Search

The ship tracking and mapping capabilities that geospatial technology provides create an opportunity to observe fishing vessels as they move through established maritime boundaries. This paper connects data availability to ground-truthing research and explores the limits of vessel movement mapping in representing worker abuse at sea through three related themes. First, a conceptual background links the advancements in maritime remote sensing to critical GIS scholarship and provides a background on worker abuse aboard Taiwanese fishing vessels. Second, the paper examines the potential of machine learning algorithms to represent worker abuse at sea, arguing that more extensive ground-truthing research with workers could help address variations in the data and limited data sets. Third, I use remote sensing data to identify and unpack Taiwanese fishing across the three EEZs with the most concentrated Taiwanese fishing activity: starting with Taiwan, followed by the Falkland Islands, and Seychelles. I argue that fishing activity and the digital representation of vessel movements are governed by terrestrial geopolitics and subject to manipulation by ship captains. Finally, the conclusion offers recommendations for how future research can capitalize on the capabilities of AIS, particularly with respect to addressing problems of working conditions and abuse at sea. [ABSTRACT FROM AUTHOR]

Evaluating the magnitude of overdiagnosis associated with stool-based service screening for colorectal cancer (CRC) beyond a randomized controlled trial is often intractable and understudied. We aim to estimate the proportion of overdiagnosis in population-based service screening programs for CRC with the fecal immunochemical test (FIT). The natural process of overdiagnosis-embedded disease was first built up to learn transition parameters that quantify the pathway of non-progressive and progressive screen-detected cases calibrated with sensitivity, while also taking competing mortality into account. The Markov algorithms were then developed for estimating these transition parameters based on Taiwan FIT service CRC screening data on 5,417,699 residents aged 50–69 years from 2004 to 2014. Following the digital twin design with the parallel universe structure for emulating the randomized controlled trial, the screened twin, mirroring the control group without screening, was virtually recreated by the application of the above-mentioned trained parameters to predict CRC cases containing overdiagnosis. The ratio of the predicted CRCs derived from the screened twin to the observed CRCs of the control group minus 1 was imputed to measure the extent of overdiagnosis. The extent of overdiagnosis for invasive CRCs resulting from FIT screening is 4.16% (95% CI: 2.61–5.78%). The corresponding figure is increased to 9.90% (95% CI: 8.41–11.42%) for including high grade dysplasia (HGD) and further inflated to 15.83% (95% CI: 15.23–16.46%) when the removal adenoma is considered. The modest proportion of overdiagnosis modelled by the digital twin method, dispensing with the randomized controlled trial design, suggests the harm done to population-based FIT service screening is negligible. [ABSTRACT FROM AUTHOR]

In the paper, we proposed a coarse-to-fine scheme to automatically detect the regions of interest (ROIs) for digitized cadastral images in Taiwan. To consider some issues such as the skew effect, image quality, and noise in digitized cadastral images, the proposed scheme is composed of four parts: pre-processing, skew correction, noise reduction, and ROI localization. In the pre-processing, each cadastral image is normalized and the prior knowledge is used to find the candidate region of the ROI. To reduce the impact of noise and poor contrast on line detection, an adaptive thresholding is adopted. To decrease the skew effect, the detected horizontal and vertical lines are analyzed to estimate the skew angle. After skew correction, an adaptive noise reduction algorithm is devised to reduce the effect of marginal, artificial, and random noise. The coordinates of the candidate region in the de-skew image with high resolution can be found and then the ROI boundary can be located correctly in the fine detection. Experimental results demonstrate that the proposed scheme can effectively and correctly localize ROIs in digitized cadastral images. [ABSTRACT FROM AUTHOR]

The development of visual tools for the timely identification of spatio-temporal clusters will assist in implementing control measures to prevent further damage. From January 2015 to June 2020, a total number of 1463 avian influenza outbreak farms were detected in Taiwan and further confirmed to be affected by highly pathogenic avian influenza subtype H5Nx. In this study, we adopted two common concepts of spatio-temporal clustering methods, the Knox test and scan statistics, with visual tools to explore the dynamic changes of clustering patterns. Since most (68.6%) of the outbreak farms were detected in 2015, only the data from 2015 was used in this study. The first two-stage algorithm performs the Knox test, which established a threshold of 7 days and identified 11 major clusters in the six counties of southwestern Taiwan, followed by the standard deviational ellipse (SDE) method implemented on each cluster to reveal the transmission direction. The second algorithm applies scan likelihood ratio statistics followed by AGC index to visualize the dynamic changes of the local aggregation pattern of disease clusters at the regional level. Compared to the one-stage aggregation approach, Knox-based and AGC mapping were more sensitive in small-scale spatio-temporal clustering. [ABSTRACT FROM AUTHOR]

