Miracle-dz/UniFAR-FTUs huggingface dataset:
Bulletin of Electrical Engineering and Informatics

Received Oct 19, 2021 Revised Mar 4, 2022 Accepted Mar 13, 2022 A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients’ dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.
Comparison of effectiveness of percutaneous nephrolithotomy versus retrograde intrarenal surgery

Background: Treatment of renal stones depends upon the stone size, location, symptoms and any other anatomical variation in the calyceal system of the kidney. Percutaneous nephrolithotomy (PCNL) is a highly effective operation with consistently high stone-free rates; it delivers good stone clearance with minimal problems and a lower retreatment rate, while it is associated with higher morbidity. Retrograde intrarenal surgery (RIRS) is a less invasive and practical therapeutic option with a short hospital stay, minimal morbidity, and a low complication rate. Objectives were to compare the effectiveness of PCNL and RIRS in the treatment of renal stones.Methods: 50 patients with renal stones with no comorbidities were divided into 2 groups. Group-1 was treated with PCNL, while group-2 underwent RIRS. The outcomes of both procedures were compared based on the operating time, duration of hospital stay and stone-free rate.Results: The mean duration of hospital stay was higher in the PCNL group (4.37±2.11 days) than the RIRS group (2.84±0.98 days). The stone free rate was more in the PCNL group (96%) as compared to the RIRS group (84%). The PCNL group (78±12.75 minutes) had a longer operative time than the RIRS group (70.59±10.09 minutes. Blood loss was more in the PCNL group.Conclusions: RIRS is an effective and safe alternative to PCNL in the treatment of renal stones. The choice of surgical approach between PCNL and RIRS should be based on the surgeon's experience and preference and the patient's financial means.
Practice and match workload of a female tennis player in two annual seasons: A single-case study

The aim of this single case study was to monitor the external workload of a professional female tennis player between 314 training sessions and 115 matches. A wearable device was used during two fully consecutive tennis seasons (24 months). External workload was determined using time indicators (total and active session times), shots indicators (shots per week, session, hour, rally and minute) and frequency distribution of rallies. This case study showed that the workload during practice sessions was higher compared to matches in terms of active time, percentage of active time, shots per hour and rally, and frequency distribution of rallies with more than nine shots. The number of shots executed per minute was lower in the practice sessions than in the match. It is concluded that the recommended number of shots per hour in a 90-min practice session is for the player to perform 400 to 800 shots. The recommended average number of rallies in practice sessions is 144 and 70% of the rallies should consist of four shots. The pace of rallies in open match situations in the practice sessions should reach the level of official matches. These conclusions could be useful guidelines for determining the workload of female tennis players participating in entry-level professional tournaments.
Advancing Database System Operators with Near-Data Processing

As applications become more data-intensive, issues like von Neumann’s bottleneck and the memory wall became more apparent since data movement is the main source of inefficiency in computer systems. Looking to mitigate this issue, Near-Data Processing (NDP) moves computation from the processor to the memory, thus reducing the data movement required by many data-intensive workloads. In this paper, we look to database query operators, common targets of NDP research as database systems often need to deal with large amounts of data. We investigate the migration of most time-consuming database operators to Vector-In-Memory Architecture (VIMA), a novel 3D-stacked memory-based NDP architecture. We consider the selection, projection, and bloom join database query operators, commonly used by data analytics applications, comparing VIMA to a high-performance x86 baseline. Our results show speedups of up to 8× for selection, 6× for projection, and 16× for join while consuming up to 99% less energy. To the best of our knowledge, these results outperform the state-of-the-art for these operators on NDP platforms.
A Systematic Review of Autonomous Emergency Braking System: Impact Factor, Technology, and Performance Evaluation

In order to track the research progress of AEB-related technologies, this paper makes a systematic analysis and research on the impact factors, key technologies, and effect evaluation of AEB. First, the paper deeply analyzes the three levels of factors affecting the performance of AEB, which are vehicle factors, driver factors, and environmental factors. Second, the paper deeply studies the technical status of the three subsystems of environment perception, decision-making, and control execution. Particularly, the performance of Mazda, Honda, NHTSA, Berkeley, and Seungwuk Moon are compared and analyzed based on MATLAB. Third, the paper summarizes the current AEB virtual test methods, closed field test methods, and its test sites. Three classic evaluation methods in the world, including the AEB test evaluation standards of ENCAP, IIHS, and i-Vista are analyzed. Finally, the paper prospects the specific research directions, including the protection of vulnerable road users, target detection method, collision avoidance strategy, complex scenarios application, and application of emerging technologies.
THE DEVELOPMENT OF SMART CITIES AND ENVIRONMENT-RELATED DOMAIN: A CASE STUDY IN INDONESIA AND FRANCE

In Indonesia, and so is France, there is a growing concern about smart city development. However, empirical evidence is still lacking in Indonesia and so is the comparison between cities in Indonesia and France. Therefore, this paper is to elucidate the smart city development in Indonesia and France in terms of the environment-related domain. This current study is qualitative in nature, focusing on two cities, Lyon in France and Bandar Lampung in Indonesia. The data collection was carried out using two types of instruments. First, we collected data to measure indicators of the environment-related domain of smart cities through interviews with government officials who were in charge of city development. Second, the data collection also involved a five-point Likert scale survey. The findings indicate that the smart city development in Lyon in terms of the environment-related domain is more developed compared to Bandar Lampung. Lyon has enlisted additional private sector support for the concept of smart city development, while everything necessary for the development of a smart city is now being prepared in Bandar Lampung. The recommendations from the findings and suggestions for further research are also discussed.
Machine Learning for Healthcare Wearable Devices: The Big Picture

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.
Vibration absorption performance of resilient wheel in metro train running on long-span cable-stayed bridge

ABSTRACT To control the vibration of long-span cable-stayed bridge (LSCSB) caused by running train on metro line, vibration suppression effect of resilient wheel on the metro train–LSCSB coupled system is deeply investigated. Firstly, metro train–LSCSB system coupled dynamic model considering resilient wheels is formed. Then, the effects of resilient wheels on the dynamic characteristics of metro train–LSCSB system are investigated. Finally, the optimal stiffness ratiois determined, and the stiffness and damping of the resilient wheel are also optimized. Results show that: The resilient wheel reduces lateral vibration of LSCSB at 2–20 Hz. For the adopted metro train–LSCSB system, satisfactory vibration absorption effect can be obtained when stiffness ratio of the resilient wheel is 0.67–1. The optimal stiffness and damping of resilient wheel are recommended to be 30 MN/m and 300 kN·s/m.
Financial Time Series Prediction Based on GGM-GAN

Financial time series prediction is challenging due to the uncertainty of financial markets, especially in China’s stock market. This paper innovates to integrate sparse Gaussian Graph Model (GGM) information into Generative Adversarial Network (GAN) to forecast stock price for the Chinese A-share market. In the GAN, we use the Long Short-Term Memory (LSTM) as generate network and Convolution Neural Network (CNN) as discriminate network. As for the input of LSTM, the features of predicted stock are combined with correlated stocks which are selected by Sparse GGM. Experimental evidence shows that our model achieves better performance in the predition error and direction accuracy than the other models, such as ARIMAGARCHA, SVM, LSTM, GAN and GGM-LSTM.
Hybrid precoding in Massive Antenna Multi-User System with Modified Hybrid Connected Structure

In this paper, a new hybrid precoding architecture is proposed for massive antenna multi-user system. The proposed architecture is an extension of the hybrid connected structure (HCS), which can realize a good tradeoff between spectral efficiency and hardware cost by adjusting the number of sub-arrays. Based on the proposed architecture, the hybrid precoding matrix is optimized by using the alternating optimization (AO) and Riemannian conjugate gradient (RCG) algorithm. The proposed scheme is evaluated based on real measured channels at 28GHz in a railway station. Simulation results show that the achievable sum rate of proposed scheme is very close to that of the optimal full-digital precoding scheme. In addition, the achievable sum rate of the proposed scheme is higher than that of the existing schemes when the number of sub-arrays takes different values.
The Rare Case of the Migrated Tubal Ligation Clip Presenting As a Bladder Stone

