Search Results (2,740)

Phenolic aerogels, owing to their low density, high char yield, large specific surface area, and well-defined three-dimensional topological networks, hold considerable promise for applications in extreme thermal protection and multifunctional material systems. The sol–gel process, a cornerstone methodology for constructing the three-dimensional nanoporous architecture of these materials, critically governs the resulting microstructural topology and macroscopic performance through its reaction kinetics, phase-separation behavior, and drying dynamics. This review systematically surveys recent advances in the sol–gel synthesis of phenolic aerogels, focusing on the polycondensation mechanisms operative under acidic and basic catalytic conditions, nucleation-and-growth kinetics, and strategies for tailoring multiscale pore structures. It further provides a comparative analysis of interfacial regulation mechanisms for capillary-stress elimination across supercritical drying, freeze-drying, and ambient-pressure drying routes. We also dissect the structure–property relationships underpinning Knudsen-effect-mediated gaseous thermal insulation, multi-scale hybrid network toughening, and inorganic phase-transition-induced in situ ceramization for thermal protection, demonstrating the synergistic optimization of thermal insulation, structural load-bearing, and ablation resistance. Finally, we summarise current applications in extreme thermal protection, environmental adsorption, electromagnetic interference shielding, and electrochemical energy storage and highlight future directions towards green, scalable manufacturing and intelligent materials design. Full article

Canopy Leaf Area Index (CLAI) is a stand attribute containing information on the real-time health and growth potential of managed pine plantations. Current remote sensing techniques for quantifying CLAI rely on simple linear models applied to satellite multispectral imagery, or on techniques based on light detection and ranging (LiDAR) data that are costly and less frequently collected. This study demonstrates a convolutional neural network (CNN) approach to retrieving CLAI from 10 m Sentinel-2 multispectral imagery with a model trained on gridded LiDAR-based CLAI estimates. We demonstrate large gains in accuracy with the CNN compared to traditional linear models based on vegetation indices (e.g., Simple Ratio), but also clear shortfalls in model skill when predicting “blind” in some spatial domains that were completely excluded during model training. Pixel-scale root mean squared error ranged from 0.34 to 0.64 by domain when exposed to CLAI training data from all available spatial domains, but rose to 0.58–1.74 when predicting without prior domain-specific training. Prediction accuracy was consistently lower when applied to completely unobserved USGS LiDAR-based CLAI estimates. Traditional linear models, in contrast, had the advantage of usually lower prediction error across unobserved spatial domains (0.43–1.98), but with lower maximum accuracy. These results demonstrate a potential route for deploying more complex models for LiDAR “mimicry”, e.g., between data acquisitions widely separated in time, but advocate for the development and use of more stable generalized approaches for use in unobserved managed pine stands. Full article

This study analyzes land use dynamics in Fălticeni Municipality over four decades (1985–2025), examining the influence of major political, socio-economic, and demographic changes on urban development. The transition from a centrally planned economy to a market economy, Romania’s democratic transformation and European integration, together with migration and population dynamics, have driven largely unregulated urban expansion at the expense of agricultural land and natural landscapes. Recent urban growth has also extended into areas with varying geomorphological vulnerability, increasing exposure to landslide hazards, while the city continues to face challenges related to abandoned industrial areas and insufficient forested land and green spaces. By integrating land use change analysis with physical vulnerability indicators, this study highlights the need for risk-informed and sustainable urban planning in medium-sized cities. It proposes a planning framework based on ecological zoning, controlled urban expansion on suitable terrain, brownfield redevelopment, the establishment of peri-urban forests, and the implementation of essential infrastructure supported by comprehensive geomorphological susceptibility assessments. The proposed approach provides practical guidance for enhancing urban resilience and can be replicated in other cities facing similar environmental and development challenges. Full article

