Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring of Group A Rotavirus Strains Circulating in the Environment and Among Children with Acute Gastroenteritis

Rotavirus A is the causative agent of 90% of acute gastroenteritis in children under 5, which kills 1 to 3 million children per year. Their strong resistance in the environment, their inter-species transmission as well as their power of genetic recombination can give rise to new reasserting that may be harmful to public health. The simultaneous search for the presence of rotavirus A in different environmental and clinical biotopes and matrices as well as the monitoring of the seasonal evolution of episodes is of major importance. At cost, genetic monitoring of rotaviruses shows a correlation between the presence of different genotypes of RVA in the environment and the rate of morbidity, Hence the need to monitor the emergence of new circulating strains with a view to integrating them into routine immunization programmes appropriate for each region in order to limit the spread of the disease.
GAIN ENHANCEMENT OF SIW CAVITY-BACKED ANTENNA USING DIELECTRIC LOADING

This article presents the design and development of a low profile substrate integrated waveguide semi-circular cavity-backed antenna loaded with dielectric cylinders of glass-reinforced epoxy and Teflon. The substrate integrated waveguide semi-circular cavity-backed antenna without dielectric loading radiates at 5.8 GHz with 3.13 dB gain. The antenna is modified by putting dielectric cylinders of different materials and different sizes at the edge of a semi-circular cavity to enhance the gain of the antenna. The new antenna thus created has improved gain of 8.13 dB. All simulations are done using high frequency structure simulation software. The proposed design is fabricated on a glass-reinforced epoxy substrate with a semi-circular cavity having a size of 60mm × 50 mm. The measured results are in good agreement with simulated ones.
Polymorphism of Two-Dimensional Halogen Bonded Supramolecular Networks on a Graphene/Iridium(111) Surface

The properties of two-dimensional supramolecular self-assemblies on surfaces depend on the fine balance between molecule–substrate and molecule–molecule interactions. In this article, we study the growth of 1,3,5-tri(4′-bromophenyl)benzene (TBB) monolayer on graphene epitaxially grown on Ir(111) by means of low temperature scanning tunneling microscopy and spectroscopy (LT-STM/STS) combined with a fully atomistic description of the molecules in interaction with the Gr/Ir(111) substrate, using density functional theory (DFT). In order to figure out the impact of the underlying metallic layer upon the self-assembling behavior of the molecules and their properties, we compare our results with those theoretically obtained on pristine graphene or experimentally achieved on highly oriented pyrolytic graphite (HOPG). We demonstrated that the use of the Ir layer allows the formation of large extended, continuous, and two-dimensional supramolecular networks lying even over Ir step edges like a carpet. In addition,.
Predicting Travel Demand of a Docked Bikesharing System Based on LSGC-LSTM Networks

The sustainable development of docked bikesharing systems has gained focus again owing to several problems in dockless bikesharing systems, including wanton destruction, theft, illegal parking, loss, and bankruptcy. The prediction of pickup/return demands is a critical issue for the sustainable operation of docked bikesharing systems. We propose a novel local spectral graph convolution (LSGC)- long short-term memory (LSTM) to predict pickup/return demands based on multi-source data. We apply LSGC to indicate the spatial dependency according to the geographic information system data that provide the location of stations, and we apply LSTM to demonstrate the temporal dependency based on the time-series data that represent pickup/return demands for public bikes. The LSGC-LSTM and six baseline models are trained with multi-source data of a month from a docked bikesharing system. The baseline models consist of a recurrent neural network, a LSTM, a gated recurrent unit, a graph attention LSTM network, an adaptive graph convolutional recurrent network, and a dynamic graph convolutional neural network. Results indicate that the LSGC-LSTM obtains a higher prediction accuracy and a higher efficiency than the baseline models.
The Formation and Control of Ice Crystal and Its Impact on the Quality of Frozen Aquatic Products: A Review

