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Gallium nitride high-electron-mobility transistor (GaN HEMT) power devices are favored in various scenarios due to their high-power density and efficiency. However, with the significant increase in the heat flux density, the junction temperature of GaN HEMT has become a crucial factor in device reliability. Since the junction temperature monitoring technology for GaN HEMT based on temperature-sensitive electrical parameters (TSEPs) is still in the exploratory stage, the TSEPs' characteristics of GaN HEMT have not been definitively established. In this paper, for the common steady-state TSEPs of GaN HEMT, the variation rules of the saturation voltage with low current injection, threshold voltage, and body-like diode voltage drop with temperature are investigated. The influences on the three TSEPs' characteristics are considered, and their stability is discussed. Through experimental comparison, it is found that the saturation voltage with low current injection retains favorable temperature-sensitive characteristics, which has potential application value in junction temperature measurement. However, the threshold voltage as a TSEP for certain GaN HEMT is not ideal in terms of linearity and stability. [ABSTRACT FROM AUTHOR]

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO2), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO2 changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO2) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO2 and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R2 = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))2/(exp(b3) + exp(b5))2, R2 = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R2 = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO2 in Chagan Lake during the ice-free period. The pCO2 in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO2 also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO2 values in the southeast of Chagan Lake were higher than those in the northwest. The CO2 fluxes were calculated based on the pCO2 and ranged from −3.69 to −2.42 mmol/m2/d, indicating that Chagan Lake was absorbing CO2 (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO2, while the Secchi disk depth was negatively correlated with pCO2. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change. [ABSTRACT FROM AUTHOR]

The quality of meat is highly correlated with muscle fiber type. However, the mechanisms via which proteins regulate muscle fiber types in pigs are not entirely understood. In the current study, we have performed proteomic profiling of fast/glycolytic biceps femoris (BF) and slow/oxidative soleus (SOL) muscles and identified several candidate differential proteins among these. We performed proteomic analyses based on tandem mass tags (TMTs) and identified a total of 26,228 peptides corresponding to 2667 proteins among the BF and SOL muscle samples. Among these, we found 204 differentially expressed proteins (DEPs) between BF and SOL muscle, with 56 up-regulated and 148 down-regulated DEPs in SOL muscle samples. KEGG and GO enrichment analyses of the DEPs revealed that the DEPs are involved in some GO terms (e.g., actin cytoskeleton, myosin complex, and cytoskeletal parts) and signaling pathways (PI3K-Akt and NF-kappa B signaling pathways) that influence muscle fiber type. A regulatory network of protein–protein interaction (PPI) between these DEPs that regulates muscle fiber types was constructed, which demonstrates how three down-regulated DEPs, including PFKM, GAPDH, and PKM, interact with other proteins to potentially control the glycolytic process. This study offers a new understanding of the molecular mechanisms in glycolytic and oxidative muscles as well as a novel approach for enhancing meat quality by transforming the type of muscle fibers in pigs. [ABSTRACT FROM AUTHOR]

Shale gas wastewater is a hypersaline industrial effluent in demand of efficient treatment or resource recovery. Membrane distillation (MD) is a heat-driven desalination process of high potential to deal with such streams. However, its application is highly limited by the unsatisfactory hydrophobic membranes that involve a trade-off between vapor permeability and fouling/wetting resistance. Our previous studies highlighted the potential role of an intermediate coating layer of a carbon nanotube (CNT) for the superhydrophobic membrane with 1H,1H,2H,2H-perfluorodecyltriethoxysilane (FAS) grafted to address the trade-off issue against synthetic saline oily wastewater. The work herein investigated its application performance in the continuous concentration and water recovery of real shale gas wastewater, with a commercial PVDF membrane as the reference. The modified membrane recycled 48.2% of the total volume as high-quality water and rejected 99% of feed salinity, achieving a superior concentration rate and flux recovery rate compared to PVDF. The value of the COD, total nitrogen, and ammonia nitrogen in the permeate after the modified membrane was less than 50, 20, and 20 mg/L, meeting the local wastewater discharge standard. It was pointed out that the inorganic fouling for the MD membrane was more of a concern in dealing with a real stream, but the modified membrane exhibited excellent fouling resistance. The cost associated with the treatment was estimated at USD 2.2/m3 for a production capacity of 2000 m3/d. The proposed superhydrophobic membrane has proven to be a feasible alternative from both technical and economic standpoints, offering the potential to improve MD effluent water quality and mitigate membrane fouling. [ABSTRACT FROM AUTHOR]