Land use and land cover (LULC) dynamics are critical indicators of environmental transformation and anthropogenic pressure on regional landscapes. Mirzapur, located in the transitional zone between the Indo-Gangetic Plain and the Vindhyan uplands in Northern India, represents a region characterized by ecological sensitivity, mineral-based industries, agricultural dependency, and rapid infrastructural growth. In recent decades, Northern India has experienced accelerated urbanization, population pressure, land fragmentation, and environmental stress, thus making systematic LULC monitoring crucial for sustainable resource management and policy planning. The present study examines the spatio-temporal changes in land use and land cover in Mirzapur for the years 2004, 2014, and 2024. The study employed a cloud-based platform and the Random Forest algorithm for supervised classification of multi-temporal satellite imagery. LULC maps were generated and post classification comparison was used to assess changes across the selected years. Accuracy assessment was conducted using standard validation metrics, including the Kappa coefficient, to evaluate classification. From 2004 to 2024, urban areas expanded by a relative increase of 169.36%, largely through the conversion of cropland, although the overall cropland area showed a slight increase due to agricultural expansion in other parts of the study area. A slight increase in forest cover was also observed during this period. Water bodies and barren lands declined, indicating ecological stress in the region. These changes reflect rapid urbanization, demographic pressure, and evolving socio-economic activities within the district. The LULC classification achieved overall accuracies of 96.50% (2004), 97.52% (2014), and 96.08% (2024), showing the reliability of the generated maps. The study demonstrates the effectiveness of cloud-based geospatial analysis combined with a machine learning algorithms for long-term LULC monitoring and provides valuable insights for sustainable land management and regional planning. Full article

Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development. The analysis covers 22,758 Scopus-indexed journal articles and conference papers published between 2020 and 2025, identified using the phrase “generative artificial intelligence” enclosed in double quotation marks in TITLE-ABS-KEY fields. A reproducible computational workflow was used to examine publication output, document types, subject areas, countries, institutions, funding sponsors, citation patterns, and keyword co-occurrence. Citation analysis incorporated annualized citation rates and cohort-normalized annual citation rates to improve comparisons across publication years. Results show a pronounced acceleration in publication output after 2022, with an approximate 105% compound annual growth rate over 2020–2025. Computer Science remained the largest subject area, while substantial representation extended across Engineering, Social Sciences, Medicine, Mathematics, and other domains. Publication activity was concentrated among leading countries and institutions, with the United States and China recording the highest output. Funding analysis identified major national and international sponsors through publication–sponsor associations. Citation performance varied substantially across cohorts, with the 2023 cohort exhibiting the highest cohort-normalized annual citation rate (1.58). Keyword analysis revealed a thematic shift from early AI and GAN-related research toward generative AI and large language model themes, alongside education, innovation, human–AI interaction, and responsible AI. The findings provide an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape. Full article

Large bibliographic databases require careful interpretation because publication trends and subject-area counts are shaped by database coverage, document-type selection, and classification practices. This study examines longitudinal changes in articles and reviews indexed in Scopus between 2010 and 2025, focusing on total Scopus-indexed output, the Business, Management and Accounting (BMA) category, and source-derived subject-area classification multiplicity. A longitudinal bibliometric design combined annual publication counts, growth rates, comparisons between the 2010–2021 baseline and the 2022–2025 recent observation window, exploratory segmented trend models, and the Subject-Area Multiplicity Ratio (SAMR). The SAMR was calculated as the annual sum of source-derived Scopus ASJC subject-area counts divided by the number of unique indexed articles and reviews. It is used as a descriptive ratio of classification multiplicity, not as a measure of article-level interdisciplinarity. The results show sustained but heterogeneous growth across subject areas. BMA recorded comparatively stronger growth during 2022–2025, whereas total Scopus-indexed output did not display a generalized discontinuity after 2022. Generative AI is treated only as contextual background, not as an explanatory factor. The SAMR increased from 1.654 in 2010 to 1.819 in 2025, indicating that summed subject-area counts increasingly exceeded unique-document totals. These findings support cautious interpretation of longitudinal bibliometric indicators in research assessment contexts. Full article

