Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Homestay Platform to enhance Sustainable Tourism Community Homestay Platform to enhance Sustainable Tourism in Developing Countries in Developing Countries

Developing countries are increasingly embracing community homestays to provide an authentic cultural experience to tourists. While these engagements enable local communities' economic and social development, homestays face various challenges. Digital innovation and transformations can aid homestays in promoting, managing and creating a resilient post-pandemic business model. This study aims to co-design and co-develop a community homestay management information system for greater scalability and sustainability in assisting homestay management committees in maintaining, monitoring and sustaining an equitable economy. Initially, a conceptual diagram of the system has been proposed based on the information extracted from existing literature and field interviews. The elicitations of requirements help ascertain the scope of ICT use in homestay. Design science research methodology will be applied to co-develop an interactive prototype after iterative evaluation. This study advances the discourse of ICT use in community homestays by identifying the opportunities and challenges and conceptualising a community homestay management system.
OPTIMIZED UNCERTAINTY HANDLING FEATURE SELECTION USING MOTH FLAME BASED DBSCAN CLUSTERING FOR DIABETIC RELATED CHRONIC KIDNEY DISEASE

In this work, to overcome the problem of voluminous dataset handling, improving the relevancy of features to detect the chronic kidney disease related diabetic patients, an uncertainty handling feature selection is constructed. The dataset is initially clustered by DBSCAN, the efficiency of clustering is achieved by two parameters they are epsilon and min-points. These are assigned in a random manner in standard DBSCAN, if the value is not assigned with any knowledge, then the clustering will not produce appropriate result. Hence, this presented work adapted the Moth Flame Optimization to search for optimized parameter values using its flight path behavior It discovers the best fittest value based on the flame position and those are fine tuned. The feature’s linear correlation among the class variable is identified using Pearson’s linear correlation based on it’s the features which doesn’t belong to any cluster are eliminated and based on the correlation score they are ranked. Thus, the significant feature subset is used for chronic kidney disease detection. The uncertainty about the outliers is well handled using the moth flame metaheuristic model by finding the best fittest and worst fittest features. The simulation results also proved that the accuracy obtained by the proposed Moth Flame based DBSCAN along with Pearson Correlation (MFDBSCAN-PC) produced highest accuracy compared to other standard feature selection models in chronic kidney disease detection with reduced error rate.
ASSESSING CUSTOMER SATISFACTION OF CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM IN AN E-COMMERCE COMPANY IN INDONESIA

This research aims to explore the customer satisfaction of customer relationship management system (CRMS) in Tokopedia, Indonesia. The growth of e-commerce in Indonesia is big, with Tokopedia as the top e-commerce company with largest visitors. However, there are still many customers that does not feel satisfied with their services which customers to move to other e-commerce platforms and downgrade the ratings of Tokopedia. The variables used in this study are perceived ease of use, perceived usefulness, system quality, user support, and social influence. A sample of 113 respondents of Tokopedia’s customers that lives in Jakarta was gathered from online questionnaire. This study uses the partial least square– structural equation modeling (PLS-SEM) method to analyze the collected data. The SmartPLS 4 4.0. 8.7 software was used to perform the data analysis. Based on the research, perceive ease of use, system quality, and user support influence the customer satisfaction, while perceive of usefulness and social influence do not influence the customer satisfaction.
Antiviral Activity of Flavonoids from Geopropolis of the Brazilian Jandaira Bee against Zika and Dengue Viruses

Arthropod-borne viruses within the Flaviviridae family such as Zika (ZIKV) and dengue (DENV) are responsible for major outbreaks in tropical countries, and there are no specific treatments against them. Naringenin and 7-O-methyl naringenin are flavonoids that can be extracted from geopropolis, a natural material that the Brazilian Jandaira stingless bee (Melipona subnitida Ducke) produces to protect its nest. Here, these flavonoids were tested against ZIKV and DENV using Vero cells as a cellular model to perform a cytotoxicity assay and to define the effective concentrations of TCID50 as the readout method. The results demonstrated the antiviral activity of the compounds against both viruses upon the treatment of infected cells. The tested flavonoids had antiviral activity comparable with 6-methylmercaptopurine riboside (6-MMPr), used here as a positive control. In addition, to identify the possible action mechanism of the antiviral candidates, we carried out a docking analysis followed by a molecular dynamics simulation to elucidate naringenin and 7-O-methyl naringenin binding sites to each virus. Altogether, these results demonstrate that both flavonoids have potent antiviral effects against both viruses and warrant further in vivo trials.
Improved Long-term In Vivo Lentiviral Gene Therapy for Chronic Granulomatous Disease

