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To attempt to relate the mean returns and price volatility of a selected sample of 30 companies listed in Athens stock exchange (ATHEX), to the introduction of the legal framework concerning corporate governance.

The essence of this approach is segmenting our whole sample into three subsamples with their key dates being the actual dates on which two legal frameworks related to the corporate governance has been introduced, we perform mean and variance equality tests to assess whether stock market returns and price volatility change, in a statistically significant way, in the three sub-periods.

From our empirical study, it can be concluded that the volatility has been altered both during the sample periods used and the companies for which our methodology has been implemented.

Our empirical research can be further extended including a larger sample of companies in order to draw more safe conclusions. In addition, and although our argument for high liquidity for selecting our sample of companies is rational, we believe that our research can be further enriched by first constructing a ranking for all listed companies based on various corporate governance measures.

One of the reasons that may have impacted on the volatility may be the introduction of corporate governance; however, other factors may have also resulted to lower volatility, argument that can be further researched in future studies.

This paper provides evidence on the relation between volatility and corporate governance. The implication is that the volatility has been altered during the period under investigation.

The purpose of this paper is to solve temperature stress for bending beam with different moduli under different constraints subject to nonlinear temperature.

The equations of neutral axis position, normal stress, and displacement of bending beam with different moduli subjected to nonlinear temperature were derived based on different moduli elasticity theory. Meanwhile, iterative procedure was programmed to solve the nonlinear equations. The analytical solution can return back into the result of the same modulus theory, and the analytical solution was compared with finite element numerical solution. It shows that the analytical model proposed in this paper is reliable to use. Furthermore, the influence of different moduli characteristics on the temperature stress and deformation is discussed.

The mechanical behavior of the bending beam with different moduli subject to nonlinear temperature is quite different from the one that is subjected to force. The bending beam maybe exist two neutral axis, and the reasonable selection of tension modulus and compression modulus can improve the distribution of the normal stress and reduce the maximum tensile stress or the maximum compressive stress.

The crack produced by temperature stress will affect the integrity and the durability of the structure. The solution for temperature problem with different moduli theory is rarely reported at home and board. In view of this, this paper will do some exploratory research-temperature stress for bending beam with different moduli.

The purpose of this paper is to propose an intelligent service recommendation model. The paper formulates the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning.

The authors formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning. Bayesian Network is used to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this the paper proposes maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed service recommendation engine and demonstrate its effectiveness.

First, it was found that there was reduction in application searching time in different contexts. For example, if user enters into the library, the proposed mobile will be adapted to the library situation automatically by configuring its desktop and internal settings to facilitate the library services like book search, web link, silent mode and friends search. Second, the design of the recommendation engine, utilizing contextual parameters like Location (class room, college campus, house, etc.) Personal (age, name), Temporal (time, date), Physical (fall, normal), and schedule agendas, was found to be of importance.

Exploitation of hybrid fuzzy system, Bayesian Networks and the utility theory (usage history and context history) for modeling and implementation.

Collaborative recommender systems play a crucial role in providing personalized services to online consumers. Most online shopping sites and many other applications now use the collaborative recommender systems. The measurement of the similarity plays a fundamental role in collaborative recommender systems. Some of the most well-known similarity measures are: Pearson’s correlation coefficient, cosine similarity and mean squared differences. However, due to data sparsity, accuracy of the above similarity measures decreases, which makes the formation of inaccurate neighborhood, thereby resulting in poor recommendations. The purpose of this paper is to propose a novel similarity measure based on potential field.

The proposed approach constructs a dense matrix: user-user potential matrix, and uses this matrix to compute potential similarities between users. Then the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors. Compared to the rating matrix, the potential matrix is much denser. Thus, the sparsity problem can be efficiently alleviated. The similarity modification scheme considers the number of common neighbors of two users, which can further improve the accuracy of similarity computation.

Experimental results show that the proposed approach is superior to the traditional similarity measures.

The research highlights of this paper are as follows: the authors construct a dense matrix: user-user potential matrix, and use this matrix to compute potential similarities between users; the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors; and the proposed approach performs better than the traditional similarity measures. The manuscript will be of particular interests to the scientists interested in recommender systems research as well as to readers interested in solution of related complex practical engineering problems.

The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.

The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.

Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.

The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.

