Siksha O Anusandhan University

In the growing landscape of intelligent diagnostic tools, the creation of multifunctional nanomaterials that serve both biomedical and forensic needs is redefining the capabilities of modern sensing systems. In this study, undoped and 1 mol% Sm3+ doped ZnAl2O4 (SZAO) nanoparticles (NPs) were prepared using a combustion synthesis strategy and incorporated into a carbon-paste electrode to fabricate a highly responsive electrochemical platform for dopamine (DA) detection. The resulting SZAO-modified electrode (SZAO-ME) produced a pronounced anodic peak current of 8.426 & micro;A far exceeding that of the unmodified electrode with optimal activity observed at physiological pH (7.0). Scan-rate analyses indicated that the electro-oxidation process follows diffusion-controlled kinetics, while differential pulse voltammetry (DPV) measurements demonstrated a clear linear dependence on dopamine concentrations in the 1-5 & micro;M range (R2 = 0.99). The system achieved a detection limit (LOD) of 0.238 & micro;M and a quantification limit (LOQ) of 0.797 & micro;M. The electrode further allowed dual detection of dopamine and uric acid (UA) with strong selectivity, yielding excellent linearity (R2 = 0.993 for DA and 0.996 for UA). Operationally, the SZAO-ME maintained more than 91% of its initial response after repeated testing cycles, evidencing notable stability. Its performance consistency was reinforced by low relative standard deviation (RSD) values 1.58% for repeatability and 1.55% for reproducibility. In addition to its electrochemical capabilities, the SZAO nanomaterial was applied for forensic studies, enabling clear visualization of latent fingerprints (LFPs). High ridge clarity, low background interference, and well-resolved minutiae across multiple surface types underscore its strong potential for advanced forensic fingerprint development.

Enhanced oil recovery operations frequently employ water injection, yet these processes can disturb clay rich sandstones and trigger fines migration that reduces permeability. This study develops and evaluates a multifunctional Fe3O4@Saponin/Ni nanocomposite designed to modulate surface charges and suppress fines mobilization under different salinity conditions. Low salinity flooding in clay rich cores resulted in 92% permeability reduction, confirming the severity of fines detachment. In contrast, injection of 1000 ppm Fe3O4@Saponin/Ni maintained permeability and minimized differential pressure by stabilizing mobilized fines. The nanocomposite was synthesized through sequential magnetite formation, saponin functionalization, and nickel incorporation, and was characterized using FTIR spectroscopy, thermogravimetric analysis, and electron microscopy. Zeta potential measurements demonstrated that the nanocomposite shifts fines surface charges toward near neutral values, acting as an electrostatic buffer while its organic shell provides steric stabilization. Core flooding tests further showed that this dual mechanism preserves flow capacity by preventing pore throat blockage during low salinity injection. Overall, the results indicate that Fe3O4@Saponin/Ni is an effective fines migration control agent with potential for enhanced oil recovery applications.

Unsupervised machine learning often faces challenges in achieving high-quality clustering, which involves grouping of data points into a predetermined number of clusters. In this paper, we have proposed a novel hybrid approach that combines the African Vultures Optimization Algorithm (AVOA) with the Harris hawks Optimizer (HHO) for data clustering. We introduce the new hybrid method, referred to as AVHH (AVOA + HHO), designed for efficient data clustering. In any optimization technique, exploration and exploitation are the two primary components. Exploration aims to discover promising cluster solutions within the given search space, while exploitation focuses on refining the clusters that have already been identified. In this proposed method, AVOA is used for exploration, while HHO is employed for exploitation. A detailed performance comparison was carried out for the proposed AVHH method against seven other state-of-the-art optimization algorithms, including AVOA, HHO, butterfly optimization algorithm (BOA), black hole algorithm (BHA), gray wolf optimizer (GWO), salp swarm algorithm (SSA), and sine cosine algorithm (SCA). The tests were conducted with 18 standard benchmark datasets often used in clustering, and key performance indicators are illustrated through the box plots, the convergence curves, and the sum of squared Euclidean distances. To evaluate the effectiveness of the proposed AVHH approach, rigorous statistical tests were performed, and the experimental results show that the proposed AVHH hybrid framework enhances the theoretical development of adaptive metaheuristics by linking exploration and exploitation controls with convergence stability, ultimately achieving superior clustering efficiency and accuracy across diverse datasets. To more rigorously evaluate the performance of the proposed AVHH algorithm on highly imbalanced and high-dimensional data, three benchmark datasets (SECOM, COIL20, and Lung Cancer) were utilized. The clustering performance of the proposed method is also evaluated on seven benchmark datasets using normalized mutual information (NMI), adjusted rand index (ARI), and accuracy (Acc) and is compared with other state-of-the-art algorithms. In addition, the computational run time (RT) of all considered methods are separately analyzed to assess their efficiency. To enhance transparency and reproducibility, the implementation of the proposed AVHH algorithm, along with the experimental configuration details, is available at https://github.com/tribhuvansingh88/AVHH-MATLAB-Code .

This work reports on the fabrication of polymer-ceramic multiferroic composites, PVDF-Bi₁₋ₓDyₓFeO₃ [with x = 0.05 (PVDF-BDFO5), 0.10 (PVDF-BDFO10), and 0.15 (PVDF-BDFO15)] (PVDF-BDFO), through a solution casting route. X-ray diffraction (XRD) analysis demonstrates the determination of all three polymorphic phases (α-, β- and γ-) of PVDF and unit cell parameters and the fundamental crystal structure of the crystalline phase. Structural analysis typically reveals that Dy-substitution at the Bi site stabilizes the rhombohedral perovskite structure of BiFeO₃. When incorporated into the PVDF matrix, the ceramic particles promote the nucleation of the β-phase of PVDF, which is responsible for strong electrical response. Electrical characterization of PVDF-BDFO ceramic composites improves dielectric constant, reduces leakage current, due to enhanced polarization and interfacial effects, though very high doping may eventually stabilize or slightly reduce it due to defects. The filler particles BDFO have been uniformly distributed throughout the polymer matrix, as observed using the scanning electron microscopy (SEM) technique. To investigate the impact of grain structure and grain boundaries on the resistive characteristics of the composite materials in terms of electric circuits, a complex impedance spectroscopy (CIS) technique was used. This suggests that these flexible fibers could be used for a variety of applications, including sensors, spintronic devices, and flexible information storage.

The demand for accurate stock market trend prediction models has surged among financial traders, prompting the exploration of machine learning techniques to enhance predictive performance and reduce computational complexity. Traditional models often rely solely on historical stock data, which may not fully capture the intricate dynamics of financial markets. This research addresses this limitation by developing a model that utilizes Open, High, Low, and Close (OHLC) prices, integrating both classification and regression machine learning models to predict stock market trends. However, not all models are favorable for trend prediction and therefore, we compare using different models to find the best performing model. The regression models tested include: Long Short-Term Memory (LSTM): Achieved 99.31% accuracy, Linear Regression: Achieved 98.85% accuracy, Decision Tree: Achieved 98.22% accuracy, Stochastic Gradient Descent (SGD): Achieved 97.63% accuracy, Temporal Convolutional Networks (TCN): Achieved 96.95% accuracy, K-Nearest Neighbors (KNN): Achieved 80.35% accuracy, Random Forest: Achieved 57.12% accuracy. The classification models evaluated include: Artificial Neural Networks (ANN): Achieved 97.28% accuracy, Stochastic Gradient Descent (SGD): Achieved 93.65% accuracy, K-Nearest Neighbors (KNN): Achieved 89.53% accuracy, XGBoost: Achieved 87.01% accuracy, Decision Tree: Achieved 86.1% accuracy, Random Forest: Achieved 85.06% accuracy, Support Vector Machine (SVM): Achieved 61.94% accuracy, AdaBoost: Achieved 58.38% accuracy, Na & iuml;ve Bayes: Achieved 51.4% accuracy. Here, it is observed that the regression models are performing better in trend prediction when compared with classification models. However, it has also been noticed that ANN in classification; LSTM, and Linear regression in regression are performing better than other models in their categories.