Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiyear versus single‐year El Niño events: Contrasting their impacts on South American seasonal precipitation

Differences in the seasonal distribution of precipitation over South America (SA) associated with single‐year (SY) and multiyear (MY) El Niño (EN) events were analysed based on reanalysis data for the 1901–2012 period. The results suggest that Pacific Sea Surface Temperature (SST) anomalies associated with MY EN events interact with the tropical Atlantic and Indian Oceans SST from austral fall to spring, after the event's first year, affecting SA precipitation distribution in subsequent seasons. Compared to SY EN events, which reproduce well the north–south dipolar precipitation anomaly pattern over SA, the MY EN events show differences in the intensity and positioning of precipitation anomalies. Precipitation decreases over northern SA during all seasons are observed for SY and MY EN events except for differences in magnitudes. Variations in the position and longitudinal extension of the downward motions of Walker circulation in response to these events explain these differences. Over southern and southeastern SA, differences in anomaly positioning are more evident. The positive precipitation anomalies over these regions in the austral spring and summer are weakened and southward shifted during the MY EN in relation to those during SY EN events. These variations are associated with the Rossby‐wave train pattern path that depends on the EN and season. Consequently, the associated local atmospheric circulation patterns also depend on the season. The intense (weak) South American low‐level jet (SALLJ) for the first year of MY EN contrasts with the weak or inexistence (intense) SALLJ for the SY EN during summer (springer). Complementary to previous studies, the results indicate that the differences in the intensity and duration of the SY and MY EN events contribute to changes in the EN‐related atmospheric teleconnection pattern that impacts SA precipitation. The results can be useful for climate prediction and monitoring purposes.
Adoption of Digital Banking: Insights from a UTAUT Model

The study examines the factors that affect the adoption of digital banking by clients of commercial banks in Nepal. The paper used a survey questionnaire to collect primary data from a sample of 384 respondents and applied statistical tools including descriptive statistics, correlation analysis, reliability testing, and regression analysis to analyse the data. The results showed that facilitating conditions, effort expectancy, and habit had a significant positive impact on digital banking adoption, while social influence and performance expectancy did not have a significant effect. The regression analysis identified habit as the strongest predictor of digital banking adoption, followed by effort expectancy and facilitating conditions. Overall, the findings suggest that banks should focus on making their digital banking services convenient and reliable to encourage more widespread adoption.
Label-setting algorithms for a polynomial bi-objective multimodal shortest path problem

Taking into account the multimodality of urban transportation networks for computing the itinerary of an individual passenger introduces a number of additional constraints such as restriction and/or preferences in using some modes. In this paper, such constraints are gathered under the concept of viable path modeled by a deterministic nite state automaton. Several polynomial algorithms are proposed to solve a bi-objective problem where the goal is to find all the nondominated viable shortest paths under the two objectives travel time and number of modal transfers. Among these algorithms, we consider an improved variant of the topological label-setting algorithm provided by Lozano and Storchi (2001), a new multilabel multi-queue algorithm and its bidirectional variant. The different algorithms are compared on a real network. The results show that, on the considered network, the proposed algorithms outperform the Lozano and Storchi (2001) algorithm, both for the time-independent and time-dependent case. Finally, A* acceleration techniques are discussed.
Examining Language Learning Strategies (LLS) Through the Lens of Gen Alpha ESL Learners

The ability to communicate with others in English is pivotal in this digital era, thus it is only natural to expect the digital natives like Generation Alpha to have proper English skills as it is a common language used in technology and communication. While numerous studies had been conducted to identify the preferred language learning strategies (LLS) by the English as Second Language (ESL) students, there is a paucity of studies on the LLS used by Generation Alpha, thus this study aims to shed some lights to the teachers on how they can offer assistance for their learners so that they can autonomously choose the best method that can help them refine their speaking skill. 50 primary students aged nine to twelve were selected using a purposive sampling method; they are all good language learners. The Strategy Inventory for Language Learning (SILL) was adapted and modified to assess the frequency of use of LLS. The results of the descriptive analysis revealed that metacognitive and social strategies are the most dominant LLS used by Generation Alpha while affective strategy was the least used by them in enhancing their speaking skill. Future studies should consider finding the correlation between the use of digital tools and Generation Alpha’s preferred LLS as digitalisation has apparently shifted the education paradigm. It is imperative that teachers are provided with clearer apprehension on how they can develop more effective teaching strategies for Generation Alpha based on the LLS that the pupils are gravitated to.
Ionic parameters identification of an inverse problem of strongly coupled PDE’s system in cardiac electrophysiology using Carleman estimates

In this paper, we consider an inverse problem of determining multiple ionic parameters of a 2 × 2 strongly coupled parabolic–elliptic reaction–diffusion system arising in cardiac electrophysiology modeling. We use the bidomain model coupled to an ordinary differential equation (ODE) system and we consider a general formalism of physiologically detailed cellular membrane models to describe the ionic exchanges at the microscopic level. Our main result is the uniqueness and a Lipschitz stability estimate of the ion channels conductance parameters of the model using subboundary observations over an interval of time. The key ingredients are a global Carleman-type estimate with a suitable observations acting on a part of the boundary.
Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions.

