Explore the vast range of titles available.

In this work, Density Functional Theory (DFT) on Gaussian 09 W software was utilized to investigate the phenylephrine (PE) molecule (C9H13NO2). Firstly, the optimized structure of the PE molecule was obtained using B3LYP/6-311 + G (d, p) and CAM-B3LYP/6-311 + G (d, p) basis sets. The electron charge density is shown in Mulliken atomic charge as a bar chart and also as a color-filled map in Molecular Electrostatic Potential (MEP). Using these properties, the possibility of different charge transfers occurring within the molecule was evaluated. The calculated values of the energy gap from HOMO-LUMO mapping, illustrated in Frontier Molecular Orbitals (FMO) and Density of State (DOS), were found to be similar for both the neutral and anion states in the gaseous and water solvent phases. Both the global and local reactivity were studied to understand the reactivity of the PE molecule. Using the thermodynamic parameters, the thermochemical property of the title molecule was understood. Non-covalent interaction was studied to understand the Van der Waals interactions, hydrogen bonds, and steric repulsion in the title molecule. Natural Bond Orbital (NBO) Analysis was performed to understand the strongest stabilization interaction. In the vibrational analysis, Total Electron Density (TED) assignments were done in the intense region where the frequency of the title molecule was shifted distinctly. For vibrational spectroscopy, FT-IR and Raman spectra in the neutral and anion states were plotted and compared. Using the TD-DFT technique, the UV-Vis spectra along with Tauc’s plot were studied. Finally, topological analysis, electron localized function (ELF), and localized orbital locator (LOL) were performed in the PE molecule.

This work used the 6-311++G(d, p) basis set in the DFT/B3LYP and DFT/CAM-B3LYP technique to build the molecular structures of the nicotine and caffeine molecules. The minimum energy gives stability to these molecules with their corresponding dipole moment. The optimized structure to compute Raman spectroscopy and UV-Vis in CCl4 and DMSO solvent, employing the basis set 6-311++G(d, p), the DFT/B3LYP and CAM-B3LYP hybrid function, with the C-PCM model. The re-optimized molecule is used to study NLOs property which also give the dipole moment, polarizability and hyperpolarizability of titled molecules. We used AIM to investigate these molecules’ intramolecular interactions, bond critical points, and interbasin paths. Multiwfn software 3.8 produces the NCI-RGD diagram, which we use to determine weak interaction, electron density, Van der Waals interaction, steric effect, and hydrogen bond. Similarly, we analyze the covalent bond with the molecular surface using ELF and LOL techniques.

The increasing number of private cars, public transportation vehicles, and pedestrians, as well as the absence of adequate space for these ground amenities, are one of the primary causes of traffic congestion and accidents in the Kathmandu Valley. Investigations have indicated that the Kathmandu Valley has the greatest traffic accidents despite the heavy presence of the government and its agencies there. Most teens and young adults suffer injuries while using motor vehicles. The study's primary objective is to foresee and prevent such complications by planning for sufficient subsurface infrastructure (a cut‐and‐cover rectangular tunnel) for the Kathmandu Valley's transportation network. The overlying pressure, lateral earth pressure, live load, uplift pressure, and live surcharge are some of the forces acting on the tunnel, creating unique stress and moment zones. The tunnel meets the following geometric requirements: (a) Each of the tunnel's two cells has a clear span of 10 m and a clear height of 5.5 m. The side walls, inner walls, top slab, and bottom slab are all 700 mm thick. Soil has built up to a height of 4 m over the tunnel's roof. The analytical method is used in the tunnel segment's analysis. Furthermore, the designed tunnel has been evaluated for stability, considering the deflection and shear resistance. The analysis indicates that the tunnel meets the stability requirements. This implies that the structure is capable of withstanding the applied forces without excessive deflection. Non‐linear dynamic time history analyses of the El Centro earthquake and the Gorkha earthquake were computed. From the El Centro earthquake, the maximum displacement was 23.63 mm at 10.59 s, and from the Gorkha earthquake, the maximum displacement was 16 mm at 0.19 s for the modeled structures. Research provides one of the alternate solutions to traffic management system by providing a subsurface infrastructure (cut and cover) rectangular underground tunnel.

Circuit board analysis plays a critical role in ensuring the reliability of electronic devices by identifying temperature distribution, assessing component health, and detecting potential defects. This study presents a novel approach to infrared image segmentation for circuit boards, integrating Markov Random Field (MRF) and Level Set (LS) techniques to enhance segmentation accuracy and reliability. The proposed method leverages the probabilistic modeling capabilities of MRF and the contour evolution strengths of LS to achieve robust segmentation of infrared images, revealing critical thermal and structural features. Experimental results demonstrate that the proposed MRF‐LS method achieves an accuracy of 86%, a precision of 92%, and a recall of 94% on a benchmark dataset of PCB infrared images. These results indicate significant improvements over conventional segmentation methods, including k‐means clustering and active contour models, which yielded accuracies of 79% and 81%, respectively. Furthermore, the method shows adaptability for identifying fine‐grained temperature anomalies and structural defects, with enhanced resolution for small components. The study also discusses the potential adaptability of the proposed method to other imaging modalities, highlighting its scalability and versatility. These findings underline the utility of the MRF‐LS framework as a valuable tool in advancing circuit board analysis, with promising applications in quality control and predictive maintenance for the electronics industry. Traditional segmentation methods typically struggle with these complexities. This encourages the use of robust and adaptable Markov Random Field (MRF) models and Level Set (MRF‐LS) approaches to segment items of interest in complicated images. Our approach improves circuit board infrared image segmentation by combining MRF models and Level Set algorithms. Noise reduction and picture quality improvement begin with preprocessing.

The global demand for concrete, driven by rapid industrialization and urbanization, has led to significant depletion of natural aggregates and increased environmental concerns. Natural aggregates, which constitute about 70%–80% of concrete mix volume, are essential for concrete production, but their extraction, processing, and transport contribute to considerable environmental degradation. The extensive demolition of old infrastructure due to urban growth generates massive construction and demolition waste (CDW), exacerbating landfill shortages and leading to contamination of groundwater and ecosystems. This paper explores the use of recycled aggregate concrete (RAC) derived from CDW as an alternative to natural aggregates, offering a sustainable solution to reduce waste, conserve natural resources, and minimize environmental impact. It examines the structural and environmental implications of RCA in concrete, particularly focusing on challenges such as high porosity, elevated water absorption, and reduced mechanical and durability performance. Strategies to overcome these limitations, such as incorporating supplementary cementitious materials (fly ash, silica fume), advanced mixing techniques like the Two‐Stage Mixing Approach (TSMA), and fiber reinforcement, are critically discussed. The review also emphasizes the role of numerical models and machine learning in optimizing RAC mix designs and predicting its behavior, offering valuable insights for sustainable construction practices. Furthermore, the study incorporates Life Cycle Assessment (LCA) to quantify environmental benefits and assesses real‐world applications and current codal provisions. The conclusions drawn suggest that RAC, if optimized with appropriate mix designs and processing methods, can significantly contribute to sustainable construction. However, more research is needed to standardize RCA processing methods and conduct long‐term field validations to further enhance its mechanical and durability properties. RAC not only presents an environmentally beneficial alternative to traditional concrete, but also supports a circular economy by reducing CDW and minimizing reliance on natural resources. Methodology flowchart.

The knitting of wool is a complicated textile process that is driven by nonlinear interactions of yarn tension, loop creation, and needle dynamics. These interactions have a considerable impact on the quality of the fabric as well as the efficiency with which it is produced. The traditional empirical and linearized models often fail to reflect the genuine dynamic behavior of knitting systems, which results in a significant gap in the optimization of the textile process. In order to overcome this constraint, the current research endeavors to establish a sophisticated mathematical framework for modeling the dynamics of wool knitting by using the fourth‐order Runge–Kutta (RK4) technique for numerical simulations. The key goals are to build a reliable model that can forecast yarn tension, loop mechanics, and subsystem reliability, as well as to quantify the influence that different subsystem failure and repair rates have on the availability of the system over the long run. The technique incorporates probabilistic reliability modeling using differential equations, which are then solved by means of Laplace transformation and RK4 integration on the basis of boundary conditions. This is then followed by lengthy simulations that are carried out over a period of three hundred and 60 days. The findings indicate that the model achieves an accuracy of 92% when forecasting yarn tension in comparison to experimental benchmarks. Furthermore, the model displays steady performance up to 1.2 m per second for the yarn speed, and it identifies the tumbler subsystem as the most crucial aspect affecting overall dependability and availability. Additionally, it was shown that differences in the failure and repair rates of winders and tumblers had a substantial impact on mean time between failures (MTBFs), which highlighted regions that need focused maintenance. In addition to advancing theoretical knowledge of knitting mechanics, the model that was built also offers a practical decision‐support tool that can be used to optimize machine design, predictive maintenance, and intelligent control in the textile manufacturing industry. Its incorporation into industrial settings is in accordance with the concepts of Industry 4.0, which makes it possible to manufacture fabric that is both environmentally friendly and of high quality.

