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【Background】 Soil and water contamination by heavy metals has become an environmental concern worldwide. In China, wastewater produced from mining or mine-tailings leaching has resulted in heavy metals migrating into downstream water bodies and entering food chains following irrigation with such waters. Removing heavy metals from water prior to irrigation is hence vital to prevent them from ending up in the food chain. 【Objective】 The aim of this paper is to present the results of an experimental study on the efficacy of a constructed wetland system to remove Cd from wastewaters emanating from different sources in rice-growing regions in Hunan province. 【Method】 The system comprises a planted pond and an adsorption pond, and we examined its efficacy by treating two types of wastewater: one was from coal mining (DX) and the other one was industrial wastewater (ST), with Cd concentration in both exceeding the national standard. For each wastewater, we measured its Cd removal rate from the system as well as Cd content in rice grains following irrigation with the two treated wastewaters. 【Result】 The average Cd concentration in the inflow water was 6.86 μg/L for ST and 4.56 μg/L for DX, and the average Cd concentration in the outflow water was 0.81 μg/L for the former and 1.33 μg/L for the latter. The system was hence more effective for removing Cd from the industrial wastewater than for the coal mining wastewater with the removal rate being 88.13% for the former and 70.76% for the latter. Irrigating brown rice using the treated ST and DX wastewater reduced Cd content in the grains by 37% and 58% respectably, compared to those irrigated with the wastewaters without any treatment. 【Conclusion】 The pilot system comprising a planted pond and an adsorption pond can move Cd from the wastewaters. In terms of removal rate, the system is more effective for industrial wastewater, while in terms of reducing Cd translation into rice grains, it is more effective for treating the coal mine wastewater.

Accurate iris segmentation is a crucial preprocessing stage for computer-aided ophthalmic disease diagnosis. The quality of iris images taken under different camera sensors varies greatly, and thus accurate segmentation of heterogeneous iris databases is a huge challenge. At present, network architectures based on convolutional neural networks (CNNs) have been widely applied in iris segmentation tasks. However, due to the limited kernel size of convolution layers, iris segmentation networks based on CNNs cannot learn global and long-term semantic information interactions well, and this will bring challenges to accurately segmenting the iris region. Inspired by the success of vision transformer (VIT) and swin transformer (Swin T), a hybrid deep learning approach is proposed to segment heterogeneous iris images. Specifically, we first proposed a bilateral segmentation backbone network that combines the benefits of Swin T with CNNs. Then, a multiscale feature information extraction module (MFIEM) is proposed to extract multiscale spatial information at a more granular level. Finally, a channel attention mechanism module (CAMM) is used in this paper to enhance the discriminability of the iris region. Experimental results on a multisource heterogeneous iris database show that our network has a significant performance advantage compared with some state-of-the-art (SOTA) iris segmentation networks.

Edge computing is a new paradigm, which provides storage, computing, and network resources between the traditional cloud data center and terminal devices. In this paper, we concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks for multiple users. Our objective is to joint optimize the total delay and energy generated by applications, while guaranteeing the quality of services of users. First, we formulate the problem for the application-driven tasks in edge computing by jointly considering the delays and the energy consumption. Based on that, we propose a novel Application-driven Task Offloading Strategy (ATOS) based on deep reinforcement learning by adding a preliminary sorting mechanism to realize the joint optimization. Specifically, we analyze the characteristics of application-driven tasks and propose a heuristic algorithm by introducing a new factor to determine the processing order of parallelism sub-tasks. Finally, extensive experiments validate the effectiveness and reliability of the proposed algorithm. To be specific, compared with the baseline strategies, the total cost reduction by ATOS can be up to 64.5% on average.

Understanding the coagulation process is critical to developing treatments for trauma and coagulopathies. Clinical studies on tranexamic acid (TXA) have resulted in mixed reports on its efficacy in improving outcomes in trauma patients. The largest study, CRASH-2, reported that TXA improved outcomes in patients who received treatment prior to 3 hours after the injury, but worsened outcomes in patients who received treatment after 3 hours. No consensus has been reached about the mechanism behind the duality of these results. In this paper we use a computational model for coagulation and fibrinolysis to propose that deficiencies or depletions of key anti-fibrinolytic proteins, specifically antiplasmin, a1-antitrypsin and a2-macroglobulin, can lead to worsened outcomes through urokinase-mediated hyperfibrinolysis.