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This study has attempted to investigate the effects of mining on groundwater quality in the Korba Coalfield region of Chhattisgarh, India. Hydrogeochemical processes and statistical methods have been used to assess groundwater quality. To achieve the objective, standard techniques and different indicators, including hydro-geochemistry, WQI, and multivariate studies, have been studied. The strong positive correlations were identified using the correlation coefficient between EC-TDS, EC-HCO3−, NO3−-Cl, and TDS-HCO3− respectively. The major cation abundance follows the order Na+ > Ca2+ > Mg2+ > K+, while the anion abundance is HCO3− > Cl− > SO42− > NO3− > F−. However, the hydro-geochemical facies in the area are predominantly of the Ca–Mg–HCO3 and Ca–Mg–Cl–SO4 types. Gibbs diagram indicates rock dominance have a significant impact on the concentration of both cations and anions. Furthermore, Hierarchical Cluster analysis reveals that 43 sampling locations were split into three clusters based on the WQI value, which represented the combination of geogenic and anthropogenic processes. As per Principal Component Analysis, PC1, PC2, and PC3 account for approximately 76% of the total variance, indicating determined variables that govern groundwater chemistry. The findings of the PCA indicate that both natural geological processes and human activities have an impact on the chemical composition of groundwater. As per WQI results, 33 (77%) samples were rated as excellent, 7 (16%) samples as good, and only 3 (7%) samples as having poor water quality, respectively. However, SAR and %Na analyses reveal that the majority of groundwater samples are classified as excellent to good for irrigation. The health risk assessment was conducted by determining the Hazard Quotient (HQ) using the intake exposure of groundwater according to the guidelines set by the United States Environmental Protection Agency (USEPA). Results indicated that some hazard quotient values exceeded 1 for both adults and children, resulting in non-carcinogenic risk. Overall, the groundwater in the vicinity of the Korba Coalfield is drinkable and suitable for irrigational applications after minor treatment. This research sets a benchmark for scientific studies conducted at both regional and global levels. [ABSTRACT FROM AUTHOR]

This study presented an automatic shoreline extraction method from synthetic aperture radar (SAR) imagery using a semantic image segmentation model, DeepLab-v3+. Shorelines extracted from optical satellite images were employed as ground truth. More than 130,000 pairs of labeled images were generated, which substantially enhances the dataset's objectivity for model training. By using different combination of SAR images from beaches with varying characteristics, several models were constructed. These models were then applied to 15 beaches of Japan to validate their accuracy and versatility. The developed machine learning model demonstrates high accuracy in shoreline extraction, particularly when trained with a diverse set of images from various beaches. This versatility is crucial for the model's applicability across different coastal environments, underscoring the importance of incorporating a wide range of beach properties into the training dataset. [ABSTRACT FROM AUTHOR]

Purpose: This paper reports a study of electromagnetic field (EMF) exposure of several adult insects: a ladybug, a honey bee worker, a wasp, and a mantis at frequencies ranging from 2.5 to 100 GHz. The purpose was to estimate the specific absorption rate (SAR) in insect tissues, including the brain, in order to predict the possible biological effects caused by EMF energy absorption. Method: Numerical dosimetry was executed using the finite-difference time-domain (FDTD) method. Insects were modeled as 3-tissue heterogeneous dielectric objects, including the cuticle, the inner tissue, and the brain tissue. The EMF source was modeled as sinusoidal plane waves at a single frequency (far-field exposure). Results: The whole-body averaged, tissue averaged, and 1 milligram SAR values were determined in insects for all considered frequencies for 10 different incident plane waves. SAR values were normalized to the incident power density of 1 mW/cm2. Maximal EMF absorption in the inner and brain tissues was observed at 6, 12, and 25 GHz for the considered insects, except the brain tissue of a ladybug (max at 60 GHz). Conclusion: The paper presented the first estimation of the SAR for multiple insects over a wide range of RF frequencies using 3-tissue heterogenous insect 3D models created for this specific research. The selection of tissues' dielectric properties was validated. The obtained results showed that EMF energy absorption in insects highly depends on frequency, polarization, and insect morphology. [ABSTRACT FROM AUTHOR]

Background: The Unified Theory of Acceptance and Use of Technology is a self-rated questionnaire to assess twelve constructs related to the level of acceptance of a robot, consisting of 41 items rated on a 5-point Likert scale. The aim of the study was to conduct a preliminary evaluation of the psychometric properties of the Italian version of the UTAUT (I-UTAUT) in a sample of Italian healthy subjects (HCs). Materials and methods: 30 HCs underwent the I-UTAUT to assess its comprehensibility. Reliability and divergent validity of the I-UTAUT were evaluated in a sample of 121 HCs, who also underwent the Montreal Cognitive Assessment (MoCA). Results: The final I-UTAUT version was easily comprehensible. There were no missing data, no floor and ceiling effects. Contrarily to the original version, the Principal Components Analysis suggested a seven-component structure; Cronbach's alpha was 0.94. The I-UTAUT score did not correlate with MoCA. Conclusion: The I-UTAUT represented a reliable and valid questionnaire to identify the level of acceptance of robotics technology in Italian healthy sample. [ABSTRACT FROM AUTHOR]

