Miracle-dz/UniFAR-FTUs huggingface dataset:
A Car Security System Based on Alerting Driver Drowsiness and Monitoring the State of the Vehicle

Accidents on the road are the primary cause of death, particularly among children and adolescents. Despite having fewer vehicles, low- and middle-income countries account for the preponderance of these fatalities. Consequently, a system that monitors vehicles and takes the necessary precautions to prevent collisions and fatalities is urgently required. This paper proposes a system of OpenCV image processing techniques to monitor the driver’s eye movements in order to prevent accidents caused by behavioral and psychological changes while driving. The main processing unit (MPU) of the system we have developed consists of Raspberry Pi, Microcontroller, and sensors. Both on day and night time, it detects driver drowsiness and alerts them with a wristband. In low-light conditions, two infrared (IR) blasters and multiple light-dependent resistors (LDR) sensors were used to accurately detect driver fatigue. During an accident, the MPU’s accident detection module will identify and send an SMS message to the vehicle owner with the vehicle’s location and data. Eventually, the local police station, fire department, and other safety agencies will be able to take immediate action. The novel aspect of this study is the combination of image processing techniques and sensors that accurately monitor driver behaviour and identify fatigue. The accident detection module of the system is also distinct and can provide emergency services with vital information in the event of an accident. If implemented, the proposed technology can enhance the safety features of cars and reduce the number of accidents caused by human error.
Method for Arabic text Summarization using statistical features and word2vector approach

Arabic text summarization is a field of natural language processing, and many algorithms for Arabic texts summarization have been developed, but these algorithms face weakness points which generated from the specifications of the Arabic language manipulation. In this research we have developed new algorithm for Arabic text summarization that the our algorithm contains two main models of summarization, the statistical model and the semantic model ,that the statistical model was employed to generate the candidate sentences for summary, and the semantic model was used to select and generate the final summary , in the statistical model we have used sentences statistical features, and in semantic model we have used word2vector technology which convert words to number and keeping the context of text, the developed algorithm has been tested on The Essex Arabic Summaries Corpus (EASC) dataset, and the tests approved the efficiency and the accuracy of the developed algorithm, that the developed algorithm summarization Precision reached to 75%.
Research on Remaining Useful Life Prediction of Dual-fuel Main EngineBased on CBAM Attention Mechanism

The entire life cycle of an engine is an asymmetric process, and the characteristics of its internal components are different. It is of great significance to extract engine degradation features and build models for improving the RUL prediction of the main engine. In this paper, a RUL prediction method based on CBAM attention mechanism is designed. For long time series engine data, using Convolutional Neural Network (CNN) to extract features and share convolutional kernels can process high-dimensional data well. The purpose of adding a Gated Recurrent Unit (GRU) network is to use the GRU network to sequentially model the feature expressions extracted from the CNN network and learn the sequential correlation of data. Convolutional block attention module (CBAM) is introduced as a mechanism to reduce the influence of different data lengths on the final prediction by focusing on the channels and spaces. This paper validates the above prediction method on two different engine datasets. The prediction method can effectively mine the internal time and space dependence between multiple sensor data, improve the precision of RUL prediction, and prove that the method has good applicability and effectiveness.
Screening rules and information criteria-based analysis of gene expression data

High-dimensional data is becoming increasingly common, and the biomedical field is no exception with the rapid development of technology. There are various methods to deal with high-dimensional gene expression data, but all of them have some shortcomings. In this paper, we address the theory and application of feature screening in ultra-high-dimensional discriminative classification data, with the aim of reducing ultra-high-dimensional data to a size appropriate for the originally proposed sample size, while retaining all important variables. To this end, we propose a variable screening method that sure independence screening methods in conjunction with EBIC information criteria, which can effectively reduce data dimensionality while improving computational efficiency and helping to discover the most informative variables relevant to the target. In this paper, a random simulation sampling method was first used to select parameters and filter variables using randomly sampled data, and the correct selection rate and correct fit rate of the simulation results were higher than those of other approaches, which verified the reliability of the method used. Finally, four sets of real gene expression data were used to further validate the effectiveness of the method in selecting gene expression data features.
An Unsupervised Network Anomaly Detection Model and Implementation

