Miracle-dz/UniFAR-FTUs huggingface dataset:
The Contribution of Perceived Brand Ethics to the Development of Consumer Trust: A Qualitative Study of Moroccan Islamic Banks

The Moroccan banking sector is facing growing challenges to adhere to emerging standards of business ethics and corporate social and environmental responsibility. A marketing ethic based on good organizational conduct is now necessary to consolidate competitive advantage, strengthen market positioning and ensure the long-term sustainability of the corporate brand. This article primarily aims to delve into a relatively overlooked subject within marketing literature: the consumer-perceived ethics in the service sector, the brand equity of Islamic banks, and their consequential influence on brand trust development.
Crawling and Analyzing Repository in GitHub

GitHub has become one of the most popular online software developing website. I have crawled the most popular software repositories (own over 500 star number) in GitHub, along with their contributors and stargazers. In total, we have crawled 10,665 repositories, 176,256 contributors, and 1,170,449 stargazers. One of the most important missions of analyzing is detecting communities from the network. While the heterogeneous Github network includes three objects, user, repository and programming languages and two kinds of relation between user and repository, i.e., star and contribute. Mining heterogeneous information network is a fresh and promising research field in data mining. A lot of algorithms has been proposed for heterogeneous network clustering. However, most of these methods directly cluster the heterogeneous networks. This thesis aims to transform the heterogeneous network to the homogeneous network using different schemes and then cluster the new network. We studied three weighting schemes, including dot product, Jaccard similarity and cosine similarity between the vector representations of objects. Then I cluster the homogeneous network by using modularity maximization optimization algorithms, in particular, greedy modularity maximization optimization algorithm and spectral modularity maximization optimization algorithm. The performance of clustering is evaluated using F-measure and rand index based on the programming language the software repository used. To compare the interaction between the weighting schemes and clustering algorithms, we applied out methods on GitHub dataset. Then we transformed the whole network to repository-repository and furthermore transformed it to the language-repository network. Based on this network, we discovered the relation between languages. Among 94 programming languages used by the top 10,000 projects, we studied their relations using several clustering methods. Overall, we find that languages fall into five communities, i.e., web and scripting languages (JavaScrip, HTML, etc.), system programming languages (C, C++, etc.), OS X and IOS programming languages (Objective-C, Swift, etc.), numerical and statistical languages (Matlab, FORTRAN, Julia and R), and functional programming (Lisp, Scheme, etc.).
Object Recognition System under Hazy Condition for Automated Driving Systems

We propose an object recognition system for vehicles controlled by "Automated Driving Systems (ADS). " In particular, the proposed system aims at operating "Minimum Risk Maneuver (MRM)" for the ADS only with image sensors. The image sensors, digital cameras for example, often face difficulties in acquiring images with foggy, rainy and/or misty weather conditions. Even in such "Hazy" conditions, the object recognition system should be reliable to perform the MRM strategy. The proposed system consists of two sub-systems: image "dehaze" processing, and object detection. The implemented system was validated by several sample movies recorded in hazy condition on real roads. Experimental results show that the proposed system is applicable to real-time applications.
The Use of Podcast to Teach Listening Comprehension to EFL Students: A Library Research

This research provides a review of the current literature on podcast in listening comprehension of EFL students. The type of research is library research study. The aim of this research is to analyze the effect of podcast in improving listening comprehension skills on EFL students. This paper reviewed six articles conducted between 2010-2020 utilizing multiple databases, they are ProQuest, EBSCOhost, and Springer Link through the digital library e- resource Perpustakaan Nasional RI. Furthermore, Google Scholar and Research Gate were used as additional resources. Each relevant publication screened according to some parameters; year of publishing, country, research purpose, research participant, methodology, research finding, type of article, and article in English text. A preliminary synthesis indicates that the majority of studies related to podcasts in improving listening comprehension to EFL students focused on the themes namely: (1) the effect of listening to podcast in improving students’ listening comprehension and (2) how podcast improve students’ positive perception and satisfiction in learning, then finally affected the improvement of students’ listening comprehension skills. A number of suggestions for future research have emerged from the quality and the content of previous studies. They are methodology and quality design of podcast to determine the long-term effects of podcasting on learning outcomes.
A Sensor-Based Data Acquisition System for Soil Parameters to Determine Suitable Crops

Soil parameters monitoring is significant in sustainable crop and food production. The standard strategy of soil parameters monitoring in developing and underdeveloped nations uses manual labor, resulting in wrong decisions in soil management. Inaccurate measurements due to sensor miscalibration or low sensor quality can lead to incorrect soil management decisions and negatively impact crop yield and environmental sustainability. Due to the mentioned challenges, this work aims to develop a Sensor-based Data Acquisition System for Soil Parameters that will enable users to observe various soil parameters like temperature, humidity, water level and soil pH. The system was developed using the combination of hardware and software components. The hardware component comprises of sensory and processing parts. The study calibrates sensors using known pH, moisture, and temperature values for specific crops to grow in Nigeria. The system will aid farmers in determining suitable crops for their farmland and increasing crop yield. The system collects data through a network of sensors installed in the soil and wirelessly transmits the data to a cloud-based server. The collected data is then analyzed and visualized in through a web-based dashboard, providing farmers with information about the state of their soil. The performance evaluation of the system was carried out using response time and accuracy. The average response time of the system was 4 seconds, and the percentage error for temperature and humidity readings when compared to weather forecast readings were 8.20% and 5.08%, respectively. The results show that the proposed system can provide accurate and reliable measurements of soil parameters and can be easily deployed and operated by small-scale farmers. Using this system can result in improved crop yields, reduced wastage, and better overall efficiency in agricultural operations
Raman Gas Sensor Based on Platinum Coated Capillary

This paper introduces a multi-component gas Raman spectroscopy sensing system based on a hollow-core optical waveguide. The principle of the sensor is similar to fiber-enhanced Raman spectroscopy (FERS), utilizing a quartz capillary coated with a Pt metal film deposited using ALD technology to enhance the Raman signals. The sensor system adopts a fiber probe structure, where the Raman probe is connected to the laser and spectrometer in the upper computer system through multimode optical fibers, completing the pumping and signal transmission. The experiments mainly focus on the performance characterization and optimization of the system. The results show that the system exhibits good qualitative and quantitative analysis capabilities for multi-component gases, achieving detection limits at the hundred ppm level for H2, CH4, $\mathbf{C}_{2}\mathbf{H}_{6}$, and other common flammable industrial gases. After miniaturizing the probe, it has the potential to be employed as a passive device in various complex working environments, effectively fulfilling various gas detection tasks.
A Fully Integrated Filtering Vivaldi Antenna With High Selectivity and Wide Out-of-Band Suppression

This paper introduces a novel filtering approach that employs integrated periodic structures with a conventional Vivaldi antenna to achieve a fully integrated bandpass filtering antenna. The approach results in a wide out-of-band suppression, high passband selectivity, adjustable operational bandwidth, and low insertion loss. The proposed filtering approach maintains the original size of the conventional Vivaldi antenna (base antenna) without requiring additional modifications. To validate the approach, we present two filtering Vivaldi antennas: filtering antenna I (center frequency: 18GHz, fractional bandwidth: 21%, insertion loss: 0.32dB) and filtering antenna II (center frequency: 6.5GHz, fractional bandwidth: 12%, insertion loss: 0.6dB). Their wide out-of-band gain suppression (typically $\geq 15$ dB) covers the conventional Vivaldi antenna’s frequency range (4-24GHz). A prototype of the filtering antenna I is manufactured. Its measurement results validate the proposed approach and show good agreement with the simulated reflection coefficient, realized gain, and radiation patterns. The features of the proposed filtering antenna approach, make it suitable for various applications requiring efficient frequency filtering.
EEG Based Emotion Recognition Using Variational Mode Decomposition and Convolutional Neural Network for Affective Computing Interfaces

Human emotion recognition plays a vital role in brain-to-brain communication, human-machine interactions and affective computing interfaces. This paper presents electroencephalogram (EEG) based emotion recognition using variational mode decomposition (VMD) and convolutional neural network (CNN) by finding optimal hyperparameters for recognizing three emotional classes: positive, neutral and negative. The two-stage VMD based EEG processing is proposed for effectively removing artifacts and noises from the EEG signal and also for decomposing EEG signal into five brain waves such as delta, theta, alpha,beta and gamma. The CNN based emotion recognition is presented based on the differential entropy feature extracted from 1 second brain waves instead of using brain waves directly in order to reduce size of CNN model. In this study, we created twelve CNN models using three number of layers (2, 5, and 7) and four activation functions with major objective of finding best CNN model(s). The standard SEED database is used to obtain trained CNN models and test their performance. Evaluation results show that the CNN architecture with rectified linear unit (ReLU) yielded higher accuracy of 90.33% among four activation functions. For predicting emotions of positive, negative and neutral, the CNN model with 2-layer and ReLU achieves an accuracy of 100%, 94.44% and 78.2%, respectively whereas the CNN model with 7-layer results in accuracy of 100%, 94.40% and 89.13%, respectively. This study also demonstrates significance of selecting optimal hyperparameters and best activation function.
Chemokine Receptor Antagonists Prevent and Reverse Cofilin-Actin Rod Pathology and Protect Synapses in Cultured Rodent and Human iPSC-Derived Neurons

Synapse loss is the principal cause of cognitive decline in Alzheimer’s disease (AD) and related disorders (ADRD). Synapse development depends on the intricate dynamics of the neuronal cytoskeleton. Cofilin, the major protein regulating actin dynamics, can be sequestered into cofilactin rods, intra-neurite bundles of cofilin-saturated actin filaments that can disrupt vesicular trafficking and cause synaptic loss. Rods are a brain pathology in human AD and mouse models of AD and ADRD. Eliminating rods is the focus of this paper. One pathway for rod formation is triggered in ~20% of rodent hippocampal neurons by disease-related factors (e.g., soluble oligomers of Amyloid-β (Aβ)) and requires cellular prion protein (PrPC), active NADPH oxidase (NOX), and cytokine/chemokine receptors (CCRs). FDA-approved antagonists of CXCR4 and CCR5 inhibit Aβ-induced rods in both rodent and human neurons with effective concentrations for 50% rod reduction (EC50) of 1–10 nM. Remarkably, two D-amino acid receptor-active peptides (RAP-103 and RAP-310) inhibit Aβ-induced rods with an EC50 of ~1 pM in mouse neurons and ~0.1 pM in human neurons. These peptides are analogs of D-Ala-Peptide T-Amide (DAPTA) and share a pentapeptide sequence (TTNYT) antagonistic to several CCR-dependent responses. RAP-103 does not inhibit neuritogenesis or outgrowth even at 1 µM, >106-fold above its EC50. N-terminal methylation, or D-Thr to D-Ser substitution, decreases the rod-inhibiting potency of RAP-103 by 103-fold, suggesting high target specificity. Neither RAP peptide inhibits neuronal rod formation induced by excitotoxic glutamate, but both inhibit rods induced in human neurons by several PrPC/NOX pathway activators (Aβ, HIV-gp120 protein, and IL-6). Significantly, RAP-103 completely protects against Aβ-induced loss of mature and developing synapses and, at 0.1 nM, reverses rods in both rodent and human neurons (T½ ~ 3 h) even in the continuous presence of Aβ. Thus, this orally available, brain-permeable peptide should be highly effective in reducing rod pathology in multifactorial neurological diseases with mixed proteinopathies acting through PrPC/NOX.
Experimental Demonstration of SDN-Controlled OFDM VLC System Based on Adaptive Multi-User Bit and Power Loading Algorithm

In this paper, we experimentally demonstrate a multi-user OFDM visible light communication system supporting software-defined-network (SDN) configuration to realize flexible resource allocation and bit/power loading for multiple users. An access point is designed based on FPGA to realize real-time generation of the OFDM signal and to interact with the SDN. Multi-user OFDM bit and power loading algorithm is implemented and configured by SDN to the real-time transmitter to flexibly allocate resources according to the demand of users and channel information. Two-user experiments with 30Mbit/s throughput show that the SDN-controlled system works properly and the bit error rate is kept below 10−3 as the receiving angle and the data rate ratio between users change.
Translation, Cultural Adaptation, Validation and Reliability of Persian Left Ventricular Dysfunction–36 Questionnaire

Background: The left ventricular dysfunction 36 (LVD-36) questionnaire is considered to be a tool to assess the impact of left ventricle impairment on patients’ daily life. This methodological study was aimed to translate and assess the validity and reliability of the Persian draft of the LVD-36 questionnaire among Iranian heart failure (HF) patients. Methods: We recruited stable HF patients who referred to an outpatient heart clinic in Isfahan, Iran. The LVD-36 questionnaire was translated using the forward-backward method. Twenty HF patients were recruited for content validity assessment and were asked to express their opinions about the comprehensibility and meaningfulness of each item. We invited 14 experts to assess validity through content validity index (CVI) and content validity ratio (CVR). Reliability was assessed by Cronbach’s alpha and intraclass correlation coefficient (ICC), with the latter evaluated after invitation of the participants to complete the questionnaire for the second time. Results: The translation process was performed uneventfully without any significant alterations. A total of 150 HF patients were recruited to assess the reliability of the questionnaire in this study (age: 64.6±16 years, males: 58.6%). All items had acceptable CVI and CVR, ranging 0.85–1.00 and 0.57–1.00, respectively. Cronbach’s alpha was 0.971. All participants completed the questionnaire for the second time with no missing data. Test-retest reliability revealed an excellent ICC value of 0.981 (95% CI: 0.977–0.985). Conclusion: The Persian version of the LVD-36 questionnaire is a simple, valid and reliable tool for evaluating the impact of left ventricle impairment on the well-being of Iranian HF patients.
Probiotic Bacillus licheniformis ZW3 Alleviates DSS-Induced Colitis and Enhances Gut Homeostasis

Despite Bacillus species having been extensively utilized in the food industry and biocontrol as part of probiotic preparations, limited knowledge exists regarding their impact on intestinal disorders. In this study, we investigated the effect of Bacillus licheniformis ZW3 (ZW3), a potential probiotic isolated from camel feces, on dextran sulfate sodium (DSS)-induced colitis. The results showed ZW3 partially mitigated body weight loss, disease activity index (DAI), colon shortening, and suppressed immune response in colitis mice, as evidenced by the reduction in the levels of the inflammatory markers IL-1β, TNF-α, and IL-6 (p < 0.05). ZW3 was found to ameliorate DSS-induced dysfunction of the colonic barrier by enhancing mucin 2 (MUC2), zonula occluden-1 (ZO-1), and occludin. Furthermore, enriched beneficial bacteria Lachnospiraceae_NK4A136_group and decreased harmful bacteria Escherichia-Shigella revealed that ZW3 improved the imbalanced gut microbiota. Abnormally elevated uric acid levels in colitis were further normalized upon ZW3 supplementation. Overall, this study emphasized the protective effects of ZW3 in colitis mice as well as some potential applications in the management of inflammation-related diseases.
Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.
Towards Cognitive Battery Monitoring on Hybrid VTOL Fixed-Wing sUAS with Maximized Safe Endurance

In this work, we explore developments toward an application of cognitive battery monitoring system (CBMS) using a hybrid vertical takeoff and landing (VTOL) fixed-wing small unmanned aircraft system (sUAS). The CBMS is focused on providing an early warning system for safe landings, given long endurance missions, when the battery's state of charge (SOC) is near critical. The CBMS is comprised of a Digital Twin (DT) model that is behavior matched from a pulsed discharge curve using state-of-the-art battery models and Coulomb counting for SOC estimation. The CBMS was subjected to experimental mission data to determine the average cost of the VTOL landing. The CBMS was back-tested on an experimental mission, such that, during the landing phase, the battery limit surpassed the critical regime. The CBMS estimated the remaining useful life (RUL) and provided an early landing warning recommendation. The RUL-based landing prediction provided a safe margin for initializing the landing sequence. Thus the CBMS can be used as a critical tool for safer long endurance missions with VTOL fixed-wing sUAS.
A User-Centric Cell-Free Network Architecture for Bidirectional VLC Based on Tri-Color LEDs

Visible light communication (VLC) technology is a potential candidate for access networking in 6G, which offers a higher spectral efficiency than radio-frequency (RF)-based femtocell networks by three orders of magnitude. This paper proposes an all-wireless optical bidirectional VLC access network architecture to solve the problem that users need to switch access frequently when VLC is deployed to the application. The networking architecture is based on tri-color LEDs, which improve the quality of user communication by partitioning the AP coverage area and determining the number of service wavelengths based on the user's location. The feasibility of the proposed access network architecture scheme was verified by theoretical analysis, simulation and experimentation. We showed that the proposed access network architecture is suitable for large-scale bidirectional VLC access networks and can be used as a potential technology for 6G.
Evaluation of a 2-D ConvNet for Early and Multiple Target Detection and Binary Classification in Sugarcane Fields Using UAV Images

2D ConvNets are deep convolution-based networks used exclusively for image modality-based computer vision operations. Used widely in image segmentation, object detection, image localization and classification, 2D ConvNets exhibit promising scope in camouflage target localization and detection. In this paper, we have explored LeNet-5 based 2D CNN to detect multiple weed patches of varying sizes using UAV images and successfully classifying them into two classes, viz weed and non-weed.
Demostration of Resource-Efficient and Load-Balanced Network Slicing in Computing Power Optical Networks

We propose a resource-efficient and load-balanced routing algorithm (RELB) to solve the low utilization and uneven load of heterogeneous resources in computing power optical networks. Additionally, we design and implement a software-defined Intelligent Computing Power Optical Network Convergence Platform (ICNCP) to demonstrate the performance of RELB. The experimental results show that the proposed RELB algorithm can perform well in terms of resource utilization and load balancing.
Fast Dynamic Walking with RH5 Humanoid Robot

Humanoid robots have the potential of becoming general purpose robots augmenting the human work-force in industries. However, they must match the agility and versatility of humans. It is particularly challenging for humanoids actuated with electric drives to achieve that as one must strive for the right balance between mass-inertial distribution in the robot as well as velocity and force transmissions in its actuation concept. In addition to optimal design of the robot, the control system must be designed to exploit the full potential of the robot. In this paper, we perform experimental investigations on the dynamic walking capabilities of a series-parallel hybrid humanoid named RH5. We demonstrate that it is possible to walk up to speeds of 0.43 m/s with a position controlled robot without full state feedback which makes it one of the fastest walking humanoids with similar size and actuation modalities. Video of the experiments is available at: https://youtu.be/39GL2vPedGY