Static Analysis of Android Secure Application Development Process with FindSecurityBugs

Mobile devices have been growing more and more powerful in recent decades, evolving from a simple device for SMS messages and phone calls to a smart device that can install third party apps. People are becoming more heavily reliant on their mobile devices. Due to this increase in usage, security threats to mobile applications are also growing explosively. Mobile app flaws and security defects can provide opportunities for hackers to break into them and access sensitive information. Defensive coding needs to be an integral part of coding practices to improve the security of our code. We need to consider data protection earlier, to verify security early in the development lifecycle, rather than fixing the security holes after malicious attacks and data leaks take place. Early elimination of known security vulnerabilities will help us increase the security of our software, reduce the vulnerabilities in the programs, and mitigate the consequences and damage caused by potential malicious attacks. However, many software developer professionals lack the necessary security knowledge and skills at the development stage, and secure mobile software development is not yet well represented in most schools\u27 computing curriculum. In this paper, we present a static security analysis approach with the FindSecurityBugs plugin for Android secure mobile software development based on OWASP mobile security recommendations to promote secure mobile software development education and meet the emerging industrial and educational needs

Simple rejection Monte Carlo algorithm and its application to multivariate statistical inference

The Monte Carlo algorithm is increasingly utilized, with its central step involving computer-based random sampling from stochastic models. While both Markov Chain Monte Carlo (MCMC) and Reject Monte Carlo serve as sampling methods, the latter finds fewer applications compared to the former. Hence, this paper initially provides a concise introduction to the theory of the Reject Monte Carlo algorithm and its implementation techniques, aiming to enhance conceptual understanding and program implementation. Subsequently, a simplified rejection Monte Carlo algorithm is formulated. Furthermore, by considering multivariate distribution sampling and multivariate integration as examples, this study explores the specific application of the algorithm in statistical inference

Assessing the impact of extreme droughts on dryland vegetation by multi-satellite solar-induced chlorophyll fluorescence

Satellite-estimated solar-induced chlorophyll fluorescence (SIF) is proven to be an effective indicator for dynamic drought monitoring, while the capability of SIF to assess the variability of dryland vegetation under water and heat stress remains challenging. This study presents an analysis of the responses of dryland vegetation to the worst extreme drought over the past two decades in Australia, using multi-source spaceborne SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) and TROPOspheric Monitoring Instrument (TROPOMI). Vegetation functioning was substantially constrained by this extreme event, especially in the interior of Australia, in which there was hardly seasonal growth detected by neither satellite-based observations nor tower-based flux measurements. At a 16-day interval, both SIF and enhanced vegetation index (EVI) can timely capture the reduction at the onset of drought over dryland ecosystems. The results demonstrate that satellite-observed SIF has the potential for characterizing and monitoring the spatiotemporal dynamics of drought over water-limited ecosystems, despite coarse spatial resolution coupled with high-retrieval noise as compared with EVI. Furthermore, our study highlights that SIF retrieved from TROPOMI featuring substantially enhanced spatiotemporal resolution has the promising capability for accurately tracking the drought-induced variation of heterogeneous dryland vegetation

Immunization against inhibin DNA vaccine as an alternative therapeutic for improving follicle development and reproductive performance in beef cattle

The objective of the present study was to investigate the potential role of immunization against INH on follicular development, serum reproductive hormone (FSH, E2, and P4) concentrations, and reproductive performance in beef cattle. A total of 196 non-lactating female beef cattle (4-5 years old) with identical calving records (3 records) were immunized with 0.5, 1.0, 1.5, or 2.0 mg [(T1, n = 58), (T2, n = 46), (T3, n = 42) and (T4, n = 36), respectively] of the pcISI plasmid. The control (C) group (n = 14) was immunized with 1.0 mL 0.9% saline. At 21d after primary immunization, all beef cattle were boosted with half of the primary immunization dose. On day 10 after primary immunization, the beef cattle immunized with INH DNA vaccine evidently induced anti-INH antibody except for the T1 group. The T3 group had the greatest P/N value peak among all the groups. The anti-INH antibody positive rates in T2, T3 and T4 groups were significantly higher than that in C and T1 groups. RIA results indicated that serum FSH concentration in T2 group increased markedly on day 45 after booster immunization; the E2 amount in T3 group was significantly increased on day 10 after primary immunization, and the levels of E2 also improved in T2 and T3 groups after booster immunization; the P4 concentration in T2 group was significantly improved on day 21 after primary immunization. Ultrasonography results revealed that the follicles with different diameter sizes were increased, meanwhile, the diameter and growth speed of ovulatory follicle were significantly increased. Furthermore, the rates of estrous, ovulation, conception, and twinning rate were also significantly enhanced. These findings clearly illustrated that INH DNA vaccine was capable of promoting the follicle development, thereby improving the behavioral of estrous and ovulation, eventually leading to an augment in the conception rates and twinning rate of beef cattle

Hydrological modeling in the Manas River Basin using soil and water assessment tool driven by CMADS

