Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote sensing-based vegetation indices for monitoring vegetation change in the semi-arid region of Sudan

Land degradation, a phenomenon referring to (drought) in arid, semi-arid and dry sub-humid regions as a result of climatic variations and anthropogenic activities most especially in the semi-arid lands of Sudan, where vast majority of the rural population depend solely on agriculture and pasture for their daily livelihood, the ecological pattern had been greatly influenced thereby leading to loss of vegetation cover coupled with climatic variability and replacement of the natural tree composition with invasive mesquite species. The principal aim of this study is to quantitatively examine the vigour of vegetation in Sudan through different vegetation indices. The assessment was done based on indicators such as soil adjusted vegetation index (SAVI). Cloud free multi-spectral remotely sensed data from LANDSAT imagery for the dry season periods of 1984 and 2009 were used in this study. Results of this study shows conversion of vegetation to other land use type. In general, an increase in area covered by vegetation was observed from the NDVI results of 2009 which is a contrast of that of 1984. The results of the vegetation indices for NDVI in 1984 (vegetated area) showed that about 21% was covered by vegetation while 49% of the area were covered with vegetation in 2009. Similar increase in vegetated area were observed from the result of SAVI. The decrease in vegetation observed in 1984 is as a result of extensive drought period which affects vegetation productivity thereby accelerating expansion of bare surfaces and sand accumulation. Although, increase in vegetated area were observed from the result of this study, this increase has a negative impact as the natural vegetation are degraded due to human induced activities which gradually led to the replacement of the natural vegetation with invasive tree species. The results of the study shows that NDVI perform better than by SAVI.
Education and the Vestiges of Colonialism: self-determination, neocolonialism and dependency in

It is not entirely coincidental that Thomas' article was placed first in this special number of Comparative Education, and that this paper is placed last. The authors were encouraged to exchange drafts of each other's work, and the editor of the special issue envisaged the articles as a pair of 'book-ends' linking with the intervening papers and complementing each other. Thomas provides a broad introductory account, setting the contextual scene and identifying key contemporary issues relating to education in the South Pacific. This article endeavours to take theoretical analysis further in the light of the other contributions and with particular reference to the concepts of self-determination, neocolonialism and dependency. One starting point for the analysis is the book Schooling in the Pacific Islands: Colonies in Transition (Thomas & Postlethwaite, 1984). Prior to the publication of this book, few works had analysed colonialism and education in a comparative perspective. [1] Thomas & Postlethwaite's work greatly contributed to understanding not only of education in the Pacific, but also of broader analytical frameworks and processes. Since the early 1980s the literature has seen some further development, [2] but our understanding of many central conceptual issues remains limited and a great deal of comparative work remains to be done. The present paper begins with an identification of key issues in the literature on colonialism and neocolonialism. It then turns specifically to the South Pacific, first with an overview of colonial legacies and then with focus on education. Linking back to the contribution by Thaman, attention is given to aspects of school curricula and the scale and nature of post-secondary education in the light of the literature on colonialism and neocolonialism. Specific attention is also given to the resourcing of education. In general terms it is argued that colonial legacies feature more strongly in some aspects of education than in others. In some respects countries are forcefully asserting their rights of self-determination; but there is also evidence of neocolonialism not only by the former colonial powers, but also by others. The paper by Luteru & Teasdale highlights the roles of Australia and New Zealand. The impact of colonial and neocolonial forces is particularly evident in the smallest of the South Pacific countries. However, several paradoxes remain. For example, it is arguable that some South Pacific states are better off with the existing colonial and neocolonial forces than they would be if all such ties were cut. Dilemmas of regionalism, which can support self-determination but can also obstruct it, are also explored.
Model Predictive-Position Sensorless Control of PMSM with Non-Sinusoidal Back-EMF

Model predictive control (MPC) has proven to be an efficient control technique for torque ripple minimization in permanent magnet synchronous motors (PMSMs). The torque ripple in practical PMSM is primarily caused by the non-sinusoidal back-EMF owing to the exact shape and structure of the rotor magnets and the equivalent non-sinusoidal flux distribution within the air gap. The predictive control techniques use the rotor flux lookup table (LUT) to estimate the motor output torque under non-sinusoidal back-EMF. This work proposes a rotor position estimation technique by using the rotor flux LUT and by reverse calculating the α and β components of the rotor flux components from the measured motor phase currents and inverter control signals. The proposed technique estimates the rotor angle irrespective of the initial position without requiring additional sensors. The sensorless control is modeled and tested in the simulation environment for a PMSM of 7 kW rated power, and the design is validated.
Hybrid Sensing and Communications Receiver Front-End for Mobile Communications Applications