Military conflicts are one of the inevitable factors that can cause countries to suffer from food insecurity due to reduced agricultural productivity, increased food prices, and the deterioration of agricultural land and infrastructure. Farmland may become fallowed and abandoned as a result of reduced investment in agricultural management caused by military conflicts. To rapidly assess the impact of conflicts on agricultural land and food security, the utilization of effective and feasible methods for the regular monitoring agricultural management status is necessary. To achieve this goal, we developed a framework for analyzing the spatiotemporal distribution of agricultural land and assessing the impact of the Ukraine–Russia war on agricultural management in Ukraine using remote sensing (RS) and geographic information system (GIS) technology. The random forest (RF) classifier, gap filling and Savitzky–Golay filtering (GF-SG) method, fallow-land algorithm based on neighborhood and temporal anomalies (FANTA) algorithm, and kernel density method were jointly used to classify and reveal the spatiotemporal distribution of fallowed and abandoned croplands from 2018 to 2022 based on Landsat time series data on the Google Earth Engine (GEE) platform. The results demonstrated that fallowed and abandoned croplands could be successfully and effectively identified through these proven methods. Hotspots of fallowed croplands frequently occurred in eastern Ukraine, and long-term consecutive fallow agricultural management caused cropland abandonment. Moreover, hotspots of war-driven fallowed croplands were found in western Kherson and the center of Luhansk, where the war has been escalated for a long time. This reveals that the war has had a significant negative impact on agricultural management and development. These results highlight the potential of developing an accessible methodological framework for conducting regular assessments to monitor the impact of military conflicts on food security and agricultural management. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitor (ICI) treatment is considered as an innovative approach for cancers. Since not every patient responded well to ICI therapy, it is imperative to screen out novel signatures to predict prognosis. Based on 407 gastric cancer (GC) samples retrieved from The Cancer Genome Atlas (TCGA), 36 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA), and eight of them (RNASE2, CGB5, INHBE, DUSP1, APOA1, CD36, PTGER3, CTLA4) were used to formulate the Cox regression model. The obtained risk score was proven to be significantly correlated with overall survival (OS), consistent with the consequence of the Gene Expression Omnibus (GEO) cohort (n = 433). Then, the relationship between the risk score and clinical, molecular and immune characteristics was further investigated. Results showed that the low-risk subgroup exhibited higher mutation rate, more M1 macrophages, CD8+ and CD4+ T cells infiltrating, more active MHC-I, and bias to "IFN-γ Dominant" immune type, which is consistent with our current understanding of tumor prognostic risk. Furthermore, it is suggested that our model can accurately predict 1-, 2-, and 3-year OS of GC patients, and that it was superior to other canonical models, such as TIDE and TIS. Thus, these eight genes are probably considered as potential signatures to predict prognosis and to distinguish patient benefit from ICI, serving as a guiding individualized immunotherapy. [ABSTRACT FROM AUTHOR]

Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R 2 of 0.9887, a mean bias error ( M B E ) of 0.19 mW/m 2 , and a root mean square error ( R M S E ) of 7.42 mW/m 2 . The model evaluated on 2017 SURFRAD data shows an R 2 of 0.9376, an M B E of 1.24 mW/m 2 , and an R M S E of 17.45 mW/m 2 , indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative R M S E of 25% and an M B E within ± 5 % , demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm. [ABSTRACT FROM AUTHOR]