Miracle-dz/UniFAR-FTUs huggingface dataset:
Compatible Sliding-Windows Algorithm for Vessel Trajectory Compression

With the development of marine economy and data positioning technology, A large amount of vessel trajectory data has been generated. Data compression has become the key issue in the research of vessel trajectory big data. Since the compression rate of the existing vessel trajectory compression algorithms was not adjustable, we proposed a vessel trajectory compression algorithm with adjustable compression rate based on Sliding-Windows(SW) algorithm. The main idea was to adjust the compression rate by dynamically updating the vertical Euclidean distance threshold. The proposed algorithm could adapt to the dynamic change of channel capacity by dynamically adjusting the compression rate in online compression. The simulation results showed that compared with the traditional SW algorithm, the proposed algorithm not only realized the dynamic adjustment of compression rate, but also the compression error was less than that of the SW algorithm. Meanwhile the increase of computational complexity was almost negligible. This algorithm could also be used for online compression of other kinds of trajectory data.
Technologies. University of Tehran Flow-induced effect of matrix fiber orientation on endothelial vasculogenesis

Topography of extracellular matrix plays a major role in many biological events including tissue healing, morphogenesis and growth. It is known that matrix constitution and mechanical properties are deciding factors in governing the fate of its inhabitant cells. Besides the direct mechanical cues, matrices also facilitate the release and uptake of certain chemicals and participate in cell-cell and cell- ECM crosstalk. Mechanical strains in the matrix are proved to direct endothelial cell migration and elongation leading to angiogenesis, and there is a consensus that matrix stiffness, fiber density and fiber orientation can enhance angiogenesis in the preferred direction of stiffness gradient. In this study, we specifically investigated the role of topography in guidance of endothelial self-reorganization prompted by the effect of fluid flow hindrance and facilitation in certain directions. We adopted our previous model of fluid flow guided angiogenesis for cellular responses. Lattice Boltzmann model of fluid flow was adopted and modified to study the effect of unidirectional and randomly oriented fibers. To study the effect of fiber orientation, we customized a previously proposed model of porosity in lattice Boltzmann to suit this purpose. This model could reproduce the effects of fiber orientations in matrix on endothelial migration and vasculogenesis. Simulations showed better confluency of formed lumens when prescribed flow is in the direction of fiber orientation. These results can have further implications in understanding endothelial complications in certain diseases as well as in tumor angiogenesis and metastasis.
Blood Donor Arrival Forecasting Using Regression Model and Analysis of Donor Behavioural Pattern

The uncertainty in supply, the short shelf life of blood products, and voluntary donations become the primary concern in the blood supply chain. This paper presents behaviour trends among the donors, an efficient and straightforward forecasting model for successful donations using OneBlood’s datasets. First, we examine a typical behavioural pattern among the donors, and with the help of logistic regression, we portray the relationship between a blood donor’s successful donation. Then a regressive analysis using a decision-tree-based method has been applied to forecast the total successful donation. Finally, we have examined the donor behaviour based on the successful donation and identified the behavioural trends seen among the donors.
Overview of Interference Management Techniques in 5G Cellular Networks

Communications technologies are becoming the driving force behind social, economic, and political advancements in recent times. Fifth-generation (5G) cellular networks are a revolutionary idea that connects a wide range of machines and devices in a different way than earlier technologies. The design and implementation of the many technical models, on the other hand, have resulted in intolerable signal interference. The total network performance has been considerably harmed by these vulnerable interferences. Communications system interference is an unwelcome annoyance. A number of these interferences have grown to be a significant source of barriers to increased cell throughput. The development of effective interference control strategies is a major enabler given the rise in interference in cellular networks. Additionally, as networks get denser, interference mitigation becomes more difficult. Despite this, interference management has the potential to increase the efficiency of the spectrum used by present and future wireless devices. To combat interference in a broad category of wireless networks, new paradigms for interference control have recently arisen. This review paper looks at the concerns of interferences that have been discovered and researched in various network topologies and techniques. It also pays attention to recent advances in its management: Advanced receiver, Joint Scheduling, and network information theory among others. Potential advantages of each interference management technique are illustrated, and it is proven that if 5G cellular networks use intricate joint scheduling, the advantages of sophisticated receivers can be effectively utilized.
Personal Data and Privacy Protection Regulations: State of compliance with Nigeria Data Protection Regulations (NDPR) in Ministries, Departments, and Agencies (MDAs)

