Miracle-dz/UniFAR-FTUs huggingface dataset:
An inherent limiting factor of human mobility prediction

Predicting how we humans move within space and time is becoming a central topic in many scientiﬁc domains, ranging from epidemic propagation, urban planning to ride-sharing. However, current works neglect individuals’ preferences for exploration and discovery of new places. Yet, novelty-seeking activities appear to have signiﬁcant consequences on the ability to understand and predict individuals’ trajectories. In this work, we propose a new approach for the identiﬁcation of moments of novelty-seeking. Subsequently, we construct individuals’ mobility proﬁles based on their exploration inclinations – Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior).
Estrogen Receptor α Gene Variation and the Risk of Stroke

Background and Purpose— Estrogen receptor α (ESR1) gene variation is associated with a range of important estrogen-dependent characteristics, including responses of lipid profile and atherosclerotic severity to hormone replacement therapy, coronary heart disease risk, and migraine. The roles that reproductive steroids play in cerebrovascular pathophysiology and ischemia are an important area of investigation. Given that there is a significantly higher risk of myocardial infarction among men with the CC genotype (PP of PvuII) of c.454-397T>C (rs2234693), we asked whether this genotype is associated with a higher risk of stroke. Methods— Relative risk of stroke by genotype was determined in 2709 participants of the Second Northwick Park Heart Study, white males with a mean baseline age 56 years and follow up 10.5 years. Results— Compared with participants with the ESR1 c.454-397CT or TT genotype, those with the CC genotype had a relative risk of stroke of 1.92 (95% confidence interval, 1.06 to 3.48, P=0.03) after adjustment for age, primary care practice; additional adjustment for body mass index, serum cholesterol and triglyceride levels, hypertension, diabetes, and smoking status. Exclusion of stroke cases with coronary heart disease gave results that were essentially unchanged. Conclusions— In this study, subjects with the common ESR1 c.454-397CC genotype have a substantial increase in risk of stroke. In another publication, other ESR1 variation was associated with migraine. We thus hypothesize that estrogen receptor variation may provide a basis for the established relationship among estrogens, migraine, and stroke.
DATA MINING AND FEATURE EXTRACTION TECHNIQUES FOR OPINION MINING ANALYSIS IN TWITTER

By keeping and gaining customers, every small and large firm hopes to increase earnings and sales. It is vital to estimate future sales in order to improve sales. Aim of this work is to find the product is liked and recommended by the customer or not. To retain customers, product sellers must assess online customer demand in order to manage product, brand, and inventory levels. To improve customer service, online retailers or sellers must first understand the needs of their customers. It's also important to think about future sales. The proposed opinion mining on Amazon products is based on various sorts of real-time data obtained from Twitter. Lexical features, sentiment features and pragmatic features have also been used to improve the classification of review opinion features. After preprocessed the collected real time reviews have been involved in the process of feature extraction. Finally positive and negative opinions about the Amazon products have been classified by the proposed classification model. The proposed work findings and results states whether the Amazon shopping customers likes and recommend the product or not recommend. If the positive opinionated reviews are higher, then the customers like the product and recommend the product to purchase. If the negative opinionated reviews are higher, then the customers not like the product and not recommend the product to purchase. This research aims to aid clients in making purchasing decisions.
USABILITY EVALUATION PRACTICES WITHIN AGILE DEVELOPMENT: ENGAGING WITH USABILITY AGILE PRACTITIONERS' CONCERNS

The success of developing software systems largely depends on the effectiveness of coordination between software developers and usability engineers. Yet its integration has shown not to be straightforward because of various priorities and approaches which make it further complicated. This paper proposes an approach for improving the coordination between agile developers and usability engineers. The contribution of this paper is to investigate the challenges that prevent such integration. The investigation involved a combined questionnaire survey and interviews with participants from software development companies. The result reveals that during the development process, there is a lack of collaboration between usability practitioners and agile developers. This lack is classified into three main aspects: activities management, artifacts management and communication management. Also, the authors describe an integration model to be used as a solid basis to mitigate the challenges and propose a policy agenda for future work toward improving the coordination in multidisciplinary agile usability software teams.
Tcp Startup Performance in Large Bandwidth Delay Networks

