Search Results (63,043)

Historic earthen cities face a dual challenge: surface thermal stress reduces livability, yet conventional greening can intensify the moisture processes that threaten earthen fabric. This study examines that tension in the Historic Town of Zabid, Yemen, a UNESCO World Heritage earthen city. Rather than a canopy-layer heat-island or comfort assessment, it develops an LST-based decision-support framework for heritage-sensitive greening, using two hot-season Landsat scenes (23 May and 8 June 2024). The method integrates land surface temperature (LST), the normalized difference vegetation index (NDVI), hotspot extraction, relative wetness-risk screening with an independent moisture cross-check, and earthen-fabric sensitivity in a GIS rule logic. Mapped green space occupies only 0.85% of the walled historic core. The core showed a recurrent daytime cool-core, 1.69 °C and 1.78 °C below the surrounding buffer on both dates, and LST declining 2.76 °C across the NDVI gradient. Overheating concentrated on vegetation-deficient surfaces: 98.40% of hotspot area fell within the two lowest NDVI classes, and 93% of hotspot area persisted across both dates. Wetness-risk screening flagged 14.10% of the buffer as the highest caution class. These diagnostics translate into five intervention classes—safe hotspot greening, courtyard micro-greening, contained planting, shade-first response, and protection/exclusion—a planning-level method whose indicative zones require field verification before implementation. Full article

The Lianggaoshan Formation in the Gongshanmiao block of the Sichuan Basin is characterized by narrow channel sand bodies and thin layers, resulting in weak seismic responses, which leads to poor identification of small-scale fault–fracture systems. Initial high-density 3D seismic data exhibit strong shallow surface waves and significant shot-to-shot variations in energy and frequency, necessitating amplitude-preserving noise attenuation and broadband wavelet consistency processing. First, pre-stack multi-domain amplitude-preserving noise attenuation is applied, integrating surface-wave forward modeling, stationary wavelet transform, and matrix singular value decomposition to suppress complex noise. Next, robust deconvolution constrained by a target wavelet improves broadband consistency. Subsequently, anisotropic depth-domain velocity modeling and imaging under rugged topography are conducted using a well-constrained TTI initial velocity model and full-azimuth angle-domain grid tomography. Compared with conventional data, the processed high-density data significantly enhance bandwidth, structural imaging, and thin-layer resolution. Imaging continuity of small faults (6–10 m throw) is markedly improved, and the channel characterization accuracy of the Liang-2 Member increases from 180 m to 60 m. This workflow delivers high-SNR, high-resolution, and high-fidelity results, providing a reliable basis for thin-sandbody prediction and reservoir evaluation. Full article

This study proposes a conceptual framework to operationalize the transition from conventional, technical, and material-centric circularity to an inclusive socio-technical paradigm through the deployment of a Co-Creative Digital Process Twin (DPT) framework in Progreso, Hidalgo, Mexico. The primary objective is to bridge the gap between technocentric industrial applications and the socio-technical needs of developing regions, specifically by integrating informal waste pickers (pepenadores) into a formal circular economy ecosystem. The methodology is structured into three distinct stages: a systematic scoping review following PRISMA-ScR protocols to identify architectural gaps in the current literature; the generation of a conceptual framework and Business Model Canvas grounded in participatory design; and the formulation of Key Performance Indicators for future longitudinal testing. The results indicate a profound bias in existing digital twin research toward industrial automation, with a near-total absence of social inclusion mechanisms. In response, this study presents a multidimensional business model featuring service-oriented cooperative architecture. This system utilizes open standards (FIWARE, BPMN 2.0) and crowdsourcing platforms (Ushahidi) to maximize material recovery while ensuring socio-economic equity. This will need to be verified in future studies. A limitation of this study is its specificity to a single municipality with certain characteristics, which could hinder its immediate generalization to metropolitan areas. Furthermore, the success of the model could depend on basic digital literacy and a stable telecommunications infrastructure. Full article

