This work represents a new method for robustness analysis of the model reference adaptive controller (MRAC) in the presence of input saturation. Saturation is one of the nonlinear factors affecting the stability of control systems which must be considered in controller design and stability analysis experiments. Various methods are presented for the stability and robustness analysis of adaptive control systems, and employment of describing function (DF) can be attractive and practical, due to the appropriate effectiveness of DF in estimating limit cycles and also the application of quasi-linearization theory. In this work, the stability analysis and a limit cycle estimation of a saturated system in the frequency domain are performed. The controller parameters are adjusted in a way that the system achieves its stable limit cycle in the presence of the initial conditions for the states. Moreover, the efficiency of the proposed method for second-order systems is reported in the presence of symmetric saturation and uncertainty model in Rohrs’s counterexample as the unmodeled dynamics. The results demonstrate the proposed method provides a proper analysis of system stability during the changes in the control parameters and the saturation amplitude.

This paper proposes a novel day-ahead energy hub scheduling framework aimed at improving resiliency. Accordingly, an energy hub including combined heat and power (CHP), boiler, electric-heat pump (EHP), absorption and electric chillers, energy storages and renewable sources is considered. This energy hub is equipped with smart grid (SG) infrastructures, making it possible to implement demand response (DR) programs and optimally operate energy storages. The hub is connected to the electricity and natural gas networks. Outage of input energy carriers causes failure of devices in the energy hub, loss of electrical loads, failure in cooling and heating and thus reduced resiliency. Maintaining the security of the hub consumers’ power supply system in the event of such severe disturbances is essential. Therefore, a new strategy based on the use of backup electric energy storages (EES) and DR program is proposed in this paper to improve resiliency. In addition, a numerical index is used to accurately calculate and evaluate resiliency. Numerical studies show that the proposed strategy improves resiliency during the outage of power and gas networks by 12.02% and 14.23% respectively when backup energy storages and DR program are implemented simultaneously.

In this paper, a new application of State- Dependent Riccati Equation (SDRE) is proposed as a framework to design a robust controller for the system of multiple cooperative arms with parametric uncertainties. The cooperative arms are tracking a trajectory holding a mass. Transforming the complicated robust control design to a parallel auxiliary sub-optimal design, leads to a considerable facility in design and extensive applicability specifically for complex systems. An auxiliary system with a modified performance criterion is firstly introduced. The modification in performance criterion is through incorporation of uncertainties upper bounds obtained from stability proof. Uncertain State- Dependent Coefficient (USDC) regarding joints’ friction for the robotic system is utilized to obtain the auxiliary USDC structure. Two control policies are considered: independent control of each arm and simultaneous control of overall multiple manipulators. The sub-optimal problem for the auxiliary system is solved. The achieved optimal control input for auxiliary system is the robust input for the equivalent uncertain system. Simulation results in both policies verify the effectiveness and satisfactory robustness (30%) of the proposed scheme in load carrying. Moreover, considering the same trajectory, payload and design parameters controlling the overall robotic system is superior with respect to separately controlling each arm. Finally, a comparison study is presented for the proposed scheme and Mixed SDRE-SMC (MiSS) for the overall robotic system carrying the same payload through simulation results.

One of the main topics these days is the use of quadrotors to transport commodities in the urban environment, and the main challenge in quadrotors for route planning in urban areas is obstacles. Quadrotors are not suitable to fly at high altitudes, because due to economic limitations, this will be a challenge. Therefore, most quadrotors in the urban environment must fly at a low altitude, and for this reason, there will be obstacles in their path. Some of the obstacles are predetermined, and some others are unpredictable. A new method to interact with these unexpected obstacles has been presented In this paper. This method combines the BUG2 or online-BUG2 path planning methods in robotics and model predictive control, which is intended to guide the quadrotor. In this method, first, the desired path of the quadrotor is determined with the help of the BUG2 and online-BUG2 algorithms, and then, with the help of model predictive control using the predictive functional control, the control law required to change the direction of the quadrotor to this reference path is obtained. According to the three scenarios implemented with the help of the introduced method, it can be seen the integrated approach has been able to detect them well and guide the quadrotor to the target by bypassing the obstacles.

Some factors can change the software and affect the quality, such as the new users' requirements and the need for compatibility with modern techniques. These factors impose a high cost on technical software maintenance. One of the techniques for software quality improvement and maintenance cost reduction is refactoring. The advantage of this method is software behavior preservation. Because the cost of refactoring manually is high, a technique called the hybrid optimization problem has been proposed. The main challenge in refactoring is to propose a technique with high accuracy and less runtime. Hence, in the present work, a refactoring method based on the multi-objective algorithms called RMMOC is proposed to tradeoff between quality and runtime. In this method, a helpful search-based method called UMOCell is used to increase refactoring quality. This method inspires both population-based and local-based search algorithms. Another novelty in this paper is using new metrics for program quality assessment that help to increase accuracy, decrease runtime of refactoring, and find the best refactoring solutions. Because software metrics play a significant role in search-based refactoring approaches, this paper introduces two effective criteria called MPC and refactoring number reduction in addition to previous presented metrics. The results of experiments show that the performance of the proposed method is remarkable and using new metrics is effectiveness.

