A Survey on Classification of Routing Protocols in Wireless Sensor Networks

Late progressions in remote innovation has prompted gigantic development in organization of Wireless Sensor Networks (WSNs). WSNs are involved sensors and actuators hubs, thickly conveyed over some geographic area to detect, gather, handle and send information remotely to focal information authority. The correspondence among various remote sensor hubs is controlled by directing conventions; consequently the execution of WSN exceedingly relies on upon embraced steering strategy. Numerous such vitality proficient and quality steering conventions have been outlined throughout the years so as to build the execution of correspondence in WSNs. In this paper, a comprehensive survey and scientific classification of steering conventions is talked about on the premise of system structures and information transmission procedures. This review will help WSN framework fashioners to choose fitting directing convention for specific application

Role of non-lipid risk factors like hs-CRP, uric acid and thyroid stimulating hormone in metabolic syndrome

Background: Metabolic syndrome is the cluster of diseases which arises due to excess of plasma glucose, cholesterol, fatty acids, blood pressure and obesity. The role of lipids in the development of MetS had been extensively studied. Though some non-lipid factors like hsCRP, uric acid and TSH level also remain elevated in the serum of the MetS patients, the role of these non-lipid risk factors remain incompletely understood. The objective of this study was to investigate which of these factors better predicts Mets, in order to help prevention and early detection of MetS and its associated type 2 diabetes mellitus and cardiovascular diseases. Aim and objectives was to study the significance of serum highly sensitive C-reactive protein (hs-CRP), serum uric acid (SUA) and thyroid stimulating hormone (TSH) levels in metabolic syndrome.Methods: A total of four hundred and fifty subjects (211 men and 239 women) aged ≥35 years attending the hospital were divided into three groups based on the components level of MetS as control (CS), normal (MS) and severe (SMS) MetS groups. Their fasting blood sample were taken and analyzed for the serum hs-CRP, uric acid and TSH levels. The result showed that the mean hs-CRP and uric acid levels were significantly higher in Metabolic Syndrome group (MS) and in Severe Metabolic Syndrome group (SMS) when compared to control group. But the mean TSH levels were more in MS group and in SMS group than the control which was statistically not significant. The analysis of relative significance of these risk factors showed that serum hs-CRP level had a positive linear correlation with the severity of MetS whereas, the TSH level was significantly high only in SMS and the uric acid level was not correlated with the MetS.Results: Our study revealed that type II (absence of sutural bones) was commoner than type I (presence of type I) asterion. The asterion was 4.82±0.58 cm from tip of the mastoid process on the right side and 4.70±0.70 cm on the left. It was greater in males than in females, p value being statistically significant (P = 0.00 and P = 0.02 for right and left sides respectively). The distance of asterion from supramastoid crest was 4.22±0.73 cm on the right and 4.23±0.58 cm on the left. The distance in males was more than in females. The P value 0.00 was statistically significant on the right side. Regarding the position of the asterion in relation to transverse sinus, it was on the transverse sinus in 62% cases, below it in 32% and above in 6%.Conclusions: In the present study, there was higher mean serum hs-CRP level in patients with metabolic syndrome which showed a linear increase with increasing number of components of the metabolic syndrome. Though available literature indicated that hyperuricemia adult subjects tend to develop MetS more frequently our findings showed this increase was not dependent on the severity of MetS. Also, significantly high TSH levels were found only in severe MetS suggest that as per this study the serum hs-CRP values may be consider as the diagnostic criteria for metabolic syndrome and helps to improve future prediction of development of type 2 DM and cardiovascular diseases

Multiple bilateral submandibular gland sialolithiasis

Sialolithiasis accounts for the most common etiology of salivary gland obstruction which leads to recurrent painful swelling of the involved gland which often exacerbates while eating. Stones may be encountered in any of the salivary glands but most frequently in the submandibular gland and its duct. Simultaneous sialolithiasis in more than one salivary gland is rare, occurring in fewer than 3% of cases. Seventy to 80% of cases feature solitary stones; only about 5% of patients have three or more stones, the case report which we are presenting here had three submandibular sialoliths involving both the submandibular glands which were removed by intraoral approach and no post‑operative complications were noted.Key words: Bilateral, multiple, sialolithiasis, submandibula

An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text

With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.Comment: 17 pages, 8 figures, 2 table

Assay for Pathogen-Associated Molecular Pattern (PAMP)-Triggered Immunity (PTI) in Plants

To perceive potential pathogens in their environment, plants use pattern recognition receptors (PRRs) present on their plasma membranes. PRRs recognize conserved microbial features called pathogen-associated molecular patterns (PAMPs) and this detection leads to PAMP-triggered immunity (PTI), which effectively prevents colonization of plant tissues by non-pathogens1,2. The most well studied system in PTI is the FLS2-dependent pathway3. FLS2 recognizes the PAMP flg22 that is a component of bacterial flagellin

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \emph{slots} or \emph{object files} that compete for local patches of an image. The competition has a weak inductive bias to preserve spatial continuity; consequently, one slot may claim patches scattered diffusely throughout the image. In contrast, the inductive bias of human vision is strong, to the degree that attention has classically been described with a spotlight metaphor. We incorporate a spatial-locality prior into state-of-the-art object-centric vision models and obtain significant improvements in segmenting objects in both synthetic and real-world datasets. Similar to human visual attention, the combination of image content and spatial constraints yield robust unsupervised object-centric learning, including less sensitivity to model hyperparameters.Comment: 16 pages, 3 figures, under review at NeurIPS 202

A Hybrid Cryptographic System for Secured Device to Device Communication

It is general fact that even after enormous expansion of wireless communication there are still dead regions that hampers the effective communication. With exponential rise in the smart phones, a new layer of communication has evolved that could address the concerns of dead regions and capacity barriers. D2D is the evolving communication technology which focuses on short distance hops between the public devices to reach the destination. The major drawback of this technology is that most of the devices are public hence trustworthiness of the entire channel needs to be addressed in order to make it a viable solution. In this paper, we introduce a novel hybrid cryptographic approach that could address multiple eavesdroppers’ scenario. This approach incorporates both Huffman coding and Binary coding to enhance the crypto benefits for the information transmitted over D2D channel that consists of several public devices. The dual-crypto nature of the proposed algorithm offers higher efficiency, better security and improved key transmission.  Thus, the proposed hybrid cryptographic approach is robust in nature while easy and simple to operate. In addition, the proposed approach could recover the original information without any distortion from the encrypted data making the approach lossless in nature. Further simulation results prove that the proposed offers confidentiality to the transmitted to data while addressing the network capacity crunch