Search Results (3,479)

Asbestos, a group 1 carcinogen, has generated a serious health and environmental liability in Pedro Leopoldo/MG, Brazil, even after its national ban in 2017. This study aims to analyze the silent epidemic of asbestos-related diseases (ARDs) through the lens of social injustice. We used a qualitative, socio-historical, and clinical approach within the framework of an Expanded Research Community (ERC), based on ergology, with content analysis of interviews with workers and institutional documents. The evidence reveals a pattern of institutional silencing and omission, marked by corporate fraud, denial of risk, and medical underreporting, perpetuating occupational, domestic, and environmental exposure. In response, the Brazilian Association of Asbestos-Exposed Individuals of Minas Gerais (ABREA/MG) emerged as a central actor in the struggle for recognition and justice. It is concluded that overcoming this injustice requires structured public policies of recognition, integrated surveillance, historical reparation, and strengthening of the SUS (Unified Health System), with collective resistance being fundamental to transforming suffering into memory and social demands. Full article

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

This article examines the avant-garde reformulation of the nature–culture dichotomy. Within avant-garde discourse, the traditional opposition between the organic and the mechanical—and, by extension, between the body and the machine—evolves into a specific dialectical form based on the principle of juxtaposition-in-identity. In this framework, a metaphysics of corporeality comes into conflict with an instrumentalist understanding of the organic. The analysis identifies a key conceptual shift in the 1920s: the notion of the body is superseded by that of the organism, which is subsequently transfigured into the machine. Focusing on Russian painting from the 1910s to the early 1930s, this study employs a comparative and typological methodology. It analyzes works by Mikhail Larionov, Mikhail Matyushin, and Pavel Filonov in relation to those of Konstantin Redko, situating this analysis within a broader art-historical and intellectual context. The research traces and exemplifies a pivotal transition in visual art: the shift from the early avant-garde mythopoetics of the machine–human to the late-1920s construct of the human–machine, as theorized in biomechanics and gesture studies. The article foregrounds electricity as a central pictorial motif, arguing that it served as a powerful visual and conceptual medium for synthesizing the organic with the mechanical and the mythological with the ideological. Ultimately, it posits that the internal social logic of this aesthetic shift contributed to the formation of the totalitarian body politic in Stalinist Russia. Full article

Extended Reality (XR) technologies are rapidly reshaping human–computer interaction; however, persistent ambiguity in the use of core terms (VR, AR, MR) hampers cumulative knowledge building, cross-study comparability, and technical standardisation. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. To address this issue, we conducted a PRISMA-guided systematic literature review across four major databases (IEEE Xplore, ACM Digital Library, Scopus, and Web of Science), complemented by seminal and industry sources. Of the 173,677 retrieved records, 59 studies were included in the synthesis. Using thematic synthesis, we mapped the historical evolution of definitions and conceptual models and identified recurring analytical dimensions. The results indicate a clear paradigm shift from Milgram’s one-dimensional Reality–Virtuality continuum—originally grounded in visual display technology—towards a multidimensional conceptual space that integrates subjective user-experience constructs (e.g., coherence and plausibility) with objective system characteristics. The included studies cover 1968–2025, with marked acceleration in the 2020s: 2022 alone accounts for the highest annual count (9 studies), and nearly half of the corpus (47.5%) was published in 2021–2025. We further show that industry actors pragmatically re-bound these academic concepts for product and market positioning, leading to systematic divergences between academic and industrial definitions. By distilling key turning points and synthesising core analytical dimensions into a structured lens, this review provides a historically grounded, actionable understanding of the XR conceptual landscape to support terminological alignment across research and practice. Full article

Lightning is a major natural ignition source of wildfires across forest, grassland, and cropland ecosystems. Accurate prediction of lightning-ignited fire occurrence remains challenging due to uncertainties in spatiotemporal alignment caused by vegetation-dependent smoldering delays and the difficulty of representing heterogeneous fuel conditions in mixed-vegetation regions. This study proposes a semi-automated lightning–fire alignment framework that integrates land cover information and historical fire records to improve spatiotemporal matching across different vegetation types and to reduce misclassification from human-induced fires in agricultural areas. To better characterize fuel conditions, two feature-level vegetation fusion parameters—total vegetation cover and leaf area index weight—are introduced and combined with hourly meteorological variables and lightning characteristics to develop a tuned random forest prediction model. The framework is applied at a regional scale in the Greater Khingan Mountains and southwestern forest regions of China, with predictions conducted at an event-based temporal scale using hourly inputs. The vegetation-fused model achieves an AUC of 0.93, outperforming models without vegetation fusion. Analysis of model outputs indicates that hourly maximum temperature, leaf area index weight, precipitation, and wind speed are key factors influencing lightning-ignited fire occurrence. This study demonstrates the value of semi-automated alignment and vegetation feature fusion for improving lightning-ignited fire prediction in heterogeneous landscapes, supporting regional wildfire risk assessment and potential early-warning applications. Full article

