Temperature Dependent Raman Studies and Thermal Conductivity of Few Layer MoS2

We report on the temperature dependence of in-plane E2g and out of plane A1g Raman modes in high quality few layers MoS2 (FLMS) prepared using a high temperature vapor-phase method. The materials obtained were investigated using transmission electron microscopy. The frequencies of these two phonon modes were found to vary linearly with temperature. The first order temperature coefficients for E2g and A1g modes were found to be 1.32*10-2 and 1.23*10-2 cm-1/K, respectively. The thermal conductivity of the suspended FLMS at room temperature was estimated to be about 52 W/mK

Surface optical Raman modes in InN nanostructures

Raman spectroscopic investigations are carried out on one-dimensional nanostructures of InN,such as nanowires and nanobelts synthesized by chemical vapor deposition. In addition to the optical phonons allowed by symmetry; A1, E1 and E2(high) modes, two additional Raman peaks are observed around 528 cm-1 and 560 cm-1 for these nanostructures. Calculations for the frequencies of surface optical (SO) phonon modes in InN nanostructures yield values close to those of the new Raman modes. A possible reason for large intensities for SO modes in these nanostructures is also discussed.Comment: 13 pages, 4 figures, Submitted in Journa

Excitation energy dependence of electron-phonon interaction in ZnO nanoparticles

Raman spectroscopic investigations are carried out on ZnO nanoparticles for various photon energies. Intensities of E1-LO and E2 modes exhibit large changes as the excitation energy varied from 2.41 to 3.815 eV, signifying substantially large contribution of Frohlich interaction to the Raman polarizability as compared to deformation potential close to the resonance. Relative strength of these two mechanisms is estimated for the first time in nanoparticles and compared with those in the bulk.Comment: 13 pages. 3 figures Journa

Synthetic seed production for germplasm storage of Hydrastis canadensis L. (Goldenseal)

Goldenseal (Hydrastis canadensis L.) is an important medicinal plant, native to Canada and Eastern United States, used as an antibiotic, anticonvulsant and in treatments for inflammations and dyspepsia. Over collection of wild population and native habitat destruction has lead the species to be listed as an endangered species in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), Appendix II. This brought a revived interest in conservation of germplasm.;Calcium alginate solid and hollow bead encapsulation protocols were evaluated. Shoot and callus propagules were encapsulated in calcium alginate solid bead (supplemented with stage II nutrient media) and hollow bead (no nutrient supplements) and were stored at 25°C and 5°C for either 4, 8 or 12 weeks. Best regeneration and viability were observed in 2.5% alginate concentrated solid shoot propagules that were stored at 5°C. By the end of 6 weeks of sub culturing 100% regeneration was achieved. Reduced (10%) regeneration was observed in callus propagules stored at 5°C, and all the callus propagules stored at 25°C were necrotic

A Secure IoT-Enabled Machine Learning Framework for Brain Tumor Classification and Prediction Using MR Image Data

Brain tumor identification and classification have improved due to the quick development of medical imaging and machine learning technology. This paper presents two approaches to secure image transmission in the Internet of Things (IoT): a comprehensive approach for brain tumor prediction and classification using a strong IoT infrastructure with cutting-edge machine learning models and a security approach with the implementation of the AES-ECC hybrid model in the MQTT communication protocol for image data encryption and decryption. We make use of a heterogeneous dataset that we sourced from the Kaggle Dataset platform, which includes four different types of MRI scans of brain tumors from 2870 patients. Our proposed methodology starts with the safe acquisition and transfer of MRI images through an IoT protocol infrastructure to a cloud-based platform. CNN, DenseNet, ResNet and G-Net are some of the sophisticated machine learning models that are used to interpret and analyse these pictures. The computer is trained to identify photos of brain tumors into the appropriate groups using all above four models. According to the data, our suggested CNN model performs better than the others, obtaining an amazing 89% accuracy rate. Nonetheless, we want to achieve even greater improvement in forecast precision by utilising ensemble boosting methodologies. Boosting the CNN model with Ada-Boost, Gradient Boost, XG Boost and Cat Boost algorithms aims to maximize prediction performance. We find that the CNN algorithm combined with XG Boost outperforms all other ensemble methods with an amazing accuracy rate of 97%. This encouraging result highlights how combining cutting-edge machine learning algorithms with IoT infrastructure can lead to better brain tumor classification and prognosis. The creation of more precise and effective diagnostic instruments for the identification of brain tumors is one of our study's many implications, one that will ultimately improve patient outcomes and the healthcare industry