Chronic granulomatous disease (CGD) is a congenital immunode�ciency characterized by lack of reactive oxygen species in phagocytes. We developed an in vivo gene therapy strategy based on intravenous (iv) injection of lentiviral vectors (LVs) in X-CGD mice. A non-myeloablative chemo-conditioning regimen using busulfan, cyclophosphamide and dexamethasone was developed to improve iv LV gene delivery e�ciency. The X-CGD mice received two LVs injections. After the second injection, antibody response to LV particle-associated p24-protein was examined by Western blot. We detected increased gene transfer without anti-p24 antibody response. However, the blood vector copy number (VCN) was gradually reduced after 3–12 months. To improve gene delivery into hematopoietic stem cells (HSCs), the mice were treated with AMD3100 to mobilize HSCs before LV injection. To con�rm HSCs gene transfer, we transplanted the HSCs from the LV-CYBB-treated CGD mice into untreated CGD mice. The result showed successful passage of LV-CYBB HSCs to recipient mice. Thus, by combining chemo-conditioning and AMD3100 mobilization prior to the iv LV injection, improved in vivo long-term LV gene transfer into HSCs could be established. This improved iv LV gene delivery strategy could reduce both the risk and the cost of CGD gene therapy with great potential in translational applications.
A NEW WEIGHTED FEATURE SELECTION AND ENSEMBLE LEARNING WITH HYBRID HEURISTIC STRATEGY FOR DEEP VEIN THROMBOSIS PREDICTION

Deep Vein Thrombosis (DVT) is caused due to blood clots in deep veins. DVT symptoms differ from patient to patient. The prediction of DVT is essential in the primary stage. Otherwise, it may lead to Pulmonary Embolism(PE). In this research, a new DVT prediction model is designed with the data augmentation concept, a technique commonly used in machine learning to enhance the dataset used in the learning process. It produces new cases from the original dataset without varying the pattern of the data. The proposed model consists of data augmentation, weighted feature selection, and classification phases. Initially, the data augmentation is performed after gathering the data related to DVT. Secondly, data cleaning and outlier removal are done on the collected data to improve the data quality. Furthermore, weighted feature extraction is used with Hybrid Lion-Sea Lion Optimization (HL-SLnO) to extract high-quality, non-redundant features from data. Finally, the obtained features are sent to the classification phase, where they are processed using Modified Ensemble Classifiers (MEC) with Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and XGBoost with parameter optimization derived from the same HL-SLnO. MEC combines the decisions from the classifiers mentioned above to improve the overall performance to obtain the best-predicted result. Finally, the experimental results show that the proposed model has effectively enhanced the prediction of DVT risk and provided decision support for other nursing and medical intervention.
Distinct mutation pro�les between primary bladder cancer and circulating tumor cells warrant the use of circulating tumors cells as cellular resource for mutation follow-up

Background: While circulating tumor cells may serve as minimally invasive cancer markers for bladder cancers, the relationship between primary bladder cancers and circulating tumor cells in terms of somatic mutations is largely unknown. Genome sequencing of bladder tumor and circulating tumor cells is highlighted to identify the somatic mutations of primary bladder cancer. Methods: Bladder cancer tissue was collected by transurethral resection of the bladder and preserved by snap-freezing. Circulating tumor cells were Isolated from the blood obtained before treatment. We performed whole exome sequencing of 20 matched pairs of primary bladder cancers and circulating tumor cells to identify and compare somatic mutations of these two different genomic resources. Results: We observed that mutation abundances of primary bladder cancers and circulating tumor cells were highly variable. The mutation abundance was not signi�cantly correlated between matched pairs. Of note, the mutation concordance between two resources was only 3 – 24% across 20 pairs examined, suggesting that the circulating tumor cell genomes of bladder cancer patients might be genetically distinct from primary bladder cancers. A relative enrichment of mutations belonging to APOBEC-related signature and a depletion of C-to-G transversions were observed for primary-and circulating tumor cells speci�c mutations, respectively, suggesting that distinct mutation forces might have been operative in respective lesions during carcinogenesis. Conclusions: The observed discrepancy of mutation abundance and low concordance level of mutations between genomes of primary bladder cancers and circulating tumor cells should be taken into account when evaluating clinical utility of circulating tumor cells for treatments and follow-up of bladder cancers. Trial registration: Patients were selected and registered retrospectively, and medical records were evaluated.
A Resting-State fMRI Study of Intrinsic Connectivity Network Mapping: Investigation of Long-Term Reproducibility

