Optimization problems of heat source operation are solved by linear (LP) or nonlinear (NLP) programming. The optimization methods can be compared on the basis of the complexity of the computational program setup, the time required to input and update data, and the computational time for optimal shifting and loading of installed power machinery. LP methods are preferred (e.g. simplex method, method characteristics), also the Lagrange multipliers NPL method is rarely applied. In this paper, the method of proportional increments (LP, NLP) is applied to compare the optimal loading of the power machinery installed in the thermal power plant using linear and quadratic economic characteristics.

The overall efficiency and optimisation of the solar cell are significantly influenced by the selective layer in perovskite solar cells. In this study, particular emphasis is placed on the perovskite solar cell architecture, where both the charge transport layer is removed from the structure are removed intentionally. In the paper, the full fabrication procedure is given free from the charge transport layer. To understand the behaviour of these architecture of perovskite material, XRD analysis, TEM analysis along with photoluminescence measurements are conducted. These measurements provide valuable insights into the efficiency of the perovskite material. Additionally, SEM analysis is employed to characterize surface morphology of proposed structure. Furthermore, the photovoltaic performance of the proposed solar cell architectures is evaluated. The results show that, with a simple manufacturing procedure, removing the charge transport layer has the greatest influence on the photovoltaic performance. Overall, this research is helpful in understanding how cesium titanium bromide functions as a absorber in perovskite solar cells.

The potential properties arising from the interaction of semiconductor quantum dots with electromagnetic fields have been studied intensely in recent years for their applications in nanophotonics and quantum technologies. The asymmetric double semiconductor quantum dot molecule is a semiconductor quantum dot nanostructure that exhibits unique optical properties, leading, for example, to important quantum optical phenomena like tunneling induced transparency, Autler-Townes splitting and slow light generation without the need of an external electromagnetic field. When semiconductor quantum dots and metal nanoparticles are placed close to each other, with distances in a few nanometers range, coupled nanostructures are created that have, in many cases, enhanced optical properties in comparison to the individual semiconductor quantum dots and metal nanoparticles. Recently, attention has been given to the optical properties of a coupled nanostructure fabricated by coupling a metal nanoparticle to a double semiconductor quantum dot molecule. In the present work, the behavior of the absorption and dispersion properties of the double semiconductor quantum dot molecule in the presence of a spherical metal nanoparticle is explored. Specifically, tunneling induced transparency, Autler-Townes splitting, and slow light generation are obtained in the semiconductor quantum dot structure under the presence of the metal nanoparticle and their properties in the interparticle distance between the semiconductor quantum dot structure and the metal nanoparticle is studied.

In this work, our aim is to perform modeling and numerical simulation of a solar cell using the SCAPS-1D simulation program. The studied solar cell has an N-I-P type structure, with its active layer based on a hybrid (organic-inorganic) semiconductor called “methylammonium tin triiodide perovskite” (CH3NH3SnI3). This semiconductor has demonstrated its efficiency in the field of photovoltaics.

The objective of this study is primarily focused on improving the performance of the solar cell, specifically enhancing the reproducibility and stability of perovskite solar cells, as they tend to degrade rapidly. To achieve this, we have proposed the use of ZnO and Spiro-OMeTAD as charge transport layers (ETL and HTL, respectively) and varying the thickness of the active layer to obtain the optimal parameters that ensure the proper functioning of the cell.

Eugenol is the major component of clove and clove-based products which is widely used in food and pharmaceutical industry and medicine. Its content can be considered as a marker of the sample quality. Therefore, simple, sensitive, and reliable methods for eugenol quantification is required. Glassy carbon electrode modified with carboxylated multi-walled carbon nanotubes and electropolymerized pyrogallol red has been developed for the determination of eugenol in essential oils. The working conditions (supporting electrolyte, pH, monomer concentration and electrolysis parameters) of pyrogallol red have been found using voltammetric characteristics of eugenol. Electrode developed has been studied using scanning electron microscopy, voltammetry and electrochemical impedance spectroscopy. The effectivity of the electrode surface modification has been proved. Eugenol determination has been performed in differential pulse mode which parameters have been optimized. The linear response of the electrode towards eugenol has been obtained in the range of 0.75-100 µM with the detection limit of 0.73 µM. High selectivity of eugenol determination has been observed in the presence of typical inorganic and organic compounds including essential oils components. The approach developed has been tested on the commercial clove, cinnamon, and nutmeg essential oils. Validation with the independent method has shown similar accuracy and the absence of systematic errors of eugenol determination.

Pathogenic microorganisms such as bacteria, viruses, fungi and protozoa have played a central role in the safety of drinking water, since they spread easily in the water network, constituting an environmental problem for human and animal health. Currently in water treatments, Advanced Oxidative Processes (AOPs) have been increasing importance in the microbiological disinfection of water. Thus, the present study aimed to inactivate C. albicans, a commensal yeast species in Vertebrates that can cause disease, using Advanced Oxidative Processes (AOPs). To achieve this objective, an advanced oxidation process based on hydroxyl radicals (HR-AOP) was tested, combining an oxidant (hydrogen peroxide) with UV radiation on the inactivation of C. albicans. In view of the results obtained, it was verified that the application of 2.5 mM, 5 mM and 10 mM H₂O₂ reached a cell reduction of 3 log after 180, 360 and 300 min, respectively. Subsequently, the use of UV-A radiation proved to be even more promising, as the H₂O₂ + UV-A system reached an inactivation of 3 log after 240, 180 and 60 min, respectively. These results support that UV-A radiation increases the conversion of H₂O₂ in hydroxyl radicals, responsible for the inactivation of C. albicans cells. Thus, HR-AOPs are capable of reducing this microorganism in an aqueous matrix, avoiding potential hazard to human and animal health.

Supply of parts to the workstations of assembly lines is a critical design and operational issue. Even if automation and collaborative robots are increasingly employed in industry, human operators are frequently employed in manual parts picking and assembly. These are time consuming activities asking to be efficiently performed at a high rate and for prolonged periods. Therefore, ergonomic analysis is necessary to reduce the risk of work-related musculoskeletal injuries due to biomechanical loads. Proper layout of shelves storing parts containers along the production line, and the location of containers on the shelves, may improve picking efficiency and reduce biomechanical risk. Several manufacturing companies use computer-aided ergonomic tools to improve the study of manual production lines, racking, shelving, and workstations. The paper describes the development of a support tool to configure industrial light shelves for feeding the assembly lines. The approach includes the development of a knowledge base to support the geometrical configuration of the shelving and the ergonomics analysis based on the RULA method, considering the shelf’s positions and the operator’s postures. As a test case, the model has been used to evaluate the ergonomic score of the configured shelving based on a prescribed picking sequence. Results show that the proposed approach can help in comparing the goodness of a candidate shelving layouts to improve the design of parts storage systems in order to reduce operators' workload and ergonomic risk.

This scientific article proposal explores the potential benefits of using artificial intelligence (AI) in prosthodontic restoration to achieve predictable and safer outcomes in dentistry. Prosthodontic restoration involves designing, fabricating, and placing dental prostheses to restore oral function and aesthetics. Although traditional prosthodontic techniques have evolved significantly, incorporating AI into the workflow can revolutionize the field by enhancing accuracy, efficiency, and patient satisfaction. The proposed study aims to investigate integrating AI algorithms and techniques into various stages of prosthodontic restoration, including treatment planning, digital impression acquisition, prosthesis design, and fabrication. By leveraging machine learning algorithms and image processing, AI can assist in diagnosing dental conditions, predicting treatment outcomes, and optimizing prosthesis design to ensure optimal fit and function. This technology can also aid in identifying potential challenges and risks before proceeding with the restorative procedures, minimizing errors, and improving patient safety. Furthermore, AI-powered systems can facilitate real-time assessment and feedback during fabrication, ensuring precise milling or 3D printing of prosthesis materials. These advancements have the potential to streamline workflows, reduce human error, and shorten treatment times, ultimately leading to enhanced treatment outcomes and increased patient satisfaction. The proposed research methodology includes a comprehensive literature review, an analysis of existing AI applications in prosthodontic restoration, and the development of a prototype AI-assisted system for prosthesis design and fabrication. The evaluation of this prototype will involve quantitative and qualitative assessments, comparing its performance with traditional methods. Overall, this article proposal seeks to highlight the transformative role of AI in prosthodontic restoration, emphasizing its potential to revolutionize traditional approaches and deliver predictable and safer dentistry. The findings from this research can contribute to advancing dental technology, fostering innovation and improving patient care in prosthodontics.

We investigate the spontaneous emission (SE) of a quantum emitter (QE) near a topological insulator BiS₂X₃(X=Se,Te) nanosphere. We calculate the Purcell factor of the QE near a nanosphere of radius between 40 nm and 100 nm by first-principle electromagnetic methods using experimental parameters for describing the optical properties of the topological insulator material, with and without taking into account the topologically protected delocalized surface states. We find exceptional Purcell factors of the QE up to 10¹⁰ at distances between the QE and the nanosphere as large as half its radius in the terahertz regime. We study the SE dynamics of a QE for various transition frequencies in the terahertz and free-space decay rates, which affects inversely proportional the coupling strength between the QE and the nanosphere, in the ns to ms range. For short free-space decay times, the dynamics has strong non-Markovian features, which correlate directly with large values of well-established non-Markovianity measures and possible significant quantum speedup of the dynamics. The dynamical features become gradually Markovian as the free-space decay times become long, while the corresponding non-Markovianity measures tend to zero and the quantum speedup diminishes. For the shortest free-space decay times, we find that population remains trapped in the QE, which manifests the formation of a hybrid bound state between the QE and the electromagnetic continuum of modes as modified by the nanosphere. This work demonstrates that a BiS₂X₃ (X=Se,Te) nanosphere can provide the conditions for strong light-matter interaction on the nanoscale.

Asymmetric ethers such as Cyclopentyl methyl ether (CPME) found their application as alternative solvents in technology; they are often labeled as “green solvents” as they can be prepared using renewable feedstock. They are almost immiscible with water and can be easily regenerated. Based on earlier experiments with CPME preparation in laboratory conditions and the estimated product yields, initial assessment of reaction mixture separation train was performed, for the chosen production capacity of 100 kg.h^-1 of CPME. Following suitable thermodynamic model selection, basic analyses in Aspen Plus software were executed to estimate potential azeotropic points and to adjust the separation train operation to avoid their formation. Reactor effluent containing eight chemical species was subjected to multiple separation steps including extraction and several rectifications including one vapor phase compression step to yield saleable products with sufficient purity and unreacted chemicals recyclable to the reactor. Basic simulations were performed to find optimal working conditions of individual columns and to estimate the associated energy needs. Basic design, without any heat or work integration measures, required total heating duty of 787 kW and total cooling duty of 614 kW. This yielded specific heat consumption of 28.3 GJ per ton of the main product (CPME) which is unacceptably high as it represents around 70 % of its chemical energy content (heating value). Further research will be devoted to reducing heating and cooling duty by integrated separation train design development to comply with reduced carbon footprint mandatory for syntheses and separations of green solvents.