Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct measurement of electron density in microdischarge at atmospheric pressure by Stark broadening

We present a method and results for measurement of electron density in atmospheric-pressure dielectric barrier discharge. The electron density of microdischarge in atmospheric pressure argon is measured by using the spectral line profile method. The asymmetrical deconvolution is used to obtain Stark broadening. The results show that the electron density in single filamentary microdischarge at atmospheric pressure argon is 3.05×1015cm−3 if the electron temperature is 10 000 K. The result is in good agreement with the simulation. The electron density in dielectric barrier discharge increases with the increase of applied voltage.
Synthesis of a new conjugated polymer for cell membrane imaging by using an intracellular targeting strategy

A water soluble polythiophene with tyrosine kinase inhibitor lapatinib as side chain moieties (PTL) was designed and synthesized. Together with its fluorescent characteristic and low cytotoxicity, PTL can be used for cell membrane imaging of living cells by targeting the intracellular domain of transmembrane proteins.
In-Wheel Traction Motor: a Case Study for Formula SAE Electric

This paper discusses in-wheel electric tractive systems, bringing as a case study the finite element design of a Fractional Slot Permanent Magnet Synchronous Machine with non-Overlaping Winding (FSPMSM) for an all-wheel drive Formula Student powertrain. The goal is to obtain a system that fits a 10” rim wheel, with a high power density that enables a 280 kg vehicle to accelerate over 75 meters in less than 4 seconds. Iterating the design method to maximize the output torque, the simulations indicate that a single 12,000 rpm machine can provide continuous 17.7 kW at the 600 Vdc system.
USING GOOGLE FORMS FOR ONLINE LISTENING TEST: DOES IT WORK?

The major objective of this study was to investigate the use of Google forms in giving listening comprehension test and to know the students’ responses toward online test by using Google forms. From this study, it is also hoped that it can provide information to English teachers or lecturers who teach listening subject about how to use Google form and how to upload an audio and video in this online platform. The methodological approach taken in this study was a descriptive qualitative method. Data for this study were collected using observation, students’ listening comprehension tests, and open-ended questionnaires. The participants of this study were 62 students in one English education program of one university in Bandar Lampung . In summary, the results in this study indicate that the students have positive responses toward the use of Google form in the listening comprehension test. It can be seen from their response and their answers to the questionnaire given, the result of observation of using Google forms , and the result of their listening comprehension test, the majority of the students got good scores. The average score of the test was 77,74. The median of the score was 80. Key words: Google form s , assessment, Listening Comprehension, students’ response .
Comparison of the Efficacy of Gabapentin Versus Levodopa-C for the Treatment of Restless Legs Syndrome in End-Stage Renal Disease on Hemodialysis Patients

Objective: We aimed to compare the efficacy of gabapentin and levodopa-c for the symptoms of restless leg syndrome in patients of end-stage renal disease (ESRD) undergoing maintenance hemodialysis therapy. Methods: In this observational, cross-sectional study, patients of ESRD on hemodialysis with restless leg syndrome were included after assessment of symptoms and quality of sleep before the treatment by completing two questionnaires: the International Restless Leg Syndrome Study Group (IRLSSG) questionnaire and the Pittsburgh Sleep Quality Index (PSQI) scale. They were randomly divided into two groups. One group was prescribed levodopa-c (110 mg) as a single dose two hours before bedtime for four weeks. The other group was given gabapentin (200 mg) after each hemodialysis session for four weeks. After the treatment, the patients completed the two questionnaires again: the IRLSSG questionnaire and the Pittsburgh Sleep Quality Index. Results: In our study, men were 14 (53.8%), and women were 12 (46.2%). Gabapentin was given to 14 (53.8%) patients, and 12 (46.2%) patients were prescribed levodopa-c. In the levodopa group, the average baseline IRLSS was 24.333 ± 7.936), and the mean baseline PSQI score was 13.583 ± 3.396. After treatment with levodopa for four weeks, the mean IRLSS was 8.666 ± 3.312, and the mean PSQI score was 4.666 ± 2.839; a P-value of 0.00001 was noted. While in the gabapentin group, the mean baseline IRLSS was 26.071 ± 7.936, and the mean baseline PSQI score was 14.857 ± 3.254. After treatment for four weeks with gabapentin, the mean IRLSS was 5.3571 ± 1.392, and the post-treatment average PSQI was 2.992 (SD: 0.916); a P-value of 0.00001 was noted. Conclusion: Both levodopa and gabapentin effectively relieve symptoms of restless leg syndrome and improve the quality of sleep and life in ESRD patients undergoing hemodialysis.
An unsupervised approach for traffic motion patterns extraction