BACKGROUND AND PURPOSE: Connectivity mapping based on resting-state fMRI is rapidly developing, and this methodology has great potential for clinical applications. However, before resting-state fMRI can be applied for diagnosis, prognosis, and monitoring treatment for an individual patient with neurologic or psychiatric diseases, it is essential to assess its long-term reproducibility and between-subject variations among healthy individuals. The purpose of the study was to quantify the long-term test-retest reproducibility of ICN measures derived from resting-state fMRI and to assess the between-subject variation of ICN measures across the whole brain. MATERIALS AND METHODS: Longitudinal resting-state fMRI data of 6 healthy volunteers were acquired from 9 scan sessions during (cid:1) 1 year. The within-subject reproducibility and between-subject variation of ICN measures, across the whole brain and major nodes of the DMN, were quantified with the ICC and COV. RESULTS: Our data show that the long-term test-retest reproducibility of ICN measures is outstanding, with (cid:1) 70% of the connectivity networks showing an ICC (cid:1) 0.60. The COV across 6 healthy volunteers in this sample was (cid:1) 0.2, suggesting significant between-subject variation. CONCLUSIONS: Our data indicate that resting-state ICN measures (eg, the correlation coefficients between fMRI signal-intensity profiles from 2 different brain regions) are potentially suitable as biomarkers for monitoring disease progression and treatment effects in clinical trials and individual patients. Because between-subject variation is significant, it may be difficult to use quantitative ICN measures in their current state as a diagnostic tool. ABBREVIATIONS: COV
Designing the M @bullet Core Tm M3 Cpu Architecture

The M•CORE microRISC architecture has been developed to address the growing need for long battery life among today’s portable applications. In this paper, we present the architectural enhancements of the M3 processor, the successor to the original M •CORE M2 architecture. Specifically, we discuss the instruction buffer and pipeline enhancements, the branch prediction algorithm, branch folding for small program loops, the fast integer multiplier, and several new instructions. We present performance comparisons between the M2 and M3 M •CORE processors. Finally, we also discuss two system implementations utilizing the M•CORE M3 processor. IEEE International Conference on Computer Design, Austin, Texas, October 10-13, 1999.
Correlation between nasal obstruction, quality of life and costs of endoscopic vs. microscopic transsphenoidal pituitary surgery in a low/middle-income country

Few studies compare the post-operative nasal symptoms, quality of life, and costs in the microscopic vs. endoscopic transsphenoidal approaches for pituitary adenomas, particularly in low/middle-income Latin American populations. This ambispective study correlated the perception of nasal obstruction, quality of life and costs of endoscopic vs. microscopic transsphenoidal pituitary surgery at the Fundación Santa Fe de Bogotá, Colombia between January 2018, and December 2019. A total of 46 patients (mean age: 50.57 years) who underwent pituitary resection for adenomas either by microscopic or endoscopic approach in the Neurosurgery and/or Otolaryngology departments of the Fundación Santa Fe de Bogotá were included. Postoperative perception of nasal obstruction was assessed through the Nasal Obstruction Symptom Evaluation (NOSE) and a visual analog scale (VAS). Quality of life was assessed with the Glasgow Bene�t Inventory (GBI). Surgical-related and hospitalization costs were extracted from the hospital’s �nances database. A median difference of 45 points favoring the microscopic approach was found in the global scores of the NOSE scale. A median difference of 4 points was found in the VAS score (p<0.001 in both interventions). The GBI median scores were 27.78 points (IQR: 55.56), and 25.00 (IQR: 25) for the microscopic and endoscopic approaches, respectively. A negative correlation between the GBI and NOSE was found ( ρ̂ =-0.44; 95% CI: -0.684 to -0.095), implying that the less postoperative nasal obstruction, the better quality of life. The difference between median surgical-related costs was $ 437.92 USD, favoring of the microscopic approach (p=0.03). Nasal obstruction and quality of life were correlated, highlighting the impact of sinonasal outcomes in patients’ quality of life. Statistically signi�cant differences were found in postoperative nasal obstruction symptoms and costs, favoring the microscopic approach.
Validation of the attitude scale for information and communications technologies

Objective: To validate a scale that identifi es the attitude of academics, from Health Sciences courses, as to the use of Information and Communications Technologies. Methods: Methodological study, carried out with 292 undergraduates from Biological Sciences, Nursing, Nutrition and Medical School, and applied to the Information and Communications Technology Attitude Scales for Health with 14 items. Validation was conducted by a committee of experts involved in the translation from the Portuguese version, with internal validation, cultural adaptation. It passed through a pre-test, and a post-test, by means of exploratory factorial analysis, consistency and representability of the scale components. Results: A total of 14 items was validated, all of which with construct adequacy, according to the measure of suitability of 0.80 and p<0.000. Factorial analyses produced a two-factor solution, positive and negative constructs, which allows better identifi cation of academic attitudes. Conclusion: The scale presented values of suitability above the average for this type of instrument. Thus, it is effective to assess the proposed object and it provides an innovative means of research within this fi eld.
Youtube And Its Role In Education Content: A Descriptive Overview

The video-sharing website YouTube is a Web 2.0 stage for appropriated video sharing that is notable among understudies, colleges, and scholastics. This article investigates the YouTube channels made by three trained professionals - Dave Cormier, Wendy Drexler, and Michael Wesch - who have research interests in development helped to learn and have made YouTube channels to share their revelations. It is the example of movies that every researcher posted and named as "instruction" on their YouTube stations that will be the focal point of the exploration. The data accumulated from the substance examination gives for a superior comprehension of what material is being shared and how it is being shared. The general discoveries suggest that analysts who have an individual channel or one that is officially associated with the college are bound to disseminate recordings made as an outcome of their work as researchers, with a portion of these movies, in any event, offering copyright to the school. Each of the movies examines points and thoughts that are tantamount to each other as far as the reconciliation and utilization of innovation in training, yet the way used to pass on their shifts.
A roadblock-and-kill model explains the action of the DNA-targeting antibiotic ciprofloxacin

Fluoroquinolones antibiotics that cause DNA damage by inhibiting DNA topoisomerases are clinically important, but their mechanism of action is not yet fully understood. Here we investigate growth inhibition of the bacterium Escherichia coli by the fluoroquinolone ciprofloxacin at low doses (up to 5x the minimum inhibitory concentration). We measure the long-term and short-term (dynamic) response of the growth rate and DNA production rate to ciprofloxacin, at both populationand single-cell level. We show that despite the molecular complexity of DNA metabolism, a simple `roadblock-and-kill’ model focusing on replication fork blockage and DNA damage by ciprofloxacinpoisoned DNA topoisomerase II (gyrase) quantitatively reproduces long-term growth rates. The model also predicts dynamical changes in DNA production rate in wild type E. coli and in a DNA recombination-deficient mutant, following a step-up of ciprofloxacin. Our work highlights the importance of including biophysical processes in biochemical-systems models to fully understand bacterial response to antibiotics.
Improvement of Radar Rainfall Estimation Using Radar Reflectivity Data from the Hybrid Lowest Elevation Angles

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR
Statistical analysis of urban air-pollution data in the Athens basin area, Greece

Air pollutant concentrations recorded in the Athens basin area by a network of stations are analysed and examined to estimate the typical behaviour of the air pollutants, especially with regard to daily, weekly and annual periodicities and meteorological dependencies. Also, an attempt is made to predict daily and monthly concentrations by utilising a statistically based model, that is, a multiple linear regression model. The results obtained show the presence of a very significant weekly periodicity for all the analysed air pollutants (e.g. SO2, O3, CO, NO and NO2). It also appears to be present a yearly periodicity for the primary (e.g. SO2, CO, NO and NO2) air pollutants analysed and studied. The statistical prediction by using the Autoregressive Integrated Moving Average (ARIMA) stochastic model in combination with the Theil index, shows good predicting capabilities (one day ahead) for sulphur dioxide (SO2), carbon monoxide (CO), ozone (O2) and nitrogen oxides (NO and NO2). The significance of the forecasting is controlled by the Theil index.
Computer aided diagnosis under the inﬂuence of heterogeneous data and imbalanced classes

α -reductase type 2 deﬁciency (SRD5A2) and P450 oxidorec-tase deﬁciency (PORD), and simultaneously handling missing and heterogenous measurements in the urine data has been introduced in [3]. In this abstract we present an overview of the state-of-the-art techniques to deal with imbalanced classes for training two classiﬁers suitable for learning with missing values. We submitted this work to the European Symposium on Artiﬁcial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2017
Opleiding Informatica Using sentiment analysis on tweets for predicting cryptocurrency price movements: A case study

Cryptocurrencies have gained significant relevance in the financial landscape and are increasingly viewed as an emerging market. The cryptocurrency market’s characteristics, mainly the abundant availability of different types of market data, make it a compelling and suitable subject for research. By employing sentiment analysis and machine learning techniques, one can explore valuable insights into market behaviour, thereby addressing the complex task of stock market prediction. This research investigates the effectiveness of different methodologies for predicting cryptocurrency price movements, utilizing machine learning models in combination with data from sentiment analysis, technical analysis, or a hybrid approach merging both methods. In this case study, we focus on gathering data from social media platforms, specifically Twitter, and built classifiers that predict whether a cryptocurrency price will increase or decrease in price in the next time window (using hourly intervals) based on sentiment analysis, technical analysis, and a combination of both. Using these classifiers we investigated four different cryptocurrencies: Bitcoin, Ethereum, Dogecoin and Ripple. Theresults reveal an average accuracy of 50.05% for sentiment analysis, 51.61% for technical analysis, and 51.46% for the combination of both. The majority class classifier obtains an average accuracy of 50.41%. A statistical significance test shows that sentiment analysis alone does not consistently outperform the baseline, while models incorporating technical analysis or the combination of both show promise in capturing market dynamics. We also investigated the effect of punctuation in text for the sentiment analysis and concluded based on our results that utilizing punctuation is a crucial factor for obtaining good results from sentiment analysis.