Global sea level variations from altimetry,GRACE and Argo data over 2005-2014

Total sea level variations（SLVs） are caused by two major components：steric variations due to thermal expansion of seawater,and mass-induced variations due to mass exchange between ocean and land.In this study,the global SLV and its steric and mass components were estimated by satellite altimetry,Argo float data and the Gravity Recovery and Climate Experiment（GRACE） data over 2005-2014.Space gravimetry observations from GRACE suggested that two-thirds of the global mean sea level rise rate observed by altimetry（i.e.,3.1 ± 0.3 mm/a from 2005 to 2014） could be explained by an increase in ocean mass.Furthermore,the global mean sea level was observed to drop significantly during the2010/2011 La Nina event,which may be attributed to the decline of ocean mass and steric SLV.Since early 2011,the global mean sea level began to rise rapidly,which was attributed to an increase in ocean mass.The findings in this study suggested that the global mean sea-level budget was closed from 2005 to 2014 based on altimetry,GRACE,and Argo data.

Predictive Modeling and Parameter Optimization of Cutting Forces During Orbital Drilling

To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.

Optimization of Steel Bar Manufacturing Process Using Six Sigma

Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and controlmethodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar（Khyber Pakhtunkhwa, Pakistan）. The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.

A blockchain-based platform for incentivizing customer reviews in the grocery industry

Nowadays,user-generated content is pivotal for many companies:people trust other customers’opinions more than any brand advertisement.Brands are aware of this and try to promote and motivate their customers to create high-quality content.However,this way of operating is still at an early stage:there is a lack of fairness,as companies typically do not provide a validation system,or if they do,it is not based on a transparent solution,and often,there is no reward for creating unique and high-quality content.In this paper,we focus on the problem of incentivizing users’creation of content in the form of customer reviews in the online grocery industry.Specifically,we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology.We develop a decentralized ecosystem of consumers,influencers,and manufacturers,where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization.Blockchain technology is used to certify the content’s authenticity and compensate content creators with a cryptographic token.We illustrate the technical choices of the solution together with its software architecture and implemented platform.In particular,we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.

An Optimal Resource Provision Policy in Cloud Computing Based on Customer Profiles

Conventional resource provision algorithms focus on how to maximize resource utilization and meet a fixed constraint of response time which be written in service level agreement（SLA）.Unfortunately,the expected response time is highly variable and it is usually longer than the value of SLA.So,it leads to a poor resource utilization and unnecessary servers migration.We develop a framework for customer-driven dynamic resource allocation in cloud computing.Termed CDSMS（customer-driven service manage system）,and the framework’s contributions are twofold.First,it can reduce the total migration times by adjusting the value of parameters of response time dynamically according to customers’profiles.Second,it can choose a best resource provision algorithm automatically in different scenarios to improve resource utilization.Finally,we perform a serious experiment in a real cloud computing platform.Experimental results show that CDSMS provides a satisfactory solution for the prediction of expected response time and the interval period between two tasks and reduce the total resource usage cost.