Miracle-dz/UniFAR-FTUs huggingface dataset:
PERBANDINGAN EFEKTIVITAS PEMBERIAN MINUMAN KUNYIT ASAM DAN MINUMAN JAHE TERHADAP PENURUNAN NYERI HAID PADA SISWI DI SMA NEGERI 3 GORONTALO UTARA

ABSTRAK Ditha Rosdiana Abdul, 2015. Perbandingan Efektivitas Pemberian Minuman Kunyit Asam dan Minuman Jahe Terhadap Penurunan Nyeri Haid Pada Siswi Di SMA Negeri 3 Gorontalo Utara. Skripsi, Jurusan Keperawatan, Fakultas Ilmu-Ilmu Kesehatan dan Keolahragaan, Universitas Negeri Gorontalo. Pembimbing I dr.Vivien Novarina A.Kasim, M.Kes dan Pembimbing II Vik Salamanja, S.Kep, Ns. , M.Kes. Pemberian minuman kunyit asam (mengandung curcumine dan anthocyanin) dan minuman jahe (mengandung gingerol dan shogaol) dapat menurunkan nyeri haid yang diakibatkan oleh kontraksi uterus dengan cara memblok produksi prostaglandin penyebab nyeri haid. Penelitian Ini bertujuan untuk mengetahui perbandingan efektivitas pemberian minuman kunyit asam dan minuman jahe terhadap penurunan nyeri haid pada siswi di SMA Negeri 3 Gorontalo Utara. Rancangan penelitian menggunakan quasi eksperimen dengan non equivalent (pretest and posttest) group design. Populasi penelitian adalah seluruh siswi kelas X dan XI yang mengalami nyeri haid saat menstruasi dengan jumlah sampel 30 siswi dengan menggunakan teknik Purposive Sampling. Instrumen yang digunakan yaitu lembar pengukuran nyeri Numeric Rating Scale. Analisa data menggunakan uji Mann Whitney. Hasil penelitian menunjukkan bahwa terdapat perbedaan antara pemberian minuman kunyit asam dan minuman jahe terhadap penurunan nyeri haid dengan p-value 0,023< I±(0,05). Kesimpulan penelitian ini menunjukan bahwa pemberian minuman kunyit asam (mean=18,70) lebih efektif terhadap penurunan nyeri haid pada siswi di SMA Negeri 3 Gorontalo Utara dibanding dengan minuman jahe (mean=12,30). Disarankan minuman kunyit asam dapat menjadi pilihan untuk mengatasi nyeri haid. Kata Kunci : Minuman Kunyit Asam, Minuman Jahe, Nyeri Haid Daftar Pustaka : 47 buah (2000-2014)
Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil

The start of the vaccination against COVID-19 was an essential step towards the end of the pandemic. In Brazil, CoronaVac was the first vaccine to be applied in the immunization campaign, and it is one of the most used today. Still, CoronaVac has specific components that have driven the spread of misinformation online. In this work, we compare the dissemination of misinformation on Twitter about the approval of such a vaccine for adults and children. The results show that misinformation is significant on Twitter and there has been a substantial change in the style of such content shared between 2021 and 2022, moving from a false narrative about the development of the vaccine to raising suspicions on the approval process by the health regulatory agency.
Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis. Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells. Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells. Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.
Offensive Language Detection in Arabic Social Networks Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings

Social networks facilitate communication between people from all over the world. Unfortunately, the excessive use of social networks leads to the rise of antisocial behaviors such as the spread of online offensive language, cyberbullying (CB), and hate speech (HS). Therefore, abusive\offensive and hate detection become a crucial part of cyberharassment. Manual detection of cyberharassment is cumbersome, slow, and not even feasible in rapidly growing data. In this study, we addressed the challenges of automatic detection of the offensive tweets in the Arabic language. The main contribution of this study is to design and implement an intelligent prediction system encompassing a two-stage optimization approach to identify and classify the offensive from the non-offensive text. In the first stage, the proposed approach fine-tuned the pre-trained word embedding models by training them for several epochs on the training dataset. The embeddings of the vocabularies in the new dataset are trained and added to the old embeddings. While in the second stage, it employed a hybrid approach of two classifiers, namely XGBoost and SVM, and a genetic algorithm (GA) to mitigate the drawback of the classifiers in finding the optimal hyperparameter values to run the proposed approach. We tested the proposed approach on Arabic Cyberbullying Corpus (ArCybC), which contains tweets collected from four Twitter domains: gaming, sports, news, and celebrities. The ArCybC dataset has four categories: sexual, racial, intelligence, and appearance. The proposed approach produced superior results, in which the SVM algorithm with the Aravec SkipGram word embedding model achieved an accuracy rate of 88.2% and an F1-score rate of 87.8%.
Relationships between Consecutive Long-Term and Mid-Term Mobility Decisions over the Life Course: A Bayesian Network Approach

