Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Feasibility of a Conductive Textile Structure to Indicate Pre-tensioning of Seat-belts

Child safety restraints and seat belts have been legally mandated in many countries as they reduce the risk of death or serious injury in a vehicle crash. However, recent studies demonstrate that pretightening or over-tightening of the safety restraints results in major physiological discomfort and severe injuries to the wearer. This occurs due to improper positioning of the belt as well as uneven load distribution, and a toddler or child may not be able to acknowledge this fact, thus highlighting the contemporary necessity for a system to indicate such a phenomenon. Therefore, the feasibility of a textile based system that incorporates the principle of a strain indicator, where the imposed strain on the safety harness would be measured relative to the surface resistivity of the developed conductive fabric has been investigated in this research. Cotton fabric was polymerized in situ with Polyaniline, where the aniline/HCl solution was polymerized by drop casting an ammonium persulphate/HCl solution to impart conductivity to the textile structure, by grafting conductive nano fibers on the surface of the cotton. An Arduino Uno interface was used to plot the variations of the voltage level in response to the strain imposed at a baud rate of 9600, with an LED indicator triggered at variations of the pre calibrated base voltage level. The study concluded that a 16.85% variation of the surface conductivity was achieved per unit elongation of the fabric structure, which is sufficient to establish a safety interval, for the tensile strain of the seat-belt.
An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet

Tomato maturity classification is the process that classifies the tomatoes based on their maturity by its life cycle. It is green in color when it starts to grow; at its pre-ripening stage, it is Yellow, and when it is ripened, its color is Red. Thus, a tomato maturity classification task can be performed based on the color of tomatoes. Conventional skill-based methods cannot fulfill modern manufacturing management's precise selection criteria in the agriculture sector since they are time-consuming and have poor accuracy. The automatic feature extraction behavior of deep learning networks is most efficient in image classification and recognition tasks. Hence, this paper outlines an automated grading system for tomato maturity classification in terms of colors (Red, Green, Yellow) using the pre-trained network, namely 'AlexNet,' based on Transfer Learning. This study aims to formulate a low-cost solution with the best performance and accuracy for Tomato Maturity Grading. The results are gathered in terms of Accuracy, Loss curves, and confusion matrix. The results showed that the proposed model outperforms the other deep learning and the machine learning (ML) techniques used by researchers for tomato classification tasks in the last few years, obtaining 100% accuracy.
Class Imbalanced Fault Diagnosis via Combining K-Means Clustering Algorithm with Generative Adversarial Networks

Recent advancements in machine learning and communication technologies have enabled new approaches to automated fault diagnosis and detection in industrial systems. Given wide variation in occurrence frequencies of different classes of faults, the class distribution of real-world industrial fault data is usually imbalanced. However, most prior machine learning-based classification methods do not take this imbalance into consideration, and thus tend to be biased toward recognizing the majority classes and result in poor accuracy for minority ones. To solve such problems, we propose a k-means clustering generative adversarial network (KM-GAN)-based fault diagnosis approach able to reduce imbalance in fault data and improve diagnostic accuracy for minority classes. First, we design a new k-means clustering algorithm and GAN-based oversampling method to generate diverse minority-class samples obeying the similar distribution to the original minority data. The k-means clustering algorithm is adopted to divide minority-class samples into k clusters, while a GAN is applied to learn the data distribution of the resulting clusters and generate a given number of minority-class samples as a supplement to the original dataset. Then, we construct a deep neural network (DNN) and deep belief network (DBN)-based heterogeneous ensemble model as a fault classifier to improve generalization, in which DNN and DBN models are trained separately on the resulting dataset, and then the outputs from both are averaged as the final diagnostic result. A series of comparative experiments are conducted to verify the effectiveness of our proposed method, and the experimental results show that our method can improve diagnostic accuracy for minority-class samples.
Globular weak $\omega$-categories as models of a type theory

We study the dependent type theory CaTT, introduced by Finster and Mimram, which presents the theory of weak $\omega$-categories, following the idea that type theories can be considered as presentations of generalized algebraic theories. Our main contribution is a formal proof that the models of this type theory correspond precisely to weak $\omega$-categories, as defined by Maltsiniotis, by generalizing a definition proposed by Grothendieck for weak $\omega$-groupoids: Those are defined as suitable presheaves over a cat-coherator, which is a category encoding structure expected to be found in an $\omega$-category. This comparison is established by proving the initiality conjecture for the type theory CaTT, in a way which suggests the possible generalization to a nerve theorem for a certain class of dependent type theories
Zinc oxide incorporated water-in-diesel emulsion fuel: Formulation, particle size measurement, and emission characteristics assessment

