Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs

Using a state-space system, I forecasted the US Treasury yields by employing frequentist and Bayesian methods after first decomposing the yields of varying maturities into its unobserved term structure factors. Then, I exploited the structure of the state-space model to forecast the Treasury yields, and compared the forecast performance of each model using the metric - mean squared forecast error, as the loss function. Among the frequentist methods, I applied the two-step Diebold-Li, two-step principal components, and one-step Kalman filter approaches. Likewise, I imposed the five different priors in Bayesian VARs -Diffuse, Minnesota, natural conjugate, independent normal inverse-Wishart, and the stochastic search variable selection priors. After forecasting the Treasury yields for 9 different forecast horizons, I found that the BVAR with Minnesota prior generally minimizes the loss function. I augmented the above BVARs by including macroeconomic variables and constructed impulse response functions with a recursive ordering identification scheme. Finally, I fitted a sign-restricted BVAR with dummy observations.
Cryopreservation of Network Bioactivity and Multi-Lineage Stromal Cell Differentiation Potential within Three-Dimensional Synthetic Hydrogels

Despite progress in many aspects of controlling cell behavior within synthetic three-dimensional hydrogels, approaches to cryopreserve these systems - encompassing the protection of both encapsulated cell viability and network bioactive functions - are lacking. Here, we demonstrate the retention of encapsulated human mesenchymal stromal cell (hMSC) viability following in situ cryopreservation regardless of cell line, material system, or storage duration. Further, the preservation extends to network bioactive functions, with hMSCs cryopreserved within degradable and adhesive hyaluronic-acid (HA) based hydrogels exhibiting degradation-mediated spreading within the gels equivalent to their non-frozen counterparts. Finally, the platform cryopreservation protocol preserves multi-lineage cellular differentiation capacity, with encapsulated hMSCs in non-degradable and adhesive/degradable HA-based hydrogels undergoing rates of adipogenesis and osteogenesis, respectively, equivalent to those in non-frozen gels on a per-cell basis. Collectively, these findings indicate a versatile platform technology that contributes to an increased understanding of three-dimensional cell-matrix interactions, and which may enable the indefinite cryopreservation of tissue engineering constructs for clinical applications.
Executive Functions as Mediator Variables between Binge Eating Symptoms and Body Mass Index: A Mediation Analysis

In recent years, the topic of executive functions has played a significant role in the context of clinical psychology, because maladaptive patterns can impair several aspects of physical and mental health. The main goal of the present study is to explore the relationship between executive functions, binge eating symptoms and body mass index. 103 participants showing binge eating symptoms have been involved in the present study, who completed the Binge Eating Screener, the short Hungarian version of the Behavior Rating Inventory of Executive Function - Adult - Self Report scale, and a demographic questionnaire. We conducted and explorative study, since contrary to previous research, we did not only point out the linear relationship between binge eating symptoms and body mass index, but also studied the mediating role of executive functions (cognitive regulation, emotion regulation and behavior regulation) in this relationship. In this model, binge eating symptoms can be defined as predictor variables, while the output variable is the body mass index. Our results show that there is a significant positive correlation between body mass index, binge eating symptoms and certain components of executive functions (cognitive regulation problems and behavior regulation problems). We also found that behavior regulation problems are present as mediators in the relationship between binge eating symptoms and body mass index.
A techno-economic comparison between technologies for biomass fractionation including liquor re-use

1 Laboratory of Cellulose and Paper, Forest Research Center INIA, Cta. La Coruña km 7.5, 28040 Madrid, Spain Department of Chemical Engineering, Faculty of Engineering, Universidad de la República, Julio Herrera y Reissig 565, 11300 Montevideo, Uruguay Chemical Engineering and Materials Department, Faculty of Chemistry, Universidad Complutense, 28040 Madrid, Spain 4 Laboratory of Process Simulation and Control (LSCP), Department of Chemical Engineering, University of São Paulo, Av. Prof. Luciano Gualberto, 380 trv. 3, 05508-900 São Paulo, SP, Brazil
The Changing Distribution of the Male Ethnic Wage Gap in Great Britain

