Reviewerly/exHarmony huggingface dataset:
Feelings and perceptions of Unified Health System-dependent users cured of COVID-19

Due to the whole context of pandemic and its social impacts, a fragile situation in public health is faced, and may not only cause physical illness, but especially mental suffering, which has a high chance of occurring in the general population and persisting with post-pandemic psychological symptoms. The study aimed to understand feelings and perceptions experienced by dependent UHS users affected by Coronavirus, as well as the sequelae resulting from the process of illness and cure of the virus. This is descriptive, exploratory study type with a qualitative approach. The collection was carried out in the municipality of Guarabira, PB, with 20 dependent UHS users, who were affected by COVID-19, and who had been cured for at least 1 month. Users were invited to participate in the research through social networks. The interviews were guided by the data collection instrument and conducted remotely, using digital audio and video platforms. The age range of the interviewees ranged from 20 to 63 years, and 17 (85%) were females and 3 (15%) were males. Most females had completed higher education and were single. Feelings such as fear, anxiety, anguish, and loneliness, and the presence of sequelae after the cure of COVID-19 were identified. These feelings were clearly exposed in the interviewees' statements and the most prevalent feeling was fear. The study allowed for the identification of feelings harmful to the mental health of the participants. Through the reports, the presence of important sequelae after the cure of COVID-19 was also identified.
Sentimentos e Percepções de usuários SUS dependentes curados da COVID-19

Due to the whole context of pandemic and its social impacts, a fragile situation in public health is faced, and may not only cause physical illness, but especially mental suffering, which has a high chance of occurring in the general population and persisting with post-pandemic psychological symptoms. The study aimed to understand feelings and perceptions experienced by dependent UHS users affected by Coronavirus, as well as the sequelae resulting from the process of illness and cure of the virus. This is descriptive, exploratory study type with a qualitative approach. The collection was carried out in the municipality of Guarabira, PB, with 20 dependent UHS users, who were affected by COVID-19, and who had been cured for at least 1 month. Users were invited to participate in the research through social networks. The interviews were guided by the data collection instrument and conducted remotely, using digital audio and video platforms. The age range of the interviewees ranged from 20 to 63 years, and 17 (85%) were females and 3 (15%) were males. Most females had completed higher education and were single. Feelings such as fear, anxiety, anguish, and loneliness, and the presence of sequelae after the cure of COVID-19 were identified. These feelings were clearly exposed in the interviewees' statements and the most prevalent feeling was fear. The study allowed for the identification of feelings harmful to the mental health of the participants. Through the reports, the presence of important sequelae after the cure of COVID-19 was also identified.
Influência do estresse e da ansiedade nos hábitos alimentares de estudantes durante a pandemia

The objective of this work was to analyze the influence of stress and anxiety on the eating habits of nutrition students during the COVID-19 pandemic, identifying how these mental disorders contributed to the consumption of some specific foods and associating them with the pandemic period. This is a quantitative, descriptive study, carried out during the second half of 2021, using nutrition students, of both sexes and aged over 18 years, and collection was done through Google Forms and sent via social networks (WhatsAppR). The sample consisted of 83 nutrition students, of which 96.4% (n=80) were female and 39.8% (n=33) were between 18 and 22 years old. Regarding eating habits, 75.9% (n=63) reported some change in their eating routine, 83.1% (n=69) reported a greater appetite or desire to consume high-calorie foods and 81.9% (n= 68) stated that the stress or anxiety of social isolation may have influenced their food choices. These results are perfectly understandable, given the stressful and anxiolytic environment caused by social isolation, showing that such food choices do not depend on the individual's level of education or even course, but on a set of factors. Likewise, one cannot ignore the fact that many reported difficulties in relaxing, discouragement and sadness during the pandemic period, conditions that favor the emergence of other diseases of a psychological nature. With that, stress and anxiety were identified as possible influencers of the food choices of the evaluated students.
Home Based e-health Electronic Medical Record Systems a Decision Support Selection Framework for Individual, Security and Privacy Concerns Using MCDM Analysis

