Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal approach for early prediction of COVID-19 disease using convolutional neural network

The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.
Leveraging social media, big data, and smart technologies for intercultural communication and effective leadership: Empirical study at the Ministry of Digital Economy and Entrepreneurship

The objective of this study was to evaluate the impact of social media, big data, and smart technology on intercultural communication and effective leadership inside the Ministry of digital & entrepreneurship. The main objective was to investigate the influence of these technical elements on organizational behavior and the efficacy of leadership within the particular setting of a government ministry dedicated to digital economy and entrepreneurship. In order to accomplish this goal, a thorough empirical inquiry was done, which included gathering data from important individuals involved in the Ministry. The study intentionally selected a sample size of 379 individuals, who represented various responsibilities within the Ministry. The process of data gathering entailed the distribution of surveys and the conduction of interviews to acquire valuable insights and viewpoints from the participants. The utilization of this approach yielded a resilient dataset that is well-suited for thorough investigation. The study explored the complex connection between the use of social media platforms, the implementation of big data analytics, and the incorporation of smart technologies in influencing the dynamics of intercultural communication and leadership inside the Ministry. The results emphasized the substantial influence of social media in promoting intercultural communication and cooperation among personnel within the Ministry. Moreover, the implementation of big data analytics has become a crucial element in improving decision-making processes, impacting several facets of leadership efficacy, strategic planning, and employee involvement. Smart technologies were recognized as crucial elements in establishing efficient communication channels and facilitating effective leadership practices. The study's findings emphasized the beneficial impacts of utilizing social media, big data, and smart technology in the Ministry of digital & entrepreneurship. The research highlighted the significance of government organizations incorporating these technologies in a proactive manner to foster a work environment characterized by improved multicultural communication, well-informed decision-making and efficient leadership. This study makes a substantial contribution to the comprehension of how technological improvements might influence organizational behavior and leadership practices in a government setting. It provides essential insights for policymakers, leaders, and researchers. The findings have broader significance beyond the Ministry, serving as a basis for additional investigation into the use of technology in intercultural communication and leadership effectiveness inside government institutions.
Sulfonic Resins as Catalysts for the Oxidation of Alcohols with H2O2/KBr

Sulfonic resins can replace homogeneous sulfonic acids in the oxidation of alcohols with the H2O2/KBr system. The performance of different resins was tested with methyl 9(10)-hydroxystearate, a secondary fatty alcohol. The structural features of the resin were more important than the acid strength for the catalytic performance of this reaction. The optimization of the reaction conditions allows the recovery of the resin, although regeneration is required due to the active role of KBr, and a significant loss of sulfonic groups can be detected after nine runs. In the case of primary fatty alcohols, the oxidation leads to carboxylic acids, which are esterified with the starting alcohol under the acidic conditions. For cyclic secondary alcohols, the steric hindrance around the hydroxyl group seems to be important for the efficiency of the oxidation to ketone.
Development of a New Post-Quantum Digital Signature Algorithm: Syrga-1

The distinguishing feature of hash-based algorithms is their high confidence in security. When designing electronic signature schemes, proofs of security reduction to certain properties of cryptographic hash functions are used. This means that if the scheme is compromised, then one of these properties will be violated. It is important to note that the properties of cryptographic hash functions have been studied for many years, but if a specific hash function used in a protocol turns out to be insecure, it can simply be replaced with another one while keeping the overall construction unchanged. This article describes a new post-quantum signature algorithm, Syrga-1, based on a hash function. This algorithm is designed to sign r messages with a single secret key. One of the key primitives of the signature algorithm is a cryptographic hash function. The proposed algorithm uses the HAS01 hashing algorithm developed by researchers from the Information Security Laboratory of the Institute of Information and Computational Technologies. The security and efficiency of the specified hash algorithm have been demonstrated in other articles by its authors. Hash-based signature schemes are attractive as post-quantum signature schemes because their security can be quantified, and their security has been proven.
A Miniaturized Tri-Polarization Reconfigurable Annular Ring Antenna Based on Slow-wave Transmission Line

A miniaturized tri-polarization reconfigurable antenna implemented by slow-wave transmission line (SW-TL)-based annular ring is presented. Due to the slow-wave propagation characteristics of the SW-TL, the size of the developed antenna is reduced by 50.9% compared with a conventional annular ring antenna. A narrow slot along the – y-axis is etched on the annular ring and two PIN diodes are utilized to reconfigure one linearly polarized (LP) state and two circularly polarized state. Simulation results manifest that the overlapped bandwidth for the three polarization states is 1.08%, and the peak gain within the overlapped bandwidthis 7.68 dBi.
Analysis of Dynamic Energy Consumption Sources and Energy Conservation Measures for Bus

This paper focuses on analyzing the influencing factors of bus dynamic energy consumption, providing a bus model related to dynamic energy consumption, as well as the relationship between coupling parameter values and process size changes. By analyzing the calculation formula of bus dynamic energy consumption, measures to reduce bus dynamic energy consumption are pointed out, which is helpful for studying on-chip bus energy conservation issues in deep submicron and nano processes.
Structural and Textural-Aware Feature Extraction for Hyperspectral Image Classification

Feature extraction is a prevalent technique in hyperspectral remote sensing. Various tasks require this technique as a preprocessing step, including image classification, anomaly detection, image denoising, and so on. Edge-preserving filtering-based methods have been extensively utilized for this purpose. However, these methods do not take the inherent structural and textural information into account, leading to poor performance in classifying hyperspectral images (HSIs). In this letter, a new structural and textural-aware feature extraction method is proposed that preserves the relevant structural information and removes useless textures. First, structural and textural-aware recursive filtering features (STRFs) are extracted along with an exponential form of windowed inherent variance (eWIV). Then, multiscale STRFs are integrated by the principal component analysis (PCA) method to obtain more discriminative features (MSTRF). Finally, the fused features are fed into a pixel-wise classifier to obtain the final results. The main difference between the MSTRF method and other feature extraction methods is that the MSTRF method can make full use of the proposed eWIV map, which can help to properly characterize structure and texture in HSIs. Experimental results on several public datasets indicate that our method leads to state-of-the-art classification performance, especially in the presence of very small training set.
A New Anisotropic Driving Force Model for SiC Device Simulations

We present a new anisotropic driving force model to compute the anisotropy of the mobility and impact ionization rate in TCAD simulations of silicon carbide (SiC) devices. We derive the model from the relationship between mobility, driving force, and carrier heating. The obtained driving force along the <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-axis and that perpendicular to the <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-axis depend on the gradient of the quasi-Fermi potential and the mobility anisotropy factor. Since mobility also depends on the driving force, we solve for the mobility anisotropy factor to determine the anisotropic mobility and driving force together. We also propose a new interpolation scheme to consider the anisotropy of the impact ionization rate using the derived anisotropic driving force. We perform the simulation of a 4H-SiC vertical power MOSFET device and compare the present model with the existing models in the commercial simulator.
Identification of regional persistent patterns through principal prediction patterns

Most persistent atmospheric circulation patterns in terms of air pressure distributions are derived for the N Atlantic / NW European region. They are identified through Principal Prediction Pattern Analysis and through Principal Oscillation Pattern Analysis. With the help of these patterns the degree of persistence in a given pressure map can be determined. The larger the initial degree of persistence, the better is the skill of the persistence forecast.
Deep Learning Approach for Directivity Prediction of Multi-Layered Leaky Wave Antenna

This work provides a solution for the forward design problem of leaky wave antennas to predict its directivity using a novel deep neural network architecture. Considering the nonlinear relation of the angle of maximum directivity and frequency at leaky wave resonance, this paper leverages the advantage of deep learning framework for such non-linear regression problem. Using the Maxwell's equations, the dataset has been generated for different permittivity and thickness values of three-layered leaky wave antenna. For training purpose, it considers 80% of total data. Remaining 20% data are used to obtain an accurate prediction of the maximum directivity within less than 1% error. The proposed method can be suitable replacement for rigorous full-wave simulation in terms of time and computational resource.
An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum

With the voluminous information being produced by the Internet of Things (IoT) smart gadgets, the consumers with their countless service requests are also growing rapidly. As there is a huge distance between the IoT devices and the Cloud datacenter, some latency is incurred in the communication between the IoT devices and the Cloud datacenter. This latency can be reduced by introducing a Fog layer in between the Cloud and the IoT layer and therefore, it is paramount to offload those tremendous data to leverage the overloaded storage and computation to the Cloud-based systems and Fog-assisted nodes. Moreover, these heavy computations consume significant energy from the distributed Fog servers as well as Cloud datacenters. Therefore, this work addresses the task migration problem in a Fog-Cloud system and load balancing to reduce the latency rate, energy utilized and service time while increasing the resource utilization for latency-sensitive systems. This paper uses a Fuzzy logic algorithm for determining the target layers for offloading considering the resource heterogeneity and the system requirements (i.e., network bandwidth, task size, resource utilization and latency sensitivity). A Binary Linear-Weight JAYA (BLWJAYA) task scheduling algorithm has been proposed to map the incoming IoT requests to computation-rich Fog nodes/virtual machines (VMs). Numerous experimental simulations have been carried out to appraise the efficacy of the suggested method and it is evident that the suggested method outperforms other baselines with an approximate improvement of 26.2%, 12%, 7%, 8.63% and 6% for Resource utilization, Service rate, Latency rate, Energy consumption and Load balancing rate. The presented approach is generic and scalable concerning addressing the unpredictability of data and the associated latency due to the task offloading criteria within the Fog layer.
Blockchain-Assisted Hierarchical Attribute-Based Encryption Scheme for Secure Information Sharing in Industrial Internet of Things

The edge devices will produce enormous quantities of data daily as the Industrial Internet of Things (IIoT) expands in scope. Still, most IIoT data is stored in data centers, making it challenging to transfer data between domains safely. Smart logistic products have dramatically changed due to the prevalence of decentralized edge computing and blockchain in the industry sector. To address the need to exchange data between logistics networks, we proposed a novel decentralized hierarchical attribute-based encryption (HABE) scheme combining edge computing and blockchain. To begin, we offer an IoT data encryption strategy in which edge devices can send data to a nearby cloud network for data processing while maintaining privacy. In addition, we developed a blockchain-integrated data-sharing scheme that makes it possible for users to share data via the use of edge and cloud storage. In particular, an IoT device incorporates an encryption-based authentication system to verify users’ access rights at the network’s periphery in a decentralized manner. Using HABE, we provide a blockchain-integrated architecture for the IoT that protects user privacy. The suggested design utilizes the edge and cloud network paradigms and HABE to maintain privacy and works well with smart logistics applications. The authentication time of the proposed model is reduced by 1.5 times compared with the centralized model. The analyses and experimental findings show that the proposed blockchain-integrated edge computing architecture is better than the existing schemes in terms of data sharing, data privacy, and security.
Genetic evidence on the causality between gut microbiota and various asthma phenotypes: a two-sample Mendelian randomization study

Introduction Asthma is a multifarious disease that manifests in various phenotypes. Among the various factors that contribute to the development of asthma, the gut microbiota has recently emerged as a compelling area of investigation. This study aims to investigate the causal relationships between gut microbiota and distinct asthma phenotypes. Methods The genome-wide association study (GWAS) summary statistics for 211 gut microbial taxa were used as study exposure. Five traits pertaining to various asthma phenotypes (asthma, allergic asthma, childhood asthma, suggestive for eosinophilic asthma and obesity-related asthma) were included as study outcome. We conducted Mendelian randomization (MR) analysis and sensitivity analysis for each bacterial taxa and asthma phenotypes. Result We discovered a total of 58 associations that exhibited evidence of causality. Out of these, 4 associations remained significant even after applying multiple correction. An increased risk of asthma was causally associated with higher abundance of genus Holdemanella (OR = 1.11; CI: 1.05-1.17; p = 0.027), genus Oxalobacter (OR = 1.09; CI: 1.04-1.15; p = 0.025) and genus Butyricimonas (OR = 1.14; CI: 1.06-1.22; p = 0.027). Order NB1n was causally linked with an increased risk of obesity-related asthma (OR = 1.17; CI: 1.07-1.29; p = 0.015). There was limited overlap among the taxa that exhibited potential causal relationships with distinct asthma phenotypes. Conclusion Our research has provided genetic evidence that establishes multiple causal relationships between the gut microbiota and distinct asthma phenotypes, supporting the role of the gut microbiota in various asthma phenotypes. It is possible that different taxa play a role in the development of distinct asthma phenotypes. The causal relationships identified in this study require further investigation.
M²–CNN: A Macro-Micro Model for Taxi Demand Prediction

