A photovoltaic emulator (PVE) is a specialized instrument that has the potential to reproduce the static and dynamic characteristics of a solar panel. This instrument has become a key milestone in the testing and validation of PV systems. Thus, despite the uncontrolled fluctuations in actual environmental conditions, the PVE offers a controllable environment, allowing smooth implementation and testing of PV subsystems. The PVE is structured around two indispensable elements, the reference PV model and the PVE power electronics controller. To enhance the simplicity of the PVE, these two elements must be inherently simple. Several advanced approaches to efficiently integrating the PV model into the PVE system have been developed in the literature. Nonetheless, these methods are still highly complex, since they often rely on iterative computation of intricate equations of solar panels. To surmount such limitations, this paper introduces a novel PVE aimed at simplifying the implementation and integration of the PV model into the PVE system. The proposed system uses a simple and iteration-free approach for providing the reference to the PVE. This approach is based on a straightforward explicit model of the solar panel. As compared to contemporary works, the proposed PVE stands out for high flexibility and simplicity, not requiring the complex iterative computations of the implicit equations of the solar panel model. The overall PVE system is implemented with a simple proportional-integral (PI) controller and a DC-DC Buck power electronic converter, and validated on a 200W solar panel. A series of experiments are conducted for both varying PVE loads and diverse environmental test profiles. The acquired results revealed that the proposed PVE efficiently reproduces the static and dynamic characteristics of the solar panel.

The optimal power flow (OPF) is one of the fundamental mathematical tools currently used to operate power systems with the technical limits of the transmission power system. To determine OPF, a highly non-linear complex problem, it is essential to research power system planning and control. This study presents a practical and trustworthy optimization approach for the OPF problem in electrical transmission power systems. Many intelligence optimization algorithms and methods have recently been developed to solve the OPF, particularly the non-linear complex optimization problems. In this paper, a novel meta-heuristic algorithm called the mountain gazelle optimizer (MGO) is suggested for solving the OPF problem. The suggested algorithm applies the improved three single objective functions to the MGO algorithm for the best OPF issue control variable settings. Three objective functions that reflect the minimization of generating fuel cost, the minimizing of active power loss, and the minimizing of voltages deviations have been used to investigate and test the proposed algorithm on the standard IEEE 30-bus test system. The simulation results demonstrate the efficiency of the proposed MGO algorithm, the fuel costs are reduced by 11.407 %, power losses are considerably decreased by 51.016 % and enhancing voltage profile is significantly reduced by 91.501 %. Furthermore, the outcomes produced by the proposed algorithm have also been contrasted with outcomes produced by applying other comparable optimization algorithms published in recent years. The optimal results are encouraging and demonstrate the resilience and efficacy of the suggested strategy.

This study focuses on studying the influence of copper Cu and magnesium Mg in Zinc Aluminate (Zn_0.9M_0.1Al₂O4: M= Cu, Mg) through comprehensive characterization. The samples were synthesized via sol-gel method followed by annealing at 900 degree celsius. Single-phase cubic spinel structure was confirmed using XRD. The lattice parameter and grain size were ascertained from the XRD data. Crystallite size determination of Cu and Mg-doped ZnAl₂O₄ using Scherrer’s formula was found to be in the range 25-40 nm. Confirmation of spinel structure formation was ascertained by FT-IR study. The optical properties of Cu and Mg-doped ZnAl₂O₄ were investigated using UV-Vis spectroscopy. The absorption spectra revealed characteristic absorption bands and allowed for the estimation of the bandgap energy. The optical bandgap values provided information about the electronic transitions and the potential application of the material in optoelectronic devices. The SEM images unveiled the generation of agglomerated particles with varying sizes and shapes. The presence of all the corresponding elements in the synthesized powders was assured using EDS analysis. Measurements of Dielectric parameters were performed in the broad frequency range (100Hz-1MHz) at temperatures varying from 25°C to 200°C. The results exhibited that the dielectric constant decreased with increasing temperature, while the dielectric loss exhibited a peak at a specific temperature. The Nyquist plots revealed semicircles, indicating the occurrence of grain and grain boundary contributions to the overall impedance response. The impedance analysis further indicated the presence of both bulk and grain boundary effects, with grain boundary contributions becoming more dominant at higher temperatures. The observed variations in dielectric properties and impedance responses can be attributed to the structural changes, such as grain growth and modifications in defect concentration, induced by temperature. These findings contribute to the understanding of the electrical behaviour of doped zinc aluminate materials and their useful applications in different electronic and energy systems.