IZA DP No. 14276 APRIL 2021 The Changing Distribution of the Male Ethnic Wage Gap in Great Britain We decompose the ethnic pay gap in Great Britain across the distribution of hourly wages, yielding a detailed insight into differences between groups and how these vary over pay percentiles and through time. While some groups experience reductions in the pay gap consistent with lower discrimination, including relatively well paid Indian workers and relatively poorly paid Bangladeshis, others specifically Black groups face an apparent glass ceiling barring access to well paid jobs. The increasing educational attainment of Britain’s ethnic groups provides some optimism around narrowing pay differentials, particularly at the top of the distribution, while the introduction and uprating of the National Minimum/ Living Wage has contributed to improvements at the lower end. JEL Classification: D31, J15, J31, J38
Bismuth Incorporation Stabilized α-CsPbI3 for Fully Inorganic Perovskite Solar Cells

All-inorganic CsPbI3 perovskite is emerging to be an alternative light-harvesting material in solar cells owing to the enhanced stability and comparable photovoltaic performance compared to organic–inorganic hybrid perovskites. However, the desirable black phase α-CsPbI3 is not stable at room temperature and degrades rapidly to a nonperovskite yellow phase δ-CsPbI3. Herein, we introduce a compositional engineering approach via incorporating Bi3+ in CsPbI3 to stabilize the α-phase at room temperature. Fully inorganic solar cells based on the Bi-incorporated α-CsPb1–xBixI3 compounds demonstrate a high PCE of 13.21% at an optimal condition (incorporation of 4 mol % Bi3+) and maintain 68% of the initial PCE for 168 h under ambient conditions without encapsulation. This is the first attempt of partial substitution of the “B”-site of the perovskite to stabilize the α-CsPbI3, which paves the way for further developments of such perovskites and other optoelectronic devices.
Seasonal variations of phytoplankton community structure in Pasur River estuary of Bangladesh

In the present study, phytoplankton community structure in relation to hydrological parameters were studied based on three seasons over a period of 12 months from March 2020 to February 2021 in the Pasur river estuary, Bangladesh. Water quality parameters namely water temperature, DO, pH, alkalinity, TDS and Salinity were determined between 23.05 to 32 °C, 4.93 to 8.78 mg/L, 7.12 to 7.99, 109 to 175 mg/L and 6.20 to 14.68 ppt respectively. Concentrations of nutrients viz. nitrate (0.05 to 1.02 mg/L) and phosphate (0.03 to 0.85 mg/L) also fluctuated seasonally. A total 68 species of phytoplankton belonging to 42 genera were identified with 40 belong to the class Bacillariophyceae, 12 to Dinophyceae, 8 to Cyanophyceae, 6 to Chlorophyceae and 2 to Euglenophyceae. Relatively higher total cell density of phytoplankton was observed during post-monsoon season (48.204 × 10 cells/L) and lower during monsoon season (37.301 × 10 cells/L). The percentage composition of phytoplankton was recorded as Diatoms > Dinoflagellates > Blue greens > Greens > Euglenoids respectively. Generally, diatoms formed the dominant group in term of total species and cell numbers during the study period. Species diversity, richness and evenness index ranged from 1.45 to 3.80, 0.25 to 0.98 and 0.48 to 0.96 with highest values were recorded during post-monsoon coinciding with the stable hydrographical conditions with a higher concentration of nutrients in the water columns.
Image Classification and Object Detection using CNN A Comparative Study using Traffic Sign Imagery

—After the revival of CNN by their impressive performance in ImageNet Classification task in 2012, they are now being widely used in Image Classification and Object Detection. There are numerous published method on image classification using CNN where they have achieved near perfect accuracy. In contrast object detection task in real life scenario is yet to produce satisfactory result. In this work we have used existing traffic sign image database, both low and high resolution, to perform the task of classification and detection. In our study we have also found highly satisfactory result on classification even with small dataset, where else detection accuracy was poor and highly dependent on training dataset size and number of training iteration. For classification task we have used a modified version of AlexNet and for the object detection task we have used Single Shot MultiBox Detector (SSD) and Faster R-CNN architecture.
A Social Representation of Organ Donation. Factors Influencing Donation Availability

