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In recent years, the Integrated Satellite Aerial Terrestrial (I‐SAT) network has garnered significant attention as an innovative and integrated communication system. However, it still encounters interference in the face of the complex external environment. In this context, reconfigurable intelligent surface (RIS) provides a key way of solving this problem and effectively improves the performance and stability of the I‐SAT network. This article considers the combination of unmanned aerial vehicle (UAV) and RIS and proposes a novel architecture for sub‐connected active RIS (ARIS) under the energy consumption constraints of UAV and ARIS. The authors first provide a UAV‐ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. The interference between each link and the total energy consumption are all taken into consideration. In addition, a deep deterministic policy gradient (DDPG) algorithm is utilized for the optimization problem, and achieves convergence in continuous action space. Finally, the simulation results affirm the precision of the proposed method in significantly enhancing performance compared to other schemes. [ABSTRACT FROM AUTHOR]

Globally, the widespread utilization of plastic products has resulted in the accumulation of microplastics (MPs) in the soil. MPs have the potential to impact the loss of soil organic carbon (SOC). Nevertheless, the influence of different types of MPs on SOC loss remains uncertain.In this study, a 38 day incubation experiment with two kinds of conventional MPs (polyethylene, polypropylene) as well as two kinds of biodegradable MPs (polyhydroxyalkanoate [PHA], polylactic acid [PLA]) were added into three types of soil (loam, sandy loam, and sandy soil) in farmland shelterbelts, and the sources of CO2 emissions was distinguished by the difference in 13C isotope abundance between the biodegradable MPs (PHA and PLA) (−10.02‰ to −9.92‰) and the soil (−24.39‰ to −22.86‰) (>10‰).In conjunction with the structural characterization of MPs, as well as soil physicochemical properties and microbial characteristics, we observed that the conventional MPs did not degrade in short term incubation, but significantly enhance soil‐derived CO2 emissions by altering the dissolved N content (NH4+‐N and dissolved total N [DTN]) and reducing microbial biomass carbon content only in sandy loam soil (p < 0.05). Biodegradable MPs degraded significantly, and enhanced soil‐derived CO2 emissions by reducing soil DTN and NO3−‐N contents in loam, sandy loam and sandy soil (p < 0.05).Overall, the input of biodegradable MPs causes a more serious loss of SOC than conventional MPs as the soil sand content increased in short term incubation, which needs to be considered in predicting the global impact of increasing biodegradable MPs pollution. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

As a plant classified under the "medicine food homology" concept, garlic offers various health benefits and comes in many different varieties. In this study, the metabolite composition of different garlic varieties were analyzed using LC–MS/MS quadrupole‐Orbitrap mass spectrometry and ICP‐MS. A total of 30 chemical elements and 1256 metabolites were identified. Significant differences in chemical elements and metabolomics profiles were observed among the five garlic groups (VIP > 1.5). Compared to WG, PG contained 5 unique compounds, HG had 15 unique compounds, SCG had 18 unique compounds, and SBG had 26 unique compounds. Furthermore, the results showed that WG had smaller differences with PG and HG, but significant differences with SBG and SCG. KEGG analysis revealed metabolic pathways associated with the formation of differential metabolites. These findings uncover the differences and mechanisms in the composition of various garlic varieties, providing a theoretical foundation for distinguishing the nutritional components of different garlic types. [ABSTRACT FROM AUTHOR]

Heavy metal ions such as cadmium, mercury, lead, and arsenic in the soil cannot be degraded naturally and are absorbed by crops, leading to accumulation in agricultural products, which poses a serious threat to human health. Therefore, establishing a rapid and efficient method for detecting heavy metal ions in agricultural products is of great significance to ensuring the health and safety. In this study, a novel optimized spectrometric method was developed for the rapid and specific colorimetric detection of cadmium ions based on N-(2-Acetamido)-iminodiacetic acid (ADA) and Victoria blue B (VBB) as the chromogenic unit. The safety evaluation of ADA showed extremely low biological toxicity in cultured cells and live animals. The standard curve is y = 0.0212x + 0.1723, R2 = 0.9978, and LOD = 0.08 μM (0.018 mg/kg). The liner concentrations detection range of cadmium is 0.1–10 μM. An inexpensive paper strip detection method was developed with a detection limit of 0.2 μM to the naked eye and a detection time of less than 1 min. The method was successfully used to assess the cadmium content of rice, soybean, milk, grape, peach, and cabbage, and the results correlated well with those determined by inductively coupled plasma–mass spectrometry (ICP-MS). Thus, our study demonstrated a novel rapid, safe, and economical method for onsite, real-time detection of cadmium ions in agricultural products. [ABSTRACT FROM AUTHOR]

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model. [ABSTRACT FROM AUTHOR]

T‐2 toxin causes renal dysfunction with proteinuria and glomerular podocyte damage. This work explores the role of metabolic disorder/reprogramming‐mediated epigenetic modification in the progression of T‐2 toxin‐stimulated podocyte injury. A metabolomics experiment is performed to assess metabolic responses to T‐2 toxin infection in human podocytes. Roles of protein O‐linked‐N‐acetylglucosaminylation (O‐GlcNAcylation) in regulating T‐2 toxin‐stimulated podocyte injury in mouse and podocyte models are assessed. O‐GlcNAc target proteins are recognized by mass spectrometry and co‐immunoprecipitation experiments. Moreover, histone acetylation and autophagy levels are measured. T‐2 toxin infection upregulates glucose transporter type 1 (GLUT1) expression and enhances hexosamine biosynthetic pathway in glomerular podocytes, resulting in a significant increase in β‐arrestin‐1 O‐GlcNAcylation. Decreasing β‐arrestin‐1 or O‐GlcNAc transferase (OGT) effectively prevents T‐2 toxin‐induced renal dysfunction and podocyte injury. Mechanistically, O‐GlcNAcylation of β‐arrestin‐1 stabilizes β‐arrestin‐1 to activate the mammalian target of rapamycin (mTOR) pathway as well as to inhibit autophagy during podocyte injury by promoting H4K16 acetylation. To sum up, OGT‐mediated β‐arrestin‐1 O‐GlcNAcylation is a vital regulator in the development of T‐2 toxin‐stimulated podocyte injury via activating the mTOR pathway to suppress autophagy. Targeting β‐arrestin‐1 or OGT can be a potential therapy for T‐2 toxin infection‐associated glomerular injury, especially podocyte injury. [ABSTRACT FROM AUTHOR]

Adequate sleep is essential for the biological maintenance of physical energy. Lack of sleep can affect thinking, lead to emotional anxiety, reduce immunity, and interfere with endocrine and metabolic processes, leading to disease. Previous studies have focused on long‐term sleep deprivation and the risk of cancer, heart disease, diabetes, and obesity. However, systematic metabolomics analyses of blood, heart, liver, spleen, kidney, brown adipose tissue, and fecal granules have not been performed. This study aims to systematically assess the metabolic changes in the target organs caused by sleep deprivation in vivo, to search for differential metabolites and the involved metabolic pathways, to further understand the impact of sleep deprivation on health, and to provide strong evidence for the need for early intervention. [ABSTRACT FROM AUTHOR]

A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（Adaptive moment）optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurrent Neural Network) and one-layer LSTM model.

On the basis of the basic principle of electromagnetic dosimetry, the paper studies the electromagnetic exposure of a passenger's body to a compound electromagnetic field caused by the combined action of DC and AC bus currents of a pure electric vehicle inverter. By building an electromagnetic model of a pure electric vehicle body, adult human body and inverter, the finite element method is used to calculate the magnetic induction intensity(| B |), induced electric field intensity(| E |) and induced current density(| J |) of the compound electromagnetic field to the human trunk and central nervous system in the driver and front seat passenger. The numerical results are compared with the exposure limits defined by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). The fields are well below the safe exposure limits defined by ICNIRP. [ABSTRACT FROM AUTHOR]