Despite major advances in cardiovascular medicine, ischemic heart disease (IHD) in women remains underrecognized and undertreated. A substantial proportion of symptomatic women have no flow-limiting epicardial stenosis, and ischemia may arise from coronary microvascular dysfunction, vasospasm, diffuse atherosclerosis, plaque-related mechanisms, spontaneous coronary artery dissection (SCAD), or combinations of these processes. Female-specific biological factors—including hormonal transitions, vascular aging, inflammation, metabolic changes, and reproductive cardiovascular risk enhancers—modify the coronary substrate across the life course. This review integrates these determinants with contemporary coronary phenotyping and proposes a clinically oriented, mechanism-based framework for IHD in women. Multimodality imaging, invasive coronary functional testing, and intracoronary imaging are considered complementary tools for identifying predominant and coexisting mechanisms. Management should combine guideline-directed cardiovascular prevention with mechanism-targeted antianginal therapy and women-specific considerations, including reproductive planning, pregnancy counseling, medication safety, and psychosocial care. This framework is intended as a practical synthesis rather than a validated classification system and aims to support more precise diagnosis and individualized treatment of women with ischemic symptoms. Full article

The objective of this study was to evaluate the solar photocatalytic degradation of the disperse textile dye 2-(4-amino-2-nitrophenyl)-1,3-benzothiazole using titanium dioxide nanoparticles (TiO₂ P25) under a mean solar irradiance of 492 ± 58 W/m², evaluating the effects of pH, photocatalyst concentration, and continuous aeration on process efficiency. The degradation of the dye was determined by monitoring its concentration by UV–Visible spectrophotometry, while the mineralization was evaluated by chemical oxygen demand (COD). Likewise, kinetic behavior was analyzed using pseudo-first-order and pseudo-second-order models. The results showed that the degradation efficiency increased with the concentration of TiO₂, reaching the highest yield with 400 ppm of TiO₂ and continuous aeration, which confirms that both variables are determining operating factors for maximizing photocatalytic efficiency. pH exerted a significant influence on the activity of the system, obtaining the highest degradation efficiencies and the greatest reductions in COD under slightly alkaline conditions (pH 8–9), a behavior attributed to the greater colloidal stability of TiO₂ and the modification of its surface properties with respect to its point of zero charge (pHpzc ≈ 6.2). The pseudo-second-order model generally provided an adequate empirical description of the experimental data; however, the best-fitting model varied depending on the experimental condition. The simultaneous decrease in the concentration of the dye and the COD confirmed that the treatment produced not only the decolorization of the solution, but also the progressive oxidation of the organic matter. Integrating kinetic analysis with the simultaneous assessment of decolorization and COD enabled clear experimental differentiation between adsorption and photocatalysis, strengthening the interpretation of TiO₂-based solar photocatalytic systems. In conclusion, solar photocatalysis using TiO₂ and the optimization of operational variables constitute an effective treatment strategy that takes advantage of solar irradiation as the primary energy source, thereby reducing dependence on conventional artificial UV irradiation. Within the scope of this study, the combination of solar radiation, continuous aeration and appropriate operating conditions demonstrated promising potential for the treatment of textile wastewater containing persistent dyes. Full article

Frailty, cognitive impairment, depression, functional dependency, low physical performance, and undernutrition are highly prevalent in older adults referred for cancer treatment, cardiovascular interventions, neurological therapies, and orthopaedic surgery, and each independently predicts treatment toxicity, post-procedural complications, and mortality. Comprehensive Geriatric Assessment (CGA) is the most accurate predictor of treatment outcomes in older patients, yet it remains structurally absent from most speciality pathways, contributing to overtreatment of biologically frail patients and undertreatment of biologically robust ones. In this Viewpoint, we propose and operationalise the FIT–FRAIL–DISABLE multidimensional geriatric stratification model as a conceptual framework for translating CGA findings into proportional, speciality-specific treatment recommendations across oncology, cardiology, neurogeriatrics, and orthogeriatrics. The model integrates six validated CGA instruments—the Mini-Mental State Examination (MMSE), the 15-item Geriatric Depression Scale (GDS-15), basic and instrumental Activities of Daily Living (ADL, IADL), the Short Physical Performance Battery (SPPB), and the Mini Nutritional Assessment Short Form (MNA-SF)—into three actionable categories (FIT, FRAIL, DISABLE) aligned with full-intensity, risk-adapted, and symptom-focused care, and is explicitly positioned in relation to the deficit-based frailty paradigm and the WHO Intrinsic Capacity (ICOPE) framework, which it complements rather than replaces. We specify a provisional, a priori decision rule for combining domain scores and describe how each stratification category should inform—but not dictate—goal-concordant treatment intensity within a shared decision-making process. FIT–FRAIL–DISABLE is presented as a proposed, hypothesis-generating framework: its composite thresholds and domain weighting have not yet been empirically derived, and the model has not undergone prospective validation. Retrospective calibration on existing CGA cohorts and prospective multicentre validation are being designed within the GERIA-NET research consortium. If validated, the framework has the potential to reduce both overtreatment-related harm and undertreatment-related therapeutic deprivation in older adults undergoing active treatment. Full article

