Search Results (6,176)

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

This research evaluates three wind data sources, the Sentinel-1 wind product, the global reanalysis ERA5, and the regional reanalysis CARRA, across Iceland’s North, South, West, and East coastal regions. The analysis mainly focuses on validating Sentinel-1 high-resolution capabilities for capturing fine-scale wind patterns in coastal zones, where traditional reanalyses may have tangible limitations. Performance is evaluated through intercomparison of datasets and analysis of regional wind speed variability, with in situ coastal meteorological observations providing ground-truth validation. The results highlight the relative strengths and limitations of each source, offering guidance for improving wind-driven and wind-dependent applications in Iceland’s coastal regions, such as hazard assessment, marine operations, and renewable energy planning. Full article

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations. Full article

The synthesis of two series of poly(lactic acid) (PLA)-based polymer nanocomposites (PNCs) filled with small amounts (0.5 and 1%) of Ag and Cu nanoparticles (NPs) was performed. Moreover, two methods for the PNC synthesis were performed, namely, ‘conventional mixing techniques’ and ‘in situ ring opening polymerization (ROP)’. The latter method was employed for the first time; moreover, it was found to be more effective in achieving very good NP dispersion in the polymer matrix as well as the formation of interfacial polymer–NP interactions. The in situ ROP for PLA/Cu was not productive due to the oxidation of Cu NPs being faster than the initiation of ROP. The presence of NPs resulted in suppression of the glass transition temperature, T_g (23–60 °C), with the effects being by far stronger in the case of ROP-based PNCs, e.g., exhibiting T_g decrease by tens of K. Due to that surprising result, the ROP-based PLA/Ag PNCs exhibited elevated ionic conductivity phenomena (at room temperature). This can be exploited in specific applications, e.g., mimicking the facilitated small molecules permeation. The effects of NPs on crystallinity (2–39%) were found opposite between the two series. Crystallinity was facilitated/suppressed in the mixing/ROP -based PNCs, respectively. The local and segmental molecular mobility map was constructed for these systems for the first time. Combining the overall data, a concluding scenario was employed, that involved the densification of the polymer close to the NPs’ surface and the free volume increase away from them. Finally, an exceptional effect was observed in PLA + 0.5% Ag (ROP). The crystallization involvement resulted in a severe suppression of T_g (−25 °C). Full article

The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and lack effective short-term LSWT forecasting and early warning capabilities. To overcome these limitations, we established a high-frequency, real-time, and accurate monitoring and forecasting method for the LSWT based on a novel hyperspectral proximal sensing system (HPSs). An LSWT inversion method was constructed based on a deep neural network (DNN) algorithm with a satisfactory accuracy of R² = 0.99, RMSE = 0.92 °C, MAE = 0.64 °C. An analysis of data collected from October 2021 to December 2023 revealed distinct seasonal fluctuations in the LSWT in the northern region of Lake Taihu, with the LSWT ranging from 2.61 °C to 38.52 °C. The hourly LSWT for the next three days was forecasted based on a long short-term memory (LSTM) model, with the accuracy having an R² = 0.99, an RMSE = 1.01 °C, and an MAE = 0.87 °C. This study complements lake water quality monitoring and early warning systems and supports a deeper understanding of dynamic processes within lake physical systems. Full article

