Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning:a newly data-driven modeling attempt

Background:Schistosomiasis control is striving forward to transmission interruption and even elimination,evidence-lead control is of vital importance to eliminate the hidden dan gers of schistosomiasis.This study attempts to ide ntify high risk areas of schistosomiasis in China by using in formation value and machine learni ng.

Fuzzy logic applied to value of information assessment in oil and gas projects

The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.

Value of big data to finance:observations on an internet credit Service Company in China

Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice.

Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow

The management of information flow for production improvement has always been a target in the research. In this paper, the focus is on the analysis model of the characteristics of information flow in shop floor operations based on the influence that dimension (support or medium), direction and the quality information flow have on the value of information flow using machine learning classification algorithms. The obtained results of classification algorithms used to analyze the value of information flow are Decision Trees (DT) and Random Forest (RF) with a score of 0.99% and the mean absolute error of 0.005. The results also show that the management of information flow using DT or RF shows that, the dimension of information such as digital information has the greatest value of information flow in shop floor operations when the shop floor is totally digitalized. Direction of information flow does not have any great influence on shop floor operations processes when the operations processes are digitalized or done by operators as machines.

A case study of a giant reactivated landslide based on NPR anchor cable Newton force early warning

In a large ancient landslide,approximately 240,000 m3 of sediments were reactivated,posing a grave threat to the safety of iron ore stopes.To trace the deformation and evolution history of reactivated Landslide,we conducted geological surveys and combined real-time monitoring equipment to analyze the landslide data since 1986 and the deformation status of the reactivated Landslide.A multi-factor comprehensive landslide monitoring method and an Newton force early warning system(NFEWS)were established,focusing on underground stress,surface deformation information and landslide stability.Furthermore,we developed a four-level early warning grading standard,employing surface cracks and changes in underground stress thresholds as early warning indicators.This standard adds expert assessment to avoid false alarms and realize real-time dynamics of mining landslides during excavation and transportation.Through the case study and analysis of Nanfen open-pit mine,the NFEWS system offers valuable insights and solution for early warning of landslides in analogous open-pit mines.Finally,the evaluation index system of landslide hazard susceptibility was established by selecting the Newton force influence factor.A landslide susceptibility zoning map is constructed using the information value model.The rationality and accuracy are assessed from three perspectives:frequency ratio,landslide hazard point density,and receiver operating characteristic(ROC)curve.The improved Newton force landslide early warning system provides a good reference for the analysis and monitoring of the creep landslide evolution process.

Intertextile Beijing Features More Informative programme of seminars and trend forums add value to fair

Intertextile Beijing Apparel Fabrics,will be held from 29-31 March 2009 at the China International Exhibition Centre,will showcase the latest textiles from around the world on 48,000 sqm of exhibition space.The event has confirmed 1100 exhibitors from 14 countries and regions including

AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks

Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node movement.We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues.First,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio.The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes.Finally,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for AUV.The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.

Research on Personal Credit Evaluation Based on Mobile Telecommunications Data

With the rapid development of big data technology, the personal credit evaluation industry has entered a new stage. Among them, the evaluation of personal credit based on mobile telecommunications data is one of the hotspots of current research. However, due to the complexity and diversity of personal credit evaluation variables, in order to reduce the complexity of the model and improve the prediction accuracy of the model, we need to reduce the dimension of the input variables. According to the data provided by a mobile telecommunications operator, this paper divides the data into a training sets and verification sets. We perform correlation analysis on each indicator of the data in the training set, and calculate the corresponding IV value based on the WOE value of the selected index, then binning data with SPSS Modeler. The selected variables were modeled using a logistic regression algorithm. In order to make the regression results more practical, we extract the scoring rules according to the results of logistic regression, convert them into the form of score cards, and finally verify the validity of the model.

