Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automotive-Grade Monolithic Masterless Fault-Tolerant Hybrid Dickson DC–DC Converter for 48-V Multi-Phase Applications

Autonomous-driving features necessitate a fault-tolerant (FT) power-management unit in 48-V automotive applications. This article presents a monolithic single-phase closed-loop masterless FT four-to-one hybrid Dickson converter IC for high-conversion-ratio 48-V automotive multi-phase applications. The chip includes: 1) a mixed-signal droop-based valley-current ON-time quasi-fixed-frequency control, which enables masterless multi-phase operation capability; 2) single-point short-circuit fault and open-circuit fault detection and blocking of any power switches or flying capacitors; 3) an optimized high-voltage (HV) power stage with fully integrated flying-capacitor-sourced gate drivers without any external bootstrap capacitors; 4) a glitch-free cross-coupled bootstrapped HV level shifter; and 5) a dual-leg bidirectional closed-loop current-sensing circuit optimized for the hybrid Dickson topology. The design was fabricated in a 0.13- $\mu \text{m}$ 90-V automotive silicon-on-insulator (SOI) bipolar-CMOS–DMOS (BCD) process. The measured results confirm the operation over 20–60-V input, 3.3-V nominal output, 3-A maximum load, and 320-kHz effective switching frequency within the automotive-grade −40 °C–+125 °C ambient temperature. The design achieves above 90% measured efficiency at maximum conversion ratio and load at room temperature. A three-phase system is used to evaluate and confirm the fault-blocking capability and continuous power delivery upon detection of a fault.
Investigation of students’ comfort and adaptation in university dormitories in humid subtropical climatic area in winter in Chongqing, China

Abstract Students’ comfort and adaption in university dormitories is important for their learning. By controlling different environmental parameters (temperature, humidity, air velocity, etc.,) this can provide a great satisfaction in students’ dormitory. In the present study, investigation was performed in students’ dormitories in university in Chongqing, China. A survey was conducted on students’ thermal comfort in typical university dormitories during winter in Chongqing, Southwest China. This also included on-site and continuous measurements of indoor physical parameters and filling out questionnaires about thermal sensation, thermal comfort, and adaptive behaviours by the students. Results showed that staying for longer periods in regions with a colder climate in winter, improved students’ adaptability to lower temperature, closely correlated to behavioural and psychological processes. Although the thermal conditions varied in the international students’ and Chinese students’ dormitories, the thermal environmental conditions in the students’ dormitories were poorer. The average indoor air temperature was 18.7°C and 18.1°C in the international students’ and Chinese students’ dormitories, respectively, which fell outside the ASHRAE thermal comfort zone, with higher indoor relative humidity. In addition, Chinese students felt more comfortable and satisfied with indoor air temperature and relative humidity compared to international students, evidently showing a higher adaptability to a severe colder winter climate. The study provides information that can support comfort researchers, designers, and policy makers towards improved design in student’s dormitories and sustainable dormitory buildings.
Vaccine Attitude of Healthcare Professionals Towards Covid19: Samsun/Turkey Example

The purpose of this study is to determine the attitudes of healthcare professionals towards CoronaVac and BioNTech vaccines applied in Turkey without the completion of phase 3 studies and to contribute to the policies that will be developed for the vaccine. The data in the study were collected between 01.03. 2021 and 31.03. 2021. The population consists of healthcare workers working in primary and secondary healthcare institutions in Samsun (N= 11.840). In determining the sample size, the unknown prevalence was taken as 50% and the margin of error as 3% by using the Open Epi Calculator program and it was determined as 979 with an error level of 0.05 and a power of 80%. Two forms were used for data collection namely Personal Introductory Information Form and Vaccination Attitude Scale. The data were evaluated with regression model and descriptive analysis such as number and percentage. The average age of the participants in the study was 38.33 ± 8.852, the average working year was 14.99 ± 8.920, 61.7%. 79% of the healthcare professionals were vaccinated for Covid 19, and all of them were vaccinated with CoronaVac vaccine. The vaccine acceptance average for CoronaVac vaccine is 3.81 ± 0.870, and the vaccine acceptance average for BioNTech vaccine is 3.76 ± 0.778. The attitude of those with more education and working years is better than others towards the BioNTech vaccine. An important finding of the study is that 79% of the healthcare workers in the research group were vaccinated;in other words, 21% of them corresponding to one out of five people were not vaccinated. The second important finding of the study is that those with higher education and more working years have a better attitude towards BioNTech vaccine than others.
The first 10 years’ experience in radical retropubic prostatectomy: Complications, lower urinary tract symptoms, and quality of life - a single-center experience