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media. [ABSTRACT FROM AUTHOR]

Advances in computer and communication technologies have engendered opportunities for developing an improved ubiquitous health care environment. One of the crucial applications is a ubiquitous clinic recommendation system, which entails recommending a suitable clinic to a mobile patient based on his/her location, hospital department, and preferences. However, patients may not be willing or able to express their preferences. To overcome this problem, some ubiquitous clinic recommendation systems mine the historical data of patients to learn their preferences, and they apply an algorithm to adjust the recommendation algorithm after receiving more patient data. Such an adjustment mechanism may operate for several periods; however, this raises a question regarding the sustainability (i.e., long-term effectiveness) of such an adjustment mechanism. To address this question, this study modeled the improvement in the successful recommendation rate of a ubiquitous clinic recommendation system that adopts an adjustment mechanism as a learning process. Both the asymptotic value and learning speed of the learning process provide valuable information regarding the long-term effectiveness of the adjustment mechanism. The proposed methodology was applied in a regional study to a ubiquitous clinic recommendation system that adjusts the recommendation mechanism by solving an integer nonlinear programming problem on a rolling basis. The experimental results revealed that the proposed method exhibited a considerably higher level of accuracy in forecasting the successful recommendation rate compared with several existing methods. Although the adjustment mechanism exhibits long-term effectiveness, the learning speed requires improvement. [ABSTRACT FROM AUTHOR]

Assuming Cox's regression model, we consider penalized full likelihood approach to conduct variable selection under nested case-control (NCC) sampling. Penalized non-parametric maximum likelihood estimates (PNPMLEs) are characterized by self-consistency equations derived from score functions. A cross-validation method based on profile likelihood is used to choose the tuning parameter within a family of penalty functions. Simulation studies indicate that the numerical performance of (P)NPMLE is better than weighted partial likelihood in estimating the log-relative risk and in identifying the covariates and the model, under NCC sampling. LASSO performs best when cohort size is small; SCAD performs best when cohort size is large and may eventually perform as well as the oracle estimator. Using the SCAD penalty, we establish the consistency, asymptotic normality, and oracle properties of the PNPMLE, as well as the sparsity property of the penalty. We also propose a consistent estimate of the asymptotic variance using observed profile likelihood. Our method is illustrated to analyze the diagnosis of liver cancer among those in a type 2 diabetic mellitus dataset who were treated with thiazolidinediones in Taiwan. [ABSTRACT FROM AUTHOR]

Objective: The long-term predictive value of the new proposed algorithm in the updated 2016 guidelines of the European Association of Cardiovascular Imaging to assess diastolic dysfunction (DD) in patients with heart failure with preserved ejection fraction (HFpEF) has not been validated.Methods: The analysis included 451 patients who were diagnosed with HFpEF as confirmed via echocardiography. The endpoints were mortality and hospitalization for HF. The Kaplan-Meier curves and Cox regression models were generated to determine the risk of all-cause mortality based on the 2016 and 2009 DD grading algorithm, respectively. We evaluated the net reclassification index of outcomes on the basis of 2009 DD grade after abiding by the 2016 recommendations.Results: After a follow-up of 2976 days, 119 patients (26.4%) died. According to the 2016 DD grading, grade III DD was associated with a significantly higher risk of mortality (hazard ratio [HR], 2.209; 95% CI 1.144-4.266) and HF hospitalization (HR, 2.047; 95% CI 1.348-3.870), as compared with grade I DD. Grade II DD was also associated with a higher risk of mortality (HR, 1.538; 95% CI 1.313-1.924). However, only grade III DD was independently associated with worse mortality based on 2009 DD grading. The net reclassification index for mortality increased significantly after grading by 2016 algorithm (10.6%, p < 0.001).Conclusions: The 2016 DD grading algorithm showed improved prognostic value of long-term mortality in patients with HFpEF. Based on the findings of the study, the appropriate grading of DD is important in the prognostication of patients with HFpEF.Key Points: • The application of the 2016 European Association of Cardiovascular Imaging recommendations diastolic dysfunction (DD) grading algorithm improves the predictive value for mortality. • Our analysis suggests DD grades II and III based on 2016 guidelines is associated with poor outcomes as compared with grade I. The echocardiographic indices of the new algorithm should be obtained and applied to effectively evaluate DD. [ABSTRACT FROM AUTHOR]

Since the year 1973, more than 54,000 M ≥ 3.0 earthquakes have occurred around Taiwan, and their magnitude-frequency relationship was found following with the Gutenberg-Richter recurrence law with b value equal to 0.923 from the least-square calculation. However, using this b value with the McGuire-Arabasz algorithm results in some disagreement between observations and expectations in magnitude probability. This study introduces a simple approach to optimize the b value for better modeling of the magnitude probability, and its effectiveness is demonstrated in this paper. The result shows that the optimal b value can better model the observed magnitude distribution, compared with two customary methods. For example, given magnitude threshold = 5.0 and maximum magnitude = 8.0, the optimal b value of 0.835 is better than 0.923 from the least-square calculation and 0.913 from maximum likelihood estimation for simulating the earthquake's magnitude probability distribution around Taiwan. [ABSTRACT FROM AUTHOR]

This paper employed two classical, popular decision-tree algorithms (C5.0 and CART), and traditional Regression to deal with reservoir operations regarding decision of the releases from a reservoir system during floods. The experiment site was in Shihmen Reservoir, located in northern Taiwan. In a typical single-peak typhoon, the rules derived include two operational stages, the stage before peakflow (Stage I) and the stage after peakflow (Stage II). This study collected 50 typhoons (1987-2009). Four cases are designed, that are discretized class labels (target fields) are run by C5.0 and CART (i.e., Cases 1 and 2, respectively), while numeric class labels are run by CART and Regression (i.e., Cases 3 and 4, respectively). The criteria of root mean square error (RMSE), coefficient of efficiency (CE), and relative error of peak discharge (EQ) were used to evaluate the forecasts. Results showed that the decision trees are skillful in the prediction of reservoir releases in the studied site. Furthermore, it was found that CART regression trees with numeric targets are more appropriate and precise than C5.0 classification trees and Regression for the prediction of releases. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the shortcoming of using ordinary digital measures in electronic medical record (EMR), along with the proposed use of forward secure digital measure in signing EMR. Topics covered include the reasons for the revocation of healthcare certificate, the use of digital signature for the authentication and non-repudiation and the advantage of forward digital signature scheme.

Brain metastases are commonly found in patients that are diagnosed with primary malignancy on their lung. Lung cancer patients with brain metastasis tend to have a poor survivability, which is less than 6 months in median. Therefore, an early and effective detection system for such disease is needed to help prolong the patients' survivability and improved their quality of life. A modified electromagnetism-like mechanism (EM) algorithm, MEM-SVM, is proposed by combining EM algorithm with support vector machine (SVM) as the classifier and opposite sign test (OST) as the local search technique. The proposed method is applied to 44 UCI and IDA datasets, and 5 cancers microarray datasets as preliminary experiment. In addition, this method is tested on 4 lung cancer microarray public dataset. Further, we tested our method on a nationwide dataset of brain metastasis from lung cancer (BMLC) in Taiwan. Since the nature of real medical dataset to be highly imbalanced, the synthetic minority over-sampling technique (SMOTE) is utilized to handle this problem. The proposed method is compared against another 8 popular benchmark classifiers and feature selection methods. The performance evaluation is based on the accuracy and Kappa index. For the 44 UCI and IDA datasets and 5 cancer microarray datasets, a non-parametric statistical test confirmed that MEM-SVM outperformed the other methods. For the 4 lung cancer public microarray datasets, MEM-SVM still achieved the highest mean value for accuracy and Kappa index. Due to the imbalanced property on the real case of BMLC dataset, all methods achieve good accuracy without significance difference among the methods. However, on the balanced BMLC dataset, MEM-SVM appears to be the best method with higher accuracy and Kappa index. We successfully developed MEM-SVM to predict the occurrence of brain metastasis from lung cancer with the combination of SMOTE technique to handle the class imbalance properties. The results confirmed that MEM-SVM has good diagnosis power and can be applied as an alternative diagnosis tool in with other medical tests for the early detection of brain metastasis from lung cancer. [ABSTRACT FROM AUTHOR]

Ovarian cancer is the deadliest gynaecological disease because of the high mortality rate and there is no any symptom in cancer early stage. It was often the terminal cancer period when patients were diagnosed with ovarian cancer and thus delays a good opportunity of treatment. The current common method for detecting ovarian cancer is blood testing for analyzing the tumor marker CA-125 of serum. However, specificity and sensitivity of CA-125 are insufficient for early detection. Therefore, it has become an urgent issue to look for an efficient method which precisely detects the tumor markers for ovarian cancer. This study aims to find the target genes of ovarian cancer by different algorithms of information science. Feature selection and decision tree were applied to analyze 9600 ovarian cancer-related genes. After screening the target genes, candidate genes will be analyzed by Ingenuity Pathway Analysis (IPA) software to create a genetic pathway model and to understand the interactive relationship in the different pathological stages of ovarian cancer. Finally, this research found 9 oncogenes associated with ovarian cancer and some genes had not been discovered in previous studies. This system will assist medical staffs in diagnosis and treatment at cancer early stage and improve the patient's survival. [ABSTRACT FROM AUTHOR]

