nils-herrmann/clean_scirepeval huggingface dataset:
Endocrowns: review

The ideal restoration of endodontically treated teeth (ETT) has been widely and controversially discussed in the literature. Prevention of healthy dental structure is essential to help mechanical stabilization of tooth-restoration integrity, increase the amount of suitable surfaces for adhesion and thus positively affect the long-term success. ETT are affected by a higher risk of biomechanical failure than vital teeth. With the development of adhesive systems, the need for post-core restorations is also reduced. Especially for restoration of excessively damaged ETT, endocrowns have been used as an alternative to the conventional post-core and fixed partial dentures. Compared to conventional methods, good aesthetics, better mechanical performance, and less cost and clinic time are the advantages of endocrowns.
Cancer: a family at risk

The diagnosis of cancer is a family experience that changes the lives of all its members, bringing an immense amount of stress and many challenging situations. The daily routine, common activities and distribution of duties all have to change. Family members follow the phases of the disease, very often suffering comparable or greater distress than the patient. They use various coping methods which aim at helping both the sick relative and themselves. These methods, together with emotional responses, change over time according to the phase of the disease. Cancer puts the family at risk since it imposes an alternation in the relations among family members. It affects the couple's relationship, their sex life, and it can also be a cause of major trauma among their children and adolescents. The diagnosis of cancer brings also individual risks for the family members in terms of psychological and physical health impairment. Family caregivers often feel overloaded with the additional obligations and roles they have to pick up. They find it increasingly burdening to care full-time for the household and provide emotional support for the patient. The family's problems and the way family members regard the disease may be also a result of the family system they are in. This article describes the nature of caregiving to a patient with cancer and the biggest concerns for the family.
Hedonic and utilitarian motivations for online retail shopping behavior

Motivations to engage in retail shopping include both utilitarian and hedonic dimensions. Business to consumer e-commerce conducted via the mechanism of web-shopping provides an expanded opportunity for companies to create a cognitively and esthetically rich shopping environment in ways not readily imitable in the nonelectronic shopping world. In this article an attitudinal model is developed and empirically tested integrating constructs from technology acceptance research and constructs derived from models of web behavior. Results of two studies from two distinct categories of the interactive shopping environment support the differential importance of immersive, hedonic aspects of the new media as well as the more traditional utilitarian motivations. In addition, navigation, convenience, and the substitutability of the electronic environment to personally examining products were found to be important predictors of online shopping attitudes. Results are discussed in terms of insights for the creation of the online shopping webmosphere through more effective design of interactive retail shopping environments.
The Relationship between Organizational Citizenship Behavior and Counterproductive Work Behavior

Job performance is the most researched concept studied in industrial and organizational psychology, with the emphasis being on organizational citizenship behavior (OCB) and counterproductive work behavior (CWB) as two dimensions of it. The relationship between these two dimensions of job performance are unclear, hence the objective of the current study was to examine the relationship between organizational citizenship behavior and counterproductive work behavior. A total of 267 students studying psychology were given a questionnaire that measured organizational citizenship behavior and counterproductive work behavior (most have had part-time work experience). Correlational analysis found OCB and CWB to have only a moderate negative correlation which suggests OCB and CWB are two separate but related constructs. It was also found that females and longer-tenured individuals tend to show more OCB but no difference was found for CWB. The findings showed that individuals can engage in OCB and CWB at the same time, which necessitates organizations to find a way to encourage their employees to engage in OCB and not in CWB.
Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.
Recent advances on the development of magnesium alloys for biodegradable implants.

In recent years, much progress has been made on the development of biodegradable magnesium alloys as "smart" implants in cardiovascular and orthopedic applications. Mg-based alloys as biodegradable implants have outstanding advantages over Fe-based and Zn-based ones. However, the extensive applications of Mg-based alloys are still inhibited mainly by their high degradation rates and consequent loss in mechanical integrity. Consequently, extensive studies have been conducted to develop Mg-based alloys with superior mechanical and corrosion performance. This review focuses on the following topics: (i) the design criteria of biodegradable materials; (ii) alloy development strategy; (iii) in vitro performances of currently developed Mg-based alloys; and (iv) in vivo performances of currently developed Mg-based implants, especially Mg-based alloys under clinical trials.
Caffeine consumption.