— Next generation networks with large bandwidth and long delay pose a major challenge to TCP performance, especially during the startup period. In this paper we evaluate the performance of TCP Reno/Newreno, Vegas and Hoe's modification in large bandwidth delay networks. We propose a modified Slow-start mechanism, called Adaptive Start (Astart), to improve the startup performance in such networks. When a connection initially begins or restarts after a coarse timeout, Astart adaptively and repeatedly resets the Slow-start Threshold (ssthresh) based on an eligible sending rate estimation mechanism proposed in TCP Westwood. By adapting to network conditions during the startup phase, a sender is able to grow the congestion window (cwnd) fast without incurring risk of buffer overflow and multiple losses. Simulation experiments show that Astart can significantly improve the link utilization under various bandwidth, buffer size and round-trip propagation times. The method avoids both under-utilization due to premature Slow-start termination, as well as multiple losses due to initially setting ssthresh too high, or increasing cwnd too fast. Experiments also show that Astart achieves good fairness and friendliness toward TCP NewReno. Lab measurements using a FreeBSD Astart implementation are also reported in this paper, providing further evidence of the gains achievable via Astart.
Digital Commons @ New Haven Digital Commons @ New Haven

The paper discusses fractional generalizations of Zakai equations arising in ﬁltering problems. The derivation of the fractional Zakai equation, existence and uniqueness of its solution, as well as some methods of solu-tion to the fractional ﬁltering problem, including fractional version of the particle ﬂow method, are presented.
Batı Karadeniz Bölgesi’ndeki karaçam ve sahil çamı ağaçlandırma alanlarının karbon birikimi ile toprak ve ölü örtü özellikleri bakımından karşılaştırılması

: Land use and land use change can positively or negatively affect carbon and nutrient stocks in ecosystems. This study was carried out to determine the effects of afforestation with black pine and maritime pine species on carbon stocks and soil and litter characteristics. The study was conducted on afforestation areas and adjacent bare lands in Bartın-Ulus county, located in the Western Black Sea Region of Turkey. Three sample plots of 20×20 m were taken from each land use and the diameter at breast height and tree heights in these sample plots were measured Using the diameter and height values and the biomass equations and carbon ratios developed for the relevant tree species, the tree biomass and carbon stock per unit area were calculated. Then, disturbed and undisturbed soil samples and litter samples were taken from 0-10, 10-20 and 20-30 cm depth intervals at three points in each sample plot. The physical and chemical properties of the soil and litter samples were determined in the laboratory and measurements in the sample plots were converted to a unit area. The data were evaluated using independent sample t-tests and analysis of variance. The results showed that afforestation significantly changed N, S, P, Fe, Zn, and Cu concentrations in the soil; and C, S, Mn, and Zn concentrations in black pine and maritime pine forest floor were different. Besides, the ecosystem carbon (C) stocks differed significantly with forest land use type; black pine plantations, maritime pine plantations and bare land accumulated 148 t C ha -1 , 89 t C ha -1 and 27 t C ha -1 , respectively. Thus, we suggest that priority should be given to black pine in afforestation of the region and in other ecosystems with similar climates. The results of the research can be used in species selection for afforestation studies in terms of reducing the impact of global climate change.
GCNIllustrator: Illustrating the Effect of Hyperparameters on Graph Convolutional Networks

An increasing number of real-world applications are using graph-structured datasets, imposing challenges to existing machine learning algorithms. Graph Convolutional Networks (GCNs) are deep learning models, specifically designed to operate on graphs. One of the most tedious steps in training GCNs is the choice of the hyperparameters, especially since they exhibit unique properties compared to other neural models. Not only machine learning beginners, but also experienced practitioners often have difficulties to properly tune their models. We hypothesize that having a tool that visualizes the effect of hyperparameters choice on the performance can accelerate the model development and improve the understanding of these black-box models. Additionally, observing clusters of certain nodes helps to empirically understand how a given prediction was made due to the feature propagation step of GCNs. Therefore, this demo introduces GCNIllustrator - a web-based visual analytics tool for illustrating the effect of hyperparameters on the predictions in a citations graph.
Antifungal activities of vineyard-habitat wild yeast for grape gray-mold disease and its initial promotion of alcohol fermentation in spontaneous winemaking

Microorganisms, including native yeasts, are abundant in vineyard �elds. Herein, we studied the possibility of using vineyard-derived wild yeast as a microbial pesticide against Botrytis cinerea, a pathogen that causes grape gray mold disease, to boost the initial alcohol production of spontaneously fermented wine. We identi�ed the Saccharomyces cerevisiae strain KONDO170908, which showed the most effective antifungal activity in an ex vivo yeast dripping experiment on grape berries. This strain was utilized in an in vivo spray test on grape bunches in vineyard �elds and was proven to signi�cantly suppress gray mold disease on the grape berries in test plot #16 when the yeast was sprayed during both the �owering and ripening periods (morbidity 11.2% against 15.3% of the control plot, χ 2 test, P < 0.0001). However, in test plot #17, spraying the yeast during only the ripening period had no effect (morbidity 16.3%). The grapes from each test plot were also submitted for spontaneous wine fermentation. Alcoholic fermentation of the grapes from test plot #16 provided the most active bubbling of CO 2 gas and the highest ethanol production and colony counts over seven days of fermentation. Unique changes in the different strains of S. cerevisiae among the plots were observed throughout the early fermentation stage. Thus, yeast spraying during the �owering period might trigger modi�cation of the entire microbiota and could ultimately contribute to promoting alcohol production in the spontaneously fermented wine.
International Journal of Education Policy and Leadership Teachers' Perspectives on Teacher Self-Efficacy and Principal Leadership Characteristics

The purpose of this study was to investigate how elementary teachers rate their level of self-efficacy and to examine the characteristics of school leaders influencing teacher self-efficacy, including when teachers worked from home during the COVID-19 school shutdown. On the Teachers’ Sense of Efficacy Scale (TSES), all 287 participating teachers rated their self-efficacy in the high or moderate range. On the Principal Rating and Ranking Scale (PRRS), teachers reported that Communication, Inspiring Group Purpose, Consideration
SOCIAL ISOLATION, A NEW VARIABLE AFFECTING BEHAVIORAL INTENTION TO USE SUBSCRIPTION VIDEO ON DEMAND

In this COVID-19 pandemic, there is a change in human behavior. One of the main changes is the increase spending towards entertainment at home such as SVOD services. There is a problem in the industry that is happening in the form of churn, customer is changing from one SVOD services towards the other. There is stiff competition between SVOD services, in order to win this competition company must know what variables affect the intention to use SVOD. This paper aim to find which variables have the most significant effect upon Behavioral Intention to use SVOD using the UTAUT Model and two new variables of Corona Fear, & Social Isolation. This study will use 140 samples of users that have at least try to use Netflix, Disney+ Hotstar, HBO GO, or Viu. The conclusions of this research are that Content and Social Isolation have significant effect upon behavioral intention to use SVOD. This creates several managerial implications for SVOD service companies. First, companies need to increase quantity, quality and suitability of content of the services by either creating new content by working together with other production company or acquire existing contents to be added to the library of contents. Second, companies need to use existing user historical data in order to increase efficiency, this could be done in the form of setting set number of views as a standard, and reviewing it at several interval in order to determine which content should be remove or extended. Third, companies could set a special package for customer when government set social distancing policy, this could be done in the form setting a special price or discount if customer activated their location from set times such as weekends from afternoon until evening, this way customer could still be engaged towards the SVOD services even in isolation
EVALUATION OF E-WALLET USAGE ON RETAIL BUSINESS IN INDONESIA

