Application of geostatistical analyst methods in discovering concealed gold and pathfinder elements as geochemical anomalies related to ore mineralisation

The study area in the West Junggar Basin is known to be rich in hydrothermal gold deposits and occurrences, even though there has been minimum exploration in the area. It is here hypothesised that this area could host more gold deposits if mineral exploration methods were to be reinforced. This research is aimed at identifying geochemical anomalies of Au, and determining possible factors and conditions which facilitate the formation of anomalies by referring to As and Hg as gold pathfinders. Geostatistical analyst techniques have been applied to 9,852 stream sediments and bedrock data collected on a total surface of 1,280 km 2 of West Junggar, Xinjiang (northwest China). The kriging interpolation and quantile-quantile plot methods, combined with statistical methods, successfully identified both Au and its pathfinders’ anomalies. In the present study, median was considered as background values (10.2 ppm for As, 9.13 ppb for Hg and 2.5 ppb for Au), whereas the 95 th percentile were threshold values (28.03 ppm for As, 16.71 ppb for Hg and 8.2 ppb for Au) and values greater than thresholds are geochemical anomalies. Moreover, the high concentrations of these three discovered elements are caused primarily by hydrothermal ore mineralisation and are found to be controlled mainly by the Hatu and Sartohay faults of a northeast-southwesterly direction as well as their related secondary faults of variable orientation, which facilitate the easy flow of hydrothermal fluids towards the surface resulting in the formation of geochemical anomalies. Most of anomalies concentration of Au are found near the mining sites, which indicates that the formation of new Au anomalies is influenced by current or previous mining sites through geological or weathering processes. In addition, the low concentration of gold and its pathfinders found far from active gold mine or faults indicates that those anomalies are formed due to primary dispersion of hosting rock

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Robust mmWave Beamforming by Self-Supervised Hybrid Deep Learning

Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G. Thus, various techniques are leveraged to improve its performance, e.g., deep learning, advanced optimization algorithms, etc. Although its performance in many previous research scenarios with deep learning is quite attractive, usually it drops rapidly when the environment or dataset is changed. Therefore, designing effective beamforming network with strong robustness is an open issue for the intelligent wireless communications. In this paper, we propose a robust beamforming self-supervised network, and verify it in two kinds of different datasets with various scenarios. Simulation results show that the proposed self-supervised network with hybrid learning performs well in both classic DeepMIMO and new WAIR-D dataset with the strong robustness under the various environments. Also, we present the principle to explain the rationality of this kind of hybrid learning, which is instructive to apply with more kinds of datasets

Effect of supply air temperature on air distribution in a room with radiant heating and mechanical ventilation

The present study focused on the effect of supply air temperature on air distribution in a room with floor heating (FH) or ceiling heating (CH) and mixing ventilation (MV) or displacement ventilation (DV). The vertical distribution of airtemperature and velocity in the occupied zone and the horizontal distribution of containment concentration in the breathing zone were measured as the supply air temperature ranged from 15.0°C (59°F)to 19.0°C (66.2°F). The results showed that the vertical air temperature differences were less than 0.3°C (32.5°F) with FH+MV or CH+MV and between 1.9°C (35.4°F) and 4.2°C (39.6°F) with FH+DV or CH+DV. The turbulence intensity varied from 12.5% to 15.5% with FH+MV or CH+MV and from 6.0% to 10.8% with FH+DV or CH+DV. The air-distribution effectiveness was close to 1.0 with FH+MV or CH+MV and between 1.06 and 1.16 with FH+DV or CH+DV. The results in this paper are relevant to the designand control of the hybrid systems with radiant heating systems and mechanical ventilation systems

Identification of a major QTL and candidate genes analysis for branch angle in rapeseed (Brassica napus L.) using QTL-seq and RNA-seq

IntroductionBranching angle is an essential trait in determining the planting density of rapeseed (Brassica napus L.) and hence the yield per unit area. However, the mechanism of branching angle formation in rapeseed is not well understood.MethodsIn this study, two rapeseed germplasm with extreme branching angles were used to construct an F2 segregating population; then bulked segregant analysis sequencing (BSA-seq) and quantitative trait loci (QTL) mapping were utilized to localize branching anglerelated loci and combined with transcriptome sequencing (RNA-seq) and quantitative real-time PCR (qPCR) for candidate gene miningResults and discussionA branching angle-associated quantitative trait loci (QTL) was mapped on chromosome C3 (C3: 1.54-2.65 Mb) by combining BSA-seq as well as traditional QTL mapping. A total of 54 genes had SNP/Indel variants within the QTL interval were identified. Further, RNA-seq of the two parents revealed that 12 of the 54 genes were differentially expressed between the two parents. Finally, we further validated the differentially expressed genes using qPCR and found that six of them presented consistent differential expression in all small branching angle samples and large branching angles, and thus were considered as candidate genes related to branching angles in rapeseed. Our results introduce new candidate genes for the regulation of branching angle formation in rapeseed, and provide an important reference for the subsequent exploration of its formation mechanism