Miracle-dz/UniFAR-FTUs huggingface dataset:
Aluminium toxicity in plants : internalization of aluminium into cells of the transition zone in Arabidopsis root apices related to changes in plasma membrane potential , endosomal behaviour , and nitric oxide production

The extent of aluminium internalization during the recovery from aluminium stress in living roots of Arabidopsis thaliana was studied by non-invasive in vivo microscopy in real time. Aluminium exposure caused rapid depolarization of the plasma membrane. The extent of depolarization depends on the developmental state of the root cells; it was much more extensive in cells of the distal than in the proximal portion of the transition zone. Also full recovery of the membrane potential after removal of external aluminium was slower in cells of the distal transition zone than of its proximal part. Using morin, a vital marker dye for aluminium, and FM4-64, a marker for endosomal/vacuolar membranes, an extensive aluminium internalization was recorded during the recovery phase into endosomal/vacuolar compartments in the most aluminiumsensitive cells. Interestingly, aluminium interfered with FM4-64 internalization and inhibited the formation of brefeldin A-induced compartments in these cells. By contrast, there was no detectable uptake of aluminium into cells of the proximal part of the transition zone and the whole elongation region. Moreover, cells of the distal portion of the transition zone emitted large amounts of nitric oxide (NO) and this was blocked by aluminium treatment. These data suggest that aluminium internalization is related to the most sensitive status of the distal portion of the transition zone towards aluminium. Aluminium in these root cells has impact on endosomes
Hybrid Residual Blocks - Transformers Improving Cardiac MRI Segmentation in Deep Learning

Cardiovascular disease is one of the primary causes of mortality and disability. By assisting in early diagnosis, segmentation techniques based on deep learning might help to lessen their severity, although extremely high levels of accuracy are required. Introduce a novel and flexible architecture that raises the bar for semantic segmentation accuracy in this study. Our contributions focus on the merging of strong self-attention mechanisms obtained from Transformer with traditional convolutional neural network (CNN) architecture. This hybrid model demonstrates improved feature representation, contextual understanding, and pixel-wise predictions, setting new standards in semantic segmentation tasks. the average dice coefficients for ACDC dataset by our model, AVG DICE 92.52%, RV 90.77%, MYO 90.00%, LV 96.2S%
Design a Circular Slot Patch Antenna for 5G and UWB Band Application

Technology for communication has improved recently for 5G and Ultra-wideband applications, a circular slot pentagonal patch antenna array is created. To enhance the antenna's effectiveness, a pentagonal patch antenna is created within a circular slot. A bandwidth and return loss both help to lower an antenna's size. It is designed to work in the 3.1 GHz– 10.6 GHz Ultra-wideband frequency range as well as the 1 GHz– 6 GHz 5G communication frequency range. For this reason, the traditional pentagonal patch antenna with various slots has good return loss. An antenna with a pentagonal shape is proposed in the work to be done in order to increase efficiency, provide good radiation performance, and reduce manufacturing costs and size. HFSS software was used to design the suggested aerial. We've looked at the antenna's properties including return loss, gain, and radiation pattern.
DAB Converter Performing Internal AC Heating and Power Transfer Simultaneously for Lithium-Ion Battery in Electronic Vehicles

Lithium-ion batteries (LIBs) significantly increased internal resistances at low temperatures, shortening the cruising distances of electric vehicles. Hence, LIBs require to be properly preheated. Internal ac heating techniques utilizing the Joule heat originating from LIB’s internal resistances have been proposed as an efficient heating method. A dual active bridge (DAB) converter integrating the internal ac heating inverter has been proposed. However, using a sinusoidal reference signal, the conventional control strategy cannot perform stable power transfer during internal ac heating. This paper proposes a control strategy to perform stable power transfer and internal ac heating simultaneously by changing the switching patterns every half-cycle of the ac heating current. Experimental results demonstrated that internal ac heating and stable power transfer can perform simultaneously.
Digital Twin-enabled Opportunistic Routing with Redundant Path Injection

In future smart cities, intelligent city services based on detection, prediction, and automation via city-scale digital twins are expected to support the daily lives of citizens. However, high traffic burdens on mobile and fixed network infrastructures exist. This is because the digital twin requires collecting large amounts of local sensing data from local nodes such as sensors and personal devices. Wireless multi-hop networks can offload mobile traffic via wireless direct communications. However, fundamental issues in path construction and maintenance efficiency exist, owing to fast topological changes caused by node mobility and radio interference. Previous studies have comprehensively addressed such issues using initial path injection via digital twin, the characteristics of opportunistic routing based on the adaptive path selection, and convergence via continual packet transmissions. However, packet transmission failures occur when the injected initial and ideal forwarding paths differ because there is no redundancy in the initial forwarding path lacks redundancy. In this study, we propose a digital twin-enabled opportunistic routing that injects initial cost information into nodes along and around a path to provide path redundancy. Finally, we analyzed the impact of initial path injection by varying the redundancy level in the proposed method, compared to the non-redundant initial path used in the previous method.
The effect of self-related humour on convergent and divergent thinking