This study improves ship classification in Synthetic Aperture Radar (SAR) imagery, focusing on few-shot datasets. We propose a data augmentation strategy combining the AlignMixup method and a detail enhancement module to optimize convolutional neural network performance. AlignMixup integrates features at intermediate layers, capturing structural information, while the detail enhancement module highlights high-frequency details to improve ship feature recognition in SAR images. Experiments on small sample datasets show that our method increases classification accuracy by a significant margin and remains practical under data-limited conditions. [ABSTRACT FROM AUTHOR]

The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79%–84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites. [ABSTRACT FROM AUTHOR]

The Mw 7.5 Noto Peninsula earthquake, which occurred on January 1, 2024, was considerably hazardous to the peninsula and surrounding regions owing to a strong motion, large-scale crustal deformation, and subsequent tsunami. Significant surface displacement was observed by the dense global navigation satellite system (GNSS) stations, including universities and SoftBank corporation sites, and synthetic aperture radar (SAR). To estimate reliable coseismic slip distribution and its uncertainties for this event, we used the dense GNSS and the line-of-sight displacements from the SAR based on the Bayesian optimization framework. Considering the listric fault structure of this source fault, we validated the fault dip angles using the grid-search approach in the slip estimation. The acquired models indicated reverse fault motion, including a right-lateral slip component, and two slip peaks were estimated in the eastern and western regions of the fault in the central peninsula, independent of the assumed dip angles. These locations correspond to regions of significant uplift and westward displacement. The dip angle assumption affects the horizontal and vertical component of the calculated displacements: a higher dip angle model (≥ 45°) well reproduces vertical components of GNSS and synthetic aperture radar displacements, whereas a lower dip angle model (< 45°) well reproduces horizontal displacements. Overall, a fault dip of 45° is plausible, although it is not consistent with the listric structure suggested by the seismic reflection survey and the aftershock distribution below the central part of the peninsula. To test such a listric fault model, we conducted a coseismic slip estimation assuming a relatively high (60°) and low (25°) dip angles for the shallow and deep sections of the fault, respectively. Even in this case, we acquired a slip model similar to that of a plain fault, which reasonably reproduced each component of the surface displacements as well as the simple plane fault models. These results suggest that listric geometry is also acceptable for the source faults of this event, although the flat geometry similarly explains the observations. [ABSTRACT FROM AUTHOR]

Paddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that systematically combines time series Sentinel-2 optical features (NDVI for bare soil/peak growth, MNDWI for the submerged stages) and Sentinel-1 SAR backscatter (VH polarization for structural dynamics). PRICOS automates key phenological stage detection through harmonic fitting and dynamic thresholding, requiring only 10–20 samples per region to define rice growth cycles. Validated across six agroclimatic regions, PRICOS achieved overall accuracy (OA) and F1 scores of 0.90–0.98, outperforming existing indices like SPRI (OA: 0.79–0.95) and TWDTW (OA: 0.85–0.92). By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to polarimetric calibration, extending classical PARC-based methods by exploring new matrix combinations that broaden the applicability of the existing techniques. By investigating alternative matrix configurations, the proposed method not only enhances the flexibility of conventional calibration approaches but also identifies matrix combinations that offer superior performance advantages. The influence of the SNR and scattering matrix error of PARC on the proposed method is evaluated by numerical simulations. The results demonstrate that the proposed method is highly accurate for PARCs with an SNR greater than 34 dB and with single-channel scattering matrix deviations less than −40 dB and four-channel scattering matrix deviations less than 0.5 dB. The effectiveness and precision of the method were validated through calibration experiments conducted on the L-band polarimetric synthetic-aperture radar aboard the LT-1 satellite. The experimental results demonstrate that the amplitude and phase estimation errors of channel unbalance are less than 0.6 dB and 4.5°, respectively, and that the crosstalk estimation error is less than −33 dB. Furthermore, the effectiveness of the method is validated through trihedral corner reflector correlation experiments and the synthesis of pseudo-color images via Pauli decomposition. The theoretical polarization characteristics of the reference target exhibited a high degree of agreement with the calibrated polarization characteristics. [ABSTRACT FROM AUTHOR]

Adaptive forest management strategies require accurate detection of forest disturbance, in various spatial scales. Synthetic Aperture Radar (SAR) data can provide information about the forest attributes, penetrating the canopy at different levels under any weather and lighting conditions. ICEYE consists of the largest constellation of SAR satellites, enabling very high spatial and temporal resolution. In this study, ICEYE data were investigated in the detection of storm damage, in a fir forest with complex topography which was recently hit by the Daniel storm, resulting in severe damage to the forest structure (FS) and alluvium depositions (AD). To identify the best potential for storm damage detection using ICEYE data, an unsupervised change detection approach was employed combining wavelet transform and adaptive thresholds at spatial scales of 0.5 m (R05), 1 m (R1), 2 m (R2) and 3 m (R3). Additionally, two morphological filters were applied in best-performing resolutions to assess the impact of post-processing on the detection accuracy. Finally, FS and AD damage were investigated separately in order to provide detailed information about the detection capabilities of ICEYE. The results showcased that R1 (UA = 32.35, PA = 18.34 and K = 0.31) provided the best detection performance, followed by R05 (UA = 20.62, PA = 20.12 and K = 0.19). Furthermore, the employment of morphological filters slightly increased UA and Kappa metrics in both R1(UA = 33.40 and K = 0.32) and R05 (UA = 25.60 and K = 0.24), suggesting that post-processing is necessary to mitigate false detections. Regarding the investigation of AD and FS damage, it was revealed that post-processed ICEYE data in both R05 and R1 are capable of identifying AD with satisfying accuracy (UA = 47.41, PA = 22.36, K = 0.47), while FS damage detection is more challenging (UA = 10.15%-14.40%, PA = 14.55%-15.11%, and Kappa = 0.10-0.17). Overall, this study demonstrated that ICEYE can be used to detect storm-affected areas in mountainous forest ecosystems, especially in cases where detection with other methods is not feasible. [ABSTRACT FROM AUTHOR]