Correspondence Asadollah Shahbahrami, Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran. Email: shahbahrami@guilan.ac. ir Abstract Automatic analysis, understanding typical activities, and identifying vehicle behaviour in crowded traffic scenes are fundamental and challenging tasks for traffic video surveillance. Some recent researches have been using machine learning approaches to extract meaningful patterns occurring in a traffic scene, for example, intersection. In this regard, we convert visual patterns and features to visual words using dense and sparse optical flow and learning traffic motion patterns with group sparse topical coding (GSTC) algorithm. In the first step of the proposed algorithm, the input traffic video is divided into non-overlapping clips. After that, motion vectors are extracted using dual TV-L1 as a dense optical flow and Lucas–Kanade as a sparse optical flow and converted to flow words. For learning traffic motion patterns, the GSTC algorithm, that is, a non-probabilistic topic model (TM) has been applied. These patterns represent priors on observable motion, which can be utilised to describe a scene and answer behaviour questions such as what are the motion patterns in a traffic scene and what is going on. The experimental results which have been obtained using a real dataset, QUML, show that the combination of the GSTC + dual TVL1 extracts more traffic motion patterns in comparison with the GSTC + Lucas–Kanade and previous studies.
Midwifery Education Institutions in Italy Creation and Validation of Clinical Preceptors’ Assessment Tool: Students’ and Expert Midwives’ Views

Background: The aim of the study is to create and validate a midwifery preceptor’s evaluation form to be used by midwifery students. The International Confederation of Midwives recommends that clinical placements need to be supervised by a preceptor in order to be efficient for students who, in this way, gain competence and proper practice within the midwifery practical area. Methods: This is an observational multi-center transversal study and leads to the validation of an evaluation questionnaire. Methodically, the following steps were followed: literature review, focus group with midwifery students, meeting between expert midwives, creation of the preceptor’s assessment form, filling in of the forms by midwifery students and expert midwives, and validation of the form. The study was carried out in eight Italian universities and included eighty-eight midwifery students and eight midwives. Results and Conclusion: A midwifery preceptor’s assessment questionnaire was created made up of four attribute areas which, as a total, included 33 items. Cronbach’s alpha score was calculated after examining the forms filled in by students and expert midwives. An alpha score of 0.97–0.85 was obtained. The result was Pearson Correlation Coefficient r = 0.78.
The role of self-control and sociosexual orientation in partner selection: A speed-dating study

Self-control is a crucial factor in maintaining an established romantic relationship, but its role in relationship formation is understudied. The current study tested whether trait self-control is related to a more selective approach toward romantic partners. Over 4 years, we organized 11 speed-date events at which a total of 342 single, heterosexual participants met potential partners. Our results indicated that there was no main effect of self-control on selectivity. However, there was an interaction between self-control and sociosexual orientation (SOI) in predicting selectivity. Specifically, self-control was positively related to selectivity for people with a restricted SOI (i.e., interested in a long-term, stable relationship). For people with an unrestricted SOI (i.e., interested in a short-term, sexual relationship), however, self-control was related to lower selectivity. Our findings point to the flexibility of self-control in facilitating goal progress, stimulating people to refrain from—or act on—their impulses, depending on their own personal mating goals.
Antistatic and Thermal Properties of Poly(Lactic Acid)/Polypropylene/Carbon Nanotube Nanocomposites

The aim of this study is to investigate the influence of carbon nanotubes (CNT) on the antistatic and thermal properties of poly(lactic acid)/polypropylene/carbon nanotubes (PLA/PP/CNT) nanocomposites. PLA/PP (blend ratio = 60:40) containing CNT (loading 1.0 to 2.5 phr) was melt-compounded followed by compression moulding. The antistatic properties of PLA/PP/CNT nanocomposites achieved at 2.5 phr CNT loading. Thermogravimetric analysis (TGA) results indicated that the thermal stability of PLA/PP/ CNT nanocomposite was higher than PLA/PP blend. Differential Scanning Calorimetry (DSC) results demonstrated that CNT reduced the cold crystallisation temperature of PLA, while increased the crystallisation temperature of PP, which evidenced the nucleatingability of CNT in the PLA/PP blends.
Far-red Fraction: An Improved Metric for Characterizing Phytochrome Effects on Morphology

Phytochrome, a well-studied photoreceptor in plants, primarily absorbs in the red (R) and far-red (FR) regions and is responsible for the perception of shade and subsequent morphological responses. Experiments performed in controlled environments have widely used the R:FR ratio to simulate the natural environment and used phytochrome photoequilibrium (PPE) to simulate the activity of phytochrome. We review why PPE may be an unreliable metric, including differences in weighting factors, multiple phytochromes, nonphotochemical reversions, intermediates, variations in the total pool of phytochrome, and screening by other pigments. We suggest that environmental signals based on R and FR photon fluxes are a better predictor of plant shape than the more complex PPE model. However, the R:FR ratio is nonintuitive and can approach infinity under electric lights, which makes it difficult to extrapolate from studies in controlled environments to the field. Here we describe an improved metric: the FR fraction (FR/R+FR) with a range from 0 to 1. This is a more intuitive metric both under electric lights and in the field compared with other ratios because it is positively correlated with phytochrome-mediated morphological responses. We demonstrate the reliability of this new metric by reanalyzing previously published data.
Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model

The scope of wildfires over the previous decade has brought these natural hazards to the forefront of risk management. Wildfires threaten human health, safety, and property, and there is a need for comprehensive and readily usable wildfire simulation platforms that can be applied effectively by wildfire experts to help preserve physical infrastructure, biodiversity, and landscape integrity. Evaluating such platforms is important, particularly in determining the platforms’ reliability in forecasting the spatiotemporal trajectories of wildfire events. This study evaluated the predictive performance of a wildfire simulation platform that implements a Monte Carlo-based wildfire model called WyoFire. WyoFire was used to predict the growth of 10 wildfires that occurred in Wyoming, USA, in 2017 and 2019. The predictive quality of this model was determined by comparing disagreement and agreement areas between the observed and simulated wildfire boundaries. Overestimation–underestimation was greatest in grassland fires (>32) and lowest in mixed-forest, woodland, and shrub-steppe fires (<−2.5). Spatial and statistical analyses of observed and predicted fire perimeters were conducted to measure the accuracy of the predicated outputs. The results indicate that simulations of wildfires that occurred in shrubland- and grassland-dominated environments had the tendency to over-predict, while simulations of fires that took place within forested and woodland-dominated environments displayed the tendency to under-predict.
LEED-EB Gold Projects for Office Spaces in Large Buildings Transitioning from Version 3 (v3) to 4 (v4): Similarities and Differences between Finland and Spain