Personal data and privacy protection has been a hot topic of discussion due to the increasing impact of computing technologies in everyday life. In recent years, there have been reports of breaches of personal data either from organizations that collected it or from mischievous third parties. The global responses against the breaches have been the enactment of various policies and regulations to protect personal data against all forms of potential abuses. Nigeria Data Protection Regulation was also issued as the regulatory framework for personal data and privacy protection on Nigerian soil. This paper, aimed at analysing the state of compliance of Ministries, Departments, and Agencies with the regulatory framework. An online questionnaire was designed and hosted on Google Forms. The link to the questionnaire was sent to 110 staff, selected based on convenience, and representing various Ministries, Departments, and Agencies. The results have shown that about 70% of the organizations were aware of the provision of Nigeria Data Protection Regulation but only 34% indicated that they started the implementation. Nonetheless, about 45% Ministries, Departments, and Agencies completed the preparatory stage of compliance as they indicated that they have conducted detailed audits on their organizational operations to determine the changes and the new practices required to comply with the Nigeria Data Protection Regulation. The surveyed organizations indicated 37% to 60% compliance with other components of Nigeria Data Protection Regulation. The paper concluded that more advocacy and enforcement are required to speed up compliance with regulations.
Biomechanical Study of Fused Lumbar Spine Comparing Ti and PEEK as Connecting Rod Considering Bone Degeneracy Using FEA

Nowadays, orthopedic fixation devices are used in treating spine diseases, but Osteoporosis is one of the most usual problems for lumbar fusion surgery with pedicle screws. Among the elderly people in the world, osteoporosis has become a common disease, which is a concern for spine surgeons, because a large number of the patients who need lumbar interbody fusion surgery are older people with osteoporosis. In this study, a finite element model was created from CT scan images of a healthy lumbar spine area of a 15-year-old teenager. This model includes three lumbar vertebrae: L2, L3 and L4. More precisely, the 3D model entailed three lumbar vertebrae, two intervertebral discs and the implant system comprises 4 pedicle screws with the precise geometry of threads and two types of titanium and polyetheretherketone (PEEK) connecting rod. The boundary conditions and loading were exerted on the model. Considering the applied loads and leaning moment on the model, stress dispensation was appraised on intervertebral discs and the whole model with implant systems entailed titanium or PEEK rods. To repute a decrease in bone quality, the bones are divided into four categories i.e., weak, normal, strong, and very strong. The derived finite element model was applied to multiple axial pressing loads (270 and 510 N). Also, a moment of 10nm was exerted for the behaviors of flexion, extension, transversal leaning, and torsion at the upside face of the L2, whereas the bottom face of the L4 settled motionless. ‘Abaqus CAE 6.14-1’ software was utilized to simulate the model with various fixed mechanisms. An inductive study among multiple fixation implant systems indicated that stress distribution amounts with the PEEK-based implant fixation system decreased over time with increasing osteoporosis. Likewise, lower equipollent pressure amounts were recorded with the PEEK-based implant fixation system, particularly in the whole model for cases with poor bone quality. In addition, both implant systems authorize to reduce the throughout loading stress in the entire spine model. Finally, it was concluded that the PEEK-based implant system significantly reduces the loading on the whole model and besides, appears as a preferable choice in reducing pressure and load multiplexing for cases with poor bone quality compared to the titanium implant system.
Plasma simulation by artificial dielectrics and parallel-plate media

A plasma in the absence of dc magnetic fields is representable, from the electromagnetic viewpoint, as a lossy dielectric with a complex index of refraction whose real part is less than one. Its electrical properties, such as propagation constant and intrinsic impedance, can be simulated by microwave structures, such as artificial dielectrics of lossy metallic rods, and by H - plane parallel-plate guides. Design techniques are presented for application to the modeling of plasma media. Examples include problems of radiation from antennas on a plasma-coated ground plane, with magnetic line sources and electric aperture radiators simulated by parallel-plate guides and rodded media, respectively. Radiation patterns of these models show good agreement with established theory. Waveguide measurements on the rodded media also confirm the expected propagation and impedance characteristics.
Evaluation of Brain Cortical Connectivity in Drug Abusers Using EEG Data

This study implements Electroencephalogram (EEG) signals in a data-driven approach based on functional brain connectivity to identify brain abnormal cortical connectivity in opioid abusers. The prime objective here is to identify opioid addiction. A 19-channel resting-state EEG signal was recorded from 22 opioid addicts and 22 normal, closely matched individuals. First, brain networks associated with two groups were constructed using the Mutual Information (MI) metric in frequency bands of the delta, theta, alpha, beta, gamma, and wideband. The groups were then divided using discriminative graph features. The results from the Support Vector Machine (SVM) classifier, evaluated by the Leave-one-out cross-validation method, achieved accuracy, sensitivity, and specificity of 100% and an F-score of 1.00 in the gamma frequency band using the AdaBoost subsampling approach. The findings indicate that the implemented pipeline, based on MI quantity between EEG data channels and combined with graph metrics, can help detect opioid addiction.
Automatic Summarization of Scientific Documents Using Transformer Architectures: A Review

