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

UWB-INS Fusion Positioning Based on a Two-Stage Optimization Algorithm

Ultra-wideband (UWB) is a carrier-less communication technology that transmits data using narrow pulses of non-sine waves on the nanosecond scale. The UWB positioning system uses the multi-lateral positioning algorithm to accurately locate the target, and the positioning accuracy is seriously affected by the non-line-of-sight (NLOS) error. The existing non-line-of-sight error compensation methods lack multidimensional consideration. To combine the advantages of various methods, a two-stage UWB-INS fusion localization algorithm is proposed. In the first stage, an NLOS signal filter is designed based on support vector machines (SVM). In the second stage, the results of UWB and Inertial Navigation System (INS) are fused based on Kalman filter algorithm. The two-stage fusion localization algorithm achieves a great improvement on positioning system, it can improve the localization accuracy by 79.8% in the NLOS environment and by 36% in the (line-of-sight) LOS environment

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Science

Role of lactobacillus in female infertility via modulating sperm agglutination and immobilization

Infertility has become a common problem in recent decades. The pathogenesis of infertility is variable, but microbiological factors account for a large proportion of it. Dysbiosis of vaginal microbiota is reportedly associated with female infertility, but the influence of normal vaginal microbiota on infertility is unclear. In this review, we summarize the physiological characteristics of the vaginal tract and vaginal microbiota communities. We mainly focus on the bacterial adherence of vaginal Lactobacillus species. Given that the adherent effect plays a crucial role in the colonization of bacteria, we hypothesize that the adherent effect of vaginal Lactobacillus may also influence the fertility of the host. We also analyze the agglutination and immobilization effects of other bacteria, especially Escherichia coli, on ejaculated spermatozoa, and speculate on the possible effects of normal vaginal microbiota on female fertility

Exploiting Redundancies and Deferred Writes to Conserve Energy in Erasure-Coded Storage Clusters

We present a power-efficient scheme for erasure-coded storage clusters---ECS 2 ---which aims to offer high energy efficiency with marginal reliability degradation. ECS 2 utilizes data redundancies and deferred writes to conserve energy. In ECS 2 parity blocks are buffered exclusively in active data nodes whereas parity nodes are placed into low-power mode. ( k + r ,  k ) RS-coded ECS 2 can achieve ⌈( r + 1)/2⌉-fault tolerance for k active data nodes and r -fault tolerance for all k + r nodes. ECS 2 employs the following three optimizing approaches to improve the energy efficiency of storage clusters. (1) An adaptive threshold policy takes system configurations and I/O workloads into account to maximize standby time periods; (2) a selective activation policy minimizes the number of power-transitions in storage nodes; and (3) a region-based buffer policy speeds up the synchronization process by migrating parity blocks in a batch method. After implementing an ECS 2 -based prototype in a Linux cluster, we evaluated its energy efficiency and performance using four different types of I/O workloads. The experimental results indicate that compared to energy-oblivious erasure-coded storage, ECS 2 can save the energy used by storage clusters up to 29.8% and 28.0% in read-intensive and write-dominated workloads when k = 6 and r = 3, respectively. The results also show that ECS 2 accomplishes high power efficiency in both normal and failed cases without noticeably affecting the I/O performance of storage clusters. </jats:p

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

A miR-137-XIAP axis contributes to the sensitivity of TRAIL-induced cell death in glioblastoma

Glioblastoma (GBM) is the most lethal primary brain tumor in the central nervous system with limited therapeutic strategies to prolong the survival rate in clinic. TNF-related apoptosis-inducing ligand (TRAIL)-based strategy has been demonstrated to induce cell death in an extensive spectrum of tumor cells, including GBM, while a considerable proportion of malignant cells are resistant to TRAIL-induced apoptosis. MiR-137 is highly expressed in the brain, but significantly decreases with advanced progression of GBM. However, the functional link between miR-137 and TRAIL-induced apoptosis in GBM cells has not been established. Here, GBM cells were transfected with miR-137, and gene expression levels were examined by qRT-PCR and western blot. Apoptotic cells were measured by Annexin-V staining and TUNEL assay. Our data showed that miR-137 sensitizes GBM cells to the TRAIL-mediated apoptosis. Mechanistically, we identified that XIAP is a bona fide target of miR-137, which is essential for miR-137-regulated sensitivity of TRAIL-induced cell death in GBM cells. Finally, in a xenograft model, combined utilization of miR-137 and TRAIL potently suppresses tumor growth in vivo. Collectively, we demonstrate that a miR-137-XIAP axis is required for the sensitivity of TRAIL-induced cell death and shed a light on the avenue for the treatment of GBM

Echo in a Single Molecule

Echo is a ubiquitous phenomenon found in many physical systems, ranging from spins in magnetic fields to particle beams in hadron accelerators. It is typically observed in inhomogeneously broadened ensembles of nonlinear objects, and is used to eliminate the effects of environmental-induced dephasing, enabling observation of proper, inherent object properties. Here, we report experimental observation of quantum wave packet echoes in a single isolated molecule. In contrast to conventional echoes, here the entire dephasing-rephasing cycle occurs within a single molecule without any inhomogeneous spread of molecular properties, or any interaction with the environment. In our experiments, we use a short laser pulse to impulsively excite a vibrational wave packet in an anharmonic molecular potential, and observe its oscillations and eventual dispersion with time. A second delayed pulsed excitation is applied, giving rise to an echo: a partial recovery of the initial coherent wavepacket. The vibrational dynamics of single molecules is visualized by time-delayed probe pulse dissociating them one at a time. Two mechanisms for the echo formation are discussed: ac Stark-induced molecular potential shaking and creation of depletion-induced "hole" in the nuclear spatial distribution. Interplay between the optically induced echoes and quantum revivals of the vibrational wave packets is observed and theoretically analyzed. The single molecule wave packet echoes may lead to the development of new tools for probing ultrafast intramolecular processes in various molecules.Comment: 9 pages, 7 figure

Preparation and properties of antistatic high-strength aramid III/MWCNTs-OH fibers

Composite fibers made from aramid III and hydroxylated multiwalled carbon nanotubes (MWCNTs-OH) combine the excellent mechanical and electrical properties of both components, resulting in strong antistatic performance. However, it is of paramount importance to ensure the homogeneous dispersion of multi-walled carbon nanotubes functionalized with hydroxyl groups (MWCNTs-OH) within the aramid III spinning solution and optimize the compatibility between the two constituents to augment the overall performance of the composite fibers. To this end, this investigation successfully accomplished the dispersion of MWCNTs-OH in the spinning solution and probed the dispersion mechanism using molecular dynamics simulations. Moreover, composite fibers, comprising 2.4 weight percent MWCNTs-OH, were initially fabricated using the wet spinning method. These fibers displayed a uniform texture and a tensile strength of 1.210 GPa, signifying a noteworthy enhancement of 113.25% in comparison to the strength prior to modification. With respect to thermal behavior, the fibers exhibited a mass reduction of 21.24% within the temperature range of 0°C–538°C. In the temperature interval from 538°C to 800°C, the mass loss diminished to 10.31%, representing a substantial 71.03% reduction when compared to the unmodified state. Remarkably, even when subjected to temperatures exceeding 800°C, the composite fibers retained a residual mass of 68.45%, indicating a notable 61.17% increase from their initial condition. In terms of electrical properties, the fibers exhibited a specific resistance (ρ) of 3.330 × 109 Ω cm, demonstrating effective antistatic behavior. In summary, the antistatic composite fibers studied in this paper can effectively mitigate the hazards of static electricity in various applications, including military protection and engineering equipment in both military and civilian fields

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