Applications of liquid crystals continue to expand. They include conventional and advanced liquid crystal displays, electrically controlled lenses, tunable optical elements such as filters, light shutters, waveplates, and spatial light modulators, smart windows and sensors, and reconfigurable antennas and microwave devices, to name a few. As a rule, liquid crystal devices are controlled by applying an external electric field. This field reorients liquid crystals in a desirable way thus leading to the tunability of their physical properties. The electric-field induced reorientation of liquid crystals can be affected by ions typically present in molecular liquid crystals. In the case of liquid crystal displays, ions in liquid crystals can lead to image sticking, reduced voltage holding ratio, and altered electro-optical performance. Therefore, the development of efficient ways to better control ions in liquid crystal devices is of utmost importance to existing and future liquid crystal technologies. In this paper, we discuss how nanomaterials can affect the electrical properties of molecular liquid crystals. In general, nanomaterials in molecular liquid crystals can behave as ion capturing objects or act as a source of ions. Ion-capturing nanomaterials in molecular liquid crystals can enhance their electrical resistivity. On the other hand, ion-releasing nanoparticles can lead to the opposite effect. By considering the competition between two nanoparticle-induced ionic processes, namely the ion capturing and ion releasing effects, the electrical resistivity of liquid crystals can be controlled in a desirable way.

Being able to calculate aerodynamic coefficients of oscillating airfoils is a prerequisite for the design of rotor blades and bio-inspired micro air vehicles. Numerous methods have been used throughout the years, however, the intricacy of the underlying physics that govern the flapping motion makes many of these models computationally expensive, resulting in a need for faster and less intensive approaches. With this objective in mind, the present work proposes an implementation of a recurrent neural network to obtain the lift and moment coefficients of an airfoil undergoing flapping motion. There has been a surge in the use of neural networks in the field of aerodynamics for their capability of, once trained, producing fast reasonably accurate predictions. The proposed network is fed with the flow's Reynolds number, reduced frequency, and nondimensional amplitude, as well as the time history of the angle of attack. From this, the network can predict the evolution of the mentioned aerodynamic coefficients with time. The neural network is trained using data from a panel code, which can be used when no massive flow separation is present. Despite its limitations, the panel method is used, since it allows for the creation of an extensive training dataset promptly. Results show that the proposed neural network is indeed capable of predicting the aerodynamic coefficients with sufficient accuracy, thus reinforcing the potential artificial intelligence-based solutions have for quick aerodynamic computation. Future work looks at retraining the network using experimental data and identifying if the inputs are sufficient to capture real-fluid physics. Other improvements to the network are on the horizon, such as taking the airfoil geometry as input and increasing the network's flexibility.

Collaborative learning has been widely acknowledged as a successful teaching method within the education field, with research indicating its positive impact on student outcomes. During the Covid-19 pandemic, when all courses transitioned online due to lockdown measures, many universities employed Learning Management Systems to facilitate continued group work among students. However, forming effective student groups remained challenging, particularly given the large number of enrolled students. To address this issue, the study proposes the application of Artificial Intelligence (Machine Learning) solution to automatically group students based on their behaviours and interactions within an e-learning environment. This paper explores the potential of Machine Learning (ML) algorithms in assisting educators to create heterogeneous groups, considering various student attributes, such as behaviour and performance, to optimise collaborative learning outcomes. Students' performance within a module was compared using a Wiki activity that employed group work over the course of two academic years. In the first experiment, groups were formed randomly, while in the second experiment, students with similar behaviours were firstly identified using a clustering algorithm and then sorted by an additional algorithm into heterogeneous groups. The results demonstrate the efficacy of the Machine Learning solution compared to the random approach in assisting educators with group formation for a collaborative activity such as the wiki, confirmed by a comparative analysis showing an improvement in student performance and satisfaction. The research contributes to the advancement of online education through the creation of more effective group dynamics using Machine Learning algorithms, thereby improving overall student learning.

Calcined soda production is one of the important parts of the industry. One of the main problems in the production of soda is the waste of raw materials. Disposing of some raw materials as waste not only causes economic damage but also has a negative impact on the environment. Absorption is the main process in the production of calcined soda. By finding the optimal consumption of the chemical components involved in absorption process, it will be possible to reduce the amount of raw materials released into the waste. In this work, initially, the kinetic model of the absorption process was found using the stoichiometric matrix method. Based on the stoichiometric matrix, the main components (NH₃, CO₂, NH₄HCO₃, NaCl) participating in the chemical reaction were identified. Using the found kinetic model, a computer model of the absorption process was simulated in the MATLAB program. Based on the simulated computer model, a diagram of changes in the concentration of components in the absorption process over time was obtained. The use of the considered mathematical model as part of the absorption process control system allows: 1) Reduce the loss of the amount of gases used in the absorption process; 2) Provide the required concentration of liquid saturated with ammonia; 3) Determine the required temperature regime along the entire length of the absorber.

Plasmonic phenomena can be used to realize sensors and biosensors in several application fields. This work presents polymer-based plasmonic sensor chips made by simple and cheap realization steps. The proposed sensors are small-size, highly sensitive, versatile, low-cost, and useful to realize disposable biosensors. In detail, to realize these sensors, a resin block with a custom-shaped trench inside is achieved by a 3D printer. This trench is filled with a UV-cured optical adhesive to achieve the core of a multimode optical waveguide when the end of the trench is closed by two plastic optical fiber (POF) patches. Then, a photoresist buffer layer is deposited by spinning on the optical waveguide, and finally, a gold nanofilm is sputtered to excite the plasmonic phenomenon. The buffer layer improves the sensor performance and the gold's adhesion on the surface. The optical waveguide's shape can be changed to match the sensor chip with different experimental setups, such as the one based on the smartphone's CCD camera and LED. In this way, receptor layers can be deposited on the highly sensitive gold surface to realize high-performance bio/chemical sensors for several application fields, such as Point-Of-Care Tests (POCT) and environmental monitoring.

Antimicrobial agents are potential additives in concrete mixes to combat biodeterioration. However, their impact on the compressive strength of high-performance concrete (HPC) remains limited. In this study, response surface methodology (RSM) was employed to develop 21 combinations of nano-sized TiO₂ and ZnO, ranging from 0-2% by weight of the cement in HPC. The 28-day compressive strength of the samples served as response factors to evaluate the effects of these combinations. The RSM modelling results revealed that ZnO reduced compressive strength as its content increased from 0-2%, while TiO₂ enhanced compressive strength by up to 17% in HPC with 2% TiO₂. Combined effects indicated that compressive strengths between 81-100 MPa could be achieved by optimizing TiO₂ content and varying ZnO content within the 0-2% range. The developed quadratic model, with a p-value of <0.0001, F-value of 23.4, and R² of 0.8863, accurately depicted the effects of the nanoparticles (NPs) on compressive strengths, with most residual values falling within ±3.

The use of environmentally friendly solvents for liquid-liquid extraction offers a promising avenue for promoting sustainability in various industries. Green solvents, including ionic liquids, deep eutectic solvents, supercritical fluids, and biobased solvents, offer several advantages compared to the traditional solvents in the present time. These solvents possess properties such as low toxicity, biodegradability, and reduced environmental impact, making them highly desirable for liquid-liquid extraction processes. Through careful adjustments in composition and physicochemical properties, these solvents can be customized to achieve efficient and selective extraction of desired compounds. Additionally, recent advances in green solvents often contribute to improved energy efficiency, reduced waste production, and the potential for developing novel products with unique characteristics. By embracing green solvents for liquid-liquid extraction, industries can actively contribute to sustainable development, minimize environmental harm, and support the transition towards an eco-friendlier future.