• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7923-7927
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• 2015

Background: The Cox PH model is one of the most significant statistical models in studying survival of patients. But, in the case of patients with long-term survival, it may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer. Materials and Methods: In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program. Results: The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence. Conclusions: Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.

• Journal of Gastric Cancer
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• v.12 no.2
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• pp.108-112
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• 2012

Purpose: Since a patient's obesity can affect the mortality and morbidity of the surgery, less drastic surgeries may have a major benefit for obese individuals. This study evaluated the feasibility of performing a totally laparoscopic distal gastrectomy, with intracorporeal anastomosis, in obese patients suffering from gastric cancer. Materials and Methods: This was a retrospective analysis of the 138 patients, who underwent a totally laparoscopic distal gastrectomy from April 2005 to March 2009, at the National Kyushu Cancer Center. The body mass index of 20 patients was ${\geq}25$, and in 118 patients, it was <25 kg/$m^2$. Results: The mean values of body mass index in the 2 groups were $27.3{\pm}2.2$ and $21.4{\pm}2.3$. Hypertension was significantly more frequent in the obese patients than in the non-obese patients. The intraoperative blood loss, duration of surgery, post-operative complication rate, post-operative hospital stay, and a number of retrieved lymph nodes were not significantly different between the two groups. Conclusions: Intracorporeal anastomosis seemed to have a benefit for obese individuals. Totally laparoscopic gastrectomy is, therefore, considered to be a safe and an effective modality for obese patients.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Journal of Chest Surgery
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• v.52 no.3
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• pp.141-147
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• 2019

Background: Uniportal video-assisted thoracoscopic surgery (VATS) has proven safe and effective for pulmonary wedge resection and lobectomy. The objective of this study was to evaluate the safety and feasibility of uniportal VATS segmentectomy by comparing its outcomes with those of the multiportal approach at a single center. Methods: The records of 84 patients who underwent VATS segmentectomy from August 2010 to August 2018, including 33 in the uniportal group and 51 in the multiportal group, were retrospectively reviewed and analyzed. Results: Anesthesia and operative times were similar in the uniportal and multiportal groups (215 minutes vs. 220 minutes, respectively; p=0.276 and 180 minutes vs. 198 minutes, respectively; p=0.396). Blood loss was significantly lower in the uniportal group (50 mL vs. 100 mL, p=0.013) and chest tube duration and hospital stay were significantly shorter in the uniportal group (2 days vs. 3 days, p=0.003 and 4 days [range, 1-14 days] vs. 4 days [range, 1-62 days], p=0.011). The number of dissected lymph nodes tended to be lower in the uniportal group (5 vs. 8, p=0.056). Conclusion: Our preliminary experience indicates that uniportal VATS segmentectomy is safe and feasible in well-selected patients. A randomized, prospective study with a large group of patients and long-term follow-up is necessary to confirm these results.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.538-550
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• 2006

XML Keyword search enables us to get information easily without knowledge of structure of documents and returns specific and useful partial document results instead of whole documents. Element level query processing makes it possible, but computational complexity, as the number of documents grows, increases significantly overhead costs. In this paper, we present document-level ranking scheme over XML documents which predicts results of element-level processing to reduce processing cost. To do this, we propose the notion of 'keyword proximity' - the correlation of keywords in a document that affects the results of element-level query processing using path information of occurrence nodes and their resemblances - for document ranking process. In benefit of document-centric view, it is possible to reduce processing time using ranked document list or filtering of low scored documents. Our experimental evaluation shows that document-level processing technique using ranked document list is effective and improves performance by the early termination for top-k query.

• Journal of Advanced Navigation Technology
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• v.21 no.3
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• pp.306-311
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• 2017

In the future ubiquitous network, information will collect data from various sensors in the field. Since the sensor nodes are equipped with small, often irreplaceable, batteries with limited power capacity, it is essential that the network be energy-efficient in order to maximize its lifetime. In this paper, we propose an effective network routing method that can operate with low power as well as the transmission of data and information obtained from sensor networks, and identified the number of sensors with the best connectivity to help with the proper placement of the sensor. These purposes of this research are the development of the sensor middle-ware to integrate the maritime information and the proposal of the routing algorithm for gathering the maritime information of various sensors. In addition, for more secure ship navigation, we proposed a method to construct a sensor network using various electronic equipments that are difficult to access in a ship, and then construct a communication system using NMEA(the national marine electronics association), a ship communication standard, in the future.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.640-652
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• 2012

Many studies have investigated the smart grid architecture and communication models in the past few years. However, the communication model and architecture for a smart grid still remain unclear. Today's electric power distribution is very complex and maladapted because of the lack of efficient and cost-effective energy generation, distribution, and consumption management systems. A wireless smart grid communication system can play an important role in achieving these goals. In this paper, we describe a smart grid communication architecture in which we merge customers and distributors into a single domain. In the proposed architecture, all the home area networks, neighborhood area networks, and local electrical equipment form a local wireless mesh network (LWMN). Each device or meter can act as a source, router, or relay. The data generated in any node (device/meter) reaches the data collector via other nodes. The data collector transmits this data via the access point of a wide area network (WAN). Finally, data is transferred to the service provider or to the control center of the smart grid. We propose a wireless cooperative communication model for the LWMN.We deploy a limited number of smart relays to improve the performance of the network. A novel relay selection mechanism is also proposed to reduce the relay selection overhead. Simulation results show that our cooperative smart grid (coopSG) communication model improves the end-to-end packet delivery latency, throughput, and energy efficiency over both the Wang et al. and Niyato et al. models.