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The accurate prediction of path loss is essential for planning and optimizing communication networks, as it directly impacts the user experience. In 5G signal propagation, the mix of varied terrain and dense high-rise buildings poses significant challenges. For example, signals are more prone to multipath effects and occlusion and shadowing occur often, leading to high nonlinearities and uncertainties in the signal path. Traditional and shallow models often fail to accurately depict 5G signal characteristics in complex terrains, limiting the accuracy of path loss modeling. To address this issue, our research introduces innovative feature engineering and prediction models for 5G signals. By utilizing smartphones as signal receivers and creating a multimodal system that captures 3D structures and obstructions in the N1 and N78 bands in China, the study aimed to overcome the shortcomings of traditional linear models, especially in mountainous areas. It employed the XGBoost algorithm with Optuna for hyperparameter tuning, improving model performance. After training on real 5G data, the model achieved a breakthrough in 5G signal path loss prediction, with an R2 of 0.76 and an RMSE of 3.81 dBm. Additionally, SHAP values were employed to interpret the results, revealing the relative impact of various environmental features on 5G signal path loss. This research enhances the accuracy and stability of predictions and offers a technical framework and theoretical foundation for planning and optimizing wireless communication networks in complex environments and terrains.

Nitrogen (N) is a key element for the production of potato. The N uptake efficiency, N use efficiency and increased N utilization efficiency can be decreased by N deficiency treatment. We performed this study to investigate the association between transcriptomic profiles and the efficiencies of N in potato. Potato cultivars \"Yanshu 4\" (short for Y), \"Xiabodi\" (cv. Shepody, short for X) and \"Chunshu 4\" (short for C) were treated with sufficient N fertilizer and deficient N fertilizer. Then, the growth parameters and tuber yield were recorded; the contents of soluble sugar and protein were measured; and the activities of enzymes were detected. Leaf and root transcriptomes were analyzed and differentially expressed genes (DEGs) in response to N deficiency were identified. The results showed that N deficiency decreased the nitrate reductase (NR), glutamine synthetase (GS) and root activity. Most of the DEGs between N-treated and N-deficiency participate the processes of transport, nitrate transport, nitrogen compound transport and N metabolism in C and Y, not in X, indicating the cultivar-dependent response to N deficiency. DEGs like glutamate dehydrogenase (StGDH), glutamine synthetase (StGS) and carbonic anhydrase (StCA) play key roles in these processes mentioned above. DEGs related to N metabolism showed a close relationship with the N utilization efficiency (UTE), but not with N use efficiency (NUE). The Major Facilitator Superfamily (MFS) members, like nitrate transporter 2.4 (StNRT2.4), 2.5 (StNRT2.5) and 2.7 (StNRT2.7), were mainly enriched in the processes associated with response to stresses and defense, indicating that N deficiency induced stresses in all cultivars.

Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness k s . It is essential to estimate k s for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict k s in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict k s based on the selected surface parameters from previous DNS, LES, and experimental results for flow behavior over various surface roughness. The PSO-BP model’s ability to predict k s in the fully rough region is evaluated and compared with both the existing roughness correction formulas as well as the traditional BP model. An optimized polynomial function is also proposed to serve as a ‘white box’ for predicting k s . It turns out that the PSO-BP method has better performance in the evaluation metrics compared to other methods, yielding a Mean Absolute Error (MAE) of 0.0390, a Mean Squared Error (MSE) of 0.0026 and a Mean Absolute Percentage Error (MAPE) of 28.12%. This novel approach for estimating k s has practical applicability and holds promise for improving the precision and efficiency of calculations related to equivalent sand-grain roughness, and thus provides more accurate and effective solutions for CFD and other engineering applications.

