Madan Mohan Malaviya University of Technology

This study presents a numerical investigation of a novel star-shaped twin core photonic crystal fiber (TC-PCF) sensor for refractive index sensing of sucrose concentrations. The structural design and electromagnetic analysis are carried out using the finite element method implemented in COMSOL Multiphysics. Sensor performance is evaluated in the mid-infrared wavelength range, where key parameters such as confinement loss, wavelength sensitivity and amplitude sensitivity are extracted through MATLAB-based analysis. The proposed geometry incorporates a compact and well-defined micro-structured arrangement, which enhances the interaction between the guided modes and the analyte-filled region. Variations in sucrose concentration led to changes in refractive index, which modulates the modal properties and transmission characteristics of the fiber. The sensor achieves a maximum wavelength sensitivity of 8262.76 nm/RIU and an amplitude sensitivity of 3.3849 RIU−1, while maintaining negligible confinement loss. The combination of high sensitivity, low loss and compact design demonstrates the potential of this PCF sensor for biochemical sensing applications. These findings indicate its effectiveness for precise and reliable detection of sucrose concentration in real-world environments.

Abstract Shallow foundations over subsurface cavities are particularly vulnerable to subsidence, sinkhole formation, and differential settlement, posing significant geotechnical hazards in urban environments. This study investigates the ultimate bearing capacity of shallow foundations above a square subsurface cavity in cohesive–frictional soils using finite-element limit analysis (FELA). The dimensionless stability number ( N s ) is employed to quantify the influence of cavity geometry, depth ( N / B ), lateral offset ( M / B ), and soil properties, including the friction angle ( ϕ ) and unit-weight factor ( γ B / c ), on foundation performance. Results indicate that foundation stability is minimum when the cavity lies directly beneath the footing ( M / B = 0 ) and progressively improves with lateral offset, becoming relatively insignificant beyond M / B ≈ 4 . Low-friction soils ( ϕ ≤ 10 ° ) and high γ B / c exacerbate crown yielding, enlarging the effective influence zone and increasing subsidence risk. Shallow cavities ( N / B ≤ 2 ) significantly reduce N s , whereas deeper cavities ( N / B ≥ 4 ) exhibit minimal influence due to overlying soil confinement and arching effects. Three distinct failure mechanisms are observed: punching failure, where the footing penetrates the cavity roof under minimal soil cover; cylindrical shear wedge, forming asymmetric deformation at moderate offsets ( M / B = 2 – 4 ); and general shear, representing symmetrical plastic wedges for deeper cavities ( N / B ≥ 4 ). Higher friction angles enhance N s , emphasizing the role of soil shear strength in mitigating instability. The study identifies critical cavity positions, depths, and soil conditions for safe foundation design, providing quantitative guidance for hazard mitigation and risk assessment in subsurface cavity-prone regions. The developed stability contours and failure transition maps provide a practical framework for preliminary hazard assessment and safe foundation planning in subsurface cavity-prone regions.

This study evaluates stability of a shallow foundation (width, B) resting above a circular subsurface tunnel in cohesive-frictional soil under eccentric loading using Finite Element Limit Analysis (FELA). The analysis is performed with respect to the key parameters: tunnel depth (N/B), horizontal tunnel offset (M/B), load eccentricity (e/B), effective friction angle (phi '), and unit weight influence parameter (gamma B/c '). The non-dimensional stability number (Ns) is adopted to characterize foundation response, while the stability reduction index (SRI) and corresponding failure mechanisms quantify tunnel-foundation interaction. The results indicate that, most critical condition occurs at M/B = 0 for N/B = 1, where Ns = 0.4-0.6 and SRI reaches 93-95%, with significant interaction within-3.5 G M/B G +3.5. As N/B increases to 2 and 2.5, the critical horizontal band contracts to-2.5 G M/B G +2.5 and-1.5 G M/B G +1.5, respectively. Increasing N/B from 1 to 2.5 raises Ns from approximately 3.0 to nearly 10.5 under concentric loading, while SRI decreases from about 80% to 35-40%. Increasing eccentricity to e/B = 0.4 reduces SRI to nearly 10%, although Ns decreases due to eccentric loading. At N/B = 1; M/B = 0, Ns decreases from about 1.6 to nearly 0.6 with increasing gamma B/c ', whereas Ns increases from 0.5 to 1.7 at the same alignment with increasing phi '. Failure mechanisms shift from punching to general shear with increasing tunnel depth and offset. An artificial neural network model predicts Ns with high accuracy, achieving coefficient of determination (R2) of 0.9819.

The perovskite solar cells (PSCs) are one of the highly prospective approaches towards solving the energy crisis around the world and utilizing sunlight to generate electricity. Such renewable and abundant energy is a way of moving toward sustainable development in order to reduce dependency on fossil fuels, promote environmental conservation, and enhance long-term energy stability. Although Pb-based PSCs show excellent efficiencies, their intrinsic toxicity raises great concern from a sustainability perspective. Despite this, Sn-based perovskites present an eco-friendly alternative, though their stability and efficiency remain comparatively lower. This work has been undertaken to systematically examine and compare the performance of Sn and Pb-based PSCs with the purpose of elucidating the fundamental causes of their contrasting photovoltaic behavior. The present article comprehensively examines the comparative modeling and performance optimization of two device configurations: FTO /TiO2 /FAPbI3 /Spiro-OMeTAD /Au (PSC1) and FTO/TiO2/FASnI3/Spiro-OMeTAD/Au (PSC2) with the help of SCAPS-1D software. The investigation involves various optimizations such as active layer thickness, bulk defect density (Nt), shallow acceptor density (NA), internal resistances, and work function to maximize the output of each cell. After optimization, the output parameters achieved for PSC1 include JSC of 26.74 mA/cm2, VOC of 1.31 V, FF of 89.37

In the existing Scenario, threats are getting larger as a consistent cybersecurity problem, which attacks computer systems, handheld devices, and ubiquitous networks. The current generation of highly critical malware has also surpassed the traditional detection methods in various aspects such as accuracy, flexibility and resilience to the original attack methods. The given research is a systematic literature review of threat detection aiming to examine the literature published since 2015 to 2025 to assess the current status of the field of research and categorize the main issues or possible directions of further research that warrant further research. A new taxonomic system of discovery modes depending on deep learning and machine learning is proposed, which is viewed by datasets, feature extaction systems and algorithmic categorizers. Moreover, it gives some discussion of experimental bias that has a major influence on the performance of malware detection and defines critical measures of effectiveness to evaluate the effectiveness, and certain undefined research problems. This paper introduces possibilities of improving detection method and creates insights on plausible solutions and future research directions. Lastly, the current paper discusses the anomalies that may be experienced in digital twin and how to identify.