Using an intelligent method for microgrid generation and operation planning while considering load uncertainty

The integration of distributed generation (DG), energy storage systems (ESS), and controllable loads near the place of consumption has led to the creation of microgrids. However, the uncertain nature of renewable energy sources (wind and photovoltaic), market prices, and loads have caused issues with guaranteeing power quality and balancing generation and consumption. To solve these issues, microgrids should be managed with an energy management system (EMS), which facilitates the minimization of operating (performance) costs, the emission of pollutants, and peak loads while meeting technical constraints. To this effect, this research attempts to adjust parameters by defining indicators related to the best possible conditions of the microgrid. Generation planning, the storage of generated power, and exchange with the main grid are carried out by defining a dual-purpose objective function, which includes reducing the operating cost of power generation, as well as the pollution caused by it in the microgrid, by means of the SALP optimization algorithm. Moreover, in order to make the process more realistic and practical for microgrid planning, some parameters are considered as indefinite values, as they do not have exact values in their natural state. The results show the effect of using the introduced intelligent optimization method on reducing the objective function value (cost and pollution)

Agent-Based Simulation of Banking Service Supply Chain Based on Service-Dominant Logic

Considering the development of services in global economy, redefinition of supply chain management in this context is necessary because the concept of supply chain management cannot be applied to service-oriented businesses. Therefore, this paper aims to identify the banking service supply chain and perform an agent-based simulation using a service dominant logic approach; this is important due to the necessity of developing an applicable model for supply chain management of banking services. This supply chain model is processed through agent-based simulation in Netlogo for 5 banks in privatized and private sectors during 1388-1404, where market share is 32.4%. This model considers the flow of profit and other important parameters related to the customers, the bank, the central bank, and other banks and corporations during the mentioned period. The results of the experiments and analyses in multiple simulation times indicated the variations and changes of the important parameters associated with above-mentioned factors during these years

Maximum Power Point Tracking for Photovoltaic Systems Operating under Partially Shaded Conditions Using SALP Swarm Algorithm

This article presents a new method based on meta-heuristic algorithm for maximum power point tracking (MPPT) in photovoltaic systems. In this new method, the SALP Swarm Algorithm (SSA) is used instead of classic methods such as the Perturb and Observe (P&O) method. In this method, the value of the duty cycle is optimally determined in an optimization problem by SSA in order to track the maximum power. The objective function in this problem is maximizing the output power of the photovoltaic system. The proposed method has been applied on a photovoltaic system connected to the load, taking into account the effect of partial shade and different atmospheric conditions. The SSA method is compared with the Particle Swarm Optimization (PSO) algorithm and P&O methods. Additionally, we evaluated the effect of changes in temperature and radiation on solving the problem. The results of the simulation in the MATLAB/Simulink environment show the optimal performance of the proposed method in tracking the maximum power in different atmospheric conditions compared to other methods. To validate the proposed algorithm, it is compared with four important indexes: ISE, ITSE, IAE, and ITAE

DNA-BSA interaction, cytotoxicity and molecular docking of mononuclear zinc complexes with reductively cleaved N2S2 Schiff base ligands

The synthesis of three potentially tetradentate, N2S2 Schiff-base-ligands, containing a disulfide bond, (LSSLThio)-S-Thio(L1), (LSSLBr)-S-Br(L2) and (LSSLDiMeO)-S-DiMeO (L3) are reported. These ligands undergo reductive disulfide bond scission upon reaction with PPh3 in the presence of Zn2+ ion. [(LS)-S-DiOMe](-), [(LS)-S-Thio](-) and [(LS)-S-Br](-) are the resulting bidentate thiolate-imine anions respectively, which upon reaction with Zn2+ produce three new zinc (II) complexes: [Zn((LSN)-S-Thio)(2)] (1), [Zn((LSN)-S-Br)(2)] (2) and [Zn((LSN)-S-DiOMe)(2)] (3). The structures of (L1) and 1-3 complexes were determined by X-ray diffraction. The interaction of 1-3 with CT-DNA have been investigated by absorption, emission, and CD spectroscopic methods and thermal denaturation measurements. The resulting data reveal that 1-3 show effective binding to CT-DNA (K-b = 2.2 x 10(4) to 1 x 10(5) L mol(-1)). The binding mode of DNA with 1-3 has also been investigated by molecular docking. The protein binding ability of 1-3 has been tested by monitoring the tryptophan emission intensity using BSA as a model protein. The quenching mechanism of BSA by the zinc complexes is static (k(q) = 1.66 to 3.4 x 10(13) L-1 mol s(-1)). It is remarkable that 1-3 exhibit effective cytotoxicity against two human tumour cell lines (HeLa and MCF-7). The potent cytotoxic effects of 2 and 3, with IC50 values of 19.93 and 20.11 respectively, are higher relative to clinically used cisplatin (IC50 = 23.50) against the MCF-7 cell line, indicating that 2 and 3 may have the potential to act as effective metal-based anticancer drugs

