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BiVO4 and Ni doped BiVO4 (Ni-BiVO4) photocatalysts were synthesised by the co-precipitation method. BiVO4 and Ni-BiVO4 photocatalysts were characterized by various analytical techniques. PXRD studies of the BiVO4 and Ni-BiVO4 catalysts shows monoclinic-scheelite structure. Density functional theory calculations were carried out to predict the density of states and band gaps. The flower bunch is arrayed in a rod-like pattern on the HRTEM images, and the particle size was estimated using d-spacing and found to be in the range of 98-415 nm. Furthermore, the charge separation mechanism of photogenerated charge carriers of BiVO4 and Ni-BiVO4 photocatalysts were studied by photoelectrochemical response and impendence spectroscopy. The result shows that the Ni-BiVO4 photocatalyst possess the higher photocatalytic performance, and this can be attributed to the enhanced absorption of visible light, and lower recombination of electron and holes. Further, enhanced photocatalytic activity of Ni-BiVO4 photocatalyst was observed by addition of hydrogen peroxide (H2O2). The order of photocatalytic activity for the degradation of Naproxen under visible light: Ni-BiVO4 +H2O2 > Ni-BiVO4 > BiVO4 > Ni-BiVO4 + IPA. The investigation of degradation products was carried out utilising liquid chromatography mass spectroscopy coupled UV-visible absorption spectrum technique. Radical quenching is studied to reveal the substantial contribution of hydroxyl free radicals in the Ni-BiVO4 with H2O2 system.

The incidence of Acute Pancreatitis (AP) was 30.0 per 100,000 population overall. The two most common aetiological factors of AP are gallstones and alcohol abuse, while other causes such as metabolic, iatrogenic, vascular, infections, and toxic factors are less important and less common. Two laboratory tests, serum amylase and lipase levels, which have relatively good sensitivity and specificity, complement imaging in the diagnosis and treatment of some cases of AP. Imaging helps confirm the clinical diagnosis, determine the cause, grade the extent of AP, and assess its severity, in addition to clinical symptoms and laboratory studies. Chronic Pancreatitis (CP) is characterised by continuous inflammatory and fibrotic changes in the pancreas, eventually resulting in exocrine and endocrine dysfunction. Alcoholism is a factor in 70-90% of CP cases. Other causes include chronic ulcerative colitis, Sjogren’s syndrome, primary sclerosing cholangitis, etc. Early diagnosis of CP is challenging, as biochemical studies do not provide definitive diagnosis in the early stages. Imaging, mainly Contrast Enhanced Computed Tomography (CECT) and Magnetic Resonance Imaging (MRI), aids in the definitive diagnosis. The present pictorial article aimed to provide an image-rich overview of the morphological features associated with the early-stage and late-stage local consequences of acute and chronic pancreatitis.

Introduction: Chronic viral hepatitis is a condition of hepatotropic viral infection associated with chronic inflammation, hepatocyte injury and progressive fibrosis. Non-invasive assessment of liver fibrosis in chronic hepatitis B and C patients has been increasing and used instead of liver biopsy. Aim: To assess the diagnostic performance of ElastPQ Point Shear Wave Elastography (ElastPQ PSWE) with liver biopsy as the gold standard and comparing these results with other serum fibrosis markers namely Aspartate to Platelet Ratio Index (APRI), Fibrosis-4 Score (FIB4). Materials and Methods: A cross-sectional study was carried out in which 73 patients underwent ElastPQ PSWE and Histopathological Examination (HPE) with METAVIR Scoring system staging. Serum fibrosis indices including APRI and FIB4 values were calculated. The diagnostic performance of ElastPQ PSWE, APRI and FIB4 was evaluated using Area Under Receiver Operating Characteristic (AUROCAUROC) curve analysis, correlations of ElastPQ PSWE, APRI and FIB4 ratio index with histopathological findings (as the reference standard) were determined using Spearman’s correlation coefficient. Results: Study included 73 patients with chronic hepatitis B and hepatitis C, of which 8 (10.96%) were no/early fibrosis (F0- F1); 20 (27.40%) were significant fibrosis (F2); 22 (30.13%) were severe fibrosis (F3), and 23 (31.51%) were cirrhosis (F4). There was a significant positive correlation between different stages of liver fibrosis by liver biopsy and liver stiffness detected by ElastPQ PSWE (r=0.912, p

Objective. To assess nurses’ knowledge, attitude and practice towards using physical restraints among psychiatric patients. Methods. A descriptive cross sectional survey was carried out among conveniently selected sample of nurses working in psychiatry departments at a tertiary care center. The data was collected using self reported questionnaires of Suen. Results. The findings revealed that nurses had good knowledge (7.2±1.7, maximum posible=11), favorable attitudes 30.8± 3.3 (maximum posible=48) and good practice 31.2±6.2 (maximun posible=42) about use of physical restraints in psychiatric patients. Females had better knowledge (p

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrixs elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.

ABSTRACTNanotubes form clusters and are found in curved bundles in nanotube films and nanocomposites. Separation phenomenon is suspected to occur in these curved bundles. In this study, the deformation of a single-wall carbon nanotube (SWCNT) interacting with curved bundle nanotubes is analyzed. It is assumed that the bundle is rigid and only van der Waals force acts between the nanotube and the bundle of nanotubes. A new method of modeling geometric nonlinear behavior of the nanotube due to finite rotation and the corresponding van der Waals force is developed using co-rotational finite element method (CFEM) formulation, combined with small deformation beam theory, with the inclusion of axial force. Current developed CFEM method overcomes the limitation of linear Finite Element Method (FEM) formulation regarding large rotations and deformations of carbon nanotubes. This study provides a numerical tool to identify the critical curvature influence on the interaction of carbon nanotubes due to van der Waals forces and can provide more insight into studying irregularities in the electronic transport properties of adsorbed nanotubes in nanocomposites.

The study aimed to investigate the therapeutic efficacy of S. dulcamara extract (SDE) in managing type II diabetes‐related parameters, including blood glucose levels, α‐amylase activity, and modulation of endogenous antioxidant enzymes (SOD, GPx, CAT). Diabetes was induced in rats using streptozocin, and the anti‐diabetics effect was evaluated by the administration of S. dulcamara extract at different doses. Blood glucose levels, α‐amylase activity, and endogenous antioxidants were estimated and found to be significant (P<0.05) in the rats treated with the extract of S. dulcamara at a dose of 200 mg/kg. The extract showed dose‐dependent inhibition of α‐amylase suggesting the potential benefits in glycemic control. Moreover, the study demonstrated a modulation in the activity of endogenous antioxidant enzymes (SOD, GPx, CAT). Importantly, the fruit extract showed no significant hemolysis or acute toxicity at varying concentrations (12.5 and 25 mg/mL) indicating the safe and biocompatible nature, as confirmed by normal biochemical parameters and histopathological examination. In conclusion, the methanolic fruit extract of S. dulcamara shows significant anti‐diabetic activity by alleviating oxidative stress associated with type II diabetes and its complications. [ABSTRACT FROM AUTHOR]

Non-contact and full-field strain mapping captures strain across an entire surface, providing a complete two-dimensional (2D) strain distribution without attachment to sensors. It is an essential technique with wide-ranging applications across various industries, significantly contributing to experimental mechanics and structural health monitoring. Although there have been reviews that focus on specific methods, such as interferometric techniques or carbon nanotube-based strain sensors, a comprehensive comparison that evaluates these diverse methods together is lacking. This paper addresses this gap by focusing on strain mapping techniques specifically used in experimental mechanics and structural health monitoring. The fundamental principles of each method are illustrated with specific applications. Their performance characteristics are compared and analyzed to highlight strengths and limitations. The review concludes by discussing future challenges in strain mapping, providing insights into potential advancements and developments in this critical field. [ABSTRACT FROM AUTHOR]

The cancer diagnosis is currently experiencing an alteration of molecular biomarkers using a paradigm for the diagnostic panel. One of the most important genomic datasets showing genetic sequences is the classification of lung cancer. Many research studies have been conducted in this area, but none of them yields satisfied outcomes on account of lower classification accuracy. At first, the data are collected through the lung cancer dataset. Afterward, data are fed into pre-processing. The pre-processing segment removes the noise and enhances the input images utilising Dynamic Context-Sensitive Filtering. The pre-processing output is fed to the feature extraction segment. Here, four statistical features are extracted based on the Force Invariant Improved Feature Extraction Model. The extracting features are fed to the feature segmentation segment. Lung cancer images can segment the ROI region using Adaptive Density-Based Spatial Clustering. After that, the segmentation features are given to ACWGAN for effectively categorizing cancer and non-cancer of the lung cancer Hence, the Hunter–prey optimisation algorithm is employed to optimise the ACWGAN classifier. It classifies the lung cancer images accurately. The LCC-ACWGAN-HPOA-CTI algorithm is activated in MATLAB under the lung cancer database. The performance of the LCC-ACWGAN-HPOA-CTI approach shows higher accuracy and lower computation time than those of the existing methods. [ABSTRACT FROM AUTHOR]