Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Networked and Distributed Control Method with Optimal Power Dispatch for Islanded Microgrids

In this paper, a two-layer network and distributed control method is proposed, where there is a top-layer communication network over a bottom-layer microgrid. The communication network consists of two subgraphs, in which the first is composed of all agents, while the second is only composed of controllable agents. The distributed control laws derived from the first subgraph guarantee the supply-demand balance, while further control laws from the second subgraph reassign the outputs of controllable distributed generators, which ensure active and reactive power are dispatched optimally. However, for reducing the number of edges in the second subgraph, generally a simpler graph instead of a fully connected graph is adopted. In this case, a near-optimal dispatch of active and reactive power can be obtained gradually, only if controllable agents on the second subgraph calculate set points iteratively according to our proposition. Finally, the method is evaluated over seven cases via simulation. The results show that the system performs as desired, even if environmental conditions and load demand fluctuate significantly. In summary, the method can rapidly respond to fluctuations resulting in optimal power sharing

Living grass mulching improves soil enzyme activities through enhanced available nutrients in citrus orchards in subtropical China

Living grass mulching (LGM) is an important orchard floor management that has been applied worldwide. Although LGM can effectively enhance soil nutrient availability and fertility, its effects on microbial-mediated soil nutrient cycling and main drivers are unclear. Meanwhile, the variation of enzyme activities and soil nutrient availability with LGM duration have been rarely studied. This study aims to explore the effects of mulching age and soil layer on enzyme activities and soil nutrients in citrus orchards. In this study, three LGM (Vicia villosa) treatments were applied, i.e., mulching for eight years, mulching for four years, and no mulching (clean tillage). Their effects on the enzyme activities and soil nutrients were analyzed in different soil layers of citrus orchards in subtropical China, i.e., 0-10, 10-20, and 20-40 cm. Compared to clean tillage, mulching for four years had fewer effects on enzyme activities and soil nutrients. In contrast, mulching for eight years significantly increased available nitrogen (N), phosphorus (P) nutrients, β-glucosidase, and cellobiohydrolase activities in the soil layer of 0-20 cm. In the soil layer of 0-40 cm, microbial biomass carbon (C), N, P, N-acetylglucosaminidase, leucine aminopeptidase, and acid phosphatase activities also increased (P < 0.05). Mulching for eight years significantly promoted C, N, and P-cycling enzyme activities and total enzyme activities by 2.45-6.07, 9.29-54.42, 4.42-7.11, and 5.32-14.91 times, respectively. Redundancy analysis shows that mulching treatments for eight and four years had soil layer-dependent positive effects on soil enzyme activities. Microbial C and P showed the most significant positive correlation with enzyme activities, followed by moisture content, organic C, and available N (P < 0.05). Available nutrients contributed almost 70% to affect enzyme activities significantly and were the main drivers of the enzyme activity variation. In summary, LGM could improve soil enzyme activities by increasing available nutrients. The promotion effect was more significant under mulching for eight years. Therefore, extending mulching age and improving nutrient availability are effective development strategies for sustainable soil management in orchard systems. Our study can provide valuable guidelines for the design and implementation of more sustainable management practices in citrus orchards

Source identification and pattern study of closed coal mines water inflow in Songzao Mining Area, Chongqing City

Accurate identification of the source of water gushing in closed coal mines and correct division of water gushing modes are of great significance for scientific disposal of water resources waste and water environment pollution caused by closed coal mine drainage. A comprehensive method for water inflow characterization, source identification, and model research for closed coal mines by multivariate analysis of “water quantity–hydrochemistry–microorganism–hydrogeological conditions” is proposed. The method is based on the dynamic monitoring data of water inflow and the water chemical and microbial indexes of several closed coal mines in the Songzao mining area of Chongqing in a hydrological year. Water quality analysis methods, such as flow dynamic analysis of water inflow and flow–rainfall hydro-logical series correlation function, descriptive statistics of water chemical indexes, and the Pearson correla-tion function of water chemical indexes between mine water samples are also used as bases. The method is further coupled with the hydrogeological conditions of the mining area. Results show that there are three types of fluctuations in the response of water inflow from closed coal mines to rainfall: sudden rise and slow drop, slow rise and slow drop, and stable. The difference in water inflow source and water diversion medium is the main reason for the dynamic change in mine water inflow and the temporal and spatial differences in its response to rainfall. It also causes the characteristics of large variability in TDS, pH, chemical correlation degree, and microbial content of mine water. Based on water source identification, four types, rainfall infiltration type, aquifer release type, old empty water overflow type, and compound type, of water gushing modes of closed coal mines in mining areas are proposed. The multivariate comprehensive analysis method identifies the source of water inrush from closed coal mines in karst mining areas effectively, deepens the understanding of the characteristics of water inrush from closed coal mines, and provides theoretical support for the scientific prevention and control of closed coal mine water inrush in Songzao mining area and the coordinated development of environment and resources

