How Principals Bridge and Buffer the New Demands of Teacher Quality and Accountability: A Mixed-Methods Analysis of Teacher Hiring

In this mixed-methods study, we examine the degree to which district-and building-level administrators accommodate teacher-quality and test-based accountability policies in their hiring practices. We find that administrators negotiated local hiring goals with characteristics emphasized by federal and state teacher-quality policies, such as knowledge of the subject and teaching skills. While district administrators and principals largely bridged to external certification requirements, some principals buffered their hiring decisions from the pressures of test-based accountability. Principals who bridged to test-based accountability gave greater weight to subject knowledge and teaching skills. We find that bridging and buffering differs by policy and cannot be easily applied to accountability policies. Specifically, separating the indirect effect of external accountability from other policies influencing principal hiring is difficult. Our analysis also highlights tensions among local, state, and federal policies regarding teacher quality and the potential of accountability to permeate noninstructional school decision making

Malignant Progression in Two Children with Multiple Osteochondromas

Multiple Osteochondromas (MO) is a disease of benign bony growths with a low incidence of malignant transformation. Secondary chondrosarcoma in children is rare even in children with MO. Making a diagnosis of malignancy in low-grade cartilage tumors is challenging and requires consideration of clinical, radiographic, and histopathological factors. We report two cases of skeletally immature patients with MO who presented with rapidly enlarging and radiographically aggressive lesions consistent with malignant transformation. Both underwent allograft reconstruction of the involved site with no signs of recurrence or metastatic disease at a minimum of four-year follow-up

MATLAB in electrochemistry: A review

International audienceMATLAB (MATrix LABoratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces and interfacing with programs written in other languages, including C, C ++ , Java, Fortran and Python. Electrochemistry is a branch of chemistry that studies the relationship between electricity, as a measurable and quantitative phenomenon, and identifiable chemical change, with either electricity considered an outcome of a particular chemical change or vice versa. MATLAB has obtained a wide range of applications in different fields of science and electrochemists are also using it for solving their problems which can help them to obtain more quantitative and qualitative information about systems under their studies. In this review, we are going to cast a look on different applications of MATLAB in electrochemistry and for each section, a number of selected articles published in the literature will be discussed and finally, the results will be summarized and concluded

Independent component analysis for the identification of sources of variation on an industrial nirs application

A Near Infrared Spectroscopy (NIRS) industrial application was developed by the LPF-Tagralia team, and transferred to a Spanish dehydrator company (Agrotécnica Extremeña S.L.) for the classification of dehydrator onion bulbs for breeding purposes. The automated operation of the system has allowed the classification of more than one million onion bulbs during seasons 2004 to 2008 (Table 1). The performance achieved by the original model (R2=0,65; SEC=2,28ºBrix) was enough for qualitative classification thanks to the broad range of variation of the initial population (18ºBrix). Nevertheless, a reduction of the classification performance of the model has been observed with the passing of seasons. One of the reasons put forward is the reduction of the range of variation that naturally occurs during a breeding process, the other is the variations in other parameters than the variable of interest but whose effects would probably be affecting the measurements [1]. This study points to the application of Independent Component Analysis (ICA) on this highly variable dataset coming from a NIRS industrial application for the identification of the different sources of variation present through seasons

Investigation of Origanum libanoticum Essential Oils Chemical Polymorphism by Independent Components Analysis (ICA)

International audienceThe essential oils obtained from Origanum libanoticum Boiss., a plant endemic to Lebanon, were analyzed by GC/MS. Seventy compounds were identified, covering till 99.8% of the total oil composition. All samples were p-cymene and/or β-caryophyllene chemotype, with variable percentage of other compounds such as α-pinene, myrcene, α-phellandrene, limonene, etc. Compared to traditional drying method, lyophilized samples provided the highest essential oil (EO) yields and yields were higher at flowering stage (Chouwen: 0.33% in 2013 and 0.32% in 2014; Qartaba: 0.27% in 2013 and 0.37% in 2014). According to independent components analysis (ICA), date and site of harvest, altitude and drying technique had no effect on the variation of O. libanoticum EO chemical composition. An annual variation of EOs composition was observed since a particular variation in some major components concentration was revealed monthly and annually between 2013 and 2014

Automatic de-noising of close-range hyperspectral images with a wavelength-specific shearlet-based image noise reduction method

Hyperspectral imaging (HSI) has become an essential tool for exploration of different spatially-resolved properties of materials in analytical chemistry. However, due to various technical factors such as detector sensitivity, choice of light source and experimental conditions, the recorded data contain noise. The presence of noise in the data limits the potential of different data processing tasks such as classification and can even make them ineffective. Therefore, reduction/removal of noise from the data is a useful step to improve the data modelling. In the present work, the potential of a wavelength-specific shearlet-based image noise reduction method was utilised for automatic de-noising of close-range HS images. The shearlet transform is a special type of composite wavelet transform that utilises the shearing properties of the images. The method first utilises the spectral correlation between wavelengths to distinguish between levels of noise present in different image planes of the data cube. Based on the level of noise present, the method adapts the use of the 2-D non-subsampled shearlet transform (NSST) coefficients obtained from each image plane to perform the spatial and spectral de-noising. Furthermore, the method was compared with two commonly used pixel-based spectral de-noising techniques, Savitzky-Golay (SAVGOL) smoothing and median filtering. The methods were compared using simulated data, with Gaussian and Gaussian and spike noise added, and real HSI data. As an application, the methods were tested to determine the efficacy of a visible-near infrared (VNIR) HSI camera to perform non-destructive automatic classification of six commercial tea products. De-noising with the shearlet-based method resulted in a visual improvement in the quality of the noisy image planes and the spectra of simulated and real HSI. The spectral correlation was highest with the shearlet-based method. The peak signal-to-noise ratio (PSNR) obtained using the shearlet-based method was higher than that for SAVGOL smoothing and median filtering. There was a clear improvement in the classification accuracy of the SVM models for both the simulated and real HSI data that had been de-noised using the shearlet-based method. The method presented is a promising technique for automatic de-noising of close-range HS images, especially when the amount of noise present is high and in consecutive wavelengths

Application of Multivariate Analysis, Support Vector Machines and Artificial Neural Network to the Processing of Nuclear Magnetic Resonance data of olive oil and fish oil samples for classification of geographic origin and discrimination between wild and farm fish.

Motivations Traceability and control of origin of food products are very important for the Consumers and for the European enforcement laboratories. For instance, The high added value of olive oil makes its control an important goal for EU producers and consumers. There is thus a need in developing analytical methods to ensure compliance with labeling, i.e.the control of geographical origin giving also support to the denominated protected origin (DPO) policy, and the determination of the genuineness of the product by the detection of eventual adulterations. Futhermore , EU regulations requires that origin, wild or farmed as well as geographic origin, of fish sold on the retail market be available to the consumers. Modern analytical techniques such as Nuclear Magnetic Resonance (NMR) provide very informative data on the composition in fatty acids and in other constituents of vegetable oils and fish oils. The combination of 1H NMR fingerprinting with multivariate analysis provides an original approach to study the profile of these oils in relation with geographical origin of olive oil or for discrimination between wild or farm origin for fish like salmons. Methods Concerning the experiment on fish oil, we used Support vector machines (SVMs) as a novel learning machine in the authentication of the origin of salmon. SVMs have the advantage of relying on a well-developed theory and have already proved to be successful in a number of practical applications. The method requires a very simple sample preparation of the fish oils extracted from the white muscle of salmon samples. Multivariate (chemometric) techniques are able to filter out the most relevant information from a spectrum, e.g. for a classification. In the experiment on olive oil samples, the principal component analysis (PCA) was carried out on the ~12,000 variables (chemical shifts) and four data sets were defined prior to PCA. Linear discriminant analysis (LDA) of the first 50 PC\u2019s was applied for classification of olive oil samples according to the geographic origin and year of production. The data analysis has been carried out with and without outliers, as well. Variable selection for LDA was achieved using: (i) the best five variables and (ii) an interactive forward stepwise manner. Results The use of SVMs for the discrimination between wild and farm salmon provides a new and effective method that eliminates the possibility of fraud through misrepresentation of the country of origin of salmon. The SVM has been able to distinguish correctly between the wild and farmed salmon; however ca. 5% of the country of origins were misclassified. Using LDA on the external validation sets the correct classification of olive oil varied between 47 and 75% (random selection), and between 35 and 92% (Kennard\u2013Stone selection (KS)) depending on geographic origin (country) and production years. A similar success rate could be achieved using partial least squares discriminant analysis (PLS DA). The success rate can be considerably improved by using probabilistic neural networks (PNN). Correct classification by PNN varied between 58 and 100% on the external validation sets. Other chemometric techniques, such as multiple linear regression, or generalized pair-wise correlation, did not give better results. Acknowledgements The authors are grateful to the Europeanproject COFAWS (European Commission DG RTD FP5 project GRD2\u20132000\u201331813) and to all the collaborators from the partners of this project (Eurofins Scientific (Nantes- France), North Atlantic Fisheries College (Scalloway, Shetland Islands - United Kingdom), SINTEF Fisheries and Aquaculture (Trondheim-Norway), Joint Research Centre (Ispra-Italy)) who contributed to the collection and preparation of fish samples, and for the authorization to exploit their NMR data in this work

Policy Brief MIX AND MATCH: WHAT PRINCIPALS REALLY LOOK FOR WHEN HIRING TEACHERS

Abstract The vast majority of research and policy related to teacher quality focuses on the supply of teachers and ignores teacher demand. In particular, the important role of school principals in hiring teachers is rarely considered. Using interviews of school principals in a midsized Florida school district, we provide an exploratory mixed methods analysis of the teacher characteristics principals prefer. Our findings contradict the conventional wisdom that principals undervalue content knowledge and intelligence. Principals in our study ranked content knowledge third among a list of twelve characteristics. Intelligence does appear less important at first glance, but this is apparently because principals believe all applicants who meet certification requirements meet a minimum threshold on intelligence and because some intelligent teachers have difficulty connecting with students. More generally, we find that principals prefer an "individual mix" of personal and professional qualities. They also create an "organizational mix," hiring teachers who differ from those already in the school in terms of race, gender, experience, and skills, and an "organizational match," in which teachers have similar work habits and a high propensity to remain with the school over time. Because of tenure rules, many principals also prefer less experienced (untenured) teachers, even though research suggests that they are less effective

Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtaine