Miracle-dz/UniFAR-FTUs huggingface dataset:
Pseudocerastes urarachnoides: the ambush specialist

Pseudocerastes urarachnoides es una especie recientemente descrita en Oriente Medio, y los datos sobre su ecologia y distribucion todavia son escasos. Durante un viaje herpetologico a Iran en la primavera de 2014 pudieron observarse dos especimenes de esta especie en su habitat natural. En este articulo se presentan datos sobre los habitos de caza y habitats usados, informacion importante para la conservacion y el manejo de la especie.
Longitudinal Speed Estimation of Multi-axle Distributed Drive Vehicle Based on Federal Kalman Filter

When the wheels of distributed drive vehicle slip, the effectiveness of longitudinal speed estimation based on dynamics model is poor. Therefore, a multi-model fusion estimation method based on Federal Kalman Filter (FKF) is proposed in this paper. In order to tackle the nonlinear problem of vehicle dynamics model, an Extended Kalman Filter (EKF) is used to estimate the longitudinal speed. Additionally, considering the large mismatch between the dynamics model and the real vehicle when the wheels slip, the acceleration and linear Kalman Filter (KF) framework are used to construct a speed estimation algorithm based on kinematics. Then a FKF is designed to fuse both dynamics and kinematics methods. The slip ratio observer is introduced as the information distribution principle of two methods, and information fusion and time update are completed in FKF. After running for a period of time, the fusion result is used to reset the filter of the kinematics method to reduce the accumulated error. Finally, Trucksim/Simulink co-simulation platform is utilized for simulation analysis. Results show that the proposed speed estimation method has good effect and adaptability in the slip condition.
An Improved Method to Estimate Energy Consumption for Light Duty Electric Vehicle Based on Road Characteristics and Ambient Temperature

Electric vehicles (EVs) have gained significant attention as a sustainable and eco-friendly alternative to conventional internal combustion engine vehicles. Accurately estimating energy consumption in EVs is crucial for enhancing their driving range and optimizing their performance. In this paper, we propose an improved method to estimate energy consumption for electric vehicles by considering the influence of road characteristics and ambient temperature. The proposed method utilizes data from various road types and temperature conditions to create a comprehensive model that better reflects real-world scenarios. Through experimental validation and comparison with existing approaches, we demonstrate the effectiveness and accuracy of our method in predicting EV energy consumption.
Enhanced Ultra Wide Band Indoor Localization in Non Line of Sight Environments Using Graph Attention Networks

A novel Ultra Wide Band (UWB) indoor localization algorithm based on Graph Attention Network (GAT) is proposed in this paper to address the challenge of decreasing accuracy in Non Line of Sight (NLOS) environments. By considering the sample points as graph nodes, the GAT can effectively capture the information from neighboring nodes. Through the integration of this information, the network’s learning capacity is enhanced, resulting in more accurate model outputs. The algorithm presented in this paper was evaluated using various datasets of NLOS environments with different complexities. The experimental results demonstrate that the graph attention neural network-based algorithm outperforms conventional methods commonly employed in UWB indoor localization systems. The algorithm achieved a significant reduction in error ranging from 62.56% to 73.94%.
Active Distance Learning: How Student Perceptions Affect Academic Performance

Active distance learning is an appealing approach that allows students to study at a distance by completing learning activities at their own pace without watching recorded or live lectures. This approach, however, raises some concerns due to the lack of teacher guidance, feedback, and scaffolding. Still, a thoughtful design of the learning activities can mitigate this issue. In this study, we aimed to investigate how students perceive distance learning activities and how their perceptions affect their performance on immediate short tests. Using regression analysis of 103 students’ data and 62 learning activities, we investigated the relationship between the grades and completion time of the learning activities and students’ ratings of the perceived level of interest, motivation, challenge, frustration, and boredom. We found out, for example, that student perceptions of interest and motivation are essentially affected by grades rather than the activity’s completion time. Furthermore, we investigated the relationship between the perceptions of the learning activities and students’ performance on immediate, short tests. The regression model showed that the perceived motivation, interest, and challenge have a remarkably strong impact on students’ performance. We conclude that active distance learning can benefit from a thoughtful design of learning activities that takes students’ perceptions seriously into consideration.
Human-Machine Shared Steering Control For Vehicle Lane Keeping System: An Observer-Based Model Prediction Control Method

