Search Results (431)

This paper reconceptualizes intellectual safety in mathematics spaces by centering the voices and lived experiences of BIPOC students. The marginalization of BIPOC students is compounded by structural racism, historical exclusion, and deficit narratives that continue to shape academic environments, especially in mathematics contexts. While definitions of intellectual safety reflect white, Eurocentric norms, we argue that for BIPOC students, intellectually safe environments must be anti-racist, culturally responsive, and rooted in belonging. We started with existing definitions of intellectual safety and incorporated a more critical approach to sense of belonging. Through ethnographic research design we gathered student interviews and daily journal entries from a 12-day anti-racist mathematics summer institute for secondary students. Analysis revealed that while existing attributes captured much of the scholar’s joy, cultural affirmation, and belonging, new themes, like pride/confidence, clarity/transparency, and being listened to, emerged directly from how students experienced intellectual safety in practice. This led to a refinement of our initial conceptualization. This study provides insight into how intellectual safety manifests in a space intentionally designed to support BIPOC youth in exploring mathematics in agentic and culturally sustaining ways. Full article

The heavy mineral-rich wadi deposits sourced from various wadis close to Gabal Homret Waggat in the central eastern Desert of Egypt are being analyzed to assess their genesis and paleoenvironment. This study integrates remote sensing (ALOS/PALSAR DEM and ASTER imagery), mineralogical, and geochemical analyses (XRF and SEM-EDX). Remote sensing analysis (ASTER and ALOS/PALSAR) delineated three main watersheds and identified granitic plutons as the primary source rocks. Mineralogical analysis revealed a diverse heavy mineral assemblage, including zircon, rutile, ilmenite, magnetite, staurolite, and sillimanite, indicative of a provenance dominated by granitic and metamorphic rocks. Grain size analysis shows that the samples range from very platykurtic to extremely leptokurtic (Kg: 0.598–5.350 φ), indicating deposition in predominantly fluvial environments. Geochemical data show enrichment in SiO₂, Al₂O₃, K₂O, and Na₂O, indicating a felsic (granitic) source with low Chemical Index of Alteration (CIA: 41.89–51.83) and Plagioclase Index of Alteration (PIA: 37.97–52.78) values, and indicating that the source rocks show low to moderate chemical weathering. Tectonic discrimination diagrams suggest that the source rocks were formed in a continental island arc or active continental margin, consistent with the Arabian–Nubian Shield. The presence of economically valuable minerals like zircon and rare-earth-element-bearing monazite and columbite highlights the significant resource potential of these placer deposits. Full article

Accurate land use/land cover (LULC) maps generated through cloud computing can support large-scale land management. Leveraging the rich resources of Google Earth Engine (GEE) is essential for developing historical maps that facilitate the analysis of regional LULC dynamics. We implemented the best-performing scheme on GEE to produce 30 m LULC maps for the Shandong Peninsula urban agglomeration (SPUA) and to detect LULC changes, while closely observing the spatio-temporal trends of landscape patterns during 2004–2024 using the Shannon Diversity Index, Patch Density, and other metrics. The results indicate that (a) Gradient Tree Boost (GTB) marginally outperformed Random Forest (RF) under identical feature combinations, with overall accuracies consistently exceeding 90.30%; (b) integrating topographic features, remote sensing indices, spectral bands, land surface temperature, and nighttime light data into the GTB classifier yielded the highest accuracy (OA = 93.68%, Kappa = 0.92); (c) over the 20-year period, cultivated land experienced the most substantial reduction (11,128.09 km²), accompanied by impressive growth in built-up land (9677.21 km²); and (d) landscape patterns in central and eastern SPUA changed most noticeably, with diversity, fragmentation, and complexity increasing, and connectivity decreasing. These results underscore the strong potential of GEE for LULC mapping at the urban agglomeration scale, providing a robust basis for long-term dynamic process analysis. Full article

