Collusion-free for Cloud Verification toward the View of Game Theory

At present, clients can outsource lots of complex and abundant computation, e.g., Internet of things (IoT), tasks to clouds by the “pay as you go” model. Outsourcing computation can save costs for clients and fully utilize the existing cloud infrastructures. However, it is hard for clients to trust the clouds even if blockchain is used as the trusted platform. In this article, we utilize the verification method as SETI@home by only two rational clouds, who hope to maximize their utilities. Utilities are defined as the incomes of clouds when they provide computation results to clients. More specifically, one client outsources two jobs to two clouds and each job contains n tasks, which include k identical sentinels. Two clouds can either honestly compute each task or collude on the identical sentinel tasks by agreeing on random values. If the results of identical sentinels are identical, then client regards the jobs as correctly computed without verification. Obviously, rational clouds have incentives to deviate by collusion and provide identical random results for a higher income. We discuss how to prevent collusion by using deposits, e.g., bit-coins. Furthermore, utilities for each cloud can be automatically assigned by a smart contract. We prove that, given proper parameters, two rational clouds will honestly send correct results to the client without collusion.

Farming-as-a-Service (FAAS) for a Sustainable Agricultural Ecosystem in India

Agriculture being the prime means of livelihood, there is a basic need of re-inventing the farming best practices, combined with tech-driven innovations in this segment to ensure sustainability and eliminate poverty and hunger. In this chapter, the authors focus on introducing relevant technology-enabled services that will ensure economic sustainability, enhance food security through data-driven decision making by various stakeholders like farmers,agri-business and agri-tech start-ups, farmpreneurs, government, agronomists, and IT suppliers. The analyzed information will be used as a vantage by farmers to select precision farming practices to aid productivity to empower personnel to provide timely assistance and industries to implement real-time monitoring using sensors and devices. The chapter will help formulate concepts, methods, practices, benefits, and introducing several case scenarios to effectively propagate the service mode of farming that will imbibe pay-as-you go model ensuring cost optimization and operational ease.

Resource Allocation Using Particle Swarm Optimization Algorithm in Cloud Computing

Cloud computing is now widely used in organisations and bussiness firms because of its on-demand accessibility of framework assets, web innovation, and pay-as-you-go principle. Despite of numerous advantages of cloud computing, such as availability and accessibility, it also has few significant drawbacks. The most fundamental issue is the resource management, where Cloud computing provides IT assets such as memory, network, storage, and so on based on a virtualization concept and a pay-as-you-go model. Much research has gone into the administration of these assets. The suggested framework employs the particle swarm optimization algorithm method for assigning and performing the tasks of an application. The proposed algorithm will likely reduce task completion time, cost by utilizing the maximum resources. The proposed system is evaluated using the cloudsim toolkit. Keywords: Particle swarm optimization algorithm, Resource allocation, Cloud computing.

Temperature and Guanidine Hydrochloride effects on the folding thermodynamics of WW domain and variants

We use simulations based on an all atom Go model to calculate the folding temperatures (Tfs) and free energies (ΔGs) of two variants of the WW domain, which is a small all β-sheet protein. The results, without adjusting any parameter, are in good agreement with experiments, thus validating the simulations. We then used the Molecular Transfer Model to predict the changes in their ΔG and Tfs as Guanidine Hydrochloride concentration is varied. The predictions can be readily tested in experiments.

Reliability Analysis of C4ISR Systems Based on Goal-Oriented Methodology

Hard-and-software integrated systems such as command and control systems (C4ISR systems) are typical systems that are comprised of both software and hardware, the failures of such devices result from complicated common cause failures and common (or shared) signals that make classical reliability analysis methods will be not applicable. To this end, this paper applies the Goal-Oriented (GO) methodology to detailed analyze the reliability of a C4ISR system. The reliability as well as the failure probability of the C4ISR system, are reached based on the GO model constructed. At the component level, the reliability of units of the C4ISR system is computed. Importance analysis of failures of such a system is completed by the qualitative analysis capability of the GO model, by which critical failures of hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are ascertained. This method of this paper contributes to the reliability analysis of all hard-and-software integrated systems.