This study investigates the capabilities of the Argentinean satellite SAOCOM, developed by the National Commission for Space Activities (CONAE), to classify native forests in various regions of Argentina using its L-band synthetic aperture radar (SAR). SAOCOM offers high spatial resolution (10 to 100 metres) and multiple acquisition modes, allowing detailed observation of diverse terrestrial ecosystems. Our research focuses on the processing and calibration of intensity and phase data generating bands to classify land cover into Forest/Non Forest. A speckle filter and polarimetric decompositions are employed to improve the performance of the Random Forest classifier bands. Key methodologies include the application of Pauli, Freeman-Durden and Cloude-Pottier decompositions, which allow discriminating the backscattering mechanisms characteristic of different cover types. Both Intensity-derived bands and polarimetric products are evaluated in forest classification (band performance, accuracy, and root mean square error) and compared by cross-tabulation with other tests performed in the same areas. As outstanding results we find that the classification model fits best for intensity and Cloude-Pottier data in arid and semi-arid forests. In the performance of the bands, cross-polarization bands and bands representing the volume backscattering effects of the polarimetric bands stand out. In cross-comparisons with national and international native forest mappings, the classifications using intensity bands and Cloude-Pottier bands stand out, especially for dry forests. [ABSTRACT FROM AUTHOR]

The backscattering coefficient of SAR data is easily influenced by the observation incidence angle. According to the two sets of SAR data from different satellite orbits studied in this paper, it was observed that the incidence angle effect was influenced by the seasonality of corn crop and varied in its impact on different polarization modes. Therefore, this study aims to propose a dynamic cosine normalization method that incorporates the phenology information (BBCH) to enhance the correction accuracy of the incidence angle effect for the corn crop. Firstly, based on the Euclidean distance (Ed) of the two sets of data, the optimal power exponent (N) in the cosine normalization function was calculated separately when the surface scattering or volume scattering was dominant. The results show that the N values range from 2.3 to 1.0 for σ0VH and from 5.7 to 2.5 for σ0VV with the corn growth as well as the changes of the crop field scattering mechanisms. Subsequently, based on a negative correlation between the N and BBCH identified in this study, the equation models between these two parameters were established using four functional relationships. And the N values corresponding to different phenology stages (BBCH) were dynamically determined. In order to quantitatively analyse the correction accuracy for the different normalization methods, the Spectral Similarity Values (SSV) were calculated using the two sets of time series backscattering coefficients (σ0VH and σ0VV) data. It shows that the dynamic cosine normalization method can effectively improve the correction accuracy compared to the static cosine method, with the SSV value reaches to 0.90 or above for the σ0VH and 0.73 for the σ0VV. The result can provide an important reference for further accurate quantitative inversion of the corn growth parameters based on SAR data derived from different satellite orbits. [ABSTRACT FROM AUTHOR]

This paper presents a revolutionary breakthrough in wearable antenna technology through an ingeniously engineered arrowhead-shaped textile butterfly design, fabricated on an eco-friendly jean's substrate marking a paradigm shift in flexible electronics and communication networks. This textile antenna's distinctive morphology transcends conventional designs by seamlessly fusing biomimetic principles with cutting-edge electromagnetic architecture for wearable antenna applications, delivering unprecedented flexibility and conformability while maintaining superior performance metrics. Rigorous electromagnetic simulations and prototype validation demonstrate exceptional results: an ultra-wideband frequency response spanning 2.359–16.76 GHz, coupled with a remarkable peak gain of 6.9 dB and a ground-breaking bandwidth enhancement of 150.64%. The antenna's biomimetic butterfly topology revolutionizes omnidirectional connectivity while achieving unprecedented miniaturization, making it ideal for vital sign monitoring systems. Most significantly, the design incorporates an innovative electromagnetic field distribution technique that yields exceptionally low Specific Absorption Rate values, surpassing FCC safety standards. This breakthrough enables transformative applications in clairvoyant medical monitoring, facilitating next-generation vital sign monitoring with zero-latency data transmission and minimal electromagnetic interference, thereby pioneering new frontiers in personalized healthcare diagnostics and real-time patient monitoring systems. The integration of flexible electronics with this innovative textile antenna design represents a significant advancement in wearable technology, offering robust solutions for future healthcare applications. [ABSTRACT FROM AUTHOR]

The automatic registration of synthetic aperture radar (SAR) and optical images is still a challenging problem due to different imaging mechanisms. This letter proposes a coarse-to-fine image registration method that leverages self-similarity structural difference information. In the coarse registration stage, a Scale-Invariant Feature Transformation-based (SIFT-like) method is employed, complemented by an improved Fast Sample Consensus (IFSC) method to eliminate mismatched point pairs by probabilistic and geometric information. This stage ensures robustness against scale and rotational variations. In the fine registration stage, robust feature points are selected by utilizing phase and edge structural information. A descriptor which based on phase consistency and autocorrelation structural difference (ASDPC) is constructed to capture the structural variations between region blocks, and a fine search is carried out within the neighbourhood of the already matched feature points, so as to find more accurate matched feature points and obtain fine registration. The experimental results demonstrate that the proposed method provides robust and accurate registration for optical-to-SAR images. [ABSTRACT FROM AUTHOR]

Ship detection, an important branch of Synthetic Aperture Radar (SAR) images interpretation, is crucial for various maritime reconnaissance and surveillance but remains challenging due to background interference, dense ship alignments and high aspect ratios. To address these challenges, this letter proposes an arbitrary direction detection method based on Real-Time Models for object Detection (RTMDet), which is one of the fastest and most accurate single-stage detection detectors. Firstly, an attention selection module is designed to replace traditional full convolution method to achieve more parameter reduction; Secondly, in order to extract more key ship feature information and accelerate the network extraction, we construct a lightweight multi-scale feature pyramid network; Thirdly, a novel loss function is introduced to enhance the detection of rotated bounding box by addressing the problem of bounding box discontinuities and lack of scale invariance. The proposed method achieves state-of-the-art detection accuracy on two the high-resolution SAR image datasets for arbitrary ship detection including HRSID (85.6% AP50) and RSDD (90.8% AP50), while demonstrating significant accuracy improvements in the complex inshore scenes. [ABSTRACT FROM AUTHOR]

Apple (Malus domestica) is a temperate fruit tree that can be affected by various plant pathogens throughout the year. Among all pathogens, scab disease incited by Venturia inaequalis Cooke (Wint.) is a serious problem in apple cultivation, as it decreases both the quality and quantity of fruits. Although there are many ways to manage plant diseases including cultural, chemical, and biological methods, managing through plant resistance is an affordable and long-term solution to biotic stress. Understanding innate immune responses in apples, such as the induction of pathogenesis-related (PR) proteins, can enhance efforts to improve disease resistance. The rapid induction of apoplastic PR proteins with antifungal activities, such as chitinases, glucanases, and proteases, can directly target the cell walls of the pathogen to restrict its growth. These mechanisms are supplemented by a durable, broad-spectrum defense response initiated by salicylic acid (SA) accumulation. This response involves the transcriptional activation of PR genes and the priming of uninfected tissues through regulatory proteins. The induction of PR proteins and systemic-acquired resistance mediated by SA confer multilayered innate immunity against Venturia invasion. Malus resistance alleles, such as HcrVf (homologue of the Cladosporium fulvum resistance genes of the Vf region) and Rvi (apple scab (V. inaequalis) resistance genes), are effective against Venturia. Overall, a deeper understanding of constitutive versus induced PR gene expression and SA-mediated immunomodulation pathways in apples provides a foundation to sustainably improve crop protection against this devastating disease. Both biotechnological and ecological approaches should be pursued to leverage ancient plant immune strategies for next-generation Venturia resistance. Therefore, this review focuses on synthesizing the current knowledge of localized and systemic defense mechanisms against Venturia infection in apples. [ABSTRACT FROM AUTHOR]

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R2 = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R2 = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R2 ranging from 0.79 to 0.95. [ABSTRACT FROM AUTHOR]

Water is a valuable natural resource, indispensable in the productive, economic, and social development of human beings, agriculture, and domestic and industrial uses throughout the world. Two samplings were established to evaluate the quality of surface and underground water for agricultural irrigation in the Sierra de Amula Region, Jalisco, Mexico. The first was performed during the dry season from November 2021 to April 2022, and the second was performed during the rainy season from July to September 2022 through completely random probabilistic sampling and a longitudinal descriptive study. In total, 25 surface water and 30 groundwater samples were taken. Each sample was evaluated for its pH, electrical conductivity, and ionic concentration (Ca2+, Mg2+, Na+, K+, CO32−, HCO3−, CI−, SO42−). For data analysis, we determined the ionic concentrations and the salinity and sodicity indexes, including the electrical conductivity, pH, sodium adsorption ratio (SAR), and cationic ratio of soil structural stability (CROSS). The results indicate that the ionic concentration is mainly due to calcium bicarbonate, probably due to the geology of the region through water–rock interactions, and the pH is between 6.64 and 7.77; with respect to EC, most of the sampled sites are concentrated in medium-salinity waters of 250–750 µS cm−1. The sodium adsorption ratio (SAR) showed that the waters have high ionic concentrations of calcium and magnesium and low sodium. The CROSS values were lower than the SAR values, showing that the concentration of potassium ions K+ is low in the evaluated waters. With respect to salinity and sodicity, the water quality of the sampled sites, both surface and groundwater, can be considered good for agricultural use. Given that it was sampled in two seasons, the concentration of ions varies in the rainy season, with the dragging of materials causing the ions to concentrate to a greater extent. This type of research benefits farmers in reducing production costs, having knowledge of water quality, and decision making. We recommend that the alkaline pH of the surface or groundwater be conditioned according to the requirements of the crop to be grown and the irrigation method to be used. [ABSTRACT FROM AUTHOR]

