Miracle-dz/UniFAR-FTUs huggingface dataset:
A multi-voiced model for decision-making in water resource management. A case study in the urban area of Mar del Plata city, Argentina

ABSTRACT Water management is concerned with action on the ground. The goal of this paper is to conduct a multi-voiced decision model for water resource management in the urban area of Mar de Plata city, Province of Buenos Aires, through the use of a multicriteria analysis tool. Using the Criterium Decision Plus system, criteria and subcriteria were integrated into a multi-voiced decision model for the prioritization of zones for water resource management. The criteria chosen were Potable Water Supply, Sanitation, Water Quantity, Potential Sources of Contamination, Flood Protection, and Equity. The model identified four of the seven zones as ‘high’ to ‘very high’ priority. Priority scoring ranged from 0.15 to 0.543. Regarding the criteria, “Potable Water Supply,” ”Water Quantity,” and ”Flood Protection” were identified as the main ones contributing to the final model results. The results show the starting point for generating proposals for water management actions in this urban area.
Structural equation modeling of eBankQual scale: a study of E-Banking in India

This study assesses the relationship between perceived quality, brand perception and perceived value with satisfaction. For the data analysis structural equation modeling (SEM) method and path analysis method were used. A result indicates that, eBankQual model is fit to assess relationship between service quality, brand perception and perceived value with overall customers’ satisfaction in e-banking service. Result of regression SEM indicates that, all 14 variables found significant and good predictors of overall satisfaction in e-banking services. However, result of SEM analysis indicates that, data supports to eBankQual model and dimensions Compensation, Convenience, Contact Facilities, Easy to Use, Responsiveness, Cost Effectiveness and System Availability including brand perception and perceived value were found more significant factors in the eBankQual model.
Optimizing subjective wellbeing with amisulpride in first episode schizophrenia or related disorders

Abstract Background Subjective response (SR) to antipsychotic medication is relevant for quality of life, adherence and recovery. Here, we evaluate (1) the extent of variation in SR in patients using a single antipsychotic; (2) the association between subjective and symptomatic response; and (3) predictors of SR. Methods Open-label, single treatment condition with amisulpride in 339 patients with a first episode of a schizophrenia spectrum disorder, at most minimally treated before inclusion. Patients were evaluated at baseline, before start with amisulpride and after four weeks of treatment with the Subjective Wellbeing under Neuroleptic scale, the Positive and Negative Syndrome Scale, and the Calgary Depression Scale for Schizophrenia. Results (1) 26.8% of the patients had a substantial favorable SR, and 12.4% of the patients experienced a substantial dysphoric SR during treatment with amisulpride. (2) Modest positive associations were found between SR and 4 weeks change on symptom subscales (r = 0.268–0.390, p values < 0.001). (3) Baseline affective symptoms contributed to the prediction of subjective remission, demographic characteristics did not. Lower start dosage of amisulpride was associated with a more favorable SR (r = −0.215, p < 0.001). Conclusions We conclude that variation in individual proneness for an unfavorable SR is substantial and only modestly associated with symptomatic response. We need earlier identification of those most at risk for unfavorable SR and research into interventions to improve SR to antipsychotic medication in those at risk.
Classification of Depression on social media using Distant Supervision

Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues have been spiking all over the world rapidly. People have taken up to social media as a platform to vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets in these times, but the downside being that majority of the population has neglected the importance of mental health issues and there are not enough resources to liberate people about it. Also, people hesitate to talk about their mental issues and seek help. So, a machine learning model using distant supervision to detect depression on Twitter is curated. Use of Sentiment140 dataset with 1.6 million records of different tweets. Our training data makes use of Twitter tweets included with emojis, which are classified as noisy labels on a dataset. Further, this paper mentions about how to use models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost to distinguishing tweets between depressive or nondepressive. The purpose behind using multiple models is to achieve highest accuracy when trained with emoticon dataset. The paper’s main contribution is the idea of using tweets with emoticons for distant supervised learning.
Digital filtering with the second moment norm

The Backus-Gilbert inverse method relates model estimates to actual earth models by use of a resolving kernel. This inversion can in turn be related to various digital filter designs. If the second moment norm is used to define the resolving kernel, two types of filters are produced by an eigenvector decomposition of a time-weighted autocorrelation matrix. The eigenvector corresponding to the largest eigenvalue of this matrix is similar to the output energy filter, while the eigenvector for the smallest eigenvalue performs more like a deconvolution filter. Synthetic and real data examples demonstrate the characteristics of these filters and compare them to the familiar square norm filters. Our experiments suggest that second moment norm filters offer no significant advantages over their Euclidean norm relatives.
Effects of Home-Based Electrical Stimulation on Plasma Cytokines Profile, Redox Biomarkers, and Metalloproteinases in the Heart Failure with Reduced Ejection Fraction: A Randomized Trial

