Characterizing stand structure in a spruce forests:effects of sampling protocols

Spatial heterogeneity is an inherent characteristic of natural forest landscapes, therefore estimation of structural variability, including the collection and analyzing of field measurements, is a growing challenge for monitoring wildlife habitat di- versity and ecosystem sustainability. In this study, we investigated the combined influence of plot shape and size on the accuracy of assessment of conventional and rare structural features in two young-growth spruce-dominated forests in northwestern China. We used a series of inventory schemes and analytical approaches. Our data showed that options for sampling protocols, especially the selection of plot size considered in structural attributes measurement, dramatically af- fect the minimum number of plots required to meet a certain accuracy criteria. The degree of influence of plot shape is related to survey objectives; thus, effects of plot shape differ for evaluations of the ＂mean＂ or ＂representative＂ stand structural conditions from that for the range of habitat （in extreme values）. Results of Monte Carlo simulations suggested that plot sizes 〈0.1 ha could be the most efficient way to sample for conventional characteristics （features with relative constancy within a site, such as stem density）. Also, 0.25 ha or even larger plots may have a greater likelihood of capturing rare structural attributes （features possessing high randomness and spatial heterogeneity, such as volume of coarse woody debris） in our forest type. These findings have important implications for advisable sampling protocol （plot size and shape） to adequately capture information on forest habitat structure and diversity; such efforts must be based on a clear definition of which types are structural attributes to measure.

Upgraded Analytical Protocols in Bauxite Refining Industry Using Composite Sampling Approach to Minimize Laboratory Analysis Load

The laboratories in the bauxite processing industry are always under a heavy workload of sample collection, analysis, and compilation of the results. After size reduction from grinding mills, the samples of bauxite are collected after intervals of 3 to 4 hours. Large bauxite processing industries producing 1 million tons of pure aluminium can have three grinding mills. Thus, the total number of samples to be tested in one day reaches a figure of 18 to 24. The sample of bauxite ore coming from the grinding mill is tested for its particle size and composition. For testing the composition, the bauxite ore sample is first prepared by fusing it with X-ray flux. Then the sample is sent for X-ray fluorescence analysis. Afterwards, the crucibles are washed in ultrasonic baths to be used for the next testing. The whole procedure takes about 2 - 3 hours. With a large number of samples reaching the laboratory, the chances of error in composition analysis increase. In this study, we have used a composite sampling methodology to reduce the number of samples reaching the laboratory without compromising their validity. The results of the average composition of fifteen samples were measured against composite samples. The mean of difference was calculated. The standard deviation and paired t-test values were evaluated against predetermined critical values obtained using a two-tailed test. It was found from the results that paired test-t values were much lower than the critical values thus validating the composition attained through composite sampling. The composite sampling approach not only reduced the number of samples but also the chemicals used in the laboratory. The objective of improved analytical protocol to reduce the number of samples reaching the laboratory was successfully achieved without compromising the quality of analytical results.

A Functional Sensing Model and a Case Study in Household Electricity Usage Sensing

Sensing is a fundamental process to acquire information in the physical world for computation. Existing models treat a sensing process as an indivisible whole, such that sampling and reconstructing of signals are designed to be highly associated with each other in a unified procedure. These strongly coupled sensing systems are efficient, but usually lack reusability and upgradeability. We propose a functional sensing model called SDR （Sampling-Design-Reconstruction） to decouple a sensing process into two modules： sampling protocol and reconstruction algorithm. The core of this decoupling is a design space, which is a common data structure constructed using functions of the sensing target as prior knowledge, to seamlessly bridge the sampling protocol and reconstruction household electricity usage sensing systems can be successfully algorithm together. We demonstrate that existing types of decoupled by introducing corresponding design spaces.