Search Results (686)

The recovery and abatement of volatile organic compounds (VOCs) have received increasing attention due to their significant environmental and health impacts. Supported sulfonic acid materials have shown great potential in converting aromatic VOCs into their non-volatile derivatives through reactive adsorption. However, the anchoring state of sulfonic acid groups, which is closely related to the properties of the support, greatly affects their performance. In this study, two supported sulfonic acid materials, SZO and SMO, were prepared by treating ZrO₂ and MgO with chlorosulfonic acid, respectively, to investigate the influence of the support properties on the anchoring state of sulfonic acid groups and their reactive adsorption performance for o-xylene. The supports, adsorbents, and adsorption products were extensively characterized, and the reactivity of SZO and SMO towards o-xylene was systematically compared. The results showed that sulfonic acid groups are anchored on the ZrO₂ surface through covalent bonding, forming positively charged sulfonic acid sites ([O_1.5Zr-O]^δ−-SO3H^δ+) with a loading of 3.6 mmol/g. As a result, SZO exhibited excellent removal efficiency (≥91.3%) and high breakthrough adsorption capacity (ranging from 38.59 to 82.07 mg/g) for o-xylene in the temperature range of 130 –150 °C. In contrast, sulfonic acid groups are anchored on the MgO surface via ion-paired bonding, leading to the formation of negatively charged sulfonic acid sites ([O_0.5Mg]⁺:OSO₃H⁻), which prevents their participation in the electrophilic sulfonation reaction with o-xylene molecules. This work provides new insights into tuning and enhancing the performance of supported sulfonic acid materials for the resource-oriented treatment of aromatic VOCs. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

SMoV and SVBV are two major viruses that pose significant threats to the global strawberry industry. Both are latent viruses, making early detection difficult due to their uneven distribution and low concentration in host tissues. Traditional RT-PCR techniques are insufficient for precise and quantitative detection. In this study, TaqMan RT-qPCR detection systems for SMoV and SVBV were established for application in practical production settings, enabling accurate, rapid, and efficient detection of strawberry viruses. When viral accumulation in plants is low, the highly sensitive TaqMan RT-qPCR technique allows for accurate quantification, facilitating the early identification of infected plants and preventing large-scale outbreaks in cultivation areas. The development of a duplex TaqMan RT-qPCR assay enables simultaneous quantification of SMoV and SVBV in a single reaction, improving detection efficiency and providing technical support for risk assessment and effective control of strawberry viral diseases. Full article

Owing to the inherently lower sensitivity of microwave aperture synthesis radiometers (ASRs), Soil Moisture and Ocean Salinity (SMOS) satellite brightness temperature (TB) measurements exhibit significantly greater system noise than real-aperture microwave radiometers do. This paper introduces a novel noise mitigation method for the SMOS L1C multi-angle TB product. The proposed method develops a multi-angle sea surface TB relationship lookup table, enabling the mapping of SMOS L1C multi-angle TB data to any single-angle TB, thereby averaging to the measurements to reduce noise. Validation experiments demonstrate that the processed SMOS TB data achieve noise levels comparable to those of the Soil Moisture Active Passive (SMAP) satellite. Additionally, the salinity retrieval experiments indicate that the noise mitigation technique has a clear positive effect on SMOS salinity retrieval. Full article

With the rapid development of wireless communication, Wireless Local Area Networks (WLANs) are widely deployed in high-density environments. Ensuring fast handovers and optimal AP selection during device roaming is critical for maintaining network throughput and user experience. However, frequent mobility, high access density, and dynamic channel fluctuations complicate throughput prediction. To address this, we propose a method combining the Snow-Melting Optimizer (SMO) with decision tree regression models to optimize feature selection and model transmission opportunities (TXOP) and AP throughput. Experimental results show that the Extreme Gradient Boosting (XGBoost) model performs best, achieving high prediction accuracy for TXOP (MSE = 1.3746, R² = 0.9842) and AP throughput (MAE = 2.5071, R² = 0.9896). This approach effectively captures the nonlinear relationships between throughput and network factors in dense WLAN scenarios, demonstrating its potential for real-world applications. Full article