Background: Low-frequency electrical stimulation (LFES) is an adjuvant method for heart failure (HF) patients with restrictions to start an exercise. However, the impact on molecular changes in circulating is unknown. We investigated the effects of 10 weeks of home-based LFES on plasma cytokines profile, redox biomarkers, metalloproteinases (MMPs) activity, and exercise performance in HF patients. Methods: Twenty-four HF patients (52.45 ± 9.15 years) with reduced ejection fraction (HFrEF) (EF < 40%), were randomly assigned to a home-based LFES or sham protocol. Plasma cytokines profile was assessed through interleukins, interferon-gamma, and tumor necrosis factor levels. Oxidative stress was evaluated through ferric reducing antioxidant power, thiobarbituric acid-reactive substances, and inducible nitric oxide synthase. The MMPs activity were analyzed by zymography. Cardiorespiratory capacity and muscle strength were evaluated by cardiopulmonary test and isokinetic. Results: LFES was able to increase the active-MMP2 activity post compared to pre-training (0.057 to 0.163, p = 0.0001), while it decreased the active-MMP9 (0.135 to 0.093, p = 0.02). However, it did not elicit changes in cytokines, redox biomarkers, or exercise performance (p > 0.05). Conclusion: LFES protocol is a promising intervention to modulate MMPs activity in HFrEF patients, although with limited functional effects. These preliminary responses may help the muscle to adapt to future mechanical demands dynamically.
Observer-based robust control of a (1⩽ a ≪ 2) fractional-order uncertain systems: a linear matrix inequality approach

The study is concerned with a method of observer-based control for fractional-order uncertain linear systems with the fractional commensurate order a (1≤a<2) based on linear matrix inequality (LMI) approach. First, a sufficient condition for robust asymptotic stability of the observer-based fractional-order control systems is presented. Next, by using matrix's singular value decomposition and LMI techniques, the existence condition and method of designing a robust observer-based controller for such fractional-order control systems are derived. Unlike previous methods, the results are obtained in terms of LMIs, which can be easily obtained by Matlab's LMI toolbox. Finally, a numerical example demonstrates the validity of this approach.
POWER SERIES OF FUZZY NUMBERS

Following Talo and Basar [Determination of the duals of classical sets of sequences of fuzzy numbers and related matrix transformations, Comput. Math. Appl. 58(2009), 717–733], we essentially deal with the power series of fuzzy numbers with real or fuzzy coefficients. We consider the different cases of power series of fuzzy numbers to be convergent and give the theorem on the term by term differentiation of power series of fuzzy numbers. Finally, we present a result on the Taylor expansion of a fuzzy valued function.
Infrared-active Vibrational Modes of Fullerene C7O

Using the Lie algebraic method, the stretching fundamental IR active modes of vibrational energies of fullerenes C70 are calculated in the one-dimensional U(2) framework. By constructing the model Hamiltonian with the help of Casimir and Majorana invariant operators in this framework, we calculate the fundamental stretching IR active modes of vibration of fullerene C70 and compare the results with theoretical and experimental values to show the precision of algebraic model.
Sampling techniques and environmental variables influence the distribution of pseudoscorpions in urban forest fragments in the central Amazon

ABSTRACT The growth of urban landscapes has genarally reduced biodiversity worldwide. Invertebrates have explored different environments, and it usually takes different sampling techniques to get a representative sample of the species assemblage in a given location. Some studies have sought to determine the minimum necessary number of sampling techniques, including ecological relationships or costs to guide the sampling protocol. In the Amazon, the effect of soil characteristics on invertebrate distribution is well known. We evaluated if sampling techniques have a complementary effect on the detection of pseudoscorpion assemblages and tested whether environmental variables affect the distribution of pseudoscorpion species. The study sites were two urban forest fragments in the city of Manaus, in the central Amazon. In each fragment, we sampled 20 palm trees using the beating technique, and installed transects with 12 sampling points for collection of soil and litter samples for extraction of arthropods in a Berlese funnel and a Winkler extractor, respectively. We collected 267 individuals of 11 species of pseudoscorpions. Most records were obtained through the Winkler extraction in both fragments. The assemblage from the palm trees was different from that in the edaphic samples. Pseudoscorpion species composition also differed significantly between soil and litter, and was influenced by potassium concentration. The number of species in the fragments and the environmental effect on the distribution of pseudoscorpions was similar to that recorded in environmental protection areas, evidencing that urban forest fragments can serve as an efficient repository of Amazonian pseudoscorpion biodiversity.
Effect of Alum Sludge (AS) and Palm Kernel Ash (PKA) with Coconut Shell as Partial Replacement of Aggregates

