Miracle-dz/UniFAR-FTUs huggingface dataset:
The Research on the Incentive Method of Consortium Blockchain Based on Practical Byzantine Fault Tolerant

In this paper, we use the credit mechanism of E-commerce platform to evaluate the transaction subject as user reviews, and evaluate the credit degree of the blockchain nodes based on the consensus algorithm of the consortium blockchain as node reviews, then associate the node credit score consisting of user and node reviews with the transaction cost to substitute the incentive effect of the block reward for ledger maintenance. According to the score-pricing strategy we will introduce later, this incentive method can ameliorate the problem of too concentrated bookkeeping permissions and improve the initiative of the accounting node.
Health Care in the Age of Interoperability: Part 4

About this series This is the fourth in a series of articles on the dramatic transformation taking place in health informatics in large part because of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The first article provided background on healthcare, electronic health record systems for physicians, and the challenges they both face along with the potential of interoperability to help overcome them. The second introduced the basics of the FHIR standard and some suggested resources for those who are interested in its further exploration. The third introduced SMART on FHIR, which, based on its wide adoption, has become the default standard FHIR app platform. This article introduces clinical decision support as an important use case for FHIR in support of health care providers. The articles in this series are intended to introduce researchers from other fields to this one and assume no prior knowledge of health care or health informatics. They are abstracted from the author’s recently published book, Health Informatics on FHIR: How HL7’s New API is Transforming Healthcare (Springer International Publishing: https://www.springer.com/us/book/9783319934136).
Depicting User Connections in Social Media by Mapping User-Shared Images

Millions of images are publicly available on various social networking sites due to their sharing behaviour. Enormous amount of such images have been collected and are trained using multiple machine learning algorithms and machine generated tags to obtain user relationships. Images have been obtained and analysed from multiple online social networking sites. These images are passed into a trained image classifier model which is based on Convolutional Neural Network (CNN) using Tensor-Flow. The obtained result is further clustered and segregated by analysing the data to obtain a behaviour in order to study the various relationship patterns between the users. Using the information obtained, a graphical representation of the studied pattern is formulated. This proposed engine is proven to be 47 percent better than other approaches.
Modeling and Analysis of a Modular Hybrid Reluctance Motor for High-Torque Applications

The main contribution of this research is to present a modular hybrid reluctance motor (MHRM) for high-torque drive systems. The main sizes and parameters of the structure are presented. To obtain the main characteristics of the suggested HRM, a two dimensional finite element method (FEM) is used. The flux distributions at the alignment and unalignment of the rotor, flux linkage, and magneto-static torque profiles of the proposed topology are provided. In addition, the magnetic circuit model (MCM) of the introduced motor is derived, the reluctances of the machine are calculated, and the static features of the motor are attained from the MCM are compared with those of the FEM. The results are indicative of the fact that there is an acceptable discrepancy between the results of the FEM and MCM. Finally, it is shown that the HRM produces higher torque than the conventional SRM.
Eye-tracking-Based Quality Assessment for Image Interpolation

In an Image Quality Assessment (IQA) scenario, the Human Vision System (HVS) always acts as the ultimate receiver and valuator of generated images. As an important feature of HVS, the visual attention data has been demonstrated to be able to effectively improve the performance of existing objective quality metrics. However, this feature has not yet been well explored in the IQA of image interpolation. In this paper, we conduct an eye-tracking test on an interpolated image database and investigate the impact of visual attention on IQA of image interpolation. Two visual attention models, saliency map and Region Of Interest (ROI), are then obtained from the eye-tracking data. We further incorporate these models into non-integer interpolated IQA metric and examine their performances. Experiments show that the introduction of eye-tracking features obviously improves the conventional IQA metric for non-integer image interpolation.
Multiplatform evaluation of global trends in wind speed and wave height

Ocean winds blowing harder Two frequently asked questions about how climate warming will affect the environment are whether windiness might change and what effects that might have on ocean waves. Young and Ribal analyzed global satellite data over the period from 1985 to 2018 to determine if there are any trends in oceanic wind speed and wave height. They found small increases in both quantities, with the strongest increases in extreme conditions and in the Southern Ocean. These findings are important for understanding air-sea exchange of energy and carbon dioxide and for projecting sea levels during storms. Science, this issue p. 548 Oceanic wind speeds and wave heights have increased measurably over the past 33 years. In this study, global satellite data were analyzed to determine trends in oceanic wind speed and significant wave height over the 33-year period from 1985 to 2018. The analysis uses an extensive database obtained from 31 satellite missions comprising three types of instruments—altimeters, radiometers, and scatterometers. The analysis shows small increases in mean wind speed and significant wave height over this period, with larger increases in extreme conditions (90th percentiles). The largest increases occur in the Southern Ocean. Confidence in the results is strengthened because the wind speed trends are confirmed by all three satellite systems. An extensive set of sensitivity analyses confirms that both the mean and 90th percentile trends are robust, with only small impacts caused by satellite calibration and sampling patterns.
Named Data Networking Architecture for VANET

The number of connected vehicles has heightened quickly in the last few years. This reason leads researchers to revise network architecture by considering future trends. Recently, Named Data Networking (NDN) has been developed to route traffic in VANET without any knowledge about the end host entity. It uses content name to float data in the network instead IP address. NDN is developed to support node mobility and the ever-expanding data traffic. In this paper, we present the principle of existing schemes based on NDN approach such as CCVN, V-NDN, VENDNET, NDVN and EVNDN. Then we study the strengths and limitations of the current proposed architecture to guide the learners to develop a future approach of VANET with an efficient caching strategy, reducing network traffic and minimum satisfaction delay.
Research on Collaborative Control Method of Manufacturing Process Based on Distributed Multi-Agent Cooperation

In view of the relatively backward situation of automation information technology in manufacturing enterprises, this paper analyzes the manufacturing process of industry, designs and constructs a HD_MAS (Hierarchical Distributed Multi-Agent Systems(MAS)) architecture for collaborative control of process industry process so as to achieve collaborative control optimization for the whole manufacturing process. Based on MAS technology, this paper constructs a collaborative control model for manufacturing system that integrates multiple production units. This model combines several intelligent modules and physical entities organically. Based on the cooperation between Multi-agents, the improved MADDPG (Multi-Agent Deep Deterministic Policy Gradient) algorithm is proposed. A new algorithm framework called DS_MADDPG (Distributed Multi-Agent Deep Deterministic Policy Gradient) is applied to this model. Through the cooperation of Multi-agents, a certain intelligent control system is formed so that it can accurately complete complex production tasks and achieve distributed collaborative control of the manufacturing process, which will drive manufacturing companies to transform into the smart manufacturing.
Acute Leukemia blast counting using RGB, HSI color spaces

Computer analysis of images usually starts with segmentation, which reduce pixel data information about the objects and their structures which are present in the system. The recognition of acute leukemia blood cell based on color image is one of the most challenging tasks in image processing. Also, the conventional method of manual counting using a microscope is a time-consuming, produces errors and put an intolerable amount of stress to technicians. As a solution to this problem, this paper proposed an image processing technique for color segmentation of acute leukemia images using HSI and RGB color spaces and the evaluation of color spaces is carried out using a segmentation algorithm. And to count the number of blasts with their size which are present in the image of leukemia. The technique segments each leukemia mage into two regions; blast and the background. The blast contains white blood cells. The experimental result shows that the proposed system has provided recognition of acute leukemia blasts and to counting blast. General terms: Pattern Recognition. Keyword: Image segmentation, acute leukemia, RGB, HSI, Blast.
Approaches for Anomaly Detection in Network: A Survey

In recent years there is a constant rise in cyber attacks. These attacks affect not only the individuals but the organizations at large also. To detect these attacks requires high-end systems, since there is continuous flow of data to-and-from the network. This huge data flow makes it difficult to analyze traffic and identify anomalous communication. Rule based engines are capable of identifying sophisticated attacks, but it fails to identify unknown and new attacks as the rules are always based on prior knowledge of the administrators. It is virtually impossible to code all the rules beforehand that may capture all the attacks. Most of the attacks have common characteristics, i.e. abnormal or anomalous network communication, authentication attempts or access attempts on objects. This work surveys different approaches to detect anomalies in network communication.
A Comparative Study of Different Intrusion Prevention Systems