The years’ in radical retropubic prostatectomy: complications, lower urinary tract symptoms, and quality of life – 20+) had a statistically significant reduction of urinary symptoms after RRP (p < 0.001). After 12 months, IPSS QoL was statistically significantly lower than preoperative (p < 0.05). Conclusion For patients with clinically localized prostate cancer RRP is a safe and effective treatment option. It is associated with a higher rate of complications from the Clavien–Dindo grade ≤ II group. Radical retropubic prostatectomy has clinically beneficial effects on LUTS in patients with moderate and severe urinary symptoms and QoL related to LUTS.
An Amphiphobic Hydraulic Triboelectric Nanogenerator for a Self‐Cleaning and Self‐Charging Power System

Raindrop falling, which is one kind of water motions, contains large amount of mechanical energy. However, harvesting energy from the falling raindrop to drive electronics continuously is not commonly investigated. Therefore, a self‐cleaning/charging power system (SPS) is reported, which can be exploited to convert and store energy from falling raindrop directly for providing a stable and durable output. The SPS consists of a hydraulic triboelectric nanogenerator (H‐TENG) and several embedded fiber supercapacitors. The surface of H‐TENG is amphiphobic, enabling the SPS self‐cleaning. The fiber supercapacitor which uses α‐Fe2O3/reduced graphene oxide composite possesses remarkable specific capacitance, excellent electrical stability, and high flexibility. These properties of the fiber supercapacitor make it suitable for a wearable power system. A power raincoat based on the SPS is demonstrated as application. After showering by water flow, which simulates falling raindrops, for 100 s, the power raincoat achieves an open‐circuit voltage of 4 V and lights a light‐emitting diode for more than 300 s. With features of low cost, easy installation, and good flexibility, the SPS harvesting energy from the falling raindrop renders as a promising sustainable power source for wearable and portable electronics.
Frail geriatric patient with covid-19 infection: a case report

