Quaid-e-Millath Government College for Women

Rapid advancements in digital technology have spurred the expansion of the digital payment realm, where virtual wallets have gained prominence in the sphere of conducting financial transactions.This paper delves into the antecedentsthat determine the users’ intention to embrace mobile wallets and examines their impacton intention to accept mobile wallets.The current researchadopts Unified Theory of Acceptance and Use of Technology (UTAUT2) framework and uses four constructs namely, performance expectancy, social influence, hedonic motivation and facilitating conditions from the framework. The framework was extended by considering trust as the fifth construct to predict behavioural intention, due to its critical role in accepting innovative technologies. Data collected through a questionnaire was analysed using Structural Equation Modelling (SEM). Four constructs namely, performance expectancy, social influence, hedonic motivation, and trust had a significant role as determinants of intention to adopt mobile wallets. Together they accounted for 74% variance in behavioural intention. However, facilitating conditions did not have a significant role as a determinant of intent to acceptmobile wallets.The findings offer valuable insights for businesses, policymakers, and researchers, about adoption dynamics and aids in devising strategies to enhance mobile wallet adoption, thereby optimising the digital payment landscape.

Natural disasters pose global challenges, and integrating AI into disaster management offers significant improvements in preparedness, response, and recovery. This study evaluates the impact of various preprocessing techniques on AI models, focusing on numerical and image data. We specifically explore the CNN-LSTM model, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for sequence modeling. We compared preprocessing methods for numerical data, including Z-Score Normalization, PCA, and feature transformations like Logarithmic Transformation, and for image data, such as normalization, augmentation, and transfer learning with Fine-Tuning (ResNet). The CNN-LSTM model, leveraging both spatial and temporal data, showed improved performance, with Z-Score Normalization and Fine-Tuning enhancing model accuracy and robustness. This study highlights the importance of tailored preprocessing techniques and advanced model architectures in optimizing AI for disaster management, demonstrating the value of combining spatial and temporal insights to improve predictive accuracy.

Cholic acid (CA) has been widely used in potential therapeutic and pharmaceutical applications due to its efficient cell adhesion properties, biocompatibility, amphiphilic nature, and easily modifiable functional group, enabling its integration into polymeric drug delivery systems. Here, we report the synthesis of poly (methacrylated cysteamine conjugated cholic acid) (MAA-CA-Cyst), a thiomeric amphiphilic polymer synthesised by free radical polymerisation. The prepared poly (MAA-CA-Cyst) thiomer was characterised by 1H NMR, ATR-IR, DLS, FE-SEM, zeta potential, contact angle, and TGA techniques. The molecular weight of the polymer was calculated to be 19,953 g/mol using GPC. The critical micelle concentration (CMC) of poly (MAA-CA-Cyst) was measured to be 1.4 mg/mL, studied using a spectrofluorometer. These self-assembled thiomeric micelles (TMs) exhibit effective mucoadhesion and sustained drug-releasing properties of 47% and 21% over 180 min, respectively, in the ex vivo gastrointestinal tract mucosa of sheep. The synthesised TMs served as a potential platform for the reduction and encapsulation of Au and Ag NPs, as confirmed by ATR-IR, UV–visible, zeta potential, DLS, and HR-TEM analysis. The anti-Alzheimer's effects of poly (MAA-CA-Cyst)-stabilised Au and Ag NPs were evaluated by MTT and AO/PI double staining, scratch wound healing, and colony formation assays. These results inferred that Ag NPs showed higher apoptotic activity than AuNPs. The poly (MAA-CA-Cyst)-based TMs offer a multifunctional platform for effective mucoadhesion and sustained release of the drug, as well as their stabilised Au and Ag NPs for potential treatment of Alzheimer's disease.

Rhamnolipids (RLs) conjugated with L-cysteine (Cyst-RLs) and L-histidine (Hist-RLs) were synthesized via acid-amine coupling reaction and confirmed by ATR-FTIR, 1H & 13C NMR spectroscopy. Primary and secondary critical micelle concentrations (CMC) were determined as 0.30/1.08 mM (Hist-RLs) and 0.19/0.76 mM (Cyst-RLs) both lower than native RLs (0.8/2.0 mM) reflecting improved amphiphilicity upon conjugation. Amino acid conjugation introduced reactive histidine and imidazole functional groups endowing these biosurfactants with enhanced reducing capacity with Au surface coordination ability relative to native RLs. Above the CMC, both conjugates served as simultaneous reducing and stabilizing agents for sunlight driven synthesis of gold nanoparticles (AuNPs) at neutral pH without any external reductant. HR-TEM confirmed spherical morphology of 7–9 nm and zeta potential values of −46 ± 0.1 mV (Cyst-RLs@AuNPs) and −57.6 ± 0.13 mV (Hist-RLs@AuNPs) which is lower than native RLs, supporting long term colloidal stability exceeding 12 months. In vitro cytotoxicity against HT-29 colorectal adenocarcinoma cells yielded IC50 values of 70 ± 2.18, 60 ± 1.94, and 50 ± 2.68 μg mL−1 for RLs@AuNPs, Cyst-RLs@AuNPs, and Hist-RLs@AuNPs, respectively notably lower than fungal extract capped AuNPs reported for the same cell line (IC50:84.58 μg mL−1). AO/EtBr staining confirmed dose dependent apoptosis induction, most pronounced for Hist-RLs@AuNPs. These results establish amino acid conjugated RLs as chemically defined, reproducible platforms for stable, biocompatible AuNPs with superior anticancer potency, warranting further in vivo evaluation.

The IIoT-based Smart Soil Health Monitoring System with Tokenized Data Representation is a highly complex agricultural automation apparatus that is supposed to bring precision farming to agricultural enterprises by providing them with industrial quality monitoring and control. The suggested system takes advantage of the state-of-the-art sensors to monitor key soil health parameters in real-time. With the help of sophisticated controllers and a Programmable Logic Controller (Selec-FL tx4) these parameters are obtained and processed so that one is guaranteed of reliable, accurate and deterministic data processing that can be used in an industrial setting. The data observed on the soil is transformed into digital values in the standardized form of tokens, with each parameter being expressed as a data token which is safe and organized information. This is a token representation which enhances data integrity, traceability and interoperability of data in digital platforms. A PLC (Selec-FL tx4)-SCADA structure is adopted to maintain the virtual monitoring of the soil health, real time visualization, analysis of the historical data and automatic production of alerts. The SCADA interface makes it possible to supervise the soil conditions remotely and helps to make informed decisions concerning the irrigation management, soil treatment, and crop productivity. The system is incorporated with an Industrial Internet of Things (IIoT) architecture to make the seamless exchange of data among field devices, control systems, and cloud-based environments possible. The proposed system will improve the efficiency of the operations, minimize the number of people working with it, and encourage data-driven and sustainable agricultural methods by integrating the latest sensing technologies, PLC–SCADA automation, IIoT connectivity, and tokenized representation of digital data.