Although freezing has been used to delay the deterioration of product quality and extend its shelf life, the formation of ice crystals inevitably destroys product quality. This comprehensive review describes detailed information on the effects of ice crystals on aquatic products during freezing storage. The affecting factors (including nucleation temperature, freezing point, freezing rate, and temperature fluctuation) on the size, number, distribution, and shape of ice crystals are also elaborated in detail. Meanwhile, the corresponding technologies to control ice crystals have been developed based on these affecting factors to control the formation of ice crystals by inhibiting or inducing ice crystallization. In addition, the effects of ice crystals on the water, texture, and protein of aquatic products are comprehensively discussed, and the paper tries to describe their underlying mechanisms. This review can provide an understanding of ice crystallization in the aquatic products during freezing and contribute more clues for maintaining frozen food quality.
TF-IDF ve Doc2Vec Tabanlı Türkçe Metin Sınıflandırma Sisteminin Başarım Değerinin Ardışık Kelime Grubu Tespiti ile Arttırılması

TF-IDF terim agirliklandirma olcumu kelimelerin metinler icinde gecme sikligi bilgisine dayalidir. Bu olcum kelimeler arasindaki anlamsal iliskiyi barindirmamaktadir. Yapay sinir aglarina dayali olan Doc2Vec metodu kelimeler ve kelimeleri iceren dokumanlar arasindaki anlamsal iliskiyi barindirmakta ve yonetilebilir boyutlu dokuman vektorlerinin elde edilmesini saglamaktadir. Ardisik kelime gurubu tespitinin metin madenciligi uzerindeki olumlu etkileri literaturde sunulan pek cok calisma tarafindan belirtilmistir. Ardisik kelime gurubu tespiti dokuman icindeki anlamsal butunlugun saglanmasi acisindan onemlidir. Bu calismada, hem geleneksel TF-IDF terim agirliklandirma olcumunun, hem de YSA’lara dayali bir yontem olan Doc2Vec yonteminin kullanimi ile vektorlestirilen dokumanlar uzerinde temel makine ogrenmesi siniflandiricilarinin ve topluluk ogrenmesi algoritmalarinin basarim degerleri kiyaslanmistir. Calismamizda temel siniflandiriclar olarak Naive Bayes, K-En yakin komsuluk, Lojistik Regresyon, Karar Destek Makineleri, Karar Agaclari, Cok Katmanli Algilayicilar ve topluluk ogrenmesi metotlarindan Rassal Orman, Torbalama ve Adaboost algoritmalari kullanilmistir. Ayrica son olarak en basarili uc siniflandirma algoritmasi Cogunluk oylamasi ile birlestirilmis ve elde edilen sonuclar paylasilmistir. Siniflandiricilar farkli uzunluklarda haber dokumanlarini iceren 4 farkli Turkce veri kumesi uzerinde uygulanmistir. Calismamizin literature olan katkisi siniflandirma asamasina gecilmeden once dokumanlarin icindeki ardisik kelime grubu tespitinin gerceklestirilmesi ve dokumanlarin bu kelime obeklerinin tek bir kelime gibi ele alinmasiyla vektorlestirildikten sonra, uygulanan siniflandiricilarin basarim degerlerinin arttiginin gosterilmesi olmustur. Ardisik kelime grubu tespiti icin kelimelerin birlikte gecme sikligi prensibine dayali olan bir prensip disinda, Turkce Vikipedi’nin kelime baglantilari da kullanilmis ve dokumanlar icinde az sayida gecmesine ragmen anlamli olan ardisik kelime obeklerinin tespiti gerceklestirilebilmistir. Ardisik kelime grubu tespiti ile siniflandirma deneylerinin hemen hemen tumunde daha yuksek basarim degerleri elde edilmistir.
The Role of Inertial Instability in Cross-Hemispheric Coupling

Recent studies suggest linkages between anomalously warm temperatures in the winter stratosphere, and the high-latitude summer mesopause. The summer temperature anomaly is manifested in the decline of polar mesospheric clouds. The 2-day wave is a strong-amplitude and transient summer feature that interacts with the background state so as to warm the high-latitude summer mesopause. This wave has been linked to a low-latitude phenomenon called inertial instability, which is organized by breaking planetary waves in the winter stratosphere. Hence, inertial instability has been identified as a possible nexus between the disturbed winter stratosphere, and summer mesopause warming. We investigate a sustained occurrence of inertial instability during 19 July–8 August 2014. During this period, stratospheric winter temperatures warmed by about 10 K, while a steep decline in polar mesospheric clouds was reported between 26 July and 6 August. We present, for the first time, wave driving associated with observed inertial instability. The effect of inertial instability is to export eastward momentum from the winter hemisphere across the equator into the summer hemisphere. Using a primitive equation model, we demonstrate that the wave stresses destabilize the stratopause summer easterly jet. The reconfigured wind profile excites the wavenumber-4 component of the 2-day wave, leading to enhanced warming of the summer mesopause. This work supports previous numerical investigations that identified planetary wave–driven inertial instability as a source of the 2-day wave.
Genetic Diversity and Population Structure of Serbian Barley (Hordeum vulgare L.) Collection during a 40-Year Long Breeding Period

Determination of genetic diversity and population structure of breeding material is an important prerequisite for discovering novel and valuable alleles aimed at crop improvement. This study’s main objective was to characterize genetic diversity and population structure of a collection representing a 40-year long historical period of barley (Hordeum vulgare L.) breeding, using microsatellites, pedigree, and phenotypic data. The set of 90 barley genotypes was phenotyped during three growing seasons and genotyped with 338 polymorphic alleles. The indicators of genetic diversity showed differentiation changes throughout the breeding periods. The population structure discriminated the breeding material into three distinctive groups. The principal coordinate analysis grouped the genotypes according to their growth habit and row type. An analysis of phenotypic variance (ANOVA) showed that almost all investigated traits varied significantly between row types, seasons, and breeding periods. A positive effect on yield progress during the 40-year long breeding period could be partly attributed to breeding for shorter plants, which reduced lodging and thus provided higher yield stability. The breeding material revealed a considerable diversity level based on microsatellite and phenotypic data without a tendency of genetic erosion throughout the breeding history and implied dynamic changes in genetic backgrounds, providing a great gene pool suitable for further barley improvement.
Arabic Tweets Sentiment Analysis about Online Learning during COVID-19 in Saudi Arabia

The COVID-19 pandemic can be considered as the greatest challenge of our time and is defining and reshaping many aspects of our life such as learning and teaching, especially in the academic year of 2020 While some people could adapt quickly to online learning, others consider it to be inefficient The re-opening of schools and universities is currently under consideration However, many experts in many countries suggested that at least one semester should be online, during the pandemic Understanding the public's emotional reaction to online learning has become significant This paper studies the attitude of people of Saudi Arabia towards online learning We have used a collection of Arabic tweets posted in 2020, collected mainly via hashtags that originated in Saudi Arabia Our sentiment analysis has shown that people have maintained a neutral response to online learning This study will allow scholars and decision makers to understand the emotional effects of online learning on communities © 2021 All Rights Reserved
Real Effective Exchange Rates and deindustrialization: Evidence from 25 Post-Communist Eastern European countries

ABSTRACT For the past three decades, Eastern European countries have exhibited a noticeable decline in their share of the industrial production sector overall, but not uniformly. Simultaneously, trade liberalisation and integration in international production networks were intensified, bringing different export sophistication levels and economic development to countries in this region. This paper aims to examine the real effective exchange rate (REER) impact on the deindustrialisation or reindustrialisation process in 25 post-communist Eastern European countries. The paper employs a heterogeneous panel common factor approach for the period 1995–2018 to exploit the effect of diverse levels of export complexity, stage of economic development and intensity of participation in global value chains on the REER–industrial production relationship. The results establish a heterogeneous yet significant negative relationship between currency strengthening and industrial production. Our findings also indicate that this negative effect of appreciation is less pronounced with the country’s higher economic complexity and its broader participation in global value chains.
Evaluación de desempeño de los usuarios del Outsourcing de TI de las Organizaciones Públicas

Con los requerimientos de utilizar nuevas herramientas de TI en los procesos de gestion de los recursos tecnologicos en las organizaciones publicas, se presentan nuevos retos en la utilizacion de servicios de valoracion de outsourcing de TI. El problema de este estudio, esta orientado en las organizaciones publicas de la provincia de Manabi que contratan servicios tecnologicos especializados en los departamentos para realizar los procesos de gestion de informacion que requiera el usuario. La valoracion del desempeno de los usuarios es fundamental para la organizacion porque genera expectativa en los resultados obtenidos. El objetivo de este articulo es analizar el desempeno de los usuarios de TI en los departamentos de la organizacion. La recopilacion de los datos se realizo aplicando encuestas y entrevistas a 200 usuarios de TI en 40 organizaciones publicas de la provincia de Manabi. Los resultados indican que todavia existen inconvenientes en el desempeno de cada departamento. Como conclusion indicamos que hay resistencia de parte de los usuarios en aceptar la tendencia de TI en cada departamento, pero esto, sera un reto para futuras investigaciones realizadas en estas organizaciones publicas.
Measuring subjective social stratification: how does the graphical layout of rating scales affect response distributions, response effort, and criterion validity in web surveys?

