Miracle-dz/UniFAR-FTUs huggingface dataset:
Saving and Spending Attitudes of College Students

Aims: To determine the saving and spending attitudes of college students. Study Design: Descriptive and semi-quantitative. Place and Duration of Study: Malita, Davao Occidental, Philippines during the first semester of 2019-2020 Methodology: The respondents of the study were chosen using the Slovin formula. One hundred thirty-three college students were involved. Data were gathered through an adapted survey questionnaire. Results: Generally, the mean age of the respondents was 20.54. Most of them were female. The respondents saved money regularly for emergency use. Their parents were their usual source of money and they kept their savings in the piggy bank. The money they obtained was commonly spent on school projects. Others did not have savings due to lack of money. Only few of the respondents deposited their savings in the banks. It was also found that age determined the saving attitude. Conversely, if considered individually, saving attitude had no significant difference with gender and marital status but when grouped together, saving attitude had significant difference with gender and marital status. It implicated that saving money should be introduced early among the children.
Information System (Is) Models: Technology as a Service for Agricultural Information Dissemination in Developing Countries (Uganda). A Systematic Literature Review

This article summarizes the current literature by reviewing the concepts, applications, and development of technology adoption models and theories that are supported by the literature review, with the novelty technology’s prospective application being the main focus. These included but were not limited to, the concepts of Diffusion of Innovations (DIT) (Rogers, 1995), Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1995), and Diffusion of Innovations (DIT) (Rogers, 1995). Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991), Theory of Planned Behaviour, (Taylor and Todd, 1995), the Technology Acceptance Model (TAM) (Davis, Bogozzi and Warshaw, 1989, Technology Acceptance Model two (TAM2) Venkatesh and Davis (2000), Technology Acceptance Model three (TAM3) Venkatesh and Bala (2008), Unified Theory of Acceptance Model (UTAUT) Venkatesh et al; 2012 and the Extended Unified Theory of Acceptance Model (UTAUT2) Venkatesh et al; 2016. These assessments can give some information on technology adoption levels and potential applications for future researchers to consider, recognize and comprehend the underlying technology models and ideas that will have an impact on the preceding, current, and future applications of technology adoption and agricultural information dissemination by smallholder rural farmers.
Monitoring and Evaluation of National Vaccination Implementation: A Scoping Review of How Frameworks and Indicators Are Used in the Public Health Literature

An effective Monitoring and Evaluation (M&E) framework helps vaccination programme managers determine progress and effectiveness for agreed indicators against clear benchmarks and targets. We aimed to identify the literature on M&E frameworks and indicators used in national vaccination programmes and synthesise approaches and lessons to inform development of future frameworks. We conducted a scoping review using Arksey and O’Malley’s six-stage framework to identify and synthesise sources on monitoring or evaluation of national vaccination implementation that described a framework or indicators. The findings were summarised thematically. We included 43 eligible sources of 4291 screened. Most (95%) were in English and discussed high-income (51%) or middle-income (30%) settings, with 13 in Europe (30%), 10 in Asia-Pacific (23%), nine in Africa (21%), and eight in the Americas (19%), respectively, while three crossed regions. Only five (12%) specified the use of an M&E framework. Most (32/43; 74%) explicitly or implicitly included vaccine coverage indicators, followed by 12 including operational (28%), five including clinical (12%), and two including cost indicators (5%). The use of M&E frameworks was seldom explicit or clearly defined in our sources, with indicators rarely fully defined or benchmarked against targets. Sources focused on ways to improve vaccination programmes without explicitly considering ways to improve assessment. Literature on M&E framework and indicator use in national vaccination programmes is limited and focused on routine childhood vaccination. Therefore, documentation of more experiences and lessons is needed to better inform vaccination M&E beyond childhood.
Automedicación en estudiantes de pregrado de enfermería

