scholarly journals Framework for cost-effective analytical modelling for sensory data over cloud environment

2019 ◽  
Vol 9 (5) ◽  
pp. 3822
Author(s):  
Manujakshi BC ◽  
K B Ramesh
Keyword(s):  
Large Scale ◽  
Mining Operation ◽  
Cost Effective ◽  
Resource Constraint ◽  
Frequent Pattern ◽  
Cloud Environment ◽  
Sensory Data ◽  
Novel Approach ◽  
Novel Concept ◽  
Data As A Service

In order to offer sensory data as a service over the cloud, it is necessary to execute a cost-effective and yet precise data analytical logic within the sensing units. However, it is quite questionable as such forms of analytical operation are quite resource dependent which cannot be offered by the resource constraint sensory units. Therefore, the proposed paper introduces a novel approach of performing cost-effective data analytical method in order to extract knowledge from big data over the cloud. The proposed study uses a novel concept of the frequent pattern along with a tree-based approach in order to develop an analytical model for carrying out the mining operation in the large-scale sensor deployment over the cloud environment. Using a simulation-based approach over the mathematical model, the proposed model exhibit reduced mining duration, controlled energy dissipation, and highly optimized memory demands for all the resource constraint nodes.

scholarly journals Variable Porous Electrode Compression for Redox Flow Battery Systems

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 53 ◽  
Author(s):  
Nicholas Gurieff ◽  
Victoria Timchenko ◽  
Chris Menictas
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Porous Electrode ◽  
Cost Effective ◽  
Electrical Energy ◽  
Limiting Current ◽  
Great Promise ◽  
Full Potential ◽  
Uniform Compression ◽  
Novel Concept

Vanadium redox flow batteries (VRFBs) offer great promise as a safe, cost effective means of storing electrical energy on a large scale and will certainly have a part to play in the global transition to renewable energy. To unlock the full potential of VRFB systems, however, it is necessary to improve their power density. Unconventional stack design shows encouraging possibilities as a means to that end. Presented here is the novel concept of variable porous electrode compression, which simulations have shown to deliver a one third increase in minimum limiting current density together with a lower pressure drop when compared to standard uniform compression cell designs.

scholarly journals Novel holistic architecture for analytical operation on sensory data relayed as cloud services

2020 ◽  
Vol 10 (4) ◽  
pp. 4322
Author(s):  
Manujakshi B. C ◽  
K. B. Ramesh
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Services ◽  
Sensor Nodes ◽  
Sensory Data ◽  
Energy Constrained ◽  
Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

Facilitating recruitment of Amphibolis as a novel approach to seagrass rehabilitation in hydrodynamically active waters

2010 ◽  
Vol 61 (10) ◽  
pp. 1123 ◽  
Author(s):  
Rachel J. Wear ◽  
Jason E. Tanner ◽  
Sonja L. Hoare
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
South Australia ◽  
Cost Effective ◽  
Belowground Biomass ◽  
Small Scale ◽  
Past Century ◽  
Wastewater Management ◽  
Novel Approach ◽  
High Wave Energy

Worldwide, 29% of seagrass habitats have been lost over the past century. Compared with large-scale losses, successful restoration programs are usually only small scale (a few hectares). One area of significant seagrass loss (>5200 ha) is Adelaide, South Australia. Improvements to wastewater management have raised the possibility of rehabilitation in this area. Traditional methods of seagrass restoration are expensive and have had limited success owing to high wave energy. We investigated a range of biodegradable substrates, mostly made of hessian (burlap), to enhance Amphibolis recruitment as an alternative. After 5 weeks, 16 514 seedlings, or 157 seedlings m–2, had recruited. Survival declined over the following 12 months to 31.4%, and down to 7.2% after 3 years, in part as a result of breakdown of the hessian, and the wave-exposed nature of the sites. During the initial 12 months, above- and belowground biomass increased 2.6- and 6.4-fold, respectively. The technique may represent a non-destructive, cost-effective (<AU$10 000 ha–1) method to restore Amphibolis over large spatial scales and in areas that are hydrodynamically too active for traditional techniques, thus helping ameliorate some of the large-scale losses of seagrasses that have occurred globally.

A large-scale water-harvesting device with β-Al(OH)3 microcone arrays by simple hydrothermal synthesis

2017 ◽  
Vol 5 (48) ◽  
pp. 25328-25337 ◽  
Author(s):  
Handong Cho ◽  
Byungrak Park ◽  
Moonsu Kim ◽  
Sangmin Lee ◽  
Woonbong Hwang
Keyword(s):  
Hydrothermal Synthesis ◽  
Large Scale ◽  
Synthesis Method ◽  
Cost Effective ◽  
Water Harvesting ◽  
Novel Approach ◽  
Aluminum Substrates

Here we report a novel approach to fabricate conical microstructures on aluminum substrates using a cost-effective and scalable hydrothermal synthesis method.

Cost Effective PSO Model for MapReduce in Cloud Environment

2018 ◽  
Vol 6 (4) ◽  
pp. 497-501 ◽  
Author(s):  
Vidhyasagar B S ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Cost Effective ◽  
Cloud Environment

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Controllable and decomposable multidirectional synchronizations

2021 ◽  
Author(s):  
Gábor Bergmann
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Mathematical Framework ◽  
Collaborative Systems ◽  
Consistency Constraints ◽  
Restoration Mechanism ◽  
Common Goals ◽  
Access Privileges ◽  
Novel Concept ◽  
Engineering Projects

AbstractStudying large-scale collaborative systems engineering projects across teams with differing intellectual property clearances, or healthcare solutions where sensitive patient data needs to be partially shared, or similar multi-user information systems over databases, all boils down to a common mathematical framework. Updateable views (lenses) and more generally bidirectional transformations are abstractions to study the challenge of exchanging information between participants with different read access privileges. The view provided to each participant must be different due to access control or other limitations, yet also consistent in a certain sense, to enable collaboration towards common goals. A collaboration system must apply bidirectional synchronization to ensure that after a participant modifies their view, the views of other participants are updated so that they are consistent again. While bidirectional transformations (synchronizations) have been extensively studied, there are new challenges that are unique to the multidirectional case. If complex consistency constraints have to be maintained, synchronizations that work fine in isolation may not compose well. We demonstrate and characterize a failure mode of the emergent behaviour, where a consistency restoration mechanism undoes the work of other participants. On the other end of the spectrum, we study the case where synchronizations work especially well together: we characterize very well-behaved multidirectional transformations, a non-trivial generalization from the bidirectional case. For the former challenge, we introduce a novel concept of controllability, while for the latter one, we propose a novel formal notion of faithful decomposition. Additionally, the paper proposes several novel properties of multidirectional transformations.

scholarly journals Multi-Country Scale Assessment of Available Energy Recovery Potential Using Micro-Hydropower in Drinking, Pressurised Irrigation and Wastewater Networks, Covering Part of the EU

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 899
Author(s):  
Djordje Mitrovic ◽  
Miguel Crespo Chacón ◽  
Aida Mérida García ◽  
Jorge García Morillo ◽  
Juan Antonio Rodríguez Diaz ◽  
...  
Keyword(s):  
Drinking Water ◽  
Northern Ireland ◽  
Energy Recovery ◽  
Large Scale ◽  
Significant Proportion ◽  
Value Added ◽  
Cost Effective ◽  
Net Energy ◽  
Water Networks ◽  
Scale Assessment

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

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