Automated Lunar Crater Identification with Chandrayaan-2 TMC-2 Images using Deep Convolutional Neural Networks.

Terrestrial planets and their moons have impact craters, contributing significantly to the complex geomorphology of planetary bodies in our Solar System. Traditional crater identification methods struggle with accuracy because of the diverse forms, locations, and sizes of the craters. Our main aim is to locate lunar craters using images from Terrain Mapping Camera-2 (TMC-2) onboard the Chandrayaan-II satellite. The crater-based U-Net model, a convolutional neural network frequently used in image segmentation tasks, is a deep learning method presented in this study. The task of crater detection was accomplished with the proposed model in two steps: initially, it was trained using Resnet18 as the backbone and U-Net based on Image Net as weights. Secondly, TMC-2 images from Chandrayaan-2 were used to detect craters based on the trained model. The model proposed in this study comprises a neural network, feature extractor, and optimization technique for lunar crater detection. The model achieves 80.95% accuracy using unannotated data and precision and recall are much better with annotated data with an accuracy of 86.91% in object detection with TMC-2 ortho images. 2000 images have been considered for the present work as manual annotation is a time-consuming process and the inclusion of more images can enhance the performance score of the model proposed.

Particle surface area, ultrafine particle number concentration, and cardiovascular hospitalizations.

While the health impacts of larger particulate matter, such as PM10 and PM2.5, have been studied extensively, research regarding ultrafine particles (UFPs or PM0.1) and particle surface area concentration (PSC) is lacking. This case-crossover study assessed the associations between exposure to PSC and UFP number concentration (UFPnc) and hospital admissions for cardiovascular diseases (CVDs) in New York State (NYS), 2013-2018. We used a time-stratified case-crossover design to compare the PSC and UFPnc levels between hospitalization days and control days (similar days without admissions) for each CVD case. We utilized NYS hospital discharge data to identify all CVD cases who resided in NYS. UFP simulation data from GEOS-Chem-APM, a state-of-the-art chemical transport model, was used to define PSC and UFPnc. Using a multi-pollutant model and conditional logistic regression, we assessed excess risk (ER)% per inter-quartile change of PSC and UFPnc after controlling for meteorological factors, co-pollutants, and time-varying variables. We found immediate and lasting associations between PSC and overall CVDs (lag0-lag0-6: ERs% (95% CI%) ranges: 0.4 (0.1,0.7) - 0.9 (0.7-1.2), and delayed and prolonged ERs%: 0.1-0.3 (95% CIs: 0.1-0.5) between UFPnc and CVDs (lag0-3-lag0-6). Exposure to larger PSC was associated with immediate ER increases in stroke, hypertension, and ischemic heart diseases (1.1%, 0.7%, 0.8%, respectively, all p < 0.05). The adverse effects of PSC on CVDs were highest among children (5-17 years old), in the fall and winter, and during cold temperatures. In conclusion, we found an immediate, lasting effects of PSC on overall CVDs and a delayed, prolonged impact of UFPnc. PSC was a more sensitive indicator than UFPnc. The PSC effects were higher among certain CVD subtypes, in children, in certain seasons, and during cold days. Further studies are needed to validate our findings and evaluate the long-term effects.

Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images.

Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms-bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)-was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.

Hydrogenative Catalysis with Three-Coordinate Zinc Complexes Supported with PN Ligands is Enhanced Compared to PNP Analogs.

This work details the synthesis, characterization, and catalytic activity of reactive low-coordinate organozinc complexes. The complexes activate hydrogen and they appear to be more active in hydrogenation of ketones and imines than their tridentate pincer analogs. This is thought, in part, to be due to the lack of trailing third phosphorus arm present in previous work. DFT computations reveal a sigma-bond metathesis mechanism is comparable to an alternative aromatization/dearomatization metal-ligand cooperative mechanism.

Elucidating sources of VOCs in the Capital Region of New York State: Implications to secondary transformation and public health exposure.

