A Review of the Frictional Classification of Iowa Aggregates, MLR-81-04, 1981

The Iowa D.O.T. has a classification system designed to rate coarse aggregates as to their skid resistant characteristics. Aggregates have been classified into five functional types, with a Type 1 being the most skid resistant. A complete description of the classification system can be found in the Office of Materials Instructional Memorandum T-203. Due to the variability of ledges within any given quarry the classification of individual ledges becomes necessary. The type of aggregate is then specified for each asphaltic concrete surface course. As various aggregates become used in a.c. paving, there is a continuing process of evaluating the frictional properties of the pavement surface. It is primarily through an effort of this sort that information on aggregate sources and individual ledges becomes more refined. This study is being conducted to provide that needed up-to-date information that can be used to monitor the aggregate classification system.

Classification and Pay Plans FY2016, June 2015

This document Classifications and Pay Plans is produced by the State of Iowa Executive Branch, Department of Administrative Services. Informational document about the pay plan codes and classification codes, how to use them.

Inheritance pattern and selection criteria for resistance to soybean cyst nematode races 3 and 9

The objective of this work was to determine soybean resistance inheritance to Heterodera glycines Ichinohe (soybean cyst nematode - SCN) races 3 and 9, as well as to evaluate the efficiency of direct and indirect selection in a soybean population of 112 recombinant inbred lines (RIL) derived from the resistant cultivar Hartwig. The experiment was conducted in a completely randomized design, in Londrina, PR, Brazil. The estimated narrow-sense heritabilities for resistance to races 3 and 9 were 80.67 and 77.97%. The genetic correlation coefficient (r g = 0.17; p<0.01) shows that some genetic components of resistance to these two races are inherited together. The greatest genetic gain by indirect selection was obtained to race 9, selecting to race 3 due to simpler inheritance of resistance to race 9 and not because these two races share common resistance genes. The resistance of cultivar Hartwig to races 3 and 9 is determined by 4 and 2 genes, respectively. One of these genes confers resistance to both races, explaining a fraction of the significant genetic correlation found between resistance to these SCN races. The inheritance pattern described indicates that selection for resistance to SCN must be performed for each race individually.

Spontaneous 24-h GHRELIN secretion pattern in fasting subjects : maintenance of a meal-related pattern

RESUME OBJECTIF: Outre la stimulation de la sécrétion d'hormone de croissance, la ghréline cause une prise pondérale par augmentation de l'assimilation d'aliments et réduction de la consommation lipidique. Il a été décrit que les taux de ghréline augmentent durant la phase pré-prandiale et diminuent juste après un repas, ceci suggérant qu'elle puisse jouer un rôle d'initiateur de la prise du repas. Cependant, la sécrétion de ghréline chez des sujets à jeun n'a pas encore été étudiée en détail. DESSIN: Les profils de sécrétion de ghréline pendant 24 heures ont été étudiés chez six sujets volontaires sains (3 femmes, 3 hommes; 25.5 ans; BMI 22.8 kg/m2) et comparés aux profils plasmatiques de l'hormone de croissance, de l'insuline et du glucose. METHODE: Des échantillons sanguins ont été prélevés toutes les 20 minutes pendant 24 heures et les taux de ghréline ont été mesurés par radio-immuno essai, utilisant un anticorps polyclonal de lapin. Le profil circadien de la sécrétion de ghréline (cluster analysis) a été évalué. RESULTATS: Une augmentation puis une diminution spontanée des taux de ghréline ont été observées aux moments où les sujets auraient habituellement mangé. La ghréline a été sécrétée de façon pulsatile avec approximativement 8 pics par 24 heures. Une diminution générale des taux de ghréline a également été observée durant la période d'étude. Aucune corrélation n'a pu être observée entre les taux de ghréline, d'homione de croissance, d'insuline et de glucose. CONCLUSIONS: Cette étude montre que pendant une période de jeûne les taux de ghréline suivent un profil similaire à ceux décrits chez des sujets mangeant 3 fois par jour. Durant le jeûne, l'hormone de croissance, l'insuline et le glucose ne semblent pas être impliqués dans la régulation de la sécrétion de ghréline. En outre, nous avons observé que la sécrétion de ghréline est pulsatile. La variation des taux de ghréline, indépendamment des repas, chez des sujets à jeun, renforce les observations préalables selon lesquelles le système nerveux central est primairement impliqué dans la régulation de la prise alimentaire. ABSTRACT: OBJECTIVE: Ghrelin stimulates GH release and causes weight gain through increased food intake and reduced fat utiIization. Ghrelin levels were shown to rise in the preprandial period and decrease shortly after meal consumption, suggesting a role as a possible meal initiator. However, ghrelin secretion in fasting subjects has not yet been studied in detail. DESIGN: 24-h ghrelin profiles were studied in six healthy volunteers (three females; 25.5 years; body mass index 22.8 kg/m2) and compared with GH, insulin and glucose levels. METHODS: Blood samples were taken every 20 min during a 24-h fasting period and total ghrelin levels were measured by RIA using a polyclonal rabbit antibody. The circadian pattern of ghrelin secretion and pulsatility (Cluster analysis) were evaluated. RESULTS: An increase and spontaneous decrease in ghrelin were seen at the timepoints of customary meals. Ghrelin was secreted in a pulsatile manner with approximately 8 peaks/24 h. An overall decrease in ghrelin levels was observed during the study period. There was no correlation of ghrelin with GH, insulin or blood glucose levels. CONCLUSIONS: This pilot study indicates that fasting ghrelin profiles display a circadian pattern similar to that described in people eating three times per day. In a fasting condition. GH, insulin and glucose do not appear to be involved in ghrelin regulation. In addition, we round that ghrelin is secreted in a pulsatile pattern. The variation in ghrelin independently of meals in fasting subjects supports previous observations that it is the brain that is primarily involved in the regulation of meal initiation.

Typing less common ovarian tumors: A training tool based on a pattern-based algorithm applied to a set of 20 virtual slides.

Context: Ovarian tumors (OT) typing is a competency expected from pathologists, with significant clinical implications. OT however come in numerous different types, some rather rare, with the consequence of few opportunities for practice in some departments. Aim: Our aim was to design a tool for pathologists to train in less common OT typing. Method and Results: Representative slides of 20 less common OT were scanned (Nano Zoomer Digital Hamamatsu®) and the diagnostic algorithm proposed by Young and Scully applied to each case (Young RH and Scully RE, Seminars in Diagnostic Pathology 2001, 18: 161-235) to include: recognition of morphological pattern(s); shortlisting of differential diagnosis; proposition of relevant immunohistochemical markers. The next steps of this project will be: evaluation of the tool in several post-graduate training centers in Europe and Québec; improvement of its design based on evaluation results; diffusion to a larger public. Discussion: In clinical medicine, solving many cases is recognized as of utmost importance for a novice to become an expert. This project relies on the virtual slides technology to provide pathologists with a learning tool aimed at increasing their skills in OT typing. After due evaluation, this model might be extended to other uncommon tumors.

Mutations in tubulin genes are frequent causes of various foetal malformations of cortical development including microlissencephaly.

Complex cortical malformations associated with mutations in tubulin genes are commonly referred to as "Tubulinopathies". To further characterize the mutation frequency and phenotypes associated with tubulin mutations, we studied a cohort of 60 foetal cases. Twenty-six tubulin mutations were identified, of which TUBA1A mutations were the most prevalent (19 cases), followed by TUBB2B (6 cases) and TUBB3 (one case). Three subtypes clearly emerged. The most frequent (n = 13) was microlissencephaly with corpus callosum agenesis, severely hypoplastic brainstem and cerebellum. The cortical plate was either absent (6/13), with a 2-3 layered pattern (5/13) or less frequently thickened (2/13), often associated with neuroglial overmigration (4/13). All cases had voluminous germinal zones and ganglionic eminences. The second subtype was lissencephaly (n = 7), either classical (4/7) or associated with cerebellar hypoplasia (3/7) with corpus callosum agenesis (6/7). All foetuses with lissencephaly and cerebellar hypoplasia carried distinct TUBA1A mutations, while those with classical lissencephaly harbored recurrent mutations in TUBA1A (3 cases) or TUBB2B (1 case). The third group was polymicrogyria-like cortical dysplasia (n = 6), consisting of asymmetric multifocal or generalized polymicrogyria with inconstant corpus callosum agenesis (4/6) and hypoplastic brainstem and cerebellum (3/6). Polymicrogyria was either unlayered or 4-layered with neuronal heterotopias (5/6) and occasional focal neuroglial overmigration (2/6). Three had TUBA1A mutations and 3 TUBB2B mutations. Foetal TUBA1A tubulinopathies most often consist in microlissencephaly or classical lissencephaly with corpus callosum agenesis, but polymicrogyria may also occur. Conversely, TUBB2B mutations are responsible for either polymicrogyria (4/6) or microlissencephaly (2/6).

Classification of very high spatial resolution imagery using mathematical morphology and support vector machines

We investigate the relevance of morphological operators for the classification of land use in urban scenes using submetric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.

Loss of mating flight and shift in the pattern of carbohydrate storage in sexuals of ants (Hymenoptera; Formicidae)

In ants, energy for flying is derived from carbohydrates (glycogen and free sugars). The amount of these substrates was compared in sexuals participating or not participating in mating flights. Results show that in participating females (Lasius niger, L. flavus, Myrmica scabrinodis, Formica rufa, F. polyctena, F. lugubris), the amount of carbohydrates, especially glycogen, was higher than in non-participating females (Cataglyphis cursor, Iridomyrmex humilis). Similarly, male C. cursor and I. humilis which fly, exhibit a much higher carbohydrate content than do the non-flying females of these species. Furthermore, the quantity of carbohydrates stored was generally higher in males than in females for each species. These results are discussed with regard to the loss of the nuptial flight by some species of ants.

Prognosis in Duke's B colorectal carcinoma: the Jass classification revisited.

OBJECTIVE: To assess whether Jass staging enhances prognostic prediction in Dukes' B colorectal carcinoma. DESIGN: A historical cohort observational study. SETTING: A university tertiary care centre, Switzerland. SUBJECTS: 108 consecutive patients. INTERVENTIONS: Curative resection of Dukes' B colorectal carcinoma between January 1985 and December 1988, Patients with familial adenomatous polyposis; hereditary non-polyposis colorectal cancer; Crohns' disease; ulcerative colitis and synchronous and recurrent tumours were excluded. A comparable group of 155 consecutive patients with Dukes' C carcinoma were included for reference purposes. MAIN OUTCOME MEASURES: Disease free and overall survival for Dukes' B and overall survival for Dukes' C tumours. RESULTS: Dukes' B tumours in Jass group III or with an infiltrated margin had a significantly worse disease-free survival (p = 0.001 and 0.0001, respectively) and those with infiltrated margins had a significantly worse overall survival (p = 0.002). Overall survival among those with Dukes' B Jass III and Dukes' B with infiltrated margins was no better than overall survival among all patients with Dukes' C tumours. CONCLUSION: Jass staging and the nature of the margin of invasion allow patients undergoing curative surgery for Dukes' B colorectal carcinoma to be separated into prognostic groups. A group of patients with Dukes' B tumours whose prognosis is inseparable from those with Dukes' C tumours can be identified, the nature of the margin of invasion being used to classify a larger number of patients.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Sulfur isotope variations from orebody to hand-specimen scale at the Mezica lead-zinc deposit, Slovenia: a predominantly biogenic pattern

The Mississippi Valley-type (MVT) Pb-Zn ore district at Mezica is hosted by Middle to Upper Triassic platform carbonate rocks in the Northern Karavanke/Drau Range geotectonic units of the Eastern Alps, northeastern Slovenia. The mineralization at Mezica covers an area of 64 km(2) with more than 350 orebodies and numerous galena and sphalerite occurrences, which formed epigenetically, both conformable and discordant to bedding. While knowledge on the style of mineralization has grown considerably, the origin of discordant mineralization is still debated. Sulfur stable isotope analyses of 149 sulfide samples from the different types of orebodies provide new insights on the genesis of these mineralizations and their relationship. Over the whole mining district, sphalerite and galena have delta(34)S values in the range of -24.7 to -1.5% VCDT (-13.5 +/- 5.0%) and -24.7 to -1.4% (-10.7 +/- 5.9%), respectively. These values are in the range of the main MVT deposits of the Drau Range. All sulfide delta(34)S values are negative within a broad range, with delta(34)S(pyrite) < delta(34)S(sphalerite) < delta(34)S(galena) for both conformable and discordant orebodies, indicating isotopically heterogeneous H(2)S in the ore-forming fluids and precipitation of the sulfides at thermodynamic disequilibrium. This clearly supports that the main sulfide sulfur originates from bacterially mediated reduction (BSR) of Middle to Upper Triassic seawater sulfate or evaporite sulfate. Thermochemical sulfate reduction (TSR) by organic compounds contributed a minor amount of (34)S-enriched H(2)S to the ore fluid. The variations of delta(34)S values of galena and coarse-grained sphalerite at orefield scale are generally larger than the differences observed in single hand specimens. The progressively more negative delta(34)S values with time along the different sphalerite generations are consistent with mixing of different H(2)S sources, with a decreasing contribution of H(2)S from regional TSR, and an increase from a local H(2)S reservoir produced by BSR (i.e., sedimentary biogenic pyrite, organo-sulfur compounds). Galena in discordant ore (-11.9 to -1.7%; -7.0 +/- 2.7%, n=12) tends to be depleted in (34)S compared with conformable ore (-24.7 to -2.8%, -11.7 +/- 6.2%, n=39). A similar trend is observed from fine-crystalline sphalerite I to coarse open-space filling sphalerite II. Some variation of the sulfide delta(34)S values is attributed to the inherent variability of bacterial sulfate reduction, including metabolic recycling in a locally partially closed system and contribution of H(2)S from hydrolysis of biogenic pyrite and thermal cracking of organo-sulfur compounds. The results suggest that the conformable orebodies originated by mixing of hydrothermal saline metal-rich fluid with H(2)S-rich pore waters during late burial diagenesis, while the discordant orebodies formed by mobilization of the earlier conformable mineralization.

Differential pattern of glycogen accumulation after protein phosphatase 1 glycogen-targeting subunit PPP1R6 overexpression, compared to PPP1R3C and PPP1R3A, in skeletal muscle cells

Background PPP1R6 is a protein phosphatase 1 glycogen-targeting subunit (PP1-GTS) abundant in skeletal muscle with an undefined metabolic control role. Here PPP1R6 effects on myotube glycogen metabolism, particle size and subcellular distribution are examined and compared with PPP1R3C/PTG and PPP1R3A/GM. Results PPP1R6 overexpression activates glycogen synthase (GS), reduces its phosphorylation at Ser-641/0 and increases the extracted and cytochemically-stained glycogen content, less than PTG but more than GM. PPP1R6 does not change glycogen phosphorylase activity. All tested PP1-GTS-cells have more glycogen particles than controls as found by electron microscopy of myotube sections. Glycogen particle size is distributed for all cell-types in a continuous range, but PPP1R6 forms smaller particles (mean diameter 14.4 nm) than PTG (36.9 nm) and GM (28.3 nm) or those in control cells (29.2 nm). Both PPP1R6- and GM-derived glycogen particles are in cytosol associated with cellular structures; PTG-derived glycogen is found in membrane- and organelle-devoid cytosolic glycogen-rich areas; and glycogen particles are dispersed in the cytosol in control cells. A tagged PPP1R6 protein at the C-terminus with EGFP shows a diffuse cytosol pattern in glucose-replete and -depleted cells and a punctuate pattern surrounding the nucleus in glucose-depleted cells, which colocates with RFP tagged with the Golgi targeting domain of β-1,4-galactosyltransferase, according to a computational prediction for PPP1R6 Golgi location. Conclusions PPP1R6 exerts a powerful glycogenic effect in cultured muscle cells, more than GM and less than PTG. PPP1R6 protein translocates from a Golgi to cytosolic location in response to glucose. The molecular size and subcellular location of myotube glycogen particles is determined by the PPP1R6, PTG and GM scaffolding.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

The glasses of the transept's rosette of the Cathedral of Tarragona: characterization, classification and decay.

The glasses of the rosette forming the main window of the transept of the Gothic Cathedral of Tarragona have been characterised by means of SEM/EDS, XRD, FTIR and electronic microprobe. The multivariate statistical treatment of these data allow to establish a classification of the samples forming groups having an historical significance and reflecting ancient restorations. Furthermore, the decay patterns and mechanisms have been determined and the weathering by-products characterised. It has been demonstrated a clear influence of the bioactivity in the decay of these glasses, which activity is partially controlled by the chemical composition of the glasses.