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The penetration of photovoltaic (PV) in electric power generation is continually increasing. On the other side, the load will receive the actual power, which is a part of power supplied by the photovoltaic. Therefore, it is necessary to extract maximum power from PV. One of a defy problem, is the tracking maximum power point (MPPT) in photovoltaic frameworks and it is a significant task. It can be a reproducer of maximum power from a photovoltaic system, which it depends on the adjusting of duty cycle of DC-DC converter. In order to produce a maximum power transfer, the impedance between the source and the load should be coincide by using of a buck boost converter. In this work, the proposed methods; Firefly algorithm (FA), Enhanced Firefly (EFA), Differential Evolution Scheme1, Differential Evolution Scheme2, and Differential Evolution Scheme 3 were tested for their performances in different conditions. Finally, the simulation results confirm that the second scheme of DE outperforms the others. Visual Basic. Net has been used to simulate the results and proceed with algorithms.

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

Hemostatic abnormalities had been reported in COVID-19 patients, which may include disseminated intravascular coagulation (DIC), hypercoagulability, and alterations in platelets parameters. Articles that investigate the alterations of hemostatic abnormalities during the COVID-19 disease (2020-2021) and their predictive value of disease outcome have been thoroughly reviewed. Among the reviewed articles, thrombocytopenia is observed in 5.0-41.7% of COVID-19 patients, which is related to disease severity. Moreover, other platelets parameters, including Platelets/lymphocytes ratio (PLR), Mean platelets volume (MPV), and aggregation, may also be affected. On the other hand, findings of coagulation tests such as D dimer; fibrinogen, Antithrombin (AT), and Fibrin degradation products (FDP) are significantly elevated in COVID-19 patients, while in a single study, most of the patients had positive Lupus anticoagulants (LA) and normal protein C (PC). In the same perspective, these alterations showed significant correlations with disease severity. Overall, hemostatic laboratory markers are significant predictors of COVID-19 disease outcome as indicated by the increased risk of venous and arterial thrombotic events, especially in ICU patients.

Purpose: This study intended to explore the contribution of CYP4F2 1347C>T and GGCX 12970C>G polymorphisms on warfarin dose requirements among Sudanese subjects.Methods: A total of 136 Sudanese patients of heart valve replacement receiving stable warfarin dose were recruited for this study. Blood samples were collected; DNA was extracted using phenol chloroform method. Genotyping for CYP4F2 1347C>T and GGCX 12970C>G polymorphisms was performed using polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) method. The association of genotype with warfarin dose requirement was measured by Kruskal Wallis and Mann Whitney U tests. Genotype, age, gender, comorbidity and concurrent medication were tested as predictors of stable warfarin dose using univariate regression analysis. For all tests, p. values < 0.05 were considered statistically significant. Results: Frequencies of CYP4F2 1347C>T genotypes were 22.8% CC, 61% CT and 16.2% TT. The frequencies of C, T alleles were 0.533, 0.466 respectively which are deviated from Hardy Weinberg equilibrium (p. value = 0.008). Regarding GGCX 12970C>G genotyping, 96.9% were CC and 3% were CG. The frequencies of C, G alleles were 0.984 and 0.015 respectively which are in accordance with Hardy Weinberg equilibrium (p. value = 0.859). Insignificant differences in the mean daily warfarin dose between different genotype groups were observed (all p. values > 0.05). None of the studied variables was significant predictor of stable warfarin dose in this study population (p. values > 0.05).Conclusion: No significant contribution of CYP4F2 1347C>T and GGCX 12970C>G polymorphisms on warfarin dose requirements was observed in this study population.

Background: Several prognostic markers are used in clinical stages of Chronic Lymphocytic Leukemia (CLL); including lactate dehydrogenase enzyme (LDH), CD38 and Zeta associated protein kinase (ZAP70). This study aimed to compare LDH with ZAP70 and CD38 as prognostic markers in Sudanese patients with CLL. Materials and Methods: Fifty newly diagnosed patients with CLL were enrolled in this cross-sectional study, the age of patients ranged between 36 - 85 years, with mean of 62 years. Of total patients; there were 34 males (68%) and 16 females (32%). Blood samples were obtained and LDH measurement was done by using Dirui CS-T240 automated analyzer while CD38 and ZAP-70 were measured by flowcytometry. Data were analyzed by statistical package for social science (SPSS) computer software program version 21. Results: The present study revealed a significant association between LDH and Rai staging (p. value= 0.002), when LDH compared with ZA-70 and CD38 as reference prognostic markers; poor correlation between CD38 and LDH was found (R2= 0.086, P value= 0.034) and no correlation between LDH level and ZAP-70 expression (R2= 000, P value= 0.960). In conclusion: According to the outcomes of this study; ZAP-70 and CD38 cannot be substituted by LDH as a prognostic marker.