The paper introduces a human-machine cooperative lane-keeping assistance driving system that relies on a Unknown Input Observer (UIO). In contrast to the traditional Linear Parameter-Varying (LPV) model, this paper simplifies the LPV polyhedral model using a polyhedral simplification approach. The UIO is designed with unknown input decoupling technology to ensure asymptotic estimation performance for vehicle dynamics and driver torque. Based on the estimation results of the unknown input observer, this study proposes a UIO-based model predictive controller (MPC) that will enable the vehicle to track the road centreline with its accuracy. Additionally, a human-machine cooperative control authority distributor is introduced to mitigate conflicts between human and machine control. The proposed method’s effectiveness is confirmed by co-simulation using Simulink-Carsim, demonstrating its performance.
Development of a Population of Digital Anthropomorphic Phantoms with Simulated Acquisitions for use in Deep Learning Improvement of DMSA Quantification and Estimation of Attenuation Maps from Emission Reconstructions in DMSA Pediatric SPECT Imaging

PEDIATRICS have increased tissue radio-sensitivity and longer potential lifespan when compared to adults. Therefore, their risk of developing health problems such as cancer per unit administered activity (AA) is significantly higher than adults [1] , emphasizing the need to the reduce activity administered. Tc-99m dimercaptosuccinic acid (DMSA) SPECT imaging is performed to evaluate children with pyelonephritis and scarring to the kidneys resulting from infection [2] . CT scans are not acquired during the DMSA imaging protocol to reduce radiation exposure to pediatric patients. Instead, typically attenuation maps are formed by filling segmentations from the emission data with uniform attenuation coefficients. This process is inaccurate and can lead to reduced image quantitation and quality, which motivates our work to develop a deep learning (DL) method to estimate these attenuation maps from emission data [3] .
An Extented Single Particle Method for Lithium-ion Batteries

In order to reduce the computational complexity of the electrochemical modelling of lithium-ion batteries and improve the computational speed, this paper proposes a simplification method for the modelling of lithium-ion batteries. Based on the existed single particle model of lithium-ion battery, this article simplifies the solid-liquid diffusion process and proposes an extended single particle model. Simulation based on COMSOL is performed to verify the proposed model. Compared with the full order electrochemical model, the calculation speed of the proposed method under 1C discharge rate only takes 0.6054 seconds, which is only 10.9% of P2D. In addition, the mean square error under constant current condition is 2.95 mV only, and the dynamic condition error is 10.12 mV. The results verify the superiority of the proposed model in calculation speed with enough accuracy.
Short Circuit Detection Method for GaN MIS-HEMT Power Devices Based on Gate Leakage Charge

To ensure the reliability and safety of GaN High Electron Mobility Transistor (GaN HEMT) power devices operating at high temperature, high frequency and high voltage, it is important to study the short circuit protection methods of GaN HEMT power devices. This paper proposes a short-circuit detection method based on gate leakage charge for GaN Metal-Insulator-Semiconductor HEMT (GaN MIS-HEMT). During a short circuit fault, a rapid spike in junction temperature can cause a gate leakage current in the gate loop. The proposed method is immune to parasitic parameters caused by the package and PCB layout, and can realize short-circuit detection within $\mathbf{1}\boldsymbol{\mu}\mathbf{s}$. With the increase of the short-circuit bus voltage and ambient temperature, the short-circuit detection time can be shorter, which is more suitable for high-temperature, medium and high voltage applications.
Difficulty-Adjusted Quizzes: An Effectiveness Analysis

To improve long-term retention, regular testing allows students to apply their knowledge to practical tasks and consequently reinforce their memory. To integrate regular testing in a university-level course, the mobile application BEACON Q was developed to provide students with a quiz for each weekly lecture. It dynamically tailors the difficulty of questions to their capabilities and presents detailed explanations to serve as immediate feedback to their answers. This work analyzes the app’s usage during a semester by examining the performance data of the users, their survey answers, as well as potential correlations between the app’s design and the students’ interactions. The results are discussed in terms of the hurdles to overcome when integrating regular testing in the learner’s curriculum while supporting the design principles which constitute the testing effect. Additionally, recommendations are provided for other educators who want to introduce a similar quiz app in their curriculum, which also serves as future work for BEACON Q.
Loss Functions in Link Prediction Based on Graph Neural Networks

GNNs are neural networks that can be used to analyse graphs directly. We're going to discuss utilising GNNs for link prediction even though they can also be used for node-level tasks (like giving each website on the internet a score) and graph-level tasks (like predicting characteristics given a molecular structure). It is feasible to predict if a link will most likely connect two network nodes. Given the pervasiveness of networks, it has a wide range of uses, including reconstructing metabolic networks and recommending friends, movies, and knowledge graphs. To train these link prediction networks. We mostly use the binary categorical cross-entropy (CE) loss with adaptive moment estimation to train these link prediction networks. A loss function, cost function, or error function is a calculation formula that returns an event or the value of one or more variable to its true number, which in turn represents some “costs” associated with this event. In this study, we evaluate the effects of various loss functions on the effectiveness of GNNs for link prediction with specified labels and various sample sizes. Our findings for GNNs demonstrate that in both of the aforementioned circumstances, there is no discernible difference between models trained using CE and other conventional loss functions.
Scalable Tree Goal Prediction Based on Decision Interactions

The road conditions for vehicle movement in urban intersection scenarios are complex. Autonomous vehicles must be able to effectively infer the goals of other vehicles to make reasonable decisions to complete the driving task. Considering the difficulty and specificity of inferring vehicle behavior in different scenarios, it is a challenge to predict the goals of vehicles in an accurate, interpretable and scalable way. Accordingly, this paper proposes a decision interaction based extensible tree (DIET), a scalable goal prediction system. DIET extracts common semantic features from road and vehicle information in the scenario, and extends the feature space and eliminates errors arising from simple behavioral inference by analyzing the decision interactions between surrounding vehicles. Extensive comparative experiments have been conducted on the proposed DIET and several other typical related works under different scenarios. The model reported in this paper outperforms state-of-the-art methods by 10.9% in accuracy of new scene predictions. Clearly, DIET is able to more accurately predict the vehicle's goal in different scenarios, and the process is interpretable.
Enhancing Safety Through Advanced Collision and Obstruction Detection and Prevention

Vehicle accidents have been one of the leading causes of death. Numerous systems have been developed to counter this problem. the present architectures for collision detection are either very simple or very complex and overpowered/overdeveloped for most cases. which makes them mostly useless in most real-life scenarios. Thus, analyzing all the approaches so that a consensus can be formed, that which sensors are necessary and which ones clutter the environment and do not produce a contributing or a result-oriented output. this research paper compares and analyzes the various systems that have been developed till and criticizes them in great detail on the basis of the number of sensors used, the industry standards followed, architecture deployed, micro controller used and cost. It also points out the possible development scopes of architectures. The present approaches are also under scrutiny in the paper.
Prediction of Alzheimer's disease - A Machine Learning Perspective with Ensemble Learning

According to the World Alzheimer's Report 2022, the current number of people affected by dementia stands at over 55 million. Projections indicate that this figure will escalate to 78 million by the year 2030. Alzheimer's is primarily characterized by dementia, a neurodegenerative condition that progressively damages brain cells. Alarmingly, 75% of Alzheimer's cases go undiagnosed due to a lack of awareness. Machine learning models have shown significant value in the early prediction of Alzheimer's disease. These models enable physicians to start medication at an early stage, which can help delay the onset of symptoms. This paper focuses on the application of five highly effective models for Alzheimer's prediction. The Open Access Series of Imaging Studies (OASIS) dataset is utilized, and an auto classifier is implemented using IBM SPSS Modeler. Eight machine learning models, including C5.0, Neural Network, and Logistic Regression, are employed. Among these, the top five influential models Support Vector Machine (SVM), C5.0 decision tree, Neural Network, eXtreme Gradient Boosting (XGBoost), and Chi-square Automatic Interaction Detection (CHAID) decision tree are compared based on their performance metrics. After evaluating the results achieved by the classifier, it is observed that ensemble learning based XGBoost, outperforms other models, it achieves an impressive accuracy of 96.75% in the prediction of Alzheimer's. In ensemble learning decision trees have demonstrated exceptional performance as weak classifiers in classification-based problems. This system proves to be highly effective, easily implementable, and capable of accurately predicting Alzheimer's using clinical data of patients. Findings of this paper hold great significance for enhancing early detection in Alzheimer's disease.
Research on State Switching Control Strategy of AC-DC Hybrid Microgrid Based on Improved Voltage Loop

Microgrids have two operational states: grid-connected and islanded. Ensuring seamless transition between these different operational states is a critical measure for enhancing the stability of microgrids. Under the above background, this paper proposes a hybrid AC-DC microgrid system and a smooth switching control strategy based on improved voltage loop. First, the typical topology, operation mode and traditional control method of the proposed microgrid are introduced. Secondly, an improved voltage loop based on current compensation is proposed to reduce the current reference shock. Finally, the control of voltage amplitude and phase Angle pre-synchronization is added to the AC/DC converter of microgrid to realize smooth grid connection. Through simulation analysis, it is verified that the proposed microgrid system and the improved voltage loop control mode can coordinate the switching of each control mode, reduce the influence of current, and improve the operating stability of the microgrid.
Implementation of Battery Electric Multiple Unit on Railways: Pavia Case Study