At present, there is a high demand for concretes with varying properties. Generally, concrete with high durability can be produced by considering the constituents of the concrete, such as cement, sand, aggregates, and water. The quality of concrete is also dependent on the mix design and production method, such as transporting and pouring of concrete. In recent years, the developed countries have been focusing on the issue of sustainability in construction to create a healthier environment and reduce the environmental impact of a building throughout its lifetime while optimizing its economic viability and ensuring the comfort and safety of the occupants. One way to achieve sustainability is using agricultural waste material such as palm oil fuel ash, rice husk, fly ash slag, sludge, and coconut shell in concrete production. This study performs a series of tests to investigate the effect of using alum sludge (AS) and palm kernel ash (PKA) with coconut shells as a partial replacement of aggregates. The concrete samples were subjected to compressive strength, flexural strength, rebound hammer, ultrasonic pulse velocity, water permeability, water absorption, carbonation, alkali-silica reaction, SEM, and shrinkage tests.
Defect Detection Scheme of Pins for Aviation Connectors Based on Image Segmentation and Improved RESNET-50

In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2% and 94.4% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91% to 96.62% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability.
Assessing the Food Quality Using Carbon Nanomaterial Based Electrodes by Voltammetric Techniques

The world is facing a global financial loss and health effects due to food quality adulteration and contamination, which are seriously affecting human health. Synthetic colors, flavors, and preservatives are added to make food more attractive to consumers. Therefore, food safety has become one of the fundamental needs of mankind. Due to the importance of food safety, the world is in great need of developing desirable and accurate methods for determining the quality of food. In recent years, the electrochemical methods have become more popular, due to their simplicity, ease in handling, economics, and specificity in determining food safety. Common food contaminants, such as pesticides, additives, and animal drug residues, cause foods that are most vulnerable to contamination to undergo evaluation frequently. The present review article discusses the electrochemical detection of the above food contaminants using different carbon nanomaterials, such as carbon nanotubes (CNTs), graphene, ordered mesoporous carbon (OMC), carbon dots, boron doped diamond (BDD), and fullerenes. The voltammetric methods, such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV), have been proven to be potential methods for determining food contaminants. The use of carbon-based electrodes has the added advantage of electrochemically sensing the food contaminants due to their excellent sensitivity, specificity, large surface area, high porosity, antifouling, and biocompatibility.
A Valid and Reliable Scale for Education 4.0 Competency Determination (E4CD)

The purpose of this study is to develop a valid and reliable “Education 4.0 Competency Determination” scale. The study was conducted in two sessions: In the first session, exploratory factor analysis was applied to 308 students, and then a confirmatory factor analysis was applied to another group and conducted with 172 students. As a result, a scale consisting of 21 items and three- factors was obtained. The internal consistency coefficient of the first factor, which is mastery of digital technology, was α = . 925 using the Cronbach’s Alpha formula and . 921 using the Guttman’s Split-half method; the internal consistency coefficient of the second factor, which is information management, was α = . 880 and . 884 according to Guttman’s Split-half method while the internal consistency coefficient of the third factor, which is active participation in the process, was α = . 802 according to the Cronbach’s Alpha formula and . 783 according to the Guttman’s Split-half method.
Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials

To be useful to clinicians, prognostic and diagnostic indices must be derived from accurate models developed by using appropriate data sets. We show that fractional polynomials, which extend ordinary polynomials by including non‐positive and fractional powers, may be used as the basis of such models. We describe how to fit fractional polynomials in several continuous covariates simultaneously, and we propose ways of ensuring that the resulting models are parsimonious and consistent with basic medical knowledge. The methods are applied to two breast cancer data sets, one from a prognostic factors study in patients with positive lymph nodes and the other from a study to diagnose malignant or benign tumours by using colour Doppler blood flow mapping. We investigate the problems of biased parameter estimates in the final model and overfitting using cross‐validation calibration to estimate shrinkage factors. We adopt bootstrap resampling to assess model stability. We compare our new approach with conventional modelling methods which apply stepwise variables selection to categorized covariates. We conclude that fractional polynomial methodology can be very successful in generating simple and appropriate models.
Multi‐payload measurement of transverse velocity shears in the topside ionosphere

