AN ALTERNATIVE MODEL OF DEVELOPMENT FINANCE

In a written interview with Beijing Review reporter Yu Shujun,K.V.Kamath,President of the BRICS New Development Bank,discusses the bank’s uniqueness and purpose,its focus and methods,and the need for such multilateral development banks to innovate to meet investment needs.Beijing Review:The BRICS bank was officially named the New Development

China＇s Electricity Industry at the Crossroads： New Challenges for Developing the Right Model of Regulation

China is facing important challenges stemming from increasing rates of urbanization and aging population. To pursue its ＂harmonious society＂ objective without disrupting its path to development major overhauls are necessary in education, health, social security and above all in public services, particularly in electricity. China＇s electricity industry is at the crossroads. To meet the challenges, new models of regulation should be developed and applied. This paper examines the current state of the Chinese electricity industry and the burden it imposes on its public finances. It also reviews and critically examines the existing FIT （Europe） and RPS （USA） models of regulation and of promotion of renewable energies and advances on whether they are advantageous for China. It is argued that the electricity industry has already undergone important reforms but cross subsidies still exist, equivalent to 1.5% of China＇s GDP. Drastic rate rebalancing policies will create sustainability problems and a deterioration of China＇s public finances. To avoid such negative results, China has to further reform its electricity industry gradually and use wisely FIT-type programs to bring renewables into the grid and fulfill the Kyoto Protocol

Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network

An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.

Pressure control method and device innovative design for deep oil in-situ exploration and coring

Deep oil exploration coring technology cannot accurately maintain the in-situ pressure and temperature of samples, which leads to a distortion of deep oil and gas resource reserve evaluations based on conventional cores and cannot guide the development of deep oil and gas resources on Earth. The fundamental reason is the lack of temperature and pressure control in in-situ coring environments. In this paper, a pressure control method of a coring device is studied. The theory and method of deep intelligent temperature-pressure coupling control are innovatively proposed, and a multifield coupling dynamic sealing model is established. The optimal cardinality three term PID (Proportional-Integral-Differential) intelligent control algorithm of pressure system is developed. The temperature-pressure characteristic of the gas-liquid two-phase cavity is analyzed, and the pressure intelligent control is carried out based on three term PID control algorithms. An in-situ condition-preserved coring (ICP-Coring) device is developed, and an intelligent control system for the temperature and pressure of the coring device is designed and verified by experiments. The results show that the temperature-pressure coupling control system can effectively realize stable sealing under temperature-pressure fields of 140 MPa and 150 °C. The temperature-pressure coupling control method can accurately realize a constant pressure inside the coring device. The maximum working pressure is 140 MPa, and the effective pressure compensation range is 20 MPa. The numerical simulation experiment of pressure system control algorithm is carried out, and the optimal cardinality and three term coefficients are obtained. The pressure steady-state error is less than 0.01%. The method of temperature-pressure coupling control has guiding significance for coring device research, and is also the basis for temperature-pressure decoupling control in ICP-Coring.

Canine on the Couch:The New Canary in the Coal Mine for Environmental Health Research

Human health is intimately connected and tied to the health of our environment and ecosystem,with only a very small fraction of the risk for chronic diseases explained by genetics alone.Companion animals are prone to disease types that are shared with people,including cancers and endocrine disorders,reinforcing the thought that environmental factors contribute to the risks for chronic diseases.These factors include air and water pollution and the built environment.As such,there is increasing interest in pursuing research with companion animals,and specifically dogs,as sentinel species to inform comparative health assessments and identify risk factors for disease.Of the canine diseases for which environmental exposure research has been published,cancers have received the most attention.This review summarizes two main aspects of this comparative approach:(1)cancers that occur in dogs and which are similar to humans and(2)research investigating environmental exposures and health outcomes in dogs.The goal of this review is to highlight the diverse conditions in which pet dogs may provide unique perspectives and advantages to examine relationships between environmental exposures and health outcomes,with an emphasis on chemical pollution and cancer.Furthermore,this review seeks to raise awareness and stimulate discussion around the best practices for the use of companion animals as environmental health sentinels.

Cloud computing for integrated stochastic groundwater uncertainty analysis

One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.