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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]

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 investigates quantum mechanical effects on surface potential and threshold voltage in a two-dimensional molybdenum disulfide (MoS 2 ) based transistor. Drain current improvement occurs as a result of the low work function for metal Ti and electron affinity of MoS 2 , thereby reducing the Schottky barrier height (SBH). The titanium (Ti) contact introduced into the mono-layer MoS 2 helps in obtaining lower contact resistance and improved drain current due to the lowering of SBH. An analytical model is proposed for evaluating various device parameters such as surface potential, threshold voltage, contact resistance, mobility, and drain current. This model provides useful aspects for understanding the physics of quantum mechanical effects. Compared to other conventional devices, this transistor incorporates Ti, a low work function metal, in the contact region, producing better current drive and lower contact resistance values. A positive Schottky barrier is noticed between the mono-layer MoS 2 and the Ti metal. Compared to other high function metals, the proposed device displayed about 11% improvement in current drive and 8% less contact resistance. By attaining an enhanced carrier injection and decreased contact resistance, this device serves as a suitable prospect for low-power applications. [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]

The expressed recombinant leptospiral surface adhesion lipoprotein (Lsa27) of pathogenic Leptospira in E. coli was evaluated for the detection of Leptospira antibodies in cattle sera by latex agglutination test (LAT). The Lsa27 lacking signal peptide coding gene sequences from L. interrogans serovar Pomona was amplified (~ 660 bp) by PCR and the amplicon was cloned into pETiteN-HisKan vector. The expressed recombinant Lsa27 histidine-tagged fusion protein (rLsa27) was Ni–NTA affinity purified under denaturation followed by renaturation methods. The purified rLsa27 was characterized by SDS-PAGE and immunoblot, which confirmed the leptospiral protein with a MW of ~ 25 kDa. Further, the prepared sensitized latex beads coated with rLsa27 were evaluated as a diagnostic antigen for detection of pathogenic Leptospira antibodies by using known microscopic agglutination test (MAT) positive (n = 74) and negative (n = 62) sera for Leptospira antibodies in LAT, which revealed the relative diagnostic sensitivity of 91.89% and specificity of 87.10% against the gold standard serological test, MAT. Furthermore, on evaluation of developed rLsa27 LAT using serum samples from cattle associated with the history of abortions and reproductive disorder (n = 309), the relative sensitivity of 96.15%, and specificity of 89.11% were observed. Therefore, this rapid field test using the rLsa27 is first of its kind and it could be used as a screening test for the detection of Leptospira antibodies or it can be complemented by other diagnostics for the diagnosis /surveillance of bovine leptospirosis. [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 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.

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]

Congestive heart failure (CHF) is a major public health problem that results in tremendous economic burden. Diastolic heart failure (DHF) forms an important subset with increasing incidence and prevalence. There are widely variable estimates of the prevalence, ranging from 13% to 74% of all CHF presentations, and this is predominantly a result of a lack of uniform criteria for establishing a diagnosis. New developments in management of DHF have lagged behind those for systolic heart failure (SHF), for which numerous new therapeutic and device strategies have been instituted. The renin-angiotensin-aldosterone system (RAAS) plays an important role in the pathophysiology of both SHF as well as DHF. The beneficial role of ACE inhibitors as well as aldosterone antagonists in SHF has been well established. Because of its unique role of the RAAS in establishing fibrosis at a molecular level, RAAS blockade provides an opportunity to expand the therapeutic options for DHF. Thus far, in patients with primary DHF only the angiotensin receptor type 1 antagonist candesartan has been reported to decrease morbidity and probably mortality. Large, ongoing randomized trials including TOPCAT (Trial of Aldosterone Antagonist Therapy in Adults with Preserved Ejection Fraction Congestive Heart) and the I-PRESERVE (Irbesartan in Heart Failure with Preserved Systolic Function) are currently underway to establish the role of aldosterone antagonists in patients with DHF. [ABSTRACT FROM AUTHOR]

Quadricuspid aortic valve is a rare congenital anomaly. It may present as an isolated anomaly but is occasionally associated with aortic regurgitation. Sinus of Valsalva aneurysm (SVA) is also an infrequent congenital anomaly, typically associated with tricuspid aortic valves. There are only a few reported cases of SVA (ruptured) associated with quadricuspid aortic valves in the literature. We report the first case of the association of quadricuspid aortic valve with an unruptured SVA in an adult patient. [ABSTRACT FROM PUBLISHER]