An Adhesive bonded surface mechanical treatment is one of necessary steps in an adhesive bonding technology application. A grit blasting ranges among prospective adhesive bonded surface treatment technologies. A resulted surface structure depends on many factors. A grit blasting material grain size together with specific fraction size belongs to basic parameters. this paper deals with an influence of the grain on surface roughness parameters as well as on a charge of a adhesive bond strength. and Mechanická úprava lepeného povrchu je jedním z nezbytných kroků při aplikaci technologie lepení. Mezi perspektivní technologie úpravy lepeného povrchu patří tryskání. Výsledná textura povrchu je závislá na mnoha parametrech. Mezi základní parametry patří zrnitost tryskaného materiálu a s tím související měrný rozměr zrna. Článek pojednává o vlivu rozměru zrna na parametry drsnosti povrchu a rovněž i jejich vliv na změnu pevnosti lepeného spoje.
An assembly neural network based on the binary Hebbian rule is suggested for pattern recognition. The network consists of several sub-networks according to the number of classes to be recognized. Each sub-network consists of several neural columns according to the dimensionality of the signal space so that the value of each signal component is encoded by activity of adjacent neurons of the column. A new recognition algorithm is presented which realizes the nearest-neighbor method in the assembly neural network. Computer simulation of the network is performed. The model is tested on a texture segmentation task. The experiments have demonstrated that the network is able to segment reasonably real-world texture images.
Software measurements provide developers and software managers with information on various aspects of software systems, such as effectiveness, functionality, maintainability, or the effort and cost needed to develop a software system. Based on collected data, models capturing some aspects of software development process can be constructed. A good model should allow software professionals to not only evaluate current or completed projects but also predict future projects with an acceptable degree of accuracy.
Artificial neural networks employ a parallel distributed processing paradigm for learning of system and data behavior. Some network models, such as multilayer perceptrons, can be used to build models with universal approximation capabilities. This paper describes an application in which neural networks are used to capture the behavior of several sets of software development related data. The goal of the experiment is to gain an insight into the modeling of software data, and to evaluate the quality of available data sets and some existing conventional models.
In this paper, we present an approach to evaluate the hydrological alterations of a temporary river. In these rivers, it is expected that anthropogenic pressures largely modify low-flow components of the flow regime with consequences for aquatic habitat and diversity in invertebrate species. First, by using a simple hydrological index (IARI) river segments of the Celone stream (southern Italy) whose hydrological regime is significantly influenced by anthropogenic activities have been identified. Hydrological alteration has been further classified through the analysis of two metrics: the degree (Mf) and the predictability of dry flow conditions (Sd6). Measured streamflow data were used to calculate the metrics in present conditions (impacted). Given the lack of data from pristine conditions, simulated streamflow time series were used to calculate the metrics in reference conditions. The Soil and Water Assessment Tool (SWAT) model was used to estimate daily natural streamflow. Hydrological alterations associated with water abstractions, point discharges and the presence of a reservoir were assessed by comparing the metrics (Mf, Sd6) before and after the impacts. The results show that the hydrological regime of the river segment located in the upper part of the basin is slightly altered, while the regime of the river segment downstream of the reservoir is heavily altered. This approach is intended for use with ecological metrics in defining the water quality status and in planning streamflow management activities.
Lagtimes and times of concentration are frequently determined parameters in hydrological design and greatly aid in understanding natural watershed dynamics. In unmonitored catchments, they are usually calculated using empirical or semiempirical equations developed in other studies, without critically considering where those equations were obtained and what basic assumptions they entailed. In this study, we determined the lagtimes (LT) between the middle point of rainfall events and the discharge peaks in a watershed characterized by volcanic soils and swamp forests in
southern Chile. Our results were compared with calculations from 24 equations found in the literature. The mean LT for 100 episodes was 20 hours (ranging between 0.6–58.5 hours). Most formulae that only included physiographic predictors severely underestimated the mean LT, while those including the rainfall intensity or stream velocity showed better agreement with the average value. The duration of the rainfall events related significantly and positively with LTs. Thus, we accounted for varying LTs within the same watershed by including the rainfall duration in the equations that showed the best results, consequently improving our predictions. Izzard and velocity methods are recommended, and we suggest that lagtimes and times of concentration must be locally determined with hyetograph-hydrograph analyses, in addition to explicitly considering precipitation patterns.
The complex environmental research (hydrology, vegetation, soils and ground water) has been carried out in nature reserves, located on the Danube banks within the zone of broad-leaved forests in Germany. Under comparison were terrestrial ecosystems along the regulated and natural rivers. It was established that the weirs, dams with low head of water and small artificial reservoirs affects upon the vegetation and soils of floodplains to be manifested some decades later. A comprehensive analysis of trends in mean annual water level, water flow and the precipitation for the long period revealed the significant influence of natural long-term variability of the water content on the vegetation dynamics in floodplains. The methods, suggested by the authors made possible to assess the after-effects upon floodplain ecosystems due to changes in the river flow regime caused by different hydrotechnical constructions. and V prírodných rezerváciách v blízkosti Dunaja (v Nemecku) v oblasti pokrytej listnatými lesmi bol vykonaný komplexný environmentálny výskum (hydrológia, vegetácia, pôdy a podzemné vody). Porovnávali sa ekosystémy pozdĺž regulovaných a prirodzených tokov. Zistilo sa, že hate a priehrady s relatívne malým vzdutím, ako aj malé vodné nádrže ovplyvňovali vegetáciu a pôdy v záplavových územiach tak, že sa to prejavilo už o niekoľko desaťročí neskôr. Vyčerpávajúca analýza trendov priemernej ročnej výšky hladín, prietokov a zrážok počas dlhého obdobia pomohla objasniť významný vplyv prirodzenej dlhodobej variability obsahu vody na dynamiku vegetácie v zátopových územiach. Metóda navrhnutá autormi umožňuje určiť následné vplyvy zmien vodného režimu tokov spôsobených hydrotechnickými stavbami na ekosystém v záplavovom území.
The analysis of the evolution of learning with graphical maps is based on the placement of the individuals in positions that are computed on the basis of their answers to certain tests. These techniques are useful for detecting similarities between the knowledge profiles of the subjects and can also be used for assessing the acquisition of capabilities after a course. In this paper, we propose to extend some graphical exploratory analysis techniques to the case where there are missing or conflicting answers in the tests. We will also consider that either a missing or unknown answer, or a set of conflictive answers to a survey, is aptly represented by an interval or a fuzzy set. This representation causes that each individual in the map is no longer a point but a figure whose shape and size determine the coherence of the answers and whose position with respect to its neighbors determines the similarities and differences between the individuals.
Accuracy alone can be deceptive when evaluating the performance of a classifier, especially if the problem involves a high number of classes. This paper proposes an approach used for dealing with multi-class problems, which tries to avoid this issue. The approach is based on the Extreme Learning Machine (ELM) classifier, which is trained by using a Differential Evolution (DE) algorithm. Two error measures (Accuracy, $C$, and Sensitivity, S) are combined and applied as a fitness function for the algorithm. The proposed approach is able to obtain multi-class classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is evaluated over seven benchmark classification problems and one real problem, obtaining promising results.
a1_This study evaluated the relationship between photosynthetic carbon accumulation and symbiotic nitrogen nutrition in young fully expanded leaves of 30 nodulated cowpea genotypes grown in the field at Manga, Ghana, in 2005 and 2006. Estimates of fixed-N in photosynthetic leaves revealed greater symbiotic N in genotypes with higher photosynthetic rates and increased leaf transpiration rate/efficiency. There was also greater C accumulation in genotypes with higher symbiotic N and/or total N. Additionally, genotypes with high contents of C per unit of leaf total N exhibited greater C per unit of leaf N-fixed. The C/N and C/Rubisco-N ratios were generally similar in their magnitude when compared to the C/N-fixed ratio due possibly to the fact that Rubisco accounts for a high proportion of photosynthetic leaf N, irrespective of whether the enzyme was formed from soil N or symbiotic N. Cowpea genotypes that relied heavily on soil N for their N nutrition exhibited much higher C/N-fixed ratios, while conversely those that depended more on symbiosis for meeting their N demands showed markedly lower C/N-fixed values. For example, genotypes Omondaw, Bensogla, IT93K-2045-29, and Sanzie, which respectively derived 83.9, 83.1, 82.9, and 76.3% N from fixation, recorded lower C/N-fixed ratios of 10.7, 12.2, 12.1, and 13.0 mg mg-1 in that order in 2005. In contrast, genotypes Botswana White, IT94D-437-1, TVu1509, and Apagbaala, which obtained 14.8, 15.0, 26.4, and 26.0% of their N nutrition from fixation, showed high C/N-fixed values of 84.0, 69.0, 35.2, and 40.6 mg.mg-1, respectively, in 2005., a2_This clearly indicates that genotypes that obtained less N from symbiosis and more N from soil revealed very high C/N-fixed values, an argument that was reinforced by the negative correlations obtained between the three C/N ratios (i.e. C/N, C/Rubisco-N, and C/N-fixed) and leaf N concentration, percentage nitrogen derived from fixation, total N content, amount of N-fixed, and Rubisco N. These data suggest a direct link between photosynthetic C accumulation and symbiotic N assimilation in leaves of nodulated cowpea, and where genotypes derived a large proportion of their N from fixation, photosynthetic C yield substantially increased., A. K. Belane, F. D. Dakora., and Obsahuje seznam literatury