Pineapples are highly susceptible to "Wilt disease", caused by the biotrophic insect Dysmicoccus brevipes that also transmits several Wilt-associated viruses (PMWaVs). Conventional farms manage mealybugs and Wilt disease using chemicals. However, many of these chemicals have been banned in Europe due to safety concerns, leading to a critical need for studies on pesticide-free control methods. During their evolution, plants have developed natural defences, such as systemic acquired resistance (SAR), against pathogens and pests. In this study, salicylic acid (10−3 M) was applied to MD2 and Queen Victoria pineapple plants as a foliar spray or soil drench, followed by mealybug infestation. This treatment enhanced defences, assessed through mealybug multiplication rates, and biochemical and molecular responses of tissue-cultured plantlets under controlled conditions. Phenylalanine ammonia-lyase activity (PAL) was measured as a potential SAR signalling enzymatic marker. Additionally, the expression levels of four genes were analyzed, which included AcPAL and AcICS2, both linked to salicylic acid synthesis; AcMYB-like, a transcription factor regulating salicylic acid biosynthesis; and AcCAT, which is involved in H2O2 level control in plants. SA elicitation reduced the mealybug multiplication rate by 70% on pineapples compared to untreated plants. In this study, the biochemical marker (PAL) and three molecular markers (AcPAL, AcICS2, and AcCAT) showed significant differences between primed and unprimed plants, indicating SAR induction and its role in the pineapple–mealybug interaction. In MD2 and Queen Victoria, PAL increased by 2.3 and 1.5, respectively, while AcPAL increased by 4 and more than 10. The other molecular markers, AcICS2, AcCAT, and AcMYB-like (a transcription factor), increased by 3, except for the last one in Queen Victoria. The reduction in mealybug populations with SAR is less effective than with pesticides, but it provides a valuable alternative on Réunion Island, where the only remaining insecticide will soon be banned. In addition, SAR priming offers a promising, eco-friendly strategy for managing mealybug populations and reducing Wilt disease in pesticide-free pineapple cropping systems. [ABSTRACT FROM AUTHOR]

Alluvial aquifers are vital for agricultural communities in semiarid regions, where groundwater quality is often constrained by seasonal and spatial salinity variations. This study employed geostatistical methods to analyze the spatial and temporal variability of electrical conductivity (EC) and the sodium adsorption ratio (SAR) and elaborate an indicative quality map in the Mimoso Alluvial Aquifer, Pernambuco, Brazil. Groundwater samples were collected and analyzed for cations, total hardness (TH), and the percentage of sodium (PS). Moreover, the relation between EC and the SAR was used to determine the groundwater quality for irrigation. Cation concentrations followed the order Ca2+ > Mg2+ > Na+ > K+. EC and the SAR exhibited medium to high variability, with spatial dependence ranging from moderate to strong, and presented a strong cross-spatial dependence. Results showed that sequential Gaussian simulation (SGS) provided a more reliable groundwater classification for agricultural purposes compared to kriging methods, enabling a more rigorous evaluation. Based on the strong geostatistical cross correlation between EC and RAS, a novel water quality index was proposed, properly identifying regions with lower groundwater quality. The resulting spatial indicator maps classified groundwater as suitable (64.7%), restricted use (2.08%) and unsuitable (2.38%) for irrigation. The groundwater quality maps indicated that groundwater was mostly suitable for agriculture, except in silty areas, also corresponding to regions with low hydraulic conductivity at the saturated zone. Soil texture, rainfall, and water extraction significantly influenced spatial and temporal patterns of groundwater quality. Such correlations allow a better understanding of the groundwater quality in alluvial valleys, being highly relevant for water resources management in semiarid areas. [ABSTRACT FROM AUTHOR]

Based on a report that 1,3,4-oxadiazol-2-ylbenzenesulfonamides act as inhibitors of monoamine oxidase B (MAO-B), the present study explored the effect of replacing the 1,3,4-oxadiazole moiety with a 1,3-thiazole heterocycle. A series of 23 primary sulfonamides were synthesized and evaluated as in vitro inhibitors of the human MAOs. The results showed that the 1,3-thiazolylbenzenesulfonamides were specific inhibitors of MAO-B with the most potent MAO-B inhibitor presenting with an IC50 value of 0.103 µM (3j). Potent MAO-B inhibition was obtained with the substitution of the sulfonamide on the meta position of the phenyl rather than the para position. This study concluded that 1,3-thiazolylbenzenesulfonamides may serve as lead MAO inhibitors for the development of new treatments for disease states such as Parkinson's disease. [ABSTRACT FROM AUTHOR]

Prevalence of microbial infections and new rising pathogens are signified as causative agent for variety of serious and lethal health crisis in past years. Despite medical advances, bacterial and fungal infections continue to be a rising problem in the health care system. As more bacteria develop resistance to antibiotics used in therapy, and as more invasive microbial species develop resistance to conventional antimicrobial drugs. Relevant published publications from the last two decades, up to 2024, were systematically retrieved from the MEDLINE/PubMed, SCOPUS, EMBASE, and WOS databases using keywords such as quinolones, anti-infective, antibacterial, antimicrobial resistance and patents on quinolone derivatives. With an approach of considerable interest towards novel heterocyclic derivatives as novel anti-infective agents, researchers have explored these as essential tools in vistas of drug design and development. Among heterocycles, quinolones have been regarded extremely essential for the development of novel derivatives, even able to tackle the associated resistance issues. The quinolone scaffold with its bicyclic structure and specific functional groups such as the carbonyl and acidic groups, is indeed considered a valuable functionalities for further lead generation and optimization in drug discovery. Besides, the substitution at N-1, C-3 and C-7 positions also subjected to be having a significant role in anti-infective potential. In this article, we intend to highlight recent quinolone derivatives based on the SAR approach and anti-infective potential such as antibacterial, antifungal, antimalarial, antitubercular, antitrypanosomal and antiviral activities. Moreover, some recent patents granted on quinolone-containing derivatives as anti-infective agents have also been highlighted in tabular form. Due consideration of this, future research in this scaffold is expected to be useful for aspiring scientists to get pharmacologically significant leads. [ABSTRACT FROM AUTHOR]