Background: The clinical value of the cerebroplacental ratio (CPR) for predicting adverse perinatal outcomes in unselected obstetric populations remains uncertain. We aimed to evaluate the prognostic performance of serial third-trimester fetal biometry and Doppler-derived CPR and to compare their ability to identify adverse perinatal outcomes and neonatal growth abnormalities. Methods: In this prospective longitudinal cohort study, 100 consecutive pregnancies from an unselected obstetric population underwent standardized ultrasonographic examinations at both 28 and 37 weeks of gestation. Fetal biometric measurements, estimated fetal weight (EFW), amniotic fluid index, umbilical and middle cerebral artery Doppler indices, and CPR were recorded. Maternal, obstetric, delivery, and neonatal data were collected prospectively. Receiver operating characteristic (ROC) curve analyses and parsimonious multivariable regression models, accompanied by 1000-sample bootstrap internal validation, were performed to evaluate the independent predictive capacity of fetal biometry and CPR metrics. Results: Gestational age-specific CPR percentiles were associated with expected physiological Doppler changes but showed limited associations with obstetric and neonatal outcomes. In separate parsimonious regression models optimized for event-per-variable ratios, neither 28-week nor 37-week CPR independently predicted composite adverse perinatal outcomes, NICU admission, low Apgar scores, or abnormal umbilical cord blood pH. Rigorous bootstrap internal validation confirmed that optimism-corrected area under the curve (AUC) values for CPR models remained close to chance (range: 0.463–0.507). In contrast, 37-week EFW independently predicted neonatal birth weight, while both 37-week EFW and EFW percentile demonstrated good discriminatory performance for identifying small-for-gestational-age neonates (AUC = 0.812 and 0.833, respectively; both p < 0.001). Maternal body mass index and late-pregnancy fetal biometry also showed moderate discriminatory performance for predicting large-for-gestational-age neonates. Overall, late-pregnancy biometry outperformed CPR for identifying growth abnormalities, whereas neither modality alone accurately predicted composite adverse perinatal outcomes. Conclusions: In an unselected obstetric population, serial third-trimester CPR provided limited additional prognostic information beyond routine fetal biometry. Late-pregnancy fetal biometry, particularly 37-week estimated fetal weight, demonstrated superior performance for identifying neonatal growth abnormalities. These findings support the continued use of conventional fetal biometry as the primary component of routine third-trimester surveillance, with CPR serving as a complementary rather than standalone Doppler parameter. Full article

Wildfires are a major factor influencing vegetation dynamics and biogeochemical cycles. Soil moisture (SM) and vegetation optical depth (VOD) control fuel availability and flammability, but their interactions and feedbacks with wildfire dynamics on large spatial scales remain insufficiently understood. To investigate soil and vegetation water dynamics in the vicinity of wildfires, we employ a multi-sensor remote sensing approach that combines long-term (2000–2020) microwave-based SM and VOD data with optical fire observations across Africa. In addition to characterizing regional SM and VOD anomaly patterns in the vicinity of fire activity, this study also examines whether wildfire alters the coupling between SM and VOD dynamics during dry-down periods—an aspect that has not yet been addressed at the continental scale. Our results reveal strong regional differences in pre-fire trajectories: in the Southern Sahel, both variables exhibit positive anomalies 5–6 months before a fire, indicating above-average conditions associated with vegetation growth, whereas Southern Africa exhibits a continuous decline prior to the fire. Across varying land cover classes, regions with sparse vegetation show a multi-year increase in SM and VOD prior to fires, while areas with high biomass do not exhibit long-term fuel accumulation. Our post-fire analysis reveals an accelerated loss of SM and enhanced recovery of VOD during comparable initial conditions. These results demonstrate that wildfires not only alter the soil and vegetation water states but also modify the coupling between SM and VOD during dry-down periods. The drying of SM and the gain of VOD following a fire are accelerated, indicating intensified water exchange between the soil and vegetation, as well as likely faster SM uptake by post-fire vegetation. This leads to temporary shifts in the ecohydrological functioning of African ecosystems. Full article