This research provides evidence toward new recommendation methods directed toward LO repositories.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

The purpose of this paper is to propose the Collaborative Search System that attempts to achieve collaboration by implicitly identifying and reflecting search behaviour of collaborators in an academic network that is automatically and dynamically formed. By using the constructed Collaborative Hit Matrix (CHM), results are obtained that are based on the search behaviour and earned preferences of specialist communities of researchers, which are relevant to the user's need and reduce the time spent on bad links.

By using the Digital Bibliography Library Project (DBLP), the research communities are formed implicitly and dynamically based on the users’ research presence in the search environment and in the publication scenario, which is also used to assign users’ roles and establish links between the users. The CHM, to store the hit count and hit list of page results for queries, is also constructed and updated after every search session to enhance the collaborative search among the researchers.

The implicit researchers community formation, the assignment and dynamic updating of roles of the researchers based on research, search presence and search behaviour on the web as well as the usage of these roles during Collaborative Web Search have highly improved the relevancy of results. The CHM that holds the collaborative responses provided by the researchers on the search query results to support searching distinguishes this system from others. Thus the proposed system considerably improves the relevancy and reduces the time spent on bad links, thus improving recall and precision.

The research findings illustrate the better performance of the system, by connecting researchers working in the same field and allowing them to help each other in a web search environment.

This paper investigates the latest achievements of studies on industrial information integration engineering (IIIE).

This paper extends the research by Chen (2016) by reviewing studies from 2016 to 2019 in IEEE Xplore and Web of Science. Altogether, 970 papers related to IIIE are grouped into 27 research categories and reviewed.

The results obtained in this study indicate that the number of research studies on IIIE rose drastically in the past three years compared with the findings in Chen (2016). Particularly, energy, engineering, industrial control, information and communications technologies, instrumentation, manufacturing and transportation are the hot topics. This change proves that the Internet of things (IoT) and IIIE have integrated closely by providing more applications, such as industrial Internet of things (IIoT), cyber-physical system (CPS), smart grids and smart manufacturing. This change also proves the research direction of IIIE identified by Chen (2016).

The results present up-to-date development of IIIE and provide directions for future research on IIIE. The review identifies that energy, engineering, industrial control, information and communications technologies, instrumentation, manufacturing and transportation are the main fields that most of the reviewed papers focus on. Applications that integrate IoT and IIIE, including IIoT, CPS, smart grids and smart manufacturing, are attracting scholars' and practitioners' attention. Some new technologies, such as 5G and blockchain, have the potential to be integrated with IoT and IIIE.

This paper aims to investigate effects of acrylic functionalisation of multiwalled carbon nanotubes (MWCNTs) on properties of carbon nanotubes/epoxy nanocomposites.

A number of analytical techniques, including Fourier transform infrared spectroscopy, Raman spectroscopy, scanning electron microscopy and transmission electron microscopy, were used to assess the effects of acid treatment on MWCNTs. Ultraviolet-curable coatings were fabricated by sonication and cast moulding process. The mechanical properties of MWCNTs/epoxy composites at different weight fractions were evaluated by performing tensile tests and dynamic mechanical analysis tests. Also, gel contents were examined.

It was found that addition of nanotubes monomer to epoxy formulations had significant effect on the viscoelastic and mechanical properties.

Improving dispersion and alignment of MWCNTs in the composite matrix will contribute to the development of resin/MWCNTs nanocomposites and promote the applications.

The paper establishes a method to introduce MWCNTs into epoxy matrix as a monomer to enhance the photo curable and dispersion properties of the MWCNT/epoxy films.

The purpose of this paper is to implement a micromechanical hybrid finite element approach in order to investigate the stress transfer behavior of composites reinforced with hexagonal boron nitride (h-BN) nanosheets.

For the analysis of the problem, a three-dimensional representative volume element, consisting of three phases, has been used. The reinforcement is modeled discretely using spring elements of specific stiffness while the matrix material is modeled as a continuum medium using solid finite elements. The third phase, the intermediate one, known as the interface, has been simulated by appropriate stiffness variations which define a heterogeneous region affecting the stress transfer characteristics of the nanocomposite.

The results show a good agreement with corresponding ones from the literature and also the effect of a number of factors is indicated in stress transfer efficiency.

This is the first time that such a modeling is employed in the stress transfer examination of h-BN nanocomposites.