Introduction: Covid 19 is a respiratory system disease caused by SARS-COV2. Geriatric patients with Covid-19 equipped with multiple comorbidities and a high vulnerability have high morbidity and mortality rates. Case illustrations: A 75-year-old woman presents with confusion and fever. She was treated in the previous hospital for 7 days, experienced a deterioration of consciousness and had respiratory failure, thus was referred to Kariadi Hospital.She had comorbid hypertension, type 2 diabetes mellitus and grade 2 osteoarthritis genu bilateral. Bodyweight 45kg, height 150cm, BMI 20kg/m2,blood pressure 100/58 mmHg,pulse 110x/minute, Respiratory rate 28x/minute, Temperature 38.5℃, 93% oxygen saturation, Frailty Index 0.7, Katz Index G, Norton score 11/20. Leucocytes 9100,Lymphocytes 10%, Neutrophil Lymphocyte Ratio 2.2, Blood Glucose 247mg/dL, CRP 4.41mg/dL, Ferritin 5472.28,D-Dimer >20,000ug/dL, Fibrinogen498 mg/dL, Procalcitonin 0.39, Chest X-ray showed pneumonia infiltrates with cardiomegaly. The RT-PCR swab examination showed positive SARS-Cov-2. Brain CT showed lacunar infarct and aging atrophy cerebral. the patient was admitted to intensive isolation room because of acute respiratory distress syndrome and covid coagulopathy. After clinical improvement, the next problems that must be addressed in isolation rooms are frail, confusion, cerebral syndrome, immobility and grade 1 decubitus. Conclusion: Management of frail geriatric patients with Covid-19 has its challenges because of the high mortality rate. It requires a comprehensive and multidisciplinary approach. © 2021, Sanglah General Hospital. All rights reserved.
ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and provides a diagnostic mean for heart-related diseases. An arrhythmia is any irregularity of heartbeat that causes an abnormality in one’s heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG signal is not sufficient for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to developed computer-aided diagnosis (CAD) systems to automatically identify arrhythmias.Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The signals are obtained from MIT-BIH arrhythmia database and are categorized according to five arrhythmia types. The proposed approach identifies arrhythmia classes by using Convolutional Neural Network (CNN) architecture trained by two-dimensional (2D) ECG beat images. CNN architecture is selected due to high image recognition performance. ECG signals are segmented into heartbeats, then each heartbeat is transformed into a 2D grayscale image. The heartbeat images are used as input for the CNN. Results: The proposed CNN model is compared to other common CNN architectures such as LeNet and ResNet-50 to evaluate the performance of our study. Overall, the proposed study achieved 99.7% test accuracy in the classification of five different ECG arrhythmias.Conclusions: Testing results demonstrate that CNN trained by ECG image representations provide outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Hence, the proposed approach provides a robust method for the classification of ECG arrhythmias.
Fingerspelling Identification For American Sign Language Based On Resnet-18

------------------------------------------------------------------- ABSTRACT --------------------------------------------------------------- Sign language as the main communication channel for deaf and hearing people, plays a very important role in daily life. With the rapid development of the field of deep learning, the field of sign language recognition has ushered in new opportunities. Aiming at the small number of sign language samples and low detection accuracy, an American sign language detection method based on Resnet-18 and data augmentation is proposed. First, the sign language picture is adjusted to 64×64 size using the filling method, and then the data is augmented by methods such as chromaticity change, rotation, and noise addition to increase the diversity of data samples and improve the robustness of the detection method. The experimental results show that the accuracy of the sign language recognition method based on Resnet-18 can reach 99%, which provides a new method for sign language recognition.
Two-Dimensional Broad Learning System for Data Analytic

Recently, a novel learning algorithm called the broad learning system (BLS) has been established by using the theory of pseudoinverse and compressed sensing technology. As an alternative to deep learning, BLS has gained extensive attention in data analytics. Unlike the existing deep structures based learning tools (such as deep neural networks), it is able to rapidly achieve incremental learning, and does not require tedious retraining to remodel the system. However, given image data modeling tasks, 1-D BLS requires a conventional vectorization operation, the dimension of input will be quite large for high resolution image. This operation may result in huge computational complexity, thereby increasing the time of model construction. To resolve this issue, a 2-D version of BLS, namely 2-D-BLS, is first proposed to fast construct randomized learner models by using matrix inputs. Specifically, the proposed method employs left and right projecting vectors to replace the usual high dimensional input weight. Moreover, the theoretical analyses on the superiority of 2-D-BLS against BLS are presented in terms of the algorithm complexity and the difference of random parameter space. Empirical results on MNIST dataset, NORB 3-D object image recognition dataset, ORL dataset, YaleB dataset, and handwritten digit dataset demonstrate that the proposed 2-D-BLS outperforms the existing extensions of BLS and state-of-the-art 2-D machine learning methods in terms of the learning efficiency, showing good potential of 2-D-BLS for image data analytics. Impact Statement—as a novel learning tool, BLS has achieved great success in many fields. Image recognition is one of the most widely used fields of artificial intelligence. But, BLS is a 1-D model, the vectorization operation is necessary to convert matrix input data into a row or a column vector. This operation will lead to “explosion of dimensionality” and increase the time of model construction when it is coming to image data analytic with 2-D input. This article first proposes a 2-D extension of BLS for high resolution image data analytic. The proposed method can not only avoid the “explosion of dimensionality,” but also construct the whole structure with matrix-inputs rapidly and dynamically. The improvement of this method is expected to provide a novel and great choice for the field of image recognition, such as face recognition, industrial inspection, behavior recognition, etc., in the future.
Seroprevalence of Hepatitis B Virus Infection among the Tribal Population of Attapady, Kerala,India