Evaluating Sustainable Urban Built Morphology (SUBM) patterns at a metropolitan-wide scale obscures the localized trends in urban morphology across space and time. This research introduces Development Morphology Units (DMUs) as a micro-scale analytical concept for investigating the fine-grained spatio-temporal dynamics within urban morphology regimes. Based on 5844 development tracts observed between 1990 and 2023 in Mecklenburg County, North Carolina, this study utilizes within-regime Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) on five previously identified regimes. These DMUs are further described according to the four descriptors of temporal position, temporal span, spatial footprint, and spatial movement. The results detect substantial heterogeneity between DMUs in terms of spatio-temporal growth patterns. Peripheral and conventional suburban regimes account for large proportions of development tracts across the county; nonetheless, they revealed a limited DMU formation ratio, with most of their tracts left unclustered, while the identified DMUs were predominantly small, localized, and short-lived. On the other hand, the accessibility-oriented regime demonstrated a cohesive DMU structure and contains sustained and spatially stable morphological units. Within the intermediate regimes, most DMUs characterize localized and episodic growth, alongside a small number of large-scale units. Also, the highest-achieving sustainability regime in the county exhibited recent, short-lived, spatially localized, and stationary units. Across all regimes, morphological growth patterns predominantly represent limited spatial movement, suggesting that developments sharing similar morphological characteristics tend to remain anchored to previously established areas. These patterns align with evolutionary urbanism, indicating that sustainability-oriented urban morphology evolves through localized, cumulative processes rather than spatially random expansion. The identified DUMs demonstrated that localized urban morphology patterns evolve distinctively across space and time. The findings inform urban sustainability practices by tracking the dynamics of sustainability-oriented urban morphology at local levels. Full article

In 2025, the oil and fat industry of the Republic of Kazakhstan showed steady growth, strengthening the country’s position as a major producer and exporter of vegetable oils. However, the production process generates large amounts of waste (up to 100 tons per day), and its recycling is an important part of the oil and fat industry’s economy. High-temperature processing of plant waste has proven to be a promising method for creating new materials for various industries. The aim of this study was to determine the influence of various technical parameters of carbonization (processing temperature and process environment) on the physical and chemical characteristics of materials obtained by carbonizing sunflower seed husks from the East Kazakhstan region at temperatures of 300, 400, 500, 600, 700 and 800 °C in an inert argon environment, as well as by processing the husks in an oxidizing environment at a temperature of 650 °C, for further use in elastomer compositions as new ingredients. Increasing the carbonization temperature in an inert environment led to an increase in the amorphous carbon content, surface porosity, and pH of the studied materials. Another parameter that showed a tendency to increase with increasing temperature in an inert environment was the BET specific surface area. In the case of using an oxidizing environment, the highest pH value was observed, and the formation of crystalline mineral phases was also observed. The differences in the phase states of the studied materials may play an important role in shaping the spatial stack of the polymer matrix when used in rubber compound formulations. Full article

With the rapid growth of installed capacity for renewable energy in recent years, the challenge of developing renewable electricity has become increasingly significant. Reforms in the electricity market have played a crucial role in facilitating the large-scale absorption of renewable energy. This study employs panel data from 2008 to 2022 to examine the specific effects of China’s electricity spot market pilot policy on the development of renewable energy by two-way fixed effects multi-period difference-in-differences (DID-TWFE) models. Through examining moderation mechanism and regional heterogeneity, the impact of pilot policy on the proportion of renewable energy in power generation is fully investigated. The findings indicate that the policy increased the proportion of renewable energy in the electricity production of the pilot regions, with this effect being significant solely in the central and western regions, which are the principal producing areas for renewable electricity. The industrial structure exerts a positive moderating effect on this outcome. Based on the findings, policy implications are provided with respect to the coverage of electricity spot market and the development of inter-provincial electricity spot markets. Full article

The Cretaceous shallow oil sands in the Dagang area, located on the western slope of the Songliao Basin, are characterized by a burial depth of ≤182 m, an average reservoir temperature of 11.8 °C, an extremely high crude oil viscosity of 1,750,000 mPa·s at 15 °C, and water-bearing layers in both the roof and floor. Conventional thermal recovery methods such as SAGD and CSS are geologically unsuitable for this deposit and suffer from high energy consumption and carbon emissions. As microbial oil recovery is a technically advanced enhanced oil recovery technology that leverages microbial growth, reproduction and metabolism in the reservoir to alter the properties of oil, rock, gas and water through interaction with these components, and petroleum biotechnology research confirms that microorganisms can degrade high-molecular-weight petroleum hydrocarbons to reduce crude oil viscosity and improve its fluidity, this study explores the technical feasibility of microbial cold recovery for in situ extraction of such low-temperature, high-viscosity oil sands. The study adopts a five-well pilot pattern (one injector and four producers) with an integrated approach combining reservoir unblocking, microbial viscosity reduction, and vibration-assisted production. Systematic screening identified Pseudomonas, Chryseobacterium, and Citrobacter as the most efficient indigenous microbial strains. Pseudomonas exhibited a crude oil degradation rate of 32.17%, reducing asphaltene content from 7.47% to 3.56%, and achieved large-scale proliferation (2.5 × 10⁸ cfu/mL) at 15 °C. It also achieved a 40.8% reduction in crude oil viscosity and a desulfurization rate, alongside 56.6% denitrification. With the optimal activator No. 3, the viscosity reduction rate reached 45.18%, and the viable cell count exceeded 9.45 × 10⁸ cfu/mL. The synergistic action of Pseudomonas and an A-type nano-microemulsion surfactant reduced the oil–water interfacial tension from 49.56 to 1.25 mN/m (a 97.48% reduction) and lowered the crude oil viscosity at 25 °C from 302,000 to 11,023 mPa·s (a 96.35% reduction). Core flooding tests demonstrated an incremental oil recovery of 7.38% compared with the water-flooded control, with interfacial tension dropping from 48.21 to 1.18 mN/m. In the field trial, composite perforation (32 shots/m, 1610 mm penetration) and two cycles of oil-based fermentation fluid huff-n-puff reduced injection pressure from 2.0 to 2.5 MPa to 1.0–1.8 MPa. A total of 1489 m³ of microbial agent was injected into five wells, followed by a 125-day shut-in period. Nano-microemulsion single-well huff-n-puff (579 m³ over 87 days) further decreased injection pressure to 0 MPa. A downhole harmonic vibration source (≤20 Hz) was also applied during the trial. During the production phase, Pseudomonas was found to dominate the produced fluid, with its peak relative abundance exceeding 70%. Cumulative fluid production reached 4114 m³, yielding 21 m³ of oil sand oil. Wells with vibration assistance showed significantly higher oil content and better emulsification performance than wells without vibration assistance. Full article

Population growth, increasing water demands for data centers, and the need for more sustainable water practices are prompting advancement of next-generation water resource management strategies including water reuse. In the Midwestern United States (U.S.), where non-traditional approaches to augmenting water supply, including water reuse, are relatively new, full advanced water treatment (AWT), which includes microfiltration/ultrafiltration (MF/UF) and reverse osmosis (RO), produces a concentrate stream that is expensive to address (i.e., brine disposal). Carbon-based advanced treatment (CBAT), which combines ozonation, biofiltration, and granular activated carbon, has been shown to be a viable alternative with select advantages over the traditional RO approach, including the lack of brine generation. Two novel pilots were conducted in the Midwestern U.S., one at a large (>100 MGD) and one at a small (~10 MGD) wastewater reclamation facility (WRF), to provide proof of concept that CBAT could meet distinct regional needs. While additional demonstration data are ultimately needed for future regulatory approvals, results from the pilots showed that water quality objectives were met with treated water quality of <0.5 mg/L total Kjeldal nitrogen (TKN), <2 mg/L total organic carbon (TOC), and substantial reduction in constituents of emerging concern (CECs). If nitrate removal is required to meet drinking water standards (10 mg/L), additional treatment optimization at the source WRFs would be required. Areas for further research identified by this effort include mitigating the potential for ozonation to contribute to the formation of disinfection byproducts such as bromate and N-nitrosodimethylamine (NDMA). Along with the treatment performance demonstrated at these pilots, the study provided an opportunity to engage with key stakeholders to build trust in the AWT approach, which is critically important for regulatory and public acceptance. Based on two field-scale pilot studies in the U.S. Midwest, this paper analyzes CBAT application advantages and challenges in municipal water reuse and identifies research directions for the industry. Full article

Background/Objectives: Breast cancer biomarker detection through immunohistochemistry (IHC) is essential for treatment planning but faces challenges including turnaround time, variability, and laboratory resource constraints. Large open-source vision-language foundation models offer a potential avenue for inferring biomarker status directly from hematoxylin-and-eosin (H&E)-stained whole-slide images (WSIs). Methods: We evaluated two open-source pathology foundation models—TITAN (Transformer-based Pathology Image and Text Alignment Network, approximately 48.5 M parameters) and CHIEF (Clinical Histopathology Imaging Evaluation Foundation Model, approximately 1.2 M parameters)—for predicting estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status from H&E-stained breast cancer WSIs. WSI data were obtained from The Cancer Genome Atlas Breast Invasive Carcinoma collection (TCGA-BRCA) via the NCI Imaging Data Commons, with biomarker labels from the NCI Genomic Data Commons. In total, 937 cases (995 WSIs; 78.3% ER-positive) were evaluated for ER, 934 cases (992 WSIs; 68.4% PR-positive) for PR, and 646 cases (691 WSIs; 21.1% HER2-positive) for HER2. All evaluation was performed under a strict patient-level 50/25/25 split with 10 independent random partitions; metrics are reported as the mean across partitions with percentile-based 95% confidence intervals. Performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, and positive predictive value (PPV). Results: TITAN and CHIEF achieved comparable performance for ER (TITAN AUROC: 0.885 [95% CI: 0.848, 0.921], AUPRC: 0.954 [0.940, 0.964]; CHIEF AUROC: 0.877 [0.831, 0.914], AUPRC: 0.955 [0.938, 0.969]) and PR (TITAN AUROC: 0.799, AUPRC: 0.868; CHIEF AUROC: 0.791, AUPRC: 0.864). At the default 0.5 operating point, ER PPV was 0.90 and PR PPV was 0.79–0.81. For HER2, both models achieved AUROC values of 0.71–0.74 and AUPRC values of 0.41–0.45—well above the prevalence-based random baseline (approximately 0.211)—but default-threshold sensitivity was very low (approximately 0.07–0.08), reflecting class imbalance and the use of an uncalibrated default threshold rather than a categorical absence of morphologic signal. Conclusions: Under retrospective evaluation, both models demonstrate strong discriminative performance for ER and moderate performance for PR; HER2 prediction at the default operating point is limited and motivates threshold-calibration and multimodal extensions before any clinical use. AUPRC summarizes precision−recall behavior across thresholds and is distinct from threshold-specific precision (PPV); the two should be reported together for clinical-utility assessment in pathology AI. The findings are hypothesis-generating and motivate prospective external validation across independent institutional cohorts before any clinical deployment is considered. Full article

Three-dimensional (3D) graphene foams are attractive as lightweight conductive scaffolds with large surface area and broadband light absorption but achieving reproducible porosity and preserving the architecture after metal-template removal remain challenging. Here we report a stepwise route to freestanding 3D multilayer graphene foams based on (i) hydrogen-bubble-assisted electrodeposition of porous Ni on Cu foils, (ii) time-controlled pre-annealing at 1000 °C to drive Cu diffusion and form porous Ni-Cu alloy templates, (iii) in situ graphene CVD at 1000 °C under fixed growth conditions, and (iv) wet etching to remove the metal scaffold without a polymer support. The influence of pre-annealing (0, 1, 3, and 7 h) on template evolution, graphene growth, and foam stability was systematically investigated via SEM, EDS, XRD and Raman studies. Before etching, Raman spectroscopy indicates low-defect graphenic coatings with locally heterogeneous few-layer-like to multilayer-like signatures. Only samples pre-annealed for at least 3 h preserved the porous 3D architecture after metal removal, indicating the formation of self-supporting graphenic networks with improved post-etch morphological stability. Raman and XRD analyses further revealed a progressive reduction in structural degradation, residual strain, and stacking disorder with increasing pre-annealing time. Among the investigated samples, the foams obtained after 3 and 7 h of template pre-annealing combined preserved 3D morphology with low sheet resistance (10–20 Ω/□), negligible optical transmittance (<5%), and strong broadband visible-light absorption (75–90%). Full article