Seed germination is recognized for enhancing the accumulation of bioactive compounds. Perilla frutescens (L.) Britt., commonly known as perilla seed, is rich in fatty acids that may be beneficial for anti-hair loss. This study investigated the hair regeneration potential of perilla seed extracts—non-germinated (NG-PS) and germinated in distilled water (0 ppm selenium; G0-PS), and germinated with 80 ppm selenium (G80-PS)—obtained from supercritical fluid extraction (SFE) and screw compression (SC). SFE extracts exhibited significantly higher levels of polyphenols, tocopherols, and fatty acids compared to SC extracts. Among the germinated groups, G0-PS showed the highest bioactive compound content and antioxidant capacity. Remarkably, treatment with SFE-G0-PS led to a significant increase in the proliferation and migration of hair follicle cells, reaching 147.21 ± 2.11% (p < 0.05), and resulted in complete wound closure. In addition, its antioxidant and anti-inflammatory properties were reflected by a marked scavenging effect on TBARS (59.62 ± 0.66% of control) and suppressed nitrite amounts (0.44 ± 0.01 µM). Moreover, SFE-G0-PS markedly suppressed SRD5A1-3 gene expression—key regulators in androgenetic alopecia—in both DU-145 and HFDPCs, with approximately 2-fold and 1.5-fold greater inhibition compared to finasteride and minoxidil, respectively. Simultaneously, it upregulated the expression of hair growth-related genes, including CTNNB1, SHH, SMO, GLI1, and VEGF, by approximately 1.5-fold, demonstrating stronger activation than minoxidil. These findings suggest the potential of SFE-G0-PS as a natural therapeutic agent for promoting hair growth and preventing hair loss. Full article

Basal cell carcinoma (BCC), the most common skin cancer, is primarily driven by Hedgehog (Hh) and TP53 pathway alterations. Although additional pathways were implicated, the mutational landscape in Asian populations, particularly Koreans, remains underexplored. We performed whole-exome sequencing of BCC tumor tissues from Korean patients and analyzed mutations in 11 established BCC driver genes (PTCH1, SMO, GLI1, TP53, CSMD1/2, NOTCH1/2, ITIH2, DPP10, and STEAP4). Mutational profiles were compared with Caucasian cohort profiles to identify ethnicity-specific variants. Ultraviolet (UV)-exposed and non-UV-exposed tumor sites were compared; genes unique to non-UV-exposed tumors were further analyzed with protein–protein interaction analysis. BCCs in Koreans exhibited distinct features, including fewer truncating and more intronic variants compared to Caucasians. Korean-specific mutations in SMO, PTCH1, TP53, and NOTCH2 overlapped with oncogenic gain-of-function/loss-of-function (GOF/LOF) variants annotated in OncoKB, with some occurring at hotspot sites. BCCs in non-exposed areas showed recurrent mutations in CSMD1, PTCH1, and NOTCH1, suggesting a UV-independent mechanism. Novel mutations in TAS1R2 and ADCY10 were exclusive to non-exposed BCCs, with protein–protein interaction analysis linking them to TP53 and NOTCH2. We found unique ethnic-specific and UV-independent mutational profiles of BCCs in Koreans. TAS1R2 and ADCY10 may contribute to tumorigenesis of BCC in non-exposed areas, supporting the need for population-specific precision oncology. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

Background: Duchenne muscular dystrophy (DMD), which affects 1 in 3500 to 5000 newborn boys worldwide, is characterized by progressive skeletal muscle weakness and degeneration. The reduced muscle regeneration capacity presented by patients is associated with increased fibrosis. Satellite cells (SCs) are skeletal muscle stem cells that play an important role in adult muscle maintenance and regeneration. The absence or mutation of dystrophin in DMD is hypothesized to impair SC asymmetric division, leading to cell cycle arrest. Methods: To overcome the limited availability of biopsies from DMD patients, we used our 3D skeletal muscle organoid (SMO) system, which delivers a stable population of myogenic progenitors (MPs) in dormant, activated, and committed stages, to perform SMO cultures using three DMD patient-derived iPSC lines. Results: The results of scRNA-seq analysis of three DMD SMO cultures versus two healthy, non-isogenic, SMO cultures indicate reduced MP populations with constant activation and differentiation, trending toward embryonic and immature myotubes. Mapping our data onto the human myogenic reference atlas, together with primary SC scRNA-seq data, indicated a more immature developmental stage of DMD organoid-derived MPs. DMD fibro-adipogenic progenitors (FAPs) appear to be activated in SMOs. Conclusions: Our organoid system provides a promising model for studying muscular dystrophies in vitro, especially in the case of early developmental onset, and a methodology for overcoming the bottleneck of limited patient material for skeletal muscle disease modeling. Full article

Muscle activity during exercise is typically assessed using oximeters, to evaluate local oxygen saturation (SmO₂), or surface electromyography (sEMG), to analyze electrical activation. Despite the importance of combining these analyses, no study has evaluated both of them during specific swimming exercises in combination with mechanical power output. This study aimed to assess muscle activity during an incremental test on a swim-bench utilizing oximeters and sEMG. Nine male swimmers performed a five-steps test: PRE (3 min at rest), STEP 1, 2, and 3 (swimming at a frequency of 25, 30, and 40 cycle/min for a duration of 2, 2, and 1 min, respectively), and POST (5 min at rest). Each swimmer wore two oximeters and sEMG, one for each triceps brachii. Stroke frequency and arm mechanical power (from ~13 to ~52 watts) estimated by the swim-bench were different among all steps, while no differences between arms were found. SmO₂ (from ~70% to ~60%) and sEMG signals (from ~20 to ~65% in signal amplitude) showed a significant increase among all steps. In both arms, a large/very large correlation was found between mechanical power and SmO₂ (r < −0.634), mechanical power and sEMG onset/amplitude (r > 0.581), and SmO₂ and sEMG amplitude (r > 0.508). No correlations were found between the slope of the sEMG spectral indexes and the slope of SmO₂; only sEMG detected electrical manifestation of muscle fatigue through the steps (p < 0.05). Increased muscle activity, assessed by both oximeters and sEMG, was found at mechanical power increases, revealing both devices can detect effort variation during exercise. However, only sEMG seems to detect peripheral manifestations of fatigue in dynamic conditions. Full article