Hidrološka simulacija u meteorološki neispitanim područjima oduvijek je težak problem u proučavanju atmosferskih i hidroloških odnosa; to je također jedan od važnih faktora koji ograničavaju razvoj modela i spoznaju o izvoru vode u porječju. U svrhu analize atmosferskih i hidroloških odnosa; u radu se daje kvantitativna procjena promjene vodotokova u porječjima prekrivenim ledenjacima i snijegom, te je izabrano porječje rijeke Manas (Manas River Basin - MRB) u Kini kao tipično područje istraživanja u svrhu provjere prilagodljivosti meteoroloških podataka u Kini (China Meteorological Assimilation Driving Datasets) – CMADS, za model alata za procjenu tla i vode - Soil and Water Assessment Tool model (SWAT). Taj se model prvenstveno koristio za simulaciju izvora vode, a zatim smo ga kalibrirali s podacima CMADS-a, lokalizirali u porječje rijeke Manas (MRB), Kina, te konačno kalibrirali simulirano oticanje s dobivenim podacima SWAT-CUP (SWAT Calibration and Uncertainty Programs). Uz to, u ovo je istraživanje također uključena analiza osjetljivosti parametara te ocjena i kalibriranje parametara. Rezultati su pokazali da se modelom SWAT može dobro reproducirati proces oticanja vode na dva položaja istraživanog područja (Kenswat i Hongshanzui) primjenom podataka iz CMADS-a. Simulacija se pokazala uspješnom na osnovu podataka od mjesec dana na oba položaja gdje su R2 = (0,556÷0,999) i NSE = (0,937÷0,998), i dala zadovoljavajuće rezultate kod R2 = (0,927÷0,993) i NSE = (0,836÷0,997). Naše istraživanje pokazuje da se modelom SWAT mogu dobiti zadovoljavajući rezultati kalibriranjem parametara u područjima s visokim dotokom vode s vodenjaka. Uz to, CMADS može osigurati potrebne meteorološke podatke za SWAT simulacije i pomoći kod kalibriranja parametara i analize prikupljenih podataka s površine.Hydrological simulation in meteorological ungauged areas has always been a difficult problem for the study on atmospheric and hydrological coupling; meanwhile, it is also one of the important factors that restrict model development and basin water resource knowledge. To analyze the mechanism of atmospheric and hydrological coupling, this study quantitatively evaluated water cycle situation in basins covered with glaciers and snow, and chose the Manas River Basin (MRB) in China as the typical research area to verify the adaptability of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS). The SWAT model was firstly built to simulate water resources, then we calibrated the model with CMADS dataset and started localization in the Manas River Basin (MRB), China, and finally calibrated simulated runoff with observed data SWAT-CUP (SWAT Calibration and Uncertainty Programs). In addition, parameter sensitivity analysis, and parameter calibration and validation were also included in the present study. Results showed that the SWAT model could well reproduce the runoff process of two stations (Kenswat and Hongshanzui) in the research area by using data from CMADS. The simulation performed well on monthly scale in both stations, where R2 = (0,556÷0,999) and NSE = (0,937÷0,998), and also showed satisfactory effects, where R2 = (0,927÷0,993) and NSE = (0,836÷0,997).Our research suggests that the SWAT model can show satisfactory results through parameter calibration in areas with high glacial recharge rate. Moreover, CMADS can provide necessary meteorological data for SWAT simulations, and support parameter calibration and historical surface data analysis

Spring Flood Forecasting Based on the WRF-TSRM Mode

The snowmelt process is becoming more complex in the context of global warming, and the current existing studies are not effective in using the short-term prediction model to drive the distributed hydrological model to predict snowmelt floods. In this study, we selected the Juntanghu Watershed in Hutubi County of China on the north slope of the Tianshan Mountains as the study area with which to verify the snowmelt flood prediction accuracy of the coupling model. The weather research and forecasting (WRF) model was used to drive a double-layer distributed snowmelt runoff model called the Tianshan Snowmelt Runoff Model (TSRM), which is based on multi-year field snowmelt observations. Moreover, the data from NASA’s moderate resolution imaging spectroradiometer (MODIS) was employed to validate the snow water equivalent during the snow-melting period. Results show that, based on the analysis of the flow lines in 2009 and 2010, the WRF-driven TSRM has an overall 80% of qualification ratios (QRs), with determination coefficients of 0.85 and 0.82 for the two years, respectively, which demonstrates the high accuracy of the model. However, due to the influence of the ablation of frozen soils, the forecasted flood peak is overestimated. This problem can be solved by an improvement to the modeled frozen soil layers. The conclusion reached in this study suggests that the WRF-driven TSRM can be used to forecast short-term snowmelt floods on the north slope of the Tianshan Mountains, which can effectively improve the local capacity for the forecasting and early warning of snowmelt floods

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

We describe the construction of a very important forcing dataset of average daily surface climate over East Asia&mdash;the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS). This dataset can either drive the SWAT model or other hydrologic models, such as the Variable Infiltration Capacity model (VIC), the Soil and Water Integrated Model (SWIM), etc. It contains several climatological elements&mdash;daily maximum temperature (&deg;C), daily average temperature (&deg;C), daily minimum temperature (&deg;C), daily average relative humidity (%), daily average specific humidity (g/kg), daily average wind speed (m/s), daily 24 h cumulative precipitation (mm), daily mean surface pressure (HPa), daily average solar radiation (MJ/m2), soil temperature (K), and soil moisture (mm3/mm3). In order to suit the various resolutions required for research, four versions of the CMADS datasets were created&mdash;from CMADS V1.0 to CMADS V1.3. We have validated the source data of the CMADS datasets using 2421 automatic meteorological stations in China to confirm the accuracy of this dataset. We have also formatted the dataset so as to drive the SWAT model conveniently. This dataset may have applications in hydrological modelling, agriculture, coupled hydrological and meteorological modelling, and meteorological analysis