Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation of bct structure and high magnetic anisotropy in Fe-Co films with added V and C elements

A high amount of 0-20 at. % of C was added into an Fe-Co-V film by sputtering method, and the crystal structure transformed from body-centered cubic (bcc) to face-centered cubic (fcc) through body-centered tetragonal (bct) structure. In the Fe-Co without V, with increasing C content, the crystal structure changed discontinuously from bcc to fcc not through bct structure. On the other hand, in the Fe-Co with added 10 at. % of V, a single bct phase with the axial ratio c/a of ~1.2 formed in samples with added 5-10at. % of C. The saturation magnetization of these samples (Fe-Co-V-C) were approximately 1200-1500 kA/m and the uniaxial magnetic anisotropy constants were approximately 106 J/m3.
Accuracy of the Personal Economic Distress Index Among Arabic Unemployed

Objectives: Studies about The Index of Personal Economic Distress (IPED) in Arab countries since the onset of the COVID-19 pandemic have been alarming. This study aimed to explore the reliability, factor structure, and criterion validity of the Arabic IPED in a sample of unemployed Tunisians during the past 6 months. Methods: The Arabic version of the IPED and the validated Hospital Anxiety and Depression Scale (HADS) were administered to a total of 2011 unemployed people originating from all Tunisian regions. Principal component analysis (PCA) with confirmatory factor analysis (CFA) was used to establish the spontaneous distribution of the 8 items and possible factors of the IPED. Receiver operating characteristic (ROC) analysis was conducted to assess the ability of the IPED to distinguish between unemployed individuals with no economic distress and those in distress. The correlation between the IPED and the HADS were explored. Results: The reliability of the Arabic version of the IPED was adequate. The PCA suggested two main components of the Arabic version of the IPED: the first component including five items focused on financial responsibilities towards state structures and the second dimension containing three items focused on well-being. A statistically significant association (p = 0.01; r = 0.05) was found between the well-being dimension of the IPED and the Anxiety subscale of HADS. Conclusion: This validated version of the IPED is an important tool to study the impact of economic crises on the mental health of unemployed people, as demonstrated in the link found in this study between economic distress and anxiety.
Dynamic Phasor Modelling of Quad Boost Converter

DC-DC converters are essentially required in many applications like renewable energy integration. It also, demands regulated voltage output with high gain for all such applications for any variations in source voltage and load. Quadratic Boost Converter (QBC) provides a reasonably high voltage gain compared to the traditional boost converter. This paper essentially presents efficient computative modelling using Dynamic Phasor (DP). DP-based model closed loop operation is analyzed for different conditions like a step change in load and source voltage and compared with a similar operation with Simulink Time Domain (STD) model. Both models are developed on the MATLAB platform. Analysis shows that the DP-based model is more computationally efficient than the STD model.
The organum vasculosum of the lamina terminalis and subfornical organ: regulation of thirst

Thirst and water intake are regulated by the organum vasculosum of the lamina terminalis (OVLT) and subfornical organ (SFO), located around the anteroventral third ventricle, which plays a critical role in sensing dynamic changes in sodium and water balance in body fluids. Meanwhile, neural circuits involved in thirst regulation and intracellular mechanisms underlying the osmosensitive function of OVLT and SFO are reviewed. Having specific Nax channels in the glial cells and other channels (such as TRPV1 and TRPV4), the OVLT and SFO detect the increased Na+ concentration or hyperosmolality to orchestrate osmotic stimuli to the insular and cingulate cortex to evoke thirst. Meanwhile, the osmotic stimuli are relayed to the supraoptic nucleus (SON) and paraventricular nucleus of the hypothalamus (PVN) via direct neural projections or the median preoptic nucleus (MnPO) to promote the secretion of vasopressin which plays a vital role in the regulation of body fluid homeostasis. Importantly, the vital role of OVLT in sleep-arousal regulation is discussed, where vasopressin is proposed as the mediator in the regulation when OVLT senses osmotic stimuli.
DETERMINANTS OF CUSTOMER SATISFACTION AND ADOPTION OF ISLAMIC BANKING IN PAKISTAN

Islamic banking system is gaining popularity however little is known about issues and barriers in adoption of Islamic banking. This study aims to understand determinants of customers’ satisfaction and adoption of Islamic banking in Pakistan. Data were collected from the sample of users of Islamic banking in major cities of Pakistan. A total of 276 responses were analyzed using multiple regression techniques through SPSS. Results suggest that religious values and service quality have positive impact on satisfaction of Islamic banking customers whereas factors like inadequate information, language barriers, and trust deficiency were negatively related to customer satisfaction. Customer satisfaction was positively related to adoption of Islamic banking. Findings of this study suggest that managers of Islamic banking should envisage policies to keep abreast their customers with useful information, remove language barriers by explaining complex terms into simple language and maintain service quality to enhance consumer trust. This study is among few studies that have incorporated multiple and diverse factors for necessary for the adoption of Islamic banking
Privacy Aware Post Quantum Secure Ant Colony Optimization Ad Hoc On-Demand Distance Vector Routing in Intent Based Internet of Vehicles for 5G Smart Cities

The Internet of Vehicles (IoV), with advanced technology in 5G communication, is considered the backbone of a smart city’s intelligent transport system. There are mainly two types of vehicular communication: (1) Vehicle to Vehicle (V2V) and (2) Vehicle to Infrastructure (V2I). In IoV, there is a dynamic change in the network topology according to the controllers, destination, vehicle movement, and road structure. Intelligent vehicles are assumed to work with the capacity of data processing, data storage devices, and communication devices to communicate with vehicles or Roadside InfraStructure (RSI). This article presents a post-quantum secure ring learning with error-based key exchange. This assumption ensures security against quantum attacks. The proposed design is an ant colony optimization-based ad hoc ordered distance vector routing algorithm that avoids suspicious vehicles in IoV broadcasting. The proposed framework consists of three parts: (i) certificate authority, (ii) suspicious vehicle detecting algorithm, and (iii) optimal path selection algorithm. The performance analysis Section contains an analysis of the proposed design with related ones, and the proposed has better results.
Simplified Magnetic Flux Density Measurement for Local Resolution Analysis of Transcranial Magnetic Stimulation

The detailed energy dynamics of transcranial magnetic stimulation of the cerebral cortex remain unclear. The present study therefore examined the energy distribution of transcranial magnetic stimulation. Magnetic energy was assessed in terms of the magnetic flux density, and the stimulation resolution was evaluated. The magnetic flux density measuring device comprised a pickup probe and simple electronic circuits (e.g., an integrator and buffer amplifier). The pickup probe comprised a sensor coil (with an inner diameter of 8 mm), twisted pair line, and lead-out cable. Magnetic stimulation was applied using a Rapid2 stimulator and a figure-8 flat coil (with a diameter of 70 mm). The brain model made from an acrylic plate was based on a subject with a 62%–63% motor threshold and a scalp-primary motor cortex distance (i.e., distance from the scalp to the center of the sensor coil) of 15 mm. The highest magnetic flux density after the removal of background noise was approximately 180 mT at the center of the stimulus coil, whereas the resolution of the transcranial magnetic stimulation was approximately 6 mm (i.e., from the center to within ± 3 mm). The stimulation area estimated from the measurements was 12 mm2 around the location of highest magnetic flux density. Relative to the magnetic flux density at the center of the stimulation coil, the stimulus area in which the magnetic flux density decreased by less than 1% was approximately 50.27 mm2 and the stimulus area in which the magnetic flux density decreased by less than 5% was 254.47 mm2. This study showed that the distribution of the magnetic stimulation energy supplied to the target cortex may be evaluated using a simple measurement device and brain model.
MFAN: A Multi-Projection Fusion Attention Network for No-Reference and Full-Reference Panoramic Image Quality Assessment

Panoramic images can provide viewers with a 360$^\circ$ perspective of the scene, and create an immersive experience to viewers, which makes it plays an important role in virtual reality (VR) applications. However, different from traditional 2D images, panoramic images are projected into 2D plane to process, e.g., compression, and viewed in VR devices, which makes their quality assessment be more challenging. In this paper, we propose a Multi-projection Fusion Attention Network (MFAN) to improve the accuracy of panoramic image quality assessment (PIQA). In particular, we propose to extract features from 2D plane images generated from multiple projection methods to overcome the distortions caused by a single projection. Furthermore, we design an attention module to improve the efficiency of features in PIQA task. In addition, our model can be utilized in both full reference and no reference scenarios. Extensive experimental results show that our proposed MFAN can significantly outperform the existing IQA methods.
Deep Learning Based Facial Obfuscation Using MobileNet

More and more daily tasks are being completed electronically rather than with pen and paper or in person. This method is more secure since spoofing techniques may be used to identify unauthorized users, and once someone has been authenticated, only they can access the system. Because it provides greater segmentation than other techniques, the ability of LBPH to capture these distinctions between genuine and spoofed faces has been employed as a method for separating features, although LBPH may not be adequate to identify all face spoofing attempts, this is why MobilenetV2 model is used to detect faces. Overall, MobileNetV2 is a promising deep-learning model for face spoofing detection due to its efficiency and effectiveness. However, to increase its accuracy and resilience in identifying various sorts of face spoofing attempts, it might need to be integrated with additional techniques like LBPH, texture analysis, or depth analysis. This architecture will be used for classification, and people may be categorized based on their student ID or employee ID. Even with a large dataset, this architecture can provide superior accuracy. Therefore, other techniques such as the MobileNetV2 model may also be used in combination with LBPH to improve the accuracy of face spoofing detection.
Model-Dependent Energy-to-Peak Control for Switched Fuzzy Singular Systems With Dynamic Quantization

This paper is concerned with the energy-to-peak (E2P) feedback control for discrete-time switched Takagi-Sugeno fuzzy (STSF) singular systems with input quantized. The main challenge of this work comes from the difficulty of solving the semi-positive problem on the main diagonal in the E2P feedback control design. In order to further study the E2P control problem in the presence of dynamic quantization strategy and fuzzy switching mechanism, a new method is proposed to ensure the E2P performance index and stochastic stability of closed-loop singular (CLS) systems. Then, the design conditions of model-dependent proportional plus derivative (MDPPD) state feedback controllers and mode-dependent (MD) dynamic quantizers for STSF singular systems are obtained in the form of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the proposed control design method.
Identifying Aging-Related Biomarkers and Immune Infiltration Features in Diabetic Nephropathy Using Integrative Bioinformatics Approaches and Machine-Learning Strategies

Background: Aging plays an essential role in the development of diabetic nephropathy (DN). This study aimed to identify and verify potential aging-related genes associated with DN using bioinformatics analysis. Methods: To begin with, we combined the datasets from GEO microarrays (GSE104954 and GSE30528) to find the genes that were differentially expressed (DEGs) across samples from DN and healthy patient populations. By overlapping DEGs, weighted co-expression network analysis (WGCNA), and 1357 aging-related genes (ARGs), differentially expressed ARGs (DEARGs) were discovered. We next performed functional analysis to determine DEARGs’ possible roles. Moreover, protein–protein interactions were examined using STRING. The hub DEARGs were identified using the CytoHubba, MCODE, and LASSO algorithms. We next used two validation datasets and Receiver Operating Characteristic (ROC) curves to determine the diagnostic significance of the hub DEARGs. RT-qPCR, meanwhile, was used to confirm the hub DEARGs’ expression levels in vitro. In addition, we investigated the relationships between immune cells and hub DEARGs. Next, Gene Set Enrichment Analysis (GSEA) was used to identify each biomarker’s biological role. The hub DEARGs’ subcellular location and cell subpopulations were both identified and predicted using the HPA and COMPARTMENTS databases, respectively. Finally, drug–protein interactions were predicted and validated using STITCH and AutoDock Vina. Results: A total of 57 DEARGs were identified, and functional analysis reveals that they play a major role in inflammatory processes and immunomodulation in DN. In particular, aging and the AGE-RAGE signaling pathway in diabetic complications are significantly enriched. Four hub DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) were further screened using the interaction network, CytoHubba, MCODE, and LASSO algorithms. The results above were further supported by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infiltration, DN had significantly more resting mast cells and delta gamma T cells but fewer regulatory T cells and active mast cells. Four DEARGs have statistical correlations with them as well. Further investigation revealed that four DEARGs were implicated in immune cell abnormalities and regulated a wide range of immunological and inflammatory responses. Furthermore, the drug–protein interactions included four possible therapeutic medicines that target four DEARGs, and molecular docking could make this association practical. Conclusions: This study identified four DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) associated with DN, which might play a key role in the development of DN and could be potential biomarkers in DN.
Free-space optical communications with quantum cascade lasers

Free space optical (FSO) communications are a potential application envisioned for Quantum Cascade Lasers technology. In this paper, we sketch out the main market scenarii for FSO communications, where data channels should reach up to Tbit/s over distances that range from 0.1 to 36000 km. We analyze where QCLs can be relevant, and what standards they must meet to be relevant at the industrial level. The following topics are discussed: competing technologies, atmospheric transmission physics, signal processing and multiplexing. We try and translate the constraints of the system-level into challenges for device-level research and development.
A Novel Approaches for Detect Liver Tumor Diagnosis using Convolution Neural Network

Liver cancer is on the rise and may now be the most common form of the disease that claims lives. Superpixel segmentation as well as a convolutional neural network (CNN) technique are used to assess liver cancer detection while cutting down on complexity and processing time. Automatic 3D segmentation of ultrasound liver images using a combination of a time-consuming but accurate technique and a statistical texture before high-energy sound waves. Each image in the area surrounding the database point has texture characteristics extracted using two orthogonal quadratic filter banks. The atlas database has segmented liver surfaces and registered photos of livers from prior patients. The CNN technique is used to first segment the pixel location segmentation of the liver imaging from a new patient, and then to train the challenge of patient-specific frequency components in the feature representation from the atlas database. Liver cancer detection datasets include both benign and malignant cases.
Exploring the Potential of Sensing for Breast Cancer Detection

Breast cancer is a generalized global problem. Biomarkers are the active substances that have been considered as the signature of the existence and evolution of cancer. Early screening of different biomarkers associated with breast cancer can help doctors to design a treatment plan. However, each screening technique for breast cancer has some limitations. In most cases, a single technique can detect a single biomarker at a specific time. In this study, we address different types of biomarkers associated with breast cancer. This review article presents a detailed picture of different techniques and each technique’s associated mechanism, sensitivity, limit of detection, and linear range for breast cancer detection at early stages. The limitations of existing approaches require researchers to modify and develop new methods to identify cancer biomarkers at early stages.
Grapevine Augmentation and Classification using Enhanced EfficientNetB5 Model

All over the world, grapevine leaves available in environment are used in a broad variety of traditional food types. Recognizing different kinds of grapevine leaves is turning into a need because their cost and flavour vary from kind to kind. Also, it takes time and effort for a human to distinguish between distinct grapevine leaf kinds. So, it is very important to create a machine learning model to categorize the classification of grapevine. As a result, this is the main subject of this research. In order to categorise grape leaves, this work converts the 86% accurate EfficientNetB5 model to a CNN-based model. The grid search hyperparameter tuning method is used to get the best values for a inclusive machine learning model. A range of values for each hyperparameter are defined before the model and is trained with every possible combination of those values. The classification success rates for the EfficientNetB5 models were 86% for accuracy prediction. The outcomes show that the categorization models for grapevine kinds utilised in the study can be successfully implemented in these kind of research work for welfare of the society.
Transfer and CNN-Based De-Authentication (Disassociation) DoS Attack Detection in IoT Wi-Fi Networks

The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this work was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for de-authentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate our own dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive research were carried out with performance evaluation through multiple metrics and the results reveal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive training and evaluation of the proposed model, and comparative analysis with existing models, show that this work allows improved early detection and prevention of de-authentication and disassociation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures.
Is the labellum informative to distinguish species of Scaphyglottis(Orchidaceae)? Insights from geometric morphometrics

Scaphyglottis Poepp. & Endl. (Orchidaceae) is a neotropical genus with 78 species, 14 of which are found in Brazil. Some of these taxa have an intricate taxonomy, especially due to the morphological similarity among species and the existence of species complexes. Our study aims to test if the labellum shape, the main source of species diagnostic characters, can confirm the proposed circumscription of Brazilian species of Scaphyglottis . We analyzed 136 flowers from 12 species, to which we applied 12 landmarks, using a geometric morphometrics approach. Our results show that most species can be distinguished. Most of the variation is observed in the claw and apices of labellum lobes, confirming that the labellum is important tool in taxon distinction. Species with distinctly 3-lobed labellum showed some overlap but, for these taxa, we have found distinction specially in the lateral lobes. Our study showed new morphologically variable features in Scaphyglottis that can be used in future taxonomic studies. For widely distributed species we suggest a population-level approach, as local environmental factors can impact morphology, causing expected intraspecific variation.
Multi-Weather Classification using Deep Learning: A CNN-SVM Amalgamated Approach

Weather detection and multi-classification based on image processing are of great importance in various fields such as transportation, agriculture, and energy management. This study presents a novel approach for the detection and multi-classification of weather conditions using an amalgamated deep learning (DL) model of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). The proposed model is evaluated on a dataset of 10,000 self-collected images consisting of five different weather conditions: sunny, rainy, windy, snowy, and cloudy. Our approach demonstrates a high level of accuracy and robustness in identifying different weather conditions. The findings demonstrate that the suggested model is superior to other state-of-the-art weather detection models, achieving an overall accuracy of 97.24% for multi-classification. Furthermore, we conducted a comprehensive analysis to determine the significance of various parameters on the performance of the proposed model. Overall, our study contributes to the development of efficient and reliable weather detection systems that can aid decision-making in various applications.