Spatiotemporal variations and its driving factors of soil conservation services in the Three Gorges Reservoir area in China

Soil conservation services play a vital role in regulating ecosystem services to prevent soil erosion and ensure regional ecological security. Therefore, effective evaluation and quantification of soil conservation services in the Three Gorges Reservoir Area (TGRA) are conducive to sustainable management under future global change. In this study, based on a basic database, including land use/cover data, soil data, topographic data, meteorological data, and NDVI (Normalized Difference Vegetation Index) data as the basic databases, to evaluate the temporal and spatial changes of soil conservation services in the TGRA from 1990 to 2015 at a regional-scale level using the general soil loss equation. The results showed that forest ecosystems (including coniferous and broad-leaved mixed forests, coniferous forests, shrub forests, and broad-leaved forests) made a greater contribution (69%) to regulating soil conservation in TGRA, followed by farmland ecosystems (29%). In total TGRA, large spatial variation in soil conservation, such as the highest appeared in the northern hinterland, whereas the lowest was mostly shown in the northwest with relatively frequent human activities and developed industry and agriculture. In general, soil conservation in the TGRA ecosystem gradually increased from 1990 to 2015, with a total increase of 6%. In this period, with the effective implementation of ecological projects, such as the conversion of farmland to forest and natural forest protection, the distributed proportion of forest land area in total TGRA showed a significant increase. In the meantime, the increase of vegetation coverage also helps the restoration of ecosystem structure and function and the improvement of soil conservation services. Our findings will aid our knowledge regarding the ecosystem services of the TGRA and provide implications for future sustainable land management and ecological protection

Thermal annealing in FHD Ge-doped SiO 2 film for applications in optical waveguides

Abstract Thermal annealing effects on the microstructures and optical properties of Ge-doped SiO 2 films fabricated by flame hydrolysis deposition were investigated. Microstructure modifications from rough to smooth were measured by atomic force microscope at different annealing temperatures. The refractive index (n) and extinction coefficient (k) were obtained by variable angle spectroscopic ellipsometry. It is concluded that k decreased and n increased with increasing annealing temperature. The results suggest the improvement of the film quality can be achieved by thermal annealing.

The Effect of Raw Soybean on Oxidative Status of Digestive Organs in Mice

The present study was undertaken to specify the effect of raw soybean on oxidative status of digestive organs in mice. For this purpose, thirty male (C57BL/6J) mice were randomly divided into three groups and fed on different diets as follows: Group 1 was fed on control diet, Group 2 was fed on raw soybean diet and Group 3 was fed on raw soybean diet supplemented with 30 mg/kg cysteamine. After two weeks of feeding, duodenum, liver and pancreas samples were collected to measure oxidative and antioxidative parameters. The results show that ingestion of raw soybean markedly increased contents of superoxide anion and malondialdehyde (MDA) and activity of inducible nitric oxide synthase (iNOS), decreased activity of superoxide dismutase (SOD), T-AOC and content of reduced glutathione (GSH) in digestive organs of mice (P < 0.05). In the group fed with raw soybean diet supplemented with cysteamine, oxidative stress was mitigated. However, oxidative parameter levels were still higher than those of control diet-fed group. The present study indicates that ingestion of raw soybean could result in an imbalance between oxidant and antioxidant, and thus induce oxidative stress in digestive organs of mice

Efficient Image Segmentation of Cardiac Conditions after Basketball Using a Deep Neural Network

The evaluation of heart health status is the reference standard for measuring the intensity of exercise performed by different individuals. Thus, the effective analysis of heart conditions is an important research topic. In this study, we propose a system designed to segment images of the right ventricle. In this system, the right ventricle of the heart is segmented using an improved model called RAU-Net. The sensitivity and specificity of the network are enhanced by improving the loss function. We adopted an extended convolution rather than ordinary convolution to increase the receptive field of the network. In the network-sampling phase, we introduce an attention module to improve the accuracy of network segmentation. In the encoding and decoding stages, we also introduce three residual modules to solve the gradient explosion problem. The results of experiments are provided to show that the proposed algorithm exhibited better segmentation accuracy than an existing algorithm. Moreover, the algorithm can also be trained more rapidly and efficiently