Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Information Quality of Wikipedia Articles through Google’s E-A-T Model

Along with the emergence of Web 2.0, User Generated Content (UGC) is becoming increasingly important for knowledge sharing. Wikipedia being the world’s largest-ever community-based collaborative encyclopedia, is also one of the biggest UGC databases in the world. Wikipedia is dealing with a significant problem of Information Quality (IQ) because of its open-source and collaborative nature. When carrying out attacks such as link spamming, malicious users take advantage of Wikipedia’s popularity on the WWW. As a result, Wikipedia is generally not recommended for academic-related work. There are, however, some articles that are both rich in information and quality. Existing approaches for assessing Wikipedia’s IQ involve statistical models and machine learning algorithms; however, the existing models do not produce satisfactory results. In this study, a novel theoretical model based on Google’s E-A-T framework is introduced to assess Wikipedia’s IQ. The model comprises three IQ constructs Expertise, Authority and Trustworthiness. Based on the empirical findings and study results, a set of IQ dimensions that influence the above three IQ constructs, as well as 45 IQ attributes to measure the IQ dimensions, were identified. The IQ attributes were automatically and inexpensively extracted from the content and meta-data statistics of Wikipedia articles using a Selenium 3.14 web automation script. A sample of 2000 articles comprising 1000 Featured Articles (FA) and 1000 non-FA articles from six WikiProjects was used for the data analysis. The proposed model was compared with three previously published models in terms of classification and clustering accuracy. It received classification and clustering accuracies of 95% and 93% respectively which is a drastic improvement over the existing models. Furthermore, an average inter-rater agreement of 84% was observed. Thus, the proposed model’s effectiveness is fairly validated by this extensive experiment. This study contributes to the related knowledge area by introducing a novel framework to assess Wikipedia articles’ IQ. The study’s limitations include the domain specificity of the chosen dataset and focusing solely on the English language. However, the results can be generalized by improving the dataset by size and replicating the study for the other domains and languages supported by Wikipedia.
Agronomic Response of 13 Spanish Red Grapevine (Vitis Vinifera L.) Cultivars Under Drought Conditions in a Semi-Arid Mediterranean Climate

Drought is perhaps the most important abiotic stressor affecting plants. Grapevine (Vitis vinifera L.) is a drought-tolerant species, and this feature makes it a traditional crop in semi-arid climate areas. However, not all cultivars respond to drought in the same way. Many studies on grapevine drought response have focused on physiological traits. This study mainly used agronomic indicators to assess the drought response of 13 red cultivars. Our results revealed high variability in must isotope ratios (δ13C and δ18O), yield components, and grape must quality. Bobal, Garnacha Peluda, Garnacha Tinta, Mazuela, and Moribel cultivars responded well to drought conditions, simultaneously maintaining high yields and must quality. By contrast, Garnacha Tintorera, Forcallat Tinta, and Tempranillo cultivars showed high water use efficiency but had low yield and must quality. Therefore, these cultivars can be considered poorly adapted to drought conditions. By knowing which cultivars perform well under drought conditions, viticulturists can reduce their reliance on water irrigation and continue to maintain vineyard sustainability in current and future semi-arid climatic conditions. This research also contributes novel information about the Castilla-La Mancha region, where there have been no previous similar assays.
Green Extraction Techniques as Advanced Sample Preparation Approaches in Biological, Food, and Environmental Matrices: A Review

Green extraction techniques (GreETs) emerged in the last decade as greener and sustainable alternatives to classical sample preparation procedures aiming to improve the selectivity and sensitivity of analytical methods, simultaneously reducing the deleterious side effects of classical extraction techniques (CETs) for both the operator and the environment. The implementation of improved processes that overcome the main constraints of classical methods in terms of efficiency and ability to minimize or eliminate the use and generation of harmful substances will promote more efficient use of energy and resources in close association with the principles supporting the concept of green chemistry. The current review aims to update the state of the art of some cutting-edge GreETs developed and implemented in recent years focusing on the improvement of the main analytical features, practical aspects, and relevant applications in the biological, food, and environmental fields. Approaches to improve and accelerate the extraction efficiency and to lower solvent consumption, including sorbent-based techniques, such as solid-phase microextraction (SPME) and fabric-phase sorbent extraction (FPSE), and solvent-based techniques (μQuEChERS; micro quick, easy, cheap, effective, rugged, and safe), ultrasound-assisted extraction (UAE), and microwave-assisted extraction (MAE), in addition to supercritical fluid extraction (SFE) and pressurized solvent extraction (PSE), are highlighted.
The Effect of Agglomeration on the Electrical and Mechanical Properties of Polymer Matrix Nanocomposites Reinforced with Carbon Nanotubes

In this work, we investigated the effect of carbon nanotubes addition and agglomeration formation on the mechanical and electrical properties of CNT–polymer-based nanocomposites. Six specimens with carbon nanotubes (CNTs) fractions of 0%, 0.5%, 1%, 2%, 4% and 5% were manufactured and characterized by dynamic mechanical analysis (DMA) and four-probe method. The stress–strain curves and electrical conductivity properties were obtained. Scanning electron microscopy (SEM) was used to characterize both agglomeration and porosity formation. By employing micromechanics, through representative volume element (RVE), finite element analysis (FEA) and resistor network model (RNM), the Young’s modulus and electrical conductivity values were calculated. The samples’ elastic moduli showed an increment, reaching the maximum value at a CNTs fraction of 2%, thereafter an adverse effect was caused in the high CNT percentage samples. The final electrical conductivity seemed greatly altered with the addition of CNTs, reaching the percolation threshold at 2%. The unavoidable formation of CNT agglomerates appeared to influence the final physical properties. The CNT agglomerates adversely affect the mechanical performance of high-CNT-percentage samples. Conversely, an exponential increment in the electrical conductivity was presented as the agglomerates formed networks allowing the transport of electrons through the tunnelling effect. These phenomena were experimentally and numerically confirmed, showing a good correlation.
Bayesian Actor-Critic Wave Energy Converter Control With Modeling Errors

This paper presents a comparison of a Reinforcement-Learning (RL) based wave energy conversion controller against standard reactive damping and model predictive control (MPC) approaches, in the presence of modeling errors. Wave energy converters (WECs) are under the influence of many non-linear hydrodynamic forces, yet for ease and expediency, it is common to formulate linear WEC models and control laws. Therefore it is expected that significant modeling errors may be present, which may degrade model-based control performance. Model-free RL approaches to control may offer a significant advantage in robustness to modeling errors, in that the model is learned by the controller by experience. It is shown that, for an annual average sea state, RL-based controllers can outperform model-based control – reactive control and MPC – by 19% and 16%, respectively, when significant modeling error is present. Furthermore, compared to similar studies of RL-based control, the proposed model can reduce the training time from 8.4 hr to 1.5 hr.
Kombucha: A systematic review and meta-analysis of experimental evidence of its effects on blood glucose, dyslipidemia and body weight in diabetes mellitus

Kombucha is a non-alcoholic fermented tea-based beverage produced by a symbiotic culture of bacteria and yeast. This growing popularity in the United States and developed countries has been part of the functional food’s movement in the use of plant infusions as a promising alternative to the benefits of the microbiome in the treatment of obesity and diabetes. However, recent studies show controversial information about the effects of Kombucha on health, often driven by different theories leading to the empirical use of the drink without standardization of quantity and forms, and preparation, which can cause harm to human health. Given that, we carried out this systematic review to evaluate the effect of Kombucha on health through pre-clinical studies with scientific evidence available in the literature, to enable future studies in humans.
Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients’ ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients’ EMRs and specialists’ long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists’ activity and patients’ perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists’ profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists’ activity and patients’ perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients’ demand and service providers’ capabilities, and providing beneficial insights for data-driven telemedicine services.
An Analysis of Factors Affecting Agricultural Tractors’ Reliability Using Random Survival Forests Based on Warranty Data

Warranty data are a valuable and easily accessible data source for manufacturers to assess the reliability of products in the field. Knowledge about the relationships between products’ reliability and reliability factors is beneficial for manufacturers to improve products’ quality. For this motivation, based on warranty data from an agricultural machinery manufacturing company in China,random survival forests (RSF), which is a machine learning method for survival analysis and provides various interpretation tools, was applied for reliability modeling in this study. Thirty-four factors from production and operation were collected. Nine most important and meaningful factors were selected to show their marginal effects and interaction effects, according to which decision rules for identifying high-risk products were extracted using classification trees. The results showed that most of the nine factors, such as “Usage Rate”, had nonlinear impacts on the reliability of tractors; the marginal effects and interaction effects can be used to generate decision rules that can significantly separate high-risk products from the population. This work provides new insights for agricultural machinery manufacturers to understand their products’ reliability and make reliability improvement plans and marketing plans.
Performance evaluation of eICIC algorithms in LTE HetNets

It is not a hidden fact that, in today’s wireless and cellular networks, businesses and end users have clearly become dependent on mobility and the freedom of being always online. Smart phones, tablets and other devices are expected to be connected all the time and the applications on these devices are required to run smoothly. That is, for a mobile network to even be considered to have the minimum requirements from its users today, network problems like dropped calls, choppy videos and slow downloads should be out of the question. However, with the fast growth rate of wireless communication devices competing for spectrum, the mobile access networks that we have today will soon reach their maximum capacy. To address these high expectations, mobile network operators are employing new strategies in densely populated areas and commercial buildings where spectrum scarcity is highest. 3GPP has been working on LTE Advanced (Long Term Evolution Advanced) in order to improve the spectral efficiency by employing Heterogeneous Networks (HetNets). HetNets improve the business model for mobile network operators and users by introducing network topologies which are less costly and are able to increase the capacity and coverage provided by the traditional macro cell mobile networks. HetNets are comprised of macro cells and low cost low power base stations like picocells, femtocells, relays and other small cells along with WiFi-APs and distributed antenna systems (DAS). With HetNets a significant network capacity gain and uniform broadband experience can be provided to the users anywhere and at low cost, since the spectrum can be re-used across the multiple tiers in the network. However, HetNet deployments come with a lot of challenges and advanced interference control and management techniques are required to get the highest possible benefit from these networks. The enhanced Inter Cell Interference Coordination (eICIC) technique has been studied previously to address the interference problem that these small cells experience from macro cells. This thesis will concentrate on the performance improvement achieved when eICIC mechanisms are introduced to a simple HetNet. The eICIC approach as described in 3GPP’s Release 10 involves two mechanisms, which this thesis also tries to take into consideration. These are the CRE (Cell Range Expansion) acheived through cell bias adjustments and ABS (Almost Blank Subframe) ratio. A discussion about the importance eICIC in SON Serlf Organizing Networks will also be done.
Decoupled H ∞ control of automated vehicular platoons with complex interaction topologies

The platooning of automated vehicles has potential to significantly benefit road traffic, while its robust performance is less investigated especially considering increasing complexity of interaction topologies. This study presents a decoupled H ∞ control method for automated vehicular platoon to comprehensively compromise multiple performances. The platoon control system is first decomposed into an uncertain part and a diagonal nominal system through the linear transformation, which is motivated by the eigenvalue decomposition of information topology. Based on this almost decoupled system, a distributed H ∞ controller is presented, which can balance the performances of robustness and disturbance attenuation ability. Moreover, a numerical method is given to solve and optimise this controller by using linear matrix inequality approach. Several comparative hardware-in-loop tests of different communication topologies and controllers have been carried out to demonstrate the effectiveness of this method.
Mediating Effects of Pain Catastrophizing on Sleep and Pain Intensity in Army Basic Trainees