Vitamin C (VC), due to its chemical properties, can provide more oxygen-containing functional groups such as hydroxyl groups for biochar (BC), which promotes the adsorption of tetracycline on biochar. Therefore, in this study, cow dung biochar (CDBC) was modified with VC and VC-modified CDBC (CDBC-VC) was synthesized. The modified biochar was characterized and related factors, adsorption kinetics, isotherms and adsorption mechanisms were investigated. Adsorption kinetics indicate a fast rate of adsorption. The adsorption isotherms showed that the maximum adsorption capacity was 31.72 mg/g (CDBC) and 50.90 mg/g (CDBC-VC), respectively, and the adsorption process was inhomogeneous with multiple molecular layers and the adsorbent has a higher affinity. Mechanistic studies showed that hydrogen bonding interactions, π-π electron donor-acceptor interactions, hydrophobic interactions, and electrostatic interactions were the key to the adsorption process. The analysis of adsorbent regeneration showed that CDBC-VC had good adsorption performance. CDBC and CDBC-VC showed the best performance in simulated industrial wastewater with removal rates of 78.81% and 93.69%. The adsorption mechanism was comprehensively analyzed using six machine learning models. The extreme gradient boosting model gave the best fit. Analysis of the weights of the input variables for predicting adsorption efficiency showed that the ratio of initial TC concentration to BC dosage (29.8%), specific surface area (23%), isoelectric point (8.8%), and ash content (7.7%) had a significant effect on the predicted results.

Sulfate aerosols affect climate by scattering radiation and by changing the microphysical properties of water clouds. In much of the continental interiors that are overwhelmed by anthropogenic sulfate today, the nature of pre-industrial atmospheric sulfate remains pure speculation, which hampers our ability to quantify anthropogenic perturbation on climate and uncertainties in global climate models. Here we show that sequential leaching and multiple-isotope measurement enabled us to effectively distinguish sulfate of different origins, including pre-industrial atmospheric sulfate, retained in certain outcropping carbonates. Data from two interior sites in northern China show that one of the sulfate endmembers consistently has an unusually positive 17O anomaly (Δʹ17O at ~+1.8‰) and characteristic δ18O (~1–5‰) and δ34S (~5–10‰) values. We interpret this sulfate endmember to be integrated pre-industrial atmospheric sulfate from at least the last a few thousands of years. A triple oxygen isotope enabled GEOS-Chem chemical transport model revealed a higher Δʹ17O value in northern China and the south-western United States in the past, consistent with our data. Overall, pre-industrial atmospheric sulfate aerosol chemistry in the interior of northern China and south-western United States had a higher pH in cloud water, which may have led to a less cloud cover due to cleaner air and coarser aerosol sizes than today.Atmospheric sulfate aerosols—which could cool the atmosphere—were formed in less acidic cloud water in continental interiors in pre-industrial time than today, according to a triple oxygen isotope analysis of sulfate in weathering carbonates.

Geological evidence indicates that the deglaciation of Marinoan snowball Earth ice age (~635 Myr ago) was associated with intense continental weathering, recovery of primary productivity, transient marine euxinia, and potentially extensive CH 4 emission. It is proposed that the deglacial CH 4 emissions may have provided positive feedbacks for ice melting and global warming. However, the origin of CH 4 remains unclear. Here we report Ni isotopes (δ 60 Ni) and Yttrium-rare earth element (YREE) compositions of syndepositional pyrites from the upper most Nantuo Formation (equivalent deposits of the Marinoan glaciation), South China. The Nantuo pyrite displays anti-correlations between Ni concentration and δ 60 Ni, and between Ni concentration and Sm/Yb ratio, suggesting mixing between Ni in seawater and Ni from methanogens. Our study indicates active methanogenesis during the termination of Marinoan snowball Earth. This suggests that methanogenesis was fueled by methyl sulfides produced in sulfidic seawater during the deglacial recovery of marine primary productivity. The deglaciation of Marinoan snowball Earth (~635 Myr ago) has been associated with potentially extensive CH 4 emissions in relation to transient marine euxinia. Here, the authors find that active methanogenesis occurred during the termination of Marinoan snowball Earth, fueled by methyl sulfide production in sulfidic seawater.

Hydrogen (H2) monitoring demonstrates significant practical importance for safety assurance in industrial production and daily life, driving the demand for gas-sensing devices with enhanced performance and reduced power consumption. This study developed a room-temperature (RT) hydrogen-sensing platform utilizing two-dimensional (2D) Ag-doped SnS2 nanomaterials activated by light illumination. The Ag-SnS2 nanosheets, synthesized through hydrothermal methods, exhibited exceptional H2 detection capabilities under blue LED light activation. The synergistic interaction between silver dopants and photo-activation enabled remarkable gas sensitivity across a broad concentration range (5.0–2500 ppm), achieving rapid response/recovery times (4 s/18 s) at 2500 ppm under RT. Material characterization revealed that Ag doping induced S vacancies, enhancing oxygen adsorption, while simultaneously facilitating photo-induced hole transfer for surface hydrogen activation. The optimized sensor maintained good response stability after five-week ambient storage, demonstrating excellent operational durability. Experimental results further demonstrated that Ag dopants enhanced hydrogen adsorption–activation, while S vacancies improved the surface oxygen affinity. This work provides fundamental insights into defect engineering strategies for the development of optically modulated gas sensors, proposing a viable pathway for the construction of energy-efficient environmental monitoring systems.