ABSTRACT The use of W/D emulsion fuel has a positive influence on the performance and emission characteristics of diesel engines, especially on NOx and PM emissions. The prolonged ignition delay of W/D emulsion fuel resulting in rough engine operation is a critical factor that has to be focused on in emulsion fuel studies. ZnO, a metal-based nanoparticle that can act as a catalyst for chemical reactions during combustion process, is adopted in the present study in an attempt to shorten the ignition delay. The problems associated with nanofluid synthesis are agglomeration of nanoparticles and measurement of particle size in base fluid. The present study focuses on the previously-mentioned issues with experiments conducted in two phases. In the first phase, ZnO nanoparticles with mass fraction of 50 ppm and 100 ppm are incorporated in 10W/D emulsion fuel and stabilized using suitable surfactant and its physiochemical properties measured. To measure the ZnO particle size in emulsion fuel, milky form of ZnO incorporated in W/D emulsion fuel is turned into transparent form using DME as a solvent and UV-visible absorption spectroscopy analysis is performed. From the results obtained, it is noted that the properties of 10W/D emulsion fuel meet the standard diesel fuel properties requirement and the incorporation of nanoparticles change the fuel properties marginally. In addition, ZnO particles size in emulsion fuel is confirmed to be at nanolevel. In the second phase, IDP and emission characteristics are studied for 50ZnO10W/D and 100ZnO10W/D, and compared with BD and 10W/D. It is observed that ZnO incorporated W/D emulsion fuel blends have a shorter IDP and lesser emission levels over BD and 10W/D.
Surface control vs. diffusion control during calcite dissolution: Dependence of step-edge velocity upon solution pH

The reaction of the (1014) calcite surface during dissolution was studied as a function of pH (HCl) by liquid-cell Atomic Force Microscopy (AFM) under ambient conditions of both temperature (22 °C) and partial pressure of carbon dioxide ( P CO2). A flow through AFM liquid reaction cell was used, and solutions were renewed at a controlled flow rate and sampled for chemical analysis. In the pH range 7.5–4.3, surface dissolution proceeds via the formation of etch pits delimited by steps of height 3.3 ± 0.3 A or a multiple of this elementary distance. Rhombohedral pits form and grow at the surface by anisotropic step retreat, at a velocity of 4.2 ± 0.2 nm/s for the steps oriented along the two equivalent directions [4441]+ and [481]+, and 1.1 ± 0.2 nm/s for the steps oriented along the other two equivalent directions [4441]− and [481]−. At the pH value of 2.7, the slower rate slightly increases and is equal to 1.6 ± 0.2 nm/s. At the pH value of 1.7, microtopography measurements indicate that micrometric steps have a meandering shape, and move as a family at a velocity higher than 10 nm/s. These meandering steps are made of nanometric segments oriented along [441]+, [481]+, [4441] −, and [481]−, and they terminate their run against sloped rough etch pits with sides made of narrow terraces delimited by steps oriented along the [481]+. Finally, the present study indicates that reactive area (i.e., the total amount of reactive atomic sites) of a calcite surface increases in response to higher solution acidity firstly by an increase in the step density, and secondly by production of additional roughness at step edges. The change in step edge-velocity observed below pH = 2.7 was interpreted as the response of the dissolving surface to the loss of buffering capacity at the Stern layer.
walk-like search patterns derived from differences in prey

Shuji Shinohara, Hiroshi Okamoto, Nobuhito Manome, Pegio-Yukio Gunji, Yoshihiro Nakajima, Toru 5 Moriyama, Ung-il Chung 6 7 1 Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan 8 2 Department of Research and Development, SoftBank Robotics Group Corp., Tokyo, Japan 9 3 Department of Intermedia Art and Science, School of Fundamental Science and Technology, Waseda 10 University, Tokyo, Japan 11 4 Graduate School of Economics, Osaka City University, Osaka, Japan 12 5 Faculty of Textile Science, Shinshu University, Ueda, Japan 13 14 * Corresponding author 15 E-mail: shinohara@bioeng.t. u-tokyo.ac. jp 16 17
Examination of Affiliate Stigma, Hopelessness and Life Satisfaction in Parents of Children with Intellectual Disabilty

Introduction. While expecting a healthy child, having a child with a disability can be quite traumatic for all family members, especially parents. The objective of the research was to evaluate affiliate stigma, hopelessness, and life satisfaction in parents of children with intellectual disability. Materials and Methods. The descriptive research included 203 parents of children with intellectual disabilities, who met the research inclusion criteria and agreed to participate in the study. The data were collected using the Personal Information Form, the Affiliate Stigma Scale, the Beck Hopelessness Scale, and the Satisfaction with Life Scale. Results. The mean age of parents included in the study was 40.74 ± 7.22 years, with 67.5% of caregiving mothers. The levels of affiliate stigma and hopelessness among parents were low, and they were satisfied with their lives. In case of increasing parental affiliate stigma, their hopelessness was found to increase, while life satisfaction was found to decrease. Conclusions. In order to increase public acceptance of people with disabilities by society, it is recommended to provide trainings and raise awareness against individuals with disabilities and their families.
CEOR_A_298992 335..342

