The Adaptive Quadratic Linear Unit (AQuLU): Adaptive Non Monotonic Piecewise Activation Function

The activation function plays a key role in influencing the performance and training dynamics of neural networks. There are hundreds of activation functions widely used as rectified linear units (ReLUs), but most of them are applied to complex and large neural networks, which often have gradient explosion and vanishing gradient problems. By studying a variety of non-monotonic activation functions, we propose a method to construct a non-monotonic activation function, x·Φ(x), with Φ(x) [0, 1]. With the hardening treatment of Φ(x), we propose an adaptive non-monotonic segmented activation function, called the adaptive quadratic linear unit, abbreviated as AQuLU, which ensures the sparsity of the input data and improves training efficiency. In image classification based on different state-of-the-art neural network architectures, the performance of AQuLUs has significant advantages for more complex and deeper architectures with various activation functions. The ablation experimental study further validates the compatibility and stability of AQuLUs with different depths, complexities, optimizers, learning rates, and batch sizes. We thus demonstrate the high efficiency, robustness, and simplicity of AQuLUs

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach

古代詩歌における季節表現の形成

筑波大学 (University of Tsukuba)201

Pollution characteristics, long-term variation trend, and health risk assessment of lead in ambient PM2.5 in Jinan

BackgroundA number of studies have shown that heavy metals in atmospheric PM2.5 have impacts on human health, while studies on the impact of long-term and low-concentration exposure to lead in PM2.5 on human health are limited. ObjectiveTo investigate the pollution characteristics of lead in ambient PM2.5 and assess its chronic health risks. MethodsDaily PM2.5 concentration data in Jinan from 2014 to 2019 were collected, and the year-by-year trend of PM2.5 concentration was analyzed. Licheng District (an industrial area) and Shizhong District (a residential area) were elected to install an ambient PM2.5 monitoring stationrespectively. The sampling instrument was a 100 L·min−1 high-flow PM2.5 sampler, with a cumulative sampling time of 20-24 h per day, using a quartz fiber filter membrane for lead detection and a glass fiber filter membrane for PM2.5 determination. The sampling frequency was 7 consecutive days per month from the 10th to the 16th (A total of 493 d were sampled and some were missing; 172 d during the heating period and 321 d during the non-heating period). Two PM2.5 samples were collected in one monitoring site each day. A total of 986 samples were collected in one monitoring site. The lead content in PM2.5 samples was detected by inductively coupled plasma mass spectrometry. The concentration of PM2.5 was measured by weighing method. The annual average concentration and enrichment factor of lead in PM2.5, the change trend of lead content per unit mass of PM2.5, and the difference between heating period and non-heating period from 2014 to 2019 were estimated. Technical guide for environment health risk assessment of chemical exposure (WS/T 777-2021) was used to assess the health risks of exposure to lead in PM2.5. ResultsThe average annual concentration of lead in PM2.5 ranged from 23.2 ng·m−3 to 154.7 ng·m−3. The average concentration in heating period from 2015 to 2019 was higher than that in non-heating period, and the differences in 2015, 2017, and 2019 were statistically significant (P < 0.01 or 0.001). The enrichment factors ranged from 200 to 1342 in 2014 to 2019. The average enrichment factors in heating period in 2015, 2017, and 2018 was higher than those in non-heating period, and the difference was statistically significant (P < 0.05 or 0.001). The lead contents per unit mass of PM2.5 ranged from 493 ng·mg−1 to 1944 ng·mg−1, and the differences between heating period and non-heating period in 2014, 2017, and 2018 were statistically significant (P < 0.05 or 0.001). The average annual concentration and enrichment factor of lead in PM2.5 showed a downward trend, and thus the lead content per unit mass of PM2.5 also decreased. From 2014 to 2019, the carcinogenic risk of lead in PM2.5 in Jinan ranged from 1.69×10−8 to 2.45×10−6, showing a significant downward trend year by year, and the 95th percentile decreased by 3%-46% from the previous year. The carcinogenic risk level of lead was reduced to an acceptable level (<1×10−6) after 2017. ConclusionFrom 2015 to 2019, lead concentration and enrichment factor in PM2.5 increase during heating period compared with non-heating period, but it is not completely consistent of lead content in PM2.5 per unit mass. From 2014 to 2016, exposure to lead in PM2.5 may elevate carcinogenic risk to human. After 2017, the carcinogenic risks of exposure to lead in PM2.5 are at an acceptable level

