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The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

The new greedy algorithm is proposed to balance the energy consumption in edge computing.

The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.

The results are shown in this paper which are better as compared to existing algorithms.

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.

This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.

First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.

This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.

This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

The representation of Heterogeneous Object (HO) is divided into two categories: Data model (DM) and material evaluation paradigm (MEP). A hybrid methodology with geometry model and volumetric dataset to represent heterogeneous properties is proposed in this paper. Geometry model of an object can guarantee the accuracy of the final HO slices; and volumetric dataset lends the flexible manipulability and other advantages to HO representation. Two MEPs, namely distance field (DF) based and Fixed Reference Features & Active Grading Source(s) (FRF&AGS) are presented to facilitate the process of HO representation according to the designer)s input parameters. The DM can be modified interactively with users until the final satisfactory result is obtained. In this paper, a scheme of HO slicing is described. In this method, we utilize the slices contour of geometrical model as constraint to reconstruct the HO slices, which can theoretically achieve the same accuracy with the geometrical shape. Some examples of Heterogeneous object represented with our scheme are provided.

The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations.

This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines.

The three transfer learning techniques are all able to outperform the baseline comparisons in several situations. Furthermore, the techniques are successfully used in an ensemble approach to achieve even higher levels of accuracy.

The techniques in this paper represent a considerable step forward in heterogeneous transfer learning by removing the need to rely on instance – instance or feature – feature co-occurrence data.

Apart from the geometries to be dealt with, rapid prototyping (RP) of heterogeneous objects requires additional material information to be processed. This generally involves a large amount of information to be processed simultaneously. The robustness and efficiency problems, which seem less critical in homogeneous solid fabrications, become an issue. The direct impetus of this paper is to present robust and efficient algorithms for RP of heterogeneous objects.

The robustness is benefited from using the proposed non‐manifold heterogeneous cellular model, which guarantees gap‐free material depositions around material interfaces. The efficiency enhancement is achieved by eliminating repetitive boundary intersections and using a heuristic material interrogation approach.

By using the proposed algorithms, the robustness and efficiency of RP of heterogeneous objects can be improved. It is found that an average 30 percent efficiency improvement is obtained using the proposed heuristic material interrogation approach.

Non‐manifold heterogeneous cell representation (HC‐Rep) is used in RP fields for the first time. Based on the HC‐Rep, the robustness and efficiency of RP of heterogeneous object is addressed in this paper.

The proposed definitional framework can be used to define housing typologies of cities in developing countries. It aims to define housing typologies in the capital city of Albania, Tirana, using the proposed hierarchical framework within the dynamics of four political periods: Ottoman, establishment, socialist and postsocialist.

This study proposes a new definitional approach for the housing typologies through a hierarchical framework that defines the typologies based on their political period and legality statuses departing from the case of Tirana, which is featured by a heterogeneous context. Such context is characterized by uncontrollable urban development, making typology definition problematic. Furthermore, beyond the form, spatial and functional features, it presents their exterior distinctiveness as an innovative element. The methodology used in the study includes archival research, image documentation, spatial, functional and exterior distinctiveness analysis of housing typologies and exploration of housing form features in different political periods.

The study identifies urban formal housing typologies in Tirana detached houses, apartments, mass housing, social housing, gated communities, informal detached houses and housing with in/formal additions.

This definitional approach can be used to define housing typologies for cities featured by heterogeneous urban context.

The purpose of this paper is to aim at modelling the trails, which are search patterns with several search systems across the heterogeneous information environment. In addition, the author seeks to examine what kinds of trails occur in routine, semi-complex and complex tasks, and what barrier types occur during the trail-blazing.

The author used qualitative task-based approach with shadowing of six molecular medicine researchers during six months, and collected their web interaction logs. Data triangulation made this kind of detailed search system integration analysis possible.

Five trail patterns emerged: branches, chains, lists, singles and berrypicking trails. The berrypicking was typical to complex work tasks, whereas the branches were common in routine work tasks. Singles and lists were employed typically in semi-complex tasks. In all kinds of trails, the barriers occurred often during the interaction with a single system, but there was a considerable number of barriers with the malfunctioning system integration, and lacking integration features. The findings propose that the trails could be used to reduce the amount of laborious manual system integration, and that there is a need for support to explorative search process in berrypicking trails.

Research of information behaviour yielding to different types of search patters with several search systems during real-world work task performance in molecular medicine have not been published previously. The author presents a task-based approach how to model search behaviour patterns. The author discusses the issue of system integration, which is a great challenge in biomedical domain, from the viewpoints of information studies and search behaviour.

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.

First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.

The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.

This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.

This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.