Molecular pathology-driven oncologic diagnostics—genomic profiling, transcriptomics, proteomics, and, increasingly, artificial intelligence applied to tissue and liquid biopsy—can only be as accurate as the biospecimens on which they are performed. Biobanks are the infrastructures that secure this foundation, linking donors, biological samples, and data to discovery and, through the pathology interface, back to diagnosis. Their scientific and diagnostic value, however, is determined less by the number of specimens stored than by the rigor of the guidelines under which those specimens are collected, processed, annotated, governed, and shared. The normative landscape has changed considerably in the last three years: the ISBER Best Practices reached their fifth edition (2023), the NCI Best Practices were comprehensively revised (2026), the Standard PREanalytical Code (SPREC) was updated to version 4.0 (2024/2025), MIABIS Core reached version 3.0 (2024), the 2024 revision of the Declaration of Helsinki explicitly anchored biobank governance to the Declaration of Taipei, the European Health Data Space Regulation (EU) 2025/327 entered into force, and ISO 20387—the accreditation standard for biobanks—is undergoing its first full revision. This review synthesizes the current (2026) status of international biobanking standards, ethical and legal frameworks, pre-analytical and quality management requirements, data and interoperability standards, and sustainability models, drawing on more than 200 sources with emphasis on the 2023–2026 literature. Standards are presented not as an inventory but as an operational system, organized along the biobanking workflow from donor consent to sample distribution and impact tracking. On this basis, we propose a phased, guideline-anchored roadmap and a start-up checklist to help new teams establish a real-world biobank—particularly hospital-integrated biobanks in settings without a mature national biobanking infrastructure—and we review comprehensively the financial, operational, regulatory, and societal challenges that determine whether a new biobank thrives or stalls. Particular attention is given to the specimen classes and quality controls on which molecular tumor diagnostics depend—FFPE tissue and its sequencing artifacts, fresh-frozen tissue, liquid biopsy analytes, and DV200-gated derivative quality—and to the interface between research biobanking and the accredited diagnostic laboratory, which is set out explicitly because the two are governed by different standards. We further provide explicit, endpoint-specific operational control of warm and cold ischemia, a quality-control framework for advanced patient-derived models (xenografts and organoids) and their interface with dedicated model cores, an explicit statement of the evidence underlying the practical recommendations, and a dedicated limitations section. The review is intended as a reference piece for biobankers, pathologists, clinician-researchers, quality managers, and institutional decision-makers building the biobanks on which the coming decade of precision oncology will depend. Full article

This paper presents a real-time browser-based platform for thermohydraulic characterization of a compact laboratory heating system, developed using the PolyVR research-grade virtual reality engine. Experimental measurements are retrieved at 1 Hz from a cloud-based database and processed via browser-native computational framework that continuously performs thermophysical modeling, hydraulic analysis and energy balance evaluation. The system calculates the rate of heat transfer (h), overall heat transfer coefficient (U), dimensionless numbers (Re, Pr, Gr, Nu), pump performance, heater efficiency and cumulative thermal energy. PolyVR provides the immersive environment in which the partial digital twin functionality is integrated alongside the browser-based thermohydraulic calculations. The whole system includes support for animations regarding flow diagrams, valve state indicators, thermal field visualization and manipulation of system elements. The system architecture is designed to work on desktops, head-mounted devices, as well as in CAVE (cave automatic virtual environment) systems with remote connection made possible via using ngrok tunnels. The experiments were separated into three categories (steady-state, dynamic and validation). Steady-state and dynamic datasets show that the browser computation with PolyVR achieves high-fidelity thermohydraulic analysis similar to that done in laboratory settings. The steady-state and transient datasets illustrate that browser-based computation provides highly accurate thermohydraulic simulation close to that of the laboratory reference computations. For all experiments performed on the platform, the deviation of measurements does not exceed ±0.5 K in temperature, ±5% in flow rate and ±1% in pressure. The energy balance is closed with a deviation of ±2–3%. Full article