With accelerating urbanization and global climate warming, Urban Heat Islands (UHIs) pose serious threats to urban development. Existing UHI research mainly focuses on inland regions, lacking systematic understanding of coastal city heat island mechanisms. We selected eight Chinese coastal cities with different backgrounds, quantitatively assessed urban heat island intensity based on summer 2023 Landsat 8 remote sensing data, established block-LCZ spatial analysis units, and employed a combination of machine learning models and causal inference methods to systematically analyze the regional differentiation characteristics of Urban Heat Island Intensity (UHII) and the influence mechanisms of multi-dimensional driving factors within land–sea interaction contexts. The results revealed the following: (1) UHII in the study area presents obvious spatial differentiation, with the highest value occurring in Hong Kong (2.63 °C). Northern cities generally had higher values than southern ones. (2) Different Local Climate Zone (LCZ) types show significant differences in thermal contributions, with LCZ2 (compact midrise) blocks presenting the highest UHII values in most cities, while LCZ G (water) and LCZ A (dense trees) blocks exhibit stable cooling effects. Nighttime light (NTL) and distance to sea (DS) are dominant factors affecting UHII, with NTL marginal effect curves generally presenting hump-shaped characteristics, while DS shows different response patterns across cities. (3) Causal inference reveals true causal driving mechanisms beyond correlations, finding that causal effects of key factors exhibit significant spatial heterogeneity. The research findings provide a new cognitive framework for understanding the formation mechanisms of thermal environments in Chinese coastal cities and offer a quantitative basis for formulating regionalized UHI mitigation strategies. Full article

Nitrogen (N) is a key nutrient for sustaining ecosystem productivity and agricultural sustainability; however, achieving high-precision monitoring in wetlands with highly heterogeneous surface types remains challenging. This study focuses on Caohai, a representative karst plateau wetland in China, and integrates Sentinel-2 multispectral and Zhuhai-1 hyperspectral remote sensing data to develop a soil nitrogen inversion model based on spectral indices, texture features, and their integrated combinations. A comparison of four machine learning models (RF, SVM, PLSR, and BPNN) demonstrates that the SVM model, incorporating Zhuhai-1 hyperspectral data with combined spectral and texture features, yields the highest inversion accuracy. Incorporating land-use type as an auxiliary variable further enhanced the stability and generalization capability of the model. The study reveals the spatial enrichment of soil nitrogen content along the wetland margins of Caohai, where remote sensing inversion results show significantly higher nitrogen levels compared to surrounding areas, highlighting the distinctive role of wetland ecosystems in nutrient accumulation. Using Caohai Wetland on the Chinese karst plateau as a case study, this research validates the applicability of integrating spectral and texture features in complex wetland environments and provides a valuable reference for soil nutrient monitoring in similar ecosystems. Full article

Plants can sense light signals using specific photoreceptors, activating light signaling pathways to precisely regulate photomorphogenesis and shade-avoidance responses. This study examines the molecular responses of Arabidopsis thaliana to the CoeLux^® lighting system, a unique LED-based light source designed to simulate natural sunlight. Previous studies found that the CoeLux^® light type, characterized by a higher blue-to-green ratio and reduced blue light levels, stimulates responses in plants comparable to those displayed in shade conditions. This research compared the effects of CoeLux^® lighting to conventional high-pressure sodium (HPS) lamps, focusing on the expression of critical photomorphogenesis-related genes under both long- and short-term light treatments. Lower HY5 and elevated HFR1 expression levels were observed under the CoeLux^® light type and low-intensity light conditions. On the contrary, the influence of the CoeLux^® light type on COP1 and PIFs expression levels seems more marginal. These responses suggest a complex regulation involving both gene expression and protein-level adjustments. Additionally, mutant plants lacking these essential regulatory genes displayed altered morphologies under CoeLux^® light, underscoring the functional contribution of these genes in the adaptation to light. Our findings are twofold, advancing the understanding of plant–light relationships and plant adaptation to artificial light environments. These may foster strategies for optimizing indoor plant growth under simulated sunlight conditions. Full article

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm² to 25.96 cm², the estimated results were close to the actual area trends (R² = 0.9425), and the area error was within 5.16 cm² (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage. Full article

Artifact style is still assessed primarily visually/descriptively, and much less morphologically/parametrically—by processing latent geometric peculiarities. This research aims to explore how the morphology of artifacts can be represented and quantitatively compared in order to validate their stylistic affiliation cost-efficiently. Since the term “style” herein refers to orders of classical architecture, (Roman) capitals of the oldest, Doric order, were chosen for such a validation-oriented assessment. The methodology included the following: (1) scaling capital 3D models to a uniform height to make the number of equidistant crosscuts invariant, (2) substituting contours thereof with circles (by retaining area values) to marginalize ornamentation factors, (3) determining stepwise dynamics of perimeter change across circles to ensure relative comparisons, (4) generating diagrammatic representatives by revolving dynamics-wise trendlines, and (5) comparing them quantitatively. To make the latter valid in a statistical sense, three groups of capitals were foreseen: Control (of indisputable Doric), 1st Experimental (of assumed Doric), and 2nd Experimental (of non-Doric, i.e., Ionic). Distances between the vertices of every compared representative (from defined groups) and their nearest neighbors on the reference (a representative obtained by “averaging” control ones) were computed. Distributions of such distances induced normal probabilities capable of “inferring” whether the analyzed capital could likely be a member of the assumed order. Full article

