基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

Chemical-Shift Perturbations Reflect Bile Acid Binding to Norovirus Coat Protein: Recognition Comes in Different Flavors

Bile acids have been reported as important cofactors promoting human and murine norovirus (NoV) infections in cell culture. The underlying mechanisms are not resolved. Through the use of chemical shift perturbation (CSP) NMR experiments, we identified a low-affinity bile acid binding site of a human GII.4 NoV strain. Long-timescale MD simulations reveal the formation of a ligand-accessible binding pocket of flexible shape, allowing the formation of stable viral coat protein–bile acid complexes in agreement with experimental CSP data. CSP NMR experiments also show that this mode of bile acid binding has a minor influence on the binding of histo-blood group antigens and vice versa. STD NMR experiments probing the binding of bile acids to virus-like particles of seven different strains suggest that low-affinity bile acid binding is a common feature of human NoV and should therefore be important for understanding the role of bile acids as cofactors in NoV infection.     

生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。     

Optimization of waste quail eggshells as biocomposites for polyaniline in ammonia gas detection

A simple, cost-effective, and novel chemical sensor for ammonia (NH₃) gas detection was developed from polyaniline (PANI)/quail eggshell (QES) composites. QES is a natural waste enriched in calcium carbonate. In this work, pure PANI was synthesized from chemical oxidation method and PANI/QES composites were prepared from physical mixing of QES with the synthesized PANI at different mass ratio. A series of complementary techniques including Fourier transform infrared and ultraviolet-visible spectrometers, scanning electron microscope with energy dispersive detection coupled with mapping, thermogravimetric analysis, and X-ray diffractometer were used to characterize the physicochemical and textural properties of the biocomposites. From the results, PANI/QES composite with a mass ratio of 1 exhibited the lowest NH₃ detection limit of 5.24 ppm with a linear correlation coefficient (R²) of close to unity (0.9932) between the signal and NH₃ gas concentration. As a whole, the PANI/QES biocomposites synthesized from this work exhibited excellent selectivity toward NH₃ gas even in the presence of other gas impurities, such as acetone, ethanol, and hexane. For the sensor reusability, the PANI/QES biocomposites can be reused in the application of NH₃ gas detection for at least 4 cycles.     

Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.     

Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.     

基于哈希链的软件定义网络路径安全

针对软件定义网络中，控制器无法保证下发的网络策略能够在转发设备上得到正确执行的安全问题，提出一种新的转发路径监控安全方案。首先以控制器的全局视图能力为基础，设计了基于OpenFlow协议的路径凭据交互处理机制；然后采用哈希链和消息验证码作为生成和处理转发路径凭据信息的关键技术；最后在此基础上，对Ryu控制器和Open vSwitch开源交换机进行深度优化，添加相应处理流程，建立轻量级的路径安全机制。测试结果表明，该机制能够有效保证数据转发路径安全，吞吐量消耗比SDN数据层可信转发方案（SDNsec）降低20%以上，更适用于路径复杂的网络环境，但时延和CPU使用率的浮动超过15%，有待进一步优化。     

Distributed wireless power transfer in sensor networks with multiple Mobile Chargers

We investigate the problem of efficient wireless power transfer in wireless sensor networks. In our approach, special mobile entities (called the Mobile Chargers) traverse the network and wirelessly replenish the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed and use limited network information. We propose four new protocols for efficient charging, addressing key issues which we identify, most notably (i) what are good coordination procedures for the Mobile Chargers and (ii) what are good trajectories for the Mobile Chargers. Two of our protocols (DC, DCLK) perform distributed, limited network knowledge coordination and charging, while two others (CC, CCGK) perform centralized, global network knowledge coordination and charging. As detailed simulations demonstrate, one of our distributed protocols outperforms a known state of the art method, while its performance gets quite close to the performance of the powerful centralized global knowledge method.