Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenStreetMap Integration into the System of People Safety and Protection in Emergencies

Over the last few decades, many countries have been challenged by the devastating consequences of natural disasters and wars which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure is key for effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. The paper is devoted to OSM integration into the system of people safety and protection in emergencies. The main features of utilizing principles of people safety are described and discussed in particular emergency situation. Examples of OSM working are given and analyzed. SWOT-analysis that assesses the possibility of integrating OSM into existing emergency protection systems has been described. The work on the formation of the algorithm, according to which the calculations will be carried out was made. The application, that includes some useful buttons for people safety was explored.
Design and Implementation of a Lightweight Bloom Filter Accelerator for IoT Applications

The Internet of Things (IoT) has evolved into a rapidly expanding web of devices surrounding our lives. This interconnected ecosystem facilitates a new level of collaboration and the collection of precious data to be distributed and analyzed. However, as the number of connected devices grows, the volume and variety of generated data increase significantly, necessitating efficient and scalable solutions.Membership testing is one of the challenging the algorithms used in most of the network applications. To make it fast and lightweight, probabilistic data structures like Bloom Filters are employed. Although variants of this algorithm are proposed in the literature, there are very few addressing low power, low resource implementations with recently developed hashing techniques. Therefore, in this work, we present a practical Bloom filter accelerator design with Murmur3 hash, implemented on a Nexys A7 FPGA board. Then, we test and analyze its performance to identify limitations and explore possible improvements.
10-Spatial-Mode 1300-Km Transmission Over 6-LP Graded Index Few-Mode Fiber With 36-Ns Modal Dispersion

We demonstrate a record-long 10-mode-multiplexed transmission over 1300 km using a 6-LP few-mode fiber (FMF). Long-haul mode-division-multiplexed (MDM) transmission systems face challenges posed by the pulse broadening effect caused by differential mode delay (DMD), which hampers performance and increases computational complexity for receiver-side multiple-input multiple-output digital signal processing (MIMO-DSP) to mitigate mode mixing effects. In this study, we extend the application of mode permutation techniques, specifically the cyclic mode-group permutation (CMGP), as a solution to address these performance limitations in 10-spatial-mode-multiplexed long-haul transmission. We present experimental results conducted over a 6-LP graded-index (GI) FMF, demonstrating the effectiveness of CMGP in improving spatial channel coupling and reducing pulse spreading with comparisons with several CMGP-adopted configurations. The introduction of CMGP realized a modal-dispersion-unmanaged link, even when employing a 6-LP FMF with a modal dispersion coefficient of 157 ps/km, achieving the world's longest 1300-km transmission utilizing 10 spatial modes while reducing the impact of the modal dispersion accumulation by 82%.
An Improvement Control Strategy of Stand-Alone PV, Wind, and Battery System

The combination of wind and solar sources to produce electricity in rural zones could be one of the best solutions. This hybrid system can be tapped as an alternative because of failure of the national power sector to supply the growing demand. In addition, Wind-PV system with battery banks is used to satisfy the transient period or peak load demands.But, the fluctuation due to PV array, wind speed and the variable load causes the power imbalance between energy sources, battery utility units and AC loads. In order to ensure the stability of a hybrid system and minimize the effects of these variations on electricity, different control strategies must be applied. In this research we consider tow renewable sources wind-solar, battery, inverter, filter and load of a remote site are coupled through a DC architecture was achieved using Matlab Simulink. This paper mainly focused on the modeling and controlling of a stand-alone PV-wind system with energy storage.
Design and Simulation of 30 GHz Micro-Electro-Mechanical Systems (MEMS) Resonators

Wireless communication networks have grown at an exponential rate in recent years. This is primarily owing to the development of small-scale, high-performance devices. A resonator is a key component in many communication devices such as oscillators, duplexers, and filters. On-chip resonators that are based on microelectromechanical systems (MEMS) have demonstrated tremendous potential for a variety of applications in recent years. This is owing to their great characteristics that distinguished them from quartz crystal resonators, such as the CMOS IC technology integration, small size, high frequency-quality factor product, low power consumption, and low batch manufacturing cost. In this paper, a novel class of MEMS resonators is designed and simulated using MATLAB 2021b. The lithium niobate (LiNbO3) thin film has been used to achieve resonance frequencies of 30 GHz using the seventh order asymmetric (A7) Lamb-wave modes.
DC-DC Buck Converter Using DTMOS Technique for Ultra Low Voltage Applications

DC-DC buck converter circuit using Dynamic Threshold-Voltage MOSFET (DTMOS) technique for ultra low voltage applications is presented. There are two main purposes of the proposed structure. First, to achieve supply voltage less than diode voltage for ultra low voltage applications. Second, to increase efficiency through using DTMOS technique, without using MOS transistor and diode. At 1.3V input, the proposed converter provides 7.3% more efficiency when compared to the conventional DC-DC buck converter. The theoretical results have been confirmed by PSPICE using CMOS process technology. The obtained results show that DTMOS technique is an effective way for power converter designs and ultra low voltage and energy efficient applications.
Gradient Maximization Regularization for Signed Adversarial Attacks

Deep learning-based computer vision models have proven their success in many fields. However, adversarial attacks show us that even the most decorated models have vulnerabilities. In this work, we investigate the difference between the effects of successful and unsuccessful adversarial attacks on the input by conducting a series of statistical tests, which leads us to find a positive correlation between success and the mean norm of the gradient map on the input that is computed by backpropagating some adversarial loss. In light of this finding, we craft a regularization method for the signed adversarial attacks, i.e. FGSM and PGD, and call the regularized algorithms GMFGSM (gradient maximized FGSM) and GM-PGD(gradient maximized PGD). We achieve considerable improvements in L2 norms and fooling rates with the correct parameters compared to standard algorithms.
CrsKGC-RSAN: Conflict Resolution Strategy for Knowledge Graph Completion

Knowledge Graph Completion (KGC) is a crucial task aimed at enhancing the completeness and accuracy of knowledge graphs (KGs) by filling in missing information. However, existing knowledge graphs face two significant challenges: 1) the negative sampling issues, where false negative samples contain potential true facts, while easy negative samples limit the generalization capacity of KGC models; and 2) the entity conflict issues, many candidate entities exhibit similarities in their explicit structural features, making it difficult for models to make accurate decisions. To address the above challenges, we propose a novel method, CrsKGC-RSAN. For the negative sampling issues, we propose a Relative Self Adversarial Negative Sampling (RSAN) module. RSAN allows for a smaller margin between positive and negative samples compared to traditional self-adversarial sampling, while assigning higher training weights to hard negative samples to address false negative and esay negative sample issues. For the entity conflict issues, we present CrsKGC module, which, in addition to learning explicit structural features of KGs, mines implicit structural features, enhancing the comprehensiveness of feature learning and effectively resolving entity conflicts. Extensive experiments on the FB15K-237 and WN18RR datasets demonstrate that our method achieves state-of-the-art results and exhibits compatibility.
Review-Enhanced Sequential Recommendation with Self-Attention and Graph Collaborative Features

Recommender systems have gained significant popularity in recent years, with research focusing on three main branches: sequential recommendation, graph-based recommendation, and review-based recommendation. These branches leverage different data modalities to provide personalized recommendations. However, these individual modalities have their own biases and limitations, making it challenging to fully capture user preferences and item relations. Naive modality combination methods often face compatibility issues, leading to suboptimal performance. In this work, we propose a comprehensive model called RSSG (Review-enhanced Sequential recommendation with Self-attention and Graph collaborative features) to address this challenge. Our model finely integrates all three data modalities to provide more accurate and personalized recommendations. We utilize a language model to capture textual representations of users and items, as well as user-item attention weights. With shared user and item representations, we employ a time-interval aware self-attention mechanism to capture long-term user behavior patterns, and leverage graph attention networks to capture collaborative information between users and items. Finally, we fuse the sequential and collaborative features to make predictions. We conduct extensive experiments on various datasets to evaluate the performance of our proposed method. The experimental results demonstrate significant performance gains compared to state-of-the-art methods. Our approach effectively leverages the strengths of different data modalities, enabling more accurate and personalized recommendations for users.
Machine Learning and Deep Learning in Chest X-Rays Images for COVID-19 Diagnosis: A Review

There is an ongoing worldwide spread of the coronavirus disease 2019 (COVID-19) pandemic. Medical imaging like Chest X-rays (CXR) is essential in the fight against COVID-19; Artificial Intelligence (AI) technologies help medical specialists and increase the efficiency of imaging tools. With various positive stories, Machine Learning (ML) and Deep Learning (DL) were utilized daily in many ways. Also, they were vital in dealing with the COVID-19 outbreak. Many scientific research institutions, companies, and states are fighting against the spread of COVID-19 through a quick diagnosis. The paper discusses the AI-based ML and DL approaches for diagnosing and treating COVID-19 by CXR. In addition, AI-based DL and ML methods, along with the provided tools, CXR images of datasets, and performance in tackling COVID-19, are summarized in this work. Dealing with data of the CXR images needs dataset preprocessing through choosing the optimal method for getting speed and best accuracy. This paper reviews different techniques for preprocessing the CXR images of the dataset before applying ML or DL models. The presented survey offers a thorough overview of current advanced ML and DL research methods and describes how ML and DL might enhance COVID-19 status diagnosis. Also, this study provides details on future directions and challenges, mainly focusing on integrating ML and DL using CXR. Finally, this survey offers a handy guide for the researchers to choose the best modality and the most convenient ML and DL algorithm to detect the COVID-19 virus automatically with high accuracy using CXR images.
A Systematic Review on Natural Antioxidant Properties of Resveratrol

Polyphenols, including anthocyanins, flavonoids and stilbenes, which constitute one of the most abundant and ubiquitous groups of plant metabolites, are an integral part of the human diet. Resveratrol (3,5,4'-trihydroxystilbene), a naturally occurring polyphenol produced by some plants as a self-defence agent, has an antifungal activity. Resveratrol has been found in some plants (such as grapevine, pine and peanuts) and is considered to have beneficial effects also on human health. The number of studies on resveratrol greatly increased in PubMed database since 1997, after the anticancer effect of this molecule was first reported. The interest in resveratrol in grape was originally sparked by epidemiological studies indicating an inverse relationship between long-standing moderate consumption of red wine and the risk of coronary heart disease; this effect has been ascribed to resveratrol, which possesses diverse biochemical and physiological properties, including antiplatelet and anti-inflammatory proprieties, and provides a wide range of health benefits ranging from chemoprevention to cardioprotection. Recently, resveratrol has been described as an anti-aging compound. The consumption of resveratrol (red wine) together with a Mediterranean diet or a fast-food meal (“McDonald'sMeal”) had a positive impact on oxidized (ox-) LDL and on the expression of oxidative and inflammatory genes. Therefore, this review summarized the most important scientific data about healing and preventive potential of resveratrol, acting as cardioprotective, neuroprotective, chemopreventive and antioxidant agent.
Experimental Validation of PV Power Prediction with ML Models for Improved Grid Integration

The need of accurate prediction models for forecasting Renewable Energies (REs) production are increasing, especially for Photovoltaic (PV) power. In this paper, we implemented and tested two models for predicting PV power, known for their good performance in wind power and load forecasting. Our results proved that these two models have much higher performance than the empirical formula and the typical Machine Learning (ML) models such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), eXtreme Gradient Boosting (XG-Boost) and Long Short-Term Memory (LSTM). The first model is based on a combination of a CNN model and a Light Gradient-Boosting Machine (LightGBM) model, which decreased the Mean Squared Error (MSE), compared to a CNN model, by 7%. In the second model, we used the Variational Mode Decomposition (VMD) technique to decompose the PV power into components before training, and used LSTM and XGBoost for predicting each of the components. The second model decreased further the MSE by 45% (80% overall decrease compared to the empirical formula). 1
Directed Networks as a Novel Way to Describe and Analyze Cardiac Excitation: Directed Graph Mapping

Networks provide a powerful methodology for a variety of biological, technological and social systems, such as analysis of brain data, social networks, internet search machines, etc. Interestingly, directed networks were not yet applied to characterize the excitation of the human heart, although this system would perfect fit for this type of analysis. This is because in clinics, cardiac activation is recorded by multiple discrete electrodes. Their successive excitation in normal sinus rhythm or during cardiac arrhythmia connects neighboring electrodes, and this naturally results in a directed network. In this work, we propose a new concept: application of directed networks to the heart to describe and characterize cardiac arrhythmia. We show that certain tools used in network theory allow us to determine the mechanism and location of cardiac arrhythmia and that the robustness of this approach could exceed the existing state-of-the-art methodologies used in clinics. We demonstrate the proof-of-principle on in-silico and clinical data. Implementing our suggested techniques in the clinic has the potential to improve the accuracy and speed of analyzing cardiac arrhythmia and to provide novel insight in arrhythmia which are still not fully understood.
A General MMF and Winding Factor Calculation Model for Electrical Machines

Calculation of MMF and winding factor of electric machines is a tedious task. Additionally, there is not a common unified method to calculate the winding factors of different types of electric machines. This is due to the lack of a universal formula for the calculation of the winding factors of both Fractional Slot Concentrated Windings (FSCWs) and Distributed Windings. It is even harder to build a generalized model in case of non-uniformities such as unequally pitched slots and different numbers of turns. However, it is possible to make such a model by using generalized winding models and complex Fourier Expansion. In this paper, a universal MMF, Fourier Coefficients, and Winding Factor calculation model is developed. The developed model is implemented to the practical stage and a calculator tool is developed in the Python environment. With this tool, it is possible to calculate and visualize the MMF, Fourier Coefficients, and Winding Factors for any winding function.
The Effect of Communication Link Quality on Navigation Schemes: A Review

Sensor networks play a vital role in today's applications, including monitoring the environment, gathering surveillance, securing a home or factory, tracking targets, or tracking military personnel. Accordingly, a growing interest has been shown in building such a kind of network, characterized by mobility which plays a significant part in the application's execution. Combining mobile robotics with wireless sensor networks created the mobile sensors network. The mobility of sensor networks can improve the coverage of sensor data both spatially and temporally, as well as their robustness to dynamic environmental change. Additionally, communication is integral to such networks since measurements and commands are communicated wirelessly. Therefore, communication efficiency is affected by the surrounding environment and the noise generated by the sensor devices that make up the network. This paper provides an overview of mobile sensor networks and their applications, especially in navigation, where communication link problems affect network elements.
Factors Associated With Perception of Stigma Among Parents of Children With Cleft Lip and Palate: Cross-Sectional Study

Background Parents of children with cleft lip with or without cleft palate (CL/P) often face stigmatization, which has a significant impact on their quality of life and mental health. However, to date, there is a lack of comprehensive, multicenter empirical research on parents of children with CL/P in China, particularly those with large-scale samples. Objective This study aimed to identify major factors that contribute to the perception of stigma experienced by parents of children with CL/P. Methods A cross-sectional survey was conducted. A total of 104 parents of children diagnosed with CL/P in 2 hospitals were selected by convenience sampling. Demographics and disease information, the Chinese Perception of Stigma Questionnaire, the Center for Epidemiological Studies Depression Scale, and the Social Anxiety Scale were used in this study. Descriptive statistics, t tests, and one-way ANOVA were used to compare the differences between participants’ demographic information and perception of stigma. Multivariable linear regression was performed to assess associations between demographic factors, social anxiety, depression, and perception of stigma. Results The mean scores for the dimensions of perception of stigma, depression, and social anxiety were 22.97 (SD 9.21), 38.34 (SD 8.25), and 22.86 (SD 6.69), respectively. Depression and social anxiety were positively associated with discrimination, while surgery status was a negatively associated variable. Parents with a college education or higher had significantly lower levels of perceived stigma compared to parents with a junior high school education (all P values <.05). These 4 factors explained 40.4% of the total model variance (F8=9.726; P<.001; R2=0.450; adjusted R2=0.404). Conclusions Our findings highlight a concerning trend of diminished quality of life among parents of children with CL/P. Factors such as parents’ education level, surgery status, depression, and social anxiety are shown to influence the level of stigma experienced. Implementing comprehensive nursing care and providing presurgical support are effective strategies for alleviating parents’ social anxiety, reducing perceived stigma, and preventing depression.
Frying Process: From Conventional to Air Frying Technology

ABSTRACT Studies have shown that products resulting from oils heated for long periods under high temperatures contain polar compounds such as polymers, dimers, free fatty acids, and acrylamide, which lead to metabolic changes, malabsorption of essential fat, and development of cancer. The air frying process is capable of reaching the characteristic color and similar flavor obtained by deep fat frying, and reduces the amount of oil content and polar compounds. However, it has not been extensively studied and should receive a greater focus on the detailed evaluation of components and properties of foods and the effects on the human health.
The Diagnostic Value of CEUS in Assessing Non-Ossified Thyroid Cartilage Invasion in Patients with Laryngeal Squamous Cell Carcinoma

Background: Accurate assessment of thyroid cartilage invasion in squamous cell carcinoma (SCC) of the larynx remains a challenge in clinical practice. The aim of this study was to assess the diagnostic performance of contrast-enhanced ultrasound (CEUS), contrast-enhanced computed tomography (CECT), and magnetic resonance imaging (MRI) in the detection of non-ossified thyroid cartilage invasion in patients with SCC. Methods: CEUS, CECT, and MRI scans of 27 male patients with histologically proven SCC were evaluated and compared. A total of 31 cases were assessed via CEUS and CECT. The MR images of five patients and six cases were excluded (one patient had two suspected sites), leaving twenty-five cases for analysis via MRI. Results: CEUS showed the highest accuracy and specificity compared with CECT and MRI (87.1% vs. 64.5% and 76.0% as well as 84.0% vs. 64.0% and 72.7%, respectively). The sensitivity and negative predictive value of CEUS and MRI were the same (100%). CEUS yielded four false-positive findings. However, there were no statistically significant differences among the imaging modalities (p > 0.05). Conclusions: CEUS showed better diagnostic performance than CECT and MRI. Therefore, CEUS has the potential to accurately assess non-ossified thyroid cartilage invasion and guide appropriate treatment decisions, hopefully leading to improved patient outcomes.