Cloud computing is a massively distributed system in which existing Resources interact with user-requested tasks to meet their requests. In such a system, the problem of optimizing Resource Allocation and Scheduling (RAS) is vital, because recourse allocation and scheduling deals with the mapping between recourses and user requests and also is responsible for optimal allocating of tasks to available Resources. In the cloud environment, a user may face hundreds of computational Resources to do his work. Therefore, manually recourse allocation and scheduling are impossible, and having a schedule between user requests and available recourses seems logical. In this paper, we used Whale Optimization Algorithm (WOA) to solve Resource allocation and task scheduling problem in cloud computing to have optimal Resource allocation and reduce the total runtime of requested services by users. The proposed algorithm is compared with the other existed algorithms. Results indicate the proper performance of the proposed algorithm than other ones.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex Optimization problem that guarantees the global optimal solution. The convex Optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

This study presents a mathematical Optimization model for Resource allocation and staff management during a pandemic, focusing on balancing patient demand, facility capacity, and Resource utilization. The model aims to minimize total costs, including staffing, Resource procurement, and penalties for unmet demand, while ensuring efficient patient assignment and facility operation. A key feature of the model is the integration of cross-training strategy to enhance workforce flexibility, enabling staff to perform multiple roles and helping address staffing shortages during peak demand periods. The model accounts for multiple patient types, each with distinct Resource requirements, and healthcare facilities with varying capacities for beds, ventilators, and staff. The results demonstrate that the model successfully optimizes Resource allocation, achieving a 14.98% improvement in Resource usage efficiency and a facility utilization rate of 69.19%. Through strategic implementation of staff transfers and cross-training policies, the model maintained high operational efficiency while improving facility utilization by 0.18%. These findings highlight the significance of a flexible workforce and strategic Resource management in improving healthcare resilience and responsiveness during a pandemic.

The aim of this study is to optimize water Resource management in the Jarrahi River Basin with an environmental sustainability approach for the Shadgan International Wetland. The Jarrahi River is one of the rivers in the Persian Gulf and Oman Sea drainage basin, with most of its course located in Khuzestan province. The river's catchment area lies on the southwestern slopes of the Middle Zagros Mountains and is located between 48°45' and 51°10' East longitude and 30°30' to 31°40' North latitude. Its area is 24,300 square kilometers. A water Resource planning model for the entire Marun and Jarrahi river system was developed using an Optimization approach. The entire Marun-Jarrahi watershed was simulated in a monthly time step over a 60-year period using the WEAP simulator, and five scenarios were defined. The results were then integrated and analyzed using the powerful Shahin Harin meta-exploration algorithm. Based on the results, the release pattern for water utilization within the standard benchmark four and ten tank system using Harris Hawks Optimization (HHO), FPA, and SOS algorithms, the exploitation policies derived from the HHO algorithm with a more optimal release pattern yielded the highest benefit. Additionally, the model with the least water shortage was identified using this approach. These results demonstrate the superior efficiency of the HHO algorithm compared to the other meta-exploration algorithms employed. As a further innovation, study proposes and develops a novel hybrid model combining HHO and Cat Swarm Optimization (CSO) algorithms, referred to as the HHO-CSO algorithm.

As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog computing is more suitable for real-time processing due to the proximity of Resources to IoT layer devices. Service providers must dynamically update the hardware and software parameters of the network infrastructure. Software defined network (SDN) proposed as a new network paradigm, whose separate control layer from data layer and provides flexible network management. This paper presents a software-defined fog platform to host real-time applications in IoT. Then, we propose a novel Resource allocation method. This method involves scheduling multi-node real-time task graphs over the fog to minimize task execution latency. The proposed method is designed to benefit the centralized structure of SDN. The simulation results show that the proposed method can find near to optimal solutions in a very lower execution time than the brute force method.

Today, thinkers and policymakers envision new roles for human Resource management, one of the most important one is "competency-basing" in all functions of human Resource management from planning and selection as input processes to the development and maintenance and output processes of human Resources. The purpose of this article is to review the “, Competency-Based Human Resource Management”,book by David Dubios et al. The book is reviewed and criticized based on two methods of formalist critique that focuses on form-elements such as structure and writing-and research critique that gives originality to the content, and also based on the criteria of the Iranian Council for Reviewing Books and Text on Human Sciences. Findings showed that the main strengths of the book are having a theoretical and practical perspective, paying attention to the organizational context in the application of competencies and confronting and comparing traditional with competency-based human Resource processes. This work also has points for improvement, the most important of which are the lack of up-to-date references despite the reprint of the book, issues unrelated to or less related to the main concept of the book, and the need to explain new methods of human Resource management processes in the competency-based approach. In the end, based on the findings, suggestions were made in two headings of form and content.

Resource Constraints Project Scheduling Problem (RSPSP) seeks proper sequence of implementation of project activities in a way that the precedence relations and different type of Resource constraints are met concurrently. RCPSP tends to optimize some measurement function as make-span, cost of implementation, number of tardy tasks and etc. As RCPSP is assumed as an NP-Hard problem so, different meta-heuristic approaches have been proposed to solve different variants of it. In this paper, a modified Ant Colony Optimization (ACO) approach has been developed to deal with RCPSP. The definition of probabilistic selection rule has been modified in proposed approach in favor of better performance. Moreover, the parameters of algorithm have been determined in an adaptive manner and the stagnation behavior has been prevented in high iterations of algorithm. Uncertainty of parameters of RCPSP has also been discussed. The proposed algorithm has been coded using Visual Basic software and tested on benchmark instance in this area. The results are promising and have been compared with optimal or best known solutions.

As Grid systems are distributing geographically, the heterogeneity and dynamicity of their Resources are increased. One of the important issues is the discovery services with more scalable and dynamic environments. In this paper, we review the decentralized discovery mechanisms based on peer-to-peer (P2P) technology. We classify the discovery approach into three main categories: Unstructured, Structured, and Super-Peer. We describe the major development in these three categories and provide a discussion in term of efficiently, scalability, and dynamicity features of these approaches.