Cropland is fundamental to food security, and monitoring cropland non-agriculturalization through satellite enforcement can effectively manage and protect cropland. However, existing research primarily focuses on optical imagery, and there are problems such as low data processing efficiency and long updating cycles, which make it difficult to meet the needs of large-scale rapid monitoring. To comprehensively and accurately obtain cropland change information, this paper proposes a method based on the Google Earth Engine (GEE) cloud platform, combining optical imagery and synthetic aperture radar (SAR) data for quick and accurate detection of cropland non-agriculturalization. The method uses existing land-use/land cover (LULC) products to quickly update cropland mapping, employs change vector analysis (CVA) for detecting non-agricultural changes in cropland, and introduces vegetation indices to remove pseudo-changes. Using Shanwei City, Guangdong Province, as a case study, the results show that (1) the cropland map generated in this study aligns well with the actual distribution of cropland, achieving an accuracy of 90.8%; (2) compared to using optical imagery alone, the combined optical and SAR data improves monitoring accuracy by 22.7%, with an overall accuracy of 73.65%; (3) in the past five years, cropland changes in Shanwei followed a pattern of an initial increase followed by a decrease. The research in this paper can provide technical reference for the rapid monitoring of cropland non-agriculturalization on a large scale, so as to promote cropland protection and rational utilization of cropland. [ABSTRACT FROM AUTHOR]

The genus Piper is the largest among plants of the Piperaceae family. Phytochemical studies on various piper species indicate the presence of bioactive compounds, with alkamides being among the most prominent. Piperine is well studied, and is usually found in abundance in most species. Guineensine is an alkamide that merits particular interest and, until now, has received less scientific attention. Therefore, in the present review, we discuss guineensine's isolation, synthesis, and pharmacological activity. Data were collected from 1974 to 2024. Databases including PubMed, Google Scholar, and Science Direct were used to retrieved information using the following keywords: guineensine, isolation, synthesis, biological activity, alkamides, Piper spp., pepper, and SAR. Guineensine is obtained using various isolation methods. However, it yields low amounts; therefore, its synthesis is important. In addition, guineensine exerts many biological activities. Its potential is connected to its terminal benzodioxolyl and isobutyamide groups and to the length of its unsaturated carbon chain of twelve atoms. Findings of the studies presented in this review provide substantiation regarding the scientific interest in guineensine. Isolation procedures present advantages and disadvantages, and the methods of its synthesis are efficient. Its biological activity seems promising and further studies may lead to the development of new therapeutic agents. [ABSTRACT FROM AUTHOR]

The study of oceanic internal waves remains a critical area of research within oceanography. With the rapid advancements in oceanic remote sensing and deep learning, it is now possible to extract valuable insights from vast datasets. In this context, by building datasets using deep learning models, we propose a novel stripe segmentation algorithm for oceanic internal waves, leveraging synthetic aperture radar (SAR) images based on the SegFormer architecture. Initially, a hierarchical transformer encoder transforms the image into multilevel feature maps. Subsequently, information from various layers is aggregated through a multilayer perceptron (MLP) decoder, effectively merging local and global contexts. Finally, a layer of MLP is utilized to facilitate the segmentation of oceanic internal waves. Comparative experimental results demonstrated that SegFormer outperformed other models, including U-Net, Fast-SCNN (Fast Segmentation Convolutional Neural Network), ORCNet (Ocular Region Context Network), and PSPNet (Pyramid Scene Parsing Network), efficiently and accurately segmenting marine internal wave stripes in SAR images. In addition, we discuss the results of oceanic internal wave detection under varying settings, further underscoring the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The design and characterisation of a novel dual-band implantable antenna with compact size is presented in this research. The antenna, which is in size and operates at two critical frequencies—0.954 GHz in the UHF band and 2.4 GHz in the ISM band—was fabricated on an RT6010 substrate. The U-shaped slot and shorting pin on the radiating element have been exploited to achieved dual-band and circular polarization. The antenna is noteworthy for achieving circular polarization with a broad axial ratio bandwidth of 24.6%, which enables strong performance throughout its operating frequencies. The proposed antennas SAR values satisfy IEEE safety standards for implantable medical devices with a gain of − 28.1 dB at 2.4 GHz and − 31.2 dB at 0.954 GHz, despite its small size. The design represents a significant advancement in the field of medical implant technology since it prioritizes effective wireless communication capabilities while upholding strict safety regulations. [ABSTRACT FROM AUTHOR]

