Miracle-dz/UniFAR-FTUs huggingface dataset:
Raman scattering from GexSi1−x/Si strained‐layer superlattices

Raman spectroscopy has been used to determine built‐up deformation in GexSi1−x/Si strained‐layer superlattice grown by molecular beam epitaxy. By comparing peak positions in commensurate superlattices and single layers with those from incommensurate thick layers of the same composition we can obtain a quantitative determination of strain. Linewidths are affected by the presence of inhomogeneous strain, dislocations, and disorder. Lines are always narrower in superlattice samples, indicating better crystalline quality. In particular, the Raman line from the Si layers of the strained‐layer superlattices is indistinguishable from that from single‐crystalline Si in both linewidth and frequency. This is consistent with the expectation that the entire lattice mismatch is accommodated as a homogeneous tetragonal strain in the alloy layers only.
An Enhanced Web-based Tools for Multiple Sequence Alignment: A Comparative Approach

Multiple sequence alignment (MSA) is one of the oldest dilemmas in computational biology, involving the alignment of more than two DNA, RNA, or protein sequences. There are numerous methods for aligning multiple sequences. According to the survey, there are 103 free MSA Tools – Software, and Resources available. Many techniques are designed to maximize speed while ignoring alignment accuracy. Similarly, many techniques prioritize accuracy over speed. The MSA tool's goal is to reduce execution time and memory requirements while increasing the accuracy of MSA on large-scale data. Choosing which tools to use is one of the most difficult problems for bioinformaticians, especially for the majority of non-bioinformaticians. As a result, a comparison study of the various multiple sequence alignment tools is required so that biologists and bioinformaticians can correctly interpret their biological data using the appropriate tools. In order to determine which tool is best for multiple sequence alignment, we used a case study to evaluate various MSA tools.
Performance of Root-Mean-Square Propagation and Adaptive Gradient Optimization Algorithms on Covid-19 Pneumonia Classification

The SARS-CoV-2 coronavirus causes inflammation of the lungs, known as Covid-19 Pneumonia. Doctors or radiologists usually use lung images from X-rays to detect the condition of a person’s lungs has Covid-19 Pneumonia or not. This research classifies x-ray images of the lungs using deep learning inti 3 categories, namely Covid-19 Pneumonia, Ordinary Pneumonia, and Normal. This method for classification uses the Convolutional Neural Network (CNN), which applies 22 layers containing 5 Convolutional Layers with dimension values 16, 32, 64, 128, and 256. This research tested the Root-Mean-Square Propagation (RMSprop) and Adaptive Gradient (Adagrad) optimization algorithms used to optimize the CNN performance model for Covid-19 Pneumonia classification. The experiment involved 3.900 lung images for the training process, 450 lung images for validation, and 225 lung images for testing. Based on the investigation, implementing the RMSprop optimizer produces an accuracy of 87.99%, a precision of 0.88, a recall of 0.86, and an f1 score of 0.87. Meanwhile, implementing the Adagrad optimizer produces an accuracy of 75.99%, a precision of 0.79, a recall of 0.72, and an f1 score of 0.75. These results provide essential information that the optimization algorithm of the RMSprop produces better performance than the Adagrad in classifying Covid-19 Pneumonia.
Amperometric Measurements by a Novel Aerosol Jet Printed Flexible Sensor for Wearable Applications

In the last years, increasing interest has been addressed on wearable devices for the continuous monitoring of biochemical profiles of patients and athletes. Among various markers, lactate represents one of the most interesting to deepen the knowledge on fatigue processes, in combination with traditional electromyographic features. In this work, we propose a new, low-cost, rapid, and flexible electrochemical amperometric lactate carbon biosensor fabricated by the novel technique of aerosol jet printing (AJP). The developed biosensors were combined with a paper-based microfluidic system and a chitosan-based stable functionalization to allow a long-term continuous monitoring of human daily activities. The sensor performances were evaluated by static and dynamic electrochemical tests in buffered media. The limit of detection is 11 ± 2 mM for the static setup and of 15 ± 6 mM for the dynamic setup, while sensitivity is 0.02 and 0.04 $\mu \text{A}$ /mM, respectively, with single sigma standard deviation lower than 8%. Increasing and decreasing concentration steps of lactate induces comparable response times below 30 s. These results obtained in vitro under controlled laboratory conditions represent a promising starting point to pursue with ex-vivo and in vivo validation for the future development of stand-alone wearable patches for noninvasive lactate measurements.
Malaysian Sign Language Recognition Using 3D Hand Pose Estimation

