Search Results (970)

This qualitative study examines how culturally responsive pedagogy (CRP) and transformative learning are fostered in higher education when structured reflection, dialogic engagement, and feedback are intentionally embedded in teacher education coursework. Drawing on data from two university courses—one undergraduate course for preservice teachers and one graduate course for in-service educators (n = 44)—the study explores how equity-focused instructional design supports development toward inclusive, globally informed practice. Data sources included student reflective writing, an anonymous pre- and post-semester survey aligned with InTASC dispositions, instructor reflexive journals, peer observation reports, and course feedback artifacts. Of the 44 enrolled participants, 39 completed the pre-survey and 19 completed the post-survey; survey results were analyzed descriptively at the group level because responses were anonymous and could not be matched across time. Analysis followed Braun and Clarke’s thematic analysis procedures, with trustworthiness strengthened through triangulation, peer debriefing, member checking with a subset of participants, and reflexive journaling. Findings revealed seven interconnected themes demonstrating how reflective writing, critical scholarship, multimedia exemplars, dialogic feedback, and iterative course design supported movement from awareness toward equity-oriented pedagogical praxis. Four overarching outcomes were especially salient: (a) expanded understandings of CRP as justice-oriented praxis; (b) increased capacity to identify and interrogate personal and systemic bias; (c) stronger connections between global and intercultural perspectives and locally grounded teaching commitments; and (d) reported pedagogical shifts toward more inclusive, equity-centered practice. Survey findings indicated a group-level shift from Agree toward Strongly Agree across equity-oriented dispositions, suggesting strengthened professional commitments while warranting cautious interpretation given unmatched responses and post-survey attrition. Comparative analysis also highlighted cohort-differentiated developmental trajectories, underscoring the importance of scaffolded, context-responsive approaches in equity-focused teacher education. Overall, the study demonstrates how intentional instructional design can position reflection as an ethical and professional stance that supports equity, inclusion, and global readiness across educator career stages. Full article

Background and Objectives: Artificial intelligence (AI) is increasingly integrated into healthcare management and pharmaceutical marketing workflows, yet determinants of AI adoption intention among non-clinical professionals remain under-studied in Central and Eastern Europe. This cross-sectional study quantified AI adoption intention (AAI) across three professional groups and examined its organizational, cognitive, attitudinal, and regulatory correlates. Methods: We surveyed 127 Romanian professionals (43 hospital administrators, 42 pharmaceutical marketing professionals, 42 community pharmacy managers) using a 46-item structured instrument. The instrument combined items adapted from UTAUT/TAM and organizational-readiness measures with study-specific AI-marketing, AI-literacy, and regulatory-literacy items; Analyses included ANOVA with Tukey HSD, Spearman correlations, age-adjusted OLS regression with HC3 robust standard errors, bootstrap indirect-effect analysis, moderation, exploratory k-means clustering, and exploratory logistic/ROC analysis. Results: AAI differed across groups: pharmaceutical marketing 4.33 ± 0.50, hospital administrators 3.39 ± 0.47, and pharmacy managers 2.88 ± 0.54; all pairwise Tukey contrasts p < 0.001. In the multivariable model (R² = 0.833)—interpreted cautiously because conceptually related adoption constructs may overlap despite acceptable collinearity diagnostics—perceived usefulness, organizational readiness, and perceived ease of use were the strongest associated factors, while data governance concern was the main negative correlate. Perceived usefulness statistically accounted for 61.7% of the AI literacy–AAI indirect association, and regulatory literacy moderated the AI literacy–AAI association. An exploratory age-adjusted logistic model showed high within-sample discrimination for top-tertile AAI but should be interpreted as convergent validity among survey constructs rather than as a validated screening tool. Conclusions: AI adoption intention in Romanian medical management and pharmaceutical marketing is associated mainly with perceived usefulness and organizational readiness, tempered by data governance concern and regulatory knowledge. Longitudinal, multi-site, real-world implementation studies with external validation are needed. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Against the backdrop of accelerating digitalization and intelligent transformation, intelligent manufacturing has emerged as a key driver of green transition in manufacturing. However, evidence on its effects and the mechanisms underlying corporate green innovation remains limited. Using panel data of Chinese A-share manufacturing firms from 2011 to 2023, this study exploits the pilot policy of intelligent manufacturing as a quasi-natural experiment and employs a difference-in-differences (DID) approach. Results indicate that intelligent manufacturing significantly enhances firms’ green innovation, with robust evidence across multiple checks. Mechanism analysis shows that this effect operates through an integrated dynamic capability channel, whereby firms strengthen their adaptive capability, absorptive capability for green knowledge and digital technologies, and innovation capability through technological integration, thereby improving green innovation. Moreover, intellectual property protection strengthens this mechanism by increasing innovation returns and enhancing the capability-to-innovation conversion efficiency. Heterogeneity results suggest stronger effects in non-high-tech firms, non–heavily polluting industries, and technology-intensive firms, reflecting differences in digital readiness and resource reconfiguration capacity. Overall, this study provides causal evidence on the green effects of intelligent manufacturing, clarifies internal mechanisms, and highlights institutional and firm-level heterogeneity, with implications for digital-driven green transformation and policy design. Full article

