Search

Intercropping systems and exogenous microorganism additives are recognized for their potential to influence silage fermentation and quality. This study aims to evaluate the impacts of maize–lablab bean intercropping and lactic acid bacteria (LAB) additives on silage yield, nutritional quality, and economic profitability. A randomized block design was employed with two cropping patterns—maize monocrop (M) and maize–lablab intercrop (ML)—and five additive treatments: No additives (CK), and varying ratios of Lactobacillus Plantarum (LP) and Lactobacillus Buchneri (LB), T1 (100% LP), T2 (9LP:LB), T3 (8LP:2LB), and T4 (100% LB). The silage was analyzed and evaluated for its nutritional quality, fermentation quality, and fermentation effect after 90 days of fermentation. ML intercropping significantly enhanced the fresh matter yields by 8.59% and crude protein content by 8.73% compared to M. From the point of view of inoculation with different lactobacilli, the pH, AA, and NH3-N/TN were lower in the T2 and T3 treatments than in the other treatments, while LA was significantly higher. The V-score, which reflects the overall fermentation quality, was excellent across all treatments, with scores exceeding 80 points; the T2 treatment in ML silage achieved the highest score of 99.58. In addition, intercropping can increase the net income of farmers by 21.67%. In conclusion, maize–lablab intercropping combined with LAB inoculation, particularly with the T2 and T3 treatments, significantly enhances the silage quality and economic returns by reducing pH, increasing the LA content, and improving the CP levels. This study is the first to comprehensively analyze the synergistic effects of altering cropping systems and adding functional microorganisms on forage yield and fermentation quality, offering strategic insights for farms, especially mixed farms, to produce high-quality feed. We recommend adopting these methods to improve feed quality and maximize the profitability of silage production systems. [ABSTRACT FROM AUTHOR]

Polyurethane is widely used on the surface of composite materials for rotor blades as sand erosion protection materials. The failure mechanism investigation of polyurethane film under service conditions is useful for developing the optimal polyurethane film for rotor blades. In this article, the sand erosion test parameters were ascertained according to the service environment of the polyurethane film. The sand erosion resistance and failure mechanism of polyurethane film at different impact angles were analyzed by an infrared thermometer, a Fourier transform infrared spectrometer (FTIR), a differential scanning calorimeter (DSC), a field emission scanning electron microscope (FESEM), and a laser confocal microscope (CLSM). The results show that the direct measurement method of volume loss can better characterize the sand erosion resistance of the polyurethane film compared to traditional mass loss methods, which avoids the influence of sand particles embedded in the polyurethane film. The sand erosion resistance of polyurethane film at low-angle impact is much lower than that at high-angle impact. At an impact rate of 220 m/s, the volume loss after sand erosion for 15 min at the impact angle of 30° is 57.8 mm3, while that at the impact angle of 90° is only 2.6 mm3. The volume loss prediction equation was established according to the experimental data. During low-angle erosion, the polyurethane film damage is mainly caused by sand cutting, which leads to wrinkling and accumulation of surface materials, a rapid increase in roughness, and the generation of long cracks. The linking of developing cracks would lead to large-scale shedding of polyurethane film. During high-angle erosion, the polyurethane film damage is mainly caused by impact. The connection of small cracks caused by impact leads to the shedding of small pieces of polyurethane, while the change in the roughness of the film is not as significant as that during low-angle erosion. The disordered arrangement of the soft and hard blocks becomes locally ordered under the action of impact and cutting loads. Then, the disordered state is restored after the erosion test finishes. The erosion of sand particles leads to an increase in the temperature of the erosion zone of the polyurethane film, and the maximum temperature rise is 6 °C, which does not result in a significant change in the molecular structure of the polyurethane film. The erosion failure mechanism is cracking caused by sand cutting and impact. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an R 2 value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications. [ABSTRACT FROM AUTHOR]

Wireless body area network (WBAN) is an emerging comprehensive technology that can deeply integrate with e-health and smart sports. As a wearable network, improving the quality of network service and user experience is crucial. Due to the miniaturized design of sensors, their available energy from batteries is limited, making the extension of their lifetime a key research challenge. Existing studies have proposed methods to improve energy efficiency, but there are still limitations in addressing dynamic adaptive aspects of differential energy distribution and channel conditions. In order to further extend the lifetime of sensor nodes and networks while ensuring quality of service, and to provide a reliable transmission mechanism for heterogeneous application data, this paper presents a cross-layer optimized MAC protocol mechanism. The protocol takes into account the transmission requirements of different types of data and redesigns the superframe. To improve the lifetime of nodes, we propose an energy-adaptive adjustment mechanism considering the channel conditions. At the same time, a cooperative transmission mechanism is proposed to further enhance network lifetime. In experiments conducted on two typical networks, compared to IEEE 802.15.6, the power adjustment scheme improves the network lifetime by 2.8 to 3.7 times, and the cooperative mechanism between nodes further increases the network lifetime by 17% to 44%. Our proposed scheme effectively extends the network lifetime while ensuring quality of service, avoiding frequent battery resets for users, and effectively improving the user experience quality. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with　K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q2 = 0.612 (cross-validated correlation coefficient) and r2 = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry. [ABSTRACT FROM AUTHOR]