Engineers often face challenges when designing foundations that are located over and near the slopes. By using new small-scale laboratory model, the Effect of eccentric loading on the bearing capacity of a strip footing located on the inclined multi-layer soil mass with a weak soil layer, was investigated as multi-effects. Thin layers have substantial effects on the ultimate bearing capacity, despite they seem to be insignificant. A series of laboratory model tests was performed on a rigid strip footing resting on surfaces with different layered slope foundations. The experimental program considered different foundation configurations by varying the footing distance from the slope’s top and inclination of the thin layer. It is found that the weak thin layer decreases the ultimate bearing capacity specifically. The laboratory results indicate that the value of eccentricity affect the final bearing capacity and increase this capacity by moving away from the weak layer and the slope. Also, The weak thin layer at the critical distance led to more reduction in the ultimate bearing capacity by 43%. The results were compared with analytical methods and the differences were 2 to 9.5%. Also, the numerical simulation of the physical data shows that the results can be developed to large-scale models as a prediction.

The reaction of dichlorostannanes R2SnCl2 (R=Me 1, Bun 2) with piperazine ligand in molar ratio 1:2, in dry methylene dichloride, in an inert atmosphere leads to the synthesis of R2Sn (C4H9N2)2(R=Me 1, Bun 2). In a similar manner, The reaction between Ph2SnCl2 and piperazine in dry ethanol in molar ratio 1:1 produces [Ph2Sn (C4H8N2)]2 (3). The yields of these new products were excellent and they have been fully characterized by FT-IR, UV–Vis, multinuclear (1H, 13C, 119Sn) NMR spectroscopy and mass spectrometry, as well as elemental analysis. The spectroscopic results indicate that the piperazine ligand is coordinated to tin atom of organotin moieties, through the nitrogen atoms. Furthermore, the ligand behaves as a bidentate fashion in (1) and (2) and gives 1:2 substitution products, while in the complex (3) the two six-membered rings bind in bidentate-chelate forms between the two Sn atoms.

The SARS-CoV-2 is the novel coronavirus that causes the pandemic COVID-19, which has originated in Wuhan, China, in December 2019. Early studies have generally shown that human Angiotensin-Converting Enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) are responsible for the viral entry of SARS-CoV-2 into target cells. TMPRSS2 as androgen-regulated is highly expressed in the prostate and other tissues including the lung. We investigated the interaction between the TMPRSS2 protein and selected antiandrogens, namely Bicalutamide, Enzalutamide, Apalutamide, Flutamide, Nilutamide, and Darolutamide using in-silico molecular docking. The results showed that Apalutamide (-8.8 Kcal/mol) and Bicalutamide (-8.6 Kcal/mol) had the highest docking score. The molecular docking process was validated by re-docking the peptide like-inhibitor-serine protease hepsin and superimposing them onto the reference complex. Last of all, the tested compounds have been evaluated for their pharmacokinetic and drug likeness properties and concluded that these compounds except Nilutamide (mutagenic) can be granted as potential inhibitors of SARS-CoV-2. This in-silico study result encourages its use as means for drug discovery of new COVID-19 treatment.

The heat exchanger, an integral component in diverse processes, finds extensive application across industrial and domestic sectors. Functioning as a mechanical apparatus, it is designed to transfer or exchange heat between different mediums, thereby enhancing energy efficiency by redirecting surplus heat from unnecessary systems to those in need. Heat exchangers have become essential equipment in various end-user applications due to their environmentally friendly nature and their ability to boost overall energy efficiency in systems. The global market for heat exchangers has undergone significant changes in recent years, with manufacturers increasingly emphasizing efficiency and performance improvements. The enhanced performance of heat exchangers, driven by technological advancements, contributes to heightened energy consumption efficiency in the systems where these devices are employed. Exergetic assessments play a crucial role in improving heat exchanger efficiency from a thermodynamic standpoint. This study provides a comprehensive review of scientific papers, examining the exergetic aspects of various heat exchanger types. The literature survey explores the impact of parameters such as entropy generation, cumulative exergy destruction, nanofluids, geometry, and two-phase fluids on heat exchanger exergetic performance. It also discusses the effectiveness of different optimization approaches on the second law’s efficiency. Primarily, the study comprehensively reviews four types of heat exchangers—double-pipe, plate, cross-flow, and shell & tube—and briefly explains new types of heat exchangers. Part 1 of this study, presented in this manuscript, focuses on the fundamentals of exergy analyses in heat exchangers, with an emphasis on double-pipe and shell & tube heat exchangers. Future research directions aim to explore advanced materials with superior properties, innovative geometries for optimal performance, integration with renewable energy sources, smart technologies for adaptive control, machine learning applications for predictive modeling, and the potential of miniaturization and microscale heat exchangers. These endeavors seek to propel the field towards greater efficiency, sustainability, and adaptability across various applications.