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Surface plasmon polariton (SPP)-based plasmonic sensors are increasingly replacing traditional bulky sensors in refractive index sensing, biosensing, food adulteration detection, and other applications due to their unique optical features and different Metal-Insulator-Metal (MIM) topologies. The present research introduces a new design for a highly sensitive MIM waveguide-based refractive index sensor. This design incorporates an innovative combination of an inverted U-shaped rectangular split-ring resonator (RSRR), a semi-circle-split-ring resonator (SCSRR), and a stub. The sensor's wave-guiding capabilities, such as electromagnetic wave transmission, field profile distribution, and sensitivity, are meticulously analyzed using a two-dimensional finite-element method. The key objective of this device is to identify slight differences in the refractive index by observing shifts in resonant wavelength through the light's interaction with the plasmonic structure. Moreover, optimizing the geometric parameters significantly enhances the sensor's performance. The optimized sensor achieves a maximum refractive index sensitivity of 2400 nm/RIU, a figure of merit (FOM) of 45.28, and a Q-factor of 39.86, as demonstrated by simulation results. Further investigations reveal the MIM structure's capacity to detect adulteration in edible oils, underscoring the sensor's adaptability and its potential for widespread biosensing applications. [ABSTRACT FROM AUTHOR]

Millions of people in Bangladesh and the world have a metabolic disease named diabetes. It is also responsible for occurring different kinds of diseases such as heart attack, kidney disease, blindness and renal failure. Diabetes is a deadly, disabling disease whose risk is increasing at an alarming rate day by day perspective to Bangladesh. The detection process of diabetes is a tedious and multilayered task from some important risk factors. Like other diseases, Type2 diabetes also depends on some factors that are known as risk factors of Type2 diabetes. Risk factors are divided into four categories like Scio-economic condition, Habits, Family History and Hard Diseases etc. in proposed system. Initially 731 diabetes and non-diabetes patient’s data have been collected from different diagnostic centers, pre-processed and clustered for identifying relevant and non-relevant data. Significant factors are discovered according to four categories. Next correlation is assessment among significant factors. Finally highly significant factors are discovered whose are directly or indirectly associated with type2 diabetes. Results indicate that Age, Area of Residence, Education Level, Social Status, Family Income, Expense, Tobacco, BMI, Family History, Physical Exercise and Hard Diseases have worst impact on Quality of Life (QoL) among all factors of type2 diabetes respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the behaviour of hollow and concrete filled steel (CFST) mild and stainless steel columns subjected to transverse static and impact loading with a pre-compressive axial load. Transverse load was applied at the mid or quarter point of the columns. A total of three test series were carried out recently at the University of Western Sydney and the University of Wollongong to obtain the performance of mild and stainless steel hollow and CFST columns subjected to lateral static as well as impact loading with or without pre-compressive load. The test results reported in this paper are from the third test series, where both axial and lateral loads were applied to the columns. This paper also investigates the finite element (FE) modelling of hollow and CFST mild and stainless steel columns due to static and impact loads. Three-dimensional nonlinear FE models were developed using ABAQUS, where nonlinear material behaviour, enhanced strength corner properties of steel, pre-compressive loads were all included to simulate the static and impact experiments. The main objective of this paper is to compare the performance of experimental results with numerical results for mild and stainless steel hollow and CFST columns. Moreover, the behaviour of in-filled tubes under impact loading is also compared with that of hollow sections. Close agreement is achieved between the experimental and finite element results in terms of load–deflection response and ultimate strength. This paper also compares the results of hollow and CFST stainless steel columns with those of mild steel columns due to both static and impact loading. Generally, the stainless steel specimens showed higher strength and improved energy-dissipating characteristics compared with the mild steel columns. [Copyright &y& Elsevier]

Abstract: Concrete-filled stainless steel tubes can be considered as a new type of composite construction technique. The characteristics of stainless steel are quite different from those of mild steel in terms of strength, ductility, corrosion resistance and maintenance costs. This paper presents the behaviour of hollow and concrete-filled stainless steel tubular columns under static and impact loading. An experimental test series has been carried out at the University of Wollongong and the University of Western Sydney to investigate the performance of stainless steel hollow and concrete-filled steel tubular (CFST) columns under static and impact loads. This paper presents the results of the first test series, where stainless steel was used and no axial load was applied. The effects of a combined axial and transverse impact loads as well as the location of the impact loading have been considered in a subsequent series. Finite element modelling was carried out to predict the behaviour of composite columns under a lateral static or impact load using ABAQUS to simulate the static and impact experiments. The comparison of the experimental results with numerical results is the main objective of this paper. Moreover, the behaviour of hollow tubes under impact loading is compared with that of the in-filled sections. This paper also compared results of hollow and CFST stainless steel columns with those of mild steel columns under both static and impact loading. Generally, the stainless steel specimens showed improved energy-dissipating characteristics compared with their mild steel counterparts, especially when concrete was used to fill the hollow tubes. [Copyright &y& Elsevier]

