• Ocean Science Journal
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• v.43 no.4
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• pp.175-182
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• 2008

Bottlenose dolphins are the only cetaceans regularly observed in the northern Adriatic Sea, but they survive at low densities and are exposed to significant threats. This study investigates some of the factors that influence habitat use by the animals in a largely homogeneous environment by combining dolphin data with hydrological and physiographical variables sampled from oceanographic ships. Surveys were conducted year-round between 2003 and 2006, totalling 3,397 km of effort. Habitat modelling based on a binary stepwise logistic regression analysis predicted between 81% and 93% of the cells where animals were present. Seven environmental covariates were important predictors: oxygen saturation, water temperature, density anomaly, gradient of density anomaly, turbidity, distance from the nearest coast and bottom depth. The model selected consistent predictors in spring and summer. However, the relationship (inverse or direct) between each predictor and dolphin presence varied among seasons, and different predictors were selected in fall. This suggests that dolphin distribution changed depending on seasonal forcing. As the study area is relatively uniform in terms of bottom topography, habitat use by the animals seems to depend on complex interactions among hydrological variables, caused primarily by seasonal change and likely to determine shifts in prey distribution.

• Computers and Concrete
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• v.19 no.6
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• pp.717-724
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• 2017

In this paper, four formulas are proposed via gene expression programming (GEP)-based models and regression analysis (RA) to predict the flexural strength ($f_s$) values of mortars containing different mineral admixtures that are ground granulated blast-furnace slag (GGBFS), silica fume (SF) and fly ash (FA) at different ages. Three formulas obtained from the GEP-I, GEP-II and GEP-III models are constituted to predict the $f_s$ values from the age of specimen, water-binder ratio and compressive strength. Besides, one formula obtained from the RA is constituted to predict the $f_s$ values from the compressive strength. To achieve these formulas in the GEP and RA models, 972 data of the experimental studies presented with mortar mixtures were gathered from the literatures. 734 data of the experimental studies are divided without pre-planned for these formulas achieved from the training and testing sets of GEP and RA models. Beside, these formulas are validated with 238 data of experimental studies un-employed in training and testing sets. The $f_s$ results obtained from the training, testing and validation sets of these formulas are compared with the results obtained from the experimental studies and the formulas given in the literature for concrete. These comparisons show that the results of the formulas obtained from the GEP and RA models appear to well compatible with the experimental results and find to be very credible according to the results of other formulas.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.289-301
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• 2011

In this paper an attempt has been made to classify Asia land cover considering climatic and vegetative characteristics. The sub-class clustering based on the 13 MODIS land cover classes (except water) over Asia was performed with the climate map and the NOVI derived from SPOT 5 VGT D10 data. The unsupervised classification for the sub-class clustering was performed in each land cover class, and total 74 clusters were determined over the study area. Via these clusters, the annual variations (from 1999 to 2007) of precipitation rate and temperature were analyzed as an example by a simple linear regression model. The various annual variations (negative or positive pattern) were represented for each cluster because of the various climate zones and NOVI annual cycles. Therefore, the detailed land cover map as the classification result by the sub-class clustering in this study can be useful information in modelling works for requiring the detailed climatic and vegetative information as a boundary condition.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.2
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• pp.154-161
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• 2014

Naval ships have hull appendages which are more exposed to the outside because of its small block coefficient compared with commercial ships. These exposed hull appendages like skeg, strut and shaft line affect the maneuverability of a ship. The effect of hull appendages has considered at initial design stage to estimate more accurate maneuverability. In this paper, sensitivity analysis is used to analyze the effect on maneuverability by hull appendages. 3 DOF maneuvering equations based on Mathematical Modelling Group (MMG) model are used and propeller & rudder model are modified to reflect the characteristics of twin propeller & twin rudder. Numerical maneuvering simulations (Turning test, Zig-zag test) for benchmark naval vessel, David Taylor Model Basin (DTMB) 5415 are performed. In every simulation, it is calculated that stability indices and maneuverability characteristics (Tactical Dia., Advance, 1st Overshoot, Time of complete cycle) with respect to the parameters (area times lift coefficient slope, attachment location) of hull appendages. As a result, two regression formulas are established. One is the relation of maneuverability characteristics and stability indices and the other is the relation of stability indices and hull appendages.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.131-142
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• 2012

Since Korea is moving towards the aged society, increasing is the social attention on overall life satisfaction of the elderly. The purpose of this study is to find the factors affecting the life satisfaction of the elderly among demographic characteristics of aged people, categorized satisfactions and sources of income, based on the 2008 national survey data of the actual living condition of the elderly and welfare need. We found that many factors have significant impact on the life satisfaction of the elderly, such as demographic characteristics, the level of physical and mental health, the economic level.

• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.491-495
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• 2015

This paper aims to provide some unique insights into the verification of organisational innovation diffusion through empirically identifying the major factors determining the level of organisational innovation diffusion. The paper presents a two-stage sequential mixed method analysis: structural equation modelling analysis and regression analysis. A questionnaire survey was administrated to a sample of 223 organisations operating in Saudi Arabia. The results suggest that participative culture and, technology availability and implementation would intensify organisational climate for innovation. The results revealed compelling evidence in support of the moderating role of technology on the relationship between country socio-culture and organisational climate for innovation. Equally important, organisational innovation characteristics could play a crucial role in the intention to adopt a particular innovation. Specifically, maintaining Saudi Arabian top management's status quo is an obstacle to organisational innovation diffusion. This paper expands and improves upon the current understanding of how organisational innovation diffusion, in particular the Project Management Office (PMO), can be accelerated. By focusing on the critical factors within the conceptual model, the paper depicts the crucial role of certain factors that could leverage improved organisational innovation diffusion outcomes.

• Wind and Structures
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• v.2 no.1
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• pp.25-40
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• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• Advances in materials Research
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• v.11 no.4
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• pp.375-390
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• 2022

Carbon Fiber Reinforced Polymer (CFRP) composite provides outstanding mechanical capabilities and is therefore popular in the automotive and aerospace industries. Drilling is a common final production technique for composite laminates however, drilling high-strength composite laminates is extremely complex and challenging. The delamination of composites during the drilling at the entry and exit of the hole has a severe impact on the results of the holes surface and the material properties. The major goal of this research is to investigate contemporary industry solutions for drilling CFRP composites: enhanced edge geometries of cutting tools. This study examined the occurrence of delamination at the entry and exit of the hole during the drilling. For each of the 80°, 90°, and 118°point angle uncoated Brad point, Dagger, and Twist solid carbide drills, Taguchi design of experiments were undertaken. Cutting parameters included three variable cutting speeds (100-125-150 m/min) and feed rates (0.1-0.2-0.3 mm/rev). Brad point drills induced less delamination than dagger and twist drills, according to the research, and the best cutting parameters were found to be a combination of maximum cutting speed, minimum feed rate, and low drill point angle (V:150 m/min, f: 0.1 mm/rev, θ: 80°). The feed rate was determined to be the most efficient factor in preventing hole entry and exit delamination using analysis of variance (ANOVA). Regression analysis was used to create first-degree mathematical models for each cutting tool's entrance and exit delamination components. The results of optimization, mathematical modelling, and experimental tests are thought to be reasonably coherent based on the information obtained.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.43-54
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• 2010

In recent years there have been studies on tour based approaches for freight travel demand modelling. The purpose of this paper is to analyze tour type choice behavior of commercial vehicles which are divided into round trips and chained tours. The methods of the study are based on the decision tree and the logit model. The results indicates that the explanation variables for classifying tour types of commercial vehicles are loading factor, average goods quantity, and total goods quantity. The results of the decision tree method are similar to those of logit model. In addition, the explanation variables for tour type classification of small trucks are not different from those for medium trucks', implying that the most important factor on the vehicle tour planning is how to load goods such as shipment size and total quantity.