Genentech, Inc, South San Francisco, CA, USA Purpose: To assess the burden of influenza transmission and care-seeking patterns over 3 influenza seasons among commercially insured households with a primary influenza infection. Patients and Methods: This retrospective cohort study used commercial claims data from the US MarketScan Commercial and Medicare Supplemental databases for the 2014, 2015, and 2016 influenza seasons. Patients with a billed diagnosis of influenza and with coverage for at least 1 household member under the same health plan policy were included. A secondary diagnosed case was defined as a diagnosis of influenza in a second household member occurring within 14 days of the index case in a household. Results: Among 1,224,808 households with ≥2 members and a primary case of influenza, a secondary case of influenza was reported in 119,883 households (9.8%). A secondary diagnosed case of influenza occurred within 4 days of the primary diagnosis in 93,883 (78.3%) of those households. Both primary and secondary diagnosed influenza cases occurred most often among children (~60%). Household size was positively correlated to both the risk of a second case (6.4% of households with 2 or 3 members versus 12.6% of households with ≥4 members, P < 0.001) and the time to diagnosis of a second case (Spearman rank correlation coefficient = 0.09; P < 0.001). Conclusion: Claims data for 3 influenza seasons (2014, 2015, 2016) showed that intrahousehold transmission of influenza occurs in approximately 10% of households with a primary case and poses a higher burden on larger households. Intrahousehold transmission of influenza represents a large healthcare resource use burden, with an unmet need for interventions that limit transmission.
Use of Intravenous Ferric Carboxymaltose: A Revolutionary Approach for Iron Deficiency Anaemia in Antenatal Women

the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/bync/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Use of Intravenous Ferric Carboxymaltose: A Revolutionary Approach for Iron Deficiency Anaemia in Antenatal Women By Dr. Renu Gupta, Dr. Pavika Lal, Dr. Shaily Agarwal & Dr. Anita Gond
Determining The Factors Of Consumer’s Attitudes And Its Influence On Continuous Usage Of Mobile Shopping Applications

– This study investigated the consumer’s attitude factors that influence continuous usage of mobile shopping applications in Malaysia. The study used self-administrated questionnaire to collect data from 384 respondents using online form who had previous experience on using mobile shopping applications to purchase products. The findings show that privacy, security, perceived usefulness, perceived ease of use and electronic words of mouth are positively related with the consumer’s attitudes to continuously use the mobile shopping application. This study enhances the researcher’s understanding on how the perceived usefulness, privacy and security, electronic words of mouth and perceived ease of use in influencing the consumer’s attitudes to continuously use the mobile shopping applications. This study extends the application of expectation-confirmation model (ECM) and technology acceptance model (TAM) which can provides the theoretical understanding on the various factors that influencing consumer’s attitudes to continuously use the mobile shopping applications. The managers should focus on those factors in order to encourage more people for using the mobile shopping application to purchase the products.
Relationships Among Athletic Identity, Mental Health and Social Support in Japanese Student-Athletes During the COVID-19 Pandemic

The purpose of this study was to investigate the relationships among student athlete’s identity and mental health during the COVID-19 pandemic. In addition, this study aimed to clarify the relationship between perceived social support from teammates and mental health in student-athletes. Two studies were conducted to investigate to clarify the mental health states of student-athletes in Japan during the COVID-19 pandemic. In Study 1 conducted in April 2020, the participants were 402 male student-athletes, and examined the relationships among student-athlete’s identity and mental health. The results of correlational analyses indicated that there were significant negative correlations between degree of student-athlete’s identity and depression and sports helplessness. In Study 2 conducted in March 2021, the participants were 136 male student-athletes, and examined the relationship between perceived social support from teammates, student-athlete’s identity and mental health. The results indicated that there was significant correlation among social support, student athlete’s identity and mental health. These results suggested that mental health may be improved if student-athletes are strongly aware of their social identity, which is their social role, when unforeseen events such as the COVID-19 pandemic occur. In addition, social support provided by significant others such as teammates may contribute to the improvement of mental health.
Phase transitions in PbZr0.72Sn0.28O3 single crystals studied by Raman spectroscopy

