Subsumption between queries to object-oriented databases

Most work on query optimization in relational and object-oriented databases has concentrated on tuning algebraic expressions and the physical access to the database contents. The attention to semantic query optimization, however, has been restricted due to its inherent complexity. We take a second look at semantic query optimization in object-oriented databases and find that reasoning techniques for concept languages developed in Artificial Intelligence apply to this problem because concept languages have been tailored for efficiency and their semantics is compatible with class and query definitions in object-oriented databases. We propose a query optimizer that recognizes subset relationships between a query and a view (a simpler query whose answer is stored) in polynomial time

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstruc tures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parame ters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Bainite is an essential constituent of modern high strength steels. In addition to the still great challenge of characterization, the classification of bainite poses difficulties. Challenges when dealing with bainite are the variety and amount of involved phases, the fineness and complexity of the structures and that there is often no consensus among human experts in labeling and classifying those. Therefore, an objective and reproducible characterization and classification is crucial. To achieve this, it is necessary to analyze the substructure of bainite using scanning electron microscope (SEM). This work will present how textural parameters (Haralick features and local binary pattern) calculated from SEM images, taken from specifically produced benchmark samples with defined structures, can be used to distinguish different bainitic microstructures by using machine learning techniques (support vector machine). For the classification task of distinguishing pearlite, granular, degenerate upper, upper and lower bainite as well as martensite a classification accuracy of 91.80% was achieved, by combining Haralick features and local binary pattern

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning

Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable since it is hardly feasible to create reproducible training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs, as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models in order to classify LOM or SEM images. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts

Control-oriented modeling of a LiBr/H2O absorption heat pumping device and experimental validation

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) are devices that use thermal energy instead of electricity to generate heating and cooling, thereby facilitating the use of waste heat and renewable energy sources such as solar or geothermal energy. Despite this benefit, widespread use of AHPDs is still limited. One reason for this is partly unsatisfactory control performance under varying operating conditions, which can result in poor modulation and part load capability. A promising approach to tackle this issue is using dynamic, model-based control strategies, whose effectiveness, however, strongly depend on the model being used. This paper therefore focuses on the derivation of a viable dynamic model to be used for such model-based control strategies for AHPDs such as state feedback or model-predictive control. The derived model is experimentally validated, showing good modeling accuracy. Its modeling accuracy is also compared to alternative model versions, that contain other heat transfer correlations, as a benchmark. Although the derived model is mathematically simple, it does have the structure of a nonlinear differential-algebraic system of equations. To obtain an even simpler model structure, linearization at an operating point is discussed to derive a model in linear state space representation. The experimental validation shows that the linear model does have slightly worse steady-state accuracy, but that the dynamic accuracy seems to be almost unaffected by the linearization. The presented new modeling approach is considered suitable to be used as a basis for the design of advanced, model-based control strategies, ultimately aiming to improve the modulation and part load capability of AHPDs

Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy

The high-temperature austenite phase is the initial state of practically all technologically relevant hot forming and heat treatment operations in steel processing. The phenomena occurring in austenite, such as recrystallization or grain growth, can have a decisive influence on the subsequent properties of the material. After the hot forming or heat treatment process, however, the austenite transforms into other microstructural constituents and information on the prior austenite morphology are no longer directly accessible. There are established methods available for reconstructing former austenite grain boundaries via metallographic etching or electron backscatter diffraction (EBSD) which both exhibit shortcomings. While etching is often difficult to reproduce and strongly depend on the investigated steel’s alloying concept, EBSD acquisition and reconstruction is rather time-consuming. But in fact, though, light optical micrographs of steels contrasted with conventional Nital etchant also contain information about the former austenite grains. However, relevant features are not directly apparent or accessible with conventional segmentation approaches. This work presents a deep learning (DL) segmentation of prior austenite grains (PAG) from Nital etched light optical micrographs. The basis for successful segmentation is a correlative characterization from EBSD, light and scanning electron microscopy to specify the ground truth required for supervised learning. The DL model shows good and robust segmentation results. While the intersection over union of 70% does not fully reflect the model performance due to the inherent uncertainty in PAG estimation, a mean error of 6.1% in mean grain size derived from the segmentation clearly shows the high quality of the result

Survey of Community Pharmacy Customers’ Medication Storage and Disposal Methods

Many people store medications in bathrooms, which provide a moist, humid environment that speeds up the breakdown process of medications. The proper way to store medicines is in a cool, dry place out of the reach of children. Every year medications are also disposed of improperly presenting a risk to both humans and the environment. About one-third of all sold medications are unused. The most common ways patients dispose of medications in the United States are flushing down the toilet or sink, and throwing them away. Because of this pharmaceuticals have been found in groundwater, and drinking water proving hazardous to both humans and ecosystems. In Congress today, both the Drug Free Water Act and the Safe Drug Disposal Act have been proposed to limit the disposal of pharmaceuticals in sewage systems, and provide the means of controlled substance disposal through take-back programs. In February 2007 the White House Office of National Drug Control Policy (ONDCP) established guidelines for the disposal of prescription medications. ONDCP guidelines are: take unused, unneeded or expired medications out of the original container, mix with an undesirable substance (such as coffee grounds), securely seal in impermeable containers, such as sealable bags, and throw into the trash. ONDCP recommends only flushing if the label or patient information specifies to do so. Taking advantage of community pharmaceutical take-back programs is highly encouraged

Global inventory of nitrogen oxide emissions constrained by space-based observations of NO2 columns

sions (37.7 Tg N yr #1 ) agrees closely with the GEIAbased a priori (36.4) and with the EDGAR 3.0 bottom-up inventory (36.6), but there are significant regional differences. A posteriori NO x emissions are higher by 50 -- 100% in the Po Valley, Tehran, and Riyadh urban areas, and by 25 -- 35% in Japan and South Africa. Biomass burning emissions from India, central Africa, and Brazil are lower by up to 50%; soil NO x emissions are appreciably higher in the western United States, the Sahel, and southern Europe

Atypical language organization following perinatal infarctions of the left hemisphere is associated with structural changes in right-hemispheric grey matter.

AIM To assess how atypical language organization after early left-hemispheric brain lesions affects grey matter in the contralesional hemisphere. METHOD This was a cross-sectional study with between-group comparisons of 14 patients (six female, 8-26 years) with perinatal left-hemispheric brain lesions (two arterial ischemic strokes, 11 periventricular haemorrhagic infarctions, one without classification) and 14 typically developing age-matched controls (TDC) with functional magnetic resonance imaging (fMRI) documented left-hemispheric language organization (six female, 8-28 years). MRI data were analysed with SPM12, CAT12, and custom scripts. Language lateralization indices were determined by fMRI within a prefrontal mask and right-hemispheric grey matter group differences by voxel-based morphometry (VBM). RESULTS FMRI revealed left-dominance in seven patients with typical language organization (TYP) and right-dominance in seven patients with atypical language organization (ATYP) of 14 patients. VBM analysis of all patients versus controls showed grey matter reductions in the middle temporal gyrus of patients. A comparison between the two patient subgroups revealed an increase of grey matter in the middle frontal gyrus in the ATYP group. Voxel-based regression analysis confirmed that grey matter increases in the middle frontal gyrus were correlated with atypical language organization. INTERPRETATION Compatible with a non-specific lesion effect, we found areas of grey matter reduction in patients as compared to TDC. The grey matter increase in the middle frontal gyrus seems to reflect a specific compensatory effect in patients with atypical language organization