Sea Surface Salinity is a crucial climatic variable due to its twofold role as both a passive and an active tracer of oceanic processes. Despite its relevance, however, it could not be measured from space, mainly because of technological limitations, until 2009. Since then, the generation and assessment of satellite salinity has become a game-changer in physical and biogeochemical oceanography, as well as in climate science. Three satellite sensors with salinity-measuring capabilities (SMOS-Soil Moisture and Ocean Salinity, Aquarius, and SMAP-Soil Moisture Active Passive) have been launched in the previous decade, each characterized by specific measurement concepts and features and ad hoc validation approaches. The increasing usage of spaceborne salinity products has produced a variety of results and applications, which are here summarized under three specific domains: climate, scientific, and operational. Finally, short-to-mid-term perspectives, indicating both the expected improvements in terms of algorithms and also looking at novel mission concepts (that will provide continuation of these measurements in the decade to come) have been described. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam deformation with high accuracy and robustness is critical for ensuring the long-term safety and operational integrity of hydraulic structures. However, traditional physics-based models often struggle to fully capture the nonlinear and time-dependent deformation responses in hydraulic structures driven by such coupled environmental influences. To address these limitations, this study presents an advanced deep learning (DL)-based deformation monitoring for hydraulic buildings using long-sequence monitoring data of hydrostatic pressure and temperature. Specifically, the Bidirectional Stacked Long Short-Term Memory (Bi-Stacked-LSTM) is proposed to capture intricate temporal dependencies and directional dynamics within long-sequence hydrostatic and thermal time series. Then, hyperparameters, including the number of LSTM layers, neuron counts in each layer, dropout rate, and time steps, are efficiently fine-tuned using the Gaussian Process-based surrogate model optimization (GP-SMO) algorithm. Multiple deformation monitoring points from hydraulic buildings and a variety of advanced machine-learning methods are utilized for analysis. Experimental results indicate that the developed GP-SMO-optimized Bi-Stacked-LSTM dam deformation monitoring model shows better comprehensive representation capability of both past and future deformation-related sequences compared with benchmark methods. By approximating the behavior of the target function, the GP-SMO algorithms allow for the optimization of critical parameters in DL models while minimizing the high computational costs typically associated with direct evaluations. This novel DL-based approach significantly improves the extraction of deformation-relevant features from long-term monitoring data, enabling more accurate modeling of temporal dynamics. As a result, the developed method offers a promising new tool for safety monitoring and intelligent management of large-scale hydraulic structures. Full article

Background/Objectives: Brain arteriovenous malformations (bAVMs) are rare vascular anomalies characterized by direct connections between arteries and veins, bypassing the capillary network. This study aimed to identify potential genetic factors contributing to the development of sporadic bAVMs. Methods: Three patients (AVM1–3) from Kazakhstan who underwent microsurgical resection at the National Centre for Neurosurgery (NCN) in Astana, Kazakhstan, were analyzed. Brain AVMs were diagnosed using magnetic resonance imaging (MRI). Genomic DNA was isolated from whole venous blood samples, and whole-exome sequencing was performed on the NovaSeq 6000 platform (Illumina). Variants were filtered according to standard bioinformatics protocols, and candidate gene prioritization was conducted using the ToppGene tool. Results: In silico analysis further revealed candidate genes likely associated with lesion development, including COL3A1, CTNNB1, LAMA1, NPHP3, SLIT2, SLIT3, SMO, MAPK3, LRRK2, TTN, ERBB2, PARD3, and OBSL1. It is essential to focus on the genetic variants affecting the following prioritized genes: ERBB2, SLIT3, SMO, MAPK3, and TTN. Mutations in these genes were predicted to be “damaging”. Most of these genes are involved in signaling pathways that control vasculogenesis and angiogenesis. Conclusions: Defects in genes associated with ciliary structure and function may be critical to the pathogenesis of brain AVMs. These findings provide valuable insights into the molecular underpinnings of bAVM development, emphasizing key biological pathways and potential candidate genes. Further research is needed to establish robust correlations between specific genetic mutations and clinical phenotypes, which could ultimately inform the development of improved diagnostic, therapeutic, and prognostic approaches. Full article