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This research will analyze the traditional Iranian buildings according to the climatic factors by the use of graph theory. By this way, the hypothesis that climate factor has a major effect on the organization of the spaces in traditional Iranian buildings will be tested. Access graphs have been used to clarify the connectivity and depth of a building’s spaces from the socio-cultural point of view. However, it cannot be applied to climate studies. Thus, this study developed the existing technique to define building layouts in terms of climate and thermal comfort. The thermal comfort was graphically evaluated by the two main factors like solar gain and wind effect, with the use of a simple multi-attribute rating technique. All the analysis had been done in the interval of zero (the worst condition) to three (the best condition). The proposed orientation-weighted graph method proved that the thermal comfort factors of the buildings under study match the seasonal movements of their inhabitants. Consequently, the developed orientation-weighted graph method can be used to study space organization in traditional Iranian building in terms of solar gain and wind effect.

The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.

The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.

Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.

To the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.

The purpose of this paper is to describe how the method recently developed for mass‐spring systems and frame structures is modified to include the free vibration of trusses.

Here, two methods are presented for calculating the eigenfrequencies of structures. The first approach is graph theoretical and uses graph symmetry. The graph models are decomposed into submodels and healing processes are employed such that the union of the eigenvalues of the healed submodels contain the eigenvalues of the entire model. The second method has an algebraic nature and uses special canonical forms. The present method is illustrated through three simple examples with odd and even number of bays.

The inter‐relation for the mechanical properties of elements is established using new weighted graphs, enabling easy calculation of the eigenvalues involved. Two methods are presented for calculating the eigenfrequencies of the truss structures.

Symmetry is used for easy calculation of the eigenfrequencies of structures.

Medians of a graph have many applications in engineering. Optimal locations for facility centers, distribution of centers and domain decomposition for parallel computation are a few examples of such applications. In this paper, a new ant system (AS) algorithm based on the idea of using two sets of ants, named active and passive ants is proposed for the problem of finding k‐medians of a weighted graph or the facility location problem on a network.

The structure of the algorithm is derived from two known heuristics; namely, rank‐based AS and max‐min ant system with some adjustments in pheromone updating and locating the ants on the graph nodes. The algorithms are designed with and without a local search.

An efficient algorithm for location finding, and the novel application of an ant colony system can be considered as the main contribution of this paper.

Combining two different tools; namely, graph theory and AS algorithm results in an efficient and accurate method for location finding. The results are compared to those of another algorithm based on the theory of graphs.

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

The purpose is to reduce round-off errors in numerical simulations. In the numerical simulation, different kinds of errors may be created during analysis. Round-off error is one of the sources of errors. In numerical analysis, sometimes handling numerical errors is challenging. However, by applying appropriate algorithms, these errors are manageable and can be reduced. In this study, five novel topological algorithms were proposed in setting up a structural flexibility matrix, and five different examples were used in applying the proposed algorithms. In doing so round-off errors were reduced remarkably.

Five new algorithms were proposed in order to optimize the conditioning of structural matrices. Along with decreasing the size and duration of analyses, minimizing analytical errors is a critical factor in the optimal computer analysis of skeletal structures. Appropriate matrices with a greater number of zeros (sparse), a well structure and a well condition are advantageous for this objective. As a result, a problem of optimization with various goals will be addressed. This study seeks to minimize analytical errors such as rounding errors in skeletal structural flexibility matrixes via the use of more consistent and appropriate mathematical methods. These errors become more pronounced in particular designs with ill-suited flexibility matrixes; structures with varying stiffness are a frequent example of this. Due to the usage of weak elements, the flexibility matrix has a large number of non-diagonal terms, resulting in analytical errors. In numerical analysis, the ill-condition of a matrix may be resolved by moving or substituting rows; this study examined the definition and execution of these modifications prior to creating the flexibility matrix. Simple topological and algebraic features have been mostly utilized in this study to find fundamental cycle bases with particular characteristics. In conclusion, appropriately conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.

(1) Five new algorithms were proposed in order to optimize the conditioning of structural flexibility matrices. (2) A JAVA programming language was written for all five algorithms and a friendly GUI software tool is developed to visualize sub-optimal cycle bases. (3) Topological and algebraic features of the structures were utilized in this study.

This is a multi-objective optimization problem which means that sparsity and well conditioning of a matrix cannot be optimized simultaneously. In conclusion, well-conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.

Engineers always finding mathematical modeling of real-world problems and make them as simple as possible. In doing so, lots of errors will be created and these errors could cause the mathematical models useless. Applying decent algorithms could make the mathematical model as precise as possible.

Errors in numerical simulations should reduce due to the fact that they are toxic for real-world applications and problems.

This is an original research. This paper proposes five novel topological mathematical algorithms in order to optimize the structural flexibility matrix.

Measurement of information security assurance (ISA) is an important but difficult task. This paper aims to propose a framework, which helps in refining information security requirements into controls whose effectiveness can be measured. This work also provides aggregation techniques to combine these measurements so as to obtain an indicator for ISA at the organizational level.

A top-down approach of refining security objectives to measurable independent tasks is carried out using assign graph as the model. This captures the various objectives and their interrelationships whose initial values and relative impacts are obtained from experts. Using fuzzy cognitive model (FCM), these initial values are combined together to obtain an indicator for ISA at the firm's level.

The two applications of the framework revealed that interrelationships do exist between the different controls employed in actual security implementations and that these dependencies are seldom accounted for. When those few controls that are to be measured are clearly identified, the security experts can focus their attention on them and ensure their correct implementation and appropriate measurement. The extent of impact of a single control on the overall security picture of the firm can also be found using this approach.

While the framework is generic, the assurance values obtained are context-sensitive. This is primarily because of the subjectivity involved in assigning impact measures and initial values.

This work helps in answering two difficult questions in information security management: “what to measure?” and “how to quantify the overall security assurance of the organization?” This assists the information security team in identifying and refining those controls that needs to be appropriately emphasized. The proposed framework helps the top management in doing “what-if” analysis, thereby aiding their decision-making for information security investments.

The novel framework proposes a top-down approach for security control refinement and a bottom-up approach for combining the confidence values to obtain an indicator for ISA. This work identifies and accommodates the possibilities of having interdependencies between security controls. The proposed aggregation method using FCM is being applied for the first time in information security context and provides convergence even in the presence of cyclic dependencies amongst the controls.

Balancing and dual domain decomposition methods (DDMs) comprise a family of efficient high performance solution approaches for a large number of problems in computational mechanics. Such DDMs are used in practice on parallel computing environments with the number of generated subdomains being generally larger than the number of available processors. This paper presents an effective heuristic technique for organizing the subdomains into subdomain clusters, in order to assign each cluster to a processor. This task is handled by the proposed approach as a graph partitioning optimization problem using the publicly available software METIS. The objective of the optimization process is to minimize the communication requirements of the DDMs under the constraint of producing balanced processor workloads. This constraint optimization procedure for treating the subdomain cluster generation task leads to increased computational efficiencies for balancing and dual DDMs.

The purpose of this study is to provide an alternative graph-based airspace model for more effective free-route flight planning.

Based on graph theory and available data sets describing airspace, as well as weather phenomena, a new FRA model is proposed. The model is applied for near to optimal flight route finding. The software tool developed during the study and complexity analysis proved the applicability and timed effectivity of the flight planning approach.

The sparse bidirectional graph with edges connecting only (geographically) closest neighbours can naturally model local airspace and weather phenomena. It can be naturally applied to effective near to optimal flight route planning.

Practical results were acquired for one country airspace model.

More efficient and applicable flight planning methodology was introduced.

Aircraft following the new routes will fly shorter trajectories, which positively influence on the natural environment, flight time and fuel consumption.

The airspace model proposed is based on standard mathematical backgrounds. However, it includes the original airspace and weather mapping idea, as well as it enables to shorten flight planning computations.

One of the techniques for improving the performance of distributed systems is data replication, wherein new replicas are created to provide more accessibility, fault tolerance and lower access cost of the data. In this paper, the authors propose a community-based solution for the management of data replication, based on the graph model of communication latency between computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. The purpose of this study if to, by using this method, minimize the latency and access cost of the data.

This paper used the Louvain algorithm for finding the best communities. In the proposed algorithm, by requesting a file according to the nodes of each community, the cost of accessing the file located out of the applicant’s community was calculated and the results were accumulated. On exceeding the accumulated costs from a specified threshold, a new replica of the file was created in the applicant’s community. Besides, the number of replicas of each file should be limited to prevent the system from creating useless and redundant data.

To evaluate the method, four metrics were introduced and measured, including communication latency, response time, data access cost and data redundancy. The results indicated acceptable improvement in all of them.

So far, this is the first research that aims at managing the replicas via community detection algorithms. It opens many opportunities for further studies in this area.