Long-term and mid-term mobility decision processes in different life trajectories generate complex dynamics, in which consecutive life events are interrelated and time dependent. This study uses the Bayesian network approach to study the dynamic relationships among residential events, household structure events, employment/education events, and car ownership events. Using retrospective data obtained from a web-based survey in Beijing, China, first structure learning is used to discover the direct and indirect relationships between these mobility decisions. Parameter learning is then applied to describe the conditional probabilities and predict the direct and indirect effects of actions and policies in the resulting network. The results confirm the interdependencies between these long-term and mid-term mobility decisions, and evidence the reactive and proactive behavior of individuals and households in the context of various life events over the course of their lives. In this regard, it is important to note that an increase in household size has a contemporaneous effect on car acquisition in the future; while residential events have a synergic relationship with employment/education events. Moreover, if people’s residential location or workplace/study location moves from an urban district to a suburban or outer suburban district, this has both lagged and concurrent effects on car acquisition.
Using the Silhouette Coefficient for Representative Search of Team Tactics in Noisy Data

: Automatically recognizing team tactics based on spatiotemporal data is challenging. Deep Learning approaches have been proposed in this area but require a tremendous amount of manual work to create training and test data. This paper presents a clustering approach to reduce the needed manual effort significantly. A method is described to transform the spatiotemporal data into a canonical form that allows to efficiently apply clustering techniques. Since noise cannot be avoided in the given application context, the silhouette coefficient is applied to filter clusters considered to be noisy in a cluster technique independent way. Then, a variant of the silhouette coefficient is introduced as an indicator regarding the overall cluster model quality which allows to select the optimal clustering technique as well as the optimal set of cluster technique parameters for the given application context.
Opportunities for Interactive Communication in Mechanically Ventilated Critically Ill Patients: A Video-Based Observational Study

Background Mechanically ventilated critically ill patients need the opportunity to communicate their physical and psychosocial concerns to nurses. However, these patients face the unique problem of lacking even the opportunity to communicate. Aims The study aimed to describe the characteristics of communication opportunities for critically ill mechanically ventilated patients. Methods The study was designed as a video-based descriptive observational study. Participants included seven mechanically ventilated critically ill patients at the intensive care unit, coronary care unit, or high care unit who were conscious and seven registered nurses (seven pairs). Videos were recorded continuously from 8 am to 4 pm, and the footage was then descriptively analyzed. Data collection took place between July 2019 and June 2020. Results The total recording time was 668.0 minutes. Of these 668.0 minutes, nurses stayed in the Conversation Area of the Patient for 279.6 minutes, and of these 279.6 minutes, two-way face-to-face communication between nurse and patient occurred for 78.0 minutes. Of these 78.0 minutes, communications were started by nurses for 47.2 minutes (174 scenes) and by patients for 24.2 minutes (36 scenes). The patient-started two-way communication scenes included 37 instances of Patient-Intentional-Action that triggered the start of communication. Actions using the upper limbs were observed in 20 instances and represented the most frequently used body part. The head/face, lower limbs, or trunk were also used in some of the actions. Gestures were the most commonly used action type (14 instances). Other types included lip movement, grimace, leg flex/extension, and cough. Conclusions We found that nurses tended to start communication more frequently than patients did and that patients demonstrated Patient-Intentional-Action with a variety of actions using various body parts. Communication opportunities for patients were created when nurses took the initiative to start communication or when they noticed and responded to the Patient-Intentional-Action. Our findings demonstrate that nurses need to recognize and always respond to Patient-Intentional-Action and to take the initiative in communicating rather than waiting for the patient to do so.
Social Cognitive Career Theory Predictors of Goal Persistence in African American College Students With Disabilities