Introduction: The use of Filshie clips for tubal ligation is a common form of contraception. Migration of Filshie clips is a rare but well published complication. We report what we believe is the first case of a Filshie clip migrating completely into the bladder and presenting as a bladder calculus approximately 40 years post-insertion. Case presentation: A 73 year old lady presented with gross hematuria with no other associated symptoms. A CT Urogram and flexible cystoscopy revealed a right posterolateral bladder diverticulum with a 2.1 x 1.4 cm calculus within and no other urinary tract abnormality. She had undergone a previous tubal ligation approximately 40 years ago. She subsequently underwent a rigid cystoscopy and cystolithotripsy. Intra-operatively, during laser cystolithotripsy, a Filshie clip was seen at the core of stone. After completion of cystolithotripsy, the Filshie clip was easily removed with a grasper. The wall of the bladder diverticulum was inspected and appeared normal and intact. The patient recovered well postoperatively and was discharged on post-operative day 1. Conclusion: Filshie clip migration is rare occurrence. There are multiple case reports describing the migration of Filshie clips. This case adds to the existing literature as a potential rare late complication of migrated Filshie clips. It raises awareness that patients with previous tubal ligation may present with complications as late as 40 years post-insertion.
Different Compensation Techniques To Compensate Chromatic Dispersion In Fiber Optics

Dispersion compensation is the most important and challenging aspect of high optical signal to noise ratio (OSNR) for good quality of the signal. and mature technology but it gives high insertion loss other techniques such as electronic dispersion dispersion compensation using digital filters are also in use Key terms: Optical fibers, optical communication system, fibers (DCF), fiber Bragg grating (FBG), digital filters.
Validation of a Pseudospectral Time-Domain (PSTD) Planetary Radar Sounding Simulator With SHARAD Radar Sounding Data

In a recent study, a 2-D pseudospectral time-domain (PSTD) full-wave simulator was developed and demonstrated to be capable of efficiently solving large-scale low-frequency (e.g., HF) electromagnetic scattering problems, for example, on the application of radar sounding simulations of planetary clutter and subsurfaces. In this article, the 2-D PSTD simulator is applied to simulate a domain as large as 4000 <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> (along-track) <inline-formula> <tex-math notation="LaTeX">$\times 1666.67\,\,\lambda $ </tex-math></inline-formula> (cross-track) <inline-formula> <tex-math notation="LaTeX">$\times 33.33\,\,\lambda $ </tex-math></inline-formula> (depth) with <inline-formula> <tex-math notation="LaTeX">$\lambda =15$ </tex-math></inline-formula> m at an HF frequency of 20 MHz. To accomplish the goal, the simulator is further improved to efficiently model/simulate large cross-track slices of dielectric scenes by allowing nonuniform grid sampling in horizontal (lateral) and vertical directions, and the cross-track results are then stitched together along the track to form the simulated radargram. By combining the SHAllow RADar (SHARAD) viewing geometry and Mars Orbital Laser Altimeter (MOLA) digital elevation model (DEM), we simulate SHARAD returns at three different sites on Mars: one at the North Pole and two at Oxia Planum. At all three sites, the PSTD simulated radargrams are compared with measured SHARAD radargrams. Through power-level calibration and reference time adjustment, the PSTD simulated power estimates are further validated by comparing with real power observations from SHARAD with a 5-dB uncertainty and Pearson correlation coefficient of 0.3–0.4 (a <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-value on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-9}$ </tex-math></inline-formula>), which justifies the use of the 2-D PSTD simulator for emulating surface clutter in planetary radar sounding. This simulator is open source and can be easily modified to support radar sounding simulations in support of other planetary missions with radar sounding instruments.
Control and Stability of Modular DC Swarm Microgrids

Direct current (dc) microgrids have not yet achieved the promise of true plug-and-play characteristics due to stability issues stemming from power converters. Swarm microgrids, a type of dc microgrids, are aimed at delivering a modular and easy-to-expand infrastructure. In this article, an application-specific control strategy is developed to ensure stable and expandable swarm microgrids. This control strategy makes use of the widely available distributed storage in swarm microgrids and includes active damping techniques at power-drawing units. Utilizing a cascaded state-space system model, experimental validation with dual-active-bridge converters, and eigenvalue robustness analysis, this article demonstrates a high margin of stability for expandable swarm microgrids. The analysis includes variation of the microgrid size and line parameters, among others. The results show that swarm microgrids are stable for up to at least 1000 participating swarm units. As part of the robustness analysis of swarm microgrid stability, specific definitions are provided for the allowed behavior of the power converters used in a swarm microgrid. The developed methodology allows assessing and designing for swarm microgrid stability even without knowledge of the internal structures of the power converters. As such, this article provides a practical framework to support the scaling up of swarm microgrid deployment.
Damage Detection Methods on Wind Turbine Blade Testing with Wired and Wireless Accelerometer Sensors

