如何在选用数据集与集合之间做出决策

在软件行业中，五年就像是一个地质时代。五年前，Microsoft宣布了自己的Micorsoft．NET Framework计划。从那以后，DataSet（数据集）才开始作为关键对象出现在基于．NET的应用程序中，执行多种数据相关的任务。也是五年前，DataSet被誉为ADO记录集（Recordset）的威力增强版。那么在．NET Framework出现之前，您是如何设计数据访问层（DAL，Data Access Layer）的呢？我想您一定是以ADO和它全能的Recordset对象——断开的、且可进行XML序列化的——为中心构建数据访问层。     

Scalable High‐Throughput Production of Modular Microgels for In Situ Assembly of Microporous Tissue Scaffolds

Hydrogel scaffolds that template the regeneration of tissue structures are widely explored; however, there is often a trade‐off between material properties, such as stiffness and interconnected pore size, that may be equally important in supporting tissue growth. Microporous annealed particle scaffolds are introduced to address this trade‐off while maintaining a flowable precursor; however, manufacturing throughput, reproducibility, and flexibility of hydrogel microparticle building blocks are limited, hindering widespread adoption. The scalable high‐throughput production of bioactive microgels for the formation of microporous tissue scaffolds in situ is presented. Using a parallelized step emulsification device, scalable high‐throughput generation of monodisperse microgels is achieved. Crosslinking is initiated downstream of droplet generation using pH modulation via proton acceptors dissolved in the oil phase. This approach enables continuous production of microgels for over 12 h while ensuring highly uniform physicochemical properties. Using this platform, the effects of local matrix stiffness on cell growth orthogonal to scaffold porosity are studied. Formation of injectable cell‐laden mechanically heterogeneous microporous scaffolds is also demonstrated. This approach is particularly suited for the formation of modular, multimaterial scaffolds in situ, which could be applied to 3D bioprinting or to form more complex scaffolds to enhance regeneration of irregular wounds.     

Further Studies on Thermal Evolution of Glass-Forming Gels

Glasses of composition Na₂O·2SiO₂ containing iron oxide are obtained by the gel technique. The results show that the iron atoms accelerate the formation of the glass, whereas when they are present in low percentage (∼2%), the temperature necessary to obtain the glass phase is higher. During heat treatment at 250°C, two silicates are formed: Na₂SiO₃ and Na₄SiO₄. When the iron content is low, they change at 650°C into Na₂Si₂O₅, but when the iron content is higher (∼6%), this silicate does not form and the system becomes vitreous.     

Stream water temperature prediction based on Gaussian process regression

The prediction of stream water temperature presents an interesting topic since the water temperature has a significant ecological and economical role, such as in species distribution, fishery, industry and agriculture water exploitation. The prediction of stream water temperature is usually based on appropriate mathematical model and measurements of different atmospheric factors. In this paper, a probabilistic approach to daily mean water temperature prediction is proposed. The resulting model is a combination of two Gaussian process regression models where the first model describes the long-term component of water temperature and the other model describes the short-term variations in water temperature. The proposed approach is developed even further by modeling the short-term variations with multiple Gaussian process regression models instead with a single one. Apart from that, variable selection procedure based on mutual information is presented which is suitable for input variable selection when nonlinear models for stream water prediction are developed. The proposed approach is compared with traditional modeling approaches on the measurements obtained on the Drava river in Croatia. The presented methodology can be used as a basis of the predictive tools for water resource managers.     

Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach

This paper presents an intelligent failure prediction system for oil and gas pipeline using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Since the past decade, the incidents of oil and gas pipeline leaks and failures which happened around the world are becoming more frequent and have caused loss of life, properties and irreversible environmental damages. This situation is due to the lack of a full-proof method of inspection on the condition of oil and gas pipelines. Onset of corrosion and other defects are undetected which cause unplanned shutdowns and disruption of energy supplies to consumers. Existing failure prediction systems for pipeline which use non-destructive testing (NDTs) methods are accurate, but they are deployed at pre-determined intervals which can be several months apart. Hence, a full-proof and reliable inspection method is required to continuously monitor the condition of oil and gas pipeline in order to provide sufficient information and time to oil and gas operators to plan and organize shutdowns before failures occur. Permanently installed long range ultrasonic transducers (LRUTs) offer a solution to this problem by providing an inspection platform that continuously monitor critical pipeline sections. Data are acquired in real-time and processed to make decision based on the condition of the pipe. The continuous nature of the data requires an automatic decision making software rather than manual inspection by operators. Support Vector Machines (SVMs) classification approach has been increasingly used in a multitude of domains including LRUT and has shown better performance than other classification algorithms. SVM is heavily dependent on the choice of kernel functions as well as fine tuning of the kernel and soft margin parameters. Hence it is unsuitable to be used in continuous monitoring of pipeline data where constant modifications of kernels and parameters are not unrealistic. This paper proposes a novel classification technique, namely Euclidean-Support Vector Machines (Euclidean-SVM), to make a decision on the integrity of the pipeline in a continuous monitoring environment. The results show that the classification accuracy of the Euclidean-SVM approach is not dependent on the choice of the kernel function and parameters when classifying data from pipes with simulated defects. Irrespective of the kernel function and parameters chosen, classification accuracy of the Euclidean-SVM is comparable and also higher in some cases than using conventional SVM. Hence, the Euclidean-SVM approach is ideally suited for classifying data from the oil and gas pipelines which are continuously monitored using LRUT.     

200 ns pulse high-voltage supply for terahertz field emission

We present a method of generating 200 ns high-voltage (up to 40 kV) pulses operating at repetition rates of up to 100 kHz, which may be synchronized with laser pulses. These supplies are simple to make and were developed for ultrafast terahertz pulse generation from GaAs photoconductive antennas using a high-repetition-rate regeneratively amplified laser. We also show an improvement in signal-to-noise ratio over a continuous dc bias field and application of the supply to terahertz pulse generation.     

Defining the relationship between fetal Doppler indices,abdominal circumference and growth rate in severe fetal growth restriction using functional linear discriminant analysis

The relationship between Doppler measurements, size and growth rate in fetal growth restriction has not been defined. We used functional linear discriminant analysis (FLDA) to investigate these parameters taking account of the difficulties inherent in exploring relationships between repeated observations from a small number of cases. In 40 fetuses with severe growth restriction, serial abdominal circumference (AC), umbilical, middle cerebral artery (MCA) and ductus venosus Doppler pulsatility index measurements were recorded. In 11 singleton fetuses with normal growth, umbilical artery pulsatility index only was measured. Data were expressed as z-scores in relation to gestation and analysed longitudinally using FLDA. In severe growth restriction, the Spearman correlation coefficients between umbilical artery pulsatility index and AC z-score, MCA pulsatility index and AC z-score and ductus venosus pulsatility index z-score and AC z-score were, respectively: −0.36, p = 4.4 × 10⁻⁷; 0.70, p = 1.1 × 10⁻¹⁷ and −0.50, p = 8.1 × 10⁻⁴. No relationship was seen between Doppler parameters and growth rate. There was no relationship between umbilical artery pulsatility index and AC nor growth rate in normally grown fetuses. In severe fetal growth restriction, Doppler changes are related to absolute fetal AC size, not growth rate.     

Untargeted metabolomics of human plasma reveal lipid markers unique to chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Chronic obstructive pulmonary disease (COPD) is characterised by airway inflammation and progressive airflow limitation, whereas idiopathic pulmonary fibrosis (IPF) is characterised by a restrictive pattern due to fibrosis and impaired gas exchange. We undertook metabolomic analysis of blood samples in IPF, COPD and healthy controls (HC) to determine differences in circulating molecules and identify novel pathogenic pathways. An untargeted metabolomics using an ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometer (UHPLC-QTOF-MS) was performed to profile plasma of patients with COPD (n = 21), and IPF (n = 24) in comparison to plasma from healthy controls (HC; n = 20). The most significant features were identified using multiple database matching. One-way ANOVA and variable importance in projection (VIP) scores were also used to highlight metabolites that influence the specific disease groups. Non-polar metabolites such as fatty acids (FA) and membrane lipids were well resolved and a total of 4805 features were identified. The most prominent metabolite composition differences in lipid mediators identified at ∼2–3 fold higher in both diseases compared to HC were palmitoleic acid, oleic acid and linoleic acid; and dihydrotestosterone was lower in both diseases. We demonstrated that COPD and IPF were characterised by systemic changes in lipid constituents such as essential FA sampled from circulating plasma.