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Due to the advancements in 5 G technologies, high-definition, and the internet of things (IoT), the capacity demand of optical networks has been exponentially increased. Optical communication networks offer several metrics such as high transmission capacity, low transmission loss, better anti-interference, robustness, etc which offers new opportunities to the communication field. To satisfy the increasing demands of optical networks, effective network resource utilization become essential. So, it is needed to design proper planning tools with superior accuracy for quality of transmission (QoT) in optical networks. Recently, artificial intelligence (AI) techniques pose new opportunities for resolving these issues and machine learning (ML) algorithms offer better performance over the analytical approaches. With this motivation, this paper presents a novel AI based cognitive QoT prediction (AI-CQoT) model for optical communication networks. The proposed AI-CQoT model aims to predict the QoT for the quality of service (QoS) link setup using AI techniques with the transmission equation based synthetic data generation. The proposed model uses the Label weighting extreme learning machine (LW-ELM) model for the prediction process which includes a link and signal characteristics as input features. Besides, the LW-ELM model is trained by the use of transmission equations. For improving the predictive performance of the LW-ELM model, the parameters such as weight matrix W and penalty factor C are optimally tuned by the use of the shuffled shepherd optimization (SSO) algorithm. A detailed experimental validation is performed to highlight the improved performance of the AI-CQoT model and the results are investigated in terms of different performance measures. [ABSTRACT FROM AUTHOR]

AA5083 is an alloy of military-grade aluminum used to make lightweight combat vehicles. Combining this material with traditional welding methods results in the formation of grain agglomerations, alloy separation, porosity, and pores. To overcome these shortcomings, Friction stir welding was utilized. In this work, the problems arising from friction stir welding are reduced by performing the process underwater. A relative study was also conducted to determine the impact of various tool pin profiles. particularly straight hexagonal, straight cylindrical, straight threaded, and tapered cylindrical. Friction stir welding and Underwater friction stir welding were used for welding Aluminium alloy AA5083 alloys of 150 x100 × 6 mm, constant tool rotational speed of 1200 rpm, tool transverse speed of 40 mm/min, and tool tilt angle of 0°. This study revealed that the straight hexagonal pin produced joint in an underwater cooling medium had a greater tensile strength (UTS) of 295 MPa and a joint efficiency of 78 %., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

The utilization of banana fiber derived from micro-cellulose (MC) was exploited as a supporting material for advanced oxidation process (AOP) on the degradation of methylene blue and methyl violet dyes in the presence of H 2 O 2 -UV in aqueous medium for the first time using green chemistry protocols. Additionally, it was also effectively utilized for the adsorption of methylene blue dye using addition of H 2 O 2 in the presence of sunlight. The MC powder was fabricated using an acid alkali process from the pseudo-stem of a banana tree. The as-fabricated MC powder was systematically characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectrometer (EDX), and zero point charge (pH zpc ). The AOP assisted degradation of dye molecules was monitored by using calorimetric techniques as a function of dye concentration and pH in a batch reactor. In a short period of time, the maximum degradation efficiency of 98 % of methylene blue was achieved using MC powder assisted H 2 O 2 under UV irradiation at a minimum irradiation time of 120 min at pH 7.0 using dosage of 0.2 g/L. However, in the absence of UV light, the degradation efficiency of MC powder assisted H 2 O 2 was only about 5-10 % without UV light irradiation. The dye removal was studied as a function of various operational parameters such as pH (3-11), catalyst dose (0.2-0.6 g/L), and initial dye concentration (100-400 mg/L). In the presence of H 2 O 2 -sunlight and 0.2 g/L of dosage at pH 7.0 at a minimum contact time of 120 min, MC fiber showed maximum adsorption capacities of 98% and 85% for 100 mg/L and 400 mg/L of methylene blue concentrations. According to the obtained data, the adsorption of methylene blue dye on MC follows the Freundlich isotherm model (R 2  = 0.9886) and pseudo-first-order kinetic model (R 2  = 0.9596) due to the higher regression coefficients. This process of dye degradation and adsorption process is a novel one and environmentally benign for an effective removal of hazardous dyes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the output power prediction of the STPV system. The performance of the semitransparent photovoltaic system is predicted for the Kovilpatti region where the climatic condition is hot and humid. The short-term power is predicted for the hourly, daily, and weekly average are considered. The feature selected for the prediction of the output power of the STPV system comprises of the solar radiation, ambient temperature, and wind velocity of the Kovilpatti region. The result reveals that the output power prediction of the hourly, daily, and weekly power have the very high value of the correlation coefficient of R. The final model produced accurate forecasts, with a Root mean square (RMSE) of 0.25 in ELMAN and 0.30 in FFN and 0.426 in GRN. These features of the training algorithm indicate that the model is not dependent on the model's position or configuration in the simulation., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The mathematical model of Oldham J. Phys. Chem. B 2000 , 104 , 4703 for rotating disc electrode in an unsupported system is discussed. This article presents a new analytical method for the calculation of concentration at a rotating disc electrode controlled by diffusion, convection, and migration. This model contains a steady-state nonlinear differential equation in a three-ion system under the assumption that all the ions have the same diffusivity. The homotopy perturbation method is employed to solve the nonlinear governing equation, where the ionic concentration and current are obtained analytically, in terms of charge numbers. The comparison between the analytical results of this study and previous studies confirms that the result of the proposed method is in stronger agreement with numerical simulations than other analytical methods.

This paper examines the robust stabilization problem of continuous-time delayed neural networks via the dissipativity-learning approach. A new learning algorithm is established to guarantee the asymptotic stability as well as the (Q,S,R) - α -dissipativity of the considered neural networks. The developed result encompasses some existing results, such as H ∞ and passivity performances, in a unified framework. With the introduction of a Lyapunov-Krasovskii functional together with the Legendre polynomial, a novel delay-dependent linear matrix inequality (LMI) condition and a learning algorithm for robust stabilization are presented. Demonstrative examples are given to show the usefulness of the established learning algorithm.

In this paper, we study the problem of finite-time stability and passivity criteria for discrete-time neural networks (DNNs) with variable delays. The main objective is how to effectively evaluate the finite-time passivity conditions for NNs. To achieve this, some new weighted summation inequalities are proposed for application to a finite-sum term appearing in the forward difference of a novel Lyapunov-Krasovskii functional, which helps to ensure that the considered delayed DNN is passive. The derived passivity criteria are presented in terms of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed results.

This paper examines the problem of asymptotic stability for Markovian jump generalized neural networks with interval time-varying delays. Markovian jump parameters are modeled as a continuous-time and finite-state Markov chain. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the linear matrix inequality (LMI) formulation, new delay-dependent stability conditions are established to ascertain the mean-square asymptotic stability result of the equilibrium point. The reciprocally convex combination technique, Jensen's inequality, and the Wirtinger-based double integral inequality are used to handle single and double integral terms in the time derivative of the LKF. The developed results are represented by the LMI. The effectiveness and advantages of the new design method are explained using five numerical examples.

Aim: The present study was thus undertaken to analyze the genetic diversity of Kilakarsal and Vembur sheep breeds using single-nucleotide polymorphism (SNP) markers within Toll-like receptor (TLR) 3, 5, 6, 9, and 10 genes., Materials and Methods: Competitive allele-specific polymerase chain reaction (PCR)-based end-point genotyping was performed using real-time PCR to type the SNPs. Allele discrimination module implemented in real-time PCR was utilized to call the genotypes based on fluorescence intensity recorded for each of the two alleles. Basic diversity indices, namely, gene frequencies, observed heterozygosity, expected heterozygosity, and inbreeding coefficient (F IS ), and testing for Hardy-Weinberg equilibrium (HWE) were estimated using package for elementary analysis of SNP data software program., Results: Of the 25 SNPs, 22 were found to be polymorphic, whereas two SNPs, namely, TLR3&#95;1081&#95;AC and TLR9&#95;2036&#95;CT, were monomorphic in both Kilakarsal and Vembur sheep populations. The SNP TLR10&#95;1180&#95;AG was monomorphic in Kilakarsal but polymorphic in Vembur sheep. The observed heterozygosities were estimated as 0.289 and 0.309 in Kilakarsal and Vembur sheep, respectively, whereas the expected heterozygosity values were 0.305 and 0.309 in the two breeds, respectively. The overall mean F IS was 0.107 ranging from -0.005 to 0.241 in Kilakarsal sheep and -0.047 ranging from -0.005 to 0.255 in Vembur sheep. In Kilakarsal sheep, the test for HWE revealed TLR9&#95;1308&#95;GC SNP locus with significant deviation (p<0.05) due to heterozygosity deficit. In Vembur sheep, TLR10&#95;82&#95;CT and TLR10&#95;292&#95;CG loci showed significant deviation (p<0.05) due to heterozygosity excess. Other SNP loci did not deviate from HWE (p>0.05) revealing that the population was in HWE proportions., Conclusions: The SNP markers within five TLR genes (TLR3, TLR5, TLR6, TLR9, and TLR10) utilized for genotyping in this study were highly polymorphic in Kilakarsal and Vembur breeds of sheep. This study on the genetic diversity analysis of the Kilakarsal and Vembur sheep breeds revealed considerable genetic variation within the breeds and it can be utilized to improve desirable traits.