Non-invasive blood glucose estimation from exhaled breath has been proposed as a painless alternative to repeated capillary measurements; however, performance evaluation remains challenging in small-sample settings. This study investigates the estimation of blood glucose from human breath using volatile organic compound (VOC) signals acquired with an electronic nose. Responses from three metal-oxide sensor channels sensitive to CO, alcohol, and acetone were collected from 58 individuals, with one measurement per subject, and analyzed using strictly patient-level five-fold cross-validation, in which test folds comprised only real subjects. Two experimental factors were examined. First, model performance was evaluated with and without an additional interpretable alcohol–acetone log-ratio capturing relative variation between compounds. Second, model training was performed using either real data only or fold-wise tabular synthetic augmentation generated via a Gaussian copula fitted exclusively on training subjects, while evaluation remained strictly real-only. Under real-only training, classical machine learning models achieved the lowest prediction errors (approximately 6–7 mg/dL), whereas under synthetic augmentation FTTransformer was the best-performing deep learning model. This findings should be understood as a constrained proof-of-concept analysis rather than as evidence of diagnostic capability or clinical readiness. Full article

Greenhouse gas (GHG) emissions from transport represent a significant environmental challenge, increasing the need for standardized calculation and reporting methodologies. This study aims to analyze and compare the approaches to GHG emissions calculation under EN 16258 and ISO 14083, for road transport services, and to discuss implementation challenges related to the transition to the new standard in the Slovak transport sector. The research is based on a case study of a model road freight transport route, in which emissions are calculated using both standards and selected emission calculators, and the results are compared. The results indicate that both methodologies yield comparable total emission values, with discrepancies arising mainly from the structure of emission factors and the inclusion of indirect emissions. ISO 14083 demonstrates a more comprehensive and detailed approach, particularly in the consideration of energy supply processes. The analysis also reveals discrepancies between emission calculators due to differences in input data, emission factor databases, and modeling approaches. The findings suggest that although awareness of ISO 14083 is increasing, its wider implementation is limited by data availability, methodological complexity, and varying levels of sector readiness. Full article

The transition to a circular economy (CE) represents a complex socio-technical evolution, requiring synchronized policy frameworks and strategic capital reallocation. Adopting a systems-thinking lens, this study combines bibliometric network mapping with exploratory econometric modelling, to examine the associations between five core policy instruments and tangible circular investments (INV_CE) across the EU-27. Bibliometric analysis identifies the “firm” and “supply chain” as central functional hubs within the CE knowledge system, acting as primary mediators for capital flows. Econometric results indicate that Tradable Permits (TPOs) and an integrated Policy Integration Index (PII), comprising subsidies and energy-based taxes, show the strongest statistical association with circular investment patterns (p ≤ 0.001). However, patterns of structural disparity emerge between OECD and non-OECD Member States (p = 0.014), where the latter often exhibit a more rigid, tax-centric approach. Spearman correlations point toward institutional maturity, specifically government effectiveness (r_s = 0.48) and eco-innovation capacity, as a potential systemic gateway for investment absorption. Furthermore, a structural decoupling appears between voluntary approaches (VAs) and governance capacity in emerging systems, suggesting that such instruments may be less effective without “institutional readiness.” The findings suggest that circular transition is path-dependent and congruent with the co-evolution of policy and institutional regimes. To bridge the investment gap, the study highlights the need for systemic interventions that move beyond “one-size-fits-all” regulations toward targeted strategies that strengthen the institutional and data reporting infrastructures of circular systems. Full article