Some personality factors and interpersonal variables have a significant impact on relationship satisfaction. The sense of relational entitlement (SRE; what individuals perceive they deserve within a romantic relationship) is an interpersonal construct that can lead to changes in satisfaction. In this study, we examined the assertive sense of relational entitlement as a predictor of couple satisfaction and the additional mechanisms that explain this relationship. 147 individuals involved in romantic relationships completed measures related to their entitlement, positive and negative emotions, gratitude and satisfaction. The results showed that individuals with a higher assertive relational entitlement reported lower satisfaction. This association was mediated by negative emotions. This study fills a gap in the literature by discussing whether assertive entitlement is related to couple satisfaction and how one’s emotional state accounts for this link. Finally, we consider that the effects of assertive relational entitlement on the functioning of a couple should be explored by further studies, especially in association with other concepts.
Wearables in the classroom-psychophysiology in education

The paper presents measuring instrumentation suitable for a classroom use and to study learning and teaching by means of objective measures. The basic types of instruments used in the classroom for different purposes are presented, such as determination of the students’ well-being, assessment of their engagement in work, evaluation of differences in their motivation, relaxation, focus and attention when different teaching approaches are used. In general, instruments can be either wearable or desktop. The former are attached to students (or their teachers) in a school environment to measure their physiology (e.g. heart rate, respiration rate, skin conductance, skin temperature, heat flow, blood pressure, etc.), and the latter allow the study of the psychological correlation between educational and physiological measures in controlled laboratory conditions. Some examples are given from various physiological studies in the field of education in the Slovenian primary schools.
CS224W Report - Multiclass Seizure Classiﬁcation from EEG with Graph Convolutional Recurrent Neural Networks

Traditionally, seizure classiﬁcation from electroencephalogram (EEG) is performed by clinical experts’ visual analyses of EEG signals. Recent advances in deep learning have enabled the development of automated seizure classiﬁcation frameworks. Most of previous efforts on modeling EEG with deep learning utilized convolutional neural networks (CNNs) and/or recurrent neural networks (RNNs), ignoring the inherent graphical structure in EEG. In this paper, we propose to model the spatiotemporal dependencies in EEG data with a graph convolutional recurrent neural network. Speciﬁcally, the spatial dependency is characterized by graph convolutions on the EEG electrode graph, and the temporal dependency is captured by a recurrent neural network. We explore various graph structures, and show that our graph-based approach improves multiclass seizure classiﬁcation compared to RNN or CNN-based methods.
Quant-Noisier: Second-Order Quantization Noise

Memory and compute constraints associated with deploying AI models on the "edge" have motivated development of compression methods designed to reduce larger models into compact forms. We aim to improve upon a state-of-the-art method developed by Facebook AI Research, Quant-Noise [1]; our novel method, Quant-Noisier, utilizes second-order noise to improve performance on compressed models. For all three datasets we test, our best Quant-Noisier variant, random jitter, outperforms Quant-Noise on two out of three quantization schemes.
Structural Equation Modeling of Student Involvement in Transition-Related Actions : The Path of Least Resistance

Structural equation modeling was used to examine the relative influence of, and interaction between, three exogenous latent factors (student skills, family climate, educational opportunities) and two endogenous factors (student self-determination, student action taking). Data were collected from 237 middle school and high school students with disabilities from three different school districts representing rural, suburban, and urban settings. The hypothesized relationships among five latent factors were partially supported by findings. When nonsignificant paths were eliminated, a final model revealed a number of significant paths between latent factors and the residuals of individual variables. The latent factor Educational Opportunities was significantly related to student self-determination. Residuals from the latent factors Student Skills and Family Climate
Effectiveness of Logotherapy Intervention Program in Reducing Drug Cravings and Preventing Relapse among Males with Substance Dependence in Ahvaz

