Changes in soil quality under different agricultural management in Chinese mollisols.

Mollisols (called Black soils) are of major agricultural importance in China. Most of these soils have been used agriculturally for the last 50 years or so. Due to intensive cultivation and improper soil management, loss of organic matter and yield suppression resulting from soil erosion are serious problems in the region. The current research studied the physical and chemical properties of a typical Mollisol in China, characterized the changes of soil organic carbon (SOC) with cultivation, investigated the effects of agricultural management systems on SOC and total organic nitrogen (TON) contents and their vertical distribution, and examined the negative impacts of continuous soybean on crop and soil productivity. The soil is characterized with a thick (60 cm) mollic epipedon, higher organic carbon (5.8%), CEC (43.7 cmol(+)/kg) and macro-aggregate (\u3e0.25 mm) stability, and greater macronutrients and water availability in the upper epipedon. Bulk density increases with depth, and total porosity declines with depth. Soil texture is clay loam. Overall characteristics make this soil fertile and productive. The SOC content declined rapidly at early years of cultivation and gradually afterwards. Compared with organic matter in the uncultivated soil, total SOC loss was 17%, 28%, and 55% in 5-, 14- and 50-year cultivation, respectively. Wheat-soybean rotation with addition wheat straw or pig manure resulted in a substantial increase in SOC content in 9 years. Compared with a wheat-corn-soybean rotation, continuous cropping reduced SOC and N contents in the profile, particularly SOC content. Moldboard plowing significantly reduced SOC and N contents whereas integrated tillage increased SOC and N relative to conventional tillage. Use of chemical fertilizers (N, P, and K) along with return of crop residues resulted in a substantial increase in SOC and N in top layers of the soil. Continuous soybean results in unbalanced soil enzymes activities, declines of SOC, total K, Zn, available K and N contents, soil pH and bacteria/fungi ratio. It is proposed that the best management for maintaining soil productivity in the area would be crop rotation along with the integrated tillage and addition of crop residues and chemical fertilizers

Deeper and Wider Networks for Performance Metrics Prediction in Communication Networks

In today's era, users have increasingly high expectations regarding the performance and efficiency of communication networks. Network operators aspire to achieve efficient network planning, operation, and optimization through Digital Twin Networks (DTN). The effectiveness of DTN heavily relies on the network model, with graph neural networks (GNN) playing a crucial role in network modeling. However, existing network modeling methods still lack a comprehensive understanding of communication networks. In this paper, we propose DWNet (Deeper and Wider Networks), a heterogeneous graph neural network modeling method based on data-driven approaches that aims to address end-to-end latency and jitter prediction in network models. This method stands out due to two distinctive features: firstly, it introduces deeper levels of state participation in the message passing process; secondly, it extensively integrates relevant features during the feature fusion process. Through experimental validation and evaluation, our model achieves higher prediction accuracy compared to previous research achievements, particularly when dealing with unseen network topologies during model training. Our model not only provides more accurate predictions but also demonstrates stronger generalization capabilities across diverse topological structures

A Multi-objective Stochastic Programming Model for Order Quantity Allocation under Supply Uncertainty

One of the basic and significant subjects in supply chain management is purchasing and supply management, in which supplier selection and order allocation occupy the critical position. Recently, it has been shown that supply uncertainty is of great concern to supply chain managers and practitioners. In this paper, by taking the constraints of minimum purchasing quota and minimum production batch into account, a multi-objective mixed-integer stochastic programming model considering uncertainty in both supply timing and quantity is presented. By means of transforming the stochastic constraints into deterministic equivalents, the model is converted into a linear programming model. An improved two-phase heuristic approach is proposed and its feasibility and efficiency is illustrated through a numerical example. Further, another numerical instance is conducted to evaluate the effects of the weight of each objective and uncertainty degree on the optimal order policy and to obtain some managerial insights for the decision-making of the manufacturers

Occurrence and Control of Soybean Aphid, Aphis glycines Matsumura

The soybean aphid, Aphis glycines Matsumura, is one of the most important pests of soybean. The A. glycines outbreak in 1998 followed another aphid outbreak after 1989, which caused enormous economic losses. The aphid infested areas exceeded 200 thousand mu, and the soybean yields decreased by 20%. Among aphid infested areas, 78 thousand mu were severely infested with a yield loss of 46%. More than 3,000 mu had no yield at all.Originating text in Chinese.Citation: Wu, Xiaobing, Ni, Wenjun, Liu, Peijing. (1999). Occurrence and Control of Soybean Aphid, Aphis glycines Matsumura. How Peasants Can Increase Wealth, 6, 20

Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable Computers (ISWC) 201

Reduction of Amine Emissions from an Aqueous Amine Carbon Dioxide Capture System Using Charged Colloidal Gas Aphrons

The present invention includes a system and process to reduce amine mist emissions (or MEA) from carbon capture systems using colloidal gas aphrons (CGA), and includes a method for separating and recovering an amine solvent (e.g., in the form of entrained droplets/mist and/or fine aerosol particles) from a carbon dioxide scrubbed flue gas stream exiting a carbon capture system (e.g., oil-fired power plants, coal-fired power plants, and/or natural gas combined cycle plants)