Search

In the evaluation of a Photovoltaic (PV) system's performance, precise calculation of the system's parameters is essential, as these parameters significantly influence its efficiency across various sunlight intensities, temperature ranges, and distinct load conditions. Addressing the intricate non-linear optimization problem of pinpointing these PV system parameters, the current research adopts a novel metaheuristic optimization approach, called Transit Search (TS). The proposed technique was rigorously tested on a monocrystalline solar panel, which included both single and double-diode model structures. The design of the objective function within this framework aims to diminish the square root of the average squared discrepancies between theoretical and measured current outputs, while remaining within the established parameter bounds. The proficiency of the TS algorithm was highlighted by employing a variety of statistical error indicators, underlining the latter's effectiveness. When pitted against other established optimization algorithms through comparative analysis, TS demonstrated outstanding capabilities, evidently outperforming its contemporaries in the accurate determination of PV system parameters. [ABSTRACT FROM AUTHOR]

Four glasses of cadmium lead-borate glasses with nominal compositions of (75-X)B2O3–XPbO–5Na2O–10CdO–10ZnO: X = 5 (BPNCZPb-5), 10 (BPNCZPb-10), 15 (BPNCZPb-15), and 20 (BPNCZPb-20) mol% were fabricated using the well-known melt quenching route.The role of B2O3/PbO substitution on the physical properties and γ-ray shielding factors have been evaluated via MCNP simulation code and PhX software in the γ-energy range of 0.015: 15 MeV. The density (ρ) and molar volume (Vm) of the examined glasses grows systematically from 4.82 to 5.664 g/cm3 and from 18.88 to 22.02 cm3/mol as the PbO content increases from 5 to 20 mol% in the glass network. The oxygen molar volume (OMV) increased following the observation of an opposing the oxygen packing density (OPD) trend. The obtained values of simulated linear-absorption coefficient (µ) were in decent agreement with the values calculated by PhX software with a maximum relative differences, φ = of 4.497%. The simulated µ values drop from 127.981 to 0.130 cm−1 for BPNCZPb-5, from 189.241 to 0.158 cm−1 for BPNCZPb-10, from 243.308 to 0.180 cm−1 for BPNCZPb-15, and from 299.755 to 0.206 cm−1 for BPNCZPb-20 at photon energy range from 0.015: 15 MeV. At 0.5, 5, and 10 MeV, the µ of the BPNCZPb-X glasses have higher values than those of concretes. The BPNCZPb-20 glass sample possessed the lowest half- (HVL) and tenth- (TVL) value layers. The radiation protection efficiency (RPE) values reduced from ≈ 100.000% at 0.015 for all BPNCZPb-X glass samples to 6.305% (BPNCZPb-5), 7.587% (BPNCZPb-10), 8.626% (BPNCZPb-15), and 9.777% (BPNCZPb-20), respectively at gamma-ray energy 15 MeV. Results confirm that the suggested samples can be applied as alternative materials for radiation safety applications. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the major threats that attack women around the world. Its detection and diagnosis in the early stages can greatly improve care efficiency and reduce mortality rate. Early detection of breast cancer allows medical professionals to use less intrusive treatments, such as lumpectomies or targeted medicines, improving survival rates and lowering morbidity. This study developed a breast cancer segmentation system based on an improved version of the U-Net 3+ neural network. Various optimizations were applied to this architecture to improve the localization and segmentation performance. An evaluation of different state-of-the-art networks was performed to improve the performance of the proposed breast cancer diagnosis system. Various experiments were carried out on the INbreast Full-Field Digital Mammographic dataset (INbreast FFDM). The results obtained demonstrated that the proposed model achieved a dice score of 98.47%, which is a new state-of-the-art segmentation finding, showcasing its efficiency in detecting breast cancer from mammography images with the possibility of implementation for real applications. [ABSTRACT FROM AUTHOR]

Classification of volcanic rocks is a fundamental task in the geologic studies. Volcanic rocks are igneous rocks that cooled rapidly above the surface of the Earth's crust. They are classified according to their oxide chemical content. Furthermore, volcanic rocks can also be classified numerically by statistical means. But these methods are mostly dependent on human expert decision making and have a high cost. In this paper, a novel approach in the classification of volcanic rocks is proposed. This method is based on the rough set mathematical theory. The continuous data of the information system are firstly discretized using the information loss method. Secondly, the discretized decision table is reduced and the decision rule sets are extracted. The results are consistent with previous methods and show that the proposed method reduces time and calculation costs. [ABSTRACT FROM AUTHOR]