In this paper, we introduce a macro-micro model for predicting taxi demands. Our model is a composite deep learning model that integrates multiple views Our network design specifically incorporates the spatial and temporal dependency of taxi or ride-hailing demand, unlike previous papers that also utilize deep learning models. In addition, we propose a hybrid of Long Short-Term Memory Networks and Temporal Convolutional Networks that incorporates real-world time series with long sequences. Finally, we introduce a microscopic component that attempts to extract insights revealed by roaming vacant taxis. In our study, we demonstrate that our approach is competitive against a large array of approaches from the literature on the basis of detailed moving logs of more than 20,000 taxis and 12 million trips per month over a three-month period. Our analysis of the effectiveness of individual components reveals that microscopic information is essential for generating high-quality predictions.
An Experimental Investigation on the Pool Boiling Heat Transfer of R-134a on Microporous Cu-MWCNT Composite Surfaces

Multiwalled carbon nanotubes (MWCNTs) exhibit outstanding physical properties, including high thermal conductivity, excellent mechanical strength, and low electrical resistivity, which make them suitable candidates for a variety of applications. The work presented in this paper focuses on the pool boiling performance of refrigerant R-134a on microporous Cu-MWCNT composite surface layers. A two-stage electrodeposition technique was used to fabricate Cu-MWCNT composite coatings. In order to achieve variation in the surface properties of the Cu-MWCNT composite surface layer, electrodeposition was carried out at various bath temperatures (25 °C, 30 °C, 35 °C, and 40 °C). All surfaces coated with the Cu-MWCNT composite demonstrated superior boiling performance compared to the uncoated surface. Heat transfer coefficient (HTC) values for Cu-MWCNT composite surface layers, prepared at bath temperatures of 25 °C, 30 °C, 35 °C, and 40 °C, exhibited improvements of up to 1.75, 1.88, 2.06, and 2.22, respectively, in comparison to the plain Cu surface.
A Novel Safe File Sharing Method Based on Cloud Storage Structure in DHT Networks

Safe file sharing mechanism in cloud storage is one of the most significant elements that affects the development of security technology. With application of homomorphic key agreement mechanism 、 Shamir secret sharing algorithm. Bloom Filter and B-tree search algorithm and Rsync data updating algorithm, a safe file sharing scheme in cloud storage is established based on cloud storage infrastructure of DHT networks. Under the circumstance of cloud storage, the scheme can make full use of the sharing mechanism and achieve the confidentiality and integrity of the file. Based on DHT networks, the analysis and test of data sharing are conducted in the paper. Together with file upload and download, the test of data encryption and decryption is done on the basis of Dropbox. As the result shows, the scheme puts forward a safe and effective solution to safe file sharing in cloud storage.
F1 Race Winner Predictor

With the help of machine learning, a winning Formula One (F1) race prediction model is what this project hopes to create. The model is trained using historical data from F1 races, such as lap times, sector times, qualification times, and information about the drivers and teams. To make the predictions we will be using Python and Support Vector Machines (SVM). The outcome of this initiative could be quite interesting for F1 fans and could influence wagering and other related activity. With this project we aim to create a machine-learning model that can forecast an F1 race winner based on a variety of inputs or display the effectiveness of SVMs by comparing predicted values and actual values.
Breaking the U: Asymmetric U-Net for Object Recognition in Muon Tomography☆

We utilize a U2 net-inspired deep learning model for object detection within a scattering muon tomography setup. Capturing muon readings from scintillators situated above and beneath the examination area, we discern the angular disparities between inbound and outbound muon paths. These angular measurements serve as inputs for a neural network, proficient in forecasting the contours of objects and, to an extent, their constituent materials within the research zone. Our model handles the inherent complexity of the input data, where the angular measurements map onto a 200 x 200 space, while producing an output image of a smaller 40 x 40 resolution, resulting in an asymmetric U-Net architecture that diverges from conventional semantic segmentation models which typically maintain the same input and output dimensions.
Harmonic Utilization Permanent Magnet Topology Design Considerations

Reducing costly permanent magnet material while increasing torque density is of great interest. A Harmonic Utilization Permanent Magnet (HUPM) machine topology, proposed in recent literature, offers the same torque density for reduced magnet utilization. However, adoption of the HUPM topology requires understanding for selection of feasible design parameters. This paper proposes an analytical technique for calculating the design parameters as well as considerations for the adoption of the HUPM topology. The analytical technique is applied to test the validity and delineate challenges associated with adoption of HUPM. Finite element analysis confirms the design considerations required for successful adoption of the HUPM topology.