An overview of anti-diabetic plants used in Gabon: Pharmacology and Toxicology

© 2017 Elsevier B.V. All rights reserved.Ethnopharmacological relevance: The management of diabetes mellitus management in African communities, especially in Gabon, is not well established as more than 60% of population rely on traditional treatments as primary healthcare. The aim of this review was to collect and present the scientific evidence for the use of medicinal plants that are in currect by Gabonese traditional healers to manage diabetes or hyperglycaemia based here on the pharmacological and toxicological profiles of plants with anti-diabetic activity. There are presented in order to promote their therapeutic value, ensure a safer use by population and provide some bases for further study on high potential plants reviewed. Materials and methods: Ethnobotanical studies were sourced using databases such as Online Wiley library, Pubmed, Google Scholar, PROTA, books and unpublished data including Ph.D. and Master thesis, African and Asian journals. Keywords including ‘Diabetes’ ‘Gabon’ ‘Toxicity’ ‘Constituents’ ‘hyperglycaemia’ were used. Results: A total of 69 plants currently used in Gabon with potential anti-diabetic activity have been identified in the literature, all of which have been used in in vivo or in vitro studies. Most of the plants have been studied in human or animal models for their ability to reduce blood glucose, stimulate insulin secretion or inhibit carbohydrates enzymes. Active substances have been identified in 12 out of 69 plants outlined in this review, these include Allium cepa and Tabernanthe iboga. Only eight plants have their active substances tested for anti-diabetic activity and are suitables for further investigation. Toxicological data is scarce and is dose-related to the functional parameters of major organs such as kidney and liver. Conclusion: An in-depth understanding on the pharmacology and toxicology of Gabonese anti-diabetic plants is lacking yet there is a great scope for new treatments. With further research, the use of Gabonese anti-diabetic plants is important to ensure the safety of the diabetic patients in Gabon.Peer reviewedFinal Accepted Versio

Analytical Simulation and Experimental Investigation of Various Characteristics of Hole Quality During Micro Drilling of PCB

Achievement of good surface quality remains a concern during micro drilling of printed circuit board (PCB). Although a great deal of work is reported on micro drilling of PCB, information on effect of drilling parameters like feed on different characteristics of hole quality is relatively scarce. Although stresses during micro drilling of PCBs are critical issues, their correlation with hole quality is yet to be reported. The current work utilizes finite element analysis based simulation of deformation and stresses to explain the various parameters of hole quality such as diameter, delamination factor and burr thickness. Effect of feed on these parameters has also been established. Results indicated that stresses play a vital role in influencing hole qualities of PCB. Increase in feed rate resulted in reduction in hole diameter, whereas delamination factor and mean burr thickness increased with feed. The study is, therefore, expected to be of help in proper selection of feed in order to achieve acceptable hole quality after micro drilling of PCB

Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction

In this research, we used a dataset from Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory containing 6,780 samples collected manually and through internet of things (IoT) sensor sources from 6,780 patients to perform a thorough investigation into liver disease stage prediction. The dataset was carefully cleaned before being sent to the machine learning pipeline. We utilised a range of machine learning models, such as Naïve Bayes (NB), sequential minimal optimisation (SMO), K-STAR, random forest (RF), and multi-class classification (MCC), using Python to predict the stages of liver disease. The results of our simulations demonstrated how well the SMO model performed in comparison to other models. We then expanded our analysis using different machine learning boosting models with SMO as the base model: adaptive boosting (AdaBoost), gradient boost, extreme gradient boosting (XGBoost), CatBoost, and light gradient boosting model (LightGBM). Surprisingly, gradient boost proved to be the most successful, producing an astounding 96% accuracy. A closer look at the data showed that when AdaBoost was combined with the SMO base model, the accuracy results were 94.10%, XGBoost 90%, CatBoost 92%, and LightGBM 94%. These results highlight the effectiveness of proposed model i.e. gradient boosting in improving the prediction of liver disease stage and provide insightful information for improving clinical decision support systems in the field of medical diagnostics

Intraoral Palatal Lipoma: A Rare Case Report

Lipomas are the commonest occurring benign neoplasms of the human body originating from adipose cells. Intraoral lipomas are rare, contributing to less than 5% of all head and neck neoplasms. Even so, lipoma should be considered as a differential diagnosis in swellings of the oral cavity. Surgical excision is the mainstay of treatment in the case of intraoral lipoma. In this case report, the lipoma was seen originating from the soft palate. It was surgically excised in toto and was subjected to histopathological investigations to reach a definitive diagnosis. This case is being reported as it is uncommon for a lipoma to have an intraoral origin and even more rare for it to be seen occurring in the palate