Home based e-health Electronic medical record.E-health is currently buzzing with discussion of the Electronic Medical Record (EMR).EMRs are essential to e-health applications because they store patient health data.Additionally, the EMR includes court documents produced in ambulatory and medical facility settings.These files provide as a source of data for electronic health records.Healthcare personnel have trouble trusting EMR systems, despite their use in hospitals.There aren't many research on EMR adoption in hospitals, especially given the privacy and security issues with EMR systems from a variety of fundamental angles.In order to better understand how individual, security, and privacy variables affect EMR uptake and use, the current study provides a decision-support choice framework.Early in the twenty-first century, the term "e-health" was developed to describe the application of contemporary information and communication technologies to the delivery of healthcare.E-health necessitates various developments in computer science, telecommunications, and the transfer of medical data over vast distances.E-health usage enhances global networking and thinking while also enhancing local, regional, and societal health.Numerous advantages are provided, such as improved patient care quality, functional hygiene effectiveness, and health promotion.Physicians and other healthcare professionals are regarded as the most significant influencers in e-health projects.Healthcare providers will not be able to gain from e-health if they do not adopt and use this technology.While both "Electronic Medical Records" (EMR) and "Electronic Health Records" (EHR) are essential components of e-health applications and both contain patient health-related information, they are distinct from one another and are used separately.When deciding between alternative multi-criteria decision-making (MCDM) techniques, MOORA is typically chosen for three main reasons.The first MOORA is a novel MCTM method that was developed with knowledge of the sensitivity parameters of earlier techniques.So, in our opinion, it should be completely worthwhile.The amount of processing time needed by MOORA to solve the given issue using the MCDM literature is known as the 2d internalisation.Finally, according to the MOORA literature, time is static and requires little to no structure [23].A tool for choosing college students who will receive scholarships is called MOORA Gadget.From the result it is seen that Individual factors is showing the highest value for Privacy factors is showing the lowest value.Resulting in Individual factors ranked first, there a Privacy factor has low rank.
Evaluation of English as a Foreigner Language Using DEMATEL Method

English as a foreign language, otherwise known as EFL, has english as its official language absent or official one that is not a second language by foreign speakers living in the country studying english.It is the study of other subjects studying english.Foreign has been shown to improve mathematics and english skills increase, and creativity develops.Are the language of society and school english and students of english basis of access to samples based on efl, in general, the language of the community and school is english it is learned in the absence of an environment.It is learned in the absence of an environment.Not as a language.Very few countries are themselves never heard of this it can be said that in the 21st century, or recognized as a universal language.English as a foreign language or learning a second language is in a person's life it has many benefits.Higher education and workforce this will help you access opportunities.Also, english is a non-native speaker useful for english speakers not only exist but all over the world teaching english to students in own looking for a rewarding job it is also useful for english speakers will be english is a foreign as language, otherwise known as efl, it has english as its official language a non-existent or official second in a country where there is no language by resident non-native speakers studying english.The decision-making trial and evaluation laboratory (DEMATEL) is regarded as an efficient technique for locating the causal links in a complex system.It focuses on assessing the interdependencies between elements and identifying the crucial ones using a visual structural model.Alternative: Improve & Widen
Pengaruh Gerakan Literasi Sekolah Terhadap Minat Menulis Siswa Sekolah Dasar

This study aimed to investigate the effect of the School Literacy Movement on students' interest in writing at SD Negeri 7 Ciamis throughout the 2019-2020 academic year. The quantitative methodology of ex-post facto research was utilized for this study. There are two factors in this study: the School Literacy Movement (X) and writing interest (Y). This study's population consisted of sixth-grade pupils from SD Negeri 7 Ciamis, with a sample size of 30 individuals. The sampling method employed was a random sample. Using a Likert scale, information was gathered via a questionnaire. Normality and linearity tests are utilized to evaluate the data analysis requirements. The Pearson product moment correlation method is utilized as the data analysis technique. The results of the study indicate that the School Literacy Movement had no influence on the students of SD Negeri 7 Ciamis' interest in writing. This is evidenced by the r value of 0.210 r table 0.361 for the association between the School Literacy Movement (X) and writing interest (Y). The significance value used in the r table is 5%. The estimated r value is less than the r table, indicating that there is no association or correlation between the variable School Literacy Movement (X) and the variable interest in writing (Y).
Model Framework for Analysis and Visualization of Climate Disaster Patterns