The Black Lives Matter movement exposed the broad and deep issues of institutional racism in the United States. Helping young African Americans with disabilities persevere in their pursuit of college degrees and obtain entry-level professional jobs as career pathways to the middle class will contribute to workplace equity for young adults who are at the intersection of race, disability, and poverty. The Social Cognitive Career Theory (SCCT) has been validated extensively as a model of goal persistence for women and minority college students majoring in science, technology, engineering, mathematics, and medicine (STEMM). The present study evaluated SCCT constructs as predictors of goal persistence in a sample of African American college students with disabilities across various academic majors, using hierarchical regression analysis. The final model accounted for 53% of the variance in goal persistence scores, a large effect size. Academic milestone self-efficacy and career self-efficacy were the most important predictors of goal persistence, followed by academic barrier self-efficacy, deep learning style, and career outcome expectancy. The SCCT interventions designed to increase academic and career efficacy and outcome expectancy will increase the likelihood that African American college students with disabilities will complete their degrees and successfully obtain professional jobs.
Weakly monotonic averaging with application to image processing

Structured information at pixel scales is essential in images produced by satellites, medical imaging systems and modern digital cameras, which generate images many times the resolution of display devices. Loss of this information degrades the visual quality of images and performance of computer vision algorithms. Traditional methods for image reduction and denoising act as low-pass filters, removing high frequency variations in images, without regard to whether these represent noise or visually relevant detail. Both reduction and denoising may be formulated as aggregation problems over contiguous subsets of an image and recent application of monotonic averaging functions showed promising results in reducing images corrupted by additive Gaussian noise or impulse noise. However monotonic averages do not sufficiently address the issues of preserving fine detail, or removing non-Gaussian and nonadditive noise. Given the prevalence of robust, non-monotonic means within the literature, this thesis proposes their application to image processing problems expressed as local averages, with the view to establishing the properties of functions suitable for image processing tasks. Within this thesis a new class of averaging functions is established, based on the proposal of weak monotonicity a restricted form of directional monotonicity. Sufficient conditions for the weak monotonicity of the Gini means, Lehmer means and generalised mixture operators are established. Penalty function methods are applied to demonstrate the weak monotonicity of robust estimators of location, x
A panel hidden cointegration analysis on the political stability and tourism receipts

ABSTRACT In the present study, the asymmetric relationships between political stability and tourism receipts in 14 MENA countries were investigated using panel data analysis with annual data of the period between 2002 and 2018. For this purpose, the panel hidden cointegration test developed by Hatemi-J (2011) and allowing the determination of asymmetric relationships between the variables was used. The obtained results confirmed that there were hidden cointegration relationships between the positive and negative components of tourism receipts and political stability variables. In light of these findings, it was determined that, according to the coefficient estimations made using FMOLS and DOLS methods, the level of political stability had significant effects on the tourism receipts. A positive shock in the political stability would positively affect the tourism receipts, whereas a negative shock would have a negative effect.
Point-based visuo-haptic simulation of multi-organ for virtual surgery

Background and Objectives: Realistically and efficiently simulating dynamic behavior of human organs under interactions is crucial for the immersive user experience of the surgical simulator. Conventional methods are time-consuming to simulate this phenomenon due to topological modifications. Materials and Methods: This paper proposes a robust and efficient point-based framework for surgical simulation, allowing realistically simulating mechanical response of human organs under interactions with visual and tactile feedback. Considering the inevitable topological modifications occurred in surgical simulation, we adopt sparse point cloud to model the mechanics of deformable bodies while employ surface mesh to represent morphological details of human organ, which can not only disconnect mechanical complexity from geometrical details, but also enable precise boundary conditions to be solved with surface mesh. Results: We validate our method on a variety of challenging surgical scenarios, and the results demonstrate that our method can realistically and efficiently provide the visuo-haptic feedback for surgical simulation. Conclusions: Our method can well tackle the inefficiency limitation of mesh-based methods related to topological modifications issue, and has great potential to be adopted in practical surgical simulators.
Accounting for Viscoelasticity When Interpreting Nano-Composite High-Deflection Strain Gauges

High-deflection strain gauges show potential as economical and user-friendly sensors for capturing large deformations. The interpretation of these sensors is much more complex than that of conventional strain gauges due to the viscoelastic nature of strain gauges. This research endeavor developed and tested a model for interpreting sensor outputs that includes the time-dependent nature of strain gauges. A model that captures the effect of quasi-static strains was determined by using a conventional approach of fitting an equation to observed data. The dynamic relationship between the strain and the resistance was incorporated by superimposing dynamic components onto the quasi-static model to account for spikes in resistances that accompany each change in sensor strain and subsequent exponential decays. It was shown that the model can be calibrated for a given sensor by taking two data points at known strains. The resulting sensor-specific model was able to interpret strain-gauge electrical signals during a cyclical load to predict strain with an average mean absolute error (MAE) of 1.4% strain, and to determine the strain rate with an average MAE of 0.036 mm/s. The resulting model and tuning procedure may be used in a wide range of applications, such as biomechanical monitoring and analysis.
Comparing Deep Learning Models for Multi-label Classification of Arabic Abusive Texts in Social Media

: Facing up to abusive texts in social networks is gradually becoming a mainstream NLP research topic. How-ever, the detection of its speciﬁc related forms is still scarce. The majority of automatic solutions cast the problem into a two-class or three-class classiﬁcation issue not taking into account its variety of aspects. Speciﬁcally in the Arabic language, as one of the most widely spoken languages, social media abusive texts are written in a mix of different dialects which further complicates the detection process. The goal of this research is to detect eight speciﬁc subtasks of abusive language in Arabic social platforms, namely Racism, Sexism, Xenophobia, Violence, Hate, Pornography, Religious hatred, and LGBTQ a Hate. To conduct our experiments, we evaluated the performance of CNN, BiLSTM, and BiGRU deep neural networks with pre-trained Arabic word embeddings (AraVec). We also investigated the recent Bidirectional Encoder Representations from Transformers (BERT) model with its special tokenizer. Results show that DNN classiﬁers achieved nearly the same performance with an overall average precision of 85%. Moreover, although all the deep learning models obtained very close results, BERT slightly outperformed the others with a precision of 90% and a micro-averaged F1 score of 79%.
Stiffness and energy dissipation of Oval Leaf Spring mounts under unidirectional line loading

The Oval Leaf Springs (OLS), a class of passive isolation devices, have been successfully used as anti-shock and anti-vibration mounts to protect equipment and machinery. Available literature is insufficient to understand the behavior of OLS mounts. To estimate the spring stiffness, we conducted theoretical and finite element analyses (FEA) on a large number of springs having different geometrical and mechanical properties. Based on the principle of minimum potential energy, this paper presents theoretical expressions, which describe the linear static stiffness of OLS mounts subjected to line loading in the vertical (compression) and lateral (bending–shear) in-plane directions. Comparison studies showed a good agreement between numerical and analytical models. We observed a negligible effect of transverse shearing on the spring stiffness. In addition, it was demonstrated that the stiffness is more sensitive to the radius compared to the other geometric properties of the spring. Nonlinear FEA considering the hyper-viscoelastic behavior of the damping compound showed that the OLS mounts have higher energy dissipation capabilities in the lateral direction, which increase at low frequency and large amplitude loadings.
Organizational Social Capital and Performance Information use: The Mediating Role of Public Service Motivation

This article examines the mediating role of public service motivation (PSM) between organizational social capital and performance information use. This topic is worth studying since it allows to understand how organizational level factors and individual level traits interact. Using a multiple informant survey distributed to county managers in Florida, this article finds support that organizational social capital is an important predictor of performance information use, and that this relationship is mediated by the role of PSM. The article concludes with recommendations on how to capitalize on these internal resources for the effective implementation of performance management reforms and practices.
A Stochastic Spectrum Trading and Resource Allocation Framework for Opportunistic Dynamic Spectrum Access Networks

In this article, the spectrum trading problem between primary users and secondary networks is investigated. The secondary network requests multiple channels with the targeted availability to satisfy its users’ demands. Due to the uncertainty about the channels availability, stochastic optimization techniques are adopted to find the optimal set of channels for each secondary network for the lowest cost. Two different constraints on the secondary demand are defined. The first one is when the throughput has to be fully satisfied for a certain percentage of time, and the second one is when the expected value of the throughput has to exceed a certain percentage of the requested one. Also, the possibility for channel subleasing among the secondary networks is investigated to reduce the demand shortage. The results show that demanding simultaneous channels increases the cost as it reaches up to 20% higher than if the same resources were requested individually. Also, channels subleasing reduces the demand shortage probability and increases the achieved throughput, especially at low value of requested demand. In this case, the demand satisfaction probability increases by around 30% while the achieved throughput increases up to 40% compared to the scenario where channels subleasing is not allowed.