This study investigates how regulatory pressure and organizational capabilities influence innovation-enabled compliance and supply chain performance in Thailand’s plastic recycling sector. Drawing on institutional theory, the resource-based view, and dynamic capability perspectives, the study develops and empirically tests a conceptual model using partial least squares structural equation modeling (PLS-SEM). Data were collected from 300 respondents across 20 plastic recycling facilities in the Bangkok Metropolitan Region. The results show that Digital Innovation Capability (DIC) is the strongest predictor of legal compliance behavior (LCB), followed by Organizational Regulatory Readiness (ORR), Regulatory Enforcement Intensity (REI), and Compliance Process Maturity (CPM). In turn, LCB significantly enhances supply chain resilience (SCR). The findings further indicate that REI exerts both direct and indirect effects on SCR through LCB. Although REI demonstrates a significant direct effect on SCR, the indirect effect through LCB is comparatively weaker than that of Digital Innovation Capability (DIC). Nevertheless, the mediation effect remains supported based on bootstrapped confidence interval analysis. These findings suggest that regulatory pressure alone may encourage compliance at a formal level, but sustainable operational performance ultimately depends on the development of internal organizational and technological capabilities. Mediation analysis further confirms that LCB serves as a key mechanism linking organizational and technological capabilities to supply chain performance. Overall, the findings position compliance as an innovation-enabled and capability-driven mechanism that supports digital transformation, operational resilience, and sustainability within the circular economy. Full article

The pet food sector has progressively evolved over the past decade from conventional nutrition toward functionally targeted and sustainability-oriented systems that are increasingly parallel developments in human health. While numerous reviews have examined individual aspects of pet food innovation, an integrated perspective linking scientific research, patent activity, and global market dynamics remains limited. This review addresses this gap by systematically synthesizing peer-reviewed literature, patent landscapes, and product launch data to identify key drivers and bottlenecks shaping contemporary pet food innovation. The analysis highlights a strong concentration of research and patent activity in health-oriented functional formulations, particularly those targeting gastrointestinal health, immune modulation, and age-related conditions, while postbiotics, precision nutrition, and digital tools remain comparatively underdeveloped. Sustainability-driven ingredients and alternative proteins show growing momentum but face persistent challenges related to scalability, regulation, and sensory acceptance. The commercial success of functional pet foods depends on translating scientific findings into stable, manufacturable, and evidence-supported products. Future innovation will therefore be shaped by technologies that connect biological function with process feasibility and market readiness. This review concludes that future progress in pet food innovation will depend on integrated frameworks that align biological efficacy, technological feasibility, and market viability, thereby bridging the gap between scientific advancement and commercial implementation. Full article

Background: Digital health technologies are increasingly integral to healthcare delivery worldwide; however, successful adoption depends on more than technological availability. In nursing, readiness is particularly important because digital systems increasingly shape documentation, communication, decision support, and care delivery. Within the context of Saudi Arabia’s healthcare transformation, doctoral nursing students are positioned as future educators, clinicians, and leaders whose perceptions can provide insight into digital health readiness and preparation. Aim: This study aimed to explore doctoral nursing students’ perceptions of their readiness to adopt digital health technologies in Saudi Arabia, guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods: This exploratory, qualitative, descriptive study recruited 9 doctoral nursing students from a public university in Saudi Arabia using purposive sampling based on predefined eligibility criteria. Individual semi-structured interviews were conducted online and audio-recorded. Data were analyzed using a hybrid inductive–deductive thematic approach. UTAUT2 informed the deductive component of the analysis, while inductive coding and cross-case comparison supported theme generation. Results: Four interrelated themes were identified. First, readiness was positive but conditional, shaped by movement from openness to professional necessity, familiarity, workflow fit, and caution about the possible weakening of foundational or manual competence. Second, adoption depended on practical value and system credibility, including access, convenience, efficiency, safety, documentation integrity, accuracy, privacy, and reliability. Third, adoption was organizationally mediated through leadership, peer culture, infrastructure, implementation conditions, training, follow-up, and academic preparation. Fourth, digital health was understood as supporting, not substituting for, nursing work by reducing avoidable burden and creating more space for direct care while preserving human presence, communication, and clinical judgment. Conclusions: In this sample of doctoral nursing students, digital health readiness was positive but conditional. The findings suggest that readiness reflects a context-sensitive professional judgment shaped by educational preparation, organizational support, system credibility, workflow compatibility, and the perceived ability of digital technologies to enhance nursing work rather than replace it. Implications: The findings suggest that nursing education and practice should strengthen applied digital health competencies through simulation-based preparation, electronic documentation training, privacy and ethics education, workflow-aligned implementation, and sustained organizational support. Full article