Agentic artificial intelligence is gaining significance in chemical engineering. Many process decisions involve coordinated actions rather than isolated predictions. These decisions are constrained by physical limitations, uncertain measurements, safety protocols, and human supervision. This PRISMA-guided systematic review asked where AI agents are applied in chemical engineering and process control-related problems. It also asked which agent families are used, what evidence supports their contributions, and what limitations condition deployment. Scopus was searched in the title, abstract, and keyword fields on 20 April 2026. Eligible records were peer-reviewed articles published from 2022 to 2026, indexed in the Chemical Engineering subject area, and explicitly relevant to AI agents. Evidence maturity, reported limitations, and risk of overinterpretation were extracted for each study. The included studies were synthesized into four domains. These are safety and risk; digitalization and process systems engineering workflows; control, scheduling, and operations; and molecular, reaction, and materials design. Most evidence still comes from simulations, computational studies, or prototypes rather than from plants in operation. The review therefore identifies six conditions for deployment backed by auditable engineering evidence. These are industrial validation, safety guarantees, digital twins grounded in ontologies, reproducible evaluation of LLM agents, governance of the interaction between engineers and agents, and integration across scales. Full article

Surface deformation triggered by coal extraction activities, together with the consequent development of unstable slopes within rugged mountainous landscapes, constitutes a critical focus for geological risk assessment and mitigation strategies. Conventional SBAS-InSAR processing pipelines suffer from inadequate tropospheric phase mitigation in topographically complex environments, while existing clustering-based recognition approaches fail to incorporate sufficient geophysical constraints. To overcome these deficiencies, the present investigation introduces a refined methodology that synergizes InSAR measurements with an enhanced clustering scheme for the automated screening of potentially unstable slope units. First, a two-stage coupled atmospheric correction framework is constructed within the SBAS-InSAR processing chain, comprising spatially varying stratified atmosphere estimation based on geographically weighted robust regression (GWRR-M) and turbulent atmosphere compensation based on structure-guided deformation-preserving interpolation (SGDPI); both stages require no external meteorological data and effectively protect deformation signals from overcorrection. Second, a spatiotemporally constrained density peak clustering algorithm (STC-DPC) is developed, which constructs a multi-dimensional feature space integrating spatial location, deformation rate, temporal evolution characteristics, and topographic-geological background, and introduces a spatiotemporally constrained distance metric together with an Unstable Slope Index (USI) to achieve automatic identification and quantitative discrimination of unstable slopes. The proposed method was evaluated using 120 ascending-track Sentinel-1A SAR images acquired from 2019 to 2023 over the coal-mining mountainous areas of Mentougou and Fangshan districts in western Beijing, China. The results show that the improved atmospheric correction reduces the phase standard deviation of a representative interferogram from 1.6 rad to 0.6 rad, with an average reduction of 42.3% across all interferograms. A total of 187 unstable slopes were identified by the STC-DPC algorithm, mainly distributed in abandoned mining areas and steep terrain with gradients of 10–35°, with a mean deformation rate of −25.3 mm/a; field investigations at representative sites confirmed significant deformation evidence (e.g., tension cracks and bulging), providing qualitative support for the identification results. Compared with the identification results obtained without atmospheric correction (79 unstable slopes), the improved method improves the detectability of weak deformation signals in areas with strong topographic relief and diverse deformation patterns. This study provides a practical technical pathway for the early screening and monitoring of geological hazards in coal-mining mountainous areas and holds great significance for mine ecological restoration and regional disaster prevention and mitigation. Full article

This state-of-the-art review critically evaluates shotcrete for concrete rehabilitation using an application-based framework covering buildings, bridges, tunnels and underground works, hydraulic and marine structures, and industrial facilities. The review synthesizes deterioration mechanisms, substrate and interface conditions, material and process variables, mechanical and durability performance, quality control, and alternative repair systems. Recent studies are integrated with established guidance and case histories, and quantitative evidence tables are used to facilitate cross-study comparison. Across structural applications, the shotcrete–substrate interface is identified as a critical factor governing rehabilitation performance, with surface condition, moisture state, shrinkage, curing, and environmental exposure affecting bond and durability. The synthesis further demonstrates that performance requirements vary by application, including section restoration, confinement, bond, and durability in buildings and bridges; early-age support, toughness, and residual capacity in tunnels; and low permeability and abrasion/erosion resistance in hydraulic and marine structures. Five research questions are proposed to address key uncertainties in interface behavior, durability, field performance, material development, and quality assurance. The resulting framework provides a systematic basis for evaluating shotcrete rehabilitation strategies and identifying priorities for future research. Full article

