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Undergraduate medical education (UGME) is the time when doctors' attitudes toward patients and their profession are formed. It is also a period of tremendous stress for future physicians, including high levels of negative stress. Such stress can be maladaptive and may sow the seeds of burnout and long-term dissatisfaction. We believe that the introduction of yoga practice in the 1st year of medical school could ameliorate the negative stressors to which undergraduate medical students are exposed. Although there are some studies in the U.S. and internationally that support the use of Yoga in UGME, they do not provide sufficient data to make a compelling case for widespread implementation of yoga programs in undergraduate curricula. We, therefore, wish to advocate for conducting a trial of the integration of yoga in the undergraduate medical curriculum to combine yoga's ancient health wisdom into the context of modern scientific medicine. Large, prospective, multicenter, and multi-method pilot projects are needed to identify how a program of yoga practice and theory could counter the UGME environment that ultimately produces depression, anxiety, and non-effective coping strategies among medical students. A curriculum for yoga for undergraduate medical students deserves serious consideration and a prominent place among efforts to improve UGME.

This article summarizes about edge proper interval-valued complex fuzzy graph. Here, degree of an edge, total degree of an edge, edge proper and edge totally proper intervalvalued complex fuzzy were introduced. A necessary and sufficient condition in which these two graphs are equivalent is provided. Many properties of edge proper interval-valued complex fuzzy graphs are examined and they were investigated for edge totally proper interval-valued complex fuzzy graphs,

This paper aims to improve the overall task processing time of mobile real-time Internet of Things (IoT) applications in fog-cloud computing, considering the various resource and security requirements along with the time constraints of the task. Fog computing extends the cloud resources to serve the IoT devices at the network edge. In such a scenario, deciding whether the tasks should be processed at the fog layer or submitted to the cloud is critical. Moreover, for real-time applications, the mobility of IoT devices and the limited bandwidth available at the edge devices endanger the low processing time of the task. Besides, the security demands of some IoT applications (i.e. healthcare) require processing the tasks by specific fog or cloud servers to assure confidentiality of information, which may also delay the task processing time. Therefore, we first address the mobility issue by proposing three different algorithms that work on allocating the mobile IoT device to the appropriate edge device (i.e. fog gateway), considering the distance and bandwidth load factors. Then, we offer a novel task scheduling algorithm that uses fuzzy logic to optimize the distribution of tasks between the fog and cloud layers, considering the task security requirements. The algorithm selects the proper processing unit to execute the task in the fog layer by exploiting the task demands (i.e. computation, storage, bandwidth, security) and deadline. Results demonstrate that considering the factors of distance and available bandwidth load while allocating the IoT device to the fog gateway improves the task processing time better than adopting one aspect. Results also show that our proposed scheduling algorithm outperforms other existing algorithms regarding makespan, turnaround time, success ratio and processing time metrics. [ABSTRACT FROM AUTHOR]

Clustering is one of the important functionalities of networking, which has a dominant impact on the performance in terms of Throughput, packet delivery ratio and communication delays. The challenge in performing the clustering process is more challenging for heterogeneous networks. The divergence between the member nodes is highly variable for a network that contains IoT nodes. As the result of vast availability of different IoT hardware, organizing them under one roof is a critical task which is very vital too. This work is intended to develop a new hierarchical based clustering to improve the performance and the lifetime of the IoT / Wireless sensor network nodes. Three legacy functional blocks such as idiosyncratic fuzzy energy estimator, cumulative fuzzy energy hierarchy builder and energy based cluster head exchanger are integrated in this work and submitted here as “Idiosyncratic Fuzzy LowEnergy Adaptive Clustering Hierarchy (IFLEACH)”. An industrial standard network simulator is used to evaluate the performance of IFLEACH in terms of throughput, packet delivery rate, latency, end-to-end delay and energy consumption by simulating the real-worlds IoT network environment replica. An achievement around 11 kbps throughput and 4 % security level are obtained by the proposed method in high density network. [ABSTRACT FROM AUTHOR]

Cloud computing is widely preferred by various organizations due to its numerous advantages such as lower costs, better security, and faster processing times. The most critical data stored in the remote servers are protected by firewalls and encryption, at the same time, the user's essential data may be accessed from the remote cloud server without their control. This kind of data storage facility introduces various security complications. In order to prevent these issues, we have proposed a method that uses a Biometric authentication method with PSO to provide high-level security. The method combines a pseudo-integer authentication scheme with a secret OTP that can be used to authenticate an individual. A Tri-level security mechanism model is proposed to store three types of data classification namely, No Privacy, Privacy with Trusted Provider and Privacy with Non-PNR. The proposed method provides a uniform security scheme for all the classifications using Adaptive ElGamal with Menezes-Qu-Verstone, that prevents data loss and corruption caused by insiders in the cloud. Further Seed Block is utilized to recover and extract data without loss. It does so by effectively extracting the data from the formatted disks. It is then followed with the decryption process, as if the data are requested by the user. Our framework aims to provide a secure and authenticated environment for accessing data. It features an improved authentication process and reduced complexity. [ABSTRACT FROM AUTHOR]

The concept of fibonacci graceful labeling and super fibonacci graceful labeling was introduced by Kathiresan and Amutha. Let G be a (p,q)= graph. An injective function f : V(G) = [[F.sub.0], [F.sub.1], [F.sub.2], ..., [F.sub.q-1], [F.sub.q+1]}, where [F.sub.q+1] is the [(q + 1).sup.th] fibonacci number, is said to be almost super fibonacci graceful graphs if the induced edge labeling [f.sup.*] (uv) = |f (u) = f (v)| is a bijection onto the set [[F.sub.1], [F.sub.2], ..., [F.sub.q]} or [[F.sub.1],[F.sub.2], ..., [F.sub.q-1], [F.sub.q+1]}. In this paper, we show that some well known graphs namely Path, Comb, subdivision of ([B.sub.2n] : [w.sub.i]), 1 ≤ i ≤ n and some special types of extension of cycle related graphs are almost super fibonacci graceful labeling. Keywords Graceful labeling, fibonacci graceful labelin[G, almost super fibonacci graceful labeling., §1. Introduction By a graph, we mean a finite undirected graph without loops or multiple edges. A path of length n is denoted by [P.sub.n+1]. A cycle of length n [...]

This paper presents the effect of quantum confinement on the potential of short channel mono-layer molybdenum disulfide (MoS2) based transistor using Ritz Galerkin finite element technique. Unlike earlier models, only two iterations are used to obtain the potential. This model makes use of the coupled Poisson-Schrödinger equation by considering quantum mechanical effects for evaluating the potential with the support of Exciton Bohr radius, bandgap, electron energy state, Density of State (DOS), and electron concentration. Furthermore, this coupled equation along with Neumann boundary condition is employed to approximate the wave function of the Schrödinger equation, which is used again in the Poisson equation for calculating the potential. This work accurately predicts the impact of quantum confinement effects in the perpendicular direction of the channel surface. [ABSTRACT FROM AUTHOR]

Agriculture is vital to the Indian economy as over 17 percent of total GDP and employs more than 60 percent of the population relies on agriculture. This research focuses on plant diseases as they create a major threat to food production as well as for small-scale farmer's livelihood. Expert workers are employed in traditional farming to visually evaluate row by row to identify plant diseases, which is a time-consuming, labor-intensive activity that is potentially error-prone because it is done by humans. The aim of this research is to develop an automated detection model that uses a combination of image processing and deep learning techniques (Faster R-CNN+ResNet50) to analyze real-time images and detect and classify the three common maize plant diseases: Common Rust, Cercospora Leaf Spot, and Northern Leaf Blight. The proposed system achieved 91% accuracy and successfully detects three maize diseases. [ABSTRACT FROM AUTHOR]

This paper elucidates a procedure for ascertaining the performance of induction generators to disseminate the inevitable role of an induction machine in an alternate energy conversion among undergraduate and postgraduate students. Initially, a circle diagram is constructed for generator mode and motor mode using a drawing tool—AutoCAD from a simplified, energy consumption predetermination test data. Later a set of data is derived and is trained using an innovative, powerful pedagogical tool-radial basis function neural network to predict the performance indices of an induction machine. The proposed technique is compared with the experimental results of a three-phase 5 HP induction machine for various loaded conditions. Further, it provides encouraging results that will help the manufacturers to become more involved in the performance strategies of induction machines. Thus, the noninvasive method avoids the manipulation of an algebraic equation of nth order polynomial equations in predicting the generator performance indices. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during Kharif, 2016 at Tamil Nadu Agricultural University, Coimbatore to study the growth and yield parameters influenced by seed priming. The proso millet and barnyard millet seeds were imposed with priming treatments and evaluated under field conditions. The results of the field experiment revealed that among the priming treatments Pseudomonas fluorescens 20% recorded significantly higher growth parameters such as field emergence, plant height, chlorophyll content, net assimilation rate, dry matter efficiency, earliness in days to booting, days to first flowering and days to 50 per cent flowering. The yield parameters like panicle weight/plant, panicle to seed recovery percentage, seed yield/plant, seed yield/plot and 1000 seed weight was higher with Pseudomonas fluorescens 20% primed seeds compared to other treatments. Thus, the present study indicates that seed priming with Pseudomonas fluorescens 20% have the potential in enhancing growth and yield parameters in proso millet and barnyard millet. [ABSTRACT FROM AUTHOR]

Summary: This study assessed the short‐run impact to poultry farmers, duck hatcheries, control costs, compensation paid to stakeholders (transfer payments) and market reactions on own and substitute product prices and backwater tourism (boat operators) due to avian influenza (AI) outbreaks in Kuttanad region of Kerala, India, during 2014. The primary data from 91 poultry farms (duck farms, broiler chicken and backyard poultry), four hatcheries and 90 backwater boat owners were collected through pre‐tested schedules. The secondary data on transfer payments and expenditure incurred to control AI were collected from developmental departments and were analysed. The estimated loss (culling live birds, eggs and feed destruction) per duck farm was USD 9,181, USD 3,889 and USD 156 in case of commercial farms reared for meat, dual‐purpose and backyard farms, respectively. The loss incurred by small‐scale broiler and backyard poultry farms was USD 453 and USD 40, respectively. The loss incurred by large and small duck hatcheries was USD 11,963 and USD 5,790, respectively, due to culling of hatchlings, young birds and destroying eggs. The government invested USD 744,890 to contain the disease spread through massive culling, surveillance and monitoring of poultry and humans due to zoonotic nature of the disease. A sharp market reaction on own and substitute product prices and eight weeks’ time lag in price recovery was observed. The consequential impact on tourism especially for the backwater boat operators amounted to a loss of USD 2,280/boat due to fall in tourist inflow. Since, control measures are post‐incidence, it is necessary to adopt appropriate preventive bio‐security measures at the farm level besides periodical screening of domestic birds in migratory birds’ flyway locations like Kuttanad to reduce the AI burden on various stakeholders including government. [ABSTRACT FROM AUTHOR]

The study was conducted to identify epidemiological sero-distribution of Leptospira serovars representing serogroups of antibodies in Odisha, Eastern part of India. Samples were collected from livestock during 2011-2014. A total of 537 non-purposive random serum samples from 12 districts in Odisha were tested at 1:100 dilutions in microscopic agglutination test (MAT) using live antigens of 18 reference Leptospira serovars. The overall prevalence of 36.69% (197/537) with 36.13% in cattle, 54.28% in buffaloes, 28.33% in goats and 44.44% in sheep was observed. Out of 197 reacted sera, 100 samples showed reactivity with more than one serovars representing 50.76% prevalence of multiple serovars. On analysis, the seroprevalence of leptospirosis in cattle (÷2 = 63.41, P <0.01), buffaloes (÷2 = 14.68, P <0.01), sheep (÷2 = 54.66, P <0.01) and goats (÷2 = 36.27, P <0.01) across different regions are significantly dependent. The predominant Leptospira serogroup specific antibodies against major serovars representing Hardjo (30.4%), Tarassovi (20.8%), Australis (19.19%), Bankinang (18.18%), Pomona (16.66%), Kaup (15.65%), Hebdomadis (11.11%), Pyrogenes (10.1%), Bataviae (9.59%), Icterohaemorrhagiae (9.09%), Shermani (7.57%), Djasiman/Javanica (6.56%), Hurstbridge (5.55%), Grippotyphosa (4.54%), Panama (4.04%), Canicola (3.03%) and Copenhageni (2.02%) against frequency distribution were noticed. The overall prevalence of antibodies detected against the serovars Hardjo, Tarassovi, Australis, Bankinang, Pomona, Kaup, Hebdomadis and Hurstbridge representing specific serogroups suggested that these serovars may be of use in the reference panels of Leptospira antigen in the MAT for this region in both human and animal state or district disease diagnostic laboratories. [ABSTRACT FROM AUTHOR]

Key management encompasses protocols related to generation, distribution, establishment and revocation of keys and subsequent protection of keys during storage and while in use. Hardware Security Module (HSM) is used for this purpose which adheres to standards such as FIPS 140-2 or IBM Common Criteria. Hardware tokens primarily serve to transport the keys to an unprotected environment. In this paper we introduce challenges to key management and propose approaches for key management in a cryptoprocessor. Advanced Encryption Standard Key Wrap algorithm which is one of the most widely used method for key protection in field programmable gate arrays (FPGAs) is also considered. [ABSTRACT FROM AUTHOR]

The article examines some issues related to cardinal principles of contract construction. Topics discussed including issues relating to coverage under commercial general liability (CGL) insurance policies; courts approaches to examine products liability claims; and use of "pro rata by time-on-the-risk allocation" to determine how to allocate longtail losses among multiple triggered policy.

The article reflects on how policyholders often apply non-cumulation clauses erratically and inconsistently. It discusses the characteristics of long-tail claims and the difficulties involved in showing the injury or damage causing such claims in any particular time period. It further describes two competing methods for assigning liability for long-tail claims, pro rata allocation and all sums liability.

The article justifies the protest of Virmati, the protagonist in the novel "Difficult Daughters" by Indian novelist Manju Kapur. It offers a background of the character, which reflects the importance given by Kapur to familial characters especially women. It also explores the significance of the meaning of the word "difficult."

The proposed scheme involves an idea of including three biometric traits of a person where in the sense even if one fails the other trait could be utilised for verification or identity. Moreover the concept of cryptosystem is involved, where one of the biometric trait – the palm vein itself acts as a key to utilise the stored template database. The main idea in using one of the biometric trait as a key is that under any circumstance no two palm veins match unless it belong to same person. It is a valid key which no one can steal or misuse. [ABSTRACT FROM AUTHOR]

Computer systems today can be seen as a problem of pattern classification, the system must deal with some intrinsic characteristics that make it very difficult to detect intrusions directly using classical pattern recognition methods. For example, normal and anomalous states are distinguished using features that are multi-dimensional, and there is extreme asymmetry and high overlap in the amount of data available for these two systems of states. Furthermore, the patterns involved cannot be recognized by linear methods without kernel maps. The natural human immune system faces the same difficulties, but successfully protects the body against a vast variety of foreign pathogens. It is a self-adaptive and self-learning classifier that can recognize and classify threats by learning, long-term memory, and association. We have adapted the mechanisms of the human immune system to construct an intrusion detection system to protect computer networks. [ABSTRACT FROM AUTHOR]