Scientific literature cites a wide range of values for caffeine content in food products. The authors suggest the following standard values for the United States: coffee (5 oz) 85 mg for ground roasted coffee, 60 mg for instant and 3 mg for decaffeinated; tea (5 oz): 30 mg for leaf/bag and 20 mg for instant; colas: 18 mg/6 oz serving; cocoa/hot chocolate: 4 mg/5 oz; chocolate milk: 4 mg/6 oz; chocolate candy: 1.5-6.0 mg/oz. Some products from the United Kingdom and Denmark have higher caffeine content. Caffeine consumption survey data are limited. Based on product usage and available consumption data, the authors suggest a mean daily caffeine intake for US consumers of 4 mg/kg. Among children younger than 18 years of age who are consumers of caffeine-containing foods, the mean daily caffeine intake is about 1 mg/kg. Both adults and children in Denmark and UK have higher levels of caffeine intake.
A Study On Advertising And Its Impact Of Brand Awareness On Fast Moving Consumer Goods With Reference To Personal Care Products In Chennai

Advertisement and Brand awareness plays an important role in brand building, brand recognition, brand loyalty and boost up the sales performance which is regarded as the foundation for brand development. To some degree advertisement and brand awareness can directly influence consumers’ buying behavior. The female consumers from IT industry have been taken as main consumers for the research purpose. The researcher seeks to inspect and investigate brand’s intention factors and consumer’s individual factors in influencing advertisement and its impact of brand awareness on fast moving consumer goods especially personal care products .The aim of the paper is to examine the advertising and its impact of brand awareness towards FMCG Products, on the other hand, to analyze the influence of advertising on personal care products among female consumers in IT industry and finally to study the impact of media on advertising & brand awareness. The prescribed survey were conducted in the form of questionnaire and found valid and reliable for this research. After evaluating some questions, better questionnaires were developed. Then the questionnaires were distributed among 200 female consumers with a response rate of 100%. We found that advertising has constantly a significant positive effect on brand awareness and consumers perceive the brand awareness with positive attitude. Findings depicts that advertising and brand awareness have strong positive influence and considerable relationship with purchase intention of the consumer. This research highlights that female consumers of personal care products in IT industry are more brand conscious and aware about their personal care products. Advertisement and brand awareness affects their purchase intention positively; also advertising media positively influences the brand awareness and purchase intention of the female consumers. The obtained data were then processed by Pearson correlation, multiple regression analysis and ANOVA. A Study On Advertising And Its Impact Of Brand Awareness On Fast Moving Consumer Goods With Reference To Personal Care Products In Chennai Paper ID IJIFR/ V2/ E9/ 068 Page No. 3325-3333 Subject Area Business Administration
Young children's productivity with word order and verb morphology.

Four studies examined English-speaking children's productivity with word order and verb morphology. Two- and 3-year-olds were taught novel transitive verbs with experimentally controlled argument structures. The younger children neither used nor comprehended word order with these verbs; older children comprehended and used word order correctly to mark agents and patients of the novel verbs. Children as young as 2 years 1 month added -ing but not -ed to verb stems; older children were productive with both inflections. These studies demonstrate that the present progressive inflection is used productively before the regular past tense marker and suggest that productivity with word order may be independent of developments in verb morphology. The findings are discussed in terms of M. Tomasello's (1992a) Verb Island hypothesis and M. Rispoli's (1991) notion of the mosaic acquisition of grammatical relations.
Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings

Methods for text simplification using the framework of statistical machine translation have been extensively studied in recent years. However, building the monolingual parallel corpus necessary for training the model requires costly human annotation. Monolingual parallel corpora for text simplification have therefore been built only for a limited number of languages, such as English and Portuguese. To obviate the need for human annotation, we propose an unsupervised method that automatically builds the monolingual parallel corpus for text simplification using sentence similarity based on word embeddings. For any sentence pair comprising a complex sentence and its simple counterpart, we employ a many-to-one method of aligning each word in the complex sentence with the most similar word in the simple sentence and compute sentence similarity by averaging these word similarities. The experimental results demonstrate the excellent performance of the proposed method in a monolingual parallel corpus construction task for English text simplification. The results also demonstrated the superior accuracy in text simplification that use the framework of statistical machine translation trained using the corpus built by the proposed method to that using the existing corpora.
An IT management assessment framework: evaluating enterprise architecture scenarios

Enterprise architecture, EA, is an established approach for the model-based and holistic management of IT. The scope of EA is however wide and the predominant EA frameworks suggest the creation of broad and detailed models. IT management cannot control all areas spanned by the present frameworks for EA models. In order to ensure well informed decisions, IT management has a series of questions that need to be answered. This paper proposes an assessment framework that can be used to identify relevant questions for assessments of EA and EA scenarios, within the areas of EA that IT management can control. Three top dimensions in the proposed framework are presented: IT organization, IT systems, and Business organization. The framework further includes sub dimensions for identifying questions. An application of the assessment framework, as it was applied to assess EA scenarios in a power company, is also described.
Advanced Security Testbed Framework for Wearable IoT Devices