Sign languages are one of those mediums for hearing-impaired people. These languages transmit meaning by visual-manual treatment. , or more simply, hand movement. Currently, there are only 95 sign language interpreters registered with the Malaysian Federation of the Deaf as of 2020, compared to 40,389 hearing-impaired individuals with disabilities registered with the welfare department which is a problem. Therefore, with the use of deep-learning technology. , this paper proposes to alleviate the scarcity of Malaysian Sign Language interpreters for the benefit of hearing-impaired persons. The paper aims to test and report a sequenced 3D keypoint hand pose estimation model for Malaysian Sign Language Recognition and evaluate the implementation of action model in decoding basic poses of Malaysian Sign Language. According to the findings, the detecting of 3D keypoints and incorporating into LSTM models using deep learning machine learning platform and framework like TensorFlow and MediaPipe enables the detection of Malaysian sign language 3D hand posture estimation. The results demonstrated that 3D hand posture estimation may be utilised to estimate sign language in real time, providing for a better interpretation approach for the deaf community.
A Novel Method of Combining Decision Making and Optimization for LiFi Resource Allocation

Light fidelity (LiFi) is a promising wireless communication technology which can be integrated into existing lighting infrastructure, fulfilling illumination and communication requirements simultaneously. Due to the relatively small coverage area of LiFi with a single access point (AP), a number of LiFi APs are usually needed to provide coverage. However, joint access point selection and resource allocation then become challenging. This issue is mainly tackled using optimization, iteration, and decision making methods. However, the current literature fail to balance the complexity and optimality well. In this paper, a novel approach that combines decision making and optimization is proposed to provide a better trade-off between optimality and complexity. Specifically, a distributed optimization is integrated into the decision making process of fuzzy logic, which is adopted to classify the user based on their channel quality and throughput requirements. Simulation results show that the mixed method can reach the near-optimal achievable throughput, reducing runtime by more than 3 orders of magnitude compared with the global optimization benchmark. Besides, the proposed method is capable of saving up to 72% runtime compared with iteration based benchmark.
Cyber-attack Against Power System in Integrated Energy Systems: Impact and Propagation Mechanism

Advanced information and communication technology (ICT) and deep coupling of cyber and physical systems make the integrated energy system (IES) face severe cyber-attack threats. Furthermore, the integration of different energy flow systems in the IES further strengthens the complexity of cyber security. In our paper, we analyze the impact of the cyber-attack on the power system and its propagation mechanism in the IES by using an analysis framework that combines bi-level optimization and sequential energy flow simulation. First, we use a bi-level optimization model to implement the false data injection attack against the power system and then analyze the attack’s impact on the parameters of the IES and heating system through the IES time series simulation. The results show that the cyber attack against the power system in IES will expand to the heating system and strengthen its destructiveness. However, the abnormal thermal parameter fluctuation reduces the stealthiness of the attack.
A Review on Non-Intrusive Load Monitoring Using Deep Learning

Smart energy management systems have become more popular as the consumption of energy is increasing rapidly. So, in order to monitor the daily energy consumptions in real time smart meters are used. Non-intrusive load monitoring helps the users to understand the appliance level energy consumption using only the smart meter data. It’s a cost-effective way of load monitoring as only a single sensor is used. The appliance level usage pattern is used to identify the energy consumption of each device, the time for which the devices work and their energy usage pattern. The main advantage of this type of a load monitoring is that we can reduce the energy wastage and increase the efficiency of energy usage. This method uses deep neural networks, which identifies the appliance level energy consumption behavior and classifies it based on the system trained.
Cross-Layer Optimization for Underlay Cognitive Radio Systems with Multicarrier Transmission

A novel scheme to enhance the spectral efficiency (SE) for an underlay cognitive radio system (UCRS) is proposed. A cross-layer optimization (CLO) of both the physical layer (PHY) and the data link control layer (DLC) of a secondary user (SU) transmission is considered. The transceiver uses discrete Fourier transform modulated filter banks. An optimum scheme for joint bit and power loading (JBPL) as well as coding adaptation (CA) using punctured convolutional codes is employed at the PHY. At the DLC, a truncated automated repeat request (TARQ) is implemented to ensure reliable transmission. A lower bound on the SE for coded transmission is derived analytically and maximized subject to different quality-of-service (QoS) and stochastic chance-based constraints as well as third-party colored Gaussian interference at the SU receiver. The system performance is analyzed in terms of the number of retransmissions and the achievable SU SE. It turns out that the bound tightens for increasing values of the average energy-per-bit-to-noise-ratio.
Minimum principle and related numerical scheme for simulating initial flow and subsequent propagation of liquefied ground