DOI:10.17014/ijog.9.3.329-336 Extreme climate change has almost begun to show symptoms just about in all countries, including Indonesia. At the beginning of 2020, some parts of Indonesia were flooded due to high rainfall intensity. In fact, in DKI Jakarta area the potential for flooding often arrive early. For this reason, an appropriate disaster pattern analysis system needs to be developed and implemented to prevent the incident from recurring by planning better handling. The model framework that was built to create a visualization system for disaster patterns in this study consisted of five stages, namely the image processing stage in the map image, the placement of the coordinates of the affected area, interpolation with the B-Spline method, extrapolation with the Von-Neumann law, and the accurate evaluation of the formed disaster pattern mapping. Through this model framework, it is expected to produce a system design for informative disaster pattern mapping.
Cyber Resilience and Cyber Security: What's the Difference?

The concepts of cyber security and cyber resilience are considered, and a comparative characterization of these concepts is also carried out. You will learn what measures cyber security includes and the elements of a proactive action plan to prevent attackers from accessing the corporate network. Measures that should be included in a cyber resilience plan in order to minimize the negative impact of cyber attacks as much as possible are considered. The mechanisms that should be applied for both cyber security and cyber resilience are explored.
Digital pathology-based histology education for medical scientists undergraduate program in Hong Kong

Background: Histology education is the cornerstone of training medical laboratory scientists for anatomical pathology. Yet, traditional histology training is limited by teacher-student ratio, specimen scarcity, and few peer-to-peer interactions. To improve learning and teaching, we revamped our histology curriculum by introducing digital pathology. Method: Undergraduates enrolled in our undergraduate program between September 2018– September 2021 (n=222, M/F=89/133) were included with IRB approvals. The first cohort underwent a traditional basic histology classroom and others were introduced to a flipped-classroom with integrated whole slide images (WSI) using Leica Aperio CS2 and Hamamatsu Nanozoomer s210 systems. Formal assessment results (quiz/spot test/practical exam), eLearning behavior and questionnaire were evaluated. Results: The participation rate in eLearning activity was more than 98% in flipped classroom. Time spent in eLearning was significantly associated with better exam performance (Pearson=0.314, p=0.001). No significant difference in exam performance between traditional and flipped classroom, but the cohort trained by digital pathology demonstrated better performance in spot test (t-test, p=0.0259) and mid-term test (t-test, p<0.0001). Approval rate of using WSI was 4.24±0.74/5.00. Conclusions: Digital pathology becomes increasingly important for diagnosis in anatomical pathology, and we have now adopted it to transform histology education in the leading medical laboratory science undergraduate program in Hong Kong.
The Constitutional Crisis and the Coronavirus

In February of 2020, just ahead of South Africa's first Covid-19 lockdowns, Bill Gates landed in Cape Town to play a charity tennis match with Trevor Noah (host of the Daily Show) and Grand Slam tennis champions Roger Federer and Rafael Nadal.His presence just prior to the first confirmed cases of Covid-19 in the country did not go unnoticed by those espousing vaccine-hesitant discourses that would emerge later that year.Conspiracy and anti-vaccine narratives surrounding the Bill & Melinda Gates Foundation's work in Africa had circulated long before the advent of the novel coronavirus, and the conspiracy discourses which emerged regarding the billionaire's new investments in the pharmaceutical industry and the Bill & Melinda Gates Foundation's involvement in the development of Covid-19 vaccines built on that foundation.One year later, in March of 2021, the Zulu King Goodwill Zwelithini died of Covid-19-related complications, and the state funeral was "disrupted" by a regiment of Amabutho (the "traditional" volunteer army of the Zulu King) who were not permitted to attend due to pandemic restrictions.Cyril Ramaphosa, the current South African President, was forced to evacuate the funeral as Amabutho overwhelmed the police and stormed the royal palace to mourn the death of their King.On live television, the leader of the Amabutho explained to a journalist that "we cannot be deterred by these government rules of COVID.If this COVID belongs to the government, [they] must take it [back] and get [out of] our way."The event prefigured a series of other spectacles of open defiance of Covid-19 restrictions within KwaZulu-Natal (the Zulu homeland, South Africa's easternmost province) by organizations and peoples associated with the politically activated elements of a "Zulu-traditionalist" political constituency -elements long associated with the support base of the previous President Jacob Zuma.This chapter maps the emergence of the relationship between Covid-19 conspiracies and Zulu-nationalist politics in South Africa.We will explore how some of the 10
Examining the Effect of Cooperative Integrated Reading and Composition (CIRC) on Students’ Reading Comprehension