This work introduces a mobile network front-end based on a direct sampling architecture, enabling simultaneous sampling of all frequency bands below 6 GHz used for mobile communications. Good channel isolation properties of above 43 dB for most channels are an essential enabler for future sensing and communications applications. Variable amplification within individual channels enables direct control over reception levels, and enhances resistance against willful and unwilling jamming of the receiver system.
A Review Paper on the Current Utilization of WhatsApp for Educational Purposes

This review paper aims to comprehensively investigate the main aspects of utilizing WhatsApp for educational purposes, explore its advantages and disadvantages for Learning Purposes, and propose strategies for effectively utilizing WhatsApp as an educational tool. This review paper illustrated that the main aspects of utilizing WhatsApp for educational purposes were using WhatsApp as a communication tool and using WhatsApp as a learning platform. In light of the findings of previous studies, the current review paper identified some Advantages and Disadvantages regarding using WhatsApp as a communication tool or learning platform. Furthermore, based on the findings of the reviewed studies, the current review paper provides some recommended practices that can ensure the effective utilization of WhatsApp for educational purposes.
ANN based Novel Energy Management System of Hybrid Renewable Energy Sources based DC Microgrid

Hybrid power supply system’s especially powered by renewable energy sources is more popular and frequently establishing at many places in the world. Among many, wind and solar energies are available at every place and commonly used as renewable energy sources for electric power generation. However both the sources are rapidly changes depends on weather conditions. Hence a storage device like battery is required to maintain power balance among all devices in Microgrid. Therefore, a battery is incorporated to the dc-link. PMSG is coupled with wind turbine for electric power generation from wind energy. Photovoltaic (PV) is used to produce electricity from solar energy. Hence, wind system, PV system and battery are connected to a common dc-link through their respective converters for establishing DC Microgrid. Usually conventional PI controllers suffer from fixed gains tuned at particular instant. Hence, Artificial Neural Network (ANN) controllers are implemented on control methods of various converters in this paper to maintain stable voltage at dc-link. Various cases are studied to evaluate results by developing Hardware-in the-Loop (HIL) through OPAL-RT modules.
Social Media Marketing & Corporate Sector Customers' Loyalty: The Case Study of the Emirati Telecommunication Sector

Today, social media is a trendsetter of many new and unique aspects, including digital marketing. Consequently, people have more exposure to products and services from all over the world. Similarly, digital marketing is also beneficial to the corporate sector in the UAE as the number of online media users is briskly increasing. As social media usage is widely accompanied by ease of access, ease of use, and useful outcomes, the current study also aims to examine it in the realms of the digital market. We employed an exploratory approach and utilized a self-proposed model in this study. First, we tested the measurement model and then the structural model by following the rules specified in Structural Equation Modelling. Results revealed the relationship between Social Media Marketing and Accessibility $(\mathbf{p} >$ 0.000). and Cost-effectiveness remained strongly significant $(\mathbf{p} > \mathbf{0.000})$. However, the relationship between Connectivity and Social Media Marketing is rejected $(\mathbf{p} > \mathbf{.065})$. Moreover, the direct effect of Social Media Marketing on Customers' Loyalty also remained strongly significant $(\mathbf{p} > \mathbf{0.000})$. Finally, the mediating role of Public Relations also remained significant with the significance value at $\mathbf{p} > \mathbf{. 014}$. Thus, despite conventional media platforms are still used, social media are mainly preferred, and the approaches regarding Public Relations are the same. What has changed is the method to deliver the marketing message more effectively and the preferences that are more focused on customers to increase their loyalty and long-term relationship.
Design Considerations for GaN-based Drive-train Inverters in Light Electric-Vehicles

Design of high efficiency and high power density drive-train inverters is needed for meeting design targets in 2-wheeler EV segment. Gallium-nitride (GaN)-based inverters offer improvements in both metrics. However, design considerations like voltage levels, operating frequency and thermal performance need to be studied in detail. This paper discusses the indispensable design considerations of device voltage rating, thermal management and operating switching frequency for GaN inverters. Results of a numerical simulation based comparative analysis show that the 150 V DC-link GaN inverter design offers the least losses at a 10 kW power rating, resulting in an estimated power density of 40 kW/L and reduction in cooling system size.
Sistema de Internet das Coisas com Código Aberto para Monitoramento de Estufas

Considerando a demanda por soluções sustentáveis na indústria siderúrgica como o uso de carvão vegetal em detrimento do carvão mineral, este artigo descreve a especificação e desenvolvimento de um sistema de Internet das Coisas (IoT) para monitoramento de estufas destinadas ao plantio de mudas para uso como termo redutor no processo produtivo da empresa Gerdau. O projeto foi realizado em 10 semanas por uma equipe de alunos de primeiro ano da graduação do Instituto de Tecnologia e Liderança (Inteli). O método RM-ODP foi utilizado para a especificação e modelagem do sistema. Uma prova de conceito funcional composta por interface de usuário, banco de dados, API (Application Programming Interface) e um protótipo impermeável de dispositivo IoT foram desenvolvidos com sucesso. O código-fonte do projeto e manual de instruções estão disponíveis sob uma licença de código aberto.
Optimising Trading Strategies using Linear Regression on Stock Prices

Stock price prediction is a challenging endeavour since it is highly dependent on stock demand, and there is no single characteristic that can correctly predict the demand of one stock each day. The proposed method intends to offer traders a reliable framework for optimizing their trading strategies based on historical data and linear regression analysis. This method involves identifying variables that affect stock prices, such as historical stock prices and economic indicators. The project outlines the steps to create two accurate models for predicting stock prices and EMA_10(Exponential Moving Average), which includes data cleaning, prioritization, training the model with appropriate algorithms like Ordinary Least Squares (OLS), and evaluating its performance using metrics like Root Mean Square Error (RMSE) and Mean Absolute Error(MAE). The proposed method is implemented using Python and can provide valuable insights into future trends of stock prices. With the proposed trading strategy investors can make informed decisions and minimize their risk in the stock market.
Calcified Tissue Detection using Deep Learning Models in Intravascular Ultrasound images

Cardiovascular disease is the world's biggest cause of mortality. Heart disease is thought to have calcification as a significant contributing component. Currently, intravascular ultrasonography (IVUS) pictures are used to visually check for the presence of calcification by medical professionals. The goal of the study is to identify dense calcification using IVUS images taken at a 40MHz frequency. To detect these tissue frames, the features were extracted using deep learning models namely, AlexNet, GoogLeNet, SqueezeNet architectures. The IVUS pullbacks of four real patients were used in the tests. According to experimental findings, AlexNet, a deep learning architecture that has been trained, performs better than the other two models. The classification accuracy of the AlexNet (98.10%) model significantly outperforms that of two other previously trained models, GoogLeNet (89.93%) and SqueezeNet (92.80%). Future research into other feature selection techniques like ReliefF, PSO, ACO, and others, together with other models like Vision Transformers, could enhance the diagnostic process' overall accuracy.
Design of a Digital Phase Lead-and-Lag Voltage Control based on Frequency Response Approach for Single-Phase Half-Bridge DC-AC Converters

Grid-forming inverters is getting special attention of industries and academia due to their important role in the realization of microgrids. A crucial feature of them is to have an accurate voltage control. This paper proposes a digital phase lead-and-lag voltage control based on frequency response approach for single-phase half-bridge DC-AC converters. Initially, the system is modeled through the small-signal technique. Then, the controller is designed based on the frequency response approach. The discretization through Tustin and Backward methods are presented and compared. Moreover, designing the controller in discrete-time domain by conventional methods using the fictitious w-domain is also presented. The system is tested in a Controller Hardware-in-Loop setup, where the power topology runs in the HIL402 device and the digital voltage controller runs in the TMS320FF28335 digital signal controllers. Scenarios like initialization, nonlinear load and oscillation at the DC voltage are performed. Results show the efficacy of the methodology proposed in this paper.
Ears In the Wild: An Efficient Ear Localisation

This research presents a novel human ear detection method that enables the utilization of the ear as a biometric trait. Classical methodologies applicable for ear recognition in controlled settings have limited resilience against many factors such as numerous ears, alterations in illumination conditions, variations in orientation, and scalability. Given the significance of detection response, a substantial increase in false positive rate can exert a considerable influence on the process of recognition. Achieving a balance between model complexity and false positive detection is a crucial aspect of designing a system that effectively minimizes the occurrence of false positive detections while maintaining an appropriate level of complexity. To tackle this issue, we provide a computationally optimized ear identification system designed to identify ears from photos and videos under uncontrolled circumstances, with the capability to operate in real-time. The suggested technique was evaluated using three databases obtained from controlled and uncontrolled situations, namely UBEAR, AWE, and UND-J2. The analyses are conducted under various and volatile settings, including a cluttered background, the presence of numerous ears, occlusions, and very small ears, in order to showcase the effectiveness and resilience of the suggested model. The obtained ear detection results demonstrate that the suggested strategy has superiority over existing contemporary ear detection techniques.
A novel medical case entity recognition method based on the BiSRU-TextCNN

the traditional BiLSTM neural network model can't extract local semantic features. What’s more, the model training focuses on relevant data and ignores irrelevant data as much as possible. Therefore, the novel medical case entity recognition called method called BiSRU-TextCNN solves the problem. This method fuses multivariate feature vectors, fully extracts local features and global features of related data. The experimental results show that the precision, recall rate and F1-Measure value of the new model are significantly improved, and the training time of the model is obviously shortened.
Stock Price Prediction Using Machine Learning: A Survey of Recent Techniques

In this paper, we conduct a survey on recent stock price prediction models and examine the effectiveness and accuracy of using RNN, LSTM, and GRU models for stock price prediction. Since an LSTM model can handle both text and time-series data, it was thought to be the best option for stock market forecasting. The LSTM model was enhanced by combining it with other techniques and developing the GRU model, a variant of the LSTM model. The input gate and forget gate from the LSTM are combined to form the update gate in the GRU model. Those recent techniques prove to be more accurate and outperform other similar and traditional techniques.
Acceleration of Multi-Direction Diffusion MRI Using EPI with Keyhole (EPIK) and Locally Low-Rank Tensor Reconstruction

Diffusion MRI is a commonly used non-invasive imaging technique with wide range of clinical applications. Many of these applications benefit from high spatial and angular resolution acquisitions as they increase the accuracy and precision of the generated quantitative maps. Higher resolution is attainable by using multi-shot EPI, which increases the scan time. To maintain the same scan efficiency as single-shot EPI, we propose an end-to-end framework that comprises an accelerated EPI-with-keyhole (EPIK) acquisition and a novel reconstruction algorithm based on space-coil-contrast locally low-rank tensor (SCC-LLRT) regularization. We compared the proposed SCC-LLRT algorithm with SPA-LLR for reconstructing EPIK data, and compared the proposed framework with a more traditional acceleration framework based on a combination of regular EPI and SPA-LLR, using retrospectively undersampled data from a healthy subject. The results showed that SCC-LLRT generated more accurate reconstruction with better preservation of high-frequency structural details compared with SPA-LLR. Furthermore, the proposed framework (EPIK+SCC-LLRT) achieved a substantially improved NRMSE, SSIM and reduction of ghosting artifacts compared with the traditional acceleration framework (EPI+SCC-LLRT). The results suggest that the proposed framework has a high feasibility for accelerating multi-direction diffusion MRI.
Predicting admission test success using SPOC interactions

: In order to start Medical or Dentistry studies in Flemish universities, prospective students have to pass a central admission test to guarantee they have the proper level of proficiency. To support those learners, a blended program with a SPOC (Small Private Online Course) was designed on Edge edX. The logs from the platform provide a great opportunity to delve into the behavior of learners and to try to predict their success in the test based on students' interactions with the SPOC. This article has the following objectives: (1) analyze the differences of user interactions between learners based on their background, (2) develop and analyze predictive models to forecast who will pass the admission test, (3) discover which variables have more effect on success in this test, and (4) discuss about the generalizability of the solution. The results show that the SPOC learning behavior differs significantly between students with different background; it is not possible to predict success the admission test until the last months; and the average grade using only first attempts stands out as the best predictor.
Face Image Inpainting Based on Mask Gridding Autoencoder and Global-Local Refinement Fusion

Face image inpainting is a technology that can accurately predict the texture and structure of missing areas in face images. In face image inpainting, it is crucial to consider both global and local context information for the missing region. However, existing methods tend to overlook the significance of this aspect. This work introduces a hierarchical framework for face inpainting, which is based on the fusion of global and local refinement using the mask griding autoencoder (MGAE). To begin with, the irregular mask in face inpainting is preprocessed using the grid strategy, which enhances by the pre-trained mask autoencoder (MAE). This preprocessing step helps in obtaining preliminary repair results. Subsequently, a global and a local context refinement network are designed to refine the preliminary repair results simultaneously. Finally, a fusion decoder is developed to effectively combine the global and local features of different scales, resulting in the final output of the face image inpainting process. The experimental results demonstrate that our proposed method is highly competitive when compared to the most advanced techniques on the face dataset.
Transformer-Driven segmentation of New Lesions in Multiple Sclerosis scans

Multiple sclerosis (MS) is a chronic neurological disorder characterized by the development of lesions in the central nervous system, typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Timely detection of new lesions is crucial to analyse disease progression and evaluate treatment efficacy. In this study, we use a novel deep learning model; ChangeFormer, for automated detection of the new and enlarging lesions in brain magnetic resonance imaging (MRI) data of MS patients. ChangeFormer utilizes a transformer-based Siamese architecture, leveraging the concept of spacial change detection we applied the model to identify the change in MRI scans, appearing as MS lesions. We evaluate the performance of the model on a publicly available MS dataset and compare it with state-of-the-art methods. Our experimental results demonstrate the effectiveness of ChangeFormer in accurately detecting new and enlarging lesions, showcasing its potential as a valuable tool in clinical practice for monitoring disease activity in MS patients.
Improving a Kernel k-Means Iterative Algorithm

Duda et al. established an elegant k-Means Iterative Algorithm. This was extended to a kernel algorithm by the author. However, unfortunately some of the shortcomings of the original algorithm already pointed out by Duda remained. In this paper these setbacks will be eliminated to some extent by introducing a judicious initialization. Moreover the algorithm will be slightly modified using a ratchet to allow for an easy prespecified number of repetitions in order to minimize the target function.