Miracle-dz/UniFAR-FTUs huggingface dataset:
Some Insights Into Financial Literacy Among Undergraduate Students: A Case of Bosnia and Herzegovina

The purpose of this research is to provide some insights into financial literacy among undergraduate students focusing primarily on the relationship between financial knowledge, financial attitudes and financial behavior and on possible gender and financial education gap in financial literacy. Using the purposive sampling technique, data collection was carried out from April to June 2020, yielding a sample of 1,046 valid responses. To gain a better understanding of the relationship between financial behaviour, financial attitudes and financial knowledge, we, primarily, use exploratory factor analysis and multiple regression model. The research findings have revealed several important issues. First, findings have suggested that financial knowledge, financial attitudes and gender may be considered as an antecedent of the financial behaviour of undergraduate students. Second, findings have also suggested a statistically - significant difference between the financial literacy of undergraduate students concerning their exposure to formal financial education.
Making sense of sustainability work: A narrative approach

Corporate sustainability is a priority for organisations, but the nature of the enabling intra-organisational activities, processes and managerial agency is not well understood. In this study, we examine the activity and agency of corporate sustainability managers through a narrative approach and the novel theoretical lens of ‘sustainability work’: purposeful and strategic activities to shape the social-symbolic context such that social and environmental outcomes are prioritised. Analysing how individuals across a range of diverse organisations and industries frame their activity, we identify three overlapping and co-occurring broad subsets of sustainability work: goal-directed, other-directed and self-directed. Through our notion of sustainability work, we contribute by recasting managerial agency in the enabling of sustainability as occurring in the social-symbolic realm and highlighting the implications in both theory and practice for the professionalisation of sustainability. JEL Classification: M10, M14
Bioinformatics Study of Plasma Differentially Expressed Proteins in H-Type Hypertension with or without Acute Cerebral Infarction

Objective: To investigate the bioinformatics of differentially expressed proteins in plasma in patients with acute cerebral infarction associated with H-type hypertension. Methods: Gene chip public database (gene expression omnibus, GEO) GDS4521 chip data, in the chip in 30 cases of H patients with acute cerebral infarction with high blood pressure and age, gender, matching the 20 H hypertension patients as the research object, collects the mononuclear cells (PBMCs) is used to detect the gene chip, using the GO (gene Ontology, GO), protein function analysis tools such as KEGG, screening and analysis of enrichment of differentially expressed genes function and related signaling pathway. Results: 31 genes in PBMCs were significantly changed in h-type hypertension and H-type hypertension with acute cerebral infarction, 32 of which were increased and 2 of which were decreased. GO analysis showed that in terms of biological processes, the genes related to inflammatory response and neutrophil chemotaxis were the most. In terms of molecular function, chemokine activity-related genes are the most. KEGG signaling pathway analysis showed that the most differentially expressed genes were located in the TNF signaling pathway. Conclusion: H-type hypertension with acute cerebral infarction has a variety of functional proteins and signaling pathways changes, suggesting that inflammatory response in H-type hypertension with acute cerebral infarction recovery period may still play a role in the prognosis and reactivation of the disease.
Geographic heterogeneity in the outcomes of patients receiving immune checkpoint inhibitors for advanced solid tumors: a meta-analysis

Background Little is known about the effect of geographic location on efficacy of immune checkpoint inhibitors (ICI). We performed a systematic review and meta-analysis to assess the heterogeneity of ICI efficacy between different geographic locations. Methods We searched PubMed, EMBASE, and the Cochrane Library through October 2019 for phase III randomized controlled trials (RCT) that provided sufficient data for hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS) or progression-free survival (PFS) according to designated geographic region. We calculated pooled HRs and 95% CIs for North American, European and Asian cancer patients, and assessed data heterogeneity using subgroup and sensitivity analysis. The INPLASY registration number was INPLASY202050062. Results Of 10151 publications identified in our research, 17 RCTs including 7462 patients met our selection criteria. The pooled HRs for OS of North American, European and Asian patients were 0.67 (95% CI: 0.57 to 0.78), 0.72 (95% CI: 0.64 to 0.81), and 0.74 (95% CI: 0.66 to 0.84) respectively; the pooled HRs for PFS of North American, European and Asian patients were 0.58 (95% CI: 0.49 to 0.69), 0.61 (95% CI: 0.41 to 0.90), and 0.87 (95% CI: 0.38 to 1.99) respectively. Both anti-PD-1 inhibitors and anti-PD-L1 inhibitors showed clinical benefit in North American and European arms while anti-PD-L1 inhibitors failed to show benefit in Asian arms. Conclusions Our meta-analysis indicates that the magnitude of benefit from ICI varies in North America, Europe, and Asia. Asian patients experience inferior outcomes compared to Western patients. Notably, anti-PD-L1 therapies do not result in survival improvements in Asian patients.
A Revised Bourgouin Precipitation-Type Algorithm