This study aims to assess the similarities and differences between Finland and Spain in terms of Leadership in Energy and Environmental Design for Existing Buildings (LEED-EB) Gold large office building-type projects transitioning from version 3 (v3) to version 4 (v4). The percentages of the average scores are used here to assess the achievements of the LEED-EB data. The natural logarithm of the odds ratio lnθ and Fisher′s exact 2 × 2 tests with a mid p-value are used to evaluate dichotomous data, while the exact Wilcoxon–Mann–Whitney and Cliff′s δ effect size tests are used to evaluate ordinary data. The results for LEED-EB Gold large offices demonstrate similar certification strategies in Finland and Spain. These results may be useful to LEED-EB practitioners in Finland and Spain for facilitating the selection of appropriate certification strategies in line with identified high-performance credits for large offices.
Evaluating water scarcity in the Southern African Development Community (SADC) region by using a climate moisture index (CMI) indicator

Increasing water scarcity in the Southern African Development Community (SADC) region has underscored the need to improve our understanding of the management of water resources. Using total evapotranspiration (TET) and precipitation (P) data of the past 16 years this study used a modified version of the climate moisture index (CMI) in order to evaluate water scarcity throughout the SADC region, by examining the relative importance of P and TET on the variability of the CMI. The CMI value for the Democratic Republic of Congo (CMI DRC = 0.347) as well as for Angola (CMI Angola = 0.351), ranged between 0.25 and 1, characteristic of a humid region, whereas CMI values for Botswana (CMI Botswana = 0.027) and South Africa (CMI South Africa = 0.075) ranged between −0.6 and 0 or 0 and 0.25, characteristic of semi-arid to sub-humid regions. Namibia (CMI Namibia = −0.125) has been experiencing drier conditions. The findings of this linear correlation analysis confirm a strong and significant relationship between DRC-Angola ( r = 0.837), and a weak but significant relationship between Botswana–Namibia ( r = 0.554) and South Africa–Namibia ( r = 0.445) with regard to CMI, and suggest the possibility of transferring water from wetter to drier regions in the SADC study area.
Durvalumab for the treatment of PD-L1 non-small cell lung cancer

ABSTRACT Introduction: Immune checkpoint inhibitors, monoclonal antibodies directed against programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1), have broadened treatment options for patients with non-small cell lung cancer (NSCLC). Durvalumab is a selective, high-affinity, human IgG1 monoclonal anti-PD-L1 antibody that blocks interactions of PD-L1 with PD-1 and CD80. Areas covered: We reviewed clinical data supporting the use of durvalumab as a monotherapy and combination therapy for the treatment of locally advanced and advanced NSCLC. Expert commentary: Durvalumab as a monotherapy or combination therapy has shown well-tolerated safety profiles for NSCLC in several trials. Durvalumab monotherapy in advanced NSCLC patients with PD-L1 ≥ 25% as later line (ATLANTIC study) therapy led to clinically meaningful improvements compared to standard of care. Combination therapy comprising durvalumab plus tremelimumab for advanced NSCLC did not show clinical efficacy in three phase III trials. Durvalumab administered after chemoradiotherapy in stage III NSCLC (PACIFIC study) significantly improved progression-free survival and overall survival. This result has led to approval of durvalumab for patients with locally advanced NSCLC as the standard of care. Ongoing trials provide insight into how durvalumab fits into the rapidly evolving therapeutic landscape for locally advanced or advanced NSCLC.
A blockchain-driven cyber-credit evaluation approach for establishing reliable cooperation among unauthentic MSMEs in social manufacturing