Humour enhances creativity, but the question is whether different types of humour have a similar effect on improving individual creativity. It is evident that negative humour style is negatively related to creativity, while positive humour style is positively related to it. However, no evidence has been found that self-related humour (self-enhancing and self-defeating humour) directly affects creative thinking in the experimental setting. Thus, this study aimed to investigate the effect of self-related humour on convergent and divergent creative thinking. We included 60 (38 male, 22 female) participants and randomly assigned 20 in each condition to conduct this experiment. To evoke humour, we used 12 stimuli (12 self-enhancing jokes, 12 self-defeating jokes, and 12 non-humorous statements) in each condition. Remote Association Task (RAT) was used to measure convergent thinking, and Alternative Uses Task (AUT) was used to measure divergent thinking. We expected that i) people who engage in self-enhancing humour would perform better at convergent thinking tasks than the control group and the self-defeating humour group and ii) people who engage in self-enhancing humour would perform better at divergent thinking tasks than the control group and the self-defeating humour group. Our results supported our hypotheses and suggested that self-enhancing humour induced individual creativity both in convergent or divergent thinking (originality, fluency, flexibility). In contrast, self-defeating humour failed to affect either convergent or divergent thinking.
Exploring Sentiment and Topic Patterns in Metaverse Marketplace-Related Tweets

This research focuses on the Metaverse Marketplace in Twitter, an emerging e-commerce platform for virtual assets. By analyzing over 356,000 tweets, we explore user perspectives using sentiment analysis and topic modeling. The study revealed that the most discussed key themes were perceived benefits, enjoyment, security & privacy, and technology platforms' choices, all influencing user adoption. Users generally express optimism about the technology, with some concerns addressed in our study. The research's theoretical and practical implications are discussed in improving the user experience in the Metaverse Marketplace.
Comparisons of Neural Networks Using Computer Vision for Agricultural Automation

With the growing demand for efficiency in precision agriculture, the popularization of autonomous agricultural vehicles (AAV) is a trend. A safer operation is then necessary, due to the risk that the operation of these machines presents. Several sensors are used for this purpose, working together with computer vision and machine learning algorithms. The literature produced on the subject is vast and rapidly updated. However, different neural network architectures are available and can be tested in embedded systems for agricultural au-tomation. This article presents comparisons between different versions of YOLO and SSD Mobilenet networks, using 82 minutes of videos recorded in the context of a sugarcane harvest. These comparisons were performed on an AGX Jetson Orin and a desktop computer with an RTX 3060 Ti graphics card. In the first case, we also compare networks that use the library provided by the manufacturer (Jetson Inference) and also networks that use the OpenCV and PyTorch libraries. For an embedded system using AGX, the best results were found with the YoloV4 network provided by the OpenCV library. Better results may be found when more advanced networking architectures are available in native AGX Jetson Orin libraries.
Analysis of Wireless Internet of Things to Medical Health Care Patient Applications

The patients and older people with serious conditions, the failure to take medications as prescribed can be quite dangerous to their health. Using IoT A medical caretakers is suggested as a solution to that issue. The purpose of this pillbox is to assist patients in taking the right medications at the proper dosage at the appropriate times while also helping to record the patient's medical history. The pillbox can be configured using an app, and once the patient presses. Additionally, it will maintain the pill count and provide an alert when the quantity is low. Thus, the purpose of this clever pillbox is to make patients' lives easier while maintaining their health. The need for healthcare is increasing due to the growing world population, increasing expectations for effective treatment, and a general rise in life satisfaction. As a result, the development of new and more advanced scientific and technology therapies is required because healthcare continues to be one of the biggest social and economic challenges in the world.
Tangential Force Estimation for Impedance Control of Power Assisted Wheelchair

