• Environmental Analysis Health and Toxicology
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• v.31
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• pp.3.1-3.11
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• 2016

Objectives This study was conducted to describe the regional malaria incidence in relation to the geographic and climatic conditions and describe the effect of altitude on the expansion of malaria over the last decade in Papua New Guinea. Methods Malaria incidence was estimated in five provinces from 1996 to 2008 using national health surveillance data. Time trend of malaria incidence was compared with rainfall and minimum/maximum temperature. In the Eastern Highland Province, time trend of malaria incidence over the study period was stratified by altitude. Spatio-temporal pattern of malaria was analyzed. Results Nationwide, malaria incidence was stationary. Regionally, the incidence increased markedly in the highland region (292.0/100000/yr, p =0.021), and remained stationary in the other regions. Seasonality of the malaria incidence was related with rainfall. Decreasing incidence of malaria was associated with decreasing rainfall in the southern coastal region, whereas it was not evident in the northern coastal region. In the Eastern Highland Province, malaria incidence increased in areas below 1700 m, with the rate of increase being steeper at higher altitudes. Conclusions Increasing trend of malaria incidence was prominent in the highland region of Papua New Guinea, while long-term trend was dependent upon baseline level of rainfall in coastal regions.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2513-2518
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• 2016

Background: In 2008, non-Hodgkin lymphoma ranked tenth among other malignancies worldwide with an incidence of around 5 cases per 100,000 in both genders. The latest available rates in Tunisia are from 2006. Materials and Methods: This study aimed to provide an update about NHL incidence for 2009 and its trend between 1998 and 2009 as well as a projection until 2024, using data from the Salah Azaiz Institute hospital registry and the Noth Tunisia cancer registry. Results: In 2009, the NHL incidence in the north of Tunisia was 4.03 cases per 100,000, 4.97 for men and 3.10 for women. Diffuse large B-cell lymphoma (DLBCL) accounted for 63.2% of all NHL subtypes. Between 1998 and 2009, the overall trend showed no significant change. When we compared the trend between two periods (1998-2005 and 2005-2009), joinpoint regression showed a significant decrease of NHL incidence in the first period with an annual percentage change (APC) of -6.7% (95% CI:[-11.2%;-2%]), then the incidence significantly increased from 2005 to 2009 with an APC of 30.5% (95% CI: [16.1%; 46.6%]. The analyses of the different subtype trends showed a significant decrease in DLBCL incidence between 1998 and 2000 (APC:-21.5; 95% CI: [-31.4%;-10.2%]) then the incidence significantly increased between 2004 and 2007 (APC: 18.5; 95% CI: [3,6%;35.5%]). Joint point analysis of the age-period-cohort model projection showed a significant increase between 2002 and 2024 with an APC of 4.5% (%95 CI: [1.5%; 7.5%]). The estimated ASR for 2024 was 4.55/100 000 (95% CI: [3.37; 6.15]). Conclusions: This study revealed an overall steady trend in the incidence of NHL in northern Tunisia between 1998 and 2009. Projection showed an increase in the incidence in NHL in both genders which draw the attention to the national and worldwide burden of this malignancy.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.113-125
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• 2011

In this study, recent trends of the annual precipitation, the annual maximum precipitation of different durations and the rain days over several thresholds(i.e. 0, 10, 20, 40, 60 and 80 mm/day) according to the different local features were analyzed using daily precipitation data of 59 weather stations between 1973 and 2009. To analyze the variability according to the regional characteristics, 59 weather stations were classified by elevations, latitudes, longitudes, river basins, inland or shore(east sea, south sea, west sea) area and the level of urbanization. Results demonstrated that overall trend of variables increases except rain day. Results according to the regional characteristics showed that the increase trend becomes stronger with elevation increase. The increase trend of Han river basin is largest and that of Youngsan river basin is smallest. Also the increase trend becomes stronger with latitude increase and that of East coast is larger than that of South coast since it may be caused by the regional difference of elevation. The increase trend of urban area is larger than that of rural area. Overall trend showed that increase trend becomes stronger with elevation and latitude increase.

• Nuclear Engineering and Technology
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• v.30 no.4
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• pp.364-376
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• 1998

The statistical analysis method was applied to the evaluation of fracture toughness in the ductile-brittle transition temperature region. Because cleavage fracture in steel is of a statistical nature, fracture toughness data or values show a similar statistical trend. Using the three-parameter Weibull distribution, a fracture toughness vs. temperature curve (K-curve) was directly generated from a set of fracture toughness data at a selected temperature. Charpy V-notch impact energy was also used to obtain the K-curve by a $K_{IC}$ -CVN (Charpy V-notch energy) correlation. Furthermore, this method was applied to evaluate the neutron irradiation embrittlement of reactor pressure vessel (RPV) steel. Most of the fracture toughness data were within the 95% confidence limits. The prediction of a transition temperature shift by statistical analysis was compared with that from the experimental data.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1133-1140
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• 2008

The goal of this paper is to analysis of the trend for stream quality in Gumi industrial complex with Winters' exponential smoothing method. It used the five different monthly time series data such as BOD, COD, TN, TP and EC from January 1998 to December 2006. The data of BOD, COD, TN, TP and EC are analyzed by time series method and forecasted the trends until December 2007. The stream qualities change for the better about BOD, COD, TN and TP, but the stream qualities resulted by EC is still serious.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.358-368
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• 2022

Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.spc
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• pp.1-8
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• 2023

To investigate the national science and technology policy direction in the field of construction machinery, an analysis was conducted on projects selected as national research and development (R&D) initiatives by the government. Assuming that the project titles contain key keywords, text mining was employed to substantiate this assumption. Project information data spanning nine years from 2014 to 2022 was collected through the National Science & Technology Information Service (NTIS). To observe changes over time, the years were divided into three-year sections. To analyze research trends efficiently, keywords were categorized into groups: 'equipment,' 'smart,' and 'eco-friendly.' Based on the collected data, keyword frequency analysis, N-gram analysis, and topic modeling were performed. The research findings indicate that domestic government R&D in the construction machinery field primarily focuses on smart-related research and development. Specifically, investments in monitoring systems and autonomous operation technologies are increasing. This study holds significance in analyzing objective research trends through the utilization of big data analysis techniques and is expected to contribute to future research and development planning, strategic formulation, and project management.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.420-428
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• 2010

The advancement of the Science for complex systems enables the analysis of many social networks. We constructed and analyzed a Korean movie star network as one of social networks, based on the 100 Korean movie selection for a main data source. Until now, the research trend has been the structural analysis of network, focused on link numbers, such as degree, betweenness and clustering coefficient. But it is time that the research is not limited by the structural analysis of networks only. Rather, the research goal should be aimed to an information analysis, performed by identifying and analyzing central modules that are regarded as the core of complex networks, using k-core analysis method. In this research, we constructed a network of movie stars who have appeared in 100 Korean movie selection, provided by Korean movie database, also we analyzed its core modules with and without weights, and the trend of seasonal expansion of the network. We expect our findings can be used as the basic data applicable to a model for understanding of the expansion and evolution of networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.181-186
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• 2012

Recently there has been many active researches on searching similar sequences from data generated with the passage of time. Those data are classified as time series data or sequence data and have different semantics from scalar data of traditional databases. In this paper similar sequence search retrieves sequences that have a similar trend of value changes. At first we have transformed the original sequences by applying DFT. The converted data are more suitable for trend analysis and they require less number of attributes for sequence comparisons. In addition we have developed a region-based query and we applied bitmap indexes which could show better performance in data warehouse. We have built bitmap indexes with varying number of attributes and we have found the least cost query plans for efficient similar sequence searches.