Miracle-dz/UniFAR-FTUs huggingface dataset:
Architectural Solutions for Low-power, Low-voltage, and Unreliable Silicon Devices

In the past several years, technology scaling has reached an impasse, where performance has become limited not by transistor switching delays but by hard limits on power consumption brought on by limits on power delivery, cooling and battery capacities. Although transistors have continued to scale down in size, power density has increased substantially. In the future, it may become impractical to power an entire chip at nominal voltage. The main tool designers have to avoid this power wall is to lower supply voltage, but this combines with the increasing e↵ects of process variation to make semiconductors slower and less reliable. We propose several solutions to these problems. For logic faults, we provide a tunable reliability target, where the tradeo↵ between reliability and energy e ciency can be adjusted dynamically for the system, application, and environment. For faults in memories, we develop a new, low-latency forward error correction technique that is a practical solution to the high bit cell failure rate of caches at low voltage. As voltage is lowered, performance is reduced both by generally increasing transistor delay and also by amplifying the e↵ects of process variation; we mitigate the e↵ects of variation through the use of dual voltage supplies and clock dividers. For e ciency, we propose two dual-voltage and dual-frequency techniques for increasing performance of unbalanced workloads. For reliability, we propose an intelligent processor wake-up schedule to eliminate voltage emergencies that can arise from sudden increases in current demand, particularly those associated with common synchronization primitives.
Assessment of Bacteriological and Antibiogram of Uropathogens among Students in the Faculty of Health Sciences, Imo State University, Owerri

The Assessment of bacteriological and antibiogram of uropathogens among students in the faculty of Health Sciences, Imo State University, Owerri with the aim to determine the prevalence of the isolates and to test which antibiotic has the greatest antagonistic activity against the different isolates in the study area. Fifty (50) students made up of both sexes were examined using their midstream urine samples as specimen. The study took note of the students’ age, sex and department. The urine samples were cultured on Nutrient agar, Mannitol salt agar, and MacConkey agar using pour plate method and were incubated at 37oC for 24 hours. After which the total bacterial counts were carried out and based on the count, it was categorized as being significant, suspected and non-significant. The colonies were then sub cultured for further identification. The pure isolates produced were stained using Gram stain, examined microscopically and further tested using relevant biochemical tests. It was found that a total of 35 bacteria were isolated which includes E.coli 14(40%) being the most predominant organism, followed by Staphylococus aureus 13(37.1%), Proteus mirabilis 7(20%) and Pseudomonas aeruginosa 1(2.9). Out of the fifty (50) urine samples investigated, 24 samples were significant for UTI (105 CFU/ml), 2 samples were suspected for UTI (102 – 104 CFU/ml), while 24 were not significant for UTI (below 102CFU/ml). Based on departmental studies, the subjects from the Department of Nutrition and Diatetics and Optometry had the highest significance of UTI, (60%) respectively. The results of susceptibility tests showed that Imipenem was the most effective antibiotic in inhibiting the bacterial growth (98.8% of antibiotic activity). The present study therefore, revealed that the urine samples collected from students in Faculty of Health sciences, Imo State University, Owerri had significant UTI and most of the isolates (98.8%) were sensitivity to Imipenem. UTI testing should be conducted periodically, and those who are infected should be treated with antibiotics like Imipenem to avoid complications. New antimicrobials and therapeutic agents with great efficacy, no side effects, ease of availability, and low cost should also be created. In order to reduce the risk of antimicrobial resistance, antibiotic medication should be started following a laboratory culture and sensitivity report. This would not only aid in the prudent use of antibiotics, but it would also help to limit the spread of antimicrobial resistant strains in the research region and the general community.
Keeping up with the Joneses: Social comparison of integrating work and family lives

Does everyone around you seem to be juggling the work and family roles more successfully than you? This research examines the influence of social comparison of work–family balance in the work role overload to emotional exhaustion relationship through two paths – a spillover path to both job and family emotional exhaustion for the job incumbent and a crossover path to the spouse to their own family emotional exhaustion. Further, we examine whether the personality trait of social comparison orientation exacerbates these processes. We used matched responses over two time periods from 403 dual-earning, married couples living in the United States to test our hypotheses. We found support for all our hypothesized relationships. Social comparison plays a role in individuals’ perception of work–family demands, which may contribute to undesirable consequences for both the job incumbent and the spouse.
Sunitinib and Pterostilbene Combination Treatment Exerts Antitumor Effects in Gastric Cancer via Suppression of PDZD8