Follow-up study of neuropsychological scores of infant patients with cobalamin C defects and influencing factors of cerebral magnetic resonance imaging characteristics

PurposeThe purpose of this study was to investigate whether baseline cerebral magnetic resonance imaging (MRI) characteristics could predict therapeutic responsiveness in patients with cobalamin C (cblC) defects.Materials and methodsThe cerebral MRI results of 40 patients with cblC defects were evaluated by a neuroradiologist. Neuropsychological scores and imaging data were collected. Neuropsychological tests were performed before and after standardized treatment.ResultsThirty-eight patients initially underwent neuropsychological testing [developmental quotient (DQ)]. CblC defects with cerebellar atrophy, corpus callosum thinning and ventricular dilation had significantly lower DQs than those without (P &lt; 0.05). Through a multivariate linear stepwise regression equation after univariate analysis, ventricular dilation was the most valuable predictor of lower DQs. Thirty-six patients (94.7%) underwent follow-up neuropsychological testing. The pre- and post-treatment DQ values were not significantly different (Z = −1.611, P = 0.107). The post-treatment DQ classification (normal, moderately low, or extremely low) showed nearly no change compared to the pretreatment DQ classification (k = 0.790, P &lt; 0.001).ConclusionVentricular dilation, cerebral atrophy and corpus callosum thinning are the main MRI abnormalities of cblC defects, and these manifestations are significantly correlated with delayed development in children. MRI findings can be considered an important tool for determining the severity of cblC defects

Sugar Protectants Improve the Thermotolerance and Biocontrol Efficacy of the Biocontrol Yeast, Candida oleophila

A variety of sugar compounds have been used as additives to protect various biocontrol yeasts from adverse environmental stresses. However, studies on maltose and lactose as sugar protectants are limited, and their protective effect is not clear. In the present study, exposure of the biocontrol yeast Candida oleophila cells to 45°C for 10 min, while immersed in either 5 or 10% (w/v) maltose or lactose, provided a significant protective effect. The addition of maltose and lactose significantly enhanced enzyme activity and gene expression of catalase, thioredoxin reductase, and glutathione reductase, relative to cells that have been immersed in sterile distilled water (controls) exposed to 45°C. In addition, C. oleophila cells suspended in maltose and lactose solutions also exhibited higher viability and ATP levels, relative to control cells. Notably, the biocontrol efficacy of C. oleophila against postharvest diseases of apple fruit was maintained after the yeast was exposed to the high temperature treatment while immersed in maltose and lactose solutions. These results demonstrate the potential of maltose and lactose as sugar protectants for biocontrol agent against heat stress

Financial risk identification and control of cross border merger and acquisition enterprises

Mergers and acquisitions are basic channels for modern companies’ growth. With globalization speeding up, multinational companies increasingly take on M_A activities to strengthen global market positions and raise competitiveness. In recent years, M_A activities have played an important role in Chinese companies. Financial risk is inherent to M_A processes in cross-border companies. Also, more than 50% of Chinese companies did not achieve their M_A goals. Therefore, recognizing and controlling financial risk is essential. This paper analyses the financial risk from different perspectives and then provides suggestions by analysing a typical M_A case: Bohai Leasing merger with Seaco Company. Complete due diligence and clear M_A strategies, combination of various financing instrument, strategic paying methods and finance integration are some ways for controlling and decreasing finance risk