Background: Given that the important characteristics of drug abuse are high relapse rate and craving, the use of new therapeutic interventions that help drug dependents can be very important. The present study aimed to investigate the effectiveness of logotherapy intervention program in reducing drug cravings and preventing relapse among males with substance dependence in Ahvaz in 2019. Methods: The research method was quasi-experimental with a pretest-posttest design and a control group. The statistical population included all the males with addiction disorder who were referred to treatment centers in Ahvaz city during 2018. Using convenience sampling, we selected 30 males with addiction disorder willing to participate in the study and randomly divided them into experimental and control groups (n=15 per group). The research instruments included Gorski's Warning Signs of a Relapse and the Craving Questionnaire. The experimental group underwent eight sessions (90-minute sessions per week) of the logotherapy intervention program. Multivariate analysis of covariance (MANCOVA) was used to analyze the data. SPSS version 22.0 was further used to analyze the data. Results: The mean ± SD of the drug craving for experimental and control groups in the post-test phase was 36.46±6.77 and 49.06±12.95, respectively. The results showed that intervention programs were effective in reducing drug craving and relapse in males with drug addiction (Pvalue= 0.001). Conclusions: Training logotherapy was shown effective in reducing drug cravings and preventing relapse in males with substance dependence, and thus it can be used in drug rehabilitation centers to prevent relapse in clients. Keywords: Logotherapy, Drug craving, Preventing relapse, Drug abuse, Health.
Machine Learning Approach for Classification of Breast Cancer

: The classification of breast cancer has been the subject of interest in the fields of healthcare and bioinformatics, because it is the second main reason of cancer-related deaths in women. Breast cancer can be analyzed using a biopsy where tissue is eliminated and studied under microscope. The identification of problem is based on the qualification and experienced of the histopathologists, who will attention for abnormal cells. However, if the histopathologist is not well-trained or experienced, this may lead to wrong diagnosis. With the recent proposition in image processing and machine learning domain, there is an interest in experiment to develop a strong pattern recognition based framework to improve the quality of diagnosis. In this work, we will use the image feature extraction approach and machine learning approach for the classification of breast cancer using histology images into benign and malignant. Using Histopathological image we can preprocess this image after that apply feature extraction and classify the final result using SVM and Naive Bayes Classification techniques.
High-Purity H2 Produced from NH3 via a Ruthenium-Based Decomposition Catalyst and Vanadium-Based Membrane

Ammonia is a prospective hydrogen transport intermediate due to its high volumetric and gravimetric H2 densities, and existing production and distribution infrastructure. However, its ultimate use in mobile proton exchange membrane (PEM) fuel cells necessitates decomposition and purification at or near the point of use. In this study, the production of high purity H2 from NH3 using a two-stage process has been demonstrated by coupling separate decomposition (150 g of 1 wt % Ru on Al2O3 catalyst) and purification (a single 150 cm2, Pd-coated tubular vanadium membrane) stages. Equilibrium NH3 decomposition and >90% H2 recovery was demonstrated with a catalyst temperature of 450 °C and membrane temperature of 340 °C, with an overall H2 production rate of 0.75 kg/day. Mass spectrometry showed that levels of N2 and NH3 impurities were below detection limits. This configuration is readily scalable by increasing the catalyst loading and membrane area (through use of multiple tubes in parallel), and could enable .
LIVED EXPERIENCES OF MOTHERS OF WITH PREMATURE BABIES IN A NEONATAL INTENSIVE CARE UNIT OF A SELECTED HOSPITAL, IN UNITED ARAB EMIRATES: A QUALITATIVE CONTENT ANALYSIS