ABSTRACT Previous research has shown that question characteristics, such as the shape of rating scales, can affect how respondents interpret and respond to questions. For example, earlier studies reported different response distributions for questions employing rating scales in the form of a ladder and in the form of a pyramid. The current experiment, implemented in a probability-based online panel (N = 4,377), re-visits and extends this research by examining how the two graphical layouts (ladder vs. pyramid) affect response behavior and data quality of a question on subjective social stratification. In line with the earlier results, we found that respondents rated their social status lower in the pyramid than in the ladder condition. No differences between the two layouts were found regarding response effort, however, the ladder layout was associated with higher criterion validity. Therefore, we recommend employing the ladder layout when measuring subjective social stratification.
ℋ ∞ state estimation for Markov jump neural networks with transition probabilities subject to the persistent dwell-time switching rule

We investigate the problem of ℋ ∞ state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule, as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously. Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an ℋ ∞ performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.
Technology acceptance model for online cinema ticketing among moviegoers in java island Indonesia: an empirical study on tix id application

Based on the technology acceptance model, factors that form the intention to use of technology product of performed by individual users are perceived ease of use and perceived usefulness. The purpose of this study is to examine the effect of these two variables along with the attitude toward using as a part of the theory of planned behavior as the intervening variable. This research is conducted under quantitative with partial least square (PLS) method. The results show that perceived ease of use has no effect on other variables while perceived usefulness has a significant effect 0.191 on attitude toward using and effect 0.490 intention to use. In addition, there is a significant effect influence perceived usefulness as 0.581to attitude toward using and effect as 0.338 intention to use.
Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.
Jointly Optimizing Throughput and Content Delivery Cost Over Lossy Cache Networks

Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric – the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential-based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.
Determinants of e-Commerce Use at Different Educational Levels: Empirical Evidence from Turkey

Rapid spread of internet has made e-commerce an essential and effective tool for commercial transactions. The purpose of this study is to investigate e-commerce use differences between individuals in Turkey according to their educational levels and to specify the relationship between demographic characteristics of individuals and e-commerce use. In this study, the cross-sectional data obtained by Household Information Technologies Usage Survey were used. Binary logistic regression analysis was utilized to determine the factors associated with the e-commerce use of individuals. This study has concluded that the variables of income level, age, gender, occupation, region, social media use, use of internet banking, use of e-government, number of information equipment in a household and the number of people in a household have relationships with e-commerce use. In addition, it has been found out that the variables in e-commerce use showed differences according to educational levels of individuals. It has been determined as a result of this study that as the education level of the individuals increased, their tendency towards online shopping increased. Higher education level refers to higher income level at both state and private institutions and more perception towards innovations. This has naturally a positive effect on online shopping behaviors of individuals.
A Depth Multiple Hierarchical StereoNet by Using Thermodynamic Color Guidance

Thermodynamics is devoted to the study of energy or materials, and few researchers have combined the laws of thermodynamics with stereoscopic vision. Thus, we propose Depth Multiple Hierarchical StereoNet (DMH-Net) by using thermodynamic color guidance, which present end-to-end deep architecture for real-time stereo matching, realize sub-pixel precision and produce high-quality refinement disparity on dataset benchmarks. At the same time, we use color thermodynamics to guide the image output of an accurate Hill disparity image. Our DMH-Net consists of four modules: the first module uses a deep spatial pooling method to extract multiple features that work on the initial left and right images. The second module use color guided to produce high-quality cost volume. We use the ahead of two modules to calculate depth disparity precision, which can segment coarse to fine to obtain background and edge of objects information. Thirdly module integrates the information about 3D cost volume and color channel to frame a 4D hierarchical cost volume. The fourth module is designed by depth hierarchical refinement to regress the final disparity. Finally, we achieve sub-pixel precision and real-time performance on benchmarks, which can produce high-quality thermodynamic disparity regression maps.