The problem of predicting the evolution of liquefied ground, modelled as a viscoplastic material, is addressed by combining a minimum principle for the velocity field, which characterizes such an evolution, and a time step integration procedure. Two different numerical schemes are then presented for the finite element implementation of this minimum principle, namely, the regularization technique and the decomposition‐co‐ordination method by augmented Lagrangian. The second method, which proves more accurate and efficient than the first, is finally applied to simulate the incipient flow failure and subsequent spreading of a liquefied soil embankment subject to gravity. The strong influence of liquefied soil residual shear strength on reducing the maximum amplitude of the ground displacement is particularly emphasized in such an analysis. Copyright © 2005 John Wiley & Sons, Ltd.
Artificial Neural Network Modeling of Users' Behavioral Intention to Adopt M-Government Services

M-government services have gained popularity recently, especially during the Covid-19 pandemic and advancements in mobile technology. However, providing the full range of government services on mobile devices is still in the early stages. Moreover, improving and expanding capacity and providing services requires understanding the factors influencing users' behavioral intentions to adopt this technology. This study investigates factors that directly affect the intention to use mobile government services by employing the technology acceptance model and two external factors, trust and social influence in Bahrain by utilizing artificial neural networks and structural equation modeling. Results indicate that the perceived usefulness and trust factors influence users' behavioral intention to use m-government services, followed by perceived usefulness and social influence. The results of the investigated model provide insights to various stakeholders, including the government and developers, to encompass additional security measures and awareness programs among users to achieve higher levels of mobile services usage.
Towards Power Grid Stability Index and Line Losses for Optimal Placement using Moth- Flame Optimization

Forecasting distribution system loads is frequently fraught with uncertainties. Such uncertain-ties impose some undesirable consequences and fluctu-ations on the power working of the system leading to a decrease in the accuracy of the results obtained through system analysis. As a result, probabilistic analysis of the distribution system is extremely important. Hence, this paper presents a new probabilistic load flow approach in the presence of a 24-hour load shifting regime in all periods by applying a normal probability distribution in seven degrees of separation for the loads and employing this distribution function using Forward-backward algorithms based on Moth-Flame Optimization (MOF) aiming to identify the optimal placement and sizing of the Distributed Generation (DG). Our new proposed approach is tested on IEEE IO-bus and 33-bus radial distribution networks. The findings are compared with the conventional approach (i.e., based on deterministic techniques), and it is more efficient with minimum power cost for the user.
Analyzing Influential Impact of Online Trends on Social Media Channels

There is a tremendous impact of online social trends on the media and marketing networks. People in all aspects of life, including business, economic, sports, or social affairs, have an increased inclination toward online social networks. People find themselves excessively attached to social media, due to its expedited responsiveness to trending online news. People can rely on social media because they get newscasts about the whole world on a single platform. Users can remain updated about anything anywhere at the click of their fingertip. This makes social media platforms quite predominant over other media platforms such as print media, electronic media, etc. Amongst other social media networks, Twitter is emerging as an authenticated platform that provides recent happenings around the world. Our research focuses on developing a model for marketing personnel who required trend analysis and profiling to reveal influential profiles for their brand promotion. Our Twitter-based analysis helps to capture the correlation from a set of influential profiles. The highly correlated profiles are further analyzed to track the outreach of their trends, along with analyzing their contribution to the diffusion of the selected trends to evolve nationwide over the advancement in the social timeline.
Adaptive Fuzzy Neural Network vs. Convolution Neural Network in Classifying COVID-19 from Chest X-rays

Detecting COVID-19 in the early time can save lives and reduce the cost of huge pressure on healthcare centers. Many machine and deep learning models have been proposed by researchers to detect and diagnose COVID-19 based on chest X-rays. However, we need to know which of those models is more effective and efficient. This paper presents a comparative study between adaptive fuzzy neural network (AFNN) and convolutional neural network (CNN) in classifying COVID-19 using chest X-rays. We present the experimental results showing the comparative performance measures with respect to the size of available dataset. We also present the relative advantage of each family of neural network in accuracy, precision, recall, F1score, and the computation time.
Turbine Blade Platform Film Cooling With Typical Stator-Rotor Purge Flow and Discrete-Hole Film Cooling

This paper is focused on the effect of film-hole configurations on platform film cooling. The platform is cooled by purge flow from a simulated stator-rotor seal combined with discrete-hole film cooling within the blade passage. The cylindrical holes and laidback fan-shaped holes are assessed in terms of film-cooling effectiveness and total pressure loss. Lined up with the freestream streamwise direction, the film holes are arranged on the platform with two different layouts. In one layout, the film-cooling holes are divided into two rows and more concentrated on the pressure side of the passage. In the other layout, the film-cooling holes are divided into four rows and loosely distributed on the platform. Four film-cooling hole configurations are investigated totally. Testing was done in a five-blade cascade with medium high Mach number condition (0.27 and 0.44 at the inlet and the exit, respectively). The detailed film-cooling effectiveness distributions on the platform were obtained using pressure sensitive paint technique. Results show that the combined cooling scheme (slot purge flow cooling combined with discrete-hole film cooling) is able to provide full film coverage on the platform. The shaped holes present higher film-cooling effectiveness and wider film coverage than the cylindrical holes, particularly at higher blowing ratios. The hole layout affects the local film-cooling effectiveness. The shaped holes also show the advantage over the cylindrical holes with lower total pressure loss.
Angular accuracy improvement in digital array radar with experimental analysis

In this paper, we propose a digital array radar (DAR) capable of detecting multiple targets accurately using multiple simultaneous digital beam forming and phase comparison monopulse. The system forms 3 simultaneous beams with azimuth coverage of 68 degrees. After beam forming, 3 parallel chains of radar signal processing are applied to the input signals. The processing chains include Range FFT, Moving Target Indication (MTI), Moving Target Detection (MTD) and Constant False Alarm Rate (CFAR) algorithms. Detection report produces Target's range, azimuth and velocity information. However due to wide beam widths, the improvement in angular accuracy is required. After initial target detections, the targets are queued for digital monopulse processing for improving angular accuracy. After performing monopulse processing, the targets are displayed on a PPI. This system can detect multiple targets simultaneously in 68° field of view with angular accuracy of 3.09° (1/12th of the beam width). The proposed system is realized using an analog devices' TinyRad kit.
Audio Processing for Tones Validation using Deep Learning

In the industry, audio verification and validation of products present special challenges. Audio signal validation is a challenging and time-consuming part. Automation is a key to throughput, but any analog system is hard to analyze. This work demonstrates automated audio tone validation that verifies the accuracy and quality of the audio. In this paper, we present a method for classifying and predicting the audio signal using a convolutional neural network, a type of deep learning that extracts the characteristics and feature from the spectrogram representation of the signal. The proposed method processes signals, analyses the model, classifies and predicts the audio signal. The selected algorithm identifies all the audio signal of different products. Microwave/Oven products tones are taken as a case study and evaluate a proposed approach. This approach thus provides a feasible solution for different categories of products audio signal(tones). The experimental results clarify the effectiveness of the proposed approach to achieve higher accuracy of audio tone verification and validation.
Qualitative Assessment of Cooking Oil using Diffuse Reflectance Spectroscopy Technique

In this study, reflection spectroscopy technique has been used in determining the oil quality. Conventional methods such as chemical testing consumes some costs and time and not accurate enough. The samples used were bottled palm cooking oil, subsidized cooking oil, and recycled cooking oil from different brands and collected area. Two different sample condition were applied which is different type of cooking oil and different concentration of recycle cooking oil mixture that were used for analysis and their reflection spectrum were measure at 200 nm – 900 nm. The computer simulation was used to process the data based on their pattern recognition. From the analysis, it is found that reflection spectrum is influenced by concentration of foreign compound in the cooking oil sample. This indicate the reflection spectroscopy technique can be used to detect the present of foreign compound in the palm cooking oil, thus providing the difference of quality assessment of cooking oil.
Fake News Detection Using Machine Learning Models

Nowadays, with the widespread use of technology, fake news and rumors are spreading too. People and society are greatly impacted by fake news, which also can be used as phishing attempts and a way of stealing their information. In many areas of our lives, Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated their effectiveness. Furthermore, Natural Language Processing (NLP) has shown promising results in text classification applications. In this study, we proposed an experimental study for detecting fake news using ML models. The proposed model analyzes the main text of the news using NLP techniques and then classifies the news into fake or real news. We used a new dataset that combined multiple fake news datasets. Moreover, we studied the impact of features extraction methods on the performance of the developed models. Eight experiments were performed using Random Forest (RF) and Support Vector Machines (SVM) models, each with a different features extraction technique. The SVM model resulted in the best performance with an accuracy level of 98%. This result proves the model ability to be deployed and used in real-world with high reliability, to detect fake news.
Multi-Modality Brain Tumor Segmentation Network Based on Collaborative Feature Fusion

Gliomas are among the most deadly tumors. Brain tumor segmentation plays an important role in diagnosing gliomas and improving survival rates. Currently, deep learning has been widely used for brain tumor segmentation and has achieved good segmentation results. Magnetic resonance imaging (MRI) is among the most commonly used techniques for brain tu-mor segmentation. In this paper, we design a network with three encoders to extract information from different MRI modalities and a fusion module based on the attention mechanism so that the network increases the attention to features in different modalities that are useful for segmentation and weakens the effect of useless features. We conduct experiments on two datasets (BraTS2020 and BraTS2021) and have achieved promising results. The segmentation results of our network have improved compared to the classical 3D U-Net on both datasets.