As technology advances, the volume of textual material produced on the web has been steadily rising. It can take a lot of time and effort to extract useful information from textual data. Automatic text summarizing aims to create concise summaries that retain the most important parts of the source document. Transformer-based architectures have demonstrated excellently in Natural Language Processing (NLP), particularly when it comes to summarizing textual content. This paper presents a thorough analysis of the most recent advancements in transformer topologies for automatic text summarization, with a focus on the Bidirectional Autoregressive Transformer (BART). This paper highlights future directions for research on transformer-like models for autonomous text summarization, such as BART and BERT.
Fiber Bragg Grating Based Goniometer for Joint Kinematics Evaluation

Joint kinematics evaluation is a vital parameter employed in the assessment of joint pathology. The present study proposes a novel, wearable device employing Fiber Bragg Grating (FBG) sensor for the dynamic joint kinematics evaluation. The efficacy of the developed Fiber Optic Goniometer (FOG) is illustrated with the joint kinematics evaluation of the elbow joint in the form of range of motion during flexion/extension of the joint. Further, the angular velocity of the elbow movement which indicates the smooth movement of the joint is evaluated. The angular movements of the elbow joint obtained from FOG are validated against the angular movements from the Polhemus Liberty sensor. The angular movements of elbow joint obtained from both methodologies simultaneously are in agreement as observed by the good correlation coefficient of 0.98, which proves the efficacy of the proposed FOG.
Removal of Reflection and Darkness Caused by Sunglass Occlusion using aPCA

This paper defines recognition of a face if the face is hidden by an object. When part of the face is hidden by an object like sunglasses, hand, or mask, the face is not visible. The performance of facial recognition systems can be affected by obstruction of the face. The face occluded by sunglass having reflection and darkness. Here a technique is described for automatically detecting when someone is trying to hide their face with sunglasses. A method called aPCA is used to remove the reflection and darkness of the sunglasses after the blocked areas are detected.
Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies

Growing electricity demand, the deployment of renewable energy sources and the widespread use of smart home appliances provide new opportunities for home energy management systems (HEMSs), which can be defined as systems that improve the overall energy production and consumption of residential buildings by controlling and scheduling the use of household equipment. By saving energy, reducing residential electricity costs, optimizing the utilization rate and reliability of utility companies’ power systems, and reducing air pollution for society, HEMSs lead to an enhancement in the socioeconomic development of low-carbon economies. This review aims to systematically analyze and summarize the development trends and challenges of HEMSs in recent years. This paper reviews the development history of the HEMS architecture and discusses the characteristics of several major communication technologies in the current HEMS infrastructure. In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. Furthermore, experimental studies and challenges in the real world are also summarized and recommendations are given. This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. Based on this review, researchers can gain a comprehensive understanding of current research trends in HEMSs and open up ideas for developing new modeling and scheduling approaches by gaining insight into the trade-offs between optimum solutions and computational complexity.
Comparative Deep Network Analysis of Speech Emotion Recognition Models using Data Augmentation

Speech Emotion Recognition (SER) is tasked with detecting emotion in speech regardless of semantic information. In this paper we have implemented and compared five different models for SER namely - CNN, BiLSTM with attention, CNN + BiLSTM with attention, Time Distributed CNN with LSTM and Time Distributed CNN with BiLSTM. The dataset used in this paper is Multimodal Emotion Lines Dataset (MELD). Since the dataset is unbalanced, data augmentation is performed to make it balanced. Some of the feature extraction techniques like MFCC, ZCR, mel scale are used to identify the differences in the audio of different emotions. Time distributed CNN with BiLSTM model has performed better than the other models with an accuracy, precision, recall and F1 score of 92%, 92%, 93% and 91% respectively. Despite using an unbalanced dataset we have achieved superior results compared to the state-of-the-art models which are trained on balanced datasets. This shows the prominence of data augmentation and time distributed layers.
Machine Learning and Deep Neural Network Techniques for Heart Disease Prediction