Background: A mother’s expectation and excitement of having a healthy newborn is cut short when a baby is born premature. She is faced with the fear and anxiety of losing her baby or overwhelming thoughts of her baby suffering inconceivable distress in Neonatal Intensive Care Unit (NICU). Aim: This study explored the lived experience of mothers who had a premature baby admitted to NICU. Methods: A qualitative design with a phenomenological approach was used for the study. It was conducted from October to January 2019 among eleven women whose premature babies were admitted in the NICU at a hospital in Ras Al Khaimah, United Arab Emirates. Purposive sampling was used to choose the participants which continued until data saturation was reached. Data was collected using in-depth, semi structured facetoface recorded interviews and analyzed using Colaizzi’s method. Results: Three themes were generated from the analysis. Mother’s expression of thoughts and emotions while their babies in NICU; Families experienced emotion while their premature babies were in the NICU and Mothers perception of care and support from health care professionals. Conclusion: Mothers and family members were anxious about the survival of their premature babies admitted to NICU. They exhibited emotions such as anxiety, anger and sadness. During the recovering process, mothers relied on family support, faith and hope to help get through the ordeal. Satisfaction of nurses providing competent care and provision of psychological support to the mothers helped them to cope.
Use of digital mobile applications and their integration in plastic surgery

Introduction: In the environment of new digital health technologies, many paradigms are being broken, and the opening to obtain further knowledge allows the plastic surgeon to use them in their professional practice. This article aims to analyze the use of digital mobile applications by patients and professionals. The purpose is to understand the importance and recognition of this type of technology to provide more quality and safety in medical care, specifically for elective care in the plastic surgery specialty. Methods: Two analysis were carried out, in parallel, in a retrospective and descriptive manner: 1) of the conversations carried out through a mobile instant messaging application on the communication channel for monitoring patients undergoing surgery in a private plastic surgery clinic; (2) of the use of digital technologies in plastic surgery, through the analysis of the responses to a research questionnaire sent to professionals in this specialty. Results: In the sample obtained (n = 61), the monthly average of 122 conversations in the postoperative period was recorded. In a satisfaction survey, patients found it useful to communicate through a mobile application directly with the clinic. Only 4.1% of the professionals who participated in the research had their own clinic application. Conclusion: The perception of the importance of technology in patient care, the need to meet the population’s requirements, and the possibility of providing more comprehensive monitoring reliably offer the basis for validating new technology resources and mobile applications with specific resources in plastic surgery care. ■ ABSTRACT
Prioritarianism and Equality of Opportunity

This paper asks whether prioritarianism--the view that social welfare orderings should give explicit priority to the worse-off--is consistent with the normative theory of equality of opportunity. We show that there are inherent tensions between some of the axioms underpinning prioritarianism and the principles underlying equality of opportunity; but also that these inconsistencies vanish under plausible adjustments to the domains of two key axioms, namely anonymity and the transfer principle. That is: reconciling prioritarianism and equality of opportunity is possible but allowing room for individual responsibility within prioritarianism requires compromises regarding the nature and scope of both impartiality and inequality aversion. The precise nature of the compromises depends on the specific variant of the theory of equality of opportunity that is adopted, and we define classes of social welfare functions and discuss relevant dominance conditions for six such variants. The conflicts and the paths to reconciliation are illustrated in an application to South Africa between 2008 and 2017, where results suggest broad empirical agreement among the different approaches. (Stone Center on Socio-Economic Inequality Working Paper Series)
ARABIC HANDWRITTEN CHARACTERS RECOGNITION VIA MULTI-SCALE HOG FEATURES AND MULTI-LAYER DEEP RULE-BASED CLASSIFICATION

Optical character recognition systems for handwritten Arabic language still face challenges, owing to high level of ambiguity, complexity and tremendous variations in human writing styles. In this paper, we propose a new and effective Arabic handwritten characters recognition framework using multi-scale histogram oriented gradient (HOG) features and the deep rule-based classifier (DRB). In the feature extraction stage, the proposed framework combines multi-scale HOG features, and then the DRB is applied on comprehensive HOG features to obtain the final classification label/class. This study involves experimental analyses that were conducted on the publicly available cursive Arabic Handwritten Characters Database (AHCD) containing 16800 characters. Experimental results demonstrate the efficacy of the proposed recognition system compared to the existing state-of-the-art-systems.