Proton exchange membrane fuel cells are a clean energy technology with wide application in transportation and stationary energy systems. Due to the problem of voltage degradation under long-term dynamic loads, predicting their performance degradation trend is of great significance for extending the life of proton exchange membrane fuel cells and improving system reliability. This study adopts a data-driven approach to construct a degradation prediction model. In view of the problem of many input parameters and complex distribution of degradation features, a neural network model based on a multi-head attention mechanism and class token is first proposed to analyze the impact of different operating parameters on the output voltage prediction. The importance of each input variable is quantified by the attention weight matrix to assist feature screening. Subsequently, a prediction model is constructed based on Transformer to characterize the voltage degradation trend of fuel cells under dynamic conditions. The experimental results show that the root mean square error and mean absolute error of the model in the test phase are 0.008954 and 0.006590, showing strong prediction performance. Based on the importance evaluation provided by the first model, 11 key parameters were selected as inputs. After this input simplification, the model still maintained a prediction accuracy comparable to that of the full-feature model. This result verifies the effectiveness of the feature screening strategy and demonstrates its contribution to improved generalization and robustness.

We focus on the competition between nitrate/nitrite ammonification (also termed dissimilatory nitrate reduction to ammonium (DNRA)) and denitrification processes taking place across partially saturated water‐soil systems. The study is motivated by the observation that the joint presence of dissolved organic nitrogen (DON) and redox fluctuation in the vadose zone poses potential risks for generation of nitrates (NO3−‐N) that can then be reduced to ammonium (NH4+‐N) through DNRA. We examine nitrogen dynamics induced in natural soil samples subject to controlled drying‐wetting cycles. Upon experimental evidences, we estimate the parameters driving the kinetics associated with nitrogen transformation. This enables us to document a competition between DNRA and denitrification during wetting periods. We find that the increasing the carbon‐to‐nitrogen (C/N) ratio in the system yields a significant increase of DNRA rates, with a corresponding increase of their contribution to nitrate reduction. The rate of DNRA is documented to be (a) significantly faster in loam than in sandy loam, due to dissolved carbon release from loam aggregates, and (b) more effective in the presence of amino acid than urea in the natural soil, due to the role of amino acid as carbon source. Our analysis further suggests the relevance of hydrogeochemical factors (e.g., moisture variation, soil texture, and C/N ratio) on DON transformation through the influence of functional microorganisms. These insights advance our understanding of nitrogen dynamics in agroecosystems, which has significant implications for environmental management practices aimed at controlling NO3−‐N pollution in partially saturated soils. Key Points Competition between denitrification and dissimilatory nitrate reduction to ammonium (DNRA) is identified in transformation of organic nitrogen Contribution of DNRA to nitrate reduction depends on soil texture, C/N ratio and nitrogen speciation Ignoring the occurrence of DNRA may underestimate nitrate leaching through vadose zone

Entrepreneurial ventures are established in large numbers in China. The success rate of these entrepreneurial ventures is lower than that of new startups. Mismanagement and a lack of creative skills among entrepreneurs are cited as reasons for entrepreneurial failure in China. The current study investigates the impact of entrepreneurial networking and new venture intention on entrepreneurial success in China, with psychological capital and entrepreneurial optimism serving as moderators. 483 responses were collected from business students in China for data analysis. The findings of the study reveal that the impact of entrepreneurial networking and new venture intention on entrepreneurial success in China, with the moderating role of psychological capital and entrepreneurial optimism, is significant. The theoretical framework of this research has novelty as it introduces new moderating relationships of psychological capital and entrepreneurial optimism in the model of entrepreneurial success. Practically, this study has revealed that entrepreneurial success can be achieved with entrepreneurial networking, entrepreneurial optimism, psychological capital, and new venture intention. The directions of this research point out additional gaps in the literature that scholars should discuss in subsequent studies.