The use of molecular-targeted drugs in the treatment of gastric cancer is increasing. However, the variety of molecular-targeted drugs in gastric cancer is still limited, and the development of new molecular-targeted therapies is required. The effect of combining sunitinib (SUN) with pterostilbene (PTE) on the human gastric cancer cell lines TMK1 and MKN74 was examined in in vitro and in vivo. Compared with SUN or PTE treatment alone, cotreatment induced pronounced suppression of cell proliferation, with a marked increase in oxidative stress. SUN was associated with a significant retention of mitochondrial Fe2+. SUN-treated cells decreased expression of PDZ domain-containing protein 8 (PDZD8). Knockdown of PDZD8 in both cells induced Fe2+ retention, and siPDZD8+PTE markedly suppressed cell proliferation with suppressed oxidative phosphorylation, as did the combination of SUN+PTE. In a nude mouse tumor model, a pronounced antitumor effect was observed with SUN+PTE treatment compared to SUN alone. PDZD8 may be a newly discovered off-target for SUN, and that the combined use of PTE with SUN significantly promotes antitumor activity in gastric cancer cell lines. The combined use of SUN and PTE might be a new molecular-targeted therapy for gastric cancer.
Assessing the Status of Tools and Methods for Evaluating Physicians' Documentation in the Emergency Department: A Review Study

Introduction: Documentation of medical records is the first and most important source of patient information collection. On the other hand, the correct registration of medical records is considered as one of the criteria of physicians' scientific skills. Therefore, the purpose of this study was a systematic review to examine the status of tools and methods for evaluating the documentation of physicians in the emergency department.Material and Methods: This systematic review was performed in studies related to the evaluation of the documentation status of emergency department physicians. The studies were available from PubMed, Web of Science, Scopus, Irandoc and SID databases by the end of 2020. Titles and abstracts were reviewed independently based on eligibility criteria. After that, the complete texts were retrieved and independently reviewed by two researchers based on eligibility criteria. A standardized form was used to extract the data including study title, first author name, years of study, place of study, number of samples, research method, tools, indicators studied and main findings.Results: A total of 4693 related studies were extracted from the database and finally 40 main articles were included in the study. In 4 cases, the level of documentation was reported to be incomplete and undesirable by examining the registered files; In the other 4 cases, they estimated the amount of documentation as moderate to favorable. In 2 cases, the effect of education and in 2 cases, the effect of feedback and encouragement on documentation were measured. None of the studies provided a comprehensive tool for evaluating physicians' documentation of emergencies; Evaluation patterns were different in each study and were partially reviewed.Conclusion: A review of research conducted in Iran and the world on documenting physicians, especially in the emergency department, emphasizes the importance of continuing the process of patients. Consequently, the consequences are the same for all stakeholders in the medical record. In addition, the effect of feedback and encouragement was more effective than training in improving documentation, so it is suggested that programs be applied for ongoing feedback to documentarians.
Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination

The purpose of this study is to analyse COVID-19 related news published across different geographical places, in order to gain insights in reporting differences. The COVID-19 pandemic had a major outbreak in January 2020 and was followed by different preventive measures, lockdown, and finally by the process of vaccination. To date, more comprehensive analysis of news related to COVID-19 pandemic are missing, especially those which explain what aspects of this pandemic are being reported by newspapers inserted in different economies and belonging to different political alignments. Since LDA is often less coherent when there are news articles published across the world about an event and you look answers for specific queries. It is because of having semantically different content. To address this challenge, we performed pooling of news articles based on information retrieval using TF-IDF score in a data processing step and topic modeling using LDA with combination of 1 to 6 ngrams.We used VADER sentiment analyzer to analyze the differences in sentiments in news articles reported across different geographical places. The novelty of this study is to look at how COVID-19 pandemic was reported by the media, providing a comparison among countries in different political and economic contexts. Our findings suggest that the news reporting by newspapers with different political alignment support the reported content. Also, economic issues reported by newspapers depend on economy of the place where a newspaper resides.
Evaluating alternative survey methodologies in human dimensions of wildlife research

ABSTRACT Surveys are frequently used in human dimensions of wildlife research. Declines in response rates from mail and telephone surveys, however, have led researchers to seek alternatives (e.g., online platforms). This article evaluated effects of both selection and response biases on estimates of demographics, wildlife-related recreation, and wildlife value orientations across mail, telephone, and online panel surveys. Selection bias revealed that the mail survey overestimated males and older respondents. By design, the online panel yielded demographic estimates proportional to the population. All methods underestimated non-participants in hunting/fishing. Response bias via acquiescence (i.e., agreement on all items) was evident in the telephone survey, which showed higher population estimates of Pluralists, defined as people emphasizing both mutualism and domination orientations. Findings showed data weighting is necessary to address selection concerns in all three methods. Combined mail/online panel approaches are recommended for measuring wildlife value orientations and similar concepts to avoid possible response bias.
Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility pipes) and reconstruct the underground scene. There are two challenges for GPR-based inspection, which are GPR data collection and 3D subsurface object imaging. To address these challenges, we first propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose with GPR measurement in real-time. Moreover, to improve the 3D GPR imaging, we introduce a learning-based GPR data analysis method, which includes a noise removal module to clear the background noise in raw GPR data and a Convolutional Recurrent Neural Network (CRNN) to estimate the dielectric value of subsurface medium in each GPR B-scan data. We use both field and synthetic data to verify the proposed methods. Experimental results demonstrate that our proposed methods can achieve higher performance and faster processing speed in 3D GPR imaging than baseline methods.
Last interglacial sea-level proxies in the glaciated Northern Hemisphere

Abstract. Because global sea level during the last interglacial (LIG; 130–115 ka) was higher than today, the LIG is a useful approximate analogue for improving predictions of future sea-level rise. Here, we synthesize sea-level proxies for the LIG in the glaciated Northern Hemisphere for inclusion in the World Atlas of Last Interglacial Shorelines (WALIS) database. We describe 82 sites from Russia, northern Europe, Greenland and North America from a variety of settings, including boreholes, riverbank exposures and along coastal cliffs. Marine sediments at these sites were constrained to the LIG using a variety of radiometric methods (radiocarbon, uranium–thorium, potassium–argon), non-radiometric methods (amino acid dating, luminescence methods, electron spin resonance, tephrochronology) as well as various stratigraphic and palaeo-environmental approaches. In general, the sites reported in this paper do not offer constraint on the global LIG highstand, but rather evidence of glacial isostatic adjustment (GIA)-influenced sea-level positions following the Marine Isotope Stage 6 glaciation (MIS 6; 191–130 ka). Most of the proxies suggest that sea level was much higher during the LIG than at the present time. Moreover, many of the sites show evidence of regression due to sea-level fall (owing to glacial isostatic uplift), and some also show fluctuations that may reflect regrowth of continental ice or increased influence of the global sea-level signal. In addition to documenting LIG sea-level sites in a large swath of the Northern Hemisphere, this compilation is highly relevant for reconstructing the size of MIS 6 ice sheets through GIA modelling. The database is available at https://doi.org/10.5281/zenodo.5602212 (Dalton et al., 2021).
Use of Smartphone Health Apps Among Patients Aged 18 to 69 Years in Primary Care: Population-Based Cross-sectional Survey

Background The World Health Organization has defined mobile health (mHealth) as the “use of mobile and wireless technologies to support the achievement of health objectives. ” Smartphones currently represent one of the main media forms for mHealth democratization. Health apps can be an interesting tool for changing health behaviors. However, their use in France is still poorly documented. Objective The main aim of this study was to evaluate the frequency of use of health apps among patients consulting in the primary care setting in France. The secondary aims were to evaluate the use of health apps according to the sociodemographic and medical characteristics of patients and to determine their use. Methods A population-based cross-sectional survey was carried out between November 2017 and January 2018 in the Grenoble area of France among patients aged between 18 and 69 years who were consulting at 13 primary care physician offices. Patients were provided with anonymous paper self-questionnaires. The main criterion for participation was the use of a smartphone health app, defined for the purpose of this study as any app supporting patients in efforts to be healthy. Results The participation rate was 49.27% (739/1500; 95% CI 46.7%-51.8%). The smartphone use was estimated at 82.6% (597/723; 95% CI 79.6%-85.2%). Of 597 smartphone owners, 47.7% (283/595; CI 43.6%-51.6%) used at least one smartphone health app. Health apps identified in this study were mainly related to wellness, prevention, and fitness (66.1%), as well as medication, treatments, and follow-up care (50.0%). The main factors associated with health app use were: use of social networks (odds ratio [OR] 3.4, 95% CI 2.1-5.3), age under 30 years (OR 2.7, CI 1.4-4.9), city size between 5001 and 10,000 inhabitants (OR 1.8, CI 1.1-2.8), and city size more than 10,000 inhabitants (OR 2.1, CI 1.4-3.2). Conclusions In this survey, nearly one out of two patients reported the use of smartphone health apps, which are currently focused on wellness, prevention, and fitness, and are largely used by the younger population. Trial Registration ClinicalTrials.gov NCT03351491; https://clinicaltrials.gov/ct2/show/NCT03351491
Using the Length and Weight of the Body and Some Dynamic Parameters to Perform Ura Mawshi Geri Skill to Predict Kumite Players' Performance

This study aimed to identify both: 1. The kind of correlative relationship among the points of performing Ura Mawshi Geri (U. M. G.) skill and the length and weight of the players' bodies and some dynamic parameters to perform (U.M.G.) skill 2. Predictive equation is used to predict the value of performing UMG skill points with the significance of the body's length and weight and some dynamic parameters to perform it. Research sample included 8 karate players in Port Said Zone under 18 . A 3-D digital camera was used to th videotape the players. The researcher used two video cameras with speed of 50 frame /sec. He also used the motor analysis and Win analysis program at Motor Analysis Unit, Faculty of Physical Education, PortSaid University. Restmeter to measure the length of player and medical scales for the weight were used as well as (SPSS) program to treat data statistically. Most important findings revealed that there was an direc t proportionateness between the rulers assessment points for both performing of (U.M.G.) (JP.) skill and the vertical forces in both preliminary and final preparation phase (Fy , Fy ). There was also contrary 1 3 proportionateness between JP and sagital velocities within preliminary phase (Vz ) in addition to the variation 1 of parameters' participation percentage under discussion in affecting (JP). The predictive equation of (JP) by function of contributed parameters is: (JP (= 6.1827 - 0.0440 (Vz ) + 0.0187 (Vy ) - 0.02620 (Vx ) - 0.00020 (Fx )
IRS Empowered UAV Wireless Communication with Resource Allocation, Reflecting Design and Trajectory Optimization

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.
Family language policy and bilingual parenting in monolingual Beijing: latent profiles and associated predictors

ABSTRACT This study explored the family language policy, parental beliefs, and bilingual parenting in Beijing, a typical monolingual society. Stratified random sampling was conducted to recruit 192 parents of Chinese preschoolers (ages 2.5, 3.5, 4.5, and 5.5 years). They were surveyed with the Home Language Environment and Practices (HLEP) developed for this study. The statistical results indicated that: (1) the majority of Beijing families (90.1%) did not have bilingual home environment or related family language policy, only 8.9% did so; (2) three latent classes of bilingual parenting emerged: ‘No Explicit Policy’ (lower parental education level, no extra-curriculum activities, no bilingual environment, no Chinese-English bilingual, and few books at home; 64.6%); ‘Monolingual Parenting’ (relatively higher parental education level, more extra-curriculum activities, the highest number of Chinese and English books at home, the longest time for Chinese storytelling, and an emphasis on Chinese literacy and language learning; 26.6%); and ‘Bilingual Parenting’ (the highest parental education level, Chinese-English bilingual parents, bilingual home environment, and relatively more Chinese and English books at home); and (3) parental education level, bilingual father, bilingual mother, and number of English books at home predicted the profile of bilingual parenting, whereas parental beliefs about early bilingualism did not.
A review and comparison of convolution neural network models under a unified framework

There has been active research in image classiﬁcation using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of e ﬃ cient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a uniﬁed framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with di ﬀ erent characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.