How does Casimir energy fall? IV. Gravitational interaction of regularized quantum vacuum energy

Several years ago we demonstrated that the Casimir energy for perfectly reflecting and imperfectly reflecting parallel plates gravitated normally, that is, obeyed the equivalence principle. At that time the divergences in the theory were treated only formally, without proper regularization, and the coupling to gravity was limited to the canonical energy-momentum-stress tensor. Here we strengthen the result by removing both of those limitations. We consider, as a toy model, massless scalar fields interacting with semitransparent ($\delta$-function) potentials defining parallel plates, which become Dirichlet plates for strong coupling. We insert space and time point-split regulation parameters, and obtain well-defined contributions to the self- energy of each plate, and the interaction energy between the plates. (This self-energy does not vanish even in the conformally-coupled, strong-coupled limit.) We also compute the local energy density, which requires regularization near the plates. In general, the energy density includes a surface energy that resides precisely on the boundaries. This energy is also regulated. The gravitational interaction of this well-defined system is then investigated, and it is verified that the equivalence principle is satisfied.Comment: 14 pages, 4 figure

A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening

Survey of Applications of Ml in Stress Detection

Stress is a common and pervasive issue that affects millions of people worldwide. It can lead to a variety of negative health outcomes, including anxiety, depression, and physical health problems. Early detection of stress is crucial for effective management and prevention of these negative outcomes. Stress detection technologies using machine learning algorithms can provide individuals with valuable information about their stress levels and help them manage their stress in more effective ways. This can lead to improved mental and physical health outcomes, as well as increased productivity and overall well-being. Therefore, stress detection is an important area of research that has the potential to positively impact the lives of many people.                This paper presents a survey of techniques applicable to the field of stress detection using machine learning (ML) algorithms. We categorize these techniques based on the approach they take and discuss various challenges, open questions, and future work in this area. We present a taxonomy of existing research and finally discuss gaps and future directions of research to advance the study of stress management using most recent ML techniques. These technologies provide individuals with valuable information about their stress levels and can help them manage their stress in more effective ways

Survey Paper on Emotion Recognition

Facial expressions give important information about emotions of a person. Understanding facial expressions accurately is one of the challenging tasks for interpersonal relationships. Automatic emotion detection using facial expressions recognition is now a main area of interest within various fields such as computer science, medicine, and psychology. HCI research communities also use automated facial expression recognition system for better results. Various feature extraction techniques have been developed for recognition of expressions from static images as well as real time videos. This paper provides a review of research work carried out and published in the field of facial expression recognition and various techniques used for facial expression recognition

Effect of UV-B radiations on mortality and enzymes level in fish larvae of Tor tor and Schizothorax richardsonii on laboratory scale

Depletion of stratospheric ozone layer is resulting into increase in solar UV-B on earth surface. Ultra violet radiation is well known to cause many detrimental effects in aquatic organisms. The present study was performed to study the effect of solar ultraviolet radiation on fish fingerlings of Tor tor and Schizothorax richardsonii as a model system on laboratory scale. The effect of different intensities of natural solar and artificial UV-B radiation on fish larvae of T. tor and S. richardsonii in a presence of retene was investigated. Solar ultraviolet intensity showed seasonal and altitudinal variations in Garhwal region. Solar UV radiation level was lower (0.390 mw/cm2) in the month of January- February at lower altitude and highest (1.192 mw/cm2) in the month of July-August 2018 at higher altitude. Fish larvae exposed to artificial UV-B (average wavelength 312 nm and intensity of 750 mw/cm2) with retene (50µg/l) showed increase in gills malandialdehyde level and caused larvae mortality as indicating that enhanced solar UV-B exposure could be lethal to fish fauna in aquatic ecosystem. Artificial UV-B had a stronger damaging effect on fish larvae than solar radiation exhibited highly toxic in presence of retene. The larvae of S. richardsonii was found more sensitive than T. tor as indicated by high mortality rate (30%) and high pigmented characteristics on dorsal side. The solar and ultraviolet radiation showed a positive effect on high pigmentation. These results suggest that on a short time scale, UV-B radiation causing developmental stress on fish larvae may contribute to assess the phototoxic behaviour of cold water fishes

Factors affecting the ecological habitat of Benthic macro-invertebrate assemblages in Asan wetland, Dehradun in Garhwal Himalaya

The Himalayan region has several freshwater resources, such as rivers, lakes, and wetlands. These freshwater resources have been adversely affected by environmental factors. Freshwater biological systems are defenseless against outcomes of environmental changes that might prompt the irreversible disintegration of these natural surroundings. The present study aimed to investigate the effect of biotic and abiotic factors on the Benthic macro-invertebrate assemblages of the Asan wetland, Dehradun in Garhwal Himalaya. A determination of the physico-chemical health status of the Asan wetland viz., electrical conductivity (EC), total dissolved solids (TDS), biochemical oxygen demand (BOD), and nutrients parameters like nitrogen, potassium, and phosphorus were investigated during this study. Three sampling sites (Site 1, Site 2 and Site 3) of wetland were selected and the water samples were collected seasonally, i.e., summer, winter, and monsoon from April 2021-March 2022. Maximum values of EC(163.75 µS/cm), TDS (232.78 (mg/l),  alkalinity (141.20 mg/l)  and pH(7.8)  were recorded in the monsoon season (June-September) and minimum values of EC( 135.80µS/cm), TDS (196.80 (mg/l),  alkalinity (119.80mg/l)  and pH(7.2) were recorded in the winter season (November–February). An overall total of 18 macrobenthos genera belonging to four classes was identified. Maximum communities of macrobenthos were observed during the winter and minimum communities during the monsoon season. Canonical correspondence analysis (CCA) was used to determine whether microbenthic genera and habitat ecological parameters and showed a positive or negative correlation. Thus, the present study contributed to the status of various factors and their impacts on the Benthic macro-invertebrate structure of the Asan wetland.        

The role of magnetic resonance imaging brain in estimating the burden of preventable and potentially curable epilepsy in developmentally normal children

Background: Epilepsy is the most common neurological condition among children. Diagnosis of epilepsy is clinical, but to find the etiology we must depend on investigations such as electroencephalography (EEG) and magnetic resonance imaging (MRI) f the brain. EEG is more useful to diagnose genetic epilepsies, and MRI is more useful in structural causes. Objective: The objective of this study was to find the etiology of epilepsy prevalent in children of our region and to evaluate the role MRI brain in diagnosis. Methods: This was a survey-based study conducted at a tertiary care hospital in central India, between April 2015 and August 2016. A total of 108 developmentally normal children in the age group of 1 month to 14 years who presented withtwo or more unprovoked seizures 24 h apart or known case of epilepsy and on treatment admitted in the inpatient department for seizures were included in this study. The patients were classified based on detailed history, personal observation of seizures in some cases, thorough clinical examination, EEG recording, MRI findings, and other relevant investigations pertaining to the case, and the data were analyzed. Results: According to the ILAE 2010 classification, 12.9% of our patients had probable genetic epilepsy, 32.4% belonged to the structural metabolic group, and 54%, the largest proportion had an unknown cause. Of total 42 patientswith abnormal MRI, 30 (71.42%) patient had underlying structural or metabolic abnormality, whereas 28.57% had a genetic cause. 10 patients had imaging results suggestive of neurocysticercosis, while 7 patients had mesial temporal sclerosis. Gliosis was seen in nine patients, tuberculoma was seen in four patients, and finding suggestive of infarcts was seen in six patients. Remaining patients had abnormalities including corpus callosal agenesis (1), cortical dysplasia (2), tuberous sclerosis (1), white matter disease (1), and hippocampal hyperintensities suggestive of encephalitis (1). Conclusion: MRI brain is the most important routinely availableinvestigation and must be utilized. Neurocysticercosis is still the most common structural cause of epilepsy in children in our region

The Impact of Lectures by Diverse Professionals on Diversity Awareness: Pre-Post Changes

Despite the increasing diversity in the United States, minorities in the field of higher education continue to be disproportionately low. Worldviews on Education Lecture Series (WELS) was created to provide opportunities for students to have interactive dialogues with diverse professionals from around the world and nation. Theeffects of these lectures on diversity awareness were examined. Participants completed 12 items from the Miami University Diversity Awareness Scale (MUDAS) before and after the lecture. A series of paired samples t-tests were conducted to determine if the scores on the post-test were significantly higher than the scores on the pre-test. Compared to the pre-test, participants reported greater diversity awareness on the post-test. The writers conclude that given an opportunity to learn from a diverse professional, students can increase their knowledge and change their perceptions in relation to diversity. Implications for higher education and future research efforts are discussed