Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression-Driven Predictive Model to Estimate Learners' Performance through Multisource Data

Predictive modeling plays an essential role in comprehending and estimating the academic performance of learners within educational environments. This research introduces an innovative approach centered around regression analysis to forecast learners' achievements by harnessing data from various sources. By amalgamating a diverse array of data, including demographic particulars, previous academic records, lifestyle behaviors, forum participation, and engagement metrics, a holistic examination of factors influencing performance is achieved. The core of the predictive model is built on regression analysis, which capitalizes on the relationships between predictor variables and the target variable to establish a predictive framework. The methodology includes data collection, preprocessing, dataset partitioning, training using five regression algorithms (Random Forest, XGBoost, k-Nearest Neighbors, Gradient Boosting Regression Trees, and Support Vector Regression), and performance assessment using metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and, Accuracy. Results unveil insights into combined data-algorithm patterns, highlighting data source impact. Distinct categories' influence underscores data's multifaceted role in model accuracy. This underscores regression-driven models' potential for precise performance estimates, contributing to understanding the complex dynamics that drive successful online learning outcomes.
Representation Learning for Knowledge Graph with Dynamic Margin

Representation learning aims to embed knowledge graphs into a low-dimensional, dense, and real-valued vector space. Existing methods such as TransE and TransH make use of a global loss function based on margin to learn embedding representation, but the margin in the loss function is fixed during the training process. Since the margin is used as optimal distance to distinguish valid and invalid triples, it is reasonable to permit the margin to evolve during the training process. Based on this idea, a dynamic margin (DM) translation principle is proposed in this paper, and by introducing this principle into the classical TransE model, a knowledge graph embedding model named TransE-DM is presented. Compared with the TransE model, a feature of TransE-DM is that the loss function can evolve during the training process. Experimental results show that TransE-DM has a certain improvement on the link prediction task.
Weed Net: Deep Learning Informed Convolutional Neural Network Based Weed Detection in Soybean Crops

This research employs Convolutional Neural Networks (CNNs) to distinguish grass and broadleaf weeds in soybean crops, addressing the challenge of weed interference in agriculture. A dataset of 15,336 curated images, featuring soil weeds, grass, broadleaf, and soybeans, forms a comprehensive image library. Utilizing deep learning architectures (ResNet, VGG16, VGG19, AlexNet, GoogleNet, LeNet), the trained neural networks achieved remarkable accuracies, with some models exceeding 97%, demonstrating robust weed identification for precision farming. The study's accuracy in detecting weeds, ranging from 90% to over 98%, highlights practical applicability in real-world scenarios. This research aids farmers in targeted weed management, promotes sustainable farming, and enhances crop yield. The paper details experimental design, evaluation metrics, dataset characteristics, and conclusive insights, offering a comprehensive overview of model effectiveness and potential impact on modern farming.
A New Permittivity Characterization Methodology for Microwave Filter-Based Microfluidic Sensor

This paper proposes a new liquid permittivity characterization methodology for the microwave filter-based microfluidic sensor: The water-ethanol mixtures with ethanol volume fractions of 10% to 90% in increments of 10% are used as the test liquids. As the test liquids are placed inside the sensing area, the group delay responses of the filter-based sensor are measured to acquire the peak frequencies and corresponding values for complex permittivity computation. Compared with the results using a commercial dielectric probe kit, the maximum measured errors of $\varepsilon_{r}^{\prime}$ and $\varepsilon_{r}^{\prime \prime}$ are 4.7% and 16.15%, respectively.
RFID Based Embedded System for Automatic Identity and Body Temperature Screening in the Working Environment

Monitoring human body temperature is an important aspect that plays a crucial role in maintaining a safe, healthy, and productive working environment. This paper presents an RFID based embedded system for recording the identity and body temperature of people in an industrial or institutional working environment. A prototype is developed and demonstrated using a commercial RFID tag attached to the person and a temperature screening module connected to the RFID reader. When a person with a tag enters the hotspot zone of the reader, the identity part will be automatically recorded, and the temperature sensor is facilitated in such a way that the infrared waves will hit the forehead or forearm of the individual. An ultrasonic sensor with distance logic is used to maintain the accuracy of body temperature measurements and prevent the misreading of ambient temperature as body temperature. The RSSI feature of an RFID tag is used to eliminate the ambiguity in mapping between the identity of a person and his body temperature. The acquired data from the module is transferred to the Thingspeak IoT platform to enable remote monitoring of body temperature. It is observed that the proposed RFID-based embedded system is a costeffective, non-contact, and remote monitoring solution to enhance the safety of people in the working environment.
Identification of Optimal Optical Character Recognition (OCR) Engine for Proposed System