Shoulder injuries are prevalent among manual wheelchair users due to the repetitive exertion required for propelling the wheels. Conversely, powered wheelchairs encourage a sedentary lifestyle, posing their own set of health risks. A potential solution to address these issues involves the prescription of wheelchairs partially assisted by an electric motor. Such wheelchairs are referred to as pushrim-activated power-assisted wheelchairs (PAPAWs). In this context, impedance control emerges as a method to determine the torque that the motor should apply to alter the parameters of the wheelchair-user system as perceived by the user. Consequently, this approach enables the specification of the user's required exertion during propulsion. However, the implementation of this control technique typically hinges on estimating the user force, which presents technical challenges when attempting direct measurement. In this study, we proposed force estimation through four methods: (i) wheel velocity measurement, (ii) ground inclination assessment, (iii) dynamic analysis, and (iv) a closed-loop controller. We evaluated the accuracy of this estimation via simulations, accounting for potential modeling errors in some system parameter values. The results indicate that moderate uncertainties in the friction coefficient and mass do not significantly compromise impedance control performance. This outcome underscores the feasibility of implementing the control system in a PAPAW chair without necessitating a tangential force sensor.
Risk management of antibacterial drugs: A decision tree classification method driven by real world data

Antibacterial drugs not only bring benefits to human health, but also pose serious risks. The purpose of this paper is to establish a classification model driven by real-world data to quantify and classify the risk level of antimicrobial drugs. The research data is sourced from Chinese spontaneous reporting database from 2010 to 2017, including 622 adverse reactions caused by 114 antibacterial drugs, totaling 237,715 reports. Firstly, we established three risk indicators to quantify the risk characteristics of antibacterial drugs, and labeled the drug risk levels based on the classification criteria of five provinces in China. Then, SMOTE was used to expand the sample for small category. Finally, J48 algorithm was used to construct a classification model. The experimental results based on the research data show that the proposed method achieves ideal classification performance. This study can provide decision-making support for the grading management and clinical use of antibacterial drugs.
A Novel Approach for Classifying DNA Barcodes Using Ensemble NLP Models

DNA Barcodes, which are distinct fragments derived from brief sections of DNA (such as mitochondria, nucleus, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose an Ensemble NLP + Multinomial Classifier workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 96 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.
Design Optimization and Comparative Analysis of Permanent-Magnet Vernier Machines with Single-Winding Design

This paper conducts a comparative study among four permanent-magnet (PM) vernier (PMV) machines with single-winding design, including the surface-mounted PMV (SPMV) machine, the spoke-array PMV (SAPMV) machine, the U-shape consequent-pole PMV (UCPMV) machine, and the dual-stator PMV (DSPMV) machine. The machine topologies and design principles of four PMV machines are introduced, and the underlying reason for the design of the half-slot pitch displacement between two stators in the DSPMV machine is revealed analytically. Then, the multi-objective optimization using the non-dominated sorting genetic algorithm (NSGA-II) is carried out among four types of PMV machines, aiming to deliver high performances such as high output torque, high efficiency, high power factor, low PM consumption, and low torque ripple for practical industrial applications. The electromagnetic performances of the optimal designs are comprehensively evaluated using finite-element analysis (FEA). The advantages and disadvantages of each type of PMV machine are summarized, offering valuable insights to researchers in machine type selection.
An Efficient Implementation of Modified Digital Synapse Design for Neural Network

Spiking Neural Networks with biologically plausible design that can me mostly used for implementation on the hardware design with low power consumption. Size and power usage restrictions have the greatest significance when it comes to developing the implementation of a hardware like large-scale neural network (NN) design for smartphone platforms. Neuron and synapse circuits make up spiking neural networks (SNN) that is optimized in terms of both size and power. In this Paper, more optimized version of Synapse circuit implemented for Spiking neural Network. The synaptic circuit injects an equivalent binary data while modulating the input spike rates according to the trained data value. All of the temporal traits of SNN have demonstrated by the suggested circuit implementation. The circuit implementation discusses the factors like size and power consumption, making it simple to construct a sizable SNN. Also, the implementation of compact circuit is better from the prior technique (combination of analog and digital) in terms of size and power.
Research on Magnetic Circuit and Electromagnetic Performance of Combined-Pole Less-Rare-Earth Permanent-Magnet Synchronous Machine Used for Fully Electric Unmanned Aerial Vehicle

The combined-pole less-rare-earth permanent-magnet synchronous machine (CP-LRE-PMSM) has a series of advantages, such as better sinusoidal air-gap field, reduced reliance on rare-earth permanent-magnet materials. and lower cost. This paper firstly founds CP-LRE-PMSM’s equivalent magnetic circuit model, then the calculation formulas of air-gap field are deduced, meanwhile the model accuracy is examined by finite element analysis (FEA). After that, based on machine performance requirements used for fully electric unmanned aerial vehicle (FE-UAV), the electromagnetic performance of CP-LRE-PMSM is analyzed. The total harmonics distortion (THD) and fundamental component of air-gap field versus permanent magnet width, permanent magnet thickness and flux-barrier position angle are studied, respectively. The torque characteristics and the machine efficiency map are investigated, which provides the reference for theoretical analysis and design of the CP-LRE-PMSM
Adverse Drug Reactions Detection from Social Media: an Empirical Evaluation of Machine Learning Techniques

