This study presents a reliability analysis of an ice cream manufacturing facility based on operational failure data. The research develops a ten-component system model that captures plant dynamics and organizes it into three subsystems to reduce computational complexity. The study derives key reliability parameters and constructs a state transition diagram to represent system behavior across multiple operating states. The analysis applies an Artificial Neural Network (ANN) to address the limitations of conventional analytical methods in modeling complex nonlinear systems. The ANN provides strong predictive performance and manages uncertainty within the operational environment. The proposed framework estimates system reliability with high precision and supports maintenance planning and cost optimization. Numerical simulations validate the effectiveness of the ANN-based model. The results demonstrate improved prediction accuracy and greater computational efficiency compared to traditional approaches. State probability deviations are evaluated over a 24-hour period. The up-state probability increases from 95% to 96.02% during the useful life period and from 95% to 96.03% during the wear-out period. The findings confirm that the proposed method enhances reliability prediction, improves maintenance scheduling, and supports cost control and equipment design optimization in ice cream production systems.

A key component of conjugate gradient (CG) algorithms is the directions used in the CG approach. Because of their low memory requirements, these directed methods have proven effective in various applications, especially image processing. For this study, we have developed a new conjugate gradient coefficient based on the well-known quadratic model, that will accelerate the convergence and increase the accuracy of the CG approach. Because the new method uses the second order curvature and provides improved direction. So, the global convergence and critical descent characteristics of the final algorithm provide reliable performance in a range of scenarios. The results of extensive numerical testing on picture restoration show that the new formulae perform noticeably better than the current techniques. In particular, the performance of the suggested conjugate gradient (CG) scheme has been better than that of the conventional Fletcher-Reeves (FR) conjugate gradient approach. This development is a key tool in the field of computational imaging and optimization as it not only increases computing efficiency but also boosts picture restoration, image quality and its' accuracy.

This study presents the development of electrochemical biosensors for the glycated albumin (GA) sensitive detection as a crucial biomarker for monitoring glycaemic control in diabetes mellitus (DM). A ferrocene-labelled DNA aptamer specific to glycated albumin was developed as an electrochemical aptamer-based sensor to detect GA. The amine-functionalized graphene nanoplatelets (GNPs), with their enhanced surface area, provided an optimal substrate for aptamer immobilization through covalent interactions. The layer-by-layer full EDC/NHS-GNPs-Aptamer biosensor, tested by cyclic voltammetry (CV), exhibited strong redox peaks due to the good electron transfer activity between the biosensing structures. The GA-aptamer binding was studied using square wave voltammetry (SWV), and the data revealed a linear relationship for a wide range of concentrations between 10 and 10,000 μ g/mL with a low limit of detection (LOD) of 0.12 μg/mL. The performance of EDC/NHS-GNPs-Aptamer-GA displayed high selectivity and good response to the low concentrations, making it a valuable tool for clinical diagnostics and the management of diabetic patients.

Density Functional Theory (DFT) was employed to investigate the effects of beryllium (Be) substitution on the vibrational and electronic properties of a single-wall carbon nano-cone. The study aims to clarify how Be incorporation modifies the structural stability and electronic characteristics of this nanostructure. Electronic parameters including ionization potential (I), electron affinity (Eea), Fermi energy (Ef), HOMO–LUMO energies, total energy (E), and band gap (Eg) were calculated. Infrared (IR) spectra, optimized geometries, electrostatic potential maps, and electron density distributions were also analyzed. The results indicate that Be substitution reduces the ionization potential while increasing the electron affinity. Incorporation of Be atoms raises the HOMO level, Fermi energy, electron affinity, and total energy, whereas it lowers the LUMO energy and narrows the band gap. Vibrational analysis shows weakened vibrational stability upon substitution. Furthermore, electrostatic potential and charge density distributions are strongly influenced by the type, number, and position of substituted atoms, as well as by local charge redistribution. Positional dependence of Be substitution leads to noticeable variations in both electronic and vibrational responses. These findings demonstrate that controlled Be substitution can effectively tune the electronic structure of carbon nano-cones, enhancing their suitability for potential applications in nanoelectronics, catalysis, sensing, and energy storage systems.

One of the most common comorbidities of rheumatoid arthritis is depression, which is an independent mood disorder. This study aims to examine the blood levels of some biochemical markers in rheumatoid arthritis patients with and without depression compared to healthy group. This study included three groups: rheumatoid arthritis patients with depression, rheumatoid arthritis patients without depression, and a healthy group. The results showed that patients groups had significantly (P ≤ 0.05) higher activity of acetylcholine esterase and ceruloplasmine, carbonyl, ischemic modified albumin (IMA), interleukin 6 (IL-6), tumor necrosis factor α (TNF-α ), Haptoglobin, and Cupper, and significantly (P ≤ 0.05) lower levels of total protein, albumin, globulin, thiol, iron, and zinc than control group. Free amino was significantly (P ≤ 0.05) lower in patients with depression group compared to the control and significantly (P ≤ 0.05) increases in patients with Rheumatoid arthritis without depression compared to control. Serotonin was significantly (P ≤ 0.05) lower in patients with depression group compared to the control and significantly (P ≤ 0.05) increases in patients with Rheumatoid arthritis, the study suggests that the presence of depression in Rheumatoid arthritis patients is associated with additional biochemical changes that may reflect more severe or complex pathophysiological interactions. The lower levels of serotonin and free amino in Rheumatoid arthritis patients with depression indicate a potential link between mood disturbances and biochemical imbalances.

The research seeks to examine a new form of equations as well as possible criteria for oscillation and non-oscillation for all solutions of first-order neutral differential equations with the piecewise constant, whether these oscillatory solutions are convergent or non-convergent, by finding sufficient conditions that guarantee the oscillation of all solutions of these equations. According to NDEPC, these equations contain three delays and three coefficients, which requires that part of the coefficients in this equation be piecewise continuous. In witch A function that assumes constant values over predetermined intervals is known as a piecewise constant argument (PCA). The biggest integer function [ω], which rounds down any real number ω to the closest integer, is a typical example. The oscillation criteria for this type of equation necessitate the acquisition of some essential and sufficient conditions to ensure the achievement of this objective. The biggest integer function represents the piecewise constant. All solutions to this type of problem must be oscillatory, and necessary and sufficient conditions have been devised to assure this. Based on these conditions, one can deduce that the technique used for producing results relied on classifying coefficients into two independent types.