Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Multilevel Inverter Topologies

In this paper, a brief review of the multilevel inverter (MLI) topologies is presented. The two-level Voltage Source Inverter (VSI) requires a suitable filter to produce sinusoidal output waveforms. The high-frequency switching and the PWM method are used to create output waveforms with the least amount of ripples. Due to the switching losses, the traditional two-level inverter has some restrictions when running at high frequencies. For addressing this problem, multilevel inverters (MLI) with lower switching frequencies and reduced total harmonic distortion (THD) are employed, eliminating the requirement for filters and bulky transformers. Furthermore, improved performance at the high switching frequency, higher power quality (near to pure sinusoidal), and fewer switching losses are just a few of the benefits of MLI inverters. However, each switch has to have its own gate driver for implementing MLI, which adds to the system's complexity. Therefore, reducing the number of switches of MLI is necessary. This paper presents a review of some of the different current topologies using a lower number of switches. Doi: 10.28991/ESJ-2022-06-01-014 Full Text: PDF
A Survey of Real-Time Ethernet Modeling and Design Methodologies: From AVB to TSN

With the development of real-time critical systems, the ever-increasing communication data traffic puts forward high-bandwidth and low-delay requirements for communication networks. Therefore, various real-time Ethernet protocols have been proposed, but these protocols are not compatible with each other. The IEEE 802.1 Working Group developed standardized protocols named Audio Video Bridging (AVB) in 2005, and renamed it Time-Sensitive Networking (TSN) later. TSN not only adds new features but also retains the original functions of AVB. Proposing real-time Ethernet modeling and design methodologies is the key to meeting high-bandwidth and low-delay communication requirements. This article surveys the modeling from AVB to TSN, mainly including: (1) AVB and TSN modeling; (2) end-to-end delay modeling; (3) real-time scheduling modeling; (4) reliability modeling; and (5) security modeling. Based on these models, this article surveys the recent advances in real-time Ethernet design methodologies from AVB to TSN: (1) end-to-end delay analysis from AVB to TSN; (2) real-time scheduling from AVB to TSN; (3) reliability-aware design for TSN; and (4) security-aware design for TSN. Among the above four points, the last two points are only for TSN, because AVB lacks reliability and security mechanisms. This article further takes the automotive use case as an example to discuss the application of TSN in automobiles. Finally, this article discusses the future trends of TSN. By surveying the recent advances and future trends, we hope to provide references for researchers interested in real-time Ethernet modeling and design methodologies for AVB and TSN.
Association between health literacy and multimorbidity: a nationwide, cross-sectional study of a Japanese population

Objectives To examine the relationship between health literacy and multimorbidity. Design Nationwide cross-sectional study. Setting Community settings across Japan. Participants Community-dwelling participants aged 20 years or older were selected based on a quota sampling method that adjusted for age, sex and residential area. In total, 3678 participants from the Health Diary Study, with a mean age of 52.3 years (SD, 18.2 years; 1943 (52.8%) female participants), were included. Primary outcome measure Multimorbidity, the primary outcome measure, was defined as the presence of two or more chronic diseases. Results Of the 3678 participants, 824 (22.4%) had multimorbidity. The mean functional health literacy (FHL) and communicative and critical health literacy (CCHL) scores were 3.2 (SD, 0.7) and 3.6 (SD, 0.9), respectively. In the univariable analysis, both scores were associated with multimorbidity (p<0.001). However, in the multivariable modified Poisson regression analysis, only the FHL score was significantly associated with multimorbidity (per 1-point increase, 0.91; 95% CI 0.84 to 0.99). Conclusions After adjusting for confounding variables, FHL, not CCHL, was significantly related to the presence of multimorbidity. Further longitudinal studies are required to examine the causal relationship between health literacy and multimorbidity.
Primary Factors Influencing the Decision to Vaccinate against COVID-19 in the United States: A Pre-Vaccine Analysis

Because vaccine hesitancy is a dynamic trait, it is critical to identify and compare the contributing factors at the different stages of a pandemic. The prediction of vaccine decision making and the interpretation of the analytical relationships among variables that encompass public perceptions and attitudes towards the COVID-19 pandemic have been extensively limited to the studies conducted after the administration of the first FDA-approved vaccine in December of 2020. In order to fill the gap in the literature, we used six predictive models and identified the most important factors, via Gini importance measures, that contribute to the prediction of COVID-19 vaccine acceptors and refusers using a nationwide survey that was administered in November 2020, before the widespread use of COVID-19 vaccines. Concerns about (re)contracting COVID-19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision making. By investigating the vaccine acceptors and refusers before the introduction of COVID-19 vaccines, we can help public health officials design and deliver individually tailored and dynamic vaccination programs that can increase the overall vaccine uptake.
Physics-Based Forward Modeling of Ocean Surface Swell Effects on SMAP L1-C NRCS Observations

This paper examines the impact of ocean surface swell waves on near-coastal L-band high-resolution synthetic aperture radar (SAR) data collected using the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active/Passive (SMAP) radar at 40° incidence angle. The two-scale model and a more efficient off-nadir approximation of the second-order small-slope-approximation are used for co- and cross-polarized backscatter normalized radar cross-section (NRCS) predictions of the ocean surface, respectively. Backscatter NRCS predictions are modeled using a combined wind and swell model where wind-driven surface roughness is characterized using the Durden–Vesecky directional spectrum, while swell effects are represented through their contribution to the long wave slope variance (mean-square slopes, or MSS). The swell-only MSS is numerically computed based on a model defined using the JONSWAP spectrum with parameters calculated using the National Data Buoy Center and Wave Watch III data. The backscatter NRCS model is further refined to include fetch-limited and low-wind corrections. The results show an improved agreement between modeled and observed HH-polarized backscatter NRCS when swell effects are included and indicate a relatively larger swell impact on L-band compared to higher radar frequencies. Preliminary investigations into the potential swell retrieval capabilities in the form of excess MSS are encouraging, however further refinements are required to make broadly applicable conclusions.
ComIM: A community-based algorithm for influence maximization under the weighted cascade model on social networks

Influence maximization (IM) is a problem of selecting k nodes from social networks to make the expected number of the active node maximum. Recently, with the popularity of Internet technology, more and more researchers have paid attention to this problem. However, the existing influence maximization algorithms with high accuracy are usually difficult to be applied to the large-scale social network. To solve this problem the paper proposes a new algorithm, called community-based influence maximization (ComIM). Its core idea is “divide and conquer”. In detail, this algorithm first utilizes the Louvain algorithm to divide the large-scale networks into some small-scale networks. Afterwards, the algorithm utilizes the one-hop diffusion value (ODV) and two-hop diffusion value (TDV) functions to calculate the influence of a node and select nodes on these small-scale networks, which can improve the accuracy of our proposed algorithm. By using the above methods, the paper proposes a community influence-estimating method called CDV, which can improve the efficiency of the algorithm. Experimental results on six real-world datasets demonstrate that our proposed algorithm outperforms all comparison algorithms when comprehensively considering the accuracy and efficiency.
The Willingness to Pay for Telemedicine Among Patients With Chronic Diseases: Systematic Review

Background Telemedicine is increasingly being leveraged, as the need for remote access to health care has been driven by the rising chronic disease incidence and the COVID-19 pandemic. It is also important to understand patients’ willingness to pay (WTP) for telemedicine and the factors contributing toward it, as this knowledge may inform health policy planning processes, such as resource allocation or the development of a pricing strategy for telemedicine services. Currently, most of the published literature is focused on cost-effectiveness analysis findings, which guide health care financing from the health system’s perspective. However, there is limited exploration of the WTP from a patient’s perspective, despite it being pertinent to the sustainability of telemedicine interventions. Objective To address this gap in research, this study aims to conduct a systematic review to describe the WTP for telemedicine interventions and to identify the factors influencing WTP among patients with chronic diseases in high-income settings. Methods We systematically searched 4 databases (PubMed, PsycINFO, Embase, and EconLit). A total of 2 authors were involved in the appraisal. Studies were included if they reported the WTP amounts or identified the factors associated with patients’ WTP, involved patients aged ≥18 years who were diagnosed with chronic diseases, and were from high-income settings. Results A total of 11 studies from 7 countries met this study’s inclusion criteria. The proportion of people willing to pay for telemedicine ranged from 19% to 70% across the studies, whereas the values for WTP amounts ranged from US $0.89 to US $821.25. We found a statistically significant correlation of age and distance to a preferred health facility with the WTP for telemedicine. Higher age was associated with a lower WTP, whereas longer travel distance was associated with a higher WTP. Conclusions On the basis of our findings, the following are recommendations that may enhance the WTP: exposure to the telemedicine intervention before assessing the WTP, the lowering of telemedicine costs, and the provision of patient education to raise awareness on telemedicine’s benefits and address patients’ concerns. In addition, we recommend that future research be directed at standardizing the reporting of WTP studies with the adoption of a common metric for WTP amounts, which may facilitate the generalization of findings and effect estimates.
Orchestration in Fog Computing: A Comprehensive Survey

Fog computing is a paradigm that brings computational resources and services to the network edge in the vicinity of user devices, lowering latency and connecting with cloud computing resources. Unlike cloud computing, fog resources are based on constrained and heterogeneous nodes whose connectivity can be unstable. In this complex scenario, there is a need to define and implement orchestration processes to ensure that applications and services can be provided, considering the settled agreements. Although some publications have dealt with orchestration in fog computing, there are still some diverse definitions and functional intersection with other areas, such as resource management and monitoring. This article presents a systematic review of the literature with focus on orchestration in fog computing. A generic architecture of fog orchestration is presented, created from the consolidation of the analyzed proposals, bringing to light the essential functionalities addressed in the literature. This work also highlights the main challenges and open research questions.
Impact of Social Media Application Qualities on Using Them for Daily News

A model is introduced to investigate the effect of social media application qualities on the using of these applications for daily news. A standard questionnaire was designed and distributed among randomly selected social media users in the city of Neyshabur in Iran. The content of the questionnaire was validated by experts and its reliability verified using Cronbach's alpha. Random sampling was used identify participants. The SmartPLS software was used to investigate the research findings. Structural equation modelling is used for data analysis. The results show that the factors of system quality, information quality, service quality and personalization of applications affect their perceived usefulness. System quality and service quality affect the perceived ease of use of applications. However, information quality does not affect perceived ease of use. Also, the results show that perceived usefulness has a greater effect (with a path coefficient of 45%) on the attitude in respect of perceived ease of use (path coefficient of 26%). Personalization has the most positive impact on perceived usefulness and service quality has a great impact on perceived ease of use. Facilitating conditions have a positive impact on the use of social media and their use for news. Furthermore, the results show that the factors that affect the use of applications in Telegram are better than those in Viber. These findings justify why Iranian people migrate from Viber to Telegram as their social media application.
Pulsed Arterial Spin Labeling and Segmented Brain Volumetry in the Diagnostic Evaluation of Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

Background: Previous studies suggest that brain atrophy can not only be defined by its morphological extent, but also by the cerebral blood flow (CBF) within a certain area of the brain, including white and gray matter. The aim of this study is to investigate known atrophy patterns in different forms of dementia and to compare segmented brain volumetrics and pulsed arterial spin labeling (pASL) data to explore the correlation between brain maps with atrophy and this non-contrast-enhanced brain-perfusion method. Methods: Our study comprised 17 patients with diagnosed cognitive impairment (five Alzheimer’s disease = AD, five frontotemporal dementia = FTD, seven mild cognitive impairment = MCI) and 19 healthy control subjects (CO). All patients and controls underwent 4D-pASL brain-perfusion MR imaging and T1w MPRAGE. The data were assessed regarding relative brain volume on the basis of 286 brain regions, and absolute and relative cerebral blood flow (CBF/rCBF) were derived from pASL data in the corresponding brain regions. Mini-Mental State Examination (MMSE) was performed to assess cognitive functions. Results: FTD patients demonstrated significant brain atrophy in 43 brain regions compared to CO. Patients with MCI showed significant brain atrophy in 18 brain regions compared to CO, whereas AD patients only showed six brain regions with significant brain atrophy compared to CO. There was good correlation of brain atrophy and pASL perfusion data in five brain regions of patients with diagnosed FTD, especially in the superior temporal gyrus (r = 0.900, p = 0.037), the inferior frontal white matter (pars orbitalis; r = 0.968, p = 0.007) and the thalami (r = 0.810, p = 0.015). Patients with MCI demonstrated a correlation in one brain region (left inferior fronto-occipital fasciculus; r = 0.786, p = 0.036), whereas patients with diagnosed AD revealed no correlation. Conclusions: pASL can detect affected brain regions in cognitive impairment and corresponds with brain atrophy, especially for patients suffering from FTD and MCI. However, there was no correlation of perfusion alterations and brain atrophy in AD. pASL perfusion might thus represent a promising tool for noninvasive brain-perfusion evaluation in specific dementia subtypes as a complimentary imaging-based bio marker in addition to brain volumetry.
Inter-Brain Synchrony Levels According to Task Execution Modes and Difficulty Levels: An fNIRS/GSR Study

Hyperscanning is a brain imaging technique that measures brain synchrony caused by social interactions. Recent research on hyperscanning has revealed substantial inter-brain synchrony (IBS), but little is known about the link between IBS and mental workload. To study this link, we conducted an experiment consisting of button-pressing tasks of three different difficulty levels for the cooperation and competition modes with 56 participants aged 23.7± 3.8 years (mean±standard deviation). We attempted to observe IBS using functional near-infrared spectroscopy (fNIRS) and galvanic skin response (GSR) to assess the activities of the human autonomic nervous system. We found that the IBS levels increased in a frequency band of 0.075–0.15 Hz, which was unrelated to the task repetition frequency in the cooperation mode according to the task difficulty level. Significant relative inter-brain synchrony (RIBS) increases were observed in three and 10 channels out of 15 for the hard tasks compared to the normal and easy tasks, respectively. We observed that the average GSR values increased with increasing task difficulty levels for the competition mode only. Thus, our results suggest that the IBS revealed by fNIRS and GSR is not related to the hemodynamic changes induced by mental workload, simple behavioral synchrony such as button-pressing timing, or autonomic nervous system activity. IBS is thus explicitly caused by social interactions such as cooperation.
The role of coaching identity and life purpose orientations in holistic athlete development

Life purpose plays an important role in the wellbeing of adults. The current study considered domain-general and domain-specific purpose of NCAA coaches to examine whether purpose orientation in life predicted coaching purpose related to holistic athlete development directly and indirectly through coaching identity. A cross-sectional design was used. Collegiate coaches (N = 380, 62.1% male) from NCAA Division I and III institutions completed questionnaires assessing purpose orientation (career-focused, others-growth, and self-growth), coaching identity (winning-centered, holistic development-centered), and coaching purpose related to athlete development (sport-specific competence, sport-general competence, confidence, connection, and character). Structural equation modeling indicated the following: Coaching purpose related to sport-specific competence was predicted directly and indirectly via winning-centered coaching identity by career-focused purpose orientation. The four dimensions of coaching purpose (sport-general competence, confidence, connection, and character) were predicted indirectly via development-centered coaching identity by others-growth purpose orientation and by career-focused purpose orientation. Others-growth purpose orientation emerged as a significant direct predictor of coaching purpose related to connection. Inconsistent mediation emerged between others-growth purpose orientation and coaching purpose related to confidence. Overall, the findings highlight that purpose orientation can predict coaching purpose and that coaching identity centered around athletes’ holistic development and growth plays a salient role in explaining these relationships.
AN AUTOMATED SIMULATION PILOT CAPABILITY TO SUPPORT ADVANCED AIR TRAFFIC CONTROLLER TRAINING

High Air Traffic Controller attrition and the transition to NextGen (Next Generation Air Transportation System) necessitate the use of innovative methods to improve the delivery and quality of ATC training while reducing costs. The MITRE Corporation’s Center for Advanced Aviation Systems Development (CAASD) and the Federal Aviation Administration (FAA) have been conducting research over the past several years to determine where improvements can be made in the Air Traffic Control (ATC) training process. To support this research, CAASD developed a high-fidelity ATC training prototype (known as the enrouteTrainer) and conducted training on Developmental Controllers at the FAA’s Indianapolis Air Route Traffic Control Center (ARTCC). Initial assessments confirm the anticipated benefits. The ATC training prototype includes an autonomous simulation-pilot capability that uses automated speech recognition and speech synthesis to mimic the role of human sim-pilots in traditional ATC training systems. CAASD defined performance measures for evaluating the realism of the autonomous simulation-pilot, with respect to speech recognition accuracy and appropriate simulated pilot behavior. During the field evaluations, several iterations of evaluation and refinement were made to the speech recognition algorithms to improve speech recognition accuracy. This paper describes the speech recognition performance measures and presents results collected during field evaluations conducted at the Indianapolis ARTCC in June 2007.
Effects of biochar source, level of inclusion, and particle size on in vitro dry matter disappearance, total gas, and methane production and ruminal fermentation parameters in a barley silage-based diet

Abstract: This study evaluated the effects of biochar differing in source, inclusion level, and particle size on dry matter disappearance (DMD), total gas and methane (CH4) production, and ruminal fermentation in a barley silage-based diet. The seven biochar products used were coconut (CP001 and CP014) or pine (CP002, CP015, CP016, CP023, CP024)-based. Experiment 1 (Exp. 1) evaluated these biochars at 4.5%, 13.5%, and 22.5% level of diet inclusion, whereas Experiment 2 (Exp. 2) evaluated CP002, CP016, and CP023 at 2.25% and 4.50% of the diet at <0.5, 0.5–2.0, >2.0 mm particle size. Data were analyzed using PROC MIXED in SAS as a randomized complete block design, with biochar source, inclusion level, and particle size (Exp. 2 only) as fixed effects with run and replicate as random effects. Increasing level of biochar inclusion linearly (P < 0.01) decreased DMD in Exp. 1 and did not influence DMD (P > 0.05) in Exp. 2. Total gas, CH4 (mL·g−1 DMD), and ruminal fermentation parameters were not affected by product, inclusion level, or particle size (P > 0.05). In conclusion, biochar of varying source and particle size did not mitigate CH4 production, but reduced DMD at higher inclusion levels in the barley silage-based diet.
Seeing is Believing: The Impacts of Visual Exemplars on American Young Adults’ Reactions to Anti-Prescription Opioid Campaigns

Many public health campaigns are designed to combat the opioid crisis in America. However, these campaigns are often ineffective or even counterproductive. To explore what contributes to the (in)effectiveness of these campaigns, an online experiment was conducted among a convenience sample of college students to test American young adults’ reactions to four anti-prescription opioid campaign messages from local and national health organizations that vary in the presence of visual exemplars and statistical information. The study found that including a visual exemplar showing the negative consequences of misusing prescription opioids increased perceived susceptibility, psychological reactance, and perceived campaign effectiveness. The impacts of visual exemplars on psychological reactance and perceived campaign effectiveness were moderated by statistical information and (partially) explained by the believability of the campaigns.