Investigating coastal wetland plant communities is of great significance for wetland monitoring due to the important functions of coastal wetlands, such as maintaining biodiversity and mitigating global climate change. Current studies on wetland plants mostly rely on optical data, with few utilizing synthetic aperture radar (SAR) data. Moreover, these studies often analysed single temporal SAR data, which limited the exploration of the valuable information present in time-series SAR data. Therefore, in this paper, we proposed a technique for mapping coastal wetland plant types based on time-series SAR coherence and intensity data to fully utilize the information from these data. We utilized Sentinel-1 Single Look Complex (SLC) images covering the Yancheng coastal wetland for the entire year of 2021 to investigate the effectiveness of using dual-polarization interferometric coherence and intensity-derived information from time-series Sentinel-1 data as features for classification. Plant classification was conducted using support vector machine (SVM) and random forest (RF) methods. Our results demonstrated that integrating time-series dual-polarization coherence and intensity-derived information resulted the best classification accuracy, with an overall accuracy (OA) of 89.79% and a Kappa coefficient of 0.858. This highlights the effectiveness of combining coherence and intensity data from time-series Sentinel-1 for monitoring plant cover in coastal wetlands. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) is a potent instrument for estimating soil moisture across vast farmland areas. However, when soil salinity rises, existing SAR retrieval models for soil moisture become less accurate. Soluble salts in soil water alter the inherent correlation between the SAR backscattering coefficients and soil water, thus compromising the performance of retrieval models. The need to get surface roughness parameters from the field also limits the widespread application of the model. To address these issues, we developed a retrieval model for salty soil moisture assessment under small-scale roughness surfaces. First, we found a method to distinguish between slightly rough and rough surfaces using scattering entropy obtained from full-polarization decomposition. Then, we harnessed the co-polarization ratio to mitigate the effect of surface roughness in slightly rough conditions. Later, we developed a soil moisture retrieval model based on the co-polarization ratio, considering incidence angle, residual roughness, and soluble salt content. The validation of the model was verified using field surveying data, yielding an RMSE of 0.026 cm3 ·cm− 3 for the estimated soil moisture. This accuracy is comparable to or surpasses the level achieved by SAR for non-salinized soil moisture retrieval. Our findings showed that Scattering entropy is an effective parameter for distinguishing scattering mechanisms on farmland surfaces effectively. Specifically, when the scattering entropy of farmland is less than 0.5, the co-polarization ratio can mitigate some surface roughness effects. Additionally, when the soil-soluble salt content is incorporated, our model can accurately estimate the soil moisture of salty soil. [ABSTRACT FROM AUTHOR]

In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset. Unfortunately, due to various constraints, the availability of labeled image data for training SAR vehicle detection networks is quite limited. This scarcity of data has become one of the main obstacles hindering the further development of SAR vehicle detection. In response to this issue, this paper collects SAR images of the Ka, Ku, and X bands to construct a labeled dataset for training Stable Diffusion and then propose a framework for data augmentation for SAR vehicle detection based on the Diffusion model, which consists of a fine-tuned Stable Diffusion model, a ControlNet, and a series of methods for processing and filtering images based on image clarity, histogram, and an influence function to enhance the diversity of the original dataset, thereby improving the performance of deep learning detection models. In the experiment, the samples we generated and screened achieved an average improvement of 2.32%, with a maximum of 6.6% in m A P 75 on five different strong baseline detectors. [ABSTRACT FROM AUTHOR]

Quad-polarization synthetic aperture radar (SAR) satellites are important detection tools in Earth observation and remote sensing; in particular, they are of great significance for accurately interpreting radar data and inverting geophysical parameters. Polarimetric calibration is particularly critical to eliminate the effects of distortion in polarized SAR data. The C-SAR/02 satellite launched by China is an important part of the C-band synthetic aperture radar (SAR) constellation, and the quad-polarization strip I (QPSI) is an important imaging mode for its sea–land observation. The relevant research on its polarimetric calibration is still lacking. This study's polarimetric calibration of C-SAR/02 was performed based on the active radar calibrator (ARC) method using four independently developed L/S/C multi-band ARCs and several trihedral corner reflectors (CRs). The polarimetric calibration distortion matrix varies along the range direction; the polarimetric calibration distortion matrix and polarimetric calibration accuracy along the range direction were analyzed, incorporating the devices in different range directions to calculate the distortion matrix. This approach improved the accuracy of the polarimetric calibration results and the effect of the quantization application of the C-SAR satellites. Moreover, our experimental results indicate that the method presented herein is suitable for the C-SAR/02 satellite and may also be more universally applicable to C-SAR-series satellites. [ABSTRACT FROM AUTHOR]

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. [ABSTRACT FROM AUTHOR]

Tropical and subtropical Asia is the major rice-producing region in the world, but the complexity of the cropping system and the diversity of the topography bring challenges to the accurate monitoring of rice cultivation. To address this difficulty, a new deep learning model, ESF-Seg, is proposed in this study to extract the annual tropical rice distribution using monthly averaged time-series Sentinel-1 VH data. The ESF-Seg adopts the Efficient Adaptive Sparse Transformer (EAT) to remove redundant information from input features. The Channel Attention Bridge Block (CAB) and Spatial Attention Bridge Block (SAB) modules are introduced to refine the information. Meanwhile, with the FreqFusion-KAN (FreqK) module, the loss of information can be reduced through the multi-scale feature fusion strategy. The proposed method is evaluated in the Hainan Province of China, an important tropical arable zone with diverse crop resources and complicated croplands. First, ablation experiments are conducted. Compared to the classical SegFormer model, the ESF-Seg model improves on the mIOU by 4.99% and on the mPA by 2.65%. Subsequently, compared to the RF, U-Net, and the original SegFormer model, the overall accuracy (OA) of the ESF-Seg model on the validation samples increased by 11.02%, 2.01%, and 1.33%, and the F1 score improved by 0.0756, 0.0624, and 0.0490, reaching 98.31% and 0.9506, respectively. Furthermore, products showing the annual rice distribution from 2019 to 2023 in Hainan are generated, which exhibit good alignments with the statistical data, surpassing other existing products with an RMSE of 5.4004 Kha. As indicated by the rice mapping products, the proposed method preserves the integrity of the rice parcels in the fragmented croplands, thus providing a new opportunity for the continuous monitoring of tropical rice distribution with high accuracy. [ABSTRACT FROM AUTHOR]

