Full article

Road defect detection is essential for timely road damage repair and traffic safety assurance. However, existing object detection algorithms suffer from insufficient accuracy in detecting small road surface defects and are prone to missed detections and false alarms under complex lighting and background conditions. To address these challenges, this study proposes SDC-YOLOv8, an improved YOLOv8-based algorithm for road defect detection that employs attention-enhanced feature learning and adaptive feature reconstruction. The model incorporates three key innovations: (1) an SPPF-LSKA module that integrates Fast Spatial Pyramid Pooling with Large Separable Kernel Attention to enhance multi-scale feature representation and irregular defect modeling capabilities; (2) DySample dynamic upsampling that replaces conventional interpolation methods for adaptive feature reconstruction with reduced computational cost; and (3) a Coordinate Attention module strategically inserted to improve spatial localization accuracy under complex conditions. Comprehensive experiments on a public pothole dataset demonstrate that SDC-YOLOv8 achieves 78.0% mAP@0.5, 81.0% Precision, and 70.7% Recall while maintaining real-time performance at 85 FPS. Compared to the baseline YOLOv8n model, the proposed method improves mAP@0.5 by 2.0 percentage points, Precision by 3.3 percentage points, and Recall by 1.8 percentage points, yielding an F1 score of 75.5%. These results demonstrate that SDC-YOLOv8 effectively enhances small-target detection accuracy while preserving real-time processing capability, offering a practical and efficient solution for intelligent road defect detection applications. Full article

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems. Full article

This study examines the tension between “substance” and “form” in standardized sustainability reporting within an emerging market context. Using 21,964 firm-year observations from Chinese A-share listed companies (2018–2023), we investigate whether the adoption of the Global Reporting Initiative (GRI) framework enhances substantive Environmental, Social, and Governance (ESG) and creates firm value. While baseline regressions suggest a positive link between GRI and ESG performance, rigorously applying Propensity Score Matching (PSM) reveals a critical nuance: the effect of mere framework adoption attenuates after controlling for selection bias, whereas independent assurance remains a robust driver of substantive governance quality. Furthermore, mediation analysis using bootstrap resampling documents a distinct “Labeling Effect”: GRI adoption directly enhances market valuation (Tobin’s Q), yet the indirect path via ESG scores is statistically insignificant. This indicates that investors utilize GRI as a heuristic signal of legitimacy rather than pricing granular performance metrics. We also identify a “Valuation Latency”, where substantive ESG improvements significantly boost operational profitability (ROA) but are not yet fully incorporated into stock prices. Heterogeneity analysis shows these effects are stronger for non-state-owned enterprises (Non-SOEs), supporting the view that private firms leverage standardized reporting and verification to mitigate legitimacy deficits. These findings provide empirical evidence for regulators and investors to distinguish between the “label” of adoption and the “substance” of verification. Full article

A method for quality inspection of food packaging based on hybrid imaging and machine-learning techniques is presented. The proposed inspection system integrates thermal and visible-light imaging, enabling detection and classification of faults such as weak seals, creases and contamination. For the purpose of the study data acquisition is automated with the use of an industrial manipulator, ensuring repeatability and consistent positioning of samples. Using the acquired images, the temperature distribution in the sealing area and selected process parameters, a predictive model for burst-pressure testing was developed. The proposed workflow includes attribute selection, hyperparameter optimization and the application of regression algorithms. The proof-of-concept results demonstrate a strong alignment between predicted and measured values, as well as high model stability. The best-performing model, ElasticNet, achieved an R² of 0.815 and an MAE of 0.028 kgf/cm², confirming its potential for non-destructive quality control of packaging. Full article

This study examines whether environmental assurance and environmental management certifications are associated with subsequent environmental performance and reputational exposure in European Union listed firms. Using Refinitiv Eikon panel data for 441 firms (1773 firm-year observations) from 2017–2023, we analyze environmental pillar sub-scores (Emissions, Resource Use, and Innovation) and three intensity indicators (energy, pollution, and recycled waste intensity). We estimate firm fixed-effects models for performance outcomes and Firth’s logistic regression models for media-reported environmental controversies, using lagged assurance/certification indicators. Environmental assurance is consistently associated with higher environmental sub-scores and with lower energy and pollution intensity, alongside higher recycled waste intensity. In contrast, certification effects are weaker and more heterogeneous across intensity-based indicators. Regarding reputational exposure, assured firms show a higher likelihood of subsequent media-reported environmental controversies, which is consistent with heightened scrutiny and visibility rather than evidence of intent. These findings inform boards, assurance providers, investors, and policymakers seeking to strengthen the credibility and use of corporate environmental information. Full article