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In this paper, a secret key generation scheme for a group of users based on wireless channel is presented in which legal nodes are connected under star topology. Instead of using the characteristics of the channel domain such as received signal strength (RSS), the channel phase is used in the proposed scheme; Because in channels with low mobility or channels with low dispersion that do not have a large entropy channel range, the channel phase can show significant changes. Based on this, in this article, a group key generation scheme based on channel phase is proposed. The proposed key generation scheme, compared to the similar scheme, needs less time intervals for execution and therefore has a high speed of the algorithm. As a result, the key production rate will be higher, which is very desirable. In the following, we will analyze and examine the proposed protocol in terms of some criteria such as the probability of generating the correct group key, scalability and vulnerable areas.

The shovel-truck loading system is one of the most important components of transportation in an open pit mine. To evaluate the performance of the excavator-truck system, the simulation modeling approach is combined with meta-heuristic methods and it has become a suitable approach to study and optimize the complex behavior of such a system. The purpose of this study is to identify the approximate optimal number of trucks and shovels in the equipment dispatch system in Sarchesmeh copper mine in Kerman province to increase monthly efficiency and reduce transportation costs. Two evolutionary multi-objective optimization algorithms, namely Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Fast Pareto Genetic Algorithm (FastPGA), have been programmed and integrated with the shovel-truck system simulation model developed in Arena software package to perform simulation, programming and integration. . The optimization process of the experimental results shows that there are near-optimal solutions that can reduce the average monthly transportation cost by 10% and increase the average monthly power by 11%.

The environmental requirements and the need of many transportation projects in the maritime sector for financing have made it important to examine the future of sustainable financing in the maritime transportation industry.Despite extensive research in the field of future study in the transportation sector,little research has been done on the future of maritime transportation and its financing.The current research seeks to identify the drivers and future scenarios of sustainable financing in the maritime transportation industry with a scenario-based approach.It is applied and a mixed study.The theoretical population of the research is consultants and specialists in sustainable financing and future study in the maritime transportation industry.The sample size was 10 people.Interview and questionnaire were two important data collection tools.25 drivers were extracted through literature review and interviews with experts and screened by distribution of expert questionnaires and fuzzy Delphi method.the screened drivers were ranked by distributing priority questionnaires and the CoCoSo method.Finally the research scenarios were developed based on priority drivers and interview method with focus groups.These scenarios were:plural financing,bank-based financing,technology-based financing and black hole financing.

In this chain, things such as lack of access to up-to-date information, lack of ability to track inventory and information, and little coordination between chain operators, more coordination between the flow of materials and financial information, have necessitated the use of these technologies. Current research has been carried out in order to fill the research gap and investigate the impact criteria on humanitarian supply chain services on the blockchain platform. Also, in this research, after preparing a list of criteria affecting humanitarian supply chain services based on blockchain, the decision-making trial and evaluation laboratory (DEMATEL) approach has been considered to draw a cause-and-effect diagram among the effective criteria. Based on the cause-and-effect diagram, criteria such as the cost of the relief supply chain, delivery time, and tracking ability, in the group of cause criteria and criteria such as the level of trust building, demand rate, cooperation rate among supply chain actors, deception rate in operations in the group of disabled criteria were categorized.

In this research, a deep reinforcement learning algorithm is proposed for the cellular manufacturing system problem considering the costs of delay and rejection of orders. Orders with different characteristics including revenue, lead time, delivery date, and delay cost are dynamically entered into the system at different times. Due to the limited capacity of the system, it is not possible to accept all orders and some of them must be rejected at the time of entry to enable timely execution of other orders. A mathematical model with two objectives of maximizing profit and minimizing the number of rejected orders is presented and a deep reinforcement learning algorithm is used to solve this problem. The proposed algorithm is compared with the algorithms available in the literature in different categories of example problems and real problems and its efficiency is proven. The results show a 36.3% advantage in profit and 13.87% in the number of accepted orders. Also, by accepting 1% more orders, the profit decreases by 2.7% on average

Nowadays one of most important organizations’ goals is to access compatibility to increase their market share and higher profitability. Therefore, it can be claimed that compatibility is considered as one of the most general organizations’ and industries’ outputs. The purpose of writing the current paper is to represent a model to measure carpet industry compatibility emphasizing quality and innovation. Statistical society in qualitative part includes 14 top managers and 582 employees in quantitative part from which 232 ones were selected as statistical sample. First of all, by the research model was obtained with three main dimensions resources and capabilities, quality and innovation with 30 indices through semi-structured and unstructured interviews with experts and during the coding process. Meanwhile resources and capabilities includes spiritual capital, human capital and technological capabilities, quality dimension includes raw materials, pre-production phase and production phase and finally innovation dimension includes product innovation, process innovation and sale innovation.