Abstract: Large, uniform and stable polyaniline (PANI) films were fabricated by ordering and drying polyaniline-coated-polystyrene (PANI-PS) latex microspheres. Particles on a glass substrate were highly ordered, closely packed and formed a two-dimensional lattice structure with the thickness of the diameter of the particle, 3.2μm. Films fabricated on the substrate were peeled off by the pressure of water to yield the floating film, which could be transferred onto other substrates. One end of the film was connected to the platinum plate electrode while the other end was put in sulfuric acid. The film to which voltage was applied showed color change corresponding to the redox reaction. The voltammogram of the film showed the cathodic and the anodic peaks at 0.20 and 0.35V versus SCE, respectively, being similar to peaks of electropolymerized PANI films. Films were inserted into tetrahydrofuran to remove the core-polystyrene. The resulting film had similar properties as polyaniline films of PANI-PS latex microspheres. [Copyright &y& Elsevier]

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain examination and advances in AI-based detection methods, building an accurate and efficient model for detecting and categorizing tumors from MRI images remains a challenge. To address this problem, we proposed a deep Convolutional Neural Network (CNN)-based architecture for automatic brain image classification into four classes and a U-Net-based segmentation model. Using six benchmarked datasets, we tested the classification model and trained the segmentation model, enabling side-by-side comparison of the impact of segmentation on tumor classification in brain MRI images. We also evaluated two classification methods based on accuracy, recall, precision, and AUC. Our developed novel deep learning-based model for brain tumor classification and segmentation outperforms existing pre-trained models across all six datasets. The results demonstrate that our classification model achieved the highest accuracy of 98.7% in a merged dataset and 98.8% with the segmentation approach, with the highest classification accuracy reaching 97.7% among the four individual datasets. Thus, this novel framework could be applicable in clinics for the automatic identification and segmentation of brain tumors utilizing MRI scan input images. • Brain tumor type depends on complex intercellular structures. • Classification of full brain MRI requires more time and resources. • Classification of segmented tumor images requires additional computational complexity. • Brain tumor classification into four classes (Glioma, meningioma, pituitary, no tumor). • CNN-based classification model and U-Net-based segmentation model implementation. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is very useful for the clinical applications, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Although handcrafted feature extraction techniques were used in the past, Artificial Neural Network (ANN) is now more popular. In this work, a model has been proposed called Gated Recurrent Unit-Inception (GRU-INC) model has been proposed, which is an Inception-Attention based approach using Gated Recurrent Unit (GRU) that effectively makes use of the temporal and spatial information of the time-series data. The proposed model achieved an F1-score of 96.27%, 90.05%, 90.30%, 99.12%, and 95.99% on the publicly available datasets such as, UCI-HAR, OPPORTUNITY, PAMAP2, WISDM, and Daphnet, respectively. GRU along with Attention Mechanism (AM) was utilized for the temporal part, and Inception module along with Convolutional Block Attention Module (CBAM) was exploited for the spatial part of the model. The proposed architecture was evaluated against state-of-the-art models and similar works. It has been proved that the GRU-INC model has a higher recognition rate as well as lower computational cost. Thus our framework could be applicable in activity associated clinical and rehabilitation applications. • A GRU-Inception based deep learning model to identify human activities. • Attention mechanism is incorporated with GRU to improve temporal feature extraction. • Inception modules along with a CBAM block further highlights the spatial features. • The model is relatively wider rather than a deep structure thus reducing complexity. [ABSTRACT FROM AUTHOR]

Electrical resistance behaviour of Fe–24 wt.%Mn SMA was studied up to a pressure of ∼6 GPa by using an opposed anvil high pressure device. The system shows a steep rise in resistance up to ∼1 GPa and thereafter a monotonic decrease up to ∼6 GPa during the forward cycle, whereas it shows a monotonic increase during the return cycle. XRD studies of the as-prepared and pressure quenched samples show a mixed α, γ and ϵ phase in the former and a predominantly ϵ phase in the latter, indicative of a possible structural transition at ∼1 GPa, as evidenced from the resistance maximum. The decrease in the transition pressure, when compared with alloys of lower Mn concentration, provides a clue that it should be possible to further reduce the transition pressure to the predominantly ϵ phase by alloying with suitable elements, which may have positive effect on the shape memory of the alloy. [Copyright &y& Elsevier]