This survey is an integrated and complete summary of the strategies and technological systems of surveying environmental hazard in marine, freshwater, and brackish environments. Contrary to the previous articles where the separate parts of the monitoring chain are investigated or certain environments/enabling technologies are considered, the given work has a cross-domain approach that unites sensing modalities, data acquisition schemes, communication schemes, operational platforms, data analytics, energy management schemes, and regulatory compliance into one consistent framework. The survey systematically examines the entire sensing-to-cloud pipeline, which includes sensor technologies, data acquisition systems, telecommunication infrastructures, and a variety of monitoring platforms such as buoy-based systems, Unmanned Surface Vehicles (USVs), Autonomous Underwater Vehicles (AUVs), and Unmanned Aerial Vehicles (UAVs). In addition, it touches on the administration and examination of mass environmental data, including cloud-based systems and AI-based methods of automated feature identification, anomaly recognition and predictive modeling. The key points of the autonomy of the system, including power supply solutions and energy-conscious management, are also mentioned, as well as the relevant regulations on the environmental monitoring nationally, at the European level, and globally. This paper presents a systematic six-step design process of aquatic environmental monitoring systems: (1) risk categorization, (2) physical data acquisition systems, (3) monitoring platforms, (4) data management & analytics, (5) energy autonomy strategies, and (6) regulatory compliance. The systematic framework offers researchers and practitioners practical guidelines to follow when designing end-to-end systems, thus completing the gaps in the historically disjointed research strands and going beyond the traditional domain- and technology-based studies. Full article

The gut microbiota plays a key role in the health of wild birds, reflecting the influence of diet, habitat, and social behavior. Migratory and highly gregarious species such as the yellow-headed blackbird (Xanthocephalus xanthocephalus) provide valuable opportunities to explore host–microbe–environment interactions within a One Health framework. During migration, birds are exposed to diverse environments and dietary sources, which can promote highly diverse intestinal microbial communities and facilitate transient acquisition of environmental microorganisms. Here, we present the first taxonomic characterization of the fecal bacterial microbiota of X. xanthocephalus in northern Mexico based on 16S rRNA gene sequencing of the V3–V4 region. In addition, we performed a conservative screening to assess whether any bacterial taxa tentatively assigned at the species level have been previously reported as human pathogens or as having potential zoonotic relevance. Fecal samples were collected noninvasively from communal roosts within an urban–agricultural landscape of the Comarca Lagunera region during a winter season. A highly diverse bacterial community (39 phyla, 369 families, and 1195 bacterial species) was identified. Firmicutes_D, Actinobacteriota, and Campylobacterota were the dominant phyla. Among the bacterial taxa tentatively assigned at the species level, only three have been reported to exhibit zoonotic potential in the literature; however, none corresponded to avian-adapted pathogens or bacterial species historically associated with major zoonotic outbreaks, and all were detected at very low relative abundances. Overall, our findings establish an initial microbiological baseline for X. xanthocephalus and underscore the role of migratory birds as indicators of environmental microbial dynamics rather than direct sources of zoonotic risk in semiarid regions of northern Mexico. Full article

Net primary production (NPP) in the ocean is fundamental to marine food webs, supporting oxygen production for heterotrophic respiration and contributing to the long-term sequestration of carbon. Rising ocean temperatures associated with climate change are expected to alter NPP dynamics. However, it remains challenging to understand how different abiotic (especially sea temperature) and biotic factors influence marine NPP due to the complex network of interactions between these factors. This study introduces a flexible machine-learning-based framework for evaluating the sensitivity of NPP to variations in key environmental drivers, particularly sea temperature, by testing and comparing alternative machine learning algorithms. In the case study presented here, Support Vector Machines (SVM) achieved the highest predictive performance among the evaluated models. Variable-importance analysis of the best-performing algorithm, within the scope of this comparative framework, revealed that variables intrinsically linked to NPP, such as chlorophyll-a and solar radiation, play a key role in determining the predictive ability of the models. Meanwhile, sea temperature emerged as the key external factor influencing the performance of the models. The NPP exhibits a correlative sensitivity to increase of 1 °C in sea temperature, with relative changes ranging between 3% and 16%. These projections reflect model-based sensitivities derived from historical co-variation. Therefore, the results represent conditional projections under observed relationships. Although SVM performed best for this case study, the proposed framework is adaptable and can incorporate alternative algorithms, predictor sets and preprocessing strategies, enabling robust and transferable assessments of the sensitivity of regional ocean productivity to climate change. Full article