The study's overarching goal is to determine whether or not CIRC instruction has a statistically significant effect on students' ability to comprehend narrative literature. In this study, we used a one-group, pre-post-test design for our experiments. Students from SMP Hasanuddin 6 Semarang's eighth grade made up the study's sample population. Twenty-four pupils from VII A were involved as the sample of this study. A reading test was carried out as this study's primary instrument of data collection. Before being taught using the CIRC method, students had a mean score of 40.91, indicating that their reading ability in narrative text was inadequate and needed improvement; after being taught using the CIRC method, students had a mean score of 64.12, indicating that their reading ability in narrative text was good and had already improved. In other words, the CIRC technique works well for teaching students to read narrative texts.
Tuna Swarm Optimization with 3D-chaotic map and DNA encoding for image encryption with lossless image compression based on FPGA

Images and video-based multimedia data are growing rapidly due to communication network technology. During image compression and transmission, images are inevitably corrupted by noise due to the influence of the environment, transmission channels, and other factors, resulting in the damage and degradation of digital images. Numerous real-time applications, such as digital photography, traffic monitoring, obstacle detection, surveillance applications, automated character recognition, etc are affected by this information loss. Therefore, the efficient and safe transmission of data has become a vital study area. In this research, an image compression–encryption system is proposed to achieve security with low bandwidth and image de-noising issues during image transmission. The Chevrolet transformation is proposed to improve image compression quality, reduce storage space, and enhance de- noising. A 3D chaotic logistic map with DNA encoding and Tuna Swarm Optimization is employed for innovative image encryption. This optimization approach may significantly increase the image's encryption speed and transmission security. The proposed system is built using the Xilinx system generator tool on a field-programmable gate array (FPGA). Experimental analysis and experimental findings show the reliability and scalability of the image compression and encryption technique designed. For different images, the security analysis is performed using several metrics and attains 32.33 dB PSNR, 0.98 SSIM, and 7.99721 information entropy. According to the simulation results, the implemented work is more secure and reduces image redundancy more than existing methods.
Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. The classification approach is based on the following two steps. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. Using this algorithm, the BF images of the signals are obtained. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. The success rate achieved is 100 % for noise free signals. The experiment is repeated for various noise levels up to -12dB SNR. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.
Posterior Covariance Information Criterion for Weighted Inference

For predictive evaluation based on quasi-posterior distributions, we develop a new information criterion, the posterior covariance information criterion (PCIC. PCIC generalises the widely applicable information criterion WAIC so as to effectively handle predictive scenarios where likelihoods for the estimation and the evaluation of the model may be different. A typical example of such scenarios is the weighted likelihood inference, including prediction under covariate shift and counterfactual prediction. The proposed criterion utilises a posterior covariance form and is computed by using only one Markov chain Monte Carlo run. Through numerical examples, we demonstrate how PCIC can apply in practice. Further, we show that PCIC is asymptotically unbiased to the quasi-Bayesian generalization error under mild conditions in weighted inference with both regular and singular statistical models.
Data-Driven Certification of Neural Networks with Random Input Noise

Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in the literature are designed for adversarial or worst-case inputs, but researchers have recently shown a need for methods that consider random input noise. In this paper, we examine the setting where inputs are subject to random noise coming from an arbitrary probability distribution. We propose a robustness certification method that lower-bounds the probability that network outputs are safe. This bound is cast as a chance-constrained optimization problem, which is then reformulated using input-output samples to make the optimization constraints tractable. We develop sufficient conditions for the resulting optimization to be convex, as well as on the number of samples needed to make the robustness bound hold with overwhelming probability. We show for a special case that the proposed optimization reduces to an intuitive closed-form solution. Case studies on synthetic, MNIST, and CIFAR-10 networks experimentally demonstrate that this method is able to certify robustness against various input noise regimes over larger uncertainty regions than prior state-of-the-art techniques.