Anomaly detection for network attacks has always been a very important part of intrusion detection. The current research focus is anomaly detection based on deep learning, which has two main problems. One is the lack of a large amount of labeled data in model training, and the other is difficult to detect unknown network attacks or variant attacks. To solve the above problems, an unsupervised anomaly detection model is constructed in this paper. The automatic encoder is used to learn normal traffic characteristics and detect abnormal traffic. Meanwhile, time correlation features and hierarchical clustering algorithm are used for data preprocessing to reduce time and space complexity, so as to further improve the efficiency of model detection. Due to the serious lack of verification data sets for unsupervised anomaly detection, this paper collects and organizes a large amount of data and designs four types of network attack data, including new attack means, worms, system vulnerabilities and botnets. The experimental results showed that the detection accuracy of worms and system vulnerabilities reached 98%, the detection accuracy of botnets reached 89%, and the attacks of the new OriginLogger software were detected.
Agnostic Performance Testing Framework

The vast majority of software developed must have the capacity to support thousands of simultaneous users. To ensure that this is the case, systems must be tested to their limits to ensure possible points of failure at an early stage of development, avoiding wasting time and money on tuning and fixing those problems. Failure points in application performance can cause users to be unable to perform key transactions, causing the customer to lose money every second. Thus, it is critical to be able to identify these issues as soon as possible, that is, as the software is developed and integrated. These kinds of issues can be developed using a performance testing tool integrated into a CI/CD pipeline. So that this tool can be reused in various types of projects with different technologies, it is proposed that it be agnostic and developed in an open-source tool. Thus, the purpose of developing an agnostic performance testing tool is essentially related to two points: the delays caused by the lack of concern for non-functional requirements at the beginning of projects and the possibility of testing the performance of a project independently of the technology it uses, thus reducing the time spent in familiarizing with the operation of the tool and technologies.
Contributions to health and safety management through agent-based modeling using the example of evacuation models

This paper presents two distinct agent-based models for simulating building evacuations, which is a central topic in the field of occupational health and safety. With the increasing availability of large computational resources, it is especially profitable to use an agent-based approach for such evacuation simulations. The first model presented in this paper examines the overall evacuation process, aiming to showcase the effectiveness of using basic rules for modeling evacuations through an agent-based approach. The second model, on the other hand, investigates whether additional obstacles in the exit area can accelerate the evacuation process. Before presenting the models, the paper provides an introduction to agent-based modeling and compares it with different approaches to evacuation modeling.
Automated staging of zebrafish embryos with deep learning

Introducing KimmelNet: A deep learning model that accurately stages populations of zebrafish embryos from 2D brightfield images, offering a rapid and objective alternative to manual observations. The zebrafish (Danio rerio) is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and quantification of these delays is often achieved through manual observation, which is both time-consuming and subjective. We present KimmelNet, a deep learning model trained to predict embryo age (hours post fertilisation) from 2D brightfield images. KimmelNet’s predictions agree closely with established staging methods and can detect developmental delays between populations with high confidence using as few as 100 images. Moreover, KimmelNet generalises to previously unseen data, with transfer learning enhancing its performance. With the ability to analyse tens of thousands of standard brightfield microscopy images on a timescale of minutes, we envisage that KimmelNet will be a valuable resource for the developmental biology community. Furthermore, the approach we have used could easily be adapted to generate models for other organisms.
Achatina fulica Haemocyanin-Derived Peptides as Novel Antimicrobial Agents

Anteriorly we found Haemocyanin-derived peptides in semi-purified fractions of mucus secretion of Achatina fulica that showed an inhibitory effect on S. aureus strains. Here, we applied in silico rational design strategy to obtain new potential Antimicrobial Peptides (AMPs) from A. fulica haemocyanin-derived peptides (AfH). Designed peptides were chemically synthetized using the Fmoc strategy and antimicrobial activity against E. coli and S. aureus strains was investigated using the broth microdilution method. Cytotoxic activity on Vero, HaCat, and human erythrocytes cells was also evaluated. The results showed that 15-residue synthetic peptides, alpha-helical and cationic, were those with the highest biological activity against Gram positive strains, with Minimum Inhibitory Concentrations (MIC) in the range of 20 to 30 µM. The positive selectivity index suggests a higher selectivity mainly on the microorganisms evaluated and not on eukaryotic cells. In this study, A. fulica hemocyanin turned out to be an appropriate protein model for the rational design of AMPs against bacteria of public health importance. Additional studies are required to evaluate the activity of the peptides on Gram negative bacteria.
Bacteriophage P22 SieA mediated superinfection exclusion

Many temperate phages encode prophage-expressed functions that interfere with superinfection of the host bacterium by external phages. Salmonella phage P22 has four such systems that are expressed from the prophage in a lysogen that are encoded by the c2 (repressor), gtrABC, sieA, and sieB genes. Here we report that the P22-encoded SieA protein is the only phage protein required for exclusion by the SieA system, and that it is an inner membrane protein that blocks DNA injection by P22 and its relatives, but has no effect on infection by other tailed phage types. The P22 virion injects its DNA through the host cell membranes and periplasm via a conduit assembled from three “ejection proteins” after their release from the virion. Phage P22 mutants were isolated that overcome the SieA block, and they have amino acid changes in the C-terminal regions of the gene 16 and 20 encoded ejection proteins. Three different single amino acid changes in these proteins are required to obtain nearly full resistance to SieA. Hybrid P22 phages that have phage HK620 ejection protein genes are also partially resistant to SieA. There are three sequence types of extant phage-encoded SieA proteins that are less than 30% identical to one another, yet comparison of two of these types found no differences in target specificity. Our data are consistent with a model in which the inner membrane protein SieA interferes with the assembly or function of the periplasmic gp20 and membrane-bound gp16 DNA delivery conduit. HIGHLIGHTS Phage P22 SieA protein blocks DNA injection by P22-like phages SieA is an inner membrane protein Hybrid P22 phages with phage HK620 ejection proteins partially escape SieA exclusion SieA escape mutants of P22 alter the gp16 and 20 proteins that form the DNA ejection tube.
Multiomic ALS signatures highlight sex differences and molecular subclusters and identify the MAPK pathway as therapeutic target

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease and lacks effective disease-modifying treatments. Here, we performed a multiomic analysis of the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, as well as four transgenic mouse models of C9orf72-, SOD1-, TDP-43-, and FUS-ALS to characterize early and sex-specific disease mechanisms in ALS. Integrated analyses of transcriptomes, (phospho)proteomes, and miRNAomes revealed more pronounced changes in males. We identified transcriptome-based human ALS subclusters driven by the immune response, ECM, mitochondrial respiration, and RNA metabolism. The molecular signatures of human subclusters were reflected in specific mouse models. Individual and integrative multiomics analysis highlighted the mitogen-activated protein kinase pathway as an early disease-relevant mechanism. Its modulation by trametinib in vitro and in vivo validated that mitogen-activated protein kinase kinase 2 is a promising therapeutic target with beneficial effects in females.
Coronaviruses Spike glycoprotein endodomains: the sequence and structure-based comprehensive study

The flexibility of proteins makes them available to interact with many biomolecules in the cell. Specifically, such interactions in viruses help them to perform more functions despite having a smaller genome. Therefore, these flexible regions can be exciting and essential targets to be explored for their role in pathogenicity and therapeutic developments as they achieve essential interactions. In the continuation with our previous study on disordered analysis of SARS-CoV-2 spike cytoplasmic tail (CTR), or endodomain, here we have explored the disordered potential endodomains of six other coronaviruses using multiple bioinformatics approaches and molecular dynamics simulations. Based on the comprehensive analysis of its sequence and structural composition, we report the varying disorder propensity in endodomains of spike proteins of coronaviruses. The observations of this study may help to understand the importance of spike glycoprotein endodomain and creating therapeutic interventions against them.
Detecting altered hepatic lipid oxidation by MRI in an animal model of NAFLD

Nonalcoholic fatty liver disease (NAFLD) prevalence is increasing annually and affects over a third of U.S. adults. NAFLD can progress to nonalcoholic steatohepatitis (NASH), characterized by severe inflammation and fibrosis. NASH is predicted to become the primary cause of liver transplant by 2030. Although the etiology of NAFLD/NASH is incompletely understood, dysregulated fatty acid oxidation is implicated in disease pathogenesis. Here, we developed a method for estimating hepatic β-oxidation from the metabolism of [D15]octanoate to deuterated water and detection with deuterium magnetic resonance methods. Application of this method to perfused liver from a mouse model of NAFLD revealed dysregulated hepatic β-oxidation, findings that we confirmed with in vivo imaging. The high-fat diet–induced NAFLD mouse studies indicated that decreased β-oxidative efficiency in the fatty liver could serve as a prognostic indicator of NAFLD progression. Furthermore, our method provides a clinically translatable imaging approach for determining hepatic β-oxidation efficiency.
Twenty-Five Years of the Journal of Global Information Technology Management: A Bibliometric Analysis

