The folded hypercube $FQ_n$ is the Cayley graph Cay$(\mathbb{Z}_2^n,S)$, where $S=\{e_1,e_2,\dots,e_n\}\cup \{u=e_1+e_2+\dots+e_n\}$, and $e_i = (0,\dots, 0, 1, 0,$ $\dots, 0)$, with 1 at the $i$th position, $1\leq i \leq n$. In this paper, we show that the folded hypercube $FQ_n$ is a distance-transitive graph. Then, we study some properties of this graph. In particular, we show that if $n\geq 4$ is an even integer, then the folded hypercube $FQ_n$ is an $automorphic$ graph, that is, $FQ_n$ is a distance-transitive primitive graph which is not a complete or a line graph.

In this paper, we give two new optimal parallel algorithms for matrix multiplication which are designed to run on a Fibonacci hypercube structure. At first, we present a broadcast algorithm on Fibonacci hypercube with O (n) time complexity. We use this broadcast algorithm and algorithms presented by Gunnels, Lin, Morrow and Geijn on a mesh structure, in order to present two optimal parallel matrix multiplication algorithms on a Fibonacci hypercube structure. We show these algorithms are cost-optimal. The performance of the algorithm has been tested on a simulative parallel system.

This study provides a review of hypercube queuing models (HQMs) in emergency service systems (ESSs). This survey presents a comprehensive review and taxonomy of models, solutions and applications related to the HQM after Larson [12]. In addition, the structural aspects of HQMs are examined. Important contributions of the existing research are addressed by taking into account multiple factors. In particular, the integration of location decisions with HQMs for designing an ESS is discussed. Finally, a list of issues for future studies are presented.

Project management in construction industry, in many cases, is imperfect with respect to the integration of Occupational Health and Safety (OHS) risks. This imperfection exhibits itself as complications affecting the riskiness of industrial procedures and is illustrated usually by poor awareness of OHS within project teams. Difficulties on OHS regularly came about in the construction industry. The integration of OHS risk is not systematic in construction areas in spite of progressing laws and management systems. As project safety and risk evaluation in construction industry is an important issue, thus, the way on doing evaluation and liability of estimation is necessary. In this paper, we propose a new systematic approach based on Latin hypercube Sampling (LHS) for integrating occupational health and safety into project risk evaluation. This approach tries to identify and evaluate reinforcement effects in a systematic approach for integrating OHS risks into project risk assessment. Furthermore, the proposed method allows evaluating and comparing OHS risks before and after the mitigation plan. A case study is used to prove the workability, credibility of the risk evaluation approach and uncomplicated integration of OHS risks at a construction project. This approach enables continual revaluation of criteria over the direction of the project or when new information is obtained. This model enables the decision makers such as project managers to integrate OHS risks toward schedule plan and compare them before and after the mitigation plan. The mentioned model is found to be useful for predicting OHS risks in construction industries and thus avoiding accidents over the path of the project.

Infill drilling is the first choice to increase the recovery factor, but the mission of selecting the best well location is considered a major challenge with the huge area of the reservoir and the time consumption to conducting the simulation runs that may reach hundreds to thousands. This paper adopted the design of an experiment plus a proxy optimization technique to solve this problem. Where the Latin hypercube represents the DoE while the radial basis function network represents the artificial intelligence proxy model. The proxy model mimics the reservoir model to reduce the computation time and speed up the well-placement optimization process. The Latin hypercube approach is used to generate data to train the proxy model to construct a reliable artificial intelligence model to predict the best wells locations. The results are very optimistic and encouraging to rely on using the art-of-state to construct a proxy model to conduct the infill wells drilling optimization. Where the increase in cumulative oil production for the optimized case is more than the un-optimized case by 6.45% and the decrease in the field cumulative water production for the optimized case is less than the un-optimized case by 16.11% from 2020 to 2040.