ABSTRACT Raman light scattering measurements in the temperature range from 80 to 830 K were performed on a PbZr0.72Sn0. 28O3 single crystal. The frequencies of the Raman lines were analyzed and discussed in terms of the sequence of structural phase transitions. It was found that Raman spectrum displays important changes near 440, 480 and 493 K. The incorporation of more than 25 mol% of Sn4+ ions into the structure of PbZrO3 enhances polar fluctuations above TC as compared to the less Sn-doped crystals. These fluctuations lead to appearance of a ferroelastic intermediate phase below TC. It is demonstrated that the structural phase transformation in PbZr0.72Sn0. 28O3 can be considered as the result of softening of a number of modes.
The Effects Of Smoking On The Relationship Between Estimated Glomerular Filtration Rate And The Severity Of Coronary Artery Disease Based On Syntax Score

Background: Chronic kidney disease (CKD) is an independent risk factor and caused high morbidity and mortality for coronary artery disease (CAD). Several scoring systems can be used in assessing the severity of CAD, one of which is using the SYNTAX score (SXscore). Smoking is one of the risk factors for the progression of CAD and CKD. Several observational studies have under-reported the smoking factor influencing the relationship between eGFR and SXscore. Methods: This research is an observational study with a cross-sectional approach conducted at the Wahidin Sudirohusodo Hospital Makassar from July-August 2020. This study involved 62 CAD and CKD patients undergoing angiography. The estimated glomerular filtration rate was based on chronic kidney disease epidemiology collaboration (CKD-EPI). European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 07, Issue 03, 202
Searching for a Better Policy Architecture for the OpenAI Bipedal Walker Environment

Deep Learning yielded multiple neural architectures effectively targeting the speciﬁcs of input data in different domains and achieved break-through results in the past decade. Deep Rein-forcement Learning brought these successes to the Reinforcement Learning setting and raised big hopes to solve previously unreachable problems, many of which are coming from the ﬁeld of robotics. An architecture specialized to the properties of input data from robotic systems indeed was not developed so far. In this work, we investigate the question if sparse architectures are better suited for robot input data compared to the default choice of a fully connected Multilayer Perceptron (MLP). We ﬁnd hints of an optimal sparsity in an MLP and reduce the size of the policy network by 85% retaining the desired performance.
Attitudes, cognition, and functional disability in individuals with self-harm and psychiatric disorders

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Management of gram pod borer Helicoverpa armigera (Hubner) in chickpea with bio-pesticides and combination

An experiment was laid during Rabi 2019 at central research field, SHUATS, Prayagraj, U.P. Biopesticides and combination were used with seven treatments and three replications. Experiment was carried out in Randomized Block Design. The results obtained based on pest population, grain yield and B: C ratio are as follows, Emamectin benzoate (220gm/ha) + Indoxacarb (30gm/ha) (2.19) and (1932.67kg/ha) was most effective treatment followed by Indoxacarb (60gm/ha) (3.3) (1880.33kg/ha), NSKE (25kg/ha) + Indoxacarb (30gm/ha) (3.6) (1584.33kg/ha), Neem oil (5ml) + Indoxacarb (30gm/ha) (3.9) (1435.00kg/ha), Beauveria bassiana (400gm/ha) + Indoxacarb (30gm/ha) (4.4) (1332.00kg/ha) and Bacillus thuringiensis (750-1000gm/ha) + Indoxacarb (30gm/ha) (5.2) (1224.33kg/ha). The highest cost benefit ratio was obtained from Emamectin benzoate + Indoxacarb (1:2.70) ˃ Indoxacarb (1:2.65) ˃ NSKE + Indoxacarb (1:2.23) ˃ Neem oil + Indoxacarb (1:2.03) ˃ Beauveria bassiana + Indoxacarb (1:1.84) ˃ Bacillus thuringiensis + Indoxacarb (1:1.64) ˃ control (1:1.21).
“Not Like Other Girls”: Implicit and Explicit Dimensions of Internalized Sexism and Behavioral Outcomes

Unlike other forms of internalized oppression, internalized sexism, or women’s bias against other women, has been rarely studied in psychology. In this study, we aimed to determine whether implicit and explicit internalized sexism would predict internally sexist comments made in focus groups. Driven primarily by System Justification Theory, we hypothesized that implicit internalized sexism would predict participants’ proportions of internalized-sexism coded comments even above and beyond the effect of explicit internalized sexism. We also hypothesized that internalized sexism would be negatively associated with self-esteem. Participants completed measures of implicit and explicit internalized sexism as well as measures of implicit and explicit self-esteem. Participants then discussed clips from the reality television show The Bachelor in small focus groups. The data did not support hypothesized associations between either implicit or explicit internalized sexism and internalized sexism-coded comments. Additionally, we did not find significant evidence of associations between internalized sexism and self-esteem. While the data did not support our hypotheses, they did prompt compelling questions to be investigated by future research. Guided by the limitations of this study and by the qualitative data, we suggest several potential directions for future studies in this area.