Heritage Building Information Modelling (HBIM) has emerged as a powerful methodology for documenting, analysing, and managing historic buildings. However, its pedagogical potential in teaching adaptive reuse and heritage-sensitive design remains underexplored, particularly in postgraduate architectural education. This study evaluates a pedagogical HBIM framework implemented in a master’s-level course, where students applied HBIM methodologies to propose adaptive reuse interventions for a historic building in Jeddah Historic District, Saudi Arabia. Student design projects were analysed to assess how HBIM informed documentation accuracy, heritage value interpretation, and design decision-making. In addition, a retrospective questionnaire was administered to former students to evaluate the long-term educational effectiveness of the HBIM-based methodology, focusing on learning quality, design comprehension, and professional preparedness. The results indicate that HBIM significantly enhanced students’ understanding of historic fabric, improved their ability to propose context-sensitive reuse strategies, and supported more informed and evidence-based design decisions. Survey findings further demonstrate the high perceived value of HBIM in architectural education, particularly in linking theoretical knowledge with real-world heritage challenges. This research contributes a validated educational framework for integrating HBIM into adaptive reuse curricula and provides evidence-based insights applicable to heritage education and professional practice. Full article

Bactrian camels (Camelus bactrianus) have long been recognized in China as key agents of long-distance connectivity, based largely on iconographic and textual evidence, while osteological data have rarely been incorporated into discussion. Because these data have seldom been examined within a unified analytical framework, current knowledge of the development and shifting patterns of camel–human relationships remains fragmentary. To address this gap, the present study provides a detailed analysis of available camel osteological material from archaeological contexts in northern China and integrates it with broader archaeological and historical evidence. Our results identify diverse forms of interaction across time and space, including camel exploitation for transport and labor, consumption, funerary practices, and craft production. Spatiotemporal patterns indicate a persistent concentration of osteological remains in China’s northern frontier zones, whereas the record remains sporadic in central regions despite increasing camel representations in material culture and texts. This enduring distribution reflects ecological suitability and sustained economic integration in arid zones. The absence of such conditions in Central China meant that camels were never fully incorporated into local everyday life; instead, they primarily operated within imperial logistical and political systems and came to be culturally important through their role in broader exchange networks. Full article

Cancer metabolism is a cornerstone of tumor biology, characterized by profound alterations in cellular energy production and biosynthetic pathways that drive malignancy. The seminal discovery of the “Warburg effect”, the preference of cancer cells for aerobic glycolysis even under oxygen-rich conditions, provided the first major insight into this field. Historically, this observation was attributed to defective mitochondria, but modern research has revealed a far more complex picture of metabolic reprogramming that is actively driven by oncogenes, tumor suppressor genes, and the tumor microenvironment (TME). This review advances a unifying framework for understanding cancer metabolism as a dynamic ecosystem defined by three interconnected adaptations: metabolic plasticity, oncometabolite-driven epigenetic remodeling, and immune-metabolic crosstalk. These adaptations extend beyond glycolysis to encompass glutamine metabolism, lipid synthesis, amino acid utilization, and mitochondrial dynamics, all coordinated to fuel rapid proliferation, promote survival, and enable metastasis. By examining the drivers, consequences, and therapeutic barriers within this framework, we highlight emerging strategies for precision intervention. Although understanding the mechanistic basis of these pathways has unveiled new therapeutic avenues, clinical translation has been limited by metabolic redundancy, microenvironmental buffering, and patient heterogeneity. Strategies such as metabolic inhibitors, dietary interventions, and immuno-metabolic combinations offer promising prospects for disrupting tumor growth when guided by biomarker-driven patient selection and emerging technologies, including spatial metabolomics and AI-driven network modeling. Full article

This study used finite element simulation and theoretical analysis to predict the crack distribution patterns that may occur during the shrinkage cracking process of rectangular timber beams. Based on the predictions, experimental specimens with six typical crack distribution patterns (I–VI) were designed. Subsequently, a four-point bending test method was employed to conduct large-sample size fracture tests on a total of 1200small-sized Pinus sylvestris var. mongolica specimens, quantifying the effects of the crack depth, location, and distribution patterns on the specimens’ load-bearing capacity. The results indicate that when multiple cracks exist in a timber beam, their collective effect is not a simple superposition of individual cracks but a spatial distribution coupling effect. Both the depth and location of the cracks play crucial roles in their interaction. This study introduces three coefficients for evaluating the influence of cracks on timber beams, namely the load-bearing capacity coefficient (R), the decline ratio of load-bearing capacity (D), and the comprehensive crack-influence coefficient (β), which can effectively quantitatively evaluate crack damage effects. The framework established in this study, which links shrinkage crack characteristics with the load-bearing capacity of timber beams, along with the experimental data provided, can serve as a reference for the safety evaluation and scientific maintenance of historical timber components and modern timber structures with shrinkage cracks. Full article

In this paper, we propose a data-driven distributionally robust optimization (DRO) framework that ensures the economical and robust operation of solar photovoltaic (PV)-integrated battery charging swapping stations (BCSSs) for electric vehicles (EVs) under uncertainties in active distribution systems with stand-alone PV systems. In the proposed framework, multiple inventory batteries in each BCSS are used through their charging and discharging real and/or reactive power scheduling to perform Volt/VAR control (VVC) along with stand-alone PV systems, and to reduce the BCSS operational cost via battery-to-battery (B2B)-based real power exchange and demand response (DR) while satisfying the desired EV battery swapping load. To handle the uncertainties in both PV generation outputs and DR-induced maximum demand reduction capability, the proposed framework is formulated as a data-driven DRO problem based on the Wasserstein metric using historical samples of the probability distributions of the uncertainties. Using a duality theory, the original Wasserstein-based DRO problem is reformulated into a tractable optimization problem that calculates the distributionally robust bounds of uncertainties using their support information. The effectiveness of the proposed framework was assessed on an IEEE 33-node power distribution system in terms of real power loss reduction via VVC and BCSS operational cost savings via B2B/DR capability. Full article

Pituitary neuroendocrine tumors (PitNETs) constitute a significant proportion of primary intracranial neoplasms and were historically differentiated based on clinical hormone excess syndromes and tinctorial properties. The 5th edition of the WHO classification introduces a paradigm shift towards the lineage-based taxonomy based on the cell-specific expression of transcription factors (TFs). This overview focuses on the biological justifications and diagnostic value of the core TFs of Pituitary-Specific Positive Transcription Factor 1 (PIT1), T-Box Pituitary Transcription Factor (TPIT), and Steroidogenic Factor 1 (SF1), which signify the somatotroph, lactotroph, thyrotroph, corticotroph, and gonadotroph lineages, respectively. By focusing on TF expressions instead of hormone immunoreactivity, pathologists can better subtype clinically non-functioning tumors, effectively relegating the previously overutilized null cell category to about 1% of cases. The TF-based classification is also essential in discriminating high-risk histotypes of silent corticotroph tumors, sparsely granulated somatotrophs, and immature PIT1-lineage PitNETs, which are linked to a higher invasiveness and recurrence. We suggest a practical, stepwise immunohistochemical diagnostic algorithm with the integration of ancillary markers (e.g., GATA3 and ERα) to refine lineage assignment. New molecular correlates such as GNAS and USP8 mutations also add to this framework and guide the use of individualized treatment involving somatostatin analogs or dopamine agonists. And lastly, we discuss the ongoing issues of diagnosis of triple-negative and multilineage tumors and the growing importance of DNA methylation profiling and artificial intelligence in standardized reporting and improving precision management. Full article

This study investigates univariate multi-horizon forecasting of national electricity demand as a controlled benchmark for settings where exogenous drivers (e.g., weather and calendar variables) are unavailable or uncertain, through a comparative evaluation of representative deep learning architectures. The examined models include the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, a Temporal Convolutional Network (TCN), and the feed-forward Neural Basis Expansion Analysis for Time Series (N-BEATS) framework. All models are trained and evaluated within a unified experimental setup based on a univariate daily time series of Finnish national electricity demand covering the period from 2016 up to 2021, enabling a controlled assessment of architectural capabilities when relying solely on historical demand. Using a common preprocessing pipeline and a chronological train–validation–test split, forecasts are generated for short-, medium-, and long-term intervals (30, 90, and 365 days), and predictive performance is assessed using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The experimental results show that N-BEATS achieves the lowest RMSE across all considered horizons in the test set, while the GRU architecture attains the smallest MAE at the longest horizon and exhibits consistently strong performance overall. These findings highlight the complementary strengths of recurrent and feed-forward deep learning paradigms for modelling nonlinear structure and long-range dynamics in electricity demand time series, and provide quantitative evidence to support horizon-aware architecture selection in national electricity demand forecasting and related applied modelling contexts. Full article