We propose an end-to-end differentiable framework for hedonic agricultural land pricing that integrates deep learning-based land cover classification with spatial econometric modeling into a single neural architecture. Traditional hedonic pricing approaches typically separate land cover extraction from price regression, leading to suboptimal feature representations that fail to capture the spatial spillover effects inherent to agricultural markets. In our system, a Swin Transformer-based semantic segmentation network extracts pixel-level land cover features from high-resolution multispectral imagery, which are then aggregated within parcel boundaries to produce composition vectors. These features are combined with static parcel attributes and fed into a graph isomorphism network that models spatial dependencies among neighboring parcels through message passing. The central methodological innovation is a differentiable Moran’s I operator that computes spatial autocorrelation from predicted parcel prices and incorporates this statistic into the training objective as a regularizing loss term. This constraint explicitly penalizes deviations from empirically observed target levels of positive spatial autocorrelation in agricultural land markets, thereby ensuring that the learned land cover features are optimized to explain spatial price clustering rather than generic class categories. The complete pipeline, including the segmentation backbone, graph neural network, and spatial autocorrelation computation, is fully differentiable, allowing gradients from the spatial loss to flow backwards and update pixel-level features. This design transforms land cover classification from a mere preprocessing step into an economically informed feature-learning process. The unified framework thereby produces parcel valuations that are both pixel-accurate and spatially coherent, capturing complex nonlinear dependencies such as irrigation network effects or soil-type continuity that conventional spatial econometric models cannot represent. By jointly optimizing segmentation features and their spatial spillover effects on market prices, our approach represents a significant departure from the two-stage hedonic pricing methodology. Full article

This study investigates the effects of applied voltage and treatment duration during electrolytic-plasma hardening (EPH) on the microstructure, phase composition, hardness, surface condition, and tribological behavior of 20X steel. EPH was performed in a 12 wt.% Na₂CO₃ aqueous electrolyte at voltages of 280, 300, and 320 V for 4 and 6 s. Scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, instrumented indentation, surface profilometry, and ball-on-disk tribological testing were used to characterize the treated specimens. The 280 V/4 s, 300 V/4 s, 320 V/4 s, and 280 V/6 s conditions produced thermally modified surface layers without visible surface damage, whereas treatment at 300 and 320 V for 6 s resulted in localized surface melting. Among the undamaged specimens, the highest hardness was obtained at 280 V, reaching 329.6 ± 15.8 HV after 4 s and 332.1 ± 26.3 HV after 6 s, corresponding to an approximately 1.83–1.85-fold increase relative to the initial value of 180 HV. XRD revealed a predominantly α-Fe-based matrix, while weak secondary reflections could not be assigned reliably to specific phases. The minimum steady-state coefficient of friction was obtained at 300 V/4 s (0.444 ± 0.054), whereas the narrowest wear track was observed at 320 V/4 s (379.48 ± 36.24 μm). No direct correlation was found between hardness and tribological response, indicating that surface roughness and microstructural state should also be considered when selecting treatment parameters. The results define a stable EPH processing window for 20X steel and demonstrate that parameter selection should be based on a combined assessment of surface integrity, hardness, and tribological performance. Full article

Text-based visual question answering (TextVQA) requires reasoning over images containing rich textual content, often involving knowledge beyond what is directly observable. Existing methods fuse visual objects and OCR tokens but struggle when questions require external knowledge. Moreover, naively incorporating retrieved knowledge often introduces irrelevant or misleading information, which may hinder reasoning rather than support it. To address these challenges, we propose a TextVQA framework that integrates external prior knowledge to support multimodal reasoning. Given an image and question, a task-aware knowledge retrieval module selects relevant candidates, which are then filtered and verified by a knowledge verification module leveraging large language models. The verified knowledge and question are compressed into compact embeddings via a perceiver-based semantic resampler and jointly processed with visual and OCR features in a multimodal reasoning module. Experiments on the TextVQA and ST-VQA datasets demonstrate that our approach effectively leverages external knowledge to improve performance on knowledge-intensive questions. Full article