The article presents the development of a triage prediction model using emergency department patient data at a Taiwanese Teaching Hospital. Topics discussed include the reference to the purpose of a triage to avoid treatment delays for patients in cases of crowded hospital facilities, the integration of principal component analysis (PCA) with the support vector machine (SVM) to determine anomaly detection (overestimate or underestimate) and the support vector regression (SVR) algorithm.

Landslides induced by typhoon Morakot during its passage across Taiwan on 7-9 Aug 2009 claimed more than 700 lives and caused heavy economic loss. Unlike earthquake monitoring, precise locations of landslides could not be determined in near-real time because their seismic phases are difficult to identify. Here, we show that large, damaging landslide events are characterized seismically by a distinct waveform pattern of frequent intermixes of P and S waves over a time window of several tens of seconds. The predominant frequency band during these time windows ranges from 0.5 to 5 Hz. The high-frequency content is clearly deficient relative to that of local earthquakes by about one to two orders. We also demonstrate that large landslide events can be located and monitored with algorithms specifically designed for real-time seismic applications. This near-real-time monitoring capability would be particularly useful for emergency responders and government organizations to coordinate effective relief-and-rescue operations. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the feasible use of the Artificial Immune Recognition System (AIRS) proposed by Andrew Watkins in assisting doctors with the prediction of the probability of type 2 diabetes in pregnant women. Topics discussed include the statistics from the World Health Organization (WHO) as well as the American Diabetes Association (ADA) on the number of people afflicted with diabetes as in diabetes mellitus, the support vector machines (SVM), and the vertebrae immune system.

Designing a heuristic approach to solve time-definite common carrier operation planning problem is the purpose of this research. The time-definite common carrier operation planning is implemented using the arcs and nodes of a hierarchical hub-and-spoke network. In light of the complexity involved in determining the types of the vehicles, as well as their associated routing and scheduling simultaneously, we design an algorithm, which employs the concept of tabu search to solve the network structure problem. The testing data are obtained from the second largest time-definite common carrier in Taiwan. When applied to the data of this test case company, the designed tabu search algorithm can obtain final solutions in a relatively short time regardless of the level of service required. Carriers can plan their operations according to the solutions on the operation networks. Although it is possible that the designed heuristic algorithm may converge to a local optimal solution, it can be a meaningful approach for the industrial application that emphasizes the value of time and can be satisfied with a near-optimal solution. [ABSTRACT FROM AUTHOR]

Forecasting and monitoring of rainfall values are increasingly important for decreasing economic loss caused by flash floods. Based on statistical learning theory, support vector regression (SVR) has been used to deal with forecasting problems. Performing structural risk minimization rather than minimizing the training errors,SVR algorithms have better generalization ability than the conventional artificial neural networks. The objective of this investigation is to examine the feasibility and applicability of SVR in forecasting volumes of rainfall during typhoon seasons. In addition, Simulated Annealing (SA) algorithms are employed to choose parameters of the SVR model. Subsequently, rainfall values during typhoon periods in Taiwan's Wu-Tu watershed are used to demonstrate the forecasting performance of the proposed model. The simulation results show that the proposed SVRSA model is a promising alternative in forecasting amounts of rainfall during typhoon seasons. [ABSTRACT FROM AUTHOR]

This study applies an optimal procedure to identify the spatial distribution of groundwater hydraulic conductivity for a confined aquifer in north Taiwan. The parameter structure is determined by the number of zones, zonation pattern, and an uniform hydraulic conductivity associated with each zone. The proposed optimal procedure uses the Voronoi diagram in describing zonation and applies simulated annealing algorithm to optimize its pattern and associated hydraulic conductivity. Three criteria are defined to stop the searching process, including the residual error, the parameter uncertainty, and the structure error. Observation hydraulic heads in years 2000 and 2001 and hydraulic conductivity value from pumping tests are used. The results show that the parameter structure with five zones conforms to the three criteria and, thus, is recommended for future groundwater simulation for the study site. Different heuristic algorithms may also play the role of simulated annealing to optimize the parameter structure. However, which optimization algorithm is more efficient is not discussed and requires further study. [ABSTRACT FROM AUTHOR]