BACKGROUND As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. METHODS To solve the aforementioned problems, nonlinear feature Lempel-Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. RESULTS The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. CONCLUSIONS This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment.
Towards a framework for a collaborative support model to assist infection prevention and control programmes in low- and middle-income countries: a scoping review

Background: Infection prevention and control (IPC) in low- and middle-income countries (LMIC) is reported to be poorly supported because of limits in financial, human and physical resources and competing priorities in health budgets. As a result, there is often a role for external agencies to assist in strengthening IPC. While there are reports of how these partnerships have been put into practice, there are no reported frameworks or guidance documents to support the development of such relationships. Aim: The aim of this study is to identify the core elements of a collaborative support framework to assist LMIC in strengthening IPC. Methods: To achieve this, a systematic scoping review of available literature was conducted based on the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020). The databases MEDLINE, CINAHL, Embase and Scopus were utilised. The search strategy included different combinations of Medical Subject Heading (MeSH) terms, Emtree and keywords that are relevant to IPC collaboration in LMIC. Literature was limited to that published between 2005 and 2020 in the English language only. Results: Six core elements of comprehensive IPC collaborative support were identified with five IPC programme areas as minimum requirements, namely: 1) Collaborative Projects, 2) Policies and Procedures, 3) Training and Professional Development, 4) Surveillance Systems and 5) Assessment and Feedback. The last element, 6) Partnerships, was identified as an enabling factor. Conclusion: These six core elements should be considered when building a collaborative support model to assist IPC in LMIC.
Use of reminder box for the amount of iron tablet consumption in pregnant women

One of the health issues affecting pregnant women in Indonesia is iron deficiency anemia. Iron tablet supplementation is used to manage anemia, but this program has not been successful because of low compliance from forgetfulness. The use of a reminder can increase the amount of iron pill consumption. This study aimed to determine how much iron pill ingestion was impacted by the reminder box. This kind of research uses a post-test-only control group, which has a quasi-experimental design. Sixty respondents were divided into 30 intervention groups and 30 control groups. The intervention group received iron tablets in a box marked with the day, date, and time as a reminder to take their prescription, while the control group received theirs in plastic. Both groups were observed for 30 days, recording their observations on a monitoring form. An impartial t-test was utilized in the data analysis. The average amount of iron tablets ingested did not differ between the intervention and control groups (p= 0,417), although the intervention group consumed more than the average amount. Reminder boxes enhance consumption, but additional proof is required. It is unclear whether placing reminders where they are simple to notice and access will impact compliance.
PERCEPTION OF ENGLISH LANGUAGE TEACHERS ON USE OF LIFE-STAGING TECHNIQUE DURING ENGLISH LANGUAGE TEACHING IN PUBLIC SECONDARY SCHOOLS IN WARENG SUB-COUNTY, KENYA.

a positive attitude towards use of life staging technique. The study recommended that teachers of English needed to use learner-centered approaches like life-staging technique since the strategy improved on learning outcomes. The findings of this study will help education stakeholders and policy makers to come up with better teaching techniques that will suit English as a second Language classroom and improve English language performance in secondary schools.
Predictors of Body Mass Index among Pregnant Women in Nigeria: A Comparison of Ordinary Least Squares Regression and Quantile Regression Models Using Machine Learning Approach

