Journal of Nature, Science & Technology (JANSET)

Mithun Ghosh Sahil Hassan Prama Debnath

The Musculoskeletal Radiographs (MURA) dataset, proposed by Stamford Machine Learning (ML) group, contains 40,561 images of bone X-rays from 14,863 studies. The X-ray images belong to seven body areas of the upper extremity- wrist, elbow, finger, humerus, forearm, hand, and shoulder. Radiologists have classified the data into two classes, namely normal and abnormal. Six board-certified Stanford radiologists labeled the data samples using majority votes, which is considered the gold standard. The 169 layers deep model, introduced by the Stamford ML group, works well on a par with the gold standard except for the humerus radiographs, despite humerus data labeled with high accuracy. We propose to develop a comparatively shallower version of a neural network and a convolutional network with 10 hidden layers each in an Adaboost framework in the humerus data and the model performance is on par or sometimes superior to the Stamford ML group model. We evaluate the performance of the model using the validation error and Cohen’s kappa coefficients. We have shown that our modeling framework is much faster in terms of the training time of the model and as accurate compared to the deep neural network introduced by the Stamford ML group. Also, with increased resources, our model will perform much better.

https://doi.org/10.36937/janset.2021.004.001

Abdusalam Umarov Xaqberdi Xamzaev

The production of metal-containing nanoparticles is one of the important problems of modern science related to the creation of nanomaterials. Nanocomposites based on polymer matrices and uniformly distributed nanoparticles (quantum dots) isolated from each other in them have unique photoluminescent properties; in addition, polymer matrices are convenient stabilizers of nanoparticle growth and have good mechanical properties. Of the nanoparticles of semiconducting materials, metal chalcogenides (CdS, Cu) are of the greatest interest. In the chemical synthesis of metal sulfides in a polymer medium, H2S or a compound containing active sulfur can act as a sulfiding agent. Samples of nanocomposites based on semiconductor sulfides and polyethylene have been synthesized. The composition, structure and structure of nanoparticles were studied by X-ray phase analysis and spectroscopy. Investigated from thermophysical properties. From studies of the temperature dependence of the heat capacity of the compositions, LDPE and CdS compositions, It can be seen that there is a peak on the curve in the temperature range of 100-2250K, which almost degenerates with an increase in the concentration of the filler. Measurements of the temperature dependence of thermal conductivity and heat capacity revealed the presence of reversible structural rearrangements in polymer composites with metal oxide fillers. Moreover, various methods, within the limits of errors, fix a constant transition temperature of electrical conductivity, thermal conductivity and heat capacity, which speaks in favor of the fact that the basis of all detected anomalies is a single mechanism, i.e. structural rearrangement of defect states of polymer composites.

https://doi.org/10.36937/janset.2021.004.002

Supratic Chakraborty Rezwana Sultana Karimul Islam

A temperature dependent x-ray photoelectron spectroscopy (XPS) study reveals that ZrO2 reduces to metallic Zr and O at ∼ 140◦C at Zr ratios of 8.38, 9.11and 11.79 at. % in co-sputtered ZrxHf1−xO2 thin-films deposited on silicon substrate. ZrO2 reduced in the form of Zr metal. The oxygen evolved in this process reacts with carbon present in the film forming CO2 gas. Further, the presence of Zr in temperature treated samples at RT indicates the reduction as an irreversible one. Despite similar atomic radii of Zr and Hf, the stability of metastable t-ZrO2 decreases probably due to influence of Hf present in ZrxHf1−xO2. This stabilization was ascribed to a decrease in the Zr coordination number upon introduction of oxygen vacancies. Less dense d band causes the formation of metallic Zr at such a low temperature. A reduction scheme for ZrO2 at low temperature is also proposed. Since generation of metallic Zr sites at lower temperature is a challenge to develop Zr-based catalysis, this combination may be used to design ZrHfO-based catalysts for low temperature catalytic applications.

https://doi.org/10.36937/janset.2021.004.003

Prerna Saurabh Amar Kumar Verma Saurabh Rai Sourabh Singla

Gene-Gene Interactions (GGI) Networks-Genomics essentially finds the driver node to understand the functional mechanism of Gene-Gene Interaction or GGI. This process can significantly improvise while examining molecular processes perturbed by genetics in human diseases. With Artificial Intelligence (AI) developments, pattern recognition and machine learning advancements can be exploited to automate from more superficial to handle any task in the medical field. In the past, deep learning-based methods have provided encouraging results in the medical field, such as breast cancer detection, skin cancer classification, brain disease classification, arrhythmia detection, pneumonia detection from X-Ray images, and lung segmentation. Interestingly, employing machine learning in the medical field has caught my great attention and motivated us to utilize machine learning-based algorithms to detect drive nodes. Several recent types of research have focused on identifying the bare minimum of driver nodes required to control underlying gene-gene interaction networks. This study is about analyzing gene interaction networks statistically. One or more abnormalities cause a genetic condition in the DNA. Disease-related genes play essential biological roles in the cell. Multiple genes often work together to cause complex genetic illnesses. So, the central concept is to create a system that can assess networks in various elements that influence them.

https://doi.org/10.36937/janset.2021.004.004

Anzelim E. Sunguti Joshua Kibet Thomas K. Kinyanjui

Studies on organic pollutants in geothermal environments have received little attention; hence this review is necessary. The presence of trace organic pollutants such as benzene and xylenes has been reported as some of the main sources of pollution in geothermal systems. Previous studies using quantitative fluid inclusion gas analysis, Fischer–Tropsch Type (FTT) experiments and Gas chromatography-Mass spectrometry (GC-MS) have shown that there is a considerable presence of organic pollutants such as trace mono aromatic hydrocarbons (MAHs), polycyclic aromatic hydrocarbons (PAHs), and emerging organic contaminants whose origin is both biogenic and abiogenic. Organic pollutants were initially not considered in geothermal development and utilization despite the fact that these toxic chemicals can precipitate severe ecological poisoning and potential risks to human health and aquatic life in a given geothermal environment, even at very low concentrations. The significant presence of benzene in various geothermal systems is of concern because it is a precursor for many aromatic compounds that are bio-accumulative and toxic to water regimes and the environment. Thermophilic and mesophilic bacteria, nonetheless, play a critical role during the biodegradation of organic pollutants in geothermal regimes. From the findings of this review, it is difficult to classify geothermal energy exploitation and utilization as an environmentally benign resource.

https://doi.org/10.36937/janset.2021.004.005