Acharya Nagarjuna University

The HIV-1 pandemic presents a multifaceted challenge across the globe, standing as the foremost public health crisis today. Global data on HIV-related morbidity and mortality are alarming. Effective HIV management hinges on minimizing transmission through highly active antiretroviral therapy (HAART), which relies on a combination of HAART and has been a cornerstone in HIV management. However, challenges such as low patient adherence, suboptimal drug pharmacokinetics, and side effects, potentially undermine the efficacy of existing treatment. Emerging nanotherapeutics, particularly lipidic and polymeric nanoparticles, have exhibited immense promise in addressing these concerns. These nanocarriers enhance targeted drug delivery, facilitate controlled release, and reduce toxicity. Notably, co-delivery strategies using nanoparticles enable the simultaneous transport of multiple drugs involved in HAART. But the question arises whether the exploration is enough to turn the tide. Hence, through this review, the authors have tried to explore and discuss the obstacles faced by the lipid and polymeric nanoparticles such as stability and encapsulation efficiency, and translating these innovations to clinical practice in detail and discussed the future potential of AI-driven nanomedicine.

The concept of hybrid nanofluid has a great attention from researchers and scientists due to the improving thermo-physical features/properties in the base fluid. As compared to the unitary nanofluid, the hybrid nanofluid has enhanced the energy production. Also, the hydro and energy transfer in the hybrid nanofluids has a great production as compared to that of regular fluid as well as mono nanofluid. The governing partial differential equations are modelled by utilizing boundary layer theory and they are simplified by employing the usual similarity analysis. The final equations are resolved numerically through the built-in function bvp4c in the software of MATLAB. The numerical simulations are reported for the velocity and thermal function for both unitary nanofluid and hybrid nanofluid and are analysed for the controlling flow variables. The thermal Grashof number and radiation parameter enhance the velocity function of nanofluid and hybrid nanofluid. The strength of each fluid temperature is enhanced with boosting magnetic field parameter M, radiation parameter Rd and Eckert number Ec. The dimensionless skin friction function is a decreasing function of Ec and Rd.

Due to their potential carcinogenic and genotoxic effects on human health, nitrosamine impurities have become a significant concern for regulatory agencies globally. Thus, there is a need for highly sensitive and specific analytical methods capable of detecting trace levels of nitrosamines. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has become the method of choice for identifying and quantifying these impurities due to its unparalleled sensitivity, selectivity, and precision. Regulatory bodies such as the United States Food and Drug Administration, European Medicines Agency, International Council for Harmonisation, and World Health Organization emphasize the importance of addressing nitrosamine hazards, providing updated guidance to manufacturers and applicants. The key objective of this review is to examine recent advancements in LC-MS/MS methods for nitrosamine analysis, focusing on detection limits, precision, accuracy, and matrix effects to ensure highly sensitive and specific detection of these potentially carcinogenic impurities in compliance with global regulatory guidelines.

Intrusion detection is one of the prime research areas in developing a effectiveSDN environment. The ever increasing demand in the network, has directlyincreased the malicious activity and cyber threats in the 5G networks. Variousresearch done in the area of intrusion detection has more room for improvement,by including the machine learners in the IDS development. In this work,a Intrusion Detection System (IDS) is developed for the SDN by including wellknown machine learners and tree based algorithms. The entire process is done asData preprocessing,Feature extraction,Dimensionality reduction & Classification.Well known NSL-KDD data set is considered for this research. Random forestclassifier aids in the feature extraction, and the principal component analysis(PCA) for the dimensionality reduction. A Fuzzy-XGBooster classifier is proposedin this work, and it handles the classification part, and detects the normaland the anomaly class. The implementation part is done on the NSL-KDD dataset, and the performance is evaluated on several metrics. The proposed Fuzzy-XGBoost classifier achieved higher performance rate with the values of 0.999246for accuracy, 0.998859 for precision, 0.998716 for recall, 0.998788 for F1 measure,0.999485 for specificity, and 0.000515 for False Alarm rate respectively. Againfor the metrics MCC, NPV, FPR, FNR, PPV, RMSE, and MAE the proposedFuzzy-XGBoost classifier has achieved suitable values as 0.9981, 0.9993, 0.000602,0.001317, 0.9988, 0.029031, and 0.000843 respectively.

The financial industry faces significant challenges in managing exponentially growing data volumes while ensuring consistency and integrity across disparate systems. Single data sourcing emerges as a strategic solution to address these challenges by establishing a unified data architecture that serves as a definitive source of truth across the organization. This article explores the compelling business case for implementing single data sourcing in financial institutions, highlighting the substantial operational efficiencies, cost reductions, and revenue enhancements achieved through this approach. Drawing from extensive industry data, the article examines methodologies for successful implementation, focusing on centralized repositories, integration techniques, and governance frameworks. Technological enablers including cloud computing, advanced integration tools, and analytics platforms are evaluated for their transformative impact on data management capabilities. Despite the clear benefits, financial institutions encounter formidable challenges in implementation, including organizational data silos, quality concerns, and resistance to change. The article presents evidence-based mitigating strategies that have proven successful in overcoming these obstacles. The findings demonstrate that financial institutions implementing unified data architectures experience significantly improved operational performance, regulatory compliance, and customer satisfaction while simultaneously reducing costs and accelerating innovation..

The development of visible-light-driven photocatalysts is a promising strategy to address the problems associated with environmental pollution and energy shortage. But, above-said applications should require photocatalysts with a broad absorption in the solar spectrum, high stability, lower electron–hole recombination rate and strong redox power. Specifically, Ag-based ternary composite photocatalysts are exposed to be able to exhibit higher photocatalytic activity due to their strong localized surface plasmon resonance and efficient charge separation along their interfaces. Therefore, the basic principles behind the synthesis and characterization of Ag-based ternary nanocomposites toward the development of efficient photocatalysts for various pollutant degradation and solar-to-fuel conversion reactions are highlighted. Accordingly, this review provides a brief overview of Ag-based ternary photocatalysts with their fabrication comprising their composition and optimization toward higher photogenerated electron–hole charge separation.

A novel Li4Zn(PO4)2 phosphor compound was synthesized using the renowned combustion synthesis method. The crystal structure and morphology of as-prepared phosphors were carefully examined along with their emission and excitation behaviors and decay profiles. The presence of constituent elements was also confirmed by energy-dispersive X‑ray spectroscopy. Photoluminescence studies revealed a noteworthy relationship between the emission intensity and the concentration of dopant Sm3+. Synthesized phosphors exhibited a remarkably intense narrow-band orange-red emission, peak at 597 nm when excited at 401 nm. The average lifetime of phosphors was found to be 1.11 ms. Furthermore, the chromaticity (CIE-Commission Internationale de I’Eclairage) coordinates of phosphors were accurately positioned in the red region, suggesting their suitability for lighting and display applications that require red-emitting materials. Overall, this novel Li4Zn(PO4)2 phosphor exhibited promising luminescent properties, making it a potential candidate for various applications in lighting, displays, and other optoelectronic devices.

Over the last decade, advancements in small object detection have been notable, yet existing models struggle with extremely small objects due to (1) their reduced informational significance compared to ground-view objects and (2) challenges in detecting unevenly distributed objects, particularly in dense regions. To tackle these challenges, this paper presents an efficient YOLOv9 model incorporating two innovative modules. The first, called RepNCSPELAN4, is designed to accelerate feature extraction. It takes input from the initial convolutional layer and splits it into two separate paths: one processed through RepNCSP layers and the other through standard convolutional layers. The outputs are then merged, optimizing gradient flow and feature reuse. This dual-path approach boosts learning efficiency and inference speed while maintaining low computational complexity. The second module SPPELAN incorporates Spatial Pyramid Pooling (SPP) into ELAN for layer aggregation. It employs a convolutional layer for channel adjustment, then performs spatial pooling operations to capture multi-scale contextual information. Concatenates outputs and consolidates features via another convolutional layer, optimizing detailed feature extraction across spatial hierarchies, and Experimental validation on the VisDrone dataset demonstrates superior performance, with our model achieving higher mAP (48.7%) and precision (78.3%) compared to existing models, particularly in detecting x-small items.

This article comprehensively analyzes Zero Trust Architecture (ZTA) as a strategic framework for data security in modern distributed computing environments. Moving beyond traditional perimeter-based security models, Zero Trust Architecture implements the principle of "never trust, always verify" through continuous authentication, granular access controls, and comprehensive monitoring. The article examines Zero Trust concepts' theoretical foundations and historical development before exploring key implementation components, including identity management, least privilege access enforcement, data classification, encryption strategies, and continuous security analytics. The article's examination of successful implementations across diverse sectors identifies measurable security improvements, including reduced breach impact, faster threat detection, and strengthened resistance to credential-based attacks. The article explores organizational implementation considerations, including maturity models, integration strategies, and common resistance factors, providing practical guidance for security practitioners. The article examines emerging trends, including integration with cloud-native architectures, AI-driven security automation, evolving regulatory requirements, and adaptations for the Internet of Things and edge computing environments. This comprehensive article framework provides security professionals with both theoretical understanding and practical approaches for implementing Zero Trust principles to protect organizational data assets in increasingly complex and distributed computing landscapes

Silver nanoparticle–polymer nanocomposites (AgNP–PNCs) represent a transformative advancement in biomedical material science, integrating the potent antimicrobial properties of AgNPs with the structural versatility of polymer matrices. This synergy enables enhanced infection control, mechanical stability, and controlled drug delivery, making these nanocomposites highly suitable for applications such as wound healing, medical coatings, tissue engineering, and biosensors. Recent progress in synthesis and functionalization has led to greater control over particle morphology, dispersion, and stability, optimizing AgNP–PNCs for clinical and translational applications. However, challenges related to cytotoxicity, long-term stability, immune response, and scalability persist, necessitating systematic improvements in surface functionalization, hybridization strategies, and biocompatibility assessments. This review critically evaluates the latest advancements in AgNP–PNC development, focusing on their functionalization techniques, regulatory considerations, and emerging strategies to overcome biomedical challenges. Additionally, it discusses preclinical and translational aspects, including commercialization barriers and regulatory frameworks such as FDA and EMA guidelines, ensuring a comprehensive outlook on their clinical feasibility. By bridging the gap between innovation and practical application, this review investigates the transformative potential of AgNP–PNCs in advancing next-generation biomedical materials.

Agricultural consumer electronics, such as drones, sensors, and robotics, play a pivotal role in addressing challenges like wheat lodging, which can significantly impact crop yield and quality. This study leverages consumer-grade UAVs to classify wheat lodging types-root lodging and stem lodging-using high-resolution RGB images captured at three altitudes (15, 45, and 91 meters). By employing automatic segmentation techniques, datasets were generated for each altitude, and a refined EfficientNetV2-C model was proposed for classification. The model incorporates a Coordinate Attention (CA) mechanism to enhance feature extraction and Class-Balanced Focal Loss (CB-Focal Loss) to address data imbalance, achieving an average accuracy of 93.58%. This research highlights the integration of advanced AI-based classification with low-carbon agricultural drones, underscoring their relevance to consumer electronics. Compared to four conventional machine learning and two deep learning models, EfficientNetV2-C demonstrated superior performance at all altitudes while maintaining minimal carbon emissions. The study also examines the influence of UAV flight altitude on classification efficacy, revealing that while machine learning models were unaffected, deep learning models showed reduced performance at higher altitudes due to feature loss. These findings emphasize the potential of UAVs as accessible, scalable, and sustainable tools for real-time agricultural monitoring in precision farming.

Adagrasib (MRTX849), developed by Mirati Therapeutics, is a Kirsten rat sarcoma viral oncogene homolog, glycine‐to‐cysteine mutation (KRAS G12C) inhibitor approved by the Food and Drug Administration in December 2022 for the treatment of adult patients with KRAS G12C‐mutated locally advanced or metastatic non‐small‐cell lung cancer (NSCLC) who have received at least one prior systemic therapy. To address the critical need for precise and robust analytical methods for the quantification and characterization of Adagrasib and its impurities, this study presents the development and validation of a highly selective and sensitive high‐performance liquid chromatography (HPLC) method tailored for the analysis of drug substance. The method was systematically optimized through extensive trials with various column chemistries and mobile‐phase compositions. The optimized conditions employed a mobile phase comprising acetonitrile, methanol, and 0.1% triethylamine (TEA) buffer (pH adjusted to 2.5 using formic acid) in a 20:30:50 ratio, ensuring baseline resolution, stability, and reproducibility. Validation, conducted per ICH Q2 guidelines (International Council for Harmonization), demonstrated the method's excellent accuracy, precision, linearity, specificity, and robustness. The method exhibited low relative standard deviation (%RSD) values and high recovery rates, confirming precision and accuracy for Adagrasib and its impurities. Forced degradation studies under acidic, alkaline, oxidative, photolytic, and thermal conditions were performed to investigate the stability profile of Adagrasib. Degradation products were characterized using liquid chromatography‐tandem mass spectrometry (LC‐MS/MS), and their possible structures were predicted based on mass fragmentation patterns. The validated method demonstrated its capability to reliably quantify Adagrasib and its impurities at trace levels, ensuring rigorous quality control of the drug substance. The integration of degradation product identification and characterization further enhances its utility in pharmaceutical development. This method offers a robust analytical tool for monitoring the quality of Adagrasib drug substance, ensuring compliance with regulatory requirements and supporting patient safety.

The ${\text{Nd}}_{1-x}{\text{Bi}}_{x}{\text{Fe}}_{0.7}{\text{Sc}}_{0.3}{\text{O}}_{3}(x=0.1- 0.4)$ compounds were successfully synthesized using a sol–gel method. X-ray diffraction analysis revealed an average crystallite size of approximately 104 nm with a strain of 0.276 for the optimized specimen (x = 0.3) sample. Raman spectroscopy further elucidated internal structural distortions arising from fluctuations in bond distances and angles. Magnetic properties were investigated using a vibrating sample magnetometer (VSM), showing promising results with magnetic moments (Msat) upto 5.9351 emu/g, remanent magnetization (Mr) of 5.2545 emu/g and coercivity (Hc) reaching 145.21 T for the ${\text{Nd}}_{1-x}{\text{Bi}}_{x}{\text{Fe}}_{0.7}{\text{Sc}}_{0.3}{\text{O}}_{3}(x=0.1- 0.4)$ samples. Reflectance differential spectroscopy was employed to determine bandgap energies (Eg) for ${\text{Nd}}_{1-x}{\text{Bi}}_{x}{\text{Fe}}_{0.7}{\text{Sc}}_{0.3}{\text{O}}_{3}(x=0.1- 0.4)$ are 1.94 eV, 1.99 eV, 2.09 eV, 2.12 eV and 2.05 eV, respectively. The photocatalytic activity of these materials was assessed, with the ${\text{Nd}}_{1-x}{\text{Bi}}_{x}{\text{Fe}}_{0.7}{\text{Sc}}_{0.3}{\text{O}}_{3}(x=0.1- 0.4)\left(x=0.3\right)$ samples demonstrating the most effective performance in photocatalytic applications.

In order to address the issue of insufficient task offloading decisions in vehicle networks of transportation cyber-physical systems (TCPS) because of multitasking and resource constraints, this study presents a quasi-Newton deep reinforcement learning-based two-stage online offloading (QNRLO) algorithm. Computer simulation experiments show that the approach performs exceptionally well in terms of convergence under various conditions and parameter configurations. Most of the trials are carried out in a simulated setting, and further real-world scenarios may be required to confirm the algorithm's efficacy. This methodology initially implements batch normalization techniques to enhance the training process of the deep neural network, subsequently utilizing the quasi-Newton method for optimization to successfully approximate the ideal answer. According to the experimental results, the QNRLO algorithm's loss function and normalized computation rate have converged after 2,000 iterations, demonstrating the algorithm's excellent stability and dependability. The findings demonstrate). Digital Object Identifier 10.1109/TITS.2025.3539934 that the computational load and training time can be further optimized by appropriately adjusting certain parameters without compromising convergence performance. Furthermore, the technique incorporates system transmission time allocation into the TCPS model, hence augmenting the model's practicality. The proposed approach markedly enhances the efficiency and stability of job offloading compared to previous algorithms, effectively addressing task offloading challenges in TCPS and exhibiting considerable applicability and reliability.

An attribute control chart is designed for the time truncated life test when the quality characteristic follows the new Lomax Rayleigh Distribution (NLRD). Control chart coefficients and performance of the proposed control chart are determined for different shift constants. Average run lengths for different shift constants and control chart limits are tabulated for reference. Time truncated attribute control charts performance in monitoring non-conforming units following NLRD distribution is validated in this study. Simulated data results and real data example reveals that the new proposed attribute control chart for NLRD shows better results in identifying even small shifts.

Carbon dots (CDs) are nanomaterials that have gained worldwide attention due to their unique properties. This study investigated the stability of polyethylene glycol (PEG) CDs over a period of 60 days. To our knowledge, the stability of microwave-synthesized PEG CDs has not been extensively investigated by previous research groups. Comprehensive characterization was conducted using spectroscopic techniques, including UV-Vis, and fluorescence and additional techniques like DLS and TEM. UV-Vis, fluorescence, and DLS analysis were performed at regular intervals (days 1, 15, 30, and 60) to monitor changes in optical properties, particle size, and dispersion. Quantum yield (QY) and lifetime measurements were conducted on day 1 and day 60 to assess the luminescence efficiency. The results demonstrated exceptional stability of the PEG CDs, evident from the consistent UV-Vis, fluorescence spectra, unchanged particle size, and preserved morphology. Moreover, the QY and lifetime values showed small changes, indicating the robustness of the CDs.

A new series of benzothiazole-oxazole derived arylethynylbenzenesulfonamides were designed and synthesised, and their chemical structures were validated using spectroscopic methods. The compounds were further tested for anticancer activity against human cell lines such as prostate cancer (PC3 and DU-145), lung cancer (A549), and breast cancer (MCF-7) using the MTT assay with the anticancer drug etoposide as a positive control. All of the studied substances showed promising anticancer properties.

Fluorescence spectroscopy has emerged as a powerful tool for studying the self‐assembly, structural integrity, and dynamic behaviors of amino acid and peptide‐based hydrogels. Unlike fluorescence microscopy, which visualizes spatial distributions, fluorescence spectroscopy delves into the molecular interactions and environmental changes within these hydrogels. This review highlights the intrinsic fluorescence of amino acids such as tryptophan, tyrosine, and phenylalanine, alongside modifications such as Fmoc groups and the use of external probes, to elucidate gelation mechanisms and aggregation dynamics. Key phenomena such as aggregation‐induced emission (AIE), aggregation‐induced quenching (AIQ), time‐resolved fluorescence, and fluorescence resonance energy transfer (FRET) techniques provide insights into favorable and unfavorable assembly processes. The ability of fluorescence spectroscopy to monitor hydrogels in their native wet state without structural disruptions caused by drying presents a significant advantage over traditional morphological techniques such as TEM and SEM. This review underscores the importance of fluorescence spectroscopy in advancing the design and application of amino acid and peptide‐based hydrogels, with potential implications for biomaterials, drug delivery, and biosensing. The integration of fluorescence spectroscopy with complementary techniques holds great promise for unlocking new possibilities in hydrogel research.

Fortunately, multi-tenant cloud environments offer a more cost-effective provisioning model that consumes infrastructure to store and access data. As the volume of sensitive data processed and stored in the cloud continues to grow, ensuring data integrity has become a top concern for organizations. Data integrity is the information that is correct, consistent, and reliable is stored in the cloud. To address this issue, we also present a solution that allows us to maintain integrity in a multi-tenant cloud environment using advanced testing. The standard approach to testing (functional and performance testing) does not identify and will enable us to prevent data integrity problems. That's because these approaches describe the system's functionality and performance rather than the accuracy of the system's data. They proposed a solution that used techniques like anomaly detection, data profiling, and data quality checks to discover possible integrity issues in the dataset. The new techniques work within the testing process, while all data is assessed for integrity before being stored in the cloud.