Ion‐sensitive field‐effect transistors (ISFETs) represent a promising technology for the precise detection of ions, particularly within diagnostic and clinical biomedical contexts. This paper details the design and implementation of an ISFET‐based device tailored specifically for efficient and reliable ion detection in biomedical settings. By meticulously selecting materials, configuring sensors, and integrating specialized readout circuitry, the device exhibits exceptional sensitivity and specificity in detecting target ions pertinent to various biomedical assays. The design process involves thorough optimization for sensitivity, durability, and compatibility with biological samples. A comprehensive review of ISFET gate materials is provided, alongside an overview of typical gate structures and signal readout methods for ISFET sensing systems. Additionally, diverse biosensing applications including ions, deoxyribonucleic acid, proteins, and microbes are explored. Rigorous calibration and testing procedures ensure precise and reproducible measurements. The resulting device holds substantial promise for applications such as pH monitoring, ion analysis, and biomarker detection in clinical diagnostics, offering a versatile platform for advancing biomedical research and healthcare practices. In the experimental investigation of pH sensor performance, five distinct measurements of current sensitivity were recorded. The obtained values were 0.025, 0.028, 0.027, 0.026, and 0.030 mA/pH, respectively. These measurements serve as crucial indicators of the sensor's responsiveness to changes in pH levels, providing insights into its effectiveness in detecting subtle variations in acidity or alkalinity. Such precise data are essential for evaluating and optimizing the sensor's design and functionality for various practical applications in analytical chemistry, environmental monitoring, and biomedical diagnostics. Development and characterization of ISFET‐based sensors.

Cardiovascular diseases (CVDs) remain a critical challenge in healthcare, requiring advanced analytical solutions for improved diagnosis and risk management. This study proposes a hybrid machine learning framework combining Random Forest (RF) and Neural Network (NN) algorithms to enhance both predictive accuracy and interpretability in CVD analysis. The hybrid approach leverages RF for effective feature selection and NN for capturing complex, nonlinear patterns in patient data. Using the UCI Heart Disease dataset, the hybrid model achieved a notable accuracy of 91%, precision of 92%, and recall of 89%. The system integrates Internet of Things (IoT)‐enabled real‐time monitoring capabilities, enhancing clinical decision support. Our findings demonstrate the model's superiority over standalone algorithms and underline its potential for personalized healthcare interventions. Future directions include real‐time model validation, scalability for multi‐disease prediction, and optimizing the model for resource‐constrained settings. Enhancing Cardiovascular Disease Analysis in Healthcare Systems

This is a compound probability distribution based on the theoretical concept of continuous mixtures of Poisson distribution. It has a single parameter and two irrational numbers. It is named as 'Polynomial-Exponential Mixture of Poisson Distribution (PEMPD) which will play a very important role in the field of statistical modeling of over-dispersed count data. Statistical characteristics have been defined and derived according to the need of the proposed paper. By applying goodness of fit to some over-dispersed count data of secondary in nature, it has been observed that it is a better alternative of Poisson-Lindley distribution of (PLD) Sankaran, Poisson-Mishra distribution (PMD) of Sah and Poisson-Modified Mishra (MMD) distribution of Sah and Sahani.

The notion of fuzzy set theory has not so far been districted over medical diagnosis. There are some added applications, for example, in image processing, pattern identification, and many medical devices. In this research article, we introduced a new mediative fuzzy ranking technique as the fuzzy extension in decision making. The proposed mediative fuzzy logic-based technique is more relevant and applicable to incomplete and doubtful situations or some contradictions present in the expert knowledge. The value of the contradictory degree for mediative fuzzy sets used in the extension principle is defined. The proposed mediative fuzzy ranking method is easily implemented in the medical field, and the proposed mediative fuzzy extension-based measured technique is useful to medical experts and doctors in many decision-making situations; the entire work is illustrated with numerical examples. We have also given some future aspects of mediative fuzzy extension in the interpretation of type-2 intuitionistic fuzzy sets.