珊溪水利枢纽工程涉淹区移民家庭社会经济状况分析

对浙江省珊溪水利枢纽工程涉淹区移民家庭当前社会经济状况进行了分析，并以分析结果为依据，提出了完善移民规划和优化移民安置方法的建议。     

Compressive sampling-based strategy for enhancing ADCs resolution

The paper deals with the problem of simultaneously enhancing both horizontal and vertical resolution of analog-to-digital converters, with specific regard to low-cost conversion systems. To this aim, the authors propose the combined exploitation of a suitable Compressive Sampling (CS) approach and a proper digital signal processing stage. In particular, starting from a reduced number of digitized samples, the proposed CS-based sampling approach allows to recover an oversampled version of the input signal, whose spectral content is properly shaped to reject the most of in-band noise. The successive processing stage, implementing a low-pass filter, is mandated to drastically attenuate out-of-band noise components.Tests carried out on an actual microcontroller (namely, PIC32MX360L512 by Microchip) evidence the promising performance of the proposed sampling strategy. Results obtained either on single tone or multisine signals highlight a gain up to 3.5 bits in vertical resolution, while the sample rate increases 50 times with respect to the actual one adopted to randomly sample the input signal of interest.     

Distribution of Quasimidranges and Associated Mixtures

In this paper are presented and discussed tabulations of the cumulative distribution functions of quasimidranges and mixtures of quasimidranges of samples from a normal distribution. The tabulations extend up to samples of size 21. For quasimidranges not involving the extreme values of the sample, the departure from normality is very small. The physical problem giving rise to the mixtures is discussed.     

Semi‐volatile organic compounds in the air and dust of 30 French schools: a pilot study

The contamination of indoor environments with chemical compounds released by materials and furniture, such as semi‐volatile organic compounds (SVOCs), is less documented in schools than in dwellings—yet children spend 16% of their time in schools, where they can also be exposed. This study is one of the first to describe the contamination of the air and dust of 90 classrooms from 30 nursery and primary schools by 55 SVOCs, including pesticides, phosphoric esters, musks, polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), phthalates, and polybromodiphenylethers (PBDEs). Air samples were collected using an active sampling method, and dust samples were collected via two sampling methods (wiping and vacuum cleaning). In air, the highest concentrations (median >100 ng/m³) were measured for diisobutyl phthalate (DiBP), dibutyl phthalate (DBP), diethyl phthalate (DEP), bis(2‐ethylhexyl) phthalate (DEHP), and galaxolide. In dust, the highest concentrations (median >30 μg/g) were found for DEHP, diisononyl phthalate (DiNP), DiBP, and DBP. An attempt to compare two floor dust sampling methods using a single unit (ng/m²) was carried out. SVOC concentrations were higher in wiped dust, but frequencies of quantification were greater in vacuumed dust.     

The Inverse of a Centrosymmetric Matrix

In this paper we have obtained, following Craig's procedure, a simplified form for the ASN of a single sampling plan under curtailment arising from observation of enough nondefectives to accept a lot or enough defectives to reject a lot.     

The effects of applying electric fields on the mass spectrometric sampling of positive and negative ions from a flame at atmospheric pressure

Flames are plasmas, because they contain free electrons and both positive and negative ions. The concentrations of ions in a flat flame, burning at 1 bar, have been measured by continuously sampling the hot (2400 K) gas into a mass spectrometer at low pressure. The voltage, Δ?, between the metallic burner and the plate holding the metallic sampling nozzle was varied; also, the flame was seeded with an alkali metal and doped with much larger quantities (mole fraction ?1.7%) of chlorine. Currents of ions such as K⁺ and Cl⁻ were measured with the mass spectrometer for different Δ? and indicated that the sampling nozzle repels free electrons, when it is at a negative potential with respect to the burner (Δ? < 0); consequently the nozzle is then covered by a cathodic sheath of positive ions. Likewise, when Δ? ? 0, the inlet orifice is covered by charged species from the plasma, forming an anodic sheath, from which some electrons reach the nozzle; also some positive and negative ions follow them and so leave the sample. Because the sampled gas is accelerated to a Mach number of unity on entering the inlet orifice, some ions have enough momentum to pass through both a sheath and the entrance hole into the mass spectrometer. The measurements enabled the non-uniform, electric potential between the burner and the plate housing the sampling nozzle to be sketched. The thicknesses of the sheaths were also measured; a cathodic sheath of positive ions is much thicker than an anodic plasma sheath. Also, for Δ? between zero and ∼+30 V, the sheath around the inlet orifice is at its thinnest and the current detected for positive ions a maximum. This is when quantitative measurements of concentrations should be made for positive or negative ions. This study reveals the importance of the electron concentration, the diameter of the inlet orifice, the presence of a halogen, and Δ?, for determining the thicknesses of these sheaths, which do affect the sampling of ions. With chlorine in the flame, the equilibrium: H + C1⁻ = e⁻ + HC1 is sufficiently fast to be maintained, whilst the sampled gas passes through the inlet orifice. This equilibrium usually freezes at some point during the sample’s subsequent, supersonic expansion into the first vacuum chamber; freezing temperatures were deduced. Also the additional cooling of a sample by heat transfer to the sampling nozzle was estimated. It can be difficult to measure accurately the concentration of a negative ion in a flame, because negative ions, unlike positive ones, are often lost during sampling by participating with free electrons in such a chemical equilibrium, which shifts while the sample is cooled.     

Selective body biasing for post-silicon tuning of sub-threshold designs: A semi-infinite programming approach with Incremental Hypercubic Sampling

Sub-threshold designs have become a popular option in many energy constrained applications. However, a major bottleneck for these designs is the challenge in attaining timing closure. Most of the paths in sub-threshold designs can become critical paths due to the purely random process variation on threshold voltage, which exponentially impacts the gate delay. In order to address timing violations caused by process variation, post-silicon tuning is widely used through body biasing technology, which incurs heavy power and area overhead. Therefore, it is imperative to select only a small group of the gates with body biasing for post-silicon-tuning. In this paper, we first formulate this problem as a linear semi-infinite programming (LSIP). Then an efficient algorithm based on the novel concept of Incremental Hypercubic Sampling (IHCS), specially tailored to the problem structure, is proposed along with the convergence analysis. Compared with the state-of-the-art approach based on adaptive filtering, experimental results on industrial designs using 65 nm sub-threshold library demonstrate that our proposed IHCS approach can improve the pass rate by up to 7.3× with a speed up to 4.1×, using the same number of body biasing gates with about the same power consumption.     

A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—over-sampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates.     

Some Statistical Aspects of the Economics of Analytical Testing

In establishing an economical testing program the costs of routine analytical tests must be weighed against losses due to shipping material which is either above or below stated specifications. A method for obtaining an optimum sampling and testing procedure is described and examples given.     

A Bayesian approach to model dispersal for decision support

In agricultural and environmental sciences dispersal models are often used for risk assessment to predict the risk associated with a given configuration and also to test scenarios that are likely to minimise those risks. Like any biological process, dispersal is subject to biological, climatic and environmental variability and its prediction relies on models and parameter values which can only approximate the real processes. In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field. This method includes the design of candidate models, their calibration, selection and evaluation on an independent dataset. A group of models was identified that is sufficiently robust to be used for prediction purpose. The group of models allows to include local information and it reflects reliably enough the observed variability in the data so that probabilistic model predictions can be performed and used to quantify risk under different scenarios or derive optimal sampling schemes.