The paper confronts the challenges in network security and compares different Intrusion Prevention systems based on their features and working mechanisms. Defense-in-depth is an IT security approach that uses multilayered approach in security, in which multiple security mechanisms are deployed over different levels of organization for protection of different assets of the organization. Defense-in-depth ensures security at every level of the network architecture. Organization's most important assets are segregated and protected with help of multilayered security. Multilayered approach can be executed to different levels of security. Not all assets required to be completely secured; it's challenging to ensure optimal security of entire system. By using multiple systems to mitigate damage, the organization can ensure failsafe mechanism, even if one system or multiple systems fail, the system itself is still protected. Organizations today often need to install multiple cyber security applications, such as Firewall, Antivirus, anti-malware, Intrusion Detection and Prevention System, Virtual Private Network, etc. Intrusion Detection/Prevention system are able to detect any anomaly in the system based on the behavior or the signatures assigned to the system network. It is able to detect any suspicious traffic getting in or out of the network and generate an alert accordingly. IDS/IPS can be implemented in multilayered security next to firewall to detect the incoming and outgoing traffic.
BB-Graph: A Subgraph Isomorphism Algorithm for Efficiently Querying Big Graph Databases

The big graph database model provides strong modeling for complex applications and efficient querying. However, it is still a big challenge to find all exact matches of a query graph in a big graph database, which is known as the subgraph isomorphism problem. The current subgraph isomorphism approaches are built on Ullmann’s idea of focusing on the strategy of pruning out the irrelevant candidates. Nevertheless, the existing pruning techniques need much more improvement to efficiently handle complex queries. Moreover, many of those existing algorithms need large indices requiring extra memory consumption. Motivated by these, we introduce a new subgraph isomorphism algorithm, named as BB-Graph, for querying big graph databases efficiently without requiring a large data structure to be stored in main memory. We test and compare our proposed BB-Graph algorithm with two popular existing approaches, GraphQL and Cypher. Our experiments are done on three different data sets; (1) a very big graph database of a real-life population database, (2) a graph database of a simulated bank database, and (3) the publicly available World Cup big graph database. We show that our solution performs better than those algorithms mentioned here for most of the query types experimented on these
DiCoExpress: a workspace to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models.

Background RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realized neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Nevertheless, performing an RNAseq analysis remains a challenge for the biologists. Results DiCoExpress is a workspace implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses the pre-existing R packages as well as FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models with no effort thanks to the automated contrast writing function. DiCoExpress proposes a list of comparisons based on the experimental design, and the user needs only to choose the one(s) of interest for his research question. A co-expression analysis is implemented using the coseq package. Identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user, and several result outputs proposed. We used DiCoExpress to analyze a publicly available Bra ssica napus L. RNAseq dataset on the transcriptional response to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress. Conclusions DiCoExpress is an R workspace to allow users without advanced statistical knowledge and programming skills to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models . Hence, with DiCoExpress, the user can focus on the statistical modeling of gene expression according to the experimental design and on the interpretation of the results of such analysis in biological terms.
Demand and Revenue Forecasting through Machine Learning

In this Contribution, we investigate and explore on logistics data from sensors and real time devices to derive insights and identify the key features which play deterministic role for predicting the demand and revenue. In this contribution we compare the performance of various time series forecasting models and supervised learning algorithms to predict the demand and revenue to maximize the customer experience and yield greater revenue yield. RMSE has used as Key performance indicator for the comparison, From our analysis results we infer that latitude, longitude, hour of the day and day of week are important in predicting the outcome, further our study indicates Multivariate Time Series forecasting seems to be better performing for revenue and random forest seems to be performing in predicting the demand.
Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches

Background: Cerebral amyloid beta (Aβ) is a hallmark of Alzheimer’s disease (AD). Aβ can be detected in vivo with amyloid imaging or cerebrospinal fluid assessments. However, these technologies can be both expensive and invasive, and their accessibility is limited in many clinical settings. Hence the current study aims to identify multivariate cost-efficient markers for Aβ positivity among non-demented individuals using machine learning (ML) approaches. Methods: The relationship between cost-efficient candidate markers and Aβ status was examined by analyzing 762 participants from the Alzheimer’s Disease Neuroimaging Initiative-2 cohort at baseline visit (286 cognitively normal, 332 with mild cognitive impairment, and 144 with AD; mean age 73.2 years, range 55–90). Demographic variables (age, gender, education, and APOE status) and neuropsychological test scores were used as predictors in an ML algorithm. Cerebral Aβ burden and Aβ positivity were measured using 18F-florbetapir positron emission tomography images. The adaptive least absolute shrinkage and selection operator (LASSO) ML algorithm was implemented to identify cognitive performance and demographic variables and distinguish individuals from the population at high risk for cerebral Aβ burden. For generalizability, results were further checked by randomly dividing the data into training sets and test sets and checking predictive performances by 10-fold cross-validation. Results: Out of neuropsychological predictors, visuospatial ability and episodic memory test results were consistently significant predictors for Aβ positivity across subgroups with demographic variables and other cognitive measures considered. The adaptive LASSO model using out-of-sample classification could distinguish abnormal levels of Aβ. The area under the curve of the receiver operating characteristic curve was 0.754 in the mild change group, 0.803 in the moderate change group, and 0.864 in the severe change group, respectively. Conclusion: Our results showed that the cost-efficient neuropsychological model with demographics could predict Aβ positivity, suggesting a potential surrogate method for detecting Aβ deposition non-invasively with clinical utility. More specifically, it could be a very brief screening tool in various settings to recruit participants with potential biomarker evidence of AD brain pathology. These identified individuals would be valuable participants in secondary prevention trials aimed at detecting an anti-amyloid drug effect in the non-demented population.
MetaboShiny - interactive processing, analysis and annotation of direct infusion metabolomics data

Direct infusion untargeted metabolomics, as mass-over-charge values and intensity of ions, allows for rapid insight into a sample’s metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database-and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted metabolomics data.
A 0.2–18 GHz Schmitt Trigger with up to 13%–85% Duty-Cycle Tuning in 130nm SiGe BiCMOS

This paper reports on the design of a Schmitt trigger for the generation of high-speed switching signals with pulse-width modulation capability. The circuit is implemented using HBT transistors in a 130nm SiGe BiCMOS process, and shows broadband operation from 200 MHz up to 18 GHz. It has a single-ended output voltage swing of 400 mV, and rise/fall times of 10 ps and 13 ps at 10 GHz. The output-waveform duty cycle is tuned by varying the circuit’s threshold levels for the switching transitions, resulting in a continuous tuning range between 13% and 85% at 1 GHz. A push-pull Totem-Pole output buffer is designed to enable measurements with 50 Ω measurement equipment. The trigger circuit has 24.6 mW power consumption, in addition to 20.8 mW for the output buffer at the 50% duty cycle setting. The circuit implementation is very compact, requiring only 0.02 mm2 of active area. To the best knowledge of the authors, this is by far the fastest reported Schmitt trigger to date.
The Response to DNA Damage at Telomeric Repeats and Its Consequences for Telomere Function

Telomeric repeats, coated by the shelterin complex, prevent inappropriate activation of the DNA damage response at the ends of linear chromosomes. Shelterin has evolved distinct solutions to protect telomeres from different aspects of the DNA damage response. These solutions include formation of t-loops, which can sequester the chromosome terminus from DNA-end sensors and inhibition of key steps in the DNA damage response. While blocking the DNA damage response at chromosome ends, telomeres make wide use of many of its players to deal with exogenous damage and replication stress. This review focuses on the interplay between the end-protection functions and the response to DNA damage occurring inside the telomeric repeats, as well as on the consequences that telomere damage has on telomere structure and function.
A novel function of anaphase promoting complex subunit 10 in tumor progression in non-small cell lung cancer

ABSTRACT The anaphase promoting complex/cyclosome (APC/C), a cell cycle-regulated E3 ubiquitin ligase, is responsible for the transition from metaphase to anaphase and the exit from mitosis. The anaphase promoting complex subunit 10 (APC10), a subunit of the APC/C, executes a vital function in substrate recognition. However, no research has reported the connection between APC10 and cancer until now. In this study, we uncovered a novel, unprecedented role of APC10 in tumor progression, which is independent of APC/C. First, aberrant increase of APC10 expression was validated in non-small cell lung cancer (NSCLC) cells and tissues, and the absence of APC10 repressed cell proliferation and migration. Of great interest, we found that APC10 inhibition induced cell cycle arrest at the G0/G1 phase and reduced the expression of the APC/C substrate, Cyclin B1; this finding is different from the conventional concept of the accumulation of Cyclin B1 and cell cycle arrest in metaphase. Further, APC10 was found to interact with glutaminase C (GAC), and the inhibition of APC10 weakened glutamine metabolism and induced excessive autophagy. Taken together, these findings identify a novel function of APC10 in the regulation of NSCLC tumorigenesis and point to the possibility of APC10 as a new target for cancer therapy.