This research addresses the critical concern of Adverse Drug Reactions (ADRs), emphasizing the need for their timely detection to safeguard patient well-being. Detecting ADRs within sentences is pivotal for effective public health monitoring. The study’s primary objective is to assess whether sentences contain ADR references, a crucial step in identifying potential ADRs early. Timely recognition can mitigate patient harm and enhance drug development processes. The rise of patient engagement on social media has turned it into a valuable real-time resource for ADR-related information. Patients increasingly share their personal narratives about drug usage on these platforms, creating an innovative avenue for gathering firsthand accounts of ADRs. This evolving trend has transformed social media into an indispensable source of information. The study aims to compare machine learning algorithms in classifying sentences as containing ADRs or not. Three diverse datasets—CADEC, TwiMed (PubMed), and ADE—are used to train and evaluate models. Rigorous experimentation highlights the superiority of the Naive Bayes classifier over other methods. Notably, this classifier achieves remarkable accuracy rates of 94.29%, 78.76%, and 64.93%, on the CADEC, ADE, and PubMed datasets, respectively. This comparative study demonstrates the effectiveness of machine learning in identifying ADRs within sentences and underscores the Naive Bayes classifier’s consistently impressive performance across different datasets.
Modified Flux Observer based Sensorless PMSM Control for Hybrid Electric Vehicles

The adoption of sensorless rotor position estimation without need for mechanical sensors, represents a prominent solution to effectively enhance robustness and making cost effective without compromising performance. In this paper, a sensorless rotor position finding approach for surface mounted PMSM by modified flux observer (FO) for solar PV array- battery fed lightweight electric vehicle (EV) with feature of regenerative braking is presented. Modified FO approach incorporates an interference observer and a quasi-proportional resonant (QPR) based controller. This new design effectively addresses main challenges encountered in conventional FO methods, offering robustness against the DC offset and integrator issues while maintaining a remarkably simple and streamlined structure. The interference observer (IO) plays a crucial role in eradicating the DC offset and harmonics from flux estimation without introducing any errors into the estimated flux. Furthermore, the utilization of quasi PR feedback control enables elimination of discrepancies between observed and actual current, leading to a harmonics-immune performance. Performance evaluation of the control and topology is done on a 3-kW PMSM based on obtained results.
A Deep Learning based Ischemic Stroke Lesion Segmentation using Modified Multi-Scale 3D CNN

Acute Ischemic Stroke (AIS) is the second-largest fatal accident that results in High morbidity, disability, and mortality worldwide. Clinicians can help patients receive the right therapies by doing an early and automated diagnosis of AIS. A computerized method for segmenting ischemia lesions should be developed to reduce time consumption and avoid clinical error. This paper introduces the segmentation techniques based on the Deep learning algorithm by creating an eleven-layer deep, double pathway, 3D Convolutional Neural Network. After analyzing a relevant multi-modal 3D MRI-like Diffusion Weighted Imaging (DWI) patch at various scales, the created system segments pathologies voxel-wise. For training and testing, this study uses 255 examples with five-fold validation. After postprocessing, it outperformed other approaches with a high Dice mean value of 0.82 on the datasets of ISLES 2022. The implemented network can process a 3D brain volume of different sizes within 3 minutes, making it relevant to broader research cohorts' automated analysis.
Robust Low-Dimensional Representation of Tensor under F -norm Metric

Tensor principal component analysis is widely adopted in the low-dimensional representation of high-dimensional tensor data, such as time-space trajectories and color images. In the traditional methods, multilinear principal component analysis (MPCA) with the squared F -norm is not robust to the noise-corrupted data that widely exists in realistic scenarios. Besides, although L1-norm based schemes can mitigate the influence of the outliers and noise-corrupted data, the reconstruction error is not taken into consideration. To tackle the above problems, a robust MPCA algorithm is proposed to boost the robustness of low-dimensional representation by using F -norm to measure the projection distance. The proposed method not only retains the rotation invariance attribute of F -norm, but also concurrently maximizes the projection distance and minimizes the reconstruction error under F -norm metric. Finally, experiments demonstrate that this robust scheme improves the robustness of low-dimensional representation for tensor data under interference of noise-corrupted data.