Poor nutrition during pregnancy is a major public health problem. Maternal under nutrition is a significant risk factor for maternal morbidity, mortality, poor birth outcomes (e.g. low birth weight), and infant mortality. Maternal under nutrition is defined as having a body mass index (BMI) <18.5 kg/m2. Previous studies on maternal BMI utilized classical statistical approach, whose criteria for model assessment are goodness-of-fit test and residual examination. The aim of this study was to identify predictors of BMI among pregnant women in Nigeria, and to compare the performance of ordinary least squares (OLS) regression and quantile regression using machine learning approach. This study utilized data from the 2013 Nigeria Demographic and Health Survey. A total of 3,049 pregnant women were included in the study. Data were summarized using descriptive statistics. The assumption of normality of the outcome variable (BMI) was tested using one-sample Kolmogorov-Smirnov test. Bivariate associations of BMI with independent variables were assessed using robust (nonparametric) statistical techniques: Kendall’s tau correlation for continuous predictors, Wilcoxon rank sum test for binary predictors and Kruskal-Wallis test for multinomial predictors. Predictors of maternal BMI were investigated using OLS and quantile regression analyses. Model assessment was made using 10-fold cross-validation. A two-tailed p-value <0.05 was considered statistically significant. The respondents had a mean age of 28.22 ± 6.30 years, and a mean BMI of 23.81 ± 4.18 kg/m2. Multivariate analyses identified respondent’s age, duration of pregnancy, wealth class, and residence as predictors of maternal BMI. The crossvalidated mean squared error for the OLS regression model was lower than that for the quantile regression model. Respondent’s age, duration of pregnancy, wealth class, and residence were significantly associated with maternal BMI. OLS regression model fit the data more than the quantile regression model. Citation: Ajayi DT, Bello S (2018) Predictors of Body Mass Index among Pregnant Women in Nigeria: A Comparison of Ordinary Least Squares Regression and Quantile Regression Models Using Machine Learning Approach. J Biom Biostat 9: 402. doi: 10.4172/2155-6180.1000402
Lung and Colorectal Cancer Disparities in Appalachian Kentucky: Spatial Analysis on the Influence of Education and Literacy

Low educational attainment and high cancer incidence and mortality rates have long been a challenge in Appalachian Kentucky. Prior studies have reported disparities in cancer incidence and mortality between Appalachian and non-Appalachian populations, but the influence of education on this disparity has not been extensively studied. Herein, all cancers and two cancer sites with available screenings (colorectal and lung) were joined with education indicators (educational attainment and literacy) and one geographic indicator across all 120 Kentucky counties. This dataset was used to build choropleth maps and perform simple linear and spatial regression to assess statistical significance and to measure the strength of the linear relationship between county-level education and cancer-related outcomes in Appalachian and non-Appalachian Kentucky. Among all cancer sites, age-adjusted cancer incidence and mortality was higher in Appalachian versus non-Appalachian Kentucky. The percentage of the population not completing high school was positively correlated with increased colorectal and lung cancer incidence and mortality in Appalachia. Similarly, counties with a higher percentage of the population lacking basic literacy had the strongest correlation with colorectal and lung cancer incidence and mortality, which were concentrated in Appalachian Kentucky. Our findings suggest a need for implementing interventions that increase educational attainment and enhance basic literacy as a means of improving cancer outcomes in Appalachia.
Exploring the Synergistic Association Between Oral Health Status and Oral Health Literacy Among College Students: A Cross-Sectional Study

Introduction: Oral health is often viewed as a significant component for an indication of good general health or for good well-being together with a decent quality of life. Health literacy is considered a crucial factor in improvising a good life or excellent health. Oral health literacy (OHL) is the ability necessary for people to identify the factors that lead to poor oral health, learn and put into practice the essentials of effective oral self-care behaviors, and communicate with oral healthcare professionals in order to schedule appointments, put their names on waiting lists for dental care, and locate the dentist's office. Aim and objectives: To evaluate students' oral health, their OHL, to determine the relationship between their oral health status and OHL, and finally to suggest preventive measures for the benefit of public health. Materials and method: At Teerthanker Mahaveer University, a cross-sectional study involving 1500 participants, ages 18 to 25, was conducted on students studying nursing, physiotherapy, paramedicine, engineering, and law. Their informed consent was obtained. The Rapid Estimate of Adult Literacy in Dentistry (REALD-99) was used to gauge OHL levels, and the WHO's 1997 Oral Health Survey was used for their clinical evaluation. Results: The mean REALD score was significantly higher in nursing students (88.32±6.46), followed by physiotherapy college (82.46±9.11), paramedical college (70.54±10.95), law college (46.52±7.74), and least in engineering college (38.80±10.65). The difference in the REALD score based on college was statistically significant. Along with this, the REALD score showed a correlation with gender and location too. Except for fluorosis, all the clinical parameters of dental caries, gingival bleeding, and pockets, loss of attachment, dental fluorosis, and dental enamel were associated with OHL. Conclusion: The results of the current study showed a relationship between educational attainment, clinical parameters examined, and OHL, leading to the conclusion that higher OHL is related to better oral health. So, we can conclude that maintaining good oral health requires OHL.
Distributed Fusion With Unknown Inputs Under Bandwidth-Aware Event-Triggered Mechanisms: Monotonicity and Boundedness

This article addresses the distributed fusion estimation problem for a class of nonlinear systems subject to both unknown inputs and sensor failures, where a bandwidth-aware dynamic event-triggered strategy is proposed to reduce communication burden and energy consumption. First, a novel distributed filter is constructed with the aid of an intermediate variable resolving unknown inputs. In light of such a structure combined with Kalman filtering theory, an upper bound of the filtering error covariance is derived and subsequently minimized by designing appropriate gains. Furthermore, the distributed fusion depends on an optimization obtained under the covariance intersection fusion strategy. The ultimate boundedness and the monotonicity with respect to triggered thresholds are discussed for the minimized upper bound of the fusing error covariance. Finally, the effectiveness of the proposed method is vivid through illustrative simulation examples.
Search for Low Mass Dark Matter Particles with the CRESST Experiment

It has been proven by several astronomical observations that dark matter exists, but no particle candidates have been observed yet. The CRESST experiment aims to directly detect dark matter particles elastically scattering off nuclei in CaWO4 crystals which are operated at mK temperatures. With nuclear recoil energy thresholds as low as 0.3 keV [2] and 0.6 keV [3], for the detector modules LISE and TUM40, respectively, CRESST is ideally suited for the detection of low-mass dark matter particles [5]. Additionally, the radiopurity of the crystals is another important factor for the detector performance. For a detailed understanding of the detector backgrounds, we simulate the radioactive contaminations of the TUM40 detector module with Geant4. The outcome of this simulation will be vital for the CRESST-III experiment. In this contribution, we discuss our results of the search for dark matter and dark photons achieved with the detector module Lise of CRESST-II. We will discuss the status of CRESST-III Phase 1 which started taking data in 2016.
Protein-Mediated Electroporation in a Cardiac Voltage-Sensing Domain Due to an nsPEF Stimulus

This study takes a step in understanding the physiological implications of the nanosecond pulsed electric field (nsPEF) by integrating molecular dynamics simulations and machine learning techniques. nsPEF, a state-of-the-art technology, uses high-voltage electric field pulses with a nanosecond duration to modulate cellular activity. This investigation reveals a relatively new and underexplored phenomenon: protein-mediated electroporation. Our research focused on the voltage-sensing domain (VSD) of the NaV1.5 sodium cardiac channel in response to nsPEF stimulation. We scrutinized the VSD structures that form pores and thereby contribute to the physical chemistry that governs the defibrillation effect of nsPEF. To do so, we conducted a comprehensive analysis involving the clustering of 142 replicas simulated for 50 ns under nsPEF stimuli. We subsequently pinpointed the representative structures of each cluster and computed the free energy between them. We find that the selected VSD of NaV1.5 forms pores under nsPEF stimulation, but in a way that significant differs from the traditional VSD opening. This study not only extends our understanding of nsPEF and its interaction with protein channels but also adds a new effect to further study.
Space Weather Nowcasting for Area‐Denied Locations: Testing All‐Sky Imaging Applications at Geomagnetic Conjugate Points

We explore the concept of using an all‐sky imager (ASI) in one hemisphere to provide nowcasting of ionospheric perturbations in the opposite hemisphere. The specific example deals with low‐latitude plasma instabilities known as equatorial spread F (ESF) that largely depend on geomagnetic field controlled electrodynamics. ASI observations of 630.0 nm airglow from ~300 km exhibit regions of low emission (“airglow depletions”) that correlate highly with ESF patterns of radio wave disruptions, for example, from GPS satellites. For both oceanographic and geopolitical reasons, there are vast regions of the globe that cannot be used for ground‐based nowcasting of local ESF effects. For such area‐denied locations, it is possible to use observations of airglow depletions from the opposite hemisphere to specify both local and conjugate location environmental impacts. We use 15 months of ASI observations from the El Leoncito Observatory (Argentina) to predict simultaneous conditions at its trans‐equatorial geomagnetic conjugate point in Villa de Leyva (Colombia)—validated by independent ASI observations at that location. We find the success rate of conjugate point nowcasting to be greater than 95% for large‐scale ESF occurrence patterns. For a different pair of stations at higher magnetic latitudes, 3 years of observations from the Arecibo Observatory (Puerto Rico) were used to make ESF nowcasting at its conjugate point in Mercedes (Argentina) with a similar high success rate.