This article discusses the role of artistic interventions in shaping communities in selected Polish cities. It especially explores marginalized urban areas that are gaining new identities through art. A crucial aspect of the analysis concentrates on the influence of artistic activities on the formation of social bonds. Moreover, it focuses on the revitalization strategies that incorporate artistic activities designed to beautify spaces and enhance residents’ sense of security. It also includes examples of grassroots initiatives undertaken by artists in degraded areas. This study employed a qualitative methodology. In addition to reviewing the literature, a comparative analysis of case studies encompassing murals, site-specific installations, graffiti, and participatory art was conducted. The selected case studies demonstrate that art is not merely an esthetic endeavor but an important tool for solving spatial and social issues. Artists’ activities in difficult areas of a city lead to perceptual, visual, and functional changes. However, the question was whether the process of co-creation with the local community translated into stronger neighborly relationships or a greater sense of security. Full article

Mental calculation is key to the development of number sense and flexibility in thinking, but in practice, it is often neglected in favour of written algorithms. The aim of this study was to examine the relationship between the success in mental calculation and the number of strategies used, as well as to explore differences between age groups and genders. The study included 233 participants from various age groups, and data were collected through a mental calculation test and individual interviews regarding the strategies employed. The study follows quantitative, cross-sectional, correlational-comparative design, and the data was analyzed using key statistical techniques including the Kolmogorov–Smirnov test, Pearson’s correlation analysis, linear regression, one-way ANOVA with Bonferroni post hoc correction, and two-way ANOVA to examine main effects and interactions. The results showed a statistically significant positive correlation between the number of strategies and success in mental calculation. Differences between age groups were marginally significant; it was found that upper primary and secondary school students used a greater number of strategies. Additionally, boys, on average, applied more strategies than girls. In conclusion, the variety of mental calculation strategies positively correlates with accuracy in mental calculation, and teaching a greater number of strategies may contribute to the development of flexibility and confidence in mathematical thinking. It is recommended that greater emphasis is placed on the development of mental strategies within formal education. Full article

There is increasing interest in the role of parks as potential cool refuges in the age of climate change. Such potential refuges result from the Park Cool Island (PCI) effect, reflecting the temperature differential between the park and surrounding urban areas. However, this study of different park typologies in Perth, Australia, illustrates that while surface temperatures are 10–15 °C lower in parks during summer afternoons (much less than at other times), air temperatures are generally no different from the adjacent streetscape for the smaller parks. Only the largest park in the study had 1–2 °C lower morning and mid-afternoon air temperature differentials. The study illustrates that while the PCI is a real phenomenon, the magnitude in terms of air temperature is small, and it is of less relevance to the conditions felt by humans in average summer daytime conditions than the direct effects of solar radiation. Many studies have assessed the PCI effect, an indicator that has shown a wide range across different studies and measurement techniques. However, this novel paper utilises satellite remote-sensed land surface temperatures, on-ground measurements of surface temperatures, air temperatures, and humidity, as well as modelling using the microclimatic simulation software ENVI-met version 5.0. A reliance on land surface temperature, which in isolation has a marginal correlation with human experience of thermal comfort, has led some researchers to overstate the PCI effect and its influence on adjoining urban areas. The research reported in this paper illustrates that it is the shade provided by the canopy in parks, rather than parks themselves, that provides meaningful thermal comfort benefits. Accordingly, adaptation to increasing temperatures requires the creation of a continuous canopy, ideally over parks, streetscapes, and private lots in an interconnected network. Full article

The social integration of artificial intelligence (AI) poses fundamental challenges to value-driven domains such as education, where the adoption of new technologies raises not merely technical but also deeply rooted ethical and identity-related questions. While dominant technology acceptance models (e.g., TAM and UTAUT) primarily focus on cognitive-rational factors (e.g., perceived usefulness), they often overlook the cultural and value-based elements that fundamentally shape adaptation processes. Addressing this research gap, the present study examines how two hitherto under-researched factors—religiosity and teacher’s sense of calling—influence teachers’ attitudes toward AI and, ultimately, its adoption. The research is based on a survey of 680 Catholic secondary school teachers in Hungary. To analyse the data, we employed structural equation modelling (PLS-SEM) to examine the mechanisms of influence among religiosity, sense of calling, and AI attitudes. The results indicate that neither religiosity nor a sense of calling exerts a significant direct effect on AI adoption, and their indirect effects are also marginal. Although statistically significant relationships were found—stronger religiosity reduces a supportive evaluation of AI, while a higher sense of calling increases AI-related concerns—their practical significance is negligible. The study’s main conclusion is that successful AI integration, building on teachers’ pragmatic attitudes, is achieved not by neglecting value-based factors, but by developing critical AI literacy that treats technology as a responsible amplifier of pedagogical work. This finding suggests that value-based extensions of technology acceptance models should be approached with caution, as the role of these factors may be more limited than theoretical considerations imply. Full article

Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a dual innovation: a real-time break detection method and a low-cost multi-core parallel sensing system design based on ACS712 Hall sensors, achieving high detection accuracy (100% with zero false positives in tests). Unlike conventional techniques, the approach leverages current differential (ΔI) monitoring across parallel cores, triggering alarms when ΔI exceeds I_rate/2 (e.g., 0.3 A for 0.6 A rated current), corresponding to a voltage deviation ≥ 110 mV (normal baseline ≤ 3 mV). The core innovation lies in the integrated sensing system design: by optimizing the parallel deployment of ACS712 sensors and LMV324-based differential circuits, the solution reduces hardware cost to USD 3 (99.99% lower than fiber optic systems), payload by 18%, and power consumption by 23% compared to traditional methods. Post-fault cable temperatures remain ≤56 °C, ensuring safety margins. The 4-core architecture enhances mean time between failures (MTBF) by 83% over traditional systems, establishing a new paradigm for low-cost, high-reliability sensing systems in terrestrial tethered UAV cable health monitoring. Preliminary theoretical analysis suggests potential extensibility to underwater scenarios with further environmental hardening. Full article

Soil organic matter (SOM) is a fundamental indicator of soil health and a major component of the global carbon cycle; its accurate quantification is essential for sustainable agriculture. Conventional chemical assays yield only point-based soil measurements and miss the spatial distribution of soil elements; airborne hyperspectral remote sensing has emerged as a promising approach for the quantitative measurement and characterization of SOM. Inversion models translate hyperspectral data into quantitative SOM estimates. However, existing models rely solely on a single preprocessing pathway, limiting their ability to fully exploit available spectral information. We address these limitations by developing a marginal contribution-driven spectral fusion network (MC-SFNet) that conducts feature-level fusion of heterogeneous preprocessing outputs within a physics-guided deep architecture. Moreover, the combination of data-driven fusion and the Kubelka–Munk (KM) model yields more physically interpretable spectral features, advancing beyond prior purely data-driven methods. We validated MC-SFNet on a self-constructed remote sensing, high-throughput hyperspectral dataset comprising 200 black soil samples from Northeastern China (400–1000 nm, 256 bands). Experimental results show that our network reduces the RMSE by 10.7% relative to the prevailing generalized hyperspectral soil-inversion model. The proposed method provides a novel preprocessing pathway for forthcoming airborne high-throughput hyperspectral missions to extract soil-specific spectral information more effectively and further enhance large-scale SOM retrieval accuracy. Full article

The disappearance of foreign minors in Italy is a long-standing and critically underexamined social phenomenon. Despite alarming figures, public and institutional attention remains episodic and media-driven, often limited to high-profile or criminal cases. This study offers a socio-forensic analysis of official data from 2014 to 2023, revealing significant inconsistencies in how these cases are reported, categorized, and followed up by Italian institutions. It highlights how unaccompanied and migrant minors are especially vulnerable within a fragmented and reactive system that lacks transparency and effective preventive measures. Rather than presenting new empirical data, the article reinterprets existing sources to expose systemic gaps, drawing comparisons with the more structured approaches adopted in countries such as the United States, United Kingdom, and Spain. These international examples show how multilingual communication, early warning systems (e.g., AMBER Alert), and public geolocation tools can offer timely, coordinated responses to disappearances—tools that remain largely absent or underused in Italy. The article further argues for the integration of forensic geospatial methods, such as locus operandi analysis, remote sensing, and forensic geoarchaeology, not as experimental techniques, but as practical tools that could strengthen Italy’s institutional capacity to respond. Ultimately, this study seeks to elevate the discussion surrounding missing foreign minors from a marginal social concern to a matter of forensic and public interest, and to encourage interdisciplinary reflection on how such disappearances are framed—and too often dismissed—within the national landscape. Full article