Introduction: Hepatitis B Virus (HBV) infection remains a significant global health concern that may cause acute or chronic hepatitis. Chronically infected patients are at risk for cirrhosis and hepatocellular carcinoma. The disease causes a problem in the tribal communities. There are lack of studies on the prevalence of HBV among the tribal population. Aim: To assess the seroprevalence of HBV infection among the tribal population of Attapady, Kerala. Materials and Methods: This was a community based cross- sectional study conducted on serum samples collected from 269 subjects among the tribal population of Attapady. Serum samples were tested for quantitative antibody to HBsAg (anti-HBs), Hepatitis B surface antigen (HBsAg) and Hepatitis B envelope antigen (HBeAg) Enzyme Linked Immunosorbent Assay (ELISA). Total hepatitis B core antibody (anti-HBc) and IgM antibody to hepatitis B core antigen (anti HBc IgM), frequencies were obtained using proportion and 95% Confidence Interval CI. Results: The seroprevalence of HBsAg was 10.4%. HBeAg was detected in 7.1% of HBsAg positive patients. 21.2% had protective anti-HBs titer. Anti-HBe was detected in five patients. Anti-HBc total and anti-HBc IgM were positive for 26.7% and 2.6%, respectively. Anti-HBc IgM alone and isolated anti-HBc were detected in 1.5% and 5.9 %, respectively. Anti-HBs and anti-HBc total both became positive in 8.6% cases. Conclusion: HBV infection poses a huge burden on tribal health. All HBsAg positive patients should be tested further to determine the stage of the disease. There is need to explore high HBV prevalence areas with studies on associated risk factors to bring out the ongoing transmission process and focus on preventive measures. HBV vaccination, antenatal screening, and health awareness should be given priority to tackle the burden.
Human Capital Management In The System Of Public Administration In The Context of COVID-19 Pandemic

The article reveals the issues of interdependence of the development of human capital in public administration on the level of its provision by the state in the context of COVID-19. In a democratic, civil, postmodern society, one of the main tasks is the development of systems for managing the efficiency of human capital in the context of public administration, as a means of obtaining higher levels of labor productivity. Today we have to state that the achievement of this efficiency in the use of human resources can be better achieved by orienting the performance management system to promote the attraction of new human potential in public administration at all levels. In this context, a new approach to the human capital management process is being actualized, which includes employee engagement and key factors of employee engagement at every stage. We are talking about a management model that includes the main ideas and offers a new perspective on how to develop and manage the involvement of human resources to achieve high levels of labor productivity in public administration in the context of COVID-19. The purpose of the article is to study the essence, characteristics and role of human capital in the context of public administration in the context of the existence of the epidemic danger of COVID-19
Adverse selection and the market for consumer credit

This study investigates the possibility of adverse selection in the UK market for consumer credit. Long- and short-run equations for the supply and demand for clearing bank consumer credit are estimated using the Johansen procedure and three-stage least squares. We identify a long-run backward-bending credit supply curve, with a bank-optimal real interest rate of 9.77%. From the cointegrating vectors we are able to estimate good error correction representations of supply and demand using three-stage least squares. We find a strong role for housing wealth in influencing demand, and that the adjustment of supply towards its own long-run equilibrium value appears to be more sluggish than is the case with demand
Force-dependent interactions between talin and full-length vinculin

Talin and vinculin are part of a multi-component system involved in mechanosensing in cell-matrix adhesions. Both exist in auto-inhibited forms, and activation of vinculin requires binding to mechanically activated talin, yet how forces affect talin’s interaction with vinculin has not been investigated. Here by quantifying the force-dependent talin-vinculin interactions and kinetics using single-molecule analysis, we show that mechanical exposure of a single vinculin binding site (VBS) in talin is sufficient to relieve the autoinhibition of vinculin resulting in high-affinity binding. We provide evidence that the vinculin undergoes dynamic fluctuations between an auto-inhibited closed conformation and an open conformation that is stabilized upon binding to the VBS. Furthermore, we discover an additional level of regulation in which the mechanically exposed VBS binds vinculin significantly more tightly than the isolated VBS alone. Molecular dynamics simulations reveal the basis of this new regulatory mechanism, identifying a sensitive force-dependent change in the conformation of an exposed VBS that modulates binding. Together, these results provide a comprehensive understanding of how the interplay between force and autoinhibition provides exquisite complexity within this major mechanosensing axis.
Area- and Energy-Efficient LDPC Decoder Using Mixed-Resolution Check-Node Processing

A practical min-sum algorithm is associated with tree-based comparison units for the check-node operation, being a major bottleneck in designing low-cost and energy-efficient low-density parity-check (LDPC) decoders. In this brief, we present a cost-effective LDPC decoder architecture by changing its internal computing resolution for the power-hungry check-node processing. The proposed mixed-resolution comparison offers significant advantages in terms of both area and energy, while achieving error-correcting performance within 0.3 dB of the previous normalized min-sum (NMS) algorithm for a (1644, 1408) quasi-cyclic LDPC code of the 5G New Radio specifications. Compared to the baseline NMS architecture, the proposed decoder in a 65-nm CMOS technology reduces the hardware complexity and the power consumption by 28.4% and 23.1%, respectively, enhancing the area efficiency by more than 88.2%.
The Use And Implications of Management Accounting Practices in Small and Medium-Sized Enterprises

Management Accounting Practices (MAPs) usage among Small and Medium-sized Enterprises (SMEs) and their implications are examined in this paper. 291 sets of questionnaires were dispersed and six interviews were conducted to gather the data. The usage frequency of individual practices and thematic analysis are deliberated, and the results denote the comparatively low MAPs usage rates among Jordanian SMEs as opposed to those in developed countries. Nonetheless, the reported rates in Jordan are comparable to those in developing countries. Among SMEs, MAPs significantly allow product cost information determination, planning and controlling, and detection of problems. Shortcomings of the traditional system, previous experiments of large companies, and external factors, are considered as the main reasons that motivated SMEs to use MAPs. Insufficient experience, lack of financial resources, and the high costs associated with implementing MAPs are the most crucial reasons that forced SMEs to neglect to use a lot of MAPs. The present study enriches the knowledge of MAPs in the context of SMEs. Also, the significance of MAPs among SME managers is highlighted. Equally, this study should stimulate additional scrutiny and analysis of MAPs amongst SMEs. Keywords: management accounting practices (MAPs), small and mediumsized enterprises (SMEs), Jordan, diffusion of innovations theory
Viral integration detection strategies and a technical update on Virus-Clip

Oncovirus infection is crucial in human malignancies. Certain oncoviruses can lead to structural variations in the human genome known as viral genomic integration, which can contribute to tumorigenesis. Existing viral integration detection tools differ in their underlying algorithms pinpointing different aspects or features of viral integration phenomenon. We discuss about major procedures in performing viral integration detection. More importantly, we provide a technical update on Virus-Clip to facilitate its usage on the latest human genome builds (hg19 and hg38) and the adoption of multi-thread mode for faster initial read alignment. By comparing the execution of Virus-Clip using single-thread and multi-thread modes of read alignment on targeted-panel sequencing data of HBV-associated hepatocellular carcinoma patients, we demonstrate the marked improvement of multi-thread mode in terms of significantly reduced execution time, while there is negligible difference in memory usage. Taken together, with the current update of Virus-Clip, it will continue supporting the in silico detection of oncoviral integration for better understanding of various human malignancies.