A large number of research efforts have been put forward that attempts to transform a document image to format understandable for machine so that it can recognize the text or the information from the image. OCR i.e. Optical Character Recognition provides a solution for this. OCR is software that converts printed text and images into digitized form such that it can be manipulated by machine. OCR systems have established a niche place in pattern recognition. OCR has two categories, online and offline. The image of the scanned document goes through various stages like preprocessing, segmentation, feature extraction, etc. in order to retrieve the information from the image. OCR is also popular among the Android applications. Tesseract is one of the most widely used open source library for implementing OCR in Android application.
A Compact Resistive Quadrature Low Noise Ka-Band VCO SiGe HBT MMIC

A compact resistive I/Q low noise Ka-band voltage-controlled oscillator is reported in this paper. The oscillator consists of two emitter-coupled logic based high-frequency inverters and a voltage-to-current converter for the oscillation frequency control. The oscillator provides differential I/Q outputs for future integration with a sub-harmonic down-conversion mixer. The oscillator demonstrates a gain of 4.31 GHz/V with a phase noise of -75 dBc/MHz at 1 MHz offset over the oscillation bandwidth. The oscillator consumes between 117 to 148 mW from a 4V supply for different tuning voltages. The oscillator was designed using a high-frequency design methodology and was then optimized for a low-noise operation.
Time Series Generation Adversarial Network Controller Based on Optimal Control of Micro-Grids

The traditional micro-grid co-generation control framework contains two time scales, which may lead to the incoherence problem. In order to avoid the incoherence problem, this paper proposes a smart generation control framework with a unified time scale, based on which a timeseries generative adversarial network controller for optimal control of micro-grids is proposed. The proposed time-series generative adversarial network controller contains reinforcement learning, generator deep neural network and discriminator deep neural network. The generator deep neural network generates predicted states based on multiple historical states, multiple historical actions, and multiple longterm actions. The discriminator deep neural network, on the other hand, determines whether the data from the generator deep neural network or real-life data. Numerical simulation results under an IEEE 300-bus power system validate that the proposed time-series generative adversarial network controller can simultaneously achieve higher control performance and smaller long-term economic cost than conventional control algorithms and optimization algorithms.
Simultaneously inhibiting undecaprenyl phosphate production and peptidoglycan synthases promotes rapid lysis in Escherichia coli

Peptidoglycan (PG) is a highly cross‐linked polysaccharide that encases bacteria, resists the effects of turgor and confers cell shape. PG precursors are translocated across the cytoplasmic membrane by the lipid carrier undecaprenyl phosphate (Und‐P) where they are incorporated into the PG superstructure. Previously, we found that one of our Escherichia coli laboratory strains (CS109) harbors a missense mutation in uppS, which encodes an enzymatically defective Und‐P(P) synthase. Here, we show that CS109 cells lacking the bifunctional aPBP PBP1B (penicillin binding protein 1B) lyse during exponential growth at elevated temperature. PBP1B lysis was reversed by: (i) reintroducing wild‐type uppS, (ii) increasing the availability of PG precursors or (iii) overproducing PBP1A, a related bifunctional PG synthase. In addition, inhibiting the catalytic activity of PBP2 or PBP3, two monofunctional bPBPs, caused CS109 cells to lyse. Limiting the precursors required for Und‐P synthesis in MG1655, which harbors a wild‐type allele of uppS, also promoted lysis in mutants lacking PBP1B or bPBP activity. Thus, simultaneous inhibition of Und‐P production and PG synthases provokes a synergistic response that leads to cell lysis. These findings suggest a biological connection that could be exploited in combination therapies.
A Machine Learning Approach to Analyse Sentiment on Social Media

Scholars have put a lot of effort into sentiment analysis over the last ten years, the computerised extraction of positive or negative attitude statements from text. Alongside developing technologies, the popularity of internet users has been rising quickly. These people frequently share their thoughts on social networks, personal blogs, and online review sites. They hold both favourable and unfavourable opinions on various individuals, groups, locations, occasions, and concepts. We can now begin to sift out social feelings, owing to the capabilities offered by techniques used to process massive amounts of text, such as machine learning and natural language processing. In this study, we explore some of the problems in thought extraction as well as some of the methods used to overcome these difficulties. Beyond just polarity, our approach of studying social media sentiment on Twitter yields insights that are used for product profiling, trend research, and forecasting. Positive outcomes have demonstrated that the strategy may be improved to better serve the requirements of the corporate environment by sentiment analysis on social media.
Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child’s wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported by clinical assessment and written tests. A definitive diagnosis is usually made based on the DSM-V criteria. There is a lot of ongoing research on ADHD, in order to determine the neurophysiological basis of the disorder and to reach a more objective diagnosis. The advent of Machine Learning (ML) opens up promising prospects for the development of systems able to predict a diagnosis from phenotypic and neuroimaging data. This was the reason why the ADHD-200 contest was launched a few years ago. Based on the publicly available ADHD-200 collection, participants were challenged to predict ADHD with the best possible predictive accuracy. In the present work, we propose instead a ML methodology which primarily places importance on the explanatory power of a model. Such an approach is intended to achieve a fair trade-off between the needs of performance and interpretability expected from medical diagnosis aid systems. We applied our methodology on a data sample extracted from the ADHD-200 collection, through the development of decision trees which are valued for their readability. Our analysis indicates the relevance of the limbic system for the diagnosis of the disorder. Moreover, while providing explanations that make sense, the resulting decision tree performs favorably given the recent results reported in the literature.
The structure of a membrane adenylyl cyclase bound to an activated stimulatory G protein

The architecture of a signaling hub Adenylyl cyclases (ACs) respond to a variety of inputs to generate the signaling molecule cyclic adenosine monophosphate. ACs are regulated by G proteins, which are activated by upstream receptors. Qi et al. determined the structure of bovine membrane AC9 bound to an activated G protein αs subunit by cryo–electron microscopy at 3.4-angstrom resolution. The structure provides the full architecture of AC9, including a helical domain that connects the transmembrane and catalytic domains. The model reveals how the domains interact to regulate enzymatic activity, including suggesting a mechanism of self-inhibition. Science, this issue p. 389 A cryo–electron microscopy structure provides insight into how a key signaling protein is regulated. Membrane-integral adenylyl cyclases (ACs) are key enzymes in mammalian heterotrimeric GTP-binding protein (G protein)–dependent signal transduction, which is important in many cellular processes. Signals received by the G protein–coupled receptors are conveyed to ACs through G proteins to modulate the levels of cellular cyclic adenosine monophosphate (cAMP). Here, we describe the cryo–electron microscopy structure of the bovine membrane AC9 bound to an activated G protein αs subunit at 3.4-angstrom resolution. The structure reveals the organization of the membrane domain and helical domain that spans between the membrane and catalytic domains of AC9. The carboxyl-terminal extension of the catalytic domain occludes both the catalytic and the allosteric sites of AC9, inducing a conformation distinct from the substrate- and activator-bound state, suggesting a regulatory role in cAMP production.
Complete Genome Sequence of Klebsiella pneumoniae Myophage Menlow

Klebsiella pneumoniae is an opportunistic pathogen that has become an increasing problem in nosocomial infections. Studying phages that infect K. pneumoniae may lead to improvements in phage therapeutics for treating these infections. ABSTRACT Klebsiella pneumoniae is an opportunistic pathogen that has become an increasing problem in nosocomial infections. Studying phages that infect K. pneumoniae may lead to improvements in phage therapeutics for treating these infections. Here, the full genome sequence of Menlow, a Vi01-like phage, is introduced and described.
An Augmented Dataset for Vision-based Unmanned Aerial Vehicles Detection and Tracking

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) or drones in military, disaster management, business, and entertainment applications has raised concerns about their potential airspace risks. Researchers are increasingly focused on developing methods for detecting and tracking UAVs with various data sources like radar, visual, acoustic, and radio-frequency data available. Among these, visual data stands out as cost-effective and amenable to analysis using Computer Vision (CV) techniques. However, vision-based tasks present challenges such as occlusions, shaky footage, and small UAVs at a distance, requiring timely and computationally efficient detection, especially given limited onboard computational power. To address these challenges, researchers are employing advanced Convolutional Neural Networks and Transformer-based deep learning models alongside classical image processing algorithms. An additional challenge is the scarcity of real-world UAV detection datasets, as existing ones often feature only similar types of UAVs and limited movement scenarios. Real-life situations involve various airborne objects, UAV movements can be erratic in any direction, and environmental conditions can be different. To overcome these limitations, we have proposed an augmented dataset that injects various drone types into existing UAV-to-UAV detection and tracking videos with different environmental conditions. Our dataset includes erratic movements of drones and other airborne objects like kites, and balloons, enhancing its realism and utility. We believe this dataset will alleviate the scarcity and constraints in the vision-based UAV-to-UAV detection and tracking research field. The augmented dataset is publicly available at https://github.com/hasiburrahman875/aud.
Multi-Retention STT-MRAM Architectures for IoT: Evaluating the Impact of Retention Levels and Memory Mapping Schemes

In recent years, the energy consumption of IoT edge nodes has significantly increased due to the communication process. This necessitates the need to offload more computation to the edge nodes to minimize data transmission over the network. To achieve this, higher-performance CPUs and memory are required on the edge nodes. In this context, we propose an energy-efficient memory architecture specifically designed for edge nodes. STT-MRAM is a promising memory technology that offers potential replacements for SRAM and Flash in IoT devices. STT-MRAM exhibits notable advantages over traditional memory technologies, such as non-volatility for data retention without continuous power supply and energy efficiency, resulting in extended battery life for portable devices and IoT applications. Its potential for higher memory density and scalability through standard fabrication processes further enhances its appeal for next-generation memory solutions. However, the high write energy consumption is its main disadvantage. Previous works have explored non-volatility relaxation in CPU cache but there is a need to extend this approach to main memory in IoT devices. In this paper, we propose a multi-retention STT-MRAM architecture for IoT main memory. Additionally, we propose a memory mapping scheme for the suggested memory architecture and examine the impact of more relaxed retention levels on energy consumption. To the best of our knowledge, this is the first study to thoroughly investigate the optimal thermal stability factor value for STT-MRAM in IoT applications while also considering optimal memory mapping. The proposed architecture reduces energy consumption by an average of 70% and up to 83% compared to the currently used non-volatile STT-MRAM architecture. Furthermore, we propose two memory mappings that are easy to use and achieve an average energy savings that is just 5% away from the ideal mapping.
Seven Sisters Optimization Algorithm

This research proposes a new swarm-based meta-heuristic algorithm named Seven Sisters Optimizer (SSO) in-spired by the Jungle Babbler bird flock (commonly known as Seven sisters or Seven Brothers (in Hindi)). Jungle Babbler's calls are structurally and functionally organized to serve as a coherent communication system. They have 15 different types of communication classified as affiliative (friendly), with seven call types, and agonistic (unfriendly), with eight call types. The flock head, the sentinel bird, is tasked with the keep tab on optimal habitat and orients the flock members as affiliative and agonistic. The proposed algorithm is tested on 13 well-known benchmark functions. The results of the proposed SSO are verified by a comparative study of of SSO with nine other existing optimization algorithms, which include the most widely used meta-heuristics, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as well as Gravitational Search Algorithm (GSA), Flower Polli-nation Algorithm (FPA), Moth-Flame Optimization (MFO), Bat Algorithm (BOA), Firefly Algorithm (FA), Cuckoo Search Algorithm (CS) along with high-performance optimizer LSHADE-SPACMA (LSP). To evaluate the constraint handling capacity of the proposed algorithm ie SSO, it is further tested on a gear design problem, and the results are compared with different classical and swarm-based optimization approaches. The results demonstrate that the proposed SSO exploration and exploitation capabilities are superior as compared to the other optimizers.
79 GHz Band Ultra-Low Profile Radar Module Using End-Fire Antenna and Lens

This paper proposes a 79 GHz band ultra-low profile radar module which has a new antenna structure that combines an end-fire antenna (EFA) with a lens, and the EFA radiates radio waves in the horizontal direction to the substrate. Both electromagnetic simulation and actual measurements demonstrate that the new antenna structure combining the EFA with a lens enables 3 dB improved antenna gain and beam angle adjustment in the desired direction. In general, automotive use case, where radars are mounted behind a bumper, multiple reflections between the bumper and the radar surface can pose an issue. The multiple reflections degrade radar performance, resulting in maximum detection range reduction and angle estimation errors. Compared to the conventional radar which uses a broadside antenna (BSA) that radiates radio waves in the vertical direction to the substrate, the proposed radar has a smaller facing area to the bumper so that it can reduce the multiple reflections. Under such mounting conditions, both simulation and measurements show that the proposed radar can improve the variation of transmission loss through the bumper by 3 dB when the distance between the bumper and the radar surface changes, compared to the conventional radar using a BSA. Furthermore, by reducing multiple reflections, the proposed radar improves the accuracy of the direction of arrival (DOA) estimation. These results prove the effectiveness of the proposed radar.
A Data-Driven Approach to Forecast Engine Torque of an Agricultural Tractor Across Varied Operational Range Using Machine Learning

Machine learning has repeatedly found its way into the realm of agricultural applications due to its advantages over traditional frameworks. Their capacity for prediction and forecasting through parallel reasoning stands as their primary strength. This research endeavour aimed to establish a predictive model for engine torque for M-575 tractor across diverse operating conditions. A total of 6 machine-learning models were developed for tractor engine torque prediction, and the Extreme Gradient Boosting Regressor was selected as the emerging optimal choice for machine-learning-based predictive model development. The performance of the developed model was calculated based on the evaluation metrics like the coefficient of determination (R2) and mean squared error. Notably, an R2 value of 0.997 for training and 0.971 for testing datasets was achieved, accompanied by a mean squared error of 0.0009 for training and 0.0074 for testing datasets. The findings indicated a direct relationship between engine torque and engine speed under different load and throttle settings. The developed ML algorithm exhibited the ability to accurately forecast engine torque within the given power range, accounting for varying load and throttle configurations (100% and 50%), with a significant level of precision (MAPE = 3.45%). Model validation involved comparing the predicted and actual engine torque data of a specific tractor model, M-575, subjected to testing at FMTTI India.