Privacy enforcement in networked systems is commonly based on static data classification (e.g., personal vs. non-personal). However, in modern data-intensive systems, operational data such as telemetry and derived features can become privacy-sensitive depending on usage context. Inference exposure, temporal linkability, and data reuse can introduce runtime privacy risks even when the underlying data is initially considered non-sensitive. This paper proposes a runtime network management decision framework that makes this shift actionable. The framework defines four observable triggers: (i) inference exposure, (ii) temporal linkability, (iii) context sensitivity, and (iv) scope expansion. These triggers are mapped to discrete enforcement states with proportional actions and structured audit-ready decision records. The framework is evaluated using real NetFlow datasets and further validated on the large-scale CSE-CIC-IDS2018 benchmark. The empirical evaluation primarily focuses on inference exposure and temporal linkability as representative runtime triggers, while context sensitivity and scope expansion are illustrated through operational decision scenarios. The extended validation uses eight daily traffic files, resulting in 1,837,144 cleaned flow records and 70 numerical features after preprocessing. The results indicate that trigger-based enforcement maintains near-baseline detection utility while avoiding the larger utility degradation observed under continuously active enforcement strategies. The evaluation additionally includes threshold sensitivity analysis, random activation baselines, and runtime stability measurements. These findings suggest that runtime-triggered enforcement supports selective and stability-aware control with limited utility degradation. The evaluation focuses on decision-level behavior rather than full system deployment, demonstrating how runtime signals can support selective and auditable enforcement decisions. Overall, the proposed framework enables selective and interpretable runtime enforcement based on observable operational evidence rather than continuously applied fixed protections. Full article

Hyperspectral imaging has emerged as a powerful tool for food quality assessment, yet most existing methods rely on supervised classification and require prior knowledge of adulterant categories. This study applies a non-targeted screening approach based on a deep spectral autoencoder to detect adulterants in Fritillaria. While autoencoder-based anomaly detection has been established in other hyperspectral domains, its application to congeneric species discrimination and exogenous adulterant screening in Fritillaria has not been systematically explored. A deep spectral autoencoder was constructed and trained exclusively on pure samples to learn the intrinsic spectral distribution of authentic materials. During inference, reconstruction error was used as an anomaly score, and samples deviating from the learned spectral manifold were identified as suspicious. Spectral data augmentation and band trimming were applied to enhance model robustness, while the anomaly threshold was determined solely from the distribution of pure samples. The proposed method achieved strong discrimination performance, with an area under the receiver operating characteristic curve (AUC) of 0.9903 and high detection rates across multiple adulterant types. Typical exogenous adulterants such as starch and talc powder were completely detected, while congeneric species also showed high detection sensitivity despite their spectral similarity to authentic samples. Latent space visualization and residual spectral analysis further revealed clear separation patterns and interpretable spectral deviations. These results demonstrate the proof-of-concept viability of the proposed non-targeted framework for open-set screening of adulteration risks. However, the authentic samples used for training originated from a single source, and only a limited set of anomaly types was tested. Therefore, the current model should be regarded as an early proof-of-concept only, not as a ready-to-deploy screening tool. Further validation with diverse authentic samples and a wider range of adulterants under realistic variability is necessary before the method can be considered a practical strategy for quality control. Full article

The use of the Internet of Things (IoT) and wearable devices to enhance construction safety has recently attracted growing attention from the construction research community. In this paper, a system-level Structural Equation Model (SEM) is proposed to examine the relationships among perceived Safety System Value (SSV), Organizational Readiness (OR), and Adoption Barriers (AB). A survey of 567 construction professionals in Saudi Arabia was used to collect the data, which was analyzed using covariance-based SEM with Robust Maximum Likelihood (MLR) estimation. SSV was found to act as a perceptual antecedent of OR (β = 0.719). OR, in turn, was found to strongly affect AB (β = 0.712). The direct effect of SSV on AB was statistically significant (β = 0.191). Furthermore, the mediation analysis showed that approximately 73% of the total effect of SSV on AB is transmitted through OR (indirect β = 0.512, total β = 0.703). The model explained 51.6% of the variance in OR and 73.9% of the variance in AB. Data were collected through a structured questionnaire survey of 567 construction professionals in Saudi Arabia. This research contributes to the broader field of systems research by presenting a framework for the adoption of safety-related construction technologies as a systems phenomenon. The research has practical implications for building readiness-driven approaches for the effective integration of safety technologies in safety-critical construction environments. Full article

Underground space is increasingly positioned in contemporary urban discourse as a strategic resource for sustainable spatial and territorial development, particularly under conditions of limited surface capacity, growing infrastructural demand, and the need for long-term urban resilience. However, its implementation remains constrained by insufficient institutional, planning, and governance integration. Starting from this problem, this paper assesses the institutional readiness of Serbia’s spatial and urban planning system for the integration of underground planning into the territorial development system. The methodological approach is based on the development of an analytical framework for institutional readiness, structured around three key dimensions: regulatory–institutional, spatial–infrastructural, and governance–coordination. This research is conducted through a qualitative analysis of legislative, strategic, planning, and supplementary sources, using stratified criteria—normative, operational, and integrative levels—which enables a structured, document-based diagnostic assessment of the current state of the system. The results indicate that institutional readiness in Serbia is at a low to medium-low level. Although a partially developed normative framework and certain technical-informational capacities exist, underground space is not clearly recognised as a distinct planning category or as an integrated three-dimensional spatial resource. The spatial–infrastructural dimension reveals the existence of relevant cadastral, geospatial, and infrastructural foundations, but without their sufficient integration into a unified 3D planning and governance system. The key limitation is identified in the governance–coordination dimension, where fragmented competences, uneven local capacities, and the absence of dedicated coordination mechanisms hinder the systematic application of underground planning. The paper concludes that the integration of underground planning in Serbia requires gradual institutional transformation toward an integrated, three-dimensional, and long-term-oriented model of spatial governance. Its contribution lies in formulating an initial diagnostic framework that connects debates on planning systems, institutional fragmentation, spatial data integration, and territorial governance, and may serve as a basis for further research and policy development in the field of integrated territorial development. Full article

This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system approaches its Operational Design Domain limits or when risk increases; late, false, or muted requests directly impact safety. The study models the TOR pipeline (perception, driver monitoring, decision logic, in-vehicle networks, and Human–Machine Interface) as assets and data flows, applies STRIDE-based threat identification using Microsoft Threat Modeling Tool and Ansys Medini Analyze, and rates risks under ISO/SAE 21434 with traceability to ISO 26262, ISO 21448, and UNECE R155/R157. The assessment produces 165 threat rows, with an initial risk distribution of 1 Critical, 113 High, 34 Medium, and 17 Low. Results show that tampering, denial of service, and spoofing dominate the TOR threat landscape, with the central processing unit, sensor-to-CPU links, and HMI channels as primary trust anchors. After applying mitigation measures including secure boot, message authentication, intrusion detection, redundancy checks, and encrypted communication, the residual post-mitigation security levels were reduced to 0 Critical, 0 High, 13 Medium, 101 Low, and 51 Negligible. Unlike other ADAS TARA studies, this TOR-focused analysis shows that cybersecurity risk is shaped by the interaction between cyber compromise, driver-readiness estimation, HMI delivery, fallback execution, and the limited handover time budget. The results support a defence-in-depth mitigation strategy for secure TOR operation in SAE Level 2–3 vehicles. Full article