Characterizing the influence of tide on the physico-chemical parameters and nutrient variability in the coastal surface water of the northern Bay of Bengal during the winter season

The spatial distribution of physico-chemical parameters（sea surface temperature（SST）, p H, sea surface salinity（SSS）, dissolved oxygen（DO） and Secchi depth） along with filterable nutrients（dissolved inorganic nitrate（DIN）,dissolved inorganic phosphate（DIP） and reactive silicate（DSi）） are measured in the winter months of November,December, January and February for four consecutive years from 2009–2010 to 2012–2013 on the shallow continental shelf（〈20 m bathymetry） of the coastal waters（up to 18 km away from shoreline） of the northern Bay of Bengal（n Bo B） during the highest high tide（HHT） and lowest low tide（LLT） hours for the first time. The variability of the coastal biogeochemical environment is assessed during the HHT and LLT hours and for this purpose, seawater samples are collected from seven different locations of a transect in the coastal region. Physicochemical parameters（except SST） show significant difference in magnitude during the HHT and LLT hours respectively. p H, SSS and DO are found to increase in the HHT hours and vice-versa. The data reveal that during the LLT hours, a relative increase of freshwater input in the n Bo B can have elevated the nutrient concentration compared with that observed during the HHT hours. The ratio of nutrient concentration is found to deviate significantly from the Redfield ratio. The abundance of DIP is much higher compared with that of DIN and DSi.The anthropogenic sources of DIP from the upstream flow（especially the domestic effluent of several metropolises） can be mainly attributed behind such an observation. In order to characterize and establish the trend of such variation in such an important bio-climatic region, long-term and systematic ecosystem monitoring in the coastal water of the n Bo B northern Bay of Bengal should be carried out throughout the year.

A new damping ratio identification method based on pattern search

In order to improve the effectiveness of traditional time domain identification methods in identifying damping ratios, a new damping ratio identification method based on pattern search is proposed by fluctuating the reliable natural frequency obtained through traditional time domain identification methods by about 10% to build the boundary conditions, using all the initial identification results to establish the free decay response of the system, and using the pattern search method to correct the initial identification results with the residual sum of squares between the free decay response and the actually measured free-decay signal as the objective function. The proposed method deals with the actually measured free-decay signal with curve fitting and avoids enlarging the identified error caused by intermediate conversion, so it can effectively improve the identified accuracy of damping ratios. Simulations for a room-sized vibration isolation foundation show that the relative errors of analyzed three damping ratios are down to 1.05%, 1.51% and 3.7% by the proposed method from 8.42%, 5.85% and 8.5% by STD method when the noise level is 10%.

Development of a mass model in estimating weight-wise particle size distribution using digital image processing

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

Development of monitoring and assessment of forest biomass and carbon storage in China

Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry.Understanding the development of monitoring and assessment of forest biomass and carbon storage in China is important for promoting the evaluation of forest carbon sequestration capacity of China.The author conducts a systematic analysis of domestic publications addressing＂monitoring and assessment of forest biomass and carbon storage＂in order to understand the development trends,describes the brief history through three stages,and gives the situation of new development.Towards the end of the 20th century,a large number of papers on biomass and productivity of the major forest types in China had been published,covering the exploration and efforts of more than 20 years,while investigations into assessment of forest carbon storage had barely begun.Based on the data of the 7th and 8th National Forest Inventories,forest biomass and carbon storage of the entire country were assessed using individual tree biomass models and carbon conversion factors of major tree species,both previously published and newly developed.Accompanying the implementation of the 8th National Forest Inventory,a program of individual tree biomass modeling for major tree species in China was carried out simultaneously.By means of thematic research on classification of modeling populations,as well as procedures for collecting samples and methodology for biomass modeling,two technical regulations on sample collection and model construction were published as ministerial standards for application.Requests for approval of individual tree biomass models and carbon accounting parameters of major tree species have been issued for approval as ministerial standards.With the improvement of biomass models and carbon accounting parameters,thematic assessment of forest biomass and carbon storage will be gradually changed into a general monitoring of forest biomass and carbon storage,in order to realize their dynamic monitoring in national forest inventories.Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will also be one of the major directions of development in the near future.

Computing Confidence Intervals for the Postal Service’s Cost-Elasticity Estimates

This paper provides methods for assessing the precision of cost elasticity estimates when the underlying regression function is assumed to be polynomial. Specifically, the paper adapts two well-known methods for computing confidential intervals for ratios: the delta-method and the Fieller method. We show that performing the estimation with mean-centered explanatory variables provides a straightforward way to estimate the elasticity and compute a confidence interval for it. A theoretical discussion of the proposed methods is provided, as well as an empirical example based on publicly available postal data. Possible areas of application include postal service providers worldwide, transportation and electricity.