European Commission has been approving strict environmental policies oriented to reduce greenhouse gas emissions. Transportation sector is primarily involved in this switch, where several solutions are being designed and implemented to fulfil the targets. The use of Battery Electric Multiple Unit (BEMU) can help to achieve such scopes. Despite these rolling stocks are already in service in some European countries, in Italy this technology is not yet used. This work aims to implement BEMU in the Italian railway network. First, a technical feasibility study is carried out for the introduction of BEMU. Secondly, a power supply system is proposed to overcome technical criticality. A BEMU model is provided to compute the energy demand of the rolling stock. This allows to simulate the State Of Charge (SOC) profile during service, size the battery pack and quantify the energy to be charged at terminus station. Then the design of a power supply system that allows charging operations being compatible with technical limits imposed by TSI - ENE (Technical Specification of Interoperability for Energy Subsystems) is discussed. This system is focused on secondary lines joining Pavia railway node, as it results critical both for BEMU charging times and for traffic volumes.
Empowering Early Diagnosis: Optimizers Revolutionizing Breast Cancer Detection with DenseNet

Breast cancer, a highly lethal disease with a significant global impact, requires early detection to reduce its mortality rate. The emergence of pre-trained Deep Learning (DL) algorithms, commonly utilized for object detection, image classification, and image segmentation, has advanced early breast cancer detection. Several researchers have explored early breast cancer detection using pre-trained DL algorithms in conjunction with classifiers or other existing methods. However, existing research lacks a thorough examination of optimizers and their potential influence on the behaviour of these pre-trained algorithms. This study aims to comprehensively investigate the behaviour of the pre-trained DenseNet201 model under various optimizer and learning rate configurations. The analysis reveals that the DenseNet model achieved superior performance (100% accuracy) with the SGD (Stochastic Gradient Descent), and RMSProp optimizers at a learning rate of 10–2 and 10–3, respectively. However, at a learning rate of 10–4, the Adam optimizer demonstrated the highest accuracy of 99%.
Deep Learning-based Identification and Classification of Rice Plant Diseases for Precision Agriculture

Because they reduce both the quality and yield of crops, plant diseases seriously threaten our food supply’s reliability. Plant diseases often result in a significant reduction or complete absence of grain yield in various agricultural contexts. Classifying rice plant diseases and insects pose a significant challenge due to the intricate structure of their composition and the striking resemblance 5bserved among various disease types and insect species. Diseases and insects in crops must be discovered and categorized as soon as possible, ideally before they may spread. There are a number of methods for spotting plant diseases, but deep learning is now the gold standard. The authors provide a novel deep-learning approach for detecting diseases in rice fields using photographs. Convolutional Neural Networks with attention mechanism (CNNAM) can be used to detect diseases in rice plants. Due to its reliability in picture identification and classification, CNNs have been widely employed by researchers for plant disease identification. The identification of rice plant diseases, however, has only been the subject of a small number of studies. This research illuminates the present state of rice plant disease and the methods of disease detection based on Deep Learning. Deep learning models excel about more conventional machine learning methods. With the suggested model, authors were able to categorize rice photos with a 99.8 percent accuracy by learning latent patterns in the raw images.
Key Technology and Application of Electricity Information Acquisition in the New Power System

This paper introduces the system framework composition of electricity consumption information acquisition in the new type of power system, elaborates the key technologies of electricity consumption information acquisition application such as $5\mathrm{G}$ and dual-mode communication, container + APP and cloud computing under the system. It analyzes the future typical application scenarios, which include household distributed photovoltaic grid-connection, residential energy storage device access, precise load control, and the asset management of charging pile. It provides technical support for the safe operation and reliable development of the new type power system.
Improved IDS for Vehicular Ad-Hoc Network using Deep Learning Approaches

In the contemporary landscape of transportation, Vehicular Adhoc networks (VANETs) have emerged as a pivotal infrastructure, facilitating real-time communication among vehicles and infrastructure to enhance road safety and transportation efficiency. However, the dynamic and open nature of VANETs exposes them to diverse security threats, necessitating robust security measures. Intrusion Detection Systems (IDS) are fundamental for safeguarding VANETs, but their effectiveness in identifying and mitigating intrusions, particularly with evolving attack strategies, remains a challenge. This research focuses on enhancing IDS for VANETs by deploying Convolutional Neural Networks (CNN), a subset of deep learning techniques known for their proficiency in image and signal processing, in intrusion detection. The background underscores the significance of VANETs in modern transportation, emphasizing the growing need for more robust security mechanisms due to increasing connectivity and the advent of Intelligent Transportation Systems (ITS). The primary objective of this research is to assess the performance of CNN-based IDS in the VANET context, specifically in their ability to detect and respond to intrusions effectively and efficiently. The contribution of this research lies in its empirical evaluation of CNN-based IDS for VANETs, offering practical insights into the implementation of advanced security measures in vehicular communication networks. By bridging the gap between theory and application, this research serves as a valuable reference for researchers, practitioners, and policymakers, aiding in the establishment of resilient and secure vehicular communication networks in the era of growing connectivity and sophisticated security challenges.