Heart plays a crucial role in all forms of life. Heart-related disorders demand higher precision, consistency, and accuracy in diagnosis and prognosis because even a small mistake might lead to death. Heart-related deaths are common, and the number of these deaths is rising rapidly day by day. Heart disease (HD) prediction with an acceptable level of accuracy is attainable by using cutting-edge machine learning (ML) and deep learning (DL) algorithms. Making an accurate model using these algorithms can predict and categorize cardiovascular illness with high accuracy and reduce medical testing and human intervention. In this study an assessment between ML and DL was carried out to improve classification models for heart disease prediction based on related performance metrics (Accuracy, Precision, Recall, F-1 score, and AUC curve) using a benchmark dataset from UCI machine learning databases of heart disease. which consists of 14 different heart disease-related features. Extreme Gradient Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, CatBoost, Gradient Boosting, Random Forest, Ridge, Decision Tree, Logistic Regression, K Neighbors, SVM-Linear Kernel, Naive Bayes, and deep neural networks, DNN3(3-layer network) and DNN4(4-layer network) are just a few of the classification models that are successfully used in this work for classification tasks. The highest classification accuracy was attained with the Extreme Gradient Boosting classifier (81.10%) (among the machine learning classifiers). The three layer deep neural network (DNN3) among deep learning approaches has provided the best accuracy of 85.41% when using selected features as input. The gathered results showed that deep neural networks outperformed machine learning techniques.
Detection Mode of the Electric Fish and Its Dynamic Signal Tracking

The behavior of electric fish shows that it has powerful electrical perception. This paper studies the detection mode and dynamic signal tracking of the electric fish by constructing the dipole model of how the electric fish creates electric signal and how the dipole model of the electric fish receives the electric signal. Through the establishment of the electric organ discharge model in the software COMSOL, the basic characteristics of the perturbation of the electric field caused by the existence of the object are studied, and the result is consistent with the “electronic image” theory. We analyze the general relationship between electronic images and object features, and predict object features through neural networks. Finally, the sensor model is established and simulated in COMSOL, the current data of the dipoles when the sensor moves around the object is calculated via the finite element method, and the untracked Kalman filter is used to realize the detection of the sphere by the sensor. The results show that the detection of underwater objects can be realized by using the electric field.
Performance Evaluation of Fake News Detection using Predictive Modeling

Fake news on the internet is more prevalent than ever, because it has the power to create chaos among people. In the digital age, the internet usage has increased, and people are now more reliable on online news portals. An intentional spread of fake news can influence people’s perception and cause devastating outcome towards an issue. To cope with the huge amount of data produced on the internet every day, a computational model is the only solution. In this paper, we experiment a predictive modeling approach to classify text document as real or fake from the linguistic pattern of the document. The task is more difficult than the other text classification tasks because there is very little dissimilarity between a real document and a fake document. Three most popular predictive models that works well in the text classification task, multinomial naïve bayes, support vector machine and long short-term memory is experimented with feature extracted from bag of words and tf-idf methods. There are previously available works can be found, that tried to predict documents credibility with these three models, however in this paper we tried to compare the performance these models in the same dataset. Another goal is to achieve high accuracy and f1 score with at least less than 5% false negative rate.
TMV-NET: Regression approach for the quantification of Tympanic Membrane

Tympanic membrane, an essential part of the human ear whose function is to help in hearing. In this paper, novelty-based extraction of tympanic membrane blood vessels using TMV-Net is performed. The neural network layers are built in a manner where most features are retained at every convolution block. The prediction of the bounding box values is more accurate as the resolution of the image is retained at every convolution layer. The image is further upscaled with the help of SR-GAN neural networks. The output image is sharpened and hence used for quantifying the blood vessels with the help of histogram approach. The histogram approach considers the use of green channel pixels over the total area of the image giving the percentage of blood vasculature.
Nano Versus Commercial [Educator’s Corner]

The NanoVNA (Nano), first introduced by that name in 2019, is a handheld, battery-powered, microwave vector-corrected network analyzer (commonly just a “VNA”) whose hardware and software are open-source <xref ref-type="bibr" rid="ref1">[1]</xref>. A version 1.0 is surprisingly cheap, with a price tag of as little as US$50 as of 2021, and yet it can reach up to 1.5 GHz in frequency. Preassembled units are available from at least one supplier in each of a number of places in Asia, Europe, and the United States <xref ref-type="bibr" rid="ref1">[1]</xref>. These machines are already used for educational <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref>, amateur/ham radio <xref ref-type="bibr" rid="ref4">[4]</xref>, and applied engineering <xref ref-type="bibr" rid="ref5">[5]</xref>, <xref ref-type="bibr" rid="ref6">[6]</xref>, <xref ref-type="bibr" rid="ref7">[7]</xref>, <xref ref-type="bibr" rid="ref8">[8]</xref> purposes.