Introduction: Self-medication is a common behavior in society and a frequent practice among university students. It is a severe health problem that affects the familiar, individual, and collective health.Objective: To determine the reasons that lead to self-medication in nursing students at the Santiago de Cali University, Colombia.Method: A descriptive cross-sectional study was conducted with 234 undergraduate nursing students. The study was carried out between April and May 2020. To calculate the sample, we applied the statistical formula with a confidence level of 95%, a margin of error of 5%, and a desired confidence interval of 5%. We used a virtual, validated questionnaire, which the students filled out voluntarily.Results: Ninety percent of the participating students were between 18–26 years old. The mean was 21.7, while the median was 20, and the mode was 19 years. A total of 81% were women. A total of 69% of the students stated that they self-medicate. Pain relievers are the drugs of choice for self-medication. Among the reasons for self-medication, it was found that: 44% of participants do not believe that it is necessary to consult a doctor, while 20% expressed not having the time to consult with a doctor. The participants obtain the medication in the pharmacy (95%). Conclusions: Self-medication is a common practice in nursing students between the first and fifth semesters of the Santiago de Cali University. Although it is recognized as a risky practice, the prevalence is high. Introducción: La automedicación es una conducta común en la sociedad y práctica frecuente en estudiantes universitarios. Es un problema severo de salud que tiene implicaciones en la salud familiar, individual y colectiva. Objetivo: Determinar las razones para la automedicación en estudiantes de enfermería de la Universidad Santiago de Cali, Colombia. Método: Se realizó un estudio descriptivo transversal entre 234 estudiantes de pregrado de enfermería. El estudio fue realizado, entre abril y mayo de 2020. Para el cálculo de la muestra se aplicó la fórmula estadística con un nivel de confianza de 95% y margen de error de 5% e intervalo de confianza deseado de 5%. Se utilizó un cuestionario validado, diligenciado de forma virtual y desarrollado de forma voluntaria por los estudiantes. Resultados: El 90% de los estudiantes estaban comprendidos entre los 18- 26 años; media de 21,7, mediana de 20 y moda de 19 años. El 81% fueron mujeres. El 69% de los estudiantes manifestaron automedicarse. Los analgésicos son los medicamentos de elección para la automedicación. Entre las razones para automedicación se encontró que: el 44% no cree que sea necesario consultar al médico, el 20% no tiene tiempo para asistir al médico. La fuente para obtener el medicamento es la farmacia (95%). Conclusiones: La automedicación es una práctica común en los estudiantes de enfermería de primero a quinto semestre de la Universidad Santiago de Cali. Aunque se reconoce que es una práctica riesgosa, la prevalencia es alta.
Nutritional Status of Elderly Population at Gokarneshwor Municipality, Kathmandu

Background: Elderly populations are the vulnerable population and yet still neglected one. The main aim of the research is to assess the nutritional status of elderly population at Gokarneshwor municipality, Kathmandu. Methods: Quantitative cross sectional study was conducted in the time frame of July 15-Jan 15, 2020 among 301 elderly population of Gokarneshwor municipality, Kathmandu. Proportionate systematic sampling was applied for elderly selection. Descriptive and inferential statistical analysis was performed by using the software Statistical Package for Social Sciences and Microsoft Excel. Results: Study reflected that most of the elderly (48.5%) had normal body mass index followed by overweight (24.6%), underweight (18.6%) and obese (8.3%) respectively. Currently 27.2% used to consume alcohol and 24.3% used to smoke. More than one third (39.1%) elderly reported psychological stress or disease. Most of the elderly (40.1%) had high pressure followed by Diabetes (27.1%), Chronic Obstructed Pulmonary Disease (23.6%), heart disease (7.1%) and heart stroke (1.9%) respectively. Elderly nutritional status was statistically significant among marital status (P=0.022), food intake in recent three month (P=0.024), weight loss (P=0.001), mobility (P=0.004), self-perception of health status (P=0.029), psychological stress (P<0.001) are associated with malnutrition. Conclusion: Considerable numbers of elderly were found to be malnourished and many were at risk of malnutrition. Intervention focusing food habit, weight status, mental status, physical activity need to be developed which ultimately helps to balance nutritional status. Further intensive research could discover the factors for improving nutritional status of elderly. Key words: Nutritional status, elderly population, health status, dietary intake, body mass index.
Electronic Structure and IR Spectra Analysis of Tetrathiafulvelene (TTF) Using RHF and DFT Quantum Mechanical Methods