Using vertical temperature profiles obtained from upper-air observations or numerical weather prediction models, the Bourgouin technique calculates areas of positive melting energy and negative refreezing energy for determining precipitation type. Energies are proportional to the product of the mean temperature of a layer and its depth. Layers warmer than 0°C consist of positive energy; those colder than 0°C consist of negative energy. Sufficient melting or freezing energy in a layer can produce a phase change in a falling hydrometeor. The Bourgouin technique utilizes these energies to determine the likelihood of rain (RA) versus snow (SN) given a surface-based melting layer and ice pellets (PL) versus freezing rain (FZRA) or RA given an elevated melting layer. The Bourgouin approach was developed from a relatively small dataset but has been widely utilized by operational forecasters and in postprocessing of NWP output. Recent analysis with a larger dataset suggests ways to improve the original technique, especially when discriminating PL from FZRA or RA. This and several other issues are addressed by a modified version of the Bourgouin technique described in this article. Additional enhancements include use of the wet-bulb profile rather than temperature, a check for heterogeneous ice nucleation, and output that includes probabilities of four different weather types (RA, SN, FZRA, PL) rather than the single most likely type. Together these revisions result in improved performance and provide a more viable and valuable tool for precipitation-type forecasts. Several National Weather Service forecast offices have successfully utilized the revised tool in recent winters.
The Truth and Myth of the Advantages of Authoritarian Countries to COVID-19

The COVID-19 pandemic—the worst pandemic since the Spanish flu—has dramatically changed the world, with a significant number of people still suffering and dying from the disease. Some scholars argue that the pandemic has severely damaged democratic countries, mainly because they cannot intervene in their citizens' lives, as opposed to their authoritarian counterparts. This paper reports that authoritarian countries tend to have lower COVID-19 deaths than their democratic counterparts, but stringent intervention is not necessarily a key determinant. A higher number of tests is a more critical determinant of authoritarian countries' advantage, using cross-national data for 163 countries. Testing seems to be a vital tool to reduce deaths (IRR=0.66, P < 0.001). This study uses statistical evidence to demonstrate that authoritarian countries are likely to conduct more tests leading to lower death rates. The result implies that authoritarian states can strengthen citizens' support for their government through the COVID-19 pandemic.
Who Spends Too Much Time Online?

This study investigated the relation between Internet addiction and several associated factors (mental health, academic performance, socioeconomic status, self-esteem, demographic characteristics) for international students in the United States. One hundred and fifty-seven international students at a U.S. university completed five questionnaires: an Internet usage behavior questionnaire, an Internet addiction scale, a self-esteem inventory, a mental health inventory, and a demographic questionnaire. Data were analyzed using SPSS to examine the relation between Internet addiction and associated factors. The results indicated that male students who speak English as a second language and who are not religious are more likely to develop Internet addiction. Academic performance and socioeconomic status were found to be positive predictors of Internet addiction, and mental health and self-esteem were found to be negative predictors of Internet addiction. The relations between other associated factors were also examined.
Nonlinear Robust Coordinated PSS-AVR Control for a Synchronous Generator Connected to an Infinite Bus

The transient stabilization and voltage regulation problem for a synchronous generator connected to an infinite bus is addressed, in this article, in the presence of all uncertain physical parameters. First, new interpretations for the desensitized four loops regulator, here referred to as robust coordinated power system stabilizer and automatic voltage regulator (PSS-AVR) are derived in terms of minimum phase properties with respect to a suitably chosen output for the linearized error system. Second, on the basis of such an output, a nonlinear generalization of the robust coordinated PSS-AVR is designed with the aim of enlarging the stability region and improving the transients about the unpredictable operating conditions determined by the faults. Its linear action coincides with the one provided by the robust coordinated PSS-AVR: the compelling simplicity of control structure (just one integrator is involved) and robust tuning procedure of the linear design are definitely inherited, with, additionally, no use of the mechanical input power. A numerical analysis, along with realistic simulations, confirms improved stability when the proposed nonlinear controller is applied.
A 15-year Review of “Corporate Social Responsibility Practices” Research in the Hospitality and Tourism Industry

ABSTRACT The purpose of this study is to investigate corporate social responsibility (CSR) practices in the hospitality and tourism industry context. Authors reviewed 124 articles covering the 15 years between 2004 and 2019. The findings show that recent CSR research has focused on organizational and stakeholders themes. Recent CSR research trend shifts from financial focus to customer focus. Practitioners could take a reference on what sort of CSR activities have been conducted and explore new CSR initiatives in their sectors. Academics are advised to address under-research fields. Seven possible further research areas in the hospitality and tourism industry are proposed: secondary stakeholders including residents, government, media, and activist; consequences of CSR practices; antecedents of CSR practices; dark side of CSR; communicating CSR initiatives, less explored contexts and topics, and new analytic methods. A new research model has been proposed. This review paper fills a literature gap on reviewing CSR practices in the hospitality and tourism industry in a holistic manner.
Optimization of a circular microchannel heat sink using entropy generation minimization method

New advances in micro and nano scales are being realized and the contributions of micro and nano heat dissipation devices are of high importance in this novel technology development. Past studies showed that microchannel design depends on its thermal resistance and pressure drop. However, entropy generation minimization (EGM) as a new optimization theory stated that the rate of entropy generation should be also optimized. Application of EGM in microchannel heat sink design is reviewed and discussed. Using EGM, majority of the published investigations are conducted based on rectangular cross section microchannel. Latest principles for deriving the entropy generation correlations are discussed to present how this approach can be achieved. The present study involves an optimization procedure using EGM method and derives the entropy generation rate in circular microchannel heat sink based upon thermal resistance and pressure drop simultaneously. The equations are solved using MATLAB and the obtained results are compared to the past studies. The effect of channel diameter and number of channels on the entropy generation rate, Reynolds number, thermal resistance and pressure drop is investigated. Analytical correlations are utilized for heat transfer and friction coefficients. A minimum entropy generation has been observed for N=40 and channel diameter of 9090µm. It is concluded that for N=40 and channel hydraulic diameter of 9090µm, the circular microchannel heat sink is on its optimum operating point based on second law of thermodynamics.
Image-Based Lifelogging: User Emotion Perspective

: Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones and configures their lifelogs with a focus on their emotions. We design an emotion classifier based on convolutional neural networks (CNN) to predict the users’ emotions. To train the model, we create a new dataset by collecting facial images from the CelebFaces Attributes (CelebA) dataset and labeling their facial emotion expressions, and by integrating parts of the Radboud Faces Database (RaFD). Our dataset consists of 4,715 high-resolution images. We propose Representative Emotional Data Extraction Scheme (REDES) to select representative photos based on inferring users’ emotions from their facial expressions. In addition, we develop a system that allows users to easily configure diaries for a special day and summaize their lifelogs. Our experimental results show that our method is able to effectively incorporate emotions into lifelog, allowing an enriched experience.
An implicit high order finite volume scheme with a posteriori limiting for advection networks

In this paper, we describe an implicit high order finite volume scheme for networks of advection equations. Possible oscillations are eliminated using an a posteriori limiting approach. A continuous transition between the different orders is achieved by a convex combination of the numerical fluxes. The scheme shows accurate results even for large CFL numbers.
Cytotoxicity and Genotoxicity of Metal Oxide Nanoparticles in Human Pluripotent Stem Cell-Derived Fibroblasts

Advances in the use of nanoparticles (NPs) has created promising progress in biotechnology and consumer-care based industry. This has created an increasing need for testing their safety and toxicity profiles. Hence, efforts to understand the cellular responses towards nanomaterials are needed. However, current methods using animal and cancer-derived cell lines raise questions on physiological relevance. In this aspect, in the current study, we investigated the use of pluripotent human embryonic stem cell- (hESCs) derived fibroblasts (hESC-Fib) as a closer representative of the in vivo response as well as to encourage the 3Rs (replacement, reduction and refinement) concept for evaluating the cytotoxic and genotoxic effects of zinc oxide (ZnO), titanium dioxide (TiO2) and silicon-dioxide (SiO2) NPs. Cytotoxicity assays demonstrated that the adverse effects of respective NPs were observed in hESC-Fib beyond concentrations of 200 µg/mL (SiO2 NPs), 30 µg/mL (TiO2 NPs) and 20 µg/mL (ZnO NPs). Flow cytometry results correlated with increased apoptosis upon increase in NP concentration. Subsequently, scratch wound assays showed ZnO (10 µg/mL) and TiO2 (20 µg/mL) NPs inhibit the rate of wound coverage. DNA damage assays confirmed TiO2 and ZnO NPs are genotoxic. In summary, hESC-Fib could be used as an alternative platform to understand toxicity profiles of metal oxide NPs.
Impact of interactive radio programming on agricultural technology adoption and crop diversification in Malawi