Allocation of Renewable Energy Resources in Distribution Systems While considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm

Given the importance of renewable energy sources in distribution systems, this article addresses the problem of locating and determining the capacity of these sources, namely, wind turbines and solar panels. To solve this optimization problem, a new algorithm based on the behavior of salp is used. The objective functions include reducing losses, improving voltage profiles, and reducing the costs of renewable energy sources. In this method, the allocation of renewable resources is considered for different load models in distribution systems and different load levels using smart meters. Due to the fact that these objective functions are multi-objective, the fuzzy decision-making method is used to select the optimal solution from the set of Pareto solutions. The considered objective functions lead to loss reduction, voltage profile improvement, and RES cost reduction (A allocating RES resources optimally without resource limitations; B: allocating RES resources optimally with resource limitations). In addition, daily wind, solar radiation, and temperature data are taken into account. The proposed method is applied to the IEEE standard 33-bus system. The simulation results show the better performance of the multi-objective salp swarm algorithm (MSSA) at improving voltage profiles and reducing losses in distribution systems. Lastly, the optimal results of the MSSA algorithm are compared with the PSO and GA algorithms

Resource allocation in an open RAN system using network slicing

Abstract The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods

The effect of oral probiotics on CD4 count in patients with HIV infection undergoing treatment with ART who have had an immunological failure

Abstract Introduction Probiotics are live microorganisms that, when administered in appropriate colonies, can delay the destruction of the immune system and contribute to the maintenance of immunity in HIV patients. Probiotics play an important role in stimulating natural killer T cells, strengthening the functional gut barrier, and reducing systemic inflammation. Methods This study was a randomized double‐blind clinical trial involving 30 patients treated with antiretroviral therapy who had experienced immunological failure despite HIV viral suppression. Patients were divided into two equal groups of 15, group (B) received two probiotic capsules daily with a colony count of 10⁹ CFU per capsule containing seven strains, after 3 months they were examined for CD4+ counts by flow cytometry, and after a 1‐month washout period the participants who had received probiotics were switched to placebo, and the participants who had received placebo were given probiotics for 3 months, and they were examined for CD4+ counts 7 months after the start of the study. Results In the first group (A), administration of the placebo resulted in a decrease in CD4 count in the first 3 months (from 202.21 to 181.79, p value < .001), which may be due to the natural history of the disease. After probiotics administration, CD4 count increased significantly (from 181.79 to 243.86, p value < .001). Overall, after 7 months of study, there was a significant increase in the mean CD count from 202.21 to 243.86 (p value < .001). In the second group (B), the administration of probiotics in the first 3 months of the study resulted in a significant increase in the mean CD4 count (from 126.45 to 175.73, p value < .001). Termination of treatment with probiotics resulted in a significant decrease (from 175.73 to 138.9, p value < .001) but overall the CD4 count at the end of the study was significantly higher than at baseline (p value < .001)

Energy efficiency through joint routing and function placement in different modes of SDN/NFV networks

Abstract Network function virtualization (NFV) and software-defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalability, and deployment flexibility. However, migration to full SDN/NFV networks in order to serve these services is a time-consuming process and costly for mobile operators. This paper focuses on energy efficiency during the transition of mobile core networks (MCN) to full SDN/NFV networks and explores how energy efficiency can be addressed during such migration. We propose a general system model containing a combination of legacy nodes and links, in addition to newly introduced NFV and SDN nodes. We refer to this system model as partial SDN and hybrid NFV MCN, which can cover different modes of SDN and NFV implementations. Based on this framework, we formulate energy efficiency by considering joint routing and function placement in the network. Since this problem belongs to the class of non-linear integer programming problems, to solve it efficiently, we present a modified Viterbi algorithm (MVA) based on multi-stage graph modeling and a modified Dijkstra’s algorithm. We simulate this algorithm for a number of network scenarios with different fractions of NFV and SDN nodes and evaluate how much energy can be saved through such transition. Simulation results confirm the expected performance of the algorithm, which saves up to 70% energy compared to a network where all nodes are always on. Interestingly, the amount of energy saved by the proposed algorithm in the case of hybrid NFV and partial SDN networks can reach up to 60%–90% of the saved energy in full NFV/SDN networks