E-wallets are now widely used as a substitute for physical money (paper money) which can be used in offline and online transactions. This is also in line with government policy regarding the National Non-Cash Movement (GNNT) which aims to form a society that uses more non-cash instruments (Less Cash Society / LCS) in transactions. Based on the monitoring of transactions with online business platforms, currently e-wallets are automatically used, but for offline transactions, especially in the retail business, it has not been seen that they have made maximum use of e-wallets. Even though e-wallet service providers have implemented many retail business owners in Indonesia. The fundamental thing that determines the success of an applied system is the frequency of system use. This study aims to find significant variables in the use of e-wallets in the retail business in the UTAUT model and add four variables, namely perceived value, perceived trust, perceived risk and promotional benefits. The research was conducted in a quantitative way, distributing questionnaires online as many as 35 questions, and analyzing demographic data using IBM SPSS 25 and PLS-SEM analysis with SmartPLS 3.2. 8. The result, of the eight proposed hypotheses, four hypotheses were declared insignificant and rejected because the path coefficient and t-test values of these hypotheses were less than 0.1 and 1.96, while the other three hypotheses were declared significant and accepted.
PREDICTING STUDENTS’ ACADEMIC PERFORMANCE: DEVELOPING AN INTELLIGENT DATA MINING CLASSIFIER

This study attempts to propose a classification model for predicting students' academic performance by using a decision tree algorithm. The algorithm was applied to relevant attributes such as gender, high school percentage, general aptitude test score, academic achievement test score, grade average point (GPA), absent rate, and other relevant attributes were subjected to the algorithm. According to the study results, the decision tree algorithm outperformed all other classification algorithms in terms of accuracy, precision, recall, and F1-score on a sample of students from Tayma University College, University of Tabuk, Saudi Arabia. The overall accuracy of the obtained algorithm was 89.7%, which means it correctly classified 89.7% of the instances. Precision, recall, and F1-score were also relatively high in both classes. The findings add significantly to the existing literature and demonstrate efficacy.
Fusarium keratitis in Brazil: genotyping, in vitro susceptibilities, and clinical outcomes

Background: The purpose of this paper is to describe clinical characteristics and determine correlations between clinical outcomes and antifungal susceptibility among molecularly char-acterized ocular Fusarium isolates in Brazil. Methods: Forty-one Fusarium isolates obtained from 41 eyes of 41 patients were retrieved from the ophthalmic microbiology laboratory at São Paulo Federal University and grown in pure culture. These isolates were genotyped and antifungal susceptibilities determined for each isolate using a broth microdilution method. The corresponding medical records were reviewed to determine clinical outcomes. Results: The 41 isolates were genotypically classified as Fusarium solani species complex (36 isolates, 88%), Fusarium oxysporum species complex (two isolates, 5%), Fusarium dimerum species complex (one isolate, 2%) and two isolates that did not group into any of the species complexes. Final best corrected visual acuity varied from 20/20 to light perception and was on average 20/800 (logarithm of the minimum angle of resolution (LogMAR) 1.6). A history of trauma was the most common risk factor, being present in 21 patients (51%). Therapeutic penetrating keratoplasty was necessary in 22 patients (54%). Amphotericin B had the lowest minimum inhibitory concentration for 90% of isolates (MIC 90 ) value (2 µ g/mL) and voriconazole had the highest (16 µ g/mL). There was an association between a higher natamycin MIC and need for therapeutic penetrating keratoplasty (Mann–Whitney test, P , 0.005). Conclusion: Trauma was the main risk factor, and therapeutic penetrating keratoplasty was necessary in 54% of patients. Amphotericin B had the lowest MIC 90 (2 µ g/mL) of the three antifungal agents tested. There was an association between higher natamycin MIC levels and corneal perforation, emphasizing the need for antifungal susceptibility testing and tailoring of antifungal strategies.
A COMPARATIVE ANALYSIS OF THE SEVERAL MATRIX FACTORIZATION PROCESS IN IMAGE RECONSTRUCTION AND A HOMOGENEOUS APPROACH TO THE FOURIER SERIES

Over the last few decades, image reconstruction has become an interesting field for the development of computer-based applications. The Decomposition of a matrix or matrix factorization is one of the most important components in many engineering and scientific applications. This technique is used to decompose one matrix into more than one matrix. One can efficiently solve a system of equations based on matrix factorization, and this, in turn, is the foundation of the inverse matrix, which is a major component of several important algorithms. Matrix factorizations are widely applied in situations that involve solving linear systems, numerical linear algebra, rank estimation, image processing, image reconstruction etc. This paper attempts to analyze the techniques of matrix factorization or decomposition techniques used in image reconstruction based on their advantages, disadvantages, limitations and computation complexity. Some techniques are examined and a comparative evaluation of these strategies is presented. This paper also shows the homogeneity between the Fourier series and matrix factorization process in image reconstruction.
RECURSIVE FUNCTIONS MODULO CO-r-MAXIMAL SETS*1)

Define the equivalence relation ~ A on the set of recursive functions of one variable byf~Ag if and only iff([x)=g(x) for all but finitely many x e Ä, where A is an r-cohesive set, to obtain the structure ¡%\A. Then the recursive functions modulo such an equivalence relation form a semiring with no zero divisors. It is shown that if A is /--maximal, then the structure obtained above is not a nonstandard model for arithmetic, a result due to Feferman, Scott, and Tennenbaum. Furthermore, if A and B are maximal sets, then a necessary and sufficient condition for ¡%\A and äl/B to be elementarily equivalent is obtained. It is also shown that many different elementary theories can be obtained for ät/Ä by proper choice of Ä.
MODELLING A DEEP KERNEL-BASED LEARNING APPROACH FOR SPINAL CORD INJURY PREDICTION

Deep Learning (DL)-based spinal cord injury (SCI) prediction is an unswerving model in medical imaging. However, the prediction process is challenging during the segmentation and classification process. Conventionally, radiologists examine the spinal cord images to indicate the abnormalities in the spinal cord manually. The manual high dimensional spinal cord feature space interpretation makes it complex to identify the severity level. However, DL approaches help in faster and more accurate predictions. The model intends to predict the abnormal and normal spinal cord images automatically. Under certain conditions, the weight of the training images is based on similarity distribution which is likely to show better performance. It does not deal with unrepresentative data. Thus, the kernel-based learning process intends to reduce the difference between the testing and training data and explore the kernel learning value for image weighting. Here, a novel kernel-based weighting method reduces the maximal mean discrepancy (MMD) among the testing and training data, facilitating kernel and weighted image optimization. Experimental results demonstrate that the anticipated kernel-based image weighting model has a higher computational disorder prediction ability than other approaches. The numerical outcomes prove that the model has superior performance to the prevailing approaches regarding prediction accuracy, recall and precision.
Coronal Dental Caries in Relation to Cognitive Status among Retirees in Middle Euphrates Region –Iraq

: Background: Cognitive impairment significantly affects the quality of daily living which compromises oral health in older adults including dental caries that remains the most important dental health problem in developing countries.The aim of this study was examination the association between cognitive status and coronal dental caries among retirees in Middle Euphrates Region-Iraq. Materials and methods: The sample included 198 dentate retirees with 50 years old and older from the offices of (Qi-Cards) who completed the study cognitive measure using Self-Administered Gerocognitive Examination test (SAGE) and dental examinationby application of DMFS index according to WHO criteria. Result:The occurrence of dental caries among the retirees was 98.5 %. Data analysis showed that the means of DMFS and missing surfaces (MS) were statistically highly significant differ among retirees with different cognitive status. Whilethe mean decayed surfaces (DS) and filled surfaces (FS) were highest among demenated retirees and Mild Cognitive Impairment group respectively howeverthese differences were statistically not significant. Retirees with different scores of each domain of SAGE showed statistically significant differences with means of DMFS and missing surfaces (MS). Conclusion: poorer cognitive ability was directly associated with poorer dental status, namely missing and total DMF surfaces.