ABSTRACT This article contributes new empirical evidence on the impact of interactive radio programming on agricultural technology adoption and crop diversification in Malawi. It uses a nationally representative household panel data, complemented by 52 focus group discussions. Radio programming is the preferred source of agricultural and nutrition advice among the rural population. Radio programming has strong positive impact on technology awareness, but a limited impact on actual adoption of most agricultural practices being promoted. Other components of the interactive programming – listening clubs and call-in services – have limited coverage and effectiveness to date.
Verification of reliability of measurement systems for swimming analysis

Sophisticated technologies and measuring devices are increasingly being used for the analysis of performance and swimming techniques. At BUT, the Tachograph II and Swimming Inertial Measure Unit (SwIMU) measuring systems were developed for swimming analysis. To evaluate the measured data, the SwimDataViewer software was developed for both measuring systems. To verify the reliability of the Tachograph, 34 race swimmers were measured (15 F, 19 M), and 28 race swimmers (16 M, 12 F) were measured with SwIMU. All swimmers swam a 6x25m front crawl. Pullout and the first three and last two strokes of the arms were not included in the data evaluation. The intraclass correlation coefficient (ICC) was used to verify the reliability of the monitored parameters. The values of measured parameters in the front crawl for Tachograph (SwIMU) were ICC = 0.992 (0.991) for the average value of speed, ICC = 0.958 (0.960) for efficiency, ICC = 0.971 (0.988) for the frequency of the arms, and finally, ICC = 0.978 (0.992) for the frequency of the legs. The ICC coefficient for all monitored parameters in both devices showed a high degree of reliability. From this perspective, both devices are suitable for use in coaching and research practice.
Research on Moving Target Recognition and Tracking Technology Based on Machine Learning

In this paper, the SIFT feature, HOG feature and SVM are adopted to design a movement vehicle recognition system, when the vehicle is relatively close to the camera and it backs to the camera, also the vehicle can be implemented to track. In this paper, two feature extraction methods, SIFT feature and HOG feature are respectively used to extract the features from 6000 images of vehicles with back facing the cameras and 6000 images without vehicles. Then SVM is used to train them to obtain a model. Then, we use the moving object recognition algorithm to extract the moving object in the given video, and put each moving object into the trained model to judge. If the model is judged as a vehicle, it will be tracked.
Application of partitioning around medoids cluster for analysis of stunting in 100 priority regencies in Indonesia

In 2018, Indonesian government set 100 districts as the priority to reduce stunting. In this study, we hypothesize that the 100 determined districts should not be treated on an equal policy, due to some underlying factors that might affect the stunting in those districts. Thus, it is necessary to identify and analyze the grouping of 100 priority districts for stunting interventions in 2018 based on the National Team’s indicators for the Acceleration of Poverty Reduction to see the severity of stunting. It is hoped that this clustering could be a reference for the government in determining priority regency groups to reduce stunting rates. Data on 100 districts, represented by eight numerical measurements and six categorical measurements were analyzed using Partitioning Around Medoids (PAM) method. Data similarity was measured using Gower distance, which can handle the clustering of mixed data types. We identified five priority district groups which provide meaningful insights. One of the groups has the worst stunting severity condition among other groups, for each indicator; implying the high priority to follow-up by the government. The majority of districts in Papua and East Nusa Tenggara Provinces are districts with poor stunting severity. We also found that poverty, proportion of the population with defecation in latrines, access to clean water and proper sanitation, number of integrated healthcare centers (posyandu) in a village, and number of doctors in each district are important factors that explain the stunting severity.
Improving the Performance of Target Tracker by Deepening Neural Network

Visual target tracking is an important topic in the field of vision research. Its main task is to automatically process and analyze the input video information by computer, so as to obtain the position and key motion information of the target in the input video sequence, and to facilitate the subsequent research and analysis of the track and behavior of the tracking target. At present, researchers in the field of visual target tracking have proposed a series of excellent algorithms and frameworks. However, in view of the problems of deformation, occlusion, illumination change and random motion in the process of target tracking, it is still a great challenge to ensure the real-time, accuracy and robustness of tracking algorithms. In recent years, with the excellent performance of depth neural network in many tasks, the fusion of depth features based on relevant filtering algorithms to achieve target tracking tasks has become a hot research topic. In this paper, an improved target tracking algorithm is proposed based on the framework of classical target tracking methods: (1) Integrating deeper ResNet-101 deep convolution neural network model to detect target features more accurately. (2) Improve the model training method to save the network training cost. Finally, the performance of the proposed tracking algorithm is tested on the OTB target tracking standard dataset, and compared with other mainstream algorithms. The results show that the improved method proposed in this paper can effectively improve the tracking effectiveness and robustness.