Shri Ramswaroop Memorial University

This study provides an in-depth numerical evaluation of a surface plasmon resonance (SPR) biosensor engineered for identifying HIV within human plasma. The biosensor’s structure draws from the Kretschmann arrangement and incorporates vital constituents, including a BaF2 prism, gallium nitride (GaN), lead titanate (PbTiO3), black phosphorus (BP), and a customized detection layer. To examine the biosensor’s functional attributes, advanced techniques such as the transfer matrix technique and angular questioning are applied at a 633 nm wavelength. The refined arrangement attains an angular sensitivity of 277.50 ^∘ RIU-1, detection accuracy (D.A.) of 0.69 deg−1, quality factor (Q.F.) of 191.3 RIU−1, figure of merit (FOM) of 191.18 RIU−1, limit of detection (LOD) of 1.8 × × 10^-5 , and CSF of 186.88, over a refractive index range from 1.36 to 1.40. The research investigates the biosensor’s specificity and operational effectiveness through the assessment of key parameters, including detection accuracy, quality factor, figure of merit, and LOD. The results highlight considerable promise for progressing biomedical diagnostic tools and deliver noteworthy insights to the realm of materials science.

In modern communication networks, particularly satellite-based systems, data security faces significant challenges from vulnerabilities such as signal interception, jamming, and latency during long distance transmissions. Traditional cryptographic methods are increasingly vulnerable to quantum computing threats, underscoring the need for advanced solutions to protect data integrity, confidentiality, and availability. This research investigates the fusion of quantum cryptography and Machine Learning (ML) to improve security in satellite communication. The Quantum Key Distribution (QKD), which is grounded in quantum mechanics, enables unbreakable encryption by detecting eavesdropping via quantum state disturbances. The CatBoost ML algorithm is applied to a dataset of 10,000 records featuring categorical attributes for prioritizing security elements such as anomaly detection, encryption types, and access controls. The model yields an accuracy of 89.23% and Area under Curve the Receiver Operating Characteristic (AUC-ROC) score of 94.56%, effectively predicting threat levels. Feature importance reveals anomaly detection (28.5%) and quantum encryption (22.3%) as primary contributors. While hurdles such as high implementation costs and transmission range limitations persist, this quantum ML synergy provides a proactive, adaptive framework for resilient, future-ready communication networks.

Human activity, urbanization, and industrialization have all contributed to the gradual increase in air pollution that has been seen in a number of nations over the course of the last several decades. techniques of Deep Learning (DL) and Machine Learning (ML) have been of great assistance in the development of techniques in a variety of sectors, including the prediction, planning, and analysis of uncertainty in smart cities and urban progress in the present state of affairs. The rapid growth of both the population and the business sector has caused many big cities to have significant problems over the quality of the air (AQ). The most common pollutants are particulate matter (PM2.5) and particulate matter (PM10), and if the levels of these pollutants in the air continue to grow, they will be a threat to the health of people. In order to enhance the accuracy of air quality forecasts, a multitude of techniques that make use of deep learning approaches have been developed. These techniques include Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. According to the findings of this study, a combined Encoder STM model might be used to predict PM2.5. In addition to that, we proposed five more criteria that would make the forecasts more accurate. After that, further models, such as the LSTM model and the Bidirectional LSTM model, are examined for their ability to forecast the concentration of PM2.5. Our findings indicate that the suggested methodologies are superior to the advanced deep learning techniques that are currently in use in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). This accomplishment can be attributed to the fact that these methodologies have low error rates and limited feature sets.

Joining dissimilar AA2060-T8 and AA2099-T83 alloys using conventional arc welding is challenging due to the presence of high heat input, complex thermal cycles, solidification cracks, coarse grain structures, and dissolution of precipitates. To overcome these limitations, the present study utilized gas tungsten arc welding (GTAW) in pulsed current mode with the aim of establishing process–microstructure–property correlations and identifying optimum welding factors. The influence of pulse frequency, pulse time ratio, and shielding gas flow rate on average grain diameter (AGD), microhardness, and wear rate was studied through the response surface method (RSM). Analysis of variance (ANOVA) results showed that the pulse frequency had the most significant impact, followed by the pulse time ratio, while the gas flow rate had the least effect. Electron backscatter diffraction (EBSD) results showed the pulsed GTAW mode yielded a significant grain boundary (GB) transformation around 70.05

This study explores how the volatility spillover mechanism and dynamic dependence among the founding BRICS equity markets, namely IBOVESPA, MICEX, Nifty 50, SSE, and JSE, have evolved over time using a multivariate DCC-GARCH model. The analysis is conducted across three distinct regimes: the pre-COVID-19 period (1 January 2010 to 10 March 2020), the COVID-19 crisis (11 March 2020 to 23 February 2022), and the Russia–Ukraine war and sanction period (24 February 2022 to 31 March 2024). The findings indicate that, prior to the COVID-19 pandemic, the BRICS equity markets experienced significant short-term volatility spillovers and significant volatility persistence, indicative of slow financial integration, as opposed to rapid contagion. In comparison, the COVID-19 pandemic resulted in significant structural shifts in the form of increased shock transmission, greater co-movement, and evident financial contagion among the markets. During the post-COVID-19 conflict period, while there was considerable persistence in volatility, the primary drivers of volatility spillovers were geopolitical. Across the three sub-periods, the volatility spillover network shows pronounced structural changes. Before COVID-19, IBOVESPA, MICEX, and SSE act as net transmitters, while Nifty 50 and JSE are net receivers. During the COVID-19 crisis, SSE and JSE become the main shock transmitters, whereas IBOVESPA, MICEX, and Nifty 50 shift to receiver roles. In the post-COVID-19 Russia–Ukraine war period, the network becomes more asymmetric, with JSE and Nifty 50 again emerging as net transmitters, while MICEX and SSE function primarily as net receivers. Overall, this study demonstrates that BRICS equity market interdependence is regime-specific and greatly dependent on exogenous global events.