Using a multi‐payload sounding rocket mission, we present the first direct measurement of velocity shear in the topside auroral ionosphere. In regions of large, ∼200 mV/m, transient electric fields we directly measure differences in the plasma drift velocity. From these differences, shear frequencies reaching ±6Hz are measured. These directly measured shears are compared with the shear inferred from single payload measurements. It is shown this traditional measurement of shear overestimates the shear frequency by a factor of two for this event, highlighting the importance of the temporal component of near‐DC electric field structures. Coincident with these strong fields and shears are enhanced emissions of broadband, extremely low frequency (BB‐ELF) plasma waves, and a narrowband wave emission near the H+‐O+bi‐ion resonant frequency.
Health Consequences of the COVID-19 Pandemic among Health-Care Workers: A Comparison between Groups Involved and Not Involved in COVID-19 Care

Health consequences have been reported among health-care workers (HCWs) exposed to COVID-19. Sweden chose to manage the pandemic with a lower and more equal long-lasting work strain and shorter periods of recovery than in other countries. Few studies have examined the health consequences among HCWs working in such conditions. This study compared the health consequences after the first wave of the COVID-19 pandemic between HCWs involved in the care of COVID-19 patients and other HCWs and between occupational groups working in COVID-19 care. Multinomial logistic regression and univariate general linear models were used to identify differences. The levels of depression, emotional and physical fatigue, sleep quality, and general health were measured 6 months after the onset of the pandemic in 3495 HCW employed in Sweden. HCWs directly involved in COVID-19 care reported significantly poorer sleep quality and higher scores on emotional and physical exhaustion than those not involved in such care. Health consequences did not differ significantly between different occupational groups involved in COVID-19 care except for specialist nurses/midwives. HCWs more frequently involved in COVID-19 care reported higher levels of emotional and physical fatigue and poorer sleep but less severe than those reported in more severely affected countries.
Exactly solvable model of electronic states in a three-dimensional disordered Hamiltonian: non-existence of localized states

An exactly solvable model of a disordered Hamiltonian, valid in three dimensions, is presented. The ensemble-averaged Green function, and hence the density of eigenstates, for the model are found exactly. It is shown that the states in the tail of the distribution are not localized as has been suggested by several authors.
FAKTOR YANG MEMPENGARUHI PARTISIPASI PASIEN DIABETES MELITUS (DM) DALAM MENGIKUTI KEGIATAN PROLANIS DI PUSKESMAS LAKESSI KOTA PAREPARE

Upaya mengurangi peningkatan penderita penyakit kronis dan meminimalisir pembiayaan kesehatan untuk penyakit kronis, salah satu upaya BPJS Kesehatan bekerjasama dengan FKTP merancang suatu program yaitu Prolanis dengan target pemenuhan rasio Prolanis yang rutin mengikuti kegiatan Prolanis dengan indikator 75% kehadiran. Tujuan penelitian ini untuk mengetahui faktor Dukungan Keluarga, Motivasi untuk Sehat, Pelayanan Petugas Prolanis, dan Sarana dan Prasarana mempengaruhi partisipasi pasien Diabetes Melitus (DM) dalam mengikuti kegiatan Prolanis di Puskesmas Lakessi Kota Parepare. Metode yang digunakan dalam penelitian ini adalah survey analitik dengan pendekatan Cross Sectional Study. dengan menggunakan kuesioner pada sampel, dimana sampel dalam penelitian ini sebanyak 56 orang. Data dianalisis secara univariat dan bivariat menggunakan uji statistik Chi Square . Hasil penelitian menunjukkan bahwa ada pengaruh dukungan keluarga (0,001 < 0,05), ada pengaruh motivasi untuk sehat (0,003 < 0,05), tidak ada pengaruh pelayanan petugas Prolanis (0,126 > 0,05), tidak ada pengaruh sarana dan prasarana (0,501 > 0,05), dalam mengikuti kegiatan Prolanis di Puskesmas Lakessi Kota Parepare. Bagi petugas Prolanis sebaiknya melakukan kerja sama dan sosialisasi kepada keluarga untuk meningkatkan dukungan kepada pasien agar aktif berpartisipasi dalam kegiatan Prolanis
Focused Information Criteria and Model Averaging for the Cox Hazard Regression Model

This article is concerned with variable selection methods for the Cox proportional hazards regression model. Including excessive covariates causes extra variability and inflated confidence intervals for regression parameters; thus regimes for discarding the less informative ones are needed. Our framework has p covariates designated as “protected,” while variables from a further set of q covariates are examined for possible inclusion or exclusion. We develop a focused information criterion (FIC) that for given interest parameter finds the best subset of covariates. Thus the FIC might find that the best model for predicting median survival time is different than the best model for estimating survival probabilities, and the best overall model for analyzing men's survival might not be the same as the best overall model for analyzing women's survival. Methodology is also developed for model averaging, wherein the final estimate of a quantity is a weighted average of estimates computed for a range of submodels. Our methods are illustrated in simulations and for a survival study of Danish skin cancer patients.