Search

Viral hepatitis is a major worldwide public health issue, affecting hundreds of millions of people and causing substantial morbidity and mortality. The majority of the worldwide burden of viral hepatitis is caused by five biologically unrelated hepatotropic viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Metabolomics is an emerging technology that uses qualitative and quantitative analysis of easily accessible samples to provide information of the metabolic levels of biological systems and changes in metabolic and related regulatory pathways. Alterations in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and amino acid metabolism. These changes in metabolites and metabolic pathways are associated with the pathogenesis and medication mechanism of viral hepatitis and related diseases. Additionally, differential metabolites can be utilized as biomarkers for diagnosis, prognosis, and therapeutic responses. In this review, we present a thorough overview of developments in metabolomics for viral hepatitis.

In order to solve the problem of harmonic waves caused by battery energy storage (BES) and distributed generation (DG) inverters in an active distribution network, an intelligent optimal dispatching method based on a modified flower pollination algorithm (MFPA) is proposed. Firstly, the active distribution network dispatching model considering the power quality (PQ) problem caused by BES and DG is proposed. In this model, the objective function considers the additional network loss caused by a harmonic wave, as well as the constraints of the harmonic wave and voltage unbalance. Then, the MFPA is an improvement of a flower pollination algorithm (FPA). Because the MFPA has the characteristics of higher solution accuracy and better convergence than the FPA and it is not easy to fall into local optimal, the MFPA is used to solve the proposed model. Finally, simulation experiments are carried out on IEEE 37 bus and IEEE 123 bus systems, respectively. The experimental results show that this method can achieve satisfactory power quality while optimizing the total active power loss of the branch. The comparative experimental results show that the developed algorithm has better convergence than the FPA.

Background: In the power Internet of Things (IoT), power consumption data faces the risk of privacy leakage. Traditional privacy-preserving schemes cannot ensure data privacy on the system, as the secret key pairs shall be shared between all the interior nodes once leaked. In addition, the general schemes only support summation algorithms, resulting in a lack of extensibility. Objective: To preserve the privacy of power consumption data, ensure the privacy of secret keys, and support multiple data processing methods we propose an improved power consumption data privacypreserving scheme. Method: Firstly, we have established a power IoT architecture based on edge computing. Then the data is encrypted with the multi-key fully homomorphic algorithm to realize the operation of ciphertext, without the restrictions of calculation type. Through the improved decryption algorithm, ciphertext that can be separately decrypted in cloud nodes is generated, which contributes to reducing communication costs and preventing data leakage. Results: The experimental results show that our scheme is more efficient than traditional schemes in privacy preservation. According to the variance calculation result, the proposed scheme has reached the application standard in terms of computational cost and is feasible for practical operation. Discussion: In the future, we plan to adopt a secure multi-party computation based scheme so that data can be managed locally with homomorphic encryption, so as to ensure data privacy. Conclusion: A privacy-preserving scheme based on improved multi-key fully homomorphic encryption is proposed for the power consumption data, and the experimental results demonstrate the effectiveness and advantage of the proposed scheme.

Background: Power distribution Internet of Things is a smart service system in which traditional distribution networks comprehensively apply modern computing technologies, such as cloud computing, mobile Internet of Things, and big data to realize the interconnection of all things in the power system and human-computer interaction. In the power distribution Internet of Things, there are many types of main power distribution equipment, and each type of power distribution equipment is connected with multiple sensor devices. These sensor devices play an important role in the smooth operation of the power distribution Internet of Things, so it is necessary to build a reliability evaluation system for these sensor devices. Objectives: The objective was to evaluate the reliability of the sensor device connected to the main equipment in the power distribution Internet of Things. Methods: Firstly, the G1-CV method (entropy weight method-coefficient of variation method) is used to construct the initial evaluation index system, and the comprehensive weight of each index is determined and ranked. Then the improved TriMap algorithm (ITriMap) is used to reduce the high-dimensional complex index system. Results: Simulation verification results show that the algorithm has a smaller reconstruction error than traditional dimensionality reduction algorithms such as t-SNE, Largr-Vias, UMAP, etc. The reduced index can scientifically and objectively measure the main equipment sensors of the power distribution network. Conclusion: An index reduction method based on the ITriMap algorithm is proposed to evaluate the reliability of sensors. Experimental results show that this algorithm greatly reduces the reconstruction error compared to other traditional methods. Discussion: In the future, on the basis of this article, we intend to construct a sensor reliability evaluation model for the main equipment of the power distribution Internet of Things using the 5- dimensional features after dimensionality reduction as input.

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

Thread-Level speculation (TLS) is an automatic parallel technique, which partitions sequential programs with data dependencies into multithreads to be executed in parallel on the multi-core platform, and it is used to aggressively exploit the parallelism of sequential programs at runtime. However, the existing implementations of the software TLS models always incur extra and high overhead, even could be revoking performance benefits from TLS when frequent data dependencies are violated. Based on the POSIX thread library, which has a high portability, this paper proposes an improved TLS programming model to exploit potential parallelism of the sequential programs. Firstly, the execution model of the proposed programming model adopts the optimized main-assistance-work thread mechanism to improve overall operational efficiency. And then a new data structure is proposed and designed to implement a thread-level speculation mechanism for solving the thread version copy. Using the benchmarks, experimental results show that the proposed TLS programming model can acquire a significant increase in speedup on a multi-core platform and the proposed TLS programming model has excellent performance benefits. Compared with the traditional TLS programming model, the improved TLS model can achieve a speedup of 2.87 to 12.22 which delivers a 9.70% performance improvement on average.

Environmental pollution due to resistance genes from livestock manure has become a serious issue that needs to be resolved. However, little studies focused on the removal of resistance genes in simultaneous processing of livestock feces and urine. This study investigated the fate of antibiotic resistance genes (ARGs), metal resistance genes (MRGs), and class 1 integron-integrase gene (intI1) during thermophilic fermentation of swine manure in an ectopic fermentation system (EFS), which has been regarded as a novel system for efficiently treating both feces and urine. The abundances of MRGs and tetracycline resistance genes were 34.44–97.71% lower in the EFS. The supplementation of heavy metals significantly increased the abundance of intI1, with the enhancement effect of copper being more prominent than that of zinc. The highest abundances of resistance genes and intI1 were observed at high Cu levels (A2), indicating that Cu can increase the spreading of resistance genes through integrons. Network analysis revealed the co-occurrence of ARGs, MRGs, and intI1, and these genes potentially shared the same host bacteria. Redundancy analysis showed that the bacterial community explained most of the variations in ARGs, and environmental factors had influences on ARGs abundances by modulating the bacterial community composition. The decreased Sphingomonas, Comamonas, Acinetobacter, Lactobacillus, Bartonella, Rhizobium, and Bacteroides were mainly responsible for the reduced resistance genes. These results demonstrate that EFS can reduce resistance genes in simultaneous processing of livestock feces and urine.

Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom. Researchers persistently devise countermeasures strategies to fight back malware. One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection. This necessitates a review to inspect the accomplished work in order to know where the endeavors have been established, identify unresolved problems, and motivate future research directions. In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions, and limitations., Comment: 15 pages, 4 tables

To study the Noether's theorem of nonholonomic systems of non-Chetaev's type with unilateral constraints in event space, firstly, the principle of D'Alembert-Lagrange for the systems with unilateral constraints in event space is presented, secondly, the Noether's theorem and the Noether's inverse theorem for the nonholonomic systems of non-Chetaev's type with unilateral constraints in event space are studied and obtained, which is based upon the invariance of the differential variational principle under the infinitestinal transformations of group, finally, an example is given to illustrate the application of the result.