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Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems—detailed X-ray spectroscopy of individual sources is too time consuming, while hardness ratios often lack accuracy, and can be difficult to use effectively. These methods fail to use the power of X-ray CCD detectors to identify X-ray emission lines and distinguish line-dominated spectra (from chromospherically active stars, supernova remnants, etc.) from continuum-dominated ones (e.g., compact objects or active galactic nuclei, AGN). In this paper, we probe the use of artificial neural networks (ANN) in differentiating Chandra spectra of young stars in the Chandra Orion Ultradeep Project (COUP) survey from AGN in the Chandra Deep Field South (CDFS) survey. We use these surveys to generate 100,000 artificial spectra of stars and AGN, and train our ANN models to separate the two kinds of spectra. We find that our methods reach an accuracy of ∼92% in classifying simulated spectra of moderate-brightness objects in typical exposures, but their performance decreases on the observed COUP and CDFS spectra (∼91%), due in large part to the relatively high background of these long-exposure data sets. We also investigate the performance of our methods with changing properties of the spectra such as the net source counts, the relative contribution of background, the absorption column of the sources, etc. We conclude that these methods have substantial promise for application to large X-ray surveys.

In the present investigation, microstructure study and tensile behavior of B4C particulate reinforced Al2024 alloy composites have been reported. Al2024 matrix composites containing boron carbide were fabricated by conventional stir casting method. The composites containing 3 and 6 wt. % of B4C (80 micron size)particulates were fabricated for the study. The micro- structure of the composite was examined by optical microscopy. Images were taken to identify the presence of B4C particle in aluminum matrix. Further, tensile behavior of as cast Al2024 alloy and Al2024-3 wt. % B4C and 6 wt. % B4C composites were studied. Tensile properties like ultimate tensile strength, yield strength and percentage elongation were evaluated as per ASTM standards. Microstructural observation revealed the uniform distribution of particles in the Al2024 alloy matrix. It was found from the analysis, that the ultimate tensile strength of composite was increased due to increasing amount of boron carbide particle in the composite and the yield strength was increased due to the higher value of boron carbide. Also percentage elongation of the composites decreases with increase of the order of boron carbide. Finally, it was found that in the stress - strain curve, the boron carbide was the main factor in improving the tensile strength of composites because of its high hardness. The composites in the present study have great potential for application as structural materials.

Background: Ischemia Modified Albumin (IMA) is an altered serum albumin that forms under the conditions of oxidative stress and is considered as a biomarker of cardiac ischemia. The objective of this study was to evaluate the ischemia modified albumin (IMA) in the serum of the individuals with different types of tobacco habits in order to investigate the possibility of using this as a biomarker for the oxidative stress induced by the tobacco products. Materials and Methods: The study included 90 subjects, who were Grouped as control (30), Group I (betel quid chewers), Group II (gutkha chewers), Group III (smokers) and Group IV (mixed). Serum was collected from subjects of all Groups and IMA estimation was done using Albumin Cobalt binding assay. The results were tabulated and analysed statistically. Results: The mean serum IMA levels in control, Group I, Group II, Group III and Group IV were 0.52547 ABSU, 0.68767 ABSU, 0.47433 ABSU,0.36540 ABSU and 0.54593 ABSU respectively. Conclusion: The results show that serum IMA levels were increased in betel quid chewers and mixed Group compared to the controls. From the results noted in this study we suggest that IMA can be used as an early marker for tobacco related oxidative stress.

This paper presents an innovative approach to improving agricultural practices using modern technologies such as machine learning, IoT, and image processing. The proposed system includes two main modules: fertilizer prediction and crop recommendation and crop disease detection. The first module utilizes machine learning algorithms to analyze data from various sources such as soil sensors, weather stations, and historical crop yield data to predict optimal fertilizer requirements and recommend suitable crops for the specific soil type and climate. The second module employs image processing and Convolutional Neural Network (CNN) algorithms to detect crop diseases by analyzing images captured by IoT devices such as drones and cameras. The proposed system aims to increase crop yield, reduce the cost of crop maintenance, and promote sustainable farming practices. The results of this research demonstrate the potential of smart agriculture technologies in improving food security and agricultural sustainability. [ABSTRACT FROM AUTHOR]

Abstract The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.

Abstract The study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen–Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41–50 year old cohort) and a change of − 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81–100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51–60 year old cohort) and a change of − 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81–100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of − 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype–phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.

Introduction: Calcium silicate-based cements has been shown to have improved antibacterial properties when incorporated into the triple antibiotic paste (TAP). However, the new material combination must withstand the tissue environment and be dimensionally stable when employed for endodontic applications. Therefore, this study aims to determine the pH and dimensional stability of mineral trioxide aggregate (MTA) and Biodentine incorporated with TAP. Materials and Methods: A total of 80 cylindrical discs of 6 mm × 3 mm dimensions (n = 20) were prepared with MTA, MTA + TAP (1:1), Biodentine, and Biodentine + TAP (1:1) in Groups 1, 2, 3, and 4, respectively. Each group's specimens had half of their samples tested for dimensional stability (n = 10) and pH (n = 10). Samples were submerged for 1 min in containers containing 10 ml of distilled water and the initial pH was recorded and after the 7th day using a digital pH meter. To evaluate the dimensional stability, the samples were wrapped in wet gauze and kept at 37°C in the incubator for 24 h. Then, their initial lengths were measured, and were stored in distilled water at 37°C for a month before being measured once more with digital calipers. The data were statistically analyzed using one-way ANOVA, post hoc Tukey, and a paired Student "t" test (P = 0.05). Results: MTA and Biodentine without TAP demonstrated a higher pH than with TAP; nonetheless, at both time points, all groups had an alkaline pH. With the exception of MTA + TAP, all the groups were dimensionally stable at both time intervals, but no significant difference was identified between the groups (P = 0.05). Conclusion: Both MTA and Biodentine combined with TAP exhibited alkaline pH throughout the study period; however, Biodentine combined with TAP was dimensionally more stable compared to MTA with TAP. [ABSTRACT FROM AUTHOR]