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This paper uses multiple linear regression model to study the correlation between the ideological and political courses and the construction of university academic style, explores the calculation process of multiple linear regression, studies the impact analysis of linear regression model and the diagnosis of abnormal points, and analyzes the underfitting and overfitting respectively. The linear regression analysis model was used to study the differences in the academic style level of college students of different grades and genders, the correlation analysis of various dimensions of academic style and ideological and political level was carried out, and the linear fit of ideological and political curriculum education and academic style was analyzed by MATLAB to infer the significance level of ideological and political education and academic style. The results show that the correlation coefficient between ideology and politics and the dimension of learning purpose is 0.053, P=0.003

To understand the ecology of species and promote biotechnology through beneficial strain selection for improving starch yield in maize wet-milling steeping, bacterial diversity and community structure during the counter-current steeping process in a commercial steeping system were characterized and investigated. The microbial diversity in the steeping liquor, which consisted of 16 phyla, 131 families, and 290 genera, was more abundant compared to those present on the surface of unsteeped maize. As the counter-current steeping progressed, exposing newer maize to the older steepwater, Lactobacillus dominated, replacing Rahnella, Pseudomonas, Pantoea, and Serratia. The thermophilic and acidophilic microbial consortia were enriched through adaptive evolution engineering and employed to improve starch yield. Several steeping strategies were evaluated, including water alone, SO2 alone, mono-culture of B. coagulans, microbial consortia, and a combination of consortium and SO2. Combining the microbial consortium with SO2 significantly increased the starch yield to, about 66.4 ± 0.5%, a 22% and 46% increase over SO2 alone and the consortium alone, respectively. Scanning electron microscope (SEM) of steeped maize structure indicated that the combination of consortium and SO2 disrupted the protein matrix and widened gaps between starch granules in maize endosperm. This released proteins into the steepwater and left starch granules in the aleurone layer. The steeping strategy of using thermophilic and acidophilic microbial consortium as additives shows potential application as an environmentally friendly alternative to conventional maize steeping procedures. Highlights: Characterization of bacterial diversity in a commercial steeping system. Adaptation of a thermophilic and acidophilic microbial consortium for maize steeping. Development of an environmentally friendly strategy for maize steeping. Consortium combining SO2 leads to 66.4% increase in starch yield. Novel strategy disrupts protein matrix making starch granules to aleurone layer. [ABSTRACT FROM AUTHOR]

The high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models. Plain Language Summary: There are two main problems I n the existing TEC prediction field: (a) The prediction model mainly adopts a sequential stacking structure, losing fine‐grained spatiotemporal features, which leads to insufficient prediction ability of the model. (b) Current research mainly focuses on TEC prediction models, with little attention paid to hyper‐parameter optimization of prediction models. Model optimization can find hyperparameter combinations that make the model's performance close to the optimal solution. This paper proposed a TEC prediction and optimization framework, MAOOA‐Residual‐Attitude‐BiConvLSTM, in which Residual‐Attitude‐BiConvLSTM is used for TEC map prediction. By incorporating skip layer design, this model can simultaneously focus on fine‐grained and coarse‐grained features, resulting in higher prediction accuracy. MAOOA is used to find a better quasi‐optimal hyperparameters for Residual‐Attitude‐BiConvLSTM. This paper provides a new approach for building TEC prediction models. At the same time, this study also indicates that in TEC prediction, in addition to focusing on designing prediction models with higher accuracy, attention should also be paid to the optimization algorithms of the models. Key Points: Proposed multi‐strategy assisted Osprey optimization algorithmuses three new strategies to boost its optimization capabilityNew TEC model Residual‐Attention‐BiConvLSTM improves feature retention and predictionMAOOA‐Residual‐Attention‐BiConvLSTM, a novel TEC prediction framework, merges deep learning with hyper‐parameter optimization [ABSTRACT FROM AUTHOR]

Considering the high demand for air quality, the development of biomass-based air filtration membranes with high air filtration efficiency and good stability is an urgent task. In this work, polyvinyl alcohol (PVA), gelatin (GA), and cellulose nanocrystals (CNC) were mixed and prepared into a membrane through an electrospinning method for air filtration. After a hydrophobic modification, the modified PVA/GA/CNC composite membrane showed excellent filtration efficiency for PM2.5 (97.65%) through the internal three-dimensional structure barrier and the electrostatic capture effect of the CNC with a negative charge, as well as a low-pressure drop (only 50 Pa). In addition, the modified PVA/GA/CNC composite membrane had good mechanical properties (maximum tensile fracture rate of 78.3%) and high stability (air filtration efficiency of above 90% after five wash-filter cycles and a high-temperature treatment at 200 °C). It is worth noting that the whole preparation process is completed without organic solvents, putting forward a new strategy for the construction of green air filtration membranes. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an essential macronutrient for crop growth; therefore, N deficit can greatly limit crop growth and production. In the North China Plain (NCP), winter wheat (Triticum aestivum L.) is one of the main food crops, and its yield has increased from approximately 4000 kg ha−1 to 6000 kg ha−1 in the last two decades. Determining the proper N application rates at different growth stages and in all seasons is very important for the sustainable and high production of wheat in the NCP. A field experiment with five N application rates (250, 200, 150, 100, and 40 kgN·ha−1, designated as N250, N200, N150, N100, and N40, respectively) was conducted during the 2017–2018 and 2018–2019 winter wheat seasons to investigate the effects of the N application rate on water- and fertilizer-utilization efficiency and on the crop growth and yield of winter wheat under sprinkler fertigation conditions. The results showed that in the N application range of 40–200 kg ha−1, crop yield and water- and fertilizer-use efficiencies increased as the N application rate increased; however, further increases in the N application rate (from N200 to N250) did not have additional benefits. The N uptake after regreening of winter wheat linearly increased with crop growth. Considering the wheat yield and N-use efficiency, the recommended optimal N application rate was 200 kg ha−1, and the best topdressing strategy was equal amounts of N applied at the regreening, jointing, and grain-filling stages. The results of this study will be useful for optimizing field N management to achieve high wheat yield production in the NCP and in regions with similar climatic and soil environment conditions. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson's correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB. [ABSTRACT FROM AUTHOR]

The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches. [ABSTRACT FROM AUTHOR]

Primary cardiac lymphomas display a low frequency, sudden onset, swift progression of illness and elevated mortality rates. The current study presents a unique instance of primary cardiac diffuse large B-cell lymphoma and examines its clinical manifestations, pathological characteristics and differential diagnosis. A 64-year-old male patient sought medical attention due to cardiac debility and exertional dyspnea persisting for >10 days. Chest enhanced computed tomography revealed a moderately enhancing irregular mass in the ventricular area, exhibiting limited demarcation from the pericardium and left atrium, accompanied by irregular thickening of the interventricular septum. The postoperative specimen showed the presence of yellow fish-like tumor tissue. Immunohistochemical analysis revealed the presence of lymphocytes positive for CD20, BCL-2, BCL-6, c-Myc-binding protein, mutated melanoma-associated antigen 1 and CD79a, along with a high Ki-67 proliferation index of 80%. Conversely, CD10, CD30, CD3, pan cytokeratin, cyclin D1, desmin and vimentin marker results were found to be negative. Additionally, in situ hybridization demonstrated a lack of Epstein-Barr virus-encoded small RNA expression. The present case report emphasizes the significance of conducting a thorough analysis of the clinical manifestations of diffuse large B-cell lymphoma to assist clinicians in establishing a diagnosis and determining an effective treatment approach, thereby enhancing the patient's prognosis. [ABSTRACT FROM AUTHOR]