Neuro-computing solution for Lorenz differential equations through artificial neural networks integrated with PSO-NNA hybrid meta-heuristic algorithms: a comparative study.

In this article, examine the performance of a physics informed neural networks (PINN) intelligent approach for predicting the solution of non-linear Lorenz differential equations. The main focus resides in the realm of leveraging unsupervised machine learning for the prediction of the Lorenz differential equation associated particle swarm optimization (PSO) hybridization with the neural networks algorithm (NNA) as ANN-PSO-NNA. In particular embark on a comprehensive comparative analysis employing the Lorenz differential equation for proposed approach as test case. The nonlinear Lorenz differential equations stand as a quintessential chaotic system, widely utilized in scientific investigations and behavior of dynamics system. The validation of physics informed neural network (PINN) methodology expands to via multiple independent runs, allowing evaluating the performance of the proposed ANN-PSO-NNA algorithms. Additionally, explore into a comprehensive statistical analysis inclusive metrics including minimum (min), maximum (max), average, standard deviation (S.D) values, and mean squared error (MSE). This evaluation provides found observation into the adeptness of proposed AN-PSO-NNA hybridization approach across multiple runs, ultimately improving the understanding of its utility and efficiency.

Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numerical algorithm.

In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel. The weights of ANN based fitness function ranging from - 10 to 10. To optimize the weights and biases of artificial neural networks (ANNs), employ a hybridization of advanced evolutionary optimization algorithms, specifically the artificial bee colony (ABC) optimization integrated with neural network algorithms (NNA). This approach allows us to identify and fine-tune the optimal weights within the neural network, enabling accurate prediction. We compare our results against the established different analytical and numerical methods to assess the effectiveness of our approach. The methodology undergoes a rigorous evaluation, encompassing multiple independent runs to ensure the robustness and reliability of our findings. Additionally, we conduct a comprehensive analysis that includes metrics such as mean squared error, minimum values, maximum values, average values, and standard deviation over these multiple independent runs. The minimum fitness function value is 1.32 × 10-8 computed across these multiple runs. The absolute error, between the HAM and machine learning approach addressed ranging from 3.55 × 10-7 to 1.90 × 10-8. This multifaceted evaluation ensures a thorough understanding of the performance and variability of our proposed approach, ultimately contributing to our understanding of entropy management in non-uniform channel flows, with valuable implications for diverse engineering applications.

Tetra-ARMS-PCR assay development for genotyping of AGT rs699 T/C polymorphism, its comparison with PCR-RFLP and application in a case-control association study of cardiovascular disease patients.

Genetic variations in the AGT gene play a significant role in controlling the plasma concentration of angiotensinogen (precursor protein of bioactive octapeptide angiotensin II) and the efficacy of antihypertensive drugs. In the current study, Tetra-Amplification Refractory Mutation System-Polymerase Chain Reaction (T-ARMS-PCR) was developed for genotyping of AGT rs699 T/C polymorphism and validated through Sanger DNA sequencing. Its efficiency was also tested using 474 human DNA samples [control, n = 181; cardiovascular disease (CVD) patients, n = 293]. Results showed that T-ARMS-PCR is superior to the commonly used PCR-Restriction Fragment Length Polymorphism (PCR-RFLP). Statistical analysis revealed that the AGT rs699 CC genotype is more prevalent in the CVD patient group (37% vs. 28%) and AGT rs699 C allele and CC genotype increased the risk of CVD by 1.4 and 1.9 fold, respectively. In summary, T-ARMS-PCR is the most suitable approach for quick and efficient genotyping of AGT rs699 T/C polymorphism in a large population in resource-limited countries, Furthermore, AGT rs699 T/C polymorphism is associated with the risk of CVD in the Punjabi Pakistani population.

Assessing indigenous and local knowledge of farmers about pollination services in cucurbit agro-ecosystem of Punjab, Pakistan.

Being the ultimate beneficiary of ecosystem services provided by on-farm agricultural biodiversity, the participation of farmers in its sustainable utilization and conservation is crucial. How much aware they are with the significance and conservation of agricultural biodiversity in order to improve their crop yield remains unclear, especially from the developing courtiers. Pollination is one of such ecosystem services, enormously contributed by the wild bees. In the present study, we have investigated the knowledge of farmers about bees and pollination in general in three districts i.e. Multan, Bahawalpur and Khanewal of southern Punjab, Pakistan. Some 300 farmers (100 cucurbit growers in each district using convenient sampling method) were interviewed using a semi-structured questionnaire. Respondents were first presented with a box of insect specimens and then were asked to identify bees among those. Those who identified correctly were asked to state about their nesting sites. Only 11% of the respondents could correctly identify the bees and half of them could report something about nesting sites. A majority (63%) of the farmers was unable to tell fertilization requirements in cucurbits, 59% could not distinguish female flower from the male flower and 64% could not state any benefit of bees. However, upon briefing about the significance of bee pollinators, 58% of the farmers showed eagerness to conserve bees at their farms. Keeping in view the inadequacies of farmers' knowledge about wild bees and pollination in general, the present study also gives some policy recommendations.

Association of ACE I/D Polymorphism with Risk of Diabetes in Cardiovascular Disease Patients.

Angiotensin converting enzyme (ACE), as part of renin angiotensin aldosterone system, is involved in blood pressure regulation and control several physiological functions. Insertion/Deletion (I/D) polymorphism of ACE has pronounced effects on development of metabolic diseases like diabetes, cardiovascular diseases (CVDs) and hypertension. However, association of I/D polymorphism with risk of diabetes in CVD patients is not known. The aim of present study was to check the association of ACE I/D polymorphism with risk of diabetes in subjects with CVD. For this, 531 subjects were sampled and divided into 3 groups; G1-H: (healthy controls, n=117), G2-CN: (cardiac patients without diabetes, n=271) and G3-CD: (cardiac patients with diabetes, n=143). Genotyping of ACE I/D polymorphism was done by polymerase chain reaction. Allelic and genotypic frequencies were in Hardy Weinberg Equilibrium (χ2=0.11, p>0.05) and revealed high prevalence of I allele (55%) among all groups. However, II genotype was more common (37%) in G3-CD: group. Level of glucose was also higher in subjects with II genotype than DD genotype (12.6±6.3 mmol/L vs. 9.7±5.1 mmol/L). Logistic regression analysis revealed that ACE II genotype increase the risk of diabetes in CVD patients by ~2 times [OR=1.94, CI: 1.24-3.01, p=0.03]; however, this association did not reach the significance level when adjusted for age and gender. In conclusion, ACE I/D polymorphism influence the risk of diabetes in CVD patients and ACE II increases this risk by ~2 fold.