Search Results (2,128)

The pore architecture and textural properties of heterogeneous catalysts affect their intrinsic and extrinsic kinetics, selectivity, and resistance to deactivation. Modelling allows the cheaper and quicker design of new catalyst products, and the optimization of the operation of existing ones. This work particularly reviews major and recent developments in pore network models (PNMs), including image-derived versions, which are a key tool for determining the impact of pore structure and mass transport on catalyst performance. It also briefly considers related areas of multi-scale modelling, first-principles modelling of active sites with DFT, intermediate-scale microkinetic modelling, and recent developments in machine-learning-based approaches. It has been seen that, for some reaction systems, PNMs can predict effectiveness factors a priori, and deliver optimized pore network designs. However, this survey also highlights issues with current models including omission of key controlling structures due to insufficient prior pore characterization, lack of the often-substantial evolution of the pore structure over the catalyst life-stages due to various on-going physical processes, and the neglect of the often-heterogeneous spatial distribution of active sites. Further, this review also considers novel experimental techniques that demonstrate, and remedy, gaps often left out from the current modelling approaches. Full article

Innovation proficiency is widely recognized as essential to organizational competitiveness; yet, how product managers and their teams develop these proficiencies across organizational contexts remains understudied. This study examines six innovation proficiencies—Customer Empathy, Insight Generation, Idea Generation, Idea Selection, Experimentation and Learning, and Mobilizing and Executing—using a longitudinal dataset of 15,842 survey responses collected across 1066 organizations over nine years (2016–2024). Responses were analyzed using descriptive statistics, nonparametric group comparisons, correlation analysis, reliability testing, longitudinal analysis, and systematic qualitative theme extraction from nearly 50,000 responses. Insight Generation is consistently the lowest-rated proficiency despite being ranked high in importance. Organization-weighted analysis finds significant, but modest, improvement in just one of the six proficiencies (Idea Selection) over the study period and positive but non-significant trends in the others. Qualitative analysis identifies customer centricity, lack of formalized processes and data-based decision making as persistent challenges over time. These findings suggest that improvement of innovation practices depends not only on Product Managers and their teams, but on organization-wide infrastructure changes to facilitate and support their innovation work. Full article

Climate-related disasters increasingly threaten agricultural sustainability and agro-ecological systems, yet public engagement with these risks often remains limited because climate impacts are perceived as psychologically distant. This study examined whether AI-generated audiovisual simulations of climate-related disasters are associated with stronger emotional and action-oriented engagement responses, particularly when scenarios are presented in a familiar local context. Using an experimental survey design, 402 participants broadly reflecting the characteristics in Israel viewed four short AI-generated films depicting wildfire and tsunami scenarios in either local (Israel) or geographically distant settings. Participants were explicitly informed that the videos were generated using artificial intelligence tools. After viewing, participants ranked the scenarios according to emotional response, concern about future implications, perceived personal relevance, and willingness to take action. The findings show a consistent pattern in which locally framed scenarios elicited stronger responses across all four dimensions than geographically distant scenarios. Wildfire scenarios set in Israel were rated as the most emotionally impactful, personally relevant, and action-motivating. Additional differences were observed across demographic groups, with higher engagement among women, younger participants, and respondents with higher educational attainment. These results suggest that AI-generated simulations, especially when locally contextualized, may serve as a potentially useful communication tool for reducing psychological distance and strengthening public engagement with climate-related environmental risks that may indirectly affect agricultural sustainability and agro-ecological resilience. Full article

Acoustic survey provides a measurement-based approach for investigating heritage spaces in which architectural morphology, environmental conditions, and sound-related practices are physically interrelated. This study applies a portable and non-invasive survey protocol to the medieval cave sanctuary of San Michele di Mezzo, located in Fisciano, Southern Italy. The site consists of stratified natural and built spaces, including a lower cave, an upper cave, and a later upper church, and represents a relevant case study for assessing the acoustic behaviour of small, irregular, and fragile cultural heritage environments. The experimental procedure combined calibrated microphone recordings, time-domain signal inspection, third-octave-band analysis, and impulse-response-derived room-acoustic indicators, including reverberation, clarity, and definition parameters. Under the adopted source–receiver configurations, the results show acoustic differentiation among the lower cave, upper cave, and later church. The caves exhibit shorter decay times than the church over most frequency bands, while clarity and definition indicators reveal a frequency-dependent behaviour that does not support a general claim of the acoustic superiority of one space over another. Comparative data from other cave and cave-like environments further contextualize the measured response of San Michele di Mezzo. The findings do not imply intentional acoustic design; rather, in the measured configuration, they show that, under the chosen conditions, the long-lasting devotional centrality of the lower cave is compatible with an acoustic response that does not contradict spoken or sung devotional use. More broadly, the study contributes to applied acoustics by demonstrating that low-invasive field surveys can provide reproducible acoustic indicators for heritage interpretation, conservation-oriented documentation, and the investigation of intangible sound-related dimensions of cultural heritage. Full article

This systematic review summarizes experimental studies in atmospheric acoustics that quantify environmental influences on long-range sound propagation. A keyword-based search was conducted in Scopus and Google Scholar to identify relevant records. Studies were included if they were published in English between January 1977 and April 2026, investigated long-range sound propagation within the human audibility range using specific sound sources, and incorporated concurrent meteorological measurements. Two reviewers worked independently to assess eligibility of the studies included in this review. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, this systematic review surveys the methodological content of these studies with respect to sound sources, signal content and processing, microphone configuration, treatment of the ground and topography, and meteorological measurements to identify common practices. Some studies provide only limited information about the acoustic source properties, postprocessing of acoustic data, and/or configuration of meteorological measurements. Key experimental details for the 40 included studies are tabulated and summarized via histograms for reference. Most experimental acoustic studies have measured propagation within a range of 2 km on relatively flat land and have utilized tower-based meteorological measurements. The results of the studies surveyed here have implications for understanding long-range outdoor sound propagation, including development of accurate numerical models. Some contributing authors were funded by the Office of Naval Research: ONR Award N00014 24-1-2400, ONR Award N00014-24-1-2437. Full article

Problematic social media use is associated with adolescents’ mental health, yet mechanisms remain underexplored. This study tested whether dispositional mindfulness mediates associations between problematic social media use and symptoms of depression, anxiety, and stress. In a school-based sample of 892 students aged 10–14 years in Switzerland, participants completed an anonymous online classroom survey. Problematic social media use, mindfulness, and symptoms of depression, anxiety and stress were examined using structural equation modeling with robust maximum likelihood estimation and cluster-robust inference for nested data structure. Model fit was acceptable (CFI = 0.921; RMSEA = 0.052). PSMU predicted lower mindfulness (β = −0.50, p < 0.001), and mindfulness was negatively related to depression (β = −0.54), anxiety (β = −0.60), and stress (β = −0.63; all ps < 0.001). Indirect effects of PSMU via mindfulness were significant for all outcomes (β = 0.27–0.32, ps < 0.001). Direct effects of PSMU remained for anxiety (β = 0.18, p < 0.001) and stress (β = 0.14, p = 0.002) but not depression (β = 0.07, p = 0.161). Overall, findings suggest mindfulness might be an important pathway linking dysregulated social media engagement to adolescent distress. Longitudinal and experimental research is needed to clarify temporal ordering. Full article

This study examined the effect of east–west orientation of willow tree (Salix alba L.) rows on soil biological activity and weed dynamics in a temperate maize (Zea mays L.) intercropped agroforestry (AF) system in Eastern Hungary. The experiment evaluated how the year (2022, 2023), location (distance from the rows), and irrigation (IR) influenced spatial patterns of earthworm (EW) parameters and weed cover. The study aimed to assess how willow-based AF systems influence soil biological and weed community dynamics under varying IR and row spacing, in comparison with monoculture cropland (MC) systems, and to evaluate their potential role in climate change adaptation in arable farming. Both soil sampling for the EW survey and vegetation studies were conducted along perpendicular transects extending from the tree rows to measure EW abundance and biomass, as well as total weed cover. Experimental results revealed clear spatial gradients in EW distribution and weed abundance near the tree rows, driven by litter input, shading, moisture, and reduced disturbance. These effects were intensified under IR at narrower row spacings. No significant differences were observed between AF-South (shaded), AF-Center, and MC plots; however, significantly higher EW abundance and biomass were found on the AF-North (sunny) side. As for the location, significantly greater total EW abundance was found at AF-North (105.0 individual m⁻²) compared with the MC plots. AF systems enhance soil biological activity and shape weed dynamics through spatial ecological gradients influenced by tree row spacing and irrigation, supporting their role as sustainable land-use systems while emphasizing the need for site-specific management and further long-term optimization. Full article

The study examines discrepancies between personally reported and declared use of generative artificial intelligence (GenAI) among university students from a public university located in the Peruvian Andes, operationalized as the AI Use Gap, an exploratory discrepancy indicator based on two self-reported measures. Drawing on a sequential explanatory mixed-methods design, the study combines survey data (N = 150), experimental vignette evaluations, and qualitative follow-up interviews to explore how students manage the visibility and disclosure of AI use in academic contexts. Findings indicate relatively high levels of AI use alongside a consistent discrepancy between personally reported and declared use, suggesting patterns of differential reporting across contexts. Quantitative analyses did not show clearly differentiated exploratory relational patterns between the AI Use Gap and the psychosocial/contextual indicators examined, including perceived stigma, concealment, normative ambiguity, and peer pressure. Given the exploratory nature and limited internal consistency of the contextual indicators, these findings were interpreted cautiously as provisional exploratory patterns rather than as evidence of stable psychosocial relationships. Qualitative findings suggest that AI disclosure practices are shaped by socially evaluative and context-dependent processes, including fear of judgment, uncertainty regarding acceptable AI use, and selective disclosure strategies. Participants frequently described AI use as widespread but not consistently disclosed across academic settings. Overall, the findings suggest that discrepancies between AI use and disclosure may be better understood as forms of visibility management shaped by institutional ambiguity and social expectations rather than by stable individual-level characteristics alone. Rather than validating stable psychosocial mechanisms, the study explores an emerging and context-sensitive phenomenon using provisional contextual indicators intended to capture heterogeneous patterns of perception and disclosure. The study contributes to ongoing discussions regarding transparency, academic integrity, and the social regulation of AI use in higher education, particularly in under-researched Global South contexts. Full article

In this study we describe the refinement process from the first to the second phase of a teaching–learning sequence development and implementation. The TLS comprises several experimental activities that aim to support understanding of Control of Variables Strategy (CVS) reasoning in the context of floating/sinking and properties of magnets. The research was carried out during a science laboratory course in a department of early childhood education. The participants numbered 67 in the first phase of the survey and 45 pre-service early childhood teachers (referred to as student teachers) in the second phase. The analysis is theoretically grounded in Pickering’s model of scientific practice, as adapted in science education, which provides the analytical framework for identifying and categorizing refinement changes. The results showed that the refinements are differentiated from each other according to the factors that guide them. Specifically, the three refinement changes guided by the educational factor were local-guided, i.e., related to a specific activity dealing with the student teachers’ educational needs, and the other two, also driven by the scientific factor, were holistic-open refinements, i.e., related to a set of activities adjusting the TLS to the new scientific trends. These findings contribute to the literature on Teaching-Learning Sequence development by illustrating how theoretically grounded analysis can make refinement processes more explicit and analytically interpretable. Full article

Gradient amplifiers are a critical subsystem in Magnetic Resonance Imaging (MRI), as their performance directly impacts image fidelity, scan time, and overall system cost. This article surveys gradient amplifier topologies and design techniques reported in the literature, with particular emphasis on the practical trade-offs between high-performance solutions and cost-driven implementations. The review covers a broad range of architectures, from stacked H-bridge configurations that can provide high slew rate with low steady-state ripple, to modified audio-amplifier-derived approaches targeting low-cost platforms. Reported experimental results are synthesized to enable a comparative discussion in terms of key figures of merit, including linearity, efficiency, current ripple, dynamic response, and power density. The paper also discusses requirements and constraints arising from emerging MRI platforms, where compactness and energy efficiency are increasingly important. Finally, persistent challenges and open research directions are outlined, highlighting the need for architectures that improve efficiency without compromising linearity under high slew-rate operation, across both high-end clinical scanners and specialized low-cost systems. Full article

The convergence of the Internet of Things (IoT), edge computing, and artificial intelligence (AI) is reshaping cyber defense in distributed cyber–physical environments. IoT-edge systems expose heterogeneous, resource-constrained, and intermittently connected devices to threats that unfold close to sensing and control processes, making purely signature-based or rule-based defenses increasingly insufficient. This article presents a structured review of AI for cybersecurity in IoT-edge systems from a systems-oriented perspective. Rather than surveying AI for IoT security in general, it organizes the literature around four practical lenses: AI methods, datasets and benchmarks, evaluation practice, and deployment constraints. The review reconstructs a workspace-verifiable corpus of 96 references, emphasizes literature published between January 2023 and April 2026 while retaining foundational benchmark papers, and uses a conservative 26-paper empirical subset for paper-level gap coding. Because this subset was purposively sampled and the original retrieval logs were not preserved, coded counts are interpreted as recoverable reporting signals and comparability indicators rather than field-level prevalence estimates. The revised synthesis further stratifies the coded evidence by task, model family, dataset, application scenario, metric type, and deployment signal, and translates deployment feasibility into a minimum reporting checklist and edge-hardware decision matrix. Within this evidence boundary, recent work remains dominated by intrusion and anomaly detection, with continued use of traditional machine learning, deep learning, federated learning, explainable AI, and graph-based approaches. However, experimentation remains concentrated around a small set of public benchmarks, while latency, memory, energy, communication overhead, operational robustness, and reproducibility are reported inconsistently. The field is therefore constrained less by classifier novelty than by benchmark concentration, weak deployment reporting, limited response-and-mitigation analysis, undercoverage of authentication, access-control, and trust-management tasks, and limited reproducible edge-aware evaluation. Full article

Heterostructured metals and alloys are designed with spatial variations in strength and hardening that produce synergy beyond the rule of mixtures. This review surveys face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal close-packed (HCP) systems, including architectures formed or modified by rolling and related severe plastic deformation routes, and examines them under tension, compression, and shear. Across material classes, mechanical incompatibility between hetero-zones drives stress partitioning and plastic strain gradients that store geometrically necessary dislocations near zone boundaries. The associated internal back and forward stresses sustain work hardening, delay instability, and influence localization and damage initiation. We evaluate continuum, crystal plasticity, dislocation-based mesoscale, and atomistic approaches by whether they predict these internal fields and whether they are validated against internal-field measurements. Key observations are that predictive models require physically identifiable intrinsic length scales, experimentally constrained interface laws, and careful separation of mechanisms to avoid double-counting when gradient and kinematic terms coexist. Major gaps remain in parameter identifiability for multi-zone and nonlocal formulations, in transferability across processing routes and loading modes, and in community benchmarks that couple well-characterized microstructures with multimodal measurements. Recommendations are provided for validation targets and benchmark campaigns to accelerate predictive design. Full article

Digital participation depends not only on the formal availability of online engagement channels but also on how citizens interpret the safety, usefulness, and feasibility of participation. This article examines whether willingness to engage digitally is shaped more strongly by procedural platform cues or by underlying behavioral beliefs about security, efficacy, and personal capability. Using a survey of 500 adults in South Korea and a 2 × 2 survey-embedded vignette experiment, the study varies participation threshold (50 vs. 500 supporters) and response specificity (generic response vs. concrete action plan and timeline). The direct experimental effects are small and statistically non-significant, indicating no detectable moderate shift in stated willingness within this vignette design. In contrast, baseline participation intention, perceived outcome efficacy, and digital ability are consistently associated with scenario-based willingness to participate, while security trust is positively associated with baseline readiness to engage. The findings suggest that digital participation is better understood as a behavioral decision shaped by perceived risk, expected consequences, and self-assessed capability than as a simple response to procedural design alone. Full article

Reliable pavement condition assessment using UAV-derived orthophotos remains challenging under manual flight conditions, where acquisition parameters are not predefined and photogrammetric quality is highly operator-dependent. This study evaluates how UAV flight configuration influences orthophoto quality and operational usability for road infrastructure assessment in real-world manual survey scenarios. Eight flight treatments combining altitude (30–40 m AGL), flight speed (low/normal), and image capture interval (2–3 s) were tested over an urban–peri-urban road segment in Misantla, Veracruz, Mexico, using a DJI Air 3S platform. Orthomosaic quality was assessed through ground sampling distance (GSD), tie-point density, multiplicity, RMS reprojection error, dense cloud size, orthomosaic continuity, and a criteria-based interpretability index supported by field observations. Results show that while altitude controls spatial resolution, resolution alone is insufficient for reliable pavement assessment. Configurations with higher image overlap and photogrammetric redundancy (notably Treatment 1 (T1) and Treatment 3 (T3)) achieved superior geometric consistency, reduced seam artifacts, and improved detection of subtle surface irregularities. In contrast, reduced-overlap configurations produced complete but less interpretable orthomosaics. The study provides experimentally validated operational guidelines for optimizing UAV flight parameters under manual conditions, bridging the gap between controlled photogrammetric theory and practical infrastructure monitoring. Full article

Background: Diabetes mellitus remains a major public health concern, particularly in sub-Saharan Africa where type 2 diabetes predominates. In West Africa, Uvaria chamae P. Beauv. is traditionally used for diabetes management. This study investigates previously reported metabolites from Uvaria chamae using an integrated in silico approach to explore their potential antidiabetic activity and underlying mechanisms. Methods: A comprehensive literature survey identified 106 phytochemicals from stems, roots, leaves, and seeds. Diabetes-related protein targets were retrieved from the RCSB Protein Data Bank, while ligand structures were obtained from PubChem and the COCONUT database. Molecular docking, MM-GBSA rescoring, induced-fit docking, QSAR, and ADMET analyses were performed to evaluate interaction profiles, predicted activity, and developability. Results: The integrated analysis supports a polypharmacological mixture-based profile with organ-associated trends. Stem- and root-derived flavonoids, particularly isouvaretin and diuvaretin, showed the most consistent profiles for PPARγ-related pathways, while uvarinol was associated with PTP1B. Leaf alkaloids were mainly linked to DPP-4 and digestive enzyme inhibition. These compounds displayed more favorable predicted pharmacokinetic and toxicity profiles compared to acetogenins, which, despite favorable binding energies, were not prioritized as drug-like candidates due to their high lipophilicity, low QED values, and predicted toxicity liabilities, but may contribute to extract-level activity. Conclusion: These findings provide a hypothesis-generating and hierarchical framework for the prioritization of Uvaria chamae metabolites and extracts, supporting further experimental validation through enzymatic, cellular, and gene expression studies. Full article