Landslide/mudslide susceptibility is of significance to socioeconomic sustainable development and emergence management. Although remote sensing datasets have been used for landslide/mudslide susceptibility interpretations, the results might be weak owing to the limitations of the single-modal remote sensing dataset. Evolving Earth observation techniques enable the automatic identification of landslide/mudslide susceptibility over a large extent from multimodal remote sensing datasets. This also poses a major challenge in effective organization, representation, and modeling for complex information on landslide/mudslide susceptibility. In this study, we propose a geospatial semantic model to formally represent the interpretation of visual features from optical remote sensing, deformation features from synthetic-aperture radar (SAR) datasets, terrain features from digital elevation models (DEMs), and descriptions by field investigations. First, we applied optical remote sensing image, DEM, and SAR datasets to detect and annotate the features of landslide/mudslide susceptibility. Then, we developed a geospatial ontology to represent these features in a machine-understandable format. Depending on the triple structure of "domain-property-range" and the rules and restriction set by the proposed geospatial ontology, we created a semantic model to conduct semantic query and reasoning for landslide/mudslide susceptibility. The proposed semantic model for landslide/mudslide susceptibility interpretation has been successfully tested in four counties in Yunnan Province, China. We expect this work to be a major contribution to the integration of knowledge from both remote sensing and GIS data, and to deepen the application of semantic web technology in landslide/mudslide susceptibility domains. [ABSTRACT FROM AUTHOR]

A spaceborne synthetic aperture radar (SAR) systematically scans the target scene along its trajectory at various positions and azimuth angles, making it a unique asset for many remote sensing applications. The contrast between ship targets and the surrounding sea can be improved in SAR images by splitting the bandwidth into subapertures (SAs) and then averaging to attain a high azimuth resolution. This letter proposes the use of subapertures decomposition (SD) for detecting moving ships. Furthermore, the ship velocity parameter in the azimuth direction is computed. The proposed SAs dual-pol descriptor (${r_{{\rm{SA}}}}$ r SA ) employed the constant false alarm rate (CFAR) approach followed by a statistical Gamma ($\Gamma $ Γ) distribution to determine the threshold value for distinguishing ship and sea. Thereafter, the ships edges are quantified using Sobel and Canny edge detectors. The eigenvalue descriptor ($\lambda $ λ) of polarimetric SAR provides the total size of the target; hence, the ship sizes are compared with $\lambda $ λ. The velocity of moving ships (${V_{\rm{S}}}$ V S ) is evaluated by utilizing the mean difference of the Doppler centroid ($\Delta {f_{{\rm{DC}}}}$ Δ f DC ) information between pairs of all SAs and is validated using the SA displacement correlation method. The experimental results demonstrate that the proposed method is feasible for real-time moving ship monitoring with 95% accuracy. [ABSTRACT FROM AUTHOR]

Indonesia is an archipelago country which has more sea than land area that become a maritime trade route, so it is necessary to improve the safety of ship navigation, such as with ship detection for ship monitoring. As technology develops, remote sensing can be utilized in the maritime field using SAR data for ship detection. With the current advanced technology of computer vision, object detection can be progressively done using deep learning. The capture of these objects especially in the sea can be provided by SAR space-borne images. This research focuses on ship detection using variety of YOLO with 3 different datasets, such as model 1 using Sentinel-1 image with RGB composite which Sigma Nought VV polarization for red, Sigma Nought VH polarization for green, and Sigma Nought VV/VH polarization for blue. While Official-SSDD was used in model 2, and a combination of Sentinel-1 using Sigma Nought VH polarization and Official-SSDD was used in model 3. Then, ship detection model evaluated and validated using AIS (Automatic Identification System) data. The result shows Model 3 on YOLOv9 is the best model for ship detection in Surabaya area with mAP 43.90%, while for Banjarmasin area Model 1 on YOLOv4 is the best model with mAP 26.78%. YOLOv4 is the best algorithm with mAP 26.78% based on every model performance. Model 3 and 1 are the most suitable data for ship detecting in Surabaya and Banjarmasin area because this data contains Surabaya and Banjarmasin scene as YOLOv9 and YOLOv4 also improve model performance due to architectural improvement. [ABSTRACT FROM AUTHOR]

The integration of a Super High Frequency (SHF) on-body antenna with Frequency Selective Surfaces (FSS) marks a significant advancement in defense beacon technology. This study presents a unique, wearable, apple-shaped SHF antenna incorporating a multifunctional FSS for use as a Defense Locator Beacon (DLB). Key features include high gain, highly directional radiation pattern, low Specific Absorption Rate (SAR), reduced Radar Cross Section (RCS), and compact dimensions. The antenna, made on denim fabric, operates across the entire SHF band. With a 9-cell FSS array on a semi-flexible RT Duroid substrate, the structure is both simulated and fabricated, showing enhanced performance: peak gain increased from 7.14 to 11.1 dBi, FBR from 3.58 dB to 19.87 dB, and RCS reduced from -25 to -50 dB. Link Budget Analysis confirms effective communication, with ranges of 67 m and 64 m for 100 Mbps and 200 Mbps. The proposed antenna ensures high-speed communication and accurate location identification for military personnel. [ABSTRACT FROM AUTHOR]