DG Vaishnav College (Dwaraka Doss Goverdhan Doss Vaishnav College), commonly known as DGVC, is a Liberal arts, commerce and science college in Chennai, India. It is an autonomous institution affiliated with the University of Madras. It is located in the Arumbakkam locality of Chennai, which is a hub for many shops and markets, of which the most famous Koyembedu market and bus stand.
This paper develops a group transformation to formulate the laminar natural convection phenomenon influenced by a magnetic field perpendicular to the surface and an incompressible viscous fluid. The study considers a vertically oriented cone where the surface heat flux varies as a power function from the cone tip at (x = 0). The ordinary differential equations (ODEs) are obtained by transforming the non-dimensional partial differential equations (PDEs) along with their boundary conditions. The Runge-Kutta approach based on the shooting technique was used to solve the non-linear ODEs. We numerically analyzed the temperature and velocity fields for different Prandtl numbers (Pr), the exponent m value, and the magnetic parameter (M) values.
The ubiquity of antibiotics such as tetracycline (TC) in aquatic environments poses serious global health issues due to their persistence and ability to enhance antimicrobial resistance. This paper presents MXene/g-C3N4/WO3/NiO (MGWN), prepared by a simple hydrothermal route, which was evaluated for its performance as a photocatalyst in the photodegradation of TC under visible light. The coupling of metal oxides (WO3 and NiO) with carbon-based materials (MXene and g-C3N4) promotes photocatalytic activity through enhanced light absorption and charge separation. In-depth characterization using FT-IR, XRD, UV-DRS, FE-SEM, HR-TEM, and EDAX validated the development of textured nanostructure with particle sizes in the range 10-35 nm and a band gap of 2.84 eV. Photocatalytic activity tests demonstrated that MGWN exhibited a remarkable TC (5 ppm) removal rate of 99.35% within 120 min, adhering closely to pseudo-first-order kinetics. The apparent rate constant (k) was calculated to be 1.55 × 10-2 min-1 (R 2 0.9931), indicating a substantial rate of reaction. Further stability and reusability assessments revealed that the MGWN nanocomposite maintained over 90% photocatalytic efficiency after five consecutive cycles, reinforcing its potential for sustainable wastewater treatment applications. The findings highlight the MGWN nanocomposite as an effective and stable photocatalyst for removing persistent organic pollutants, paving the way for future advancements in environmental remediation technologies.
The growing demand for sustainable energy storage and clean hydrogen production has driven the search for multifunctional electrode materials with high performance and durability. Perovskite materials with high concentrations of oxygen vacancies can store substantial energy without requiring large surface areas due to the bulk intercalation mechanism. In this study, the double perovskite Ni1.5Co0.5ZnMoO6 (NCZMO DPs) was successfully synthesized using a simple sol-gel method. The resulting NCZMO DPs exhibit high capacitance and act as an efficient non-stoichiometric oxygen-defect electrode material for both pseudocapacitors and hydrogen evolution reactions (HER). Electrochemical measurements reveal outstanding capacitive performance in both alkaline and acidic media, with specific capacitances of 2558 F g-1 and 2425 F g-1, respectively, at a current density of 0.5 A g-1. The fabricated solid-state supercapacitors achieve a high energy density of 36.45 Wh/kg at a power density of 896.4 W/kg, demonstrating excellent energy storage capability. Furthermore, the electrode retains 86% of its initial capacitance after 5000 GCD cycles, highlighting its long-term stability and practical applicability. In addition, NCZMO DPs display remarkable electrocatalytic activity for HER, featuring a low overpotential of 141 mV dec-1 and excellent durability under acidic conditions. These results underscore the potential of NCZMO as a multifunctional perovskite-based material for high-performance energy storage and electrocatalytic applications.
Post-harvest deterioration in fruits and vegetables is a major cause of loss in food quality and marketability. It even threatens global food security. Naturally derived biopolymers in the form of edible coatings provide an eco-friendly alternative to traditional storage methods. Different mixtures of guava leaf extract, aloe vera gel, neem gum, starch, vegetable oil, and citric acid were used to create herbal-infused polysaccharide-based edible coatings. Three formulations (F1, F2, and F3) were made and tested for antimicrobial activity, antioxidant potential, FTIR structural characterization, and shelf-life extension of guavas and carrots. F1 showed higher antioxidant activity with a 96.58% DPPH radical scavenging effect. It also had better antimicrobial effects against Staphylococcus aureus and Pseudomonas aeruginosa, as well as significant delays in physicochemical changes. Furthermore, shelf-life experiments revealed that the coated samples could be stored for 10 days at room temperature and 20 days in the refrigerator, while uncoated controls spoiled quickly. FTIR analysis identified functional groups such as -OH, C-H, C=O, and C-O, which contribute to film integrity and barrier properties. Overall, these findings indicate that the herbal-infused edible coating is a clean-label, biodegradable, and effective way to maintain freshness in perishable produce.
As electronic trading technology accelerates the way trades occur in financial markets, the ability to manipulate the markets in many different ways has be come stronger through this same technology. Two ways this happens are via spoofing and layering, which pervert the way prices are determined, confuse in vestors, and reduce the level of trust in the integrity of financial markets. Exist ing surveillance mechanisms were predominantly rule-based or relied on opaque machine learning models, limiting their adaptability and interpretability. This study proposed SECURE, an explainable artificial intelligence (AI)-based in trusion detection system designed to detect and interpret spoofing and layering activities in real time. The framework integrated ensemble machine learning models, cybersecurity-inspired monitoring logic, and shapley additive expla nations (SHAP)-based explainability to achieve accurate detection with trans parent decision-making. Using Level-II order book data, the system analysed behavioural and microstructural trading features to classify manipulative pat terns while generating feature-level explanations for each alert. Experimental evaluation demonstrated that the proposed framework achieved high detection accuracy, low false-positive rates, and dependable real-time performance. The results indicated that combining intrusion detection principles with explainable AI improved regulatory interpretability and strengthened the effectiveness of au tomated market surveillance systems.
