Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach's accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

• Klíčová slova
• Enhanced differential evolution, Metaheuristic algorithm, Optimization technique, PV model, Parameter identification,
• Publikační typ
• časopisecké články MeSH

Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.

• Klíčová slova
• cat and mouse, optimization, optimization problem, population-based, stochastic,
• MeSH
• algoritmy * MeSH
• pohyb MeSH
• řešení problému MeSH
• teoretické modely * MeSH
• učení MeSH
• Publikační typ
• časopisecké články MeSH

The sorption ability of Lewatit FO 36-DGT resin gel, which has been developed for arsenic determination, towards uranium was tested by batch experiments within this study for the first time. Since the uptake efficiency of uranium was 99.0 ± 0.4% and the maximum uptake capacity was not achieved even at the U spike of 1250 μg in the solution, the Lewatit FO 36 resin seems to be a suitable binding phase for DGT resin gels for the determination of uranium. The resin gel also does not display any significant sorption selectivity in favour of one element over another. A novel protocol for simultaneous elution of arsenic and uranium from Lewatit FO 36 resin gel was therefore proposed in this study. The elution efficiencies of 90.3 ± 3.9% and 85.2 ± 3.1% for As and U, respectively, were obtained using 5 mL of 1 M NaOH at 70 °C for 24 h. The comparison with the original elution protocol using microwave-assisted elution by 0.25 M NaOH and 0.17 M NaCl at 130 °C for 16 min indicates, that the novel elution protocol provides good results in the performance of arsenic elution and, in addition, allows simultaneous elution of uranium. Moreover, the elimination of NaCl from the elution process allows a fast and simple analysis of both elements using ICP-MS, and therefore, the Lewatit FO 36-DGT technique can become more commonplace among laboratories without the need to modify the analytical method as proposed in the original study.

• Klíčová slova
• Arsenic, Diffusive gradients in thin films technique, Elution procedure, Uranium,
• Publikační typ
• časopisecké články MeSH

OBJECTIVES: Intracoronary cell transplantation during catheter balloon inflations may be associated with adverse events. We studied the effectiveness of an alternative transplantation technique--intracoronary cell infusion. METHODS: Fourteen pigs, which had survived acute myocardial infarction, were randomized into 2 treatment groups and 2 controls. Three days after infarction, 12 pigs underwent allogeneic intracoronary mononuclear bone marrow cell transplantation using either the standard technique (short-term cell injections during repeat balloon inflations, technique A, n = 6) or continuous intracoronary cell infusion without balloon inflations (technique B, n = 6). Implanted cells were stained with fluorescent dye. After transplantation, the pigs were euthanized and myocardial samples were analyzed by fluorescent microscopy. RESULTS: The mean numbers of fluorescently labeled bone marrow cells in the infarction border zone, in the infarction mid-area and in the center of myocardial infarction were 84, 72 and 55 using technique A, and 29, 57 and 46 using technique B, respectively. The mean cell retention in the infarction border zone of 84 cells for technique A and 29 cells for technique B differed significantly (p = 0.034, two-tailed t test). CONCLUSION: The continuous intracoronary cell infusion technique is a less efficient cell delivery technique as compared with the standard technique using repeat intracoronary balloon inflations.

• MeSH
• fluoresceiny MeSH
• fluorescenční barviva MeSH
• fluorescenční mikroskopie MeSH
• infarkt myokardu terapie   MeSH
• katetrizace MeSH
• modely nemocí na zvířatech MeSH
• prasata MeSH
• srdeční katetrizace * MeSH
• transplantace kostní dřeně metody   MeSH
• zvířata MeSH
• Check Tag
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• 5-chloromethylfluorescein MeSH Prohlížeč
• fluoresceiny MeSH
• fluorescenční barviva MeSH

We have developed a new microextraction technique for equilibrium, non-exhaustive analyte preconcentration from aqueous solutions into organic solvents lighter than water. The key point of the method is application of specially designed and optimized bell-shaped extraction device, BSED. The technique has been tested and applied to the preconcentration of selected volatile and semi volatile compounds which were determined by gas chromatography/mass spectrometry in spiked water samples. The significant parameters of the extraction have been found using chemometric procedures and these parameters were optimized using the central composite design (CCD) for two solvents. The analyte preconcentration factors were in a range from 8.3 to 161.8 (repeatability from 7 to 14%) for heptane, and 50.0-105.0 (repeatability from 0 to 5%) for tert-butyl acetate. The reproducibility of the technique was within 1-8%. The values of limits of detection and determination were 0.1-3.3 ng mL(-1) for heptane and 0.3-10.7 ng mL(-1) for tert-butyl acetate. The new microextraction technique has been found to be a cheap, simple and flexible alternative to the common procedures, such as SPME or LLME. This BSED-LLME technique can also be combined with other separation methods, e.g., HPLC or CE.

• MeSH
• chemické látky znečišťující vodu analýza   izolace a purifikace   MeSH
• design vybavení MeSH
• mikroextrakce kapalné fáze přístrojové vybavení   metody   MeSH
• minerální vody analýza   MeSH
• pitná voda analýza   MeSH
• plynová chromatografie s hmotnostně spektrometrickou detekcí metody   MeSH
• reprodukovatelnost výsledků MeSH
• voda analýza   MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH
• Názvy látek
• chemické látky znečišťující vodu MeSH
• minerální vody MeSH
• pitná voda MeSH
• voda MeSH

Roller compacted concrete (RCC) has gained prominence in the construction industry due to its durability, cost-effectiveness, and environmental benefits, particularly with the incorporation of high-volume fly ash (HVFA). However, traditional experimental approaches to evaluating RCC's mechanical properties, such as compressive strength (CS) and splitting tensile strength (STS), are resource-intensive and time-consuming. To address these challenges, this study explores the application of artificial intelligence (AI), specifically artificial neural networks (ANN) and a hybrid ANN-Biogeography-Based Optimization (ANN-BBO) model, to predict the CS and STS of RCC. A dataset comprising 168 RCC mixtures, incorporating various material and process parameters, was analyzed. The ANN-BBO model demonstrated superior predictive accuracy compared to a standalone ANN, with R2 values exceeding 0.98 for both CS and STS, significantly reducing error margins. The findings highlight the effectiveness of AI-driven modeling in optimizing RCC mix designs, minimizing experimental costs, and enhancing the sustainability of concrete production. This research underscores the potential of integrating AI with optimization techniques to refine RCC performance assessment, which enables and facilitates more efficient and sustainable infrastructure development.

• Klíčová slova
• Artificial intelligence, Compressive strength, Fly ash, Optimization technique, Roller compacted concrete, Splitting tensile strength,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: High-throughput bioinformatics analyses of next generation sequencing (NGS) data often require challenging pipeline optimization. The key problem is choosing appropriate tools and selecting the best parameters for optimal precision and recall. RESULTS: Here we introduce ToTem, a tool for automated pipeline optimization. ToTem is a stand-alone web application with a comprehensive graphical user interface (GUI). ToTem is written in Java and PHP with an underlying connection to a MySQL database. Its primary role is to automatically generate, execute and benchmark different variant calling pipeline settings. Our tool allows an analysis to be started from any level of the process and with the possibility of plugging almost any tool or code. To prevent an over-fitting of pipeline parameters, ToTem ensures the reproducibility of these by using cross validation techniques that penalize the final precision, recall and F-measure. The results are interpreted as interactive graphs and tables allowing an optimal pipeline to be selected, based on the user's priorities. Using ToTem, we were able to optimize somatic variant calling from ultra-deep targeted gene sequencing (TGS) data and germline variant detection in whole genome sequencing (WGS) data. CONCLUSIONS: ToTem is a tool for automated pipeline optimization which is freely available as a web application at https://totem.software .

• Klíčová slova
• Benchmarking, Next generation sequencing, Parameter optimization, Variant calling,
• MeSH
• reprodukovatelnost výsledků MeSH
• software MeSH
• výpočetní biologie metody   MeSH
• vysoce účinné nukleotidové sekvenování metody   MeSH
• výzkumný projekt MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study's outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

• Klíčová slova
• cluster head (CH), energy consumption, metaheuristics, particle swarm optimization (PSO), wireless energy transfer,
• Publikační typ
• časopisecké články MeSH

The rising energy demand, substantial transmission and distribution losses, and inconsistent power quality in remote regions highlight the urgent need for innovative solutions to ensure a stable electricity supply. Microgrids (MGs), integrated with distributed generation (DG), offer a promising approach to address these challenges by enabling localized power generation, improved grid flexibility, and enhanced reliability. This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. To enhance search efficiency, ILOA incorporates the Levy Flight technique for local search, which introduces adaptive step sizes with long-distance jumps, improving the exploration-exploitation balance. Unlike conventional local search strategies that rely on fixed step sizes, Levy Flight prevents premature convergence by allowing the algorithm to escape local optima and explore the solution space more effectively. Additionally, a chaotic sine map is integrated to enhance global search capability, ensuring better diversity and superior optimization performance compared to traditional algorithms. Simulation studies are conducted on a modified 33-bus distribution system segmented into three independent microgrids. The algorithm is evaluated under single-objective scenarios (cost and loss minimization) and a multi-objective optimization framework combining both objectives. In single-objective optimization, ILOA achieves a generation cost of $19,254.64/hr with 0.7118 kW of power loss, demonstrating marginal improvements over the standard Lyrebird Optimization Algorithm and significant gains over Genetic Algorithm (GA) and Jaya Algorithm (JAYA). In multi-objective optimization, ILOA surpasses competing methods by achieving a generation cost of $89,792.18/hr and 10.26 kW of power loss. The optimization results indicate that, for the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm achieves savings of approximately 0.0014%, 0.0041%, and 0.657% in operation costs compared to LOA, JAYA, and GA, respectively, when MG-1, MG-2, and MG-3 are operational. The analysis of real power loss reduction demonstrates that, in the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm effectively minimizes power loss by approximately 0.692%, 1.696%, and 1.962% in comparison to LOA, JAYA, and GA, respectively, under the operational conditions of MG-1, MG-2, and MG-3. Additionally, reliability constraints based on the Energy Index of Reliability (EIR) are effectively incorporated, further validating the robustness of the proposed approach. Considering EIR, the real power loss analysis for the IEEE-33 bus system highlights that the proposed ILOA algorithm achieves a reduction of approximately 1.319%, 2.069%, and 2.134% in comparison to LOA, JAYA, and GA, respectively, under the operational scenario where MG-1, MG-2, and MG-3 are active. The results confirm that ILOA is a highly efficient and reliable solution for distributed generation scheduling and multi-microgrid sectionalizing, showcasing its potential for real-world applications such as dynamic economic dispatch and demand response integration in smart grid systems.

• Klíčová slova
• Active power loss, Distributed generators, Energy index of reliability (EIR), Generation cost minimization, Lyrebird optimization algorithm, Multi-microgrids, Multi-objective optimization, Optimal scheduling,
• Publikační typ
• časopisecké články MeSH

Denaturant gradient gel electrophoresis (DGGE) enables insight into the diversity of the studied microbial communities on the basis of separation of PCR amplification products according to their nucleotide sequence composition. However, the success of the method is accompanied by the inherent appearance of various sequence artifacts that bias the impression of community structure by generating additional bands representing no virtual microbes. PCR-DGGE artifacts require optimization of the method when aiming at the phylogenetic identification of the selected DGGE bands. The aim of our study was to develop a procedure which will increase the reliability of the identification. Samples of rumen fluid were used for the optimization since they contain a complex microbial community that supports the generation of artifactual bands. An optimized procedure following band excision and elution of microbial DNA is proposed including nuclease treatment, selection of DNA polymerase with proofreading activity, and cloning prior to sequencing and identification analysis.

• MeSH
• bachor mikrobiologie   MeSH
• Bacteria klasifikace   genetika   izolace a purifikace   MeSH
• denaturační gradientová gelová elektroforéza metody   MeSH
• DNA bakterií genetika   MeSH
• fylogeneze MeSH
• techniky typizace bakterií metody   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH
• Názvy látek
• DNA bakterií MeSH