Exposure to ambient volatile organic compounds (VOCs) in urban areas is of interest because of their potential adverse effects to public health. A study was carried out to elucidate ambient sources of VOCs in the Capital Region of New York State for the period 2015-2019. A combined dataset of VOCs and PM2.5 species was used in positive matrix factorization (PMF) model to better interpret the complex nature of different sources. Ten sources were revealed, where background source (3.8 µg/m3, 30%) was the largest contributor to VOCs, followed by petroleum-related emissions (2.9 µg/m3, 22%) and pyrolyzed oxygen (OP)-Elemental Carbon (EC2)-aldehydes-rich (2.7 µg/m3, 21%). Other notable VOC sources included methyl ethyl ketone (MEK)-rich, vehicular traffic, and biomass burning. Both OP-EC2-aldehydes-rich and petroleum-related emissions showed notable contribution to ozone (O3) and secondary organic aerosol (SOA) formation, respectively. Observed mean carcinogenic risk values of benzene and formaldehyde and 95th percentiles risk values of 1,3-butadiene and acetaldehyde were above the USEPA acceptable level of 1x10-6 but below a tolerable risk of 1x10-4. Estimated carcinogenic risk values of OP-EC2-aldehydes-rich, vehicular traffic, background and petroleum-related emissions were above the USEPA acceptable cancer risk and posed greater risk to public health (more than 80% of total carcinogenic risk) compared to other sources. Due to lack of some VOC species data (e.g., alkanes, alkenes, terpenes, alcohols), other urban VOC sources e.g., fugitive emissions, fuel evaporation, unburned fuel were not identified. More work is needed to better understand the contribution of VOC sources to O3 and SOA formation in Albany and surrounding region. Findings can support policy makers in developing appropriate air quality management initiatives for the Capital Region in New York State.

Implementation and Analysis of Classification Algorithms for Diabetes.

BACKGROUND: In this era of cutting edge research, though one of the ubiquitous facilities accessible to modern man is state of the art medical care yet diabetes has emerged as one of the major ailments afflicting the present generation. So the prime necessity of this age has transformed into providing cheap and sustainable medical care against such major diseases like diabetes. In layman's terms Diabetes may be defined as a physiological condition wherein the blood glucose level is more than the prescribed level on a regular basis. OBJECTIVES: So the prime objective of this work is to provide a novel classification technique for detection of diabetes in a timely and effective manner. METHODS: The proposed work comprises of four phases: In the first phase a "Localized Diabetes Dataset" has been compiled and collected from Bombay Medical Hall, Mahabir Chowk, Pyada Toli, Upper Bazar, Jharkhand, Ranchi, India. In the second phase various classification techniques namely RBF NN, MLP NN, NBs, and J48graft DT have been applied on the Localized Diabetes Dataset. In the third phase, Genetic algorithm (GA) has been utilized as an attribute selection technique through which six attributes among twelve attributes have been filtered. Lastly in the fourth phase RBF NN, MLP NN, NBs and J48graft DT has been utilized for classification on relevant attributes obtained by GA. RESULTS: In this study, comparative analysis of outcomes obtained by with and without the use of GA for the same set of classification technique has been done w.r.t performance assessment. It has been conclusively inferred that GA is helpful in removing insignificant attributes, reducing the cost and computation time while enhancing ROC and accuracy. CONCLUSION: The utilized strategy may likewise be executed for other medical issues.

Detection of Depression and Scaling of Severity Using Six Channel EEG Data.

Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling of depression. Any degradation in the working of the brain can be identified through change in the electroencephalogram (EEG) signal. Thus detection as well as severity scaling of depression is done in this study using EEG signal. In this study, features are extracted from the temporal region of the brain using six (FT7, FT8, T7, T8, TP7, TP8) channels. The linear features used are delta, theta, alpha, beta, gamma1 and gamma2 band power and their corresponding asymmetry as well as paired asymmetry. The non-linear features used are Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA). The classifiers used are: Bagging along with three different kernel functions (Polynomial, Gaussian and Sigmoidal) of Support Vector Machine (SVM). Feature selection technique used is ReliefF. Highest classification accuracy of 96.02% and 79.19% was achieved for detection and severity scaling of depression using SVM (Gaussian Kernel Function) and ReliefF as feature selection. From the analysis, it was found that depression affects the temporal region of the brain (temporo-parietal region).It was also found that depression affects the higher frequency band features more and it affects each hemisphere differently. It can also be analysed that out of all the kernel of SVM, Gaussian kernel is more efficient to other kernels. Of all the features, combination of all paired asymmetry and asymmetry showed high classification accuracy (accuracy of 90.26% for detection of depression and accuracy of 75.31% for severity scaling).

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.