The Dual of the Maximum Likelihood

The Maximum Likelihood method estimates the parameter values of a statistical model that maximizes the corresponding likelihood function, given the sample information. This is the primal approach that, in this paper, is presented as a mathematical programming specification whose solution requires the formulation of a Lagrange problem. A result of this setup is that the Lagrange multipliers associated with the linear statistical model (where sample observations are regarded as a set of constraints) are equal to the vector of residuals scaled by the variance of those residuals. The novel contribution of this paper consists in deriving the dual model of the Maximum Likelihood method under normality assumptions. This model minimizes a function of the variance of the error terms subject to orthogonality conditions between the model residuals and the space of explanatory variables. An intuitive interpretation of the dual problem appeals to basic elements of information theory and an economic interpretation of Lagrange multipliers to establish that the dual maximizes the net value of the sample information. This paper presents the dual ML model for a single regression and provides a numerical example of how to obtain maximum likelihood estimates of the parameters of a linear statistical model using the dual specification.

Application of machine learning to assess the value of information in polymer flooding

In this work,we provide a more consistent alternative for performing value of information(VOI)analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir decision-making.These sequential decision problems are often solved and modeled as stochastic dynamic programs,but once the state space becomes large and complex,traditional techniques,such as policy iteration and backward induction,quickly become computationally demanding and intractable.To resolve these issues and utilize fewer computational resources,we instead make use of a viable alternative called approximate dynamic programming(ADP),which is a powerful solution technique that can handle complex,large-scale problems and discover a near-optimal solution for intractable sequential decision making.We compare and test the performance of several machine learning techniques that lie within the domain of ADP to determine the optimal time for beginning a polymer flooding process within a reservoir development plan.The approximate dynamic approach utilized here takes into account both the effect of the information obtained before a decision is made and the effect of the information that might be obtained to support future decisions while significantly improving both the timing and the value of the decision,thereby leading to a significant increase in economic performance.

Characteristic Analysis and Harmonic Feature Identification of Micro-Vibration on Flywheels

To avoid the negative effects of disturbances on satellites,the characteristics of micro-vibration on flywheels are studied.Considering rotor imbalance,bearing imperfections and structural elasticity,the extended model of micro-vibration is established.In the feature extraction of micro-vibration,singular value decomposition combined with the improved Akaike Information Criterion(AIC-SVD)is applied to denoise.More robust and self-adaptable than the peak threshold denoising,AIC-SVD can effectively remove the noise components.Subsequently,the effective harmonic coefficients are extracted by the binning algorithm.The results show that the harmonic coefficients have great identification in frequency domain.Except for the fundamental frequency caused by rotor imbalance,the harmonics are also caused by the coupling of imperfections on bearing components.

A conjecture on mechanism of information understanding

All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.

Research on the Platform-Based Retail Business Model Innovation

With the rise of the platform economy,the retail industry is facing great pressure to shift from a traditional growth model to a platform-based growth model.The one-sided market theory applicable to the traditional business models cannot adequately explain business model innovation in the platform-based growth model.Based on a systematic analysis of the platform economy theory,business model innovation theory,value co-creation theory,and dynamic capability theory and in light of the characteristics of the retail industry,this paper proposes that to achieve platform-based growth,retail business model innovation should be focused on changing the ways to create customer value and capture enterprise value and leveraging modern information systems to achieve value co-creation during the transformation of key business processes ranging from procurement and sales to distribution.In addition,enterprises must get rid of the mindset of making profi ts from the difference between buying and selling prices and should instead explore new sources of revenue through linking dividend and information value.As platform operators,retail enterprises need to develop dynamic capabilities through the interaction of heterogeneous resources of various platform participants and especially the introduction of social resources,so as to propel innovation in retail business models and ultimately the transformation from traditional growth to platform-based growth.

Ultrasound-guided prostate percutaneous intervention robot system and calibration by informative particle swarm optimization

Applying a robot system in ultrasound-guided percutaneous intervention is an effective approach for prostate cancer diagnosis and treatment.The limited space for robot manipulation restricts structure volume and motion.In this paper,an 8-degree-of-freedom robot system is proposed for ultrasound probe manipulation,needle positioning,and needle insertion.A novel parallel structure is employed in the robot system for space saving,structural rigidity,and collision avoidance.The particle swarm optimization method based on informative value is proposed for kinematic parameter identification to calibrate the parallel structure accurately.The method identifies parameters in the modified kinematic model stepwise according to parameter discernibility.Verification experiments prove that the robot system can realize motions needed in targeting.By applying the calibration method,a reasonable,reliable forward kinematic model is built,and the average errors can be limited to 0.963 and 1.846 mm for insertion point and target point,respectively.