PurposeSocial manufacturing has emerged. It aims to integrate the manufacturing resources of micro- and small-scale manufacturing enterprises (MSMEs) and help MSMEs cope with the dynamic, service-oriented and personalized market demands. In social manufacturing, MSMEs cooperate with each other through manufacturing resource sharing. However, because MSMEs are distributed and decentralized, the efficiency of establishing reliable cooperation between MSMEs is relatively low. Therefore, this paper presents a blockchain-driven cyber-credit evaluation system (BCCES) to implement distributed cyber-credit evaluation. BCCES can provide reliable cyber-credit for distributed MSMEs without the trusted third party. This can improve the efficiency of establishing reliable cooperation among unauthentic MSMEs.Design/methodology/approachThe paper proposes a BCCES to evaluate MSMEs' cyber-credit in decentralized environment. In BCCES, a cyber-credit evaluation model is proposed by improving set pair analysis (SPA) method, and cyber-credit smart contract and distributed consensus mechanism are designed according to the runtime logic of distributed cyber-credit evaluation.FindingsThe results confirmed that BCCES is feasible and effective to implement cyber-credit evaluation without the trusted third party. With the advantages of blockchain, BCCES can automatically realize cyber-credit evaluation through smart contract and distributed consensus. At the same time, BCCES can evaluate the real-time cyber-credit of MSMEs based on their latest service evaluation. In addition, we can design corresponding smart contracts according to actual requirements, which makes blockchain applicable to different distributed scenarios.Originality/valueThe paper combines blockchain and SPA to implement cyber-credit evaluation in social manufacturing and provides a new feasible idea for cyber-credit evaluation without the trusted third party. This can also provide MSMEs a reference of applying blockchain to other distributed scenarios through combining smart contract and different algorithms.
Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals

Background: Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered. Objective: This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively. Material and Methods: In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal–Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification. Results: The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8−12 Hz) - Beta1 (12 − 15 Hz) frequency band using GPDC method. Conclusion: This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.
Multi-scale promoting effects of lead for palladium catalyzed aerobic oxidative coupling of methylacrolein with methanol

A highly efficient Pd2Pb8/alumina catalyst was prepared, which provided the highest turnover number (TON) of 302 for aerobic oxidative coupling of methylacrolein with methanol. The enhanced catalytic efficiency could be attributed to the multi-scale (micron, nano and atom scales) promoting effects of the pre-loaded Pb species.
Tackling misinformation: What researchers could do with social media data

Written by Michelle A. Amazeen, Fabrício Benevenuto, Nadia M. Brashier, Robert M. Bond, Lia C. Bozarth, Ceren Budak, Ullrich K. H. Ecker, Lisa K. Fazio, Emilio Ferrara, Andrew J. Flanagin, Ales-sandro Flammini, Deen Freelon, Nir Grinberg, Ralph Hertwig, Kathleen Hall Jamieson, Kenneth Jo-seph, Jason J. Jones, R. Kelly Garrett, Daniel Kreiss, Shannon McGregor, Jasmine McNealy, Drew Margolin, Alice Marwick, FiIippo Menczer, Miriam J. Metzger, Seungahn Nah, Stephan Lewan-dowsky, Philipp Lorenz-Spreen, Pablo Ortellado, Irene Pasquetto, Gordon Pennycook, Ethan Porter, David G. Rand, Ronald Robertson, Briony Swire-Thompson, Francesca Tripodi, Soroush Vosoughi, Chris Vargo, Onur Varol, Brian E. Weeks, John Wihbey, Thomas J. Wood, & Kai-Cheng Yang
Named data networking with neural networks for intelligent image processing information systems

ABSTRACT Rapid retrieval of differentiated name data and efficient storage of information is essential in intelligent image processing information systems. In order to meet the requirements and challenges of rapid retrieval of differentiated name data and efficient storage of image information under different application scenarios of pending interest table (PIT) of named data network, a learning PIT overall scheme is proposed. This concept is known as the Learning Tree PIT (LT-PIT), which is based on a neural network framework. The index structure of LT-PIT, improves the image information storage efficiency by learning the distribution of index content in memory. The experimental results show that the comprehensive performance of LT-PIT is better than other schemes. When the number of names reaches 2 million, the index structure memory consumption of LT-PIT is 253.129 MB, including 53.129 MB of on-chip memory consumption, which can be deployed on high-speed memory SRAM. This technique is applicable to intelligent image processing information systems and can be used to process big data of images in the future.