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Lung cancer detection is the process of detecting the presence of lung tumor or abnormalities in the lungs. Early diagnosis is crucial for increasing the chances of patient survival and successful treatment. When compared to X-rays, Computed Tomography (CT) images are more sensitive and are increasingly being used for the diagnosis and screening of lung tumors. They provide complete cross-sectional images of the lungs and it will even detect small lesions. AI and Machine learning (ML) approaches are most commonly employed to analyse medical images (e.g. CT scans) and detect lung cancer. This algorithm can help radiologists identify patterns indicative or subtle abnormalities of cancer. Medical diagnosis, particularly in complex diseases such as lung cancer, frequently involves ambiguity. The diagnostic system can alleviate ambiguity via cross-verifying findings from various sources by fusing multimodal features. Multimodal feature fusion using deep learning (DL) algorithm is an advanced technology that leverages the abilities of deep neural networks to combine data from three different modalities or sources for better robustness in several applications, namely natural language processing, image, and data analysis, etc. This study introduces a Multimodal Feature Fusion using an Optimal Transfer Learning Method for Lung Cancer Detection and Classification (MFFOTL-LCDC) methodology on CT images. The chief objective of the MFFOTL-LCDC methodology is to exploit the feature fusion process for the identification and classification of lung tumor. To attain this, the MFFOTL-LCDC model undergoes a multimodal feature fusion approach to derive feature vectors using 3 DL approaches such as SqueezeNet, CapsNet, and Inception v3 models. Besides, the MFFOTL-LCDC technique applies the remora optimization algorithm (ROA) for the hyperparameter choice of 3 DL models. For lung cancer recognition, the MFFOTL-LCDC algorithm exploits the deep extreme learning machine (DELM) algorithm. A series of simulations were conducted to ensure the greater lung cancer recognition outcomes of the MFFOTL-LCDC methodology. The extensive outcomes determine the improved results of the MFFOTL-LCDC technique over recent DL approaches. [ABSTRACT FROM AUTHOR]

Bee pollen, a bee product, is considered as one of the valuable products in natural medicine, for its excellent therapeutic properties. Cold maceration technique was employed to extract bee pollen and the phytochemical constituents were analysed. The preliminary phytochemical analysis showed the presence of acids, proteins, carbohydrates, phenol, flavonoids, glycosides, anthraquinone, quinone, resins, saponin, tannin, and terpenoids. The advanced phytochemical screening of volatile chemical constituents analyzed using Gas Chromatography and Mass Spectrometry showed 40 volatile chemical compounds from the bee pollen extract. Among those, the hypocholesterolemic effect-related compound of bee pollen was analyzed by in silico docking method, where Oxidosqualene: Ianosterolcyclase (OSC) was taken as a target enzyme which helps in cholesterol biosynthesis. Hexadecanoic acid, Pentadecanoic acid, Pyrroloindole, and Alpha amyrin selected as ligands, and Ro 48-8071 fumarate used as a standard enzyme inhibitor for molecular docking analysis, showed that all the ligands efficiently bound with the target enzyme OSC. Among the four compounds, alpha amyrin had the highest binding interaction energy with the target enzyme (-13.37 kJ mole-1). The OSC enzyme inhibition activity may be responsible for the hypocholesterolemic effect and weight-reducing property of bee pollen. [ABSTRACT FROM AUTHOR]

Extreme value theory is a method for modelling and evaluating risks in unusual or rare situations, and it has gained popularity in risk management. In general, the probability of extreme occurrences can be assessed by fitting a probability distribution to a sample of extreme observations. It is crucial to examine the tail shape of the distribution since it affects the estimation of parameters associated with extreme events. The extreme value index is a shape parameter that quantifies the tail behaviour of a distribution. Selecting an appropriate threshold is a major challenge in extreme value analysis, especially in peaks over threshold methods. The choice of threshold significantly impacts the bias-variance trade-off in extreme value models. The choice of the threshold based on the stability plot is often subjective. To address this challenge, we have developed an efficient dual-phase threshold selection method. This approach involves trimming non-exceedances through a two-phase procedure and using the Cramér-von Mises test to assess exceedances above a suitable threshold, thereby determining the optimal threshold. Exceedances above a certain threshold typically follow the generalized Pareto distribution asymptotically. Estimates of the return levels associated with their return periods can be used to predict the statistical properties (magnitude and frequency) of upcoming exceedances. A simulation study is conducted to evaluate the effectiveness of the proposed threshold selection method. [ABSTRACT FROM AUTHOR]

In this paper, we consider a record-based transmuted version of Power Lomax distribution and it is named as Record-based Transmuted Power Lomax (RTPL) distribution. Further, we present several statistical properties of the proposed distribution such as moments, quantiles, stochastic ordering, order statistics, and its explicit expressions. Some of its reliability measures such as survival function, hazard function, cumulative hazard function, mean residual time, and mean inactivity time is also discussed. The maximum likelihood method is used to estimate the parameters of the RTPL distribution and this new extended model is applied to a real datasets to access the suitability and applicability of the model based on well-known information criteria and test for goodness of fit. The simulation study is performed to verify the efficiency and asymptotic behavior of the maximum likelihood estimators. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increased use of large displacement vessels for cargo transport around the world. Due to space constraints in the matured ports, the clearance available between the approach channels and berths could not be increased or modified. Hence, the vessels passing in close vicinity to the berthed vessels has become a routine. It is essential to study the passing vessel and berthed vessel interaction for ensuring safety so that the mooring systems could be designed for the increased forces. In this paper, a study envisaging such interaction using Computational Fluid Dynamics (CFD) simulations has been attempted to determine the passing vessel effect on a moored tanker. The CFD solution used is based on a three-dimensional, unsteady, inviscid flow. Initially, the CFD simulation has been carried out for existing experimental data from published paper for validation and has been extended to a range of parameters such as passing vessel displacement, velocity, separation distance and water depth. The effect of each parameter on passing vessel forces is explained in detail. It is observed from the results that the longitudinal and transverse forces on the moored vessel become negligible for separation distance more than three times the width of the vessel. Also, the study provides a proper insight into the effect of full hull forms of various displacements on the passing vessel forces. [ABSTRACT FROM AUTHOR]

The paper describes a unique approach for a label-free biosensor designed for early-stage detection of malignant and invasive melanoma. Refractive Index variation analysis plays a vital role in the photonic crystal-based sensor design. The photonic crystal-based micro interferometer biosensor has been designed in rods in air configuration. The design comprises a hexagonal ring structure between two bus waveguides forming sensing and reference arms. The early-stage diagnosis of melanoma is dependent on the comparison of normal cells against affected cells. The diagnosis is conducted using the arms of the interferometer. The sensitivity of the biosensor is determined by the phase shift/wavelength difference between the arms of the interferometer sensor. The sensitivity of the designed sensor is 4000nm/RIU. The simulations are done using the FDTD technique. The relative shifts in frequency and wavelength are due to the refractive index deviation in the sensing arm. Quality factor is obtained at 10654.3 for the wavelength 1550 nm. [ABSTRACT FROM AUTHOR]