PRL-1 overexpressed placenta-derived mesenchymal stem cells suppress adipogenesis in Graves’ ophthalmopathy through SREBP2/HMGCR pathway

Background Graves’ ophthalmopathy (GO) is a disorder, in which orbital connective tissues get in inflammation and increase in volume. Stimulants such as thyroid-stimulating hormone (TSH), insulin-like growth factor 1(IGF-1), IL-1, interferon γ, and platelet-derived growth factor cause differentiation into adipocytes of orbital fibroblasts (OFs) in the orbital fat and extraocular muscles. Human placental mesenchymal stem cells (hPMSCs) are known to have immune modulation effects on disease pathogenesis. Some reports suggest that hPMSCs can elicit therapeutic effects, but to date, research on this has been insufficient. In this study, we constructed PRL-1 overexpressed hPMSCs (hPMSCsPRL-1) in an attempt to enhance the suppressive function of adipogenesis in GO animal models. Methods In order to investigate the anti-adipogenic effects, primary OFs were incubated with differentiation medium for 10 days. After co-culturing with hPMSCsPRL-1, the characteristics of the OFs were analyzed using Nile red stain and quantitative real-time polymerase chain reaction. We then examined the in vivo regulatory effectiveness of hPMSCsPRL-1 in a GO mouse model that immunized by leg muscle electroporation of pTriEx1.1Neo-hTSHR A-subunit plasmid. Human PMSCsPRL-1 injection was performed in left orbit. We also analyzed the anti-adipogenic effects of hPMSCsPRL-1 in the GO model. Results We found that hPMSCsPRL-1 inhibited adipogenic activation factors, specifically PPARγ, C/EBPα, FABP4, SREBP2, and HMGCR, by 75.1%, 50%, 79.6%, 81.8%, and 87%, respectively, compared with naïve hPMSCs in adipogenesis-induced primary OFs from GO. Moreover, hPMSCsPRL-1 more effectively inhibited adipogenic factors ADIPONECTIN and HMGCR by 53.2% and 31.7%, respectively, than hPMSCs, compared with 15.8% and 29.8% using steroids in the orbital fat of the GO animal model. Conclusion Our findings suggest that hPMSCsPRL-1 would restore inflammation and adipogenesis of GO model and demonstrate that they could be applied as a novel treatment for GO patients.

Impact of the Intermittency Movement of Center Pivots on Irrigation Uniformity

A computer model was developed to simulate the varying depths of water applied to the ground due to the intermittent movements of a typical center pivot. The stop–go model inputs include the sprinkler application depth, the sprinkler pattern, that pattern’s wetted radius, the center pivot’s % timer setting, the move cycle time, and the end tower maximum travel speed. The model outputs were the depth of application in the pivot’s movement direction, the distribution uniformity (DU), and the coefficient of uniformity (CU). The results revealed that the pivot circular application uniformity is mostly a function of the move distance as a percent of the sprinklers’ wetted radius. This, in turn, is a function of the percent timer setting, the cycle time, and the end tower travel speed. Due to this, the high-speed center pivots had corresponding lower application uniformities compared to low-speed machines, and sprinklers with larger wetted radii resulted in better uniformity. Shortening the cycle time also resulted in higher uniformity coefficients. Based on these results, it is recommended that the run time be set based on the pivot end-tower’s travel speed and sprinkler wetted radius, such that the end tower move distance is equivalent to the sprinkler wetted radius. This will reduce wear to the tower motors due to the on–off cycling, especially for slow travel settings and for sprinklers with larger wetted radii. The ponding depths at different percent of the move distance, for the potential runoff, were estimated, which were set to be equivalent to the wetted radius.

An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation

Precision irrigation represents those strategies aiming to feed the plant needs following the soil’s spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.

Resource pooling in vehicular fog computing

Vehicular fog computing (VFC) provisions computing services at the edge of networks by fully exploiting the idle resources of vehicle loaded computer systems. Task scheduling and resource allocation revolved around VFC have gained tremendous attention recently. Currently, most of these works in VFC have focused on response time optimization or energy reduction. Computing services are provisioned in a pay-as-you-go model and vehicles as resource contributors are stimulated by the benefits obtained by leasing these resources. How to maximize their own benefits is one of big concerns but few of current works have recognized its importance in VFC. We in this paper introduce the notion of resource pooling into VFC where the computing resources of vehicles are pooled together to jointly provision computational services in a community. A genetic algorithm based strategy is proposed to solve the optimization problem for the sake of benefit maximization. Extensive experiments have been carried out to evaluate the approach and the numeric results have demonstrated that our strategy outstands other approaches with regards to the optimization objective.