Analyzing the security of Wearable Internet-of-Things (WIoT) devices is considered a complex task due to their heterogeneous nature. In addition, there is currently no mechanism that performs security testing for WIoT devices in different contexts. In this article, we propose an innovative security testbed framework targeted at wearable devices, where a set of security tests are conducted, and a dynamic analysis is performed by realistically simulating environmental conditions in which WIoT devices operate. The architectural design of the proposed testbed and a proof-of-concept, demonstrating a preliminary analysis and the detection of context-based attacks executed by smartwatch devices, are presented.
Queuing Algorithem Based Quality of Service ( Qos ) For Scheduling Environment Model in Wimax Network with Opnet Modeler

The name "WiMAX" was created by the “WiMAX Forum”, which was formed in June. WiMAX (Worldwide Interoperability for Microwave Access) standards define formal specifications for deployment of broadband wireless metropolitan area networks (wireless MANs).Wireless MANs as needed in WiMAX standards provide wireless broadband access anywhere, anytime, and on virtually any device. Introducing the various type of scheduling algorithm, like FIFO,PQ,WFQ, for comparison of four type of scheduling service, with its own QoS needs and also introducing OPNET modeler support for Worldwide Interoperability for Microwave Access (WiMAX) network. The simulation results indicate the correctness and the effectiveness of theses algorithm. This paper presents a WiMAX simulation model designed with OPNET modeler 14 to measure the delay, load and the throughput performance factors.
Thermal Conductivity of Nanoparticle – Fluid Mixture

Effective thermal conductivity of mixtures of  uids and nanometer-size particles is measured by a steady-state parallel-plate method. The tested  uids contain two types of nanoparticles, Al2O3 and CuO, dispersed in water, vacuum pump  uid, engine oil, and ethylene glycol. Experimental results show that the thermal conductivities of nanoparticle– uid mixtures are higher than those of the base  uids. Using theoretical models of effective thermal conductivity of a mixture, we have demonstrated that the predicted thermal conductivities of nanoparticle– uid mixtures are much lower than our measured data, indicating the de ciency in the existing models when used for nanoparticle– uid mixtures. Possible mechanisms contributing to enhancement of the thermal conductivity of the mixtures are discussed. A more comprehensive theory is needed to fully explain the behavior of nanoparticle– uid mixtures.
Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low falsepositive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward. Phishing websites; fuzzy models; feature model; intelligent detection; neuro fuzzy; fuzzy inference system
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users’ task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users’ offloading utility over traditional approaches.
A surrogate model for traffic optimization of congested networks: an analytic queueing network approach

Congested networks involve complex traffic dynamics that can be accurately captured with detailed simulation models. However, when performing optimization of such networks the use of simulators is limited due to their stochastic nature and their relatively high evaluation cost. This has lead to the use of general-purpose analytical metamodels, that are cheaper to evaluate and easier to integrate within a classical optimization framework, but do not capture the specificities of the underlying congested conditions. In this paper, we argue that to perform efficient optimization for congested networks it is important to develop analytical surrogates specifically tailored to the context at hand so that they capture the key components of congestion (e.g. its sources, its propagation, its impact) while achieving a good tradeoff between realism and tractability. To demonstrate this, we present a surrogate that provides a detailed description of congestion by capturing the main interactions between the different network components while preserving analytical tractable. In particular, we consider the optimization of vehicle traffic in an urban road network. The proposed surrogate model is an approximate queueing network model that resorts to finite capacity queueing theory to account for congested conditions. Existing analytic queueing models for urban networks are formulated for a single intersection, and thus do not take into account the interactions between queues. The proposed model considers a set of intersections and analytically captures these interactions. We show that this level of detail is sufficient for optimization in the context of signal control for peak hour traffic. Although there is a great variety of signal control methodologies in the literature, there is still a need for solutions that are appropriate and efficient under saturated conditions, where the performance of signal control strategies and the formation and propagation of queues are strongly related. We formulate a fixed-time signal control problem where the network model is included as a set of constraints. We apply this methodology to a subnetwork of the Lausanne city center and use a microscopic traffic simulator to validate its performance. We also compare it with several other methods. As congestion increases, the new method leads to improved average performance measures. The results highlight the importance of taking the interaction between consecutive roads into account when deriving signal plans for congested urban road networks.
Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. This work performs a predictive study of the principal index of the Brazilian stock market through artificial neural networks and the adaptive exponential smoothing method, respectively. The objective is to compare the forecasting performance of both methods on this market index, and in particular, to evaluate the accuracy of both methods to predict the sign of the market returns. Also the influence on the results of some parameters associated to both methods is studied. Our results show that both methods produce similar results regarding the prediction of the index returns. On the contrary, the neural networks outperform the adaptive exponential smoothing method in the forecasting of the market movement, with relative hit rates similar to the ones found in other developed markets.