Testing was performed on a 34 meter blade at a facility in DTU RisA Campus, featuring both wired accelerometers and lowpower MEMsbased wireless accelerometers. Testing was focused on an induced delamination area on the trailing edge of the blade, which was subject to various configurations in order to simulate different degrees of damage. Excitation was performed in two ways: near the delamination zone in a simulation of operational wind excitations, and with a bar designed to excite torsional modes of the wind turbine blade. We compare the data collected from the wireless sensors against wired sensors to demonstrate their performance. We explore methods for determining damage. We first explore results of autoregressive coefficients for indicating damage levels. Finally, we demonstrate the use of damage sensitive features from the wavelet transforms of input and output signals to provide a method suitable for nonstationary blade excitations.
Non-Coding RNAs in Tuberculosis Epidemiology: Platforms and Approaches for Investigating the Genome’s Dark Matter

A growing amount of information about the different types, functions, and roles played by non-coding RNAs (ncRNAs) is becoming available, as more and more research is done. ncRNAs have been identified as potential therapeutic targets in the treatment of tuberculosis (TB), because they may be essential regulators of the gene network. ncRNA profiling and sequencing has recently revealed significant dysregulation in tuberculosis, primarily due to aberrant processes of ncRNA synthesis, including amplification, deletion, improper epigenetic regulation, or abnormal transcription. Despite the fact that ncRNAs may have a role in TB characteristics, the detailed mechanisms behind these occurrences are still unknown. The dark matter of the genome can only be explored through the development of cutting-edge bioinformatics and molecular technologies. In this review, ncRNAs’ synthesis and functions are discussed in detail, with an emphasis on the potential role of ncRNAs in tuberculosis. We also focus on current platforms, experimental strategies, and computational analyses to explore ncRNAs in TB. Finally, a viewpoint is presented on the key challenges and novel techniques for the future and for a wide-ranging therapeutic application of ncRNAs.
Towards a Dimensional, Multifactorial, and Integrative Approach to Procrastination in Everyday Life: An Illustration through Interviews

Procrastination is a widespread phenomenon that has been extensively studied but about which a clear and integrated picture is still lacking, as reflected in the multiplicity and diversity of its definitions, causes and consequences. In addition, its examination in everyday life has been somewhat overlooked. The aim of this paper is to further the understanding of procrastination, first by providing an overview of its various definitions, causes, and consequences. Using a qualitative approach, we then provide an in-depth descriptive account of procrastination episodes retrospectively reported by six participants from the general population in diverse situations of their daily life, focusing in particular on the definitions, causes, and consequences of procrastination behaviours. Finally, this descriptive account of procrastination is discussed in terms of a dimensional, multifactorial, and integrative approach.
Advantage of a Thermoelectric Generator with Hybridization of Segmented Materials and Irregularly Variable Cross-Section Design

As a direct energy converter between heat and electricity, thermoelectric generators (TEGs) have potential applications including recovery of waste heat, and solar thermoelectric power generation. Geometric parameter and material are two critical factors to improve the TEG performance. However, the strategies base on structure design and material development are always separated. There are limited studies on the effects of consolidating them simultaneously. Here, an idea of segmented material coupled with irregularly variable cross-section design was conceived to further improve the TEG output power. The performance of TEGs with rectangular leg, segmented leg, variable cross-sectional leg, and the new design are compared. The coupling effects between various mechanisms are revealed, which are responsible for the superior performance provided by the developed design. Based on this knowledge, a multiparameters optimization was performed through the genetic algorithm to reach the optimal combination of design parameters. The results show that, with a constraint of certain material volume, the optimal performance of the TEG can be further enhanced by coupling segmented material and irregularly variable cross-section design. An improvement of 51.71% was achieved when compared with the conventional counterpart. This work offers a simple route to enhance the TEG performance when the device materials are specified, without an increase in the cost of manufacturing.