Frequency analysis of OGLE-IV photometry for classical Cepheids in Galactic fields: non-radial modes and modulations

We analyse photometry of $\sim$2000 Galactic Cepheids available in the OGLE Collection of Variable Stars. We analyse both Galactic disk and Galactic bulge fields; stars classified both as single- and multi-periodic. Our goal was to search for additional low-amplitude variability. We extend the sample of multi-mode radial pulsators by identifying ten new candidates for double-mode and six new candidates for triple-mode pulsation. In the first overtone OGLE sample, we found twelve Cepheids with additional periodicity having period ratio $P_{\rm x}/P_{\rm 1O}\in (0.60,\, 0.65)$. These periodicities do not correspond to any other radial mode. While such variables are abundant in the Magellanic Clouds, only one Cepheid of this class was known in the Galaxy before our analysis. Comparing our sample with the Magellanic Cloud Cepheids we note a systematic shift towards longer pulsation periods for more metal rich Galactic stars. Moreover in eleven stars we find one more type of additional variability, with characteristic frequencies close to half of that reported in the group with (0.60,\, 0.65) period ratios. Two out of the above inventory show simultaneous presence of both signals. Most likely, origin of these signals is connected to excitation of non-radial pulsation modes. We report three Cepheids with low-amplitude periodic modulation of pulsation: two stars are single-mode fundamental and first overtone Cepheids and one is a double-mode Cepheid pulsating simultaneously in fundamental and in first overtone modes. Only the former mode is modulated. It is a first detection of periodic modulation of pulsation in this type of double-mode Cepheids.Comment: 16 pages, 14 figures, Accepted in MNRA

Analysis of Genetic Diversity in White Clover (\u3cem\u3eTrifolium Repens\u3c/em\u3e) Breeding Populations Using Agro-Morphological and RAPD Markers

White clover is an important forage legume for temperate regions, but very little is known about the genetic organisation of its breeding populations. The low amount of variability in the Indian collections of white clover for genetic improvement warrants the introduction of new germplasm and collecting local ecotypes for characterisation, utilisation and conservation. Several molecular techniques have been used for germplasm characterisation, variety identification, marker development and identification, molecular diagnostics, phylogenetic studies and diversity analysis. Because of its simplicity, rapidity and reliability, the RAPD technique has been used extensively for diversity analysis. The present study aims at characterising white clover genotypes of distinct geographical origin using standard descriptors and RAPD markers

Asteroseismology of the heartbeat star KIC 5006817

This paper summarizes the project work on asteroseismology at the ERASMUS+ GATE 2020 Summer school on space satellite data. The aim was to do a global asteroseismic analysis of KIC 5006817 and quantify its stellar properties using the high-quality, state of the art space missions data. We employed the aperture photometry to analyze the data from the Kepler space telescope and the Transiting Exoplanet Survey Satellite (TESS). Using the lightkurve Python package, we have derived the asteroseismic parameters and calculated the stellar parameters using the scaling relations. Our analysis of KIC 5006817 confirmed its classification as a heartbeat binary. The rich oscillation spectrum facilitate estimating power excess ($\nu_{\rm max}$) at 145.50$\pm$0.50 $\mu$Hz and large frequency separation ($\Delta\nu$) to be 11.63$\pm$0.10 $\mu$Hz. Our results showed that the primary component is a low-luminosity, red-giant branch star with a mass, radius, surface gravity, and luminosity of 1.53$\pm$0.07 M$_\odot$, 5.91$\pm$0.12 R$_\odot$, 3.08$\pm$0.01 dex, and 19.66$\pm$0.73 L$_\odot$, respectively. The orbital period of the system is 94.83$\pm$0.05 d.Comment: 13 pages, 4 figures, 2 tables; Based on the project work at ERASMUS+ GATE 2020 Summer school; To be published in Contrib. Astron. Obs. Skalnat\'e Ples

Reduced level of arousal and increased mortality in adult acute medical admissions: a systematic review and meta-analysis

Abstract Background Reduced level of arousal is commonly observed in medical admissions and may predict in-hospital mortality. Delirium and reduced level of arousal are closely related. We systematically reviewed and conducted a meta-analysis of studies in adult acute medical patients of the relationship between reduced level of arousal on admission and in-hospital mortality. Methods We conducted a systematic review (PROSPERO: CRD42016022048), searching MEDLINE and EMBASE. We included studies of adult patients admitted with acute medical illness with level of arousal assessed on admission and mortality rates reported. We performed meta-analysis using a random effects model. Results From 23,941 studies we included 21 with 14 included in the meta-analysis. Mean age range was 33.4 - 83.8 years. Studies considered unselected general medical admissions (8 studies, n=13,039) or specific medical conditions (13 studies, n=38,882). Methods of evaluating level of arousal varied. The prevalence of reduced level of arousal was 3.1%-76.9% (median 13.5%). Mortality rates were 1.7%-58% (median 15.9%). Reduced level of arousal was associated with higher in-hospital mortality (pooled OR 5.71; 95% CI 4.21-7.74; low quality evidence: high risk of bias, clinical heterogeneity and possible publication bias). Conclusions Reduced level of arousal on hospital admission may be a strong predictor of in-hospital mortality. Most evidence was of low quality. Reduced level of arousal is highly specific to delirium, better formal detection of hypoactive delirium and implementation of care pathways may improve outcomes. Future studies to assess the impact of interventions on in-hospital mortality should use validated assessments of both level of arousal and delirium

Bio-efficacy of different entomopathogenic nematode species against Scarab Grubs

9-14Entomopathogenic Nematodes (EPNs) of the families Steinernematidae and Heterorhabditidae are being used as potential biological control agents against several insect-pests of turf grass, citrus, vegetables, fruits and other agriculturally important crops, as well as many veterinary and house-hold insect pests including termites. They carry symbiotic bacteria Xenorhabdus spp. (Steinernematidae) and Photorhabdus spp.(Heterorhabditidae) which is required to kill the insect host and to digest its tissues, thereby providing nutrient conditions for nematode growth and development. These nematodes kill the host insect within 24-48 h. by causing septicemia, complete their life cycle inside the cadavers and emerge en-masses within 4-5 days after mortality in case of steinernematidae and 9-15 days in case of helerorhabditids. Scarabs (Order Coleoptera) are serious pests of several field crops. Their adults feed on foliage while their larval forms, commonly called as white grubs, live in soil and feed on roots. Our recent surveys showed heavy population of grubs in the rhizosphere of sugarcane in Meerut. The grubs were collected from the fields for use in the present study. Managing scarab grubs by entomopathogenic nematodes is highly challenging because they tend to avoid infection by frequent defecation. The biocontrol efficacy of 8 EPN species/strains viz. Steinernema thermophilum; S. glaseri; S. riobrave strain GAU-M; S. carpocapsae strain Megha-1; Heterorhabditidae bacteriophora strain GAU; indica strain Megha-3; Heterorhabditiditis strain Haryana-2 and Haryana-5 was tested against scarab (Hototrichia cosanguina) larvae. All the species/strain were found to be highly efficacious against scarab larvae and could induce 80-100% mortality with in 5-6 DAI (days after inoculation). Among the tested species/strain S. glaseri was found to be highly efficacious inducing 100% mortality within 5 DAI; followed by S. thermophilum, and H. indica causing 100% mortality within 6 DAI. The other tested species/strain S. riobrave, S. carpocapsae, Heterorhabditis strain Haryana-2. H. bacteriophora and Heterorhabditis strain Haryana-4 could induce only 80% mortality up to 6 DAI. The higher efficacty of S. glaseri is attributed to its cruising type behavior best suited for accessing scarab larvae. In view of the pesticide resistance being developed in several insects, EPNs can be a suitable eco-friendly option for incorporating in integrated pest management programmes

An improvement over regression method of estimation

This paper suggested a class of estimators for the population mean of the study variable using information on an auxiliary variable with its properties under large sample approximation. The asymptotic optimum estimator in the proposed class has been identified with its properties. In addition, some existing estimators have been founded members of proposed class. It has been identified theoretically that the proposed class of estimators is better than the some traditional methods of estimation. An empirical study is carried out to judge the merits of proposed class over other competitors by using two natural population data sets

Improved class of estimators of finite population mean using sampling fraction and information on two auxiliary variables in sample surveys

This paper suggested a generalized class of estimators using information on two auxiliary va-riables and sampling fraction in simple random sampling. The bias and mean squared error for-mulae of suggested class have been derived under large sample approximation and compared with usual unbiased estimator and Singh’s (1967) ratio-cum-product estimator. The theoretical findings have been satisfied with an empirical study

Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system

IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral)