Heterogeneous Protocols Research Articles

Network performance improvement by using TI-LEACH heterogeneous clustered protocol for wireless sensor networks

Background and Objective: Current advancements in technology have encouraged heterogeneous networks with multi-sensors in Wireless Sensor Networks (WSN). These multi-sensors deployed in a particular region formed WSN and are continuously grabbing attention of researchers in the field of IoT application. In this research work, the clustering protocols, which have significant effect on saving energy of the network, increasing its lifetime and throughput are discussed. Based on such clustering techniques, a new clustering algorithm named as TI-LEACH is proposed which poses to an improvement over I-LEACH and LEACH in terms of network lifetime, instability period and throughput. Results and Conclusion: The simulation results divulge that TI-LEACH routing protocol is an energy efficient protocol as compare to I-LEACH and LEACH routing protocol. The results are compared in terms of dead nodes, alive nodes, network throughput and number of cluster heads (CHs) elected in each round.

Honey Bee Optimization based Sink Mobility Aware Heterogeneous Protocol for Wireless Sensor Network

Wireless Sensor Network (WSN) has emerged as one of the most important technologies serving an array of solutions for critical applications such as defense, industrial monitoring and decision purposes. Data routing in WSN is effective or non-effective depending upon the energy saving for nodes while transferring data packets to the sink. Mainly WSN divided into two modes; heterogeneous and homogeneous. Heterogeneous network in WSN mainly focused on the cluster head selection. Sink mobility in the heterogeneous network has still many open research issues, it is observed that it makes the network more energy efficient. The optimization in the network leads to the stability of the network at a much higher level. In this paper, the sink mobility is optimized for WSN using Honey Bee Optimization (HBO) technique by considering the parameters such as energy and distance. The proposed protocol shows significant improvement in the stability period by 33 % by covering 2928 rounds and enhanced network lifetime by 1500 rounds in compared with 2033 and 14084 rounds for iMBEENISH protocol respectively.

Topical Approach to Delivering Targeted Therapies in Lymphedema Treatment: A Systematic Review.

It is estimated that 140 to 200 million people are affected by lymphedema worldwide. Many studies have proposed targeted therapies that can be delivered systemically or locally to treat lymphedema. Since lymphedema primarily affects the skin and subcutaneous tissues, topical approaches to therapy should be considered as an attractive proposition as they can avoid systemic complications. In light of this, we conducted a systematic review of publications that analyzed the use of topical approaches to delivering targeted therapies in the treatment of lymphedema. We hypothesized that topical approaches resulted in the satisfactory treatment of lymphedema. We conducted a systematic review of publications on PubMed. The main eligibility criterion was that the articles should primarily investigate the use of topical approaches to delivering targeted therapies in the treatment of lymphedema. Consequently, we excluded papers that investigated any other delivery approaches or medical conditions. Of the 174 potential studies found in the literature, six were found to fulfill our eligibility criteria. All these studies were experimental ones on small animals (mice). The authors generally proposed different types of therapies, which could be clustered into two main groups: 1) induction of lymphangiogenesis [vascular endothelial growth factor C (VEGF-C) hydrogel or fibroblast growth factor]; and 2) modulation of inflammation (tacrolimus or topical collagen gel or troxerutin-phosphatidylcholine). All studies presented positive outcomes, demonstrating that topical therapy is a promising route for delivering growth factors and anti-inflammatory agents in the treatment of lymphedema. However, studies were conducted under heterogeneous protocols, and the safe application of these therapies in humans has not been assessed. Further studies are necessary to confirm the benefits and safety of targeted topical therapy on patients with lymphedema.

Semantic Mediation Model to Promote Improved Data Sharing Using Representation Learning in Heterogeneous Healthcare Service Environments

Interoperability has become a major challenge for the development of integrated healthcare applications. This is mainly because of the reason that data is collected, processed, and managed using heterogeneous protocols, different data formats, and diverse technologies, respectively. Moreover, interoperability among healthcare applications has been limited because of the lack of mutually agreed standards. This article proposes a semantic mediation model for the interoperability provision in heterogeneous healthcare service environments. To enhance semantic mediation, the Web of Objects (WoO) framework has been used to support abstraction and aggregation of healthcare concepts using virtual objects and composite virtual objects with ontologies. Besides, semantic annotation of healthcare data has been achieved with a simplified annotation algorithm. The alignment of diverse data models has been supported with the deep representation learning method. Semantic annotation and alignment provide a common understanding of data and cohesive integration, respectively. The semantic mediation model is backed with a target ontology catalog and standard vocabulary. Healthcare data is modeled using the standard Resource Description Framework (RDF), which provides triples structure to describe the healthcare concepts in a unified way. We demonstrate the semantic mediation process with the experimental settings and provide details on the utilization of the proposed model.

Enhanced Heterogeneous Cross-Layer Protocol for Wireless Sensor Networks

Enhanced Heterogeneous Cross-Layer Protocol for Wireless Sensor Networks

Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study

Background and purposeAdvanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis that a machine learning approach can distinguish grade 1 from higher gradings in meningioma patients using radiomics features derived from a heterogenous multicenter dataset of multi-paramedic MRI. MethodsA total of 138 patients from 5 international centers that underwent MRI prior to surgical resection of intracranial meningiomas were included. Segmentation was performed manually on co-registered multi-parametric MR images using apparent diffusion coefficient (ADC) maps, T1-weighted (T1), post-contrast T1-weighted (T1c), subtraction maps (Sub, T1c – T1), T2-weighted fluid-attenuated inversion recovery (FLAIR) and T2-weighted (T2) images. Feature selection was performed and using cross-validation to separate training from testing data, four machine learning classifiers were scored on combinations of MRI modalities: random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP). ResultsThe best AUC of 0.97 (1.0 and 0.97 for sensitivity and specificity) was observed for the combination of ADC, ADC of the peritumoral edema, T1, T1c, Sub and FLAIR-derived features using only 16 of the 10,914 possible features and XGBoost. ConclusionsMachine learning using radiomics features derived from multi-parametric MRI is capable of high AUC scores with high sensitivity and specificity in classifying meningiomas between low and higher gradings despite heterogeneous protocols across different centers. Feature selection can be performed effectively even when extracting a large amount of data for radiomics fingerprinting.

Iridium-Catalyzed Alkene-Selective Transfer Hydrogenation with 1,4-Dioxane as Hydrogen Donor.

The iridium-catalyzed transfer hydrogenation of alkenes using 1,4-dioxane as a hydrogen donor is described. The use of 1,2-bis(dicyclohexylphosphino)ethane (DCyPE), featuring bulky and highly electron-donating properties, led to high catalytic activity. A polystyrene-cross-linking bisphosphine PS-DPPBz produced a reusable heterogeneous catalyst. These homogeneous and heterogeneous protocols achieved chemoselective transfer hydrogenation of alkenes over other potentially reducible functional groups such as carbonyl, nitro, cyano, and imino groups in the same molecule.

Salient Central Lesion Volume: A Standardized Novel Fully Automated Proxy for Brain FLAIR Lesion Volume in Multiple Sclerosis.

Quantitative neuroimaging is an important part of multiple sclerosis research and clinical trials, and measures of lesion volume (LV) and brain atrophy are key clinical trial endpoints. However, translation of these endpoints to heterogeneous historical datasets and nonstandardized clinical routine imaging has been difficult. The NeuroSTREAM technique was recently introduced as a robust and broadly applicable surrogate for brain atrophy measurement, but no such surrogate currently exists for conventional T2-LV. Therefore, we sought to develop a fully automated proxy for T2-LV with similar analytic value but increased robustness to common issues arising in clinical routine imaging. We created an algorithm to identify salient central lesion volume (SCLV), comprised of the subset of lesion voxels within a specific distance to the lateral ventricles (centrality) and with intensity at least a quantitatively-derived amount brighter than normal appearing tissue (salience). We evaluated this method on four datasets (clinical, inter-scanner, scan-rescan, and real-world multi-center), including 1.5T, 3T, Philips, Siemens, and GE scanners with heterogeneous protocols, to assess agreement with conventional T2-LV, comparative relationship with disability, reliability across scanners and between scans, and applicability to real-world scans. SCLV correlated strongly with conventional T2-LV in both research-quality (r = .90, P < .001) and real-world (r = 0.87, P < 0.001) datasets. It also showed similar correlations with Expanded Disability Status Scale, as conventional T2-LV (r = 0.48 for T2-LV vs. r = 0.45 for SCLV). Inter-scanner reproducibility (ICC) was 0.86, p < 0.001 for SCLV compared to 0.84, p < 0.001 for conventional T2-LV, whereas scan-rescan ICC was 0.999 for SCLV versus 0.997 for T2-LV. SCLV is a robust, fully-automated proxy for T2-LV in situations where conventional T2-LV is not easily or reliably calculated.

AN IMPROVED ROUTING PROTOCOL SCHEME IN ADHOC NETWORKS

Nowadays, with the rapid development of science and technology and the ever increasing demand in every field, wireless sensor networks are emerging as a necessary scientific achievement to meet the demand of human in modern society. The wireless sensor network (WSN) is designed to help us not lose too much energy, workforce, avoid danger and they bring high efficiency to work. Various routing protocols are being used to increase the energy efficiency of the network, with two distinct types of protocols, homogenous and heterogeneous. In these two protocols, the SEP (Stable Election Protocol) is one of the most effective heterogeneous protocols which increase the stability of the network. In this paper, we propose an approaching the eFCM algorithm in clustering the SEP protocol which makes the WSN network more energy efficient. The simulation results showed that the SEP-eFCM proposed protocol performed better than the conventional SEP protocol.

Humoral and cellular immunity against both ZIKV and poxvirus is elicited by a two-dose regimen using DNA and non-replicating vaccinia virus-based vaccine candidates

The Zika virus (ZIKV) and poxvirus infection are considered as public health emergencies, necessitating the development of effective vaccines. Here, we report novel recombinant DNA-based and non-replicating vaccinia virus (NTV)-based vaccine candidates that express the precursor membrane-envelope (prME) or envelope (E) glycoproteins of ZIKV. After immunization of BABL/c mice with the vaccines using a homologous protocol (DNA/DNA, NTV/NTV) or heterogeneous (DNA/NTV) protocol, a similar level of anti-E IgG and neutralizing antibodies (microneutralization test) were detected in the mice. However, a significantly higher level of E-specific T cell responses was elicited in mice when a heterogeneous prime-boost protocol was used (DNA/NTV) with either the DNA-based or NTV-based vaccines. Furthermore, neutralizing antibodies and a T cell immune response against the vaccinia virus (VV) were detected in mice that were subjected to the prime-boost protocol (DNA/NTV), whereas those subjected to a homologous NTV/NTV protocol had higher levels of anti-VV IgG and neutralizing antibodies. Lastly, a novel H-2d-restricted CD8 T-cell epitope, VRSYCYEASISDMAS, was identified in the ZIKV E protein. These data demonstrate proof of concept of a bivalent vaccine candidate against ZIKV and orthopoxvirus, and support the use of DNA-prME prime and NTV-E boost protocols to protect against ZIKV and orthopoxvirus infections.

Amide proton transfer-weighted MRI in distinguishing high- and low-grade gliomas: a systematic review and meta-analysis.

Grading of brain gliomas is of clinical importance, and noninvasive molecular imaging may help differentiate low- and high-grade gliomas. We aimed to evaluate the diagnostic performance of amide proton transfer-weighted (APTw) MRI for differentiating low- and high-grade gliomas on 3-T scanners. A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to March 28, 2018. Original articles evaluating the diagnostic performance of APTw MRI for differentiating low- and high-grade gliomas were selected. The pooled sensitivity and specificity were calculated using a bivariate random-effects model. A coupled forest plot and a hierarchical summary receiver operating characteristic curve were obtained. Heterogeneity was investigated using Higgins inconsistency index (I2) test. Meta-regression was performed. Ten original articles with a total of 353 patients were included. High-grade gliomas showed significantly higher APT signal intensity than low-grade gliomas. The pooled sensitivity and specificity for the diagnostic performance of APTw MRI for differentiating low-grade and high-grade gliomas were 88% (95% CI, 77-94%) and 91% (95% CI, 82-96%), respectively. Higgins I2 statistic demonstrated heterogeneity in the sensitivity (I2= 68.17%), whereas no heterogeneity was noted in the specificity (I2= 44.84%). In meta-regression, RF saturation power was associated with study heterogeneity. Correlation coefficients between APT signal intensity and Ki-67 cellular proliferation index ranged from 0.430 to 0.597, indicating moderate correlation. All studies showed excellent interobserver agreement. Although heterogeneous protocols were used, APTw MRI demonstrated excellent diagnostic performance for differentiating low- and high-grade gliomas. APTw MRI could be a reliable technique for glioma grading in clinical practice.

