• School Mathematics
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• v.2 no.1
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• pp.203-241
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• 2000

This study aims to suggest a model of task development for mathematics performance assessment and to develop performance tasks for the fifth graders in the primary school on the basis of this model. In order to achieve these aims, the following inquiry questions were set up: (1) to develop open-ended tasks and projects for the fifth graders, (2) to develop checklists for measuring the abilities of mathematical reasoning, problem solving, connection, communication of the fifth graders more deeply when performance assessment tasks are implemented and (3) to examine the appropriateness of performance tasks and checklists and to modify them when is needed through applying these tasks to pupils. The consequences of applying some tasks and analysing some work samples of pupils are as follows. Firstly, pupils need more diverse thinking ability. Secondly, pupils want in the ability of analysing the meaning of mathematical concepts in relation to real world. Thirdly, pupils can calculate precisely but they want in the ability of explaining their ideas and strategies. Fourthly, pupils can find patterns in sequences of numbers or figures but they have difficulty in generalizing these patterns, predicting and demonstrating. Fifthly, pupils are familiar with procedural knowledge more than conceptual knowledge. From these analyses, it is concluded that performance tasks and checklists developed in this study are improved assessment tools for measuring mathematical abilities of pupils, and that we should improve mathematics instruction for pupils to understand mathematical concepts deeply, solve problems, reason mathematically, connect mathematics to real world and other disciplines, and communicate about mathematics.

• International journal of thyroidology
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• v.11 no.2
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• pp.143-151
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• 2018

Background and Objectives: Sirtuins (SIRTs) play important roles in cellular and organismal homeostasis. They have distinct gene expression patterns in various cancers; however, the relationship between SIRT expression and the progression of thyroid cancer is unclear. We investigated the expression of SIRTs in patients with papillary thyroid carcinoma (PTC) and their role as biomarkers for predicting the aggressiveness of this disease. Materials and Methods: We used immunohistochemical staining to evaluate the expression of SIRT1 and SIRT3 in tumor specimens from 270 patients with PTC. We also evaluated the potential association between SIRT expression and diverse clinicopathological features. Results: High SIRT1 expression was negatively correlated with lymphovascular invasion, central lymph node metastasis, and lateral lymph node metastasis. Multivariate analyses revealed that high SIRT1 expression was a negative independent risk factor for lateral lymph node metastasis. By contrast, high SIRT3 expression was positively correlated with locoregional recurrence. Interestingly, when patients were grouped by tumor SIRT expression patterns, the group with low SIRT1 expression and high SIRT3 expression was correlated with more aggressive cancer phenotypes including central lymph node metastasis and lateral lymph node metastasis. Conclusion: Our results suggest that SIRTs play dual roles in tumor progression, and the combination of decreased SIRT1 expression and increased SIRT3 expression is significantly associated with a poor prognosis in patients with PTC.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• Computers and Concrete
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• v.33 no.5
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• pp.481-496
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• 2024

Internal erosion around pipes can lead to the failure of earth dams through various mechanisms. This study investigates the displacement patterns in earth dam models under three different failure modes due to internal erosion, using digital image correlation (DIC) methods. Three failure modes—erosion along a pipe (FM1), pipe leakage leading to soil erosion (FM2), and erosion in a pipe due to defects (FM3)—are analyzed using two- and three-dimensional image- processing techniques. The internal displacement of the cross-sectional area and the surface displacement of the downstream slope in the dam models are monitored using an image acquisition system. Physical model tests reveal that FM1 exhibits significant displacement on the upper surface of the downstream slope, FM2 shows focused displacement around the pipe defect, and FM3 demonstrates increased displacement on the upstream slope. The variations in internal and surface displacements with time depend on the segmented area and failure mode. Analyzing the relationships between internal and surface displacements using Pearson correlation coefficients reveals various displacement patterns for the segmented areas and failure modes. Therefore, the image-based characterization methods presented in this study may be useful for analyzing the displacement distribution and behavior of earth dams around pipes, and further, for understanding and predicting their failure mechanisms.

• Journal of Chest Surgery
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• v.47 no.1
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• pp.13-19
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• 2014

Background: The aim of this study is to evaluate prognostic factors for survival in pathologic stage IIIA/N2 non-small-cell lung cancer (NSCLC), to identify the prognostic significance of the metastatic patterns of mediastinal lymph nodes (MLNs) relating to survival and to recurrence and metastasis. Methods: A total of 129 patients who underwent radical resection for pathologic stage IIIA-N2 NSCLC from July 1998 to April 2011 were retrospectively reviewed. The end points of this study were rates of loco-regional recurrence and distant metastasis, and survival. Results: The overall 5-year survival rate was 47.4%. A univariate analysis showed that age, pathologic T stage, and adjuvant chemotherapy were significant prognostic factors, while in multivariate analysis, pathologic T stage and adjuvant chemotherapy were significant prognostic factors. The metastasis rate was higher in patients with multi-station N2 involvement and with more than 3 positive MLNs. Further, non-regional MLN metastasis was associated with a higher loco-regional recurrence rate. Conclusion: Pathologic T stage and adjuvant chemotherapy were independent prognostic factors for long-term survival in pathologic stage IIIA/N2 NSCLC. The recurrence and the metastasis rate were affected by the metastatic patterns of MLNs. These results may be helpful for planning postoperative therapeutic strategies and predicting outcomes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• Journal of Gastric Cancer
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• v.22 no.4
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• pp.369-380
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• 2022

Purpose: Tumor recurrence is the principal cause of poor outcomes in remnant gastric cancer (RGC) after resection. We sought to elucidate the recurrent patterns according to tumor locations in RGC. Materials and Methods: Data were collected from the Shanghai Cancer Center between January 2006 and December 2020. A total of 129 patients with RGC were included in this study, of whom 62 had carcinomas at the anastomotic site (group A) and 67 at the non-anastomotic site (group N). The clinicopathological characteristics, surgical results, recurrent diseases, and survival were investigated according to tumor location. Results: The time interval from the previous gastrectomy to the current diagnosis was 32.0±13.0 and 21.0±13.4 years in groups A and N, respectively. The previous disease was benign in 51/62 cases (82.3%) in group A and 37/67 cases (55.2%) in group N (P=0.002). Thirty-three patients had documented sites of tumor recurrence through imaging or pathological examinations. The median time to recurrence was 11.0 months (range, 1.0-35.1 months). Peritoneal recurrence occurred in 11.3% (7/62) of the patients in group A versus 1.5% (1/67) of the patients in group N (P=0.006). Hepatic recurrence occurred in 3.2% (2/62) of the patients in group A versus 13.4% (9/67) of the patients in group N (P=0.038). Patients in group A had significantly better overall survival than those in group N (P=0.046). Conclusions: The tumor location of RGC is an essential factor for predicting recurrence patterns and overall survival. When selecting an optimal postoperative follow-up program for RGC, physicians should consider recurrent features according to the tumor location.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.37-47
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• 2024

In the summer of 2018, the Korea-Japan (KJ) region experienced an extremely severe and prolonged heatwave. This study examines the GloSea6 model's prediction performance for the 2018 KJ heatwave event and investigates how its prediction skill is related to large-scale circulation patterns identified by the k-means clustering method. Cluster 1 pattern is characterized by a KJ high-pressure anomaly, Cluster 2 pattern is distinguished by an Eastern European high-pressure anomaly, and Cluster 3 pattern is associated with a Pacific-Japan pattern-like anomaly. By analyzing the spatial correlation coefficients between these three identified circulation patterns and GloSea6 predictions, we assessed the contribution of each circulation pattern to the heatwave lifecycle. Our results show that the Eastern European high-pressure pattern, in particular, plays a significant role in predicting the evolution of the development and peak phases of the 2018 KJ heatwave approximately two weeks in advance. Furthermore, this study suggests that an accurate representation of large-scale atmospheric circulations in upstream regions is a key factor in seasonal forecast models for improving the predictability of extreme weather events, such as the 2018 KJ heatwave.

• Animal Bioscience
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• v.37 no.8
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• pp.1333-1344
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• 2024

Objective: Litter size and piglet loss at birth significantly impact piglet production and are closely associated with sow parity. Understanding how these traits vary across different parities is crucial for effective herd management. This study investigates the patterns of the number of born alive piglets (NBA), number of piglet losses (NPL), and the proportion of piglet losses (PPL) at birth in Landrace sows under tropical conditions. Additionally, it aims to identify the most suitable model for describing these patterns. Methods: A dataset comprising 2,322 consecutive reproductive records from 258 Landrace sows, spanning parities from 1 to 9, was analyzed. Modeling approaches including 2nd and 3rd degree polynomial models, the Wood gamma function, and a longitudinal model were applied at the individual level to predict NBA, NPL, and PPL. The choice of the best-fitting model was determined based on the lowest mean and standard deviation of the difference between predicted and actual values, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results: Sow parity significantly influenced NBA, NPL, and PPL (p<0.0001). NBA increased until the 4th parity and then declined. In contrast, NPL and PPL decreased until the 2nd parity and then steadily increased until the 8th parity. The 2nd and 3rd degree polynomials, and longitudinal models showed no significant differences in predicting NBA, NPL, and PPL (p>0.05). The 3rd degree polynomial model had the lowest prediction standard deviation and yielded the smallest AIC and BIC. Conclusion: The 3rd degree polynomial model offers the most suitable description of NBA, NPL, and PPL patterns. It holds promise for applications in genetic evaluations to enhance litter size and reduce piglet loss at birth in sows. These findings highlight the importance of accounting for sow parity effects in swine breeding programs, particularly in tropical conditions, to optimize piglet production and sow performance.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.