ABSTRACT The Journal of Global Information Technology Management (JGITM) is an established and globally reputed journal in the Information Technology area, focusing on publishing international IS research. It is international in all respects: content, authors’ origin, readers’ origin, and the editorial board. The journal was established in January 1998 and completed 25 years of publication in 2022. In celebration, this study presents a comprehensive and historical overview of the leading trends of the journal since its inception. The bibliographic data was retrieved from the Scopus database and analyzed with the help of the R software. This analysis considers indicators such as the most cited articles, productive authors, influential authors, leading institutes, top countries publishing in JGITM, co-citation networks, and keyword analysis. The study also develops a graphical visualization of the networks of bibliographic data. This graphical visual helps to analyze co-citation and bibliographic coupling among the published research studies. Our results indicate a continuous and substantial increase in the quality of publications, impact factor, and a growing interest in publishing articles in the journal by a broader community of academicians and scholars. We hope this information will be of significant value to JGITM’s audience: the readers and past and future authors.
Cell cycle progression mechanisms: slower cyclin-D/CDK4 activation and faster cyclin-E/CDK2

Dysregulation of cyclin-dependent kinases (CDKs) impacts cell proliferation, driving cancer. Here, we ask why the cyclin-D/CDK4 complex governs cell cycle progression through the longer G1 phase, whereas cyclin-E/CDK2 regulates the short G1/S phase transition. We consider the experimentally established high-level bursting of cyclin-E, and sustained duration of elevated cyclin-D expression in the cell, available experimental cellular and structural data, and comprehensive explicit solvent molecular dynamics simulations to provide the mechanistic foundation of the distinct activation scenarios of cyclin-D/CDK4 and cyclin-E/CDK2 in the G1 phase and G1/S transition of the cell cycle, respectively. These lead us to propose slower activation of cyclin-D/CDK4 and rapid activation of cyclin-E/CDK2. Importantly, we determine the mechanisms through which this occurs, offering innovative CDK4 drug design considerations. Our insightful mechanistic work addresses the compelling cell cycle regulation question and illuminates the distinct activation speeds in the G1 versus G1/S phases, which are crucial for cell function. Statement Our work provides an unprecedented mechanistic understanding of the distinct activation scenarios of cyclin-D/CDK4 and cyclin-E/CDK2 in cell cycle regulation, underpinning the slower activation of cyclin-D/CDK4 in the more extended G1 phase and the rapid activation of cyclin-E/CDK2 in the brief G1/S transition. Our findings address a long-standing question in cell cycle biology and suggest the design of targeted CDK4 inhibitors.
DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

Motivation Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Results Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. Availability and implementation DifferentialRegulation is distributed as a Bioconductor R package.
The terminal area simulation system. Volume 2: Verification cases

The numerical simulation of five case studies are presented and are compared with available data in order to verify the three-dimensional version of the Terminal Area Simulation System (TASS). A spectrum of convective storm types are selected for the case studies. Included are: a High-Plains supercell hailstorm, a small and relatively short-lived High-Plains cumulonimbus, a convective storm which produced the 2 August 1985 DFW microburst, a South Florida convective complex, and a tornadic Oklahoma thunderstorm. For each of the cases the model results compared reasonably well with observed data. In the simulations of the supercell storms many of their characteristic features were modeled, such as the hook echo, BWER, mesocyclone, gust fronts, giant persistent updraft, wall cloud, flanking-line towers, anvil and radar reflectivity overhang, and rightward veering in the storm propagation. In the simulation of the tornadic storm a horseshoe-shaped updraft configuration and cyclic changes in storm intensity and structure were noted. The simulation of the DFW microburst agreed remarkably well with sparse observed data. The simulated outflow rapidly expanded in a nearly symmetrical pattern and was associated with a ringvortex. A South Florida convective complex was simulated and contained updrafts and downdrafts in the form of discrete bubbles. The numerical simulations, in all cases, always remained stable and bounded with no anomalous trends.