Abstract Numerous stressors during Army Basic Combat Training can elevate pain. Previous research has shown that disrupted sleep may increase pain and pain catastrophizing. The purpose of this study is to examine the interrelationships between pain catastrophizing, sleep duration, sleep quality, and pain intensity in a military population. Measures included three standardized self-report instruments related to sleep and pain intensity. Linear mixed-model mediation analyses evaluated the direct effect of sleep on pain intensity and the indirect effect of pain catastrophizing on pain intensity. Participants (N = 478, 57.7% male, 20.8 ± 3.96 years) averaged 6.39 ± 1.01 h of sleep per night and reported mild pain intensity ratings of 3.4 ± 2.02. Significant main effects of sleep duration (b = −0.23) and quality (b = 0.11) on pain intensity (p < 0.0001) were observed, however pain catastrophizing did not mediate these relationships (p = 0.30 and p = 0.12). Overall, data suggest that female trainees with poorer sleep quality also report greater levels of pain, a relationship that was significantly mediated by pain catastrophizing. Female trainees also reported greater levels of pain intensity and rumination, which reflects observations made within the general population.
Advantages of intraoral scanning in prosthetic dentistry

Advances in digital technologies create the opportunity for much more predictable and safe dentistry. From the examination of the patient to the final treatment plan, digital technologies can provide a much comprehensive visualization and understanding. A key aspect for any long-lasting prosthetic restoration is the accuracy of the impression, which generates a working model (digital or conventional). The precision of digital impressions is essential to obtain high-quality restorations, as well as to obtain the predictability of the treatment. There are some challenging factors that can influence the precision of direct digital impressions, that’s why the clinician must be aware and find methods to counteract them. The main advantages of intraoral scanners are the possibility to completely replace the conventional impression, reducing the discomfort for the patient, and the overall costs of the impression materials. Switching from digital to conventional in impression technique increase the predictability, the precision, and the efficiency. From the clinician point of view, the overall working time and the effort of the dentist can be reduced, in order to obtain the most appropriate treatment outcome for every patient.
A data-driven machine learning approach for the 3D printing process optimisation

ABSTRACT 3D printing has become highly applicable in modern life recently. The industry has brought a facelift to most others. However, this technology still exists some shortcomings, and it therefore has not been generalised to bring the best benefits to users. In this paper, based on multilayer perceptron and convolution neural network models, we propose a new data-driven machine learning platform for predicting optimised parameters of the 3D printing process from a model design to a complete product. This finding can open up great advances in the current 3D printing technology. Accordingly, the results obtained allow us to predict quickly and accurately some decisive parameters of the traditional 3D printing process such as time, weight and length while the input was fuzzy with a part of the initial information missing. The proposed approach does not need to account for the shape, size and material of the printed object, but it can perform the process automatically without other extra factors. After completing the model, a configurator is proposed to set the parameters for the respective printer types, which makes the 3D printing process simple and fast.
EXAMINING OF THE METAPHORIC PERCEPTIONS OF PRIMARY EDUCATION TEACHERS ABOUT THE CONCEPT OF “SAVING”

This study analyzes the conceptual metaphors of primary school teacher candidates’ perceptions of “saving”. In the study, which was carried out according to the qualitative research methodology, the forms, frequencies and functions of 93 metaphorical usages related to saving were shown. In the study carried out using the qualitative research method, a basic qualitative research pattern was used and qualitative data were obtained through metaphors. The participants of the research, 205 primary education teachers participated in in-service training courses/seminars in the central Tarsus, Akdeniz, Yenişehir and Mezitli districts of Mersin province and Tarsus, Erdemli and Silifke districts, in the 2018-2019 academic year. The convenience sampling method was preferred in determining the study group of the research. The data of the research were collected through an opinion form, which was created by using structured pattern questions. Pattern questions used in this form are expressed as “Saving is …; because it is . ” and “When I think of saving, the first word that comes to mind is …” and “When I think of saving, the first person that comes to mind is …”. Content analysis technique was used in the analysis and interpretation of the data. The results showed that 93 different metaphors related to the concept of “saving” were produced by 205 primary education teachers participating in the research and the most mentioned metaphors were “future”, “water” and “life”. Primary education teachers stated 68 kinds of metaphors related to the first word that came to their minds about the concept of “savings” and the metaphors they mentioned the most were “Money”, “accumulation” and “thrift”. Regarding the first person that comes to mind about the concept of “saving”, it was seen that the primary education teachers stated 23 types of metaphors, and the metaphors they stated the most were “mother”, “father” and “wife”. Article visualizations: