• Asian-Australasian Journal of Animal Sciences
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• v.28 no.10
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• pp.1407-1418
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• 2015

A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre's polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield ($PS_i$) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre's polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from -0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from -0.98 to 1.00, respectively. The use of $PS_7$ would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.

• Journal of Information Processing Systems
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• v.5 no.1
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• pp.33-40
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• 2009

Ever since the network-based malicious code commonly known as a 'worm' surfaced in the early part of the 1980's, its prevalence has grown more and more. The RCS (Random Constant Spreading) worm has become a dominant, malicious virus in recent computer networking circles. The worm retards the availability of an overall network by exhausting resources such as CPU capacity, network peripherals and transfer bandwidth, causing damage to an uninfected system as well as an infected system. The generation and spreading cycle of these worms progress rapidly. The existing studies to counter malicious code have studied the Microscopic Model for detecting worm generation based on some specific pattern or sign of attack, thus preventing its spread by countering the worm directly on detection. However, due to zero-day threat actualization, rapid spreading of the RCS worm and reduction of survival time, securing a security model to ensure the survivability of the network became an urgent problem that the existing solution-oriented security measures did not address. This paper analyzes the recently studied efficient dynamic network. Essentially, this paper suggests a model that dynamically controls the RCS worm using the characteristics of Power-Law and depth distribution of the delivery node, which is commonly seen in preferential growth networks. Moreover, we suggest a model that dynamically controls the spread of the worm using information about the depth distribution of delivery. We also verified via simulation that the load for each node was minimized at an optimal depth to effectively restrain the spread of the worm.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.1-12
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• 2024

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

• Safety and Health at Work
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• v.7 no.4
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• pp.331-339
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• 2016

Background: Occupational risk management is known as a catalyst in generating superior returns for all stakeholders on a sustainable basis. A number of companies in Ghana implemented health and safety measures adopted from international companies to ensure the safety of their employees. However, there exist great threats to employees' safety in these companies. The purpose of this paper is to investigate the level of compliance of Occupational Health and Safety management systems and standards set by international and local legislation in power producing companies in Ghana. Methods: The methodology is conducted by administering questionnaires and in-depth interviews as measuring instruments. A random sampling technique was applied to 60 respondents; only 50 respondents returned their responses. The questionnaire was developed from a literature review and contained questions and items relevant to the initial research problem. A factor analysis was also carried out to investigate the influence of some variables on safety in general. Results: Results showed that the significant factors that influence the safety of employees at the hydroelectric power plant stations are: lack of training and supervision, non-observance of safe work procedures, lack of management commitment, and lack of periodical check on machine operations. The study pointed out the safety loopholes and therefore helped improve the health and safety measures of employees in the selected company by providing effective recommendations. Conclusion: The implementation of the proposed recommendations in this paper, would lead to the prevention of work-related injuries and illnesses of employees as well as property damage and incidents in hydroelectric power plants. The recommendations may equally be considered as benchmark for the Safety and Health Management System with international standards.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.291-299
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• 2008

This paper discusses model building for repeated binary response data with different time-dependent covariates each occasion. Since repeated measurements data are having correlated structure, weighed least squares(WLS) methodology is applied. Repeated measures designs are usually having different sizes of experimental units like split-plot designs. However repeated measures designs differ from split-plot designs in that the levels of one or more factors cannot be randomly assigned to one or more of the sizes of experimental units in the experiment. In this case, the levels of time cannot be assigned at random to the time intervals. Because of this nonrandom assignment, the errors corresponding to the respective experimental units may have a covariance matrix. So, the estimates of effects included in a suggested logit model are obtained by using covariance structures.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.619-630
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• 2011

In order to estimate an appropriate threshold and evaluate its performance for the data mixed with two different distributions, nine kinds of well-known classification accuracy measures such as MVD, Youden's index, the closest-to- (0,1) criterion, the amended closest-to- (0,1) criterion, SSS, symmetry point, accuracy area, TA, TR are clustered into five categories on the basis of their characters. In credit evaluation study, it is assumed that the score random variable follows normal mixture distributions of the default and non-default states. For various normal mixtures, optimal cut-off points for classification measures belong to each category are obtained and type I and II error rates corresponding to these cut-off points are calculated. Then we explore the cases when these error rates are minimized. If normal mixtures might be estimated for these kinds of real data, we could make use of results of this study to select the best classification accuracy measure which has the minimum error rate.

• Journal of Forest and Environmental Science
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• v.32 no.3
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• pp.280-290
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• 2016

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

• Journal of the Society of Disaster Information
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• v.5 no.1
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• pp.78-100
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• 2009

This study is to protect peoples' life, minimize the property damage by coping with threats quickly and take more preventive measures in advance against nuclear bomb, CBR, and potential explosive. For this, CBRNE(Chemical, Biological, Radiological, Nuclear, Explosive) program research was used. Thanks to advance in technology, terrorist groups and even individuals make or keep nuclear and CBR weapons. And also it's likely that disaster and threats from a toxic gas, acute pathogens, accidents in the nuclear power plants and a high explosive could be happened a lot. Recently more organized terrorist groups maintain random attacks for unspecified individuals and also it's highly likely that a large-scale terrorist attack by WMD and CBRNEwill be done. To take strict measures against CBRNE attacks by terrorists is on the rise as an urgent national task. Moreover biological weapons are relatively easy and inexpensive to obtain or produce and cause mass casualties with a small amount. For this reason, more than 25 countries have already possessed them. In the 21 st century, the international safety environment marks the age of complicated threats : transnational threats such as comprehensive security and terror, organized crime, drug smuggling, illegal trade of weapons of mass destruction, and environmental disruption along with traditional security threats. These cause military threats, terror threats, and CBRNE threats in our daily life to grow. Therefore it needs to come up with measures in such areas as research development, policy, training program. Major industrial nations on CBRNE like USA, Canada, Switzerland, and Israel have implemented various educational programs. These researches could be utilized as basic materials for drawing up plans for civil defense, emergency services and worldwide countermeasures against CBRNE.

• Safety and Health at Work
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• v.7 no.1
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• pp.72-77
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• 2016

Background: This study aimed to determine the prevalence and factors related to needle stick injuries (NSIs) and to assess related safety measures among a sample of Iranian nurses. Methods: In this cross-sectional study, a random sample of 168 registered active nurses was selected from different wards of one of the hospitals of Shiraz University of Medical Sciences (SUMS). Data were collected by an anonymous questionnaire and a checklist based observational method among the 168 registered active nurses. Results: The prevalence of NSIs in the total of work experience and the last year was 76% and 54%, respectively. Hollow-bore needles were the most common devices involved in the injuries (85.5%). The majority of NSIs occurred in the morning shift (57.8%) and the most common activity leading to NSIs was recapping needles (41.4%). The rate of underreporting NSIs was 60.2% and the major reasons for not reporting the NSIs were heavy clinical schedule (46.7%) and perception of low risk of infection (37.7%). A statistically significant relationship was found between the occurrence of NSIs and sex, hours worked/week, and frequency of shifts/month. Conclusion: The study showed a high prevalence of NSIs among nurses. Supportive measures such as improving injection practices, modification of working schedule, planning training programs targeted at using personal protective equipment, and providing an adequate number of safety facilities such as puncture resistant disposal containers and engineered safe devices are essential for the effective prevention of NSI incidents among the studied nurses.