An increase in the production of cationic dyes is expected over the next decade, which will have an impact on health and the environment. This work reports an adsorbent hydrogel composed of poly(acrylic acid) [poly(AA)] and Fe₃O₄ particles, prepared by radical polymerization and in situ co-precipitation of Fe³⁺ and Fe²⁺. This Fe₃O₄/poly(AA) composite hydrogel was used to evaluate its potential for removing the cationic dyes methylene blue (MB) and crystal violet (CV) from aqueous solutions. Instrumental characterization of the hydrogel was performed by FTIR, XRD, TGA, VSM, and physicochemical analysis (swelling and response to changes in pH). The results show that the incorporation of Fe₃O₄ particles improves the adsorption capacity of MB and CV dyes to a maximum adsorption of 571 and 321 mg/g, respectively, under the best conditions (pH 6.8, dose 1 g/L, time 24 h). The adsorption data best fit the pseudo-first order (PFO) kinetic model and the Freundlich isothermal model, indicating mass transfer via internal and/or external diffusion and active sites with different adsorption potentials. Moreover, the thermodynamic analysis confirmed that the adsorption process was spontaneous and exothermic, with physisorption as the dominant mechanism. In addition, the Fe₃O₄/poly(AA) hydrogel is capable of removing 95% of the dyes after ten consecutive adsorption–desorption cycles, demonstrating the potential of hydrogels loaded with Fe₃O₄ particles for the treatment of wastewater contaminated with dyes. Full article

Stone cultural heritage structures built from pyroclastic rocks are susceptible to deterioration due to their sensitivity to atmospheric processes. Detecting such deterioration and periodically examining it using non-destructive testing (NDT) techniques is one of the most critical measures for ensuring its transmission to future generations. In recent years, assessing the properties of building stones through NDT methods has been widely applied in planning the preservation of stone cultural heritages. In this study, deterioration observed on the interior walls of the Emir Ali Tomb, a structure distinguished from other tombs in the region by its exceptional architecture, was investigated through laboratory tests and NDT techniques, including deep moisture measurement, P-wave velocity, and infrared thermography. It was determined that the monument was constructed from four different types of pyroclastic rock, classified according to their textural and geomechanical characteristics. Using data obtained from in situ tests, NDT distribution maps were generated. The deep moisture, P-wave velocity, and infrared thermography maps revealed that the primary cause of deterioration in the monument was related to capillary water rise. Full article

Effective management of Micropterus salmoides resources requires accurate assessment of their abundance and distribution. Fisheries acoustics is a key method for such evaluations, yet its application is limited by insufficient target strength (TS) data. This study combines the Sobel edge detection technique with the Kirchhoff ray-mode model to estimate the TS of Micropterus salmoides cultured in Guangdong, China, and validates the results through in situ measurements. The relationships between TS and fish body length were established at 38 kHz, 70 kHz, 120 kHz, and 200 kHz. At 200 kHz, the average in situ TS was –42.41 dB, with a fitted formula of TS = 32.00 lgL − 88.24. Further validation was performed using time- and frequency-domain analyses of echo signals. The results show that TS increases with swim bladder volume, indicating its dominant influence. The relationship between TS and frequency is nonlinear and affected by the swim bladder angle, swimming posture, and behavioral patterns. This study also improves the computational efficiency of the Kirchhoff ray-mode model. Overall, it provides essential parameters for acoustic stock assessment of Micropterus salmoides, providing a scientific basis for their sustainable management and conservation. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article

In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is a valuable tool for providing critical data. This study investigates whether node-level WSE data from the SWOT satellite can effectively function as virtual hydrological stations under such extreme conditions. The study was applied in all of RS state considering 100 in situ gauges and was subdivided into three sections: (i) an evaluation of the variation in SWOTʹs WSE data compared to the variation in in situ levels from telemetric gauges, considering subsequent cycles of passes between July 2023 and April 2025, yielding an MAE = 35 cm and an RMSE = 73 cm after outlier removal; (ii) an evaluation of the variation in SWOTʹs WSE data compared to the variation in telemetric level data, considering one window prior to and another during the extreme event, resulting an MAE = 26 cm and an RMSE = 34 cm; (iii) an analysis of SWOTʹs data availability during the extreme event, when in situ telemetric data were unavailable. The results demonstrate an agreement between the variation observed in SWOT data and that in telemetric gauges in RS, even during extreme events. Moreover, in the absence of in situ data, SWOT was still able to capture WSE data. Full article

Harmful algal blooms (HABs) are an escalating global concern due to their increasing frequency, duration, intensity, and geographic spread. These events threaten public health by contaminating drinking water sources, recreational areas, and food production systems with cyanotoxins. Effective monitoring is critical but remains limited by the spatial and temporal variability of blooms. Unmanned aerial vehicles (UAVs) have recently emerged as a flexible, high-resolution tool for HAB monitoring that can complement satellite and in situ methods. This review synthesizes recent applications of UAVs in HAB detection, mapping, and sampling, with a focus on how these approaches can support public health interventions. Key UAV platforms, sensor types, and data processing workflows are summarized, along with considerations related to flight regulations. Studies linking UAV data to indicators like chlorophyll-a and phycocyanin are discussed, highlighting their relevance for early warning systems and water treatment responses. Finally, the review identifies persistent challenges—including validation, regulatory gaps, and integration with health risk frameworks—and provides recommendations to advance UAV-based monitoring. These insights support the continued development of UAV systems as part of comprehensive strategies to mitigate HAB-related health risks. Full article

Background: Leukemia, a hematologic malignancy, is the major fluid tumor. However, there is a paucity in laboratory characterization in South Africa due to limited diagnostic infrastructure. Chromosomal aberrations play a crucial role in leukemia pathogenesis, influencing classification, prognosis, and treatment. Aim: This study aimed to characterize chromosomal aberrations in leukemia patients using the fluorescence in situ hybridization (FISH) method, with the goal of improving diagnostic precision and guiding tailored treatment in resource-limited settings. Methodology: This study was a retrospective analysis of 349 leukemia patient records from the NHLS Corporate Data Warehouse, covering cases diagnosed between January 2019 and January 2024. Chromosomal aberrations were assessed using FISH, including cases of CML, AML, CLL, and ALL. Results: CML was the most prevalent leukemia subtype (40%), followed by AML (31%). Age-specific distributions were significant across subtypes (p < 0.0001). FISH detected subtype-specific aberrations: t(1;19) and t(12;21) in 25% of ALL cases; t(8;21) and t(15;17) in 22–33% of AML cases; and t(9;22) in 100% of CML cases. In CLL, 13q deletions were most common (53% complex, 33% simple). Conclusions: This study reveals distinct chromosomal aberration patterns in leukemia patients in Gauteng, with CML as the most prevalent subtype. Distinct patterns were observed across ALL, AML, and CLL, with age and gender-specific trends. Findings highlight regional genetic influences, diagnostic gaps, and healthcare challenges, emphasizing the urgent need to expand cytogenetic and molecular testing to enable targeted diagnostics, risk stratification, and personalized therapies in sub-Saharan Africa. Full article

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser–matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field. Full article

We developed a forecast model for cyanobacteria bloom formation in two contrasting inland lakes in Australia by combining in situ sampling and continuous remote sensing hyperspectral reflectance (HydraSpectra) with hydrodynamic and algal growth models. Cyanobacterial distribution of a buoyant species was simulated with an algal growth model, driven by forecasted meteorological data, and modeled temperature stratification and mixing dynamics from a one-dimensional, vertical k-epsilon turbulence hydrodynamic model. The cyanobacteria model was re-initialized daily with measured cell counts derived from the hyperspectral reflectance data. Simulations of cyanobacterial concentrations (cell counts) reflected the dynamic mixing behavior in the lakes with daily phases of near-surface accumulation and subsequent daily mixing due to wind or night-time cooling. To determine the surface concentration of cyanobacteria on sub-daily time scales, it was demonstrated that the combined use of high-resolution water temperature profiles, HydraSpectra reflectance data, and a hydrodynamic model to quantify the mixing dynamics is essential. Overall, the model results demonstrated a prototype for a cyanobacteria short-term forecast model. Having these tools in place allows us to quantify the risks of cyanobacterial blooms in advance to inform options for lake management. Full article