Tetrathiafulvalene (C6H4S4) is an organosulfur compound used in the manufacture of organic optoelectronic materials. Restricted Hartree-Fock (RHF) and Density Functional Theory (DFT) were employed for the study of the optimized molecular structure of TTF using different basis sets. All computations were performed using Gaussian 03 package. Parameters such as minimum energy, bond lengths and bond angles, HOMO-LUMO energy gap, chemical reactivity descriptors, Non-linear optical properties (such as isotropic polarizability (α), anisotropy of polarizability (∆∝), and total first hyper-polarizability (βtot)), density of states, vibrational frequencies, and intensities were computed and reported in order to determine the relative stability as well as chemical reactivity of the molecule. The results obtained show that RHF has the lowest average value of bond length of 1. 0729Å while that obtained using DFT has the lowest average value of 1. 0812Å using the same basis set 6-311++G (d, p). This shows that the value is a bit higher using DFT than RHF which implies that the bonds of TTF molecule will be slightly stronger when optimized using RHF than DFT. The bond angles were found to be slightly higher by using DFT than RHF. The calculated HOMO-LUMO energy gap shows that the molecule will be slightly more stable in chemical reaction using RHF than DFT. The DFT values, 3.59 eV and 3.60 eV obtained for the energy gap are closer to the reported value of 3.63eV compared to those obtained by RHF. From the results obtained for vibrational frequencies using both methods, TTF is stable due to the absence of imaginary frequencies. This confirms the stability of the molecule as stated in the results of HOMO-LUMO energy gap. The calculated vibrational frequencies show that the most intense frequency was obtained to be 752.6293 cm-1 at corresponding intensity of 145.9063KM/mole by RHF/6-31++G (d, p) while at B3LYP/6-31+G(d), the most intense frequency is about 635.0243 cm-1 with corresponding intensity of 138.5738MK/mole. By and large, the studyunveil the potential of Tetrathiafulvalene for optoelectronic applications.
Influence of key site parameters on the thermal performance of double skin facades in naturally ventilated buildings in a tropical climate.

Double skin facades (DSFs) have gained recognition as architectural elements in modern office buildings which, when appropriately applied, can potentially lead to improvements in the indoor thermal comfort and reduction in building energy consumption even in warm or tropical climates. This technology that utilises the renewable resources of solar and wind to reduce the air-conditioning demand in such climates is a potential solution to the current environmental challenges. This study examines the influence of key ‘site’ parameters, by keeping the ‘building’ parameters constant, on the thermal behaviour of an optimized building model with DSF. The site parameters represent the variables of two local environmental conditions: the level of local solar incidence, which relates to the influence of hours of the day, solar angle, sky conditions (cloudy and clear) and facade orientation on the building behaviour; and the wind conditions, which account for the effects of speed and direction acting on the DSF. Using the climate data of Rio de Janeiro city as the tropical environmental context, building energy simulations are performed to the defined DSF models. Airflow levels and periods of thermal acceptance, based on the adaptive comfort criteria in relation to the outdoor environmental variations, are analysed to demonstrate the site conditions under which the technology is likely to operate effectively.
PENGARUH LEVERAGE, FIRM SIZE, LIKUIDITAS DAN PROFITABILITAS TERHADAP CASH HOLDING

The purpose of this research is to obtain empirical evidence regarding the effect of leverage, company size, liquidity and profitability on cash holding manufacturing companies listed on the Indonesia Stock Exchange in the period 2015-2017. This research uses 68 selected manufacturing companies using a purposive sampling method from a total of 148 companies for 3 years. Data is processed using multiple regression analysis using SPSS version 24. The results of this study indicate that company size, liquidity and profitability have a significant effect on cash while leverage has a insignificant effect on holding cash.
An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of O(NpNm2), where Np is the number of particles, and Nm is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of O(Nm2). We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF.
Self-Supervised Robust Feature Matching Pipeline for Teach and Repeat Navigation

The performance of deep neural networks and the low costs of computational hardware has made computer vision a popular choice in many robotic systems. An attractive feature of deep-learned methods is their ability to cope with appearance changes caused by day–night cycles and seasonal variations. However, deep learning of neural networks typically relies on large numbers of hand-annotated images, which requires significant effort for data collection and annotation. We present a method that allows autonomous, self-supervised training of a neural network in visual teach-and-repeat (VT&R) tasks, where a mobile robot has to traverse a previously taught path repeatedly. Our method is based on a fusion of two image registration schemes: one based on a Siamese neural network and another on point-feature matching. As the robot traverses the taught paths, it uses the results of feature-based matching to train the neural network, which, in turn, provides coarse registration estimates to the feature matcher. We show that as the neural network gets trained, the accuracy and robustness of the navigation increases, making the robot capable of dealing with significant changes in the environment. This method can significantly reduce the data annotation efforts when designing new robotic systems or introducing robots into new environments. Moreover, the method provides annotated datasets that can be deployed in other navigation systems. To promote the reproducibility of the research presented herein, we provide our datasets, codes and trained models online.
Degenerative injuries of the metatarsophalangeal plantar plate on magnetic resonance imaging: a new perspective

ABSTRACT Objective The magnetic resonance imaging diagnostic criteria for a complete tear of metatarsophalangeal plantar plate are well-established. However, more subtle abnormalities can also occur and be a source of pain. The objective of this study is to determine the prevalence of degenerative plantar plate injuries in patients with metatarsalgia who underwent forefoot magnetic resonance imaging and describe the main abnormalities found. The hypothesis is that mild capsular fibrosis will have high sensitivity but low specificity for plantar plate degenerative injuries. Methods A retrospective cross-sectional study was conducted with 85 patients (105 feet) with metatarsalgia who underwent forefoot magnetic resonance imaging using a specific protocol to study metatarsophalangeal plantar plate. The experiment observer classified second toe plantar plate as normal, complete rupture or degenerative lesion and described the main magnetic resonance imaging findings. Results A normal plantar plate was observed in 75 (71.4%) of the 105 feet assessed, in 25 (24%) feet there were degenerative plantar lesions, and in 5 (4.6%) feet there were complete ruptures. Degenerative injury of the plantar plate was best identified in coronal short axis intermediate-weighted images, with high sensitivity (92%). Pericapsular fibrosis below the intermetatarsal ligament was identified in 96% of cases, with high sensitivity (96%) for diagnosis of degenerative plantar plate injury. Conclusion Degenerative lesions of the metatarsophalangeal plantar plate were more prevalent than complete ruptures and were best viewed in coronal short axis intermediate-weighted sequences. Pericapsular fibrosis below the intermetatarsal ligament was the indirect finding most strongly associated with degenerative plantar plate injury.
Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach

The deep integration of industrial internet of things technologies in the industrial smart grid (ISG) brings many privacy and security attacks, threatening the trustworthiness of underlying system infrastructures and associated services. That, in turn, raises the necessity for anomaly detection staged by appropriate authorities. Deep learning can provide a promising solution for anomaly detection, but it remains untrustworthy as it fails to do well with small-size labeled data and class-imbalanced data. To solve these issues, this article introduces a novel privacy-preserving federated semisupervised class-rebalanced (Fed-SCR) framework for the detection of anomalous power data in fog-assisted smart grids. Fed-SCR introduces a semisupervised generative network to enhance the quality of generated minority samples and model the relationships between labeled and unlabeled data. Moreover, the generator and discriminator are block-structured leveraging temporal convolutions to improve the representation power during the training. A novel aggregation scheme [termed federated geometric median aggregation (Fed-GMA)] is also introduced based on selective and periodic geometric-median-based aggregation with the main aim to increase the robustness of the federated semi supervised class-rebalanced (Fed-SRC) against noisy gradients while maintaining communication efficiency. The evaluations of Fed-SCR on public power grid datasets reveal its efficiency in improving the trustworthiness of the ISG platform, outperforming the competing methods in terms of binary classification (accuracy: 97.28) and multiclass classification (accuracy: 96.36–95.04).
Hierarchical Disentangling Network for Building Extraction from Very High Resolution Optical Remote Sensing Imagery

Building extraction using very high resolution (VHR) optical remote sensing imagery is an essential interpretation task that impacts human life. However, buildings in different environments exhibit various scales, complicated spatial distributions, and different imaging conditions. Additionally, with the spatial resolution of images increasing, there are diverse interior details and redundant context information present in building and background areas. Thus, the above-mentioned situations would create large intra-class variances and poor inter-class discrimination, leading to uncertain feature descriptions for building extraction, which would result in over- or under-extraction phenomena. In this article, a novel hierarchical disentangling network with an encoder–decoder architecture called HDNet is proposed to consider both the stable and uncertain feature description in a convolution neural network (CNN). Next, a hierarchical disentangling strategy is set up to individually generate strong and weak semantic zones using a newly designed feature disentangling module (FDM). Here, the strong and weak semantic zones set up the stable and uncertain description individually to determine a more stable semantic main body and uncertain semantic boundary of buildings. Next, a dual-stream semantic feature description is built to gradually integrate strong and weak semantic zones by the designed component feature fusion module (CFFM), which is able to generate a powerful semantic description for more complete and refined building extraction. Finally, extensive experiments are carried out on three published datasets (i.e., WHU satellite, WHU aerial, and INRIA), and the comparison results show that the proposed HDNet outperforms other state-of-the-art (SOTA) methods.