Effect of the position of base station in square fields of energy efficient small, medium and large-scale wireless sensor networks - internet-of-things

In the recent time, wireless sensor networks (WSNs) are an essential component of the internet-of-things (IoTs). This has given new platforms for applications of integrated WSN-IoTs. The connection between the physical and virtual world of internet demands heterogeneous sensor nodes either deployed in an independent manner or embedded within other wireless devices. Stability and lifespan of the system are also decided by an important factor of the location of base station (BS) in the clustering of sensor nodes. Coverage, connectivity and an efficient communication in WSN-IoT integrated systems is decided by the position of base station in the midpoints of the four sides of a square field. The clustering in heterogeneous broad transmission distance protocol (HT-BTDP) with different base station locations shows the results of stability and lifetime. HT-BTDP is compared to the previous existing heterogeneous protocols in small, medium and large-scale networks for peripheral midpoint placement of BS.

A Realization of Cyber-Physical Manufacturing Control System Through Industrial Internet of Things

A Cyber-Physical System in Industry 4.0 is with interconnected elements of cyber and physical worlds. However, the interconnection relies on the success of communication among the equipment, sensors, controllers with different data and communication standards. (Protocols) This research takes an approach of applying Industrial Internet of Things (IIoT) to integrate the sensing data from various equipment and sources. Industrial Micro Control Unit (MCU) is applied to interface with the data sources, actuators, and equipment. The MCU transmits the sensing data/control commands back and forth with the cloud/fog computing platform. By deploying data analytics and reasoning for the manufacturing knowledge ontology in the cloud platform, the cyber world is able to recognize the situations and problems in the physical world. Thus, a decision of preventive or predicative actions can be made in the cyber world and then be implemented in the physical world. The results show that IIoT can eliminate the problems of heterogeneous protocols and databases in manufacturing data transmission. Based on IIoT, CPS is realized without the concerns or difficulties of data transmission

Effect of the position of base station in square fields of energy efficient small, medium and large-scale wireless sensor networks - internet-of-things

In the recent time, wireless sensor networks (WSNs) are an essential component of the internet-of-things (IoTs). This has given new platforms for applications of integrated WSN-IoTs. The connection between the physical and virtual world of internet demands heterogeneous sensor nodes either deployed in an independent manner or embedded within other wireless devices. Stability and lifespan of the system are also decided by an important factor of the location of base station (BS) in the clustering of sensor nodes. Coverage, connectivity and an efficient communication in WSN-IoT integrated systems is decided by the position of base station in the midpoints of the four sides of a square field. The clustering in heterogeneous broad transmission distance protocol (HT-BTDP) with different base station locations shows the results of stability and lifetime. HT-BTDP is compared to the previous existing heterogeneous protocols in small, medium and large-scale networks for peripheral midpoint placement of BS.

Consistent Registration and Discovery Scheme for Devices and Web Service Providers Based on RAML Using Embedded RD in OCF IoT Network

The Internet of Things (IoT) is comprised of connected devices which are equipped with sensors, actuators, and applications to provide services for enabling those objects to connect and exchange data. For these connected devices and the IoT network environment, heterogeneous protocols and frameworks have been published and applied to provide novel IoT services in our daily life. On the Internet, most of the services are provided by existing web service providers which are based on the high-performance processor, storage, and stable power supply. However, devices of the IoT are developed for constrained environments using the small size of equipment to provide seamless services ubiquitously. For accessing the constrained devices and existing web service providers using the clients in the IoT networks such as smart homes, the services of servers from the devices and web service providers shall be discovered by the clients using a consistent discovery service. In this paper, a consistent registration and discovery scheme is proposed for the devices and web service providers in the Open Connectivity Foundation (OCF)-based IoT network. For supporting the proposed scheme, an embedded resource directory (RD) server is proposed to provide a consistent registration service that is used for publishing information of devices and web service providers. For the registration, a unified profile format is used that is based on the RESTful API Modeling Language to describes the information of devices and web service providers. Furthermore, the discovery service provides the consistent interface to discover the registered devices and web service providers by the client using the unified user interface. Accordingly, the client can access the resources of devices and web service providers based on the discovered information.

Networking and communication challenges for post-exascale systems

With the significant advancement in emerging processor, memory, and networking technologies, exascale systems will become available in the next few years (2020–2022). As the exascale systems begin to be deployed and used, there will be a continuous demand to run next-generation applications with finer granularity, finer time-steps, and increased data sizes. Based on historical trends, next-generation applications will require postexascale systems during 2025–2035. In this study, we focus on the networking and communication challenges for post-exascale systems. Firstly, we present an envisioned architecture for post-exascale systems. Secondly, the challenges are summarized from different perspectives: heterogeneous networking technologies, high-performance communication and synchronization protocols, integrated support with accelerators and field-programmable gate arrays, fault-tolerance and quality-of-service support, energy-aware communication schemes and protocols, softwaredefined networking, and scalable communication protocols with heterogeneous memory and storage. Thirdly, we present the challenges in designing efficient programming model support for high-performance computing, big data, and deep learning on these systems. Finally, we emphasize the critical need for co-designing runtime with upper layers on these systems to achieve the maximum performance and scalability.

A New Energy Efficient Clustering Protocol for a Novel Concentric Circular Wireless Sensor Network

Researchers concentrate on big data. Wireless sensor network is one of the sources of big data. Wireless sensor network has hundreds of sensor nodes with limited energy and computational capability. Clustering is a technique used to reduce the energy expended and extend the network lifetime. Generally, nodes are deployed in a square network field. The nodes at the edges of the network have to transmit longer distance to the sink than the nodes at the sides. This depletes the node energy and reduces the network lifetime. Our proposed work Efficient Energy Heterogeneous Circular field Clustering Protocol (EEHCCP), deploys two tier energy heterogeneity nodes: normal and advance nodes in different zones of concentric circular network field. A hybrid direct and clustered communication in a circular network field has increased the network lifetime and throughput of the sensor network. The network lifetime and throughput of EEHCCP is better than SEP, DEEC and EDEEC. Also, performance metric of heterogeneous clustered EEHCCP is compared with Efficient Energy Homogeneous Circular field Protocol with no clustering.

CuO Nanoparticles as An Efficient Heterogeneous Catalyst for the 1,3‐Dipolar Cycloaddition of Dicarbonyl Compounds to Azides

AbstractSubstituted 1,2,3‐triazoles are important heterocyclic molecules with applications in diverse research areas. The organocatalytic (3+2) cycloaddition reaction of azides with active methylene compounds has proven to be a powerful strategy for the synthesis of a variety of 1,2,3‐triazoles with different substituents. However the reported methods in the literature, commonly used a homogenous organocatalyst. Considering this point, here we have reported a novel heterogeneous protocol for the synthesis of 1,4,5‐trisubstituted 1,2,3‐triazoles by reaction of different dicarbonyl compounds with aryl azides catalyzed by copper oxide nanoparticles under microwave irradiation. The important features of this methodology are high yield products, short reaction times and recyclability of the CuO catalyst.

Pre-operative MRI staging of endometrial cancer in a multicentre cancer network: can we match single centre study results?

To evaluate the staging accuracy of magnetic resonance imaging (MRI) for endometrial cancer in daily practice over a 3-year period at a tertiary referral centre receiving scans from a large number of hospitals with varying protocols. To compare these daily practice results to published data from single-centre studies. After ethical approval, MRI staging records for 270 studies from nine network and three centre hospitals were retrospectively collected and compared with final operative histopathology. The International Federation of Gynaecology and Obstetrics (FIGO) stage, depth of invasion assessment and cervical stromal invasion were analysed and reasons for discrepancies reviewed. MRI-based complete FIGO stage was fully concordant with histopathology in 65.6%. MRI accuracy for depth of myometrial invasion and cervical stromal invasion was 73.3% and 89.3% respectively. Our results did not match the high accuracy previously reported in studies based on single centres. Published MRI staging accuracy from small single-centre studies were not replicated in a tertiary referral centre receiving scans with heterogeneous protocols over a 3-year period. These results highlight the challenges faced in daily practice and may reflect achievable and realistic MRI staging accuracies in large rapid throughput referral networks. Adherence to standardised high-quality protocols may help to improve future results. • Three-year MRI-staging accuracy for endometrial cancer in a multicentre cancer network • Daily practice MRI-staging accuracy did not meet results of single-centre studies • Large scale cancer network MRI-staging accuracies should be further evaluated • Treatment recommendations should be based on achievable MRI-staging accuracies.

Mathematical Basis of Predicting Dominant Function in Protein Sequences by a Generic HMM\u2013ANN Algorithm

The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence.