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"Liu, Yu-Chih"
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Reciprocity, evolution, and decision games in network and data science
\"Learn how to analyze and manage evolutionary and sequential user behaviors in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision-making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behavior and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing\"-- Provided by publisher.
Book
A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment
This article presents a geographic information system (GIS)-based artificial neural network (GANN) model for flood susceptibility assessment of Keelung City, Taiwan. Various factors, including elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index, were generated using a digital elevation model and LANDSAT 8 imagery. Historical flood data from 2015 to 2019, including 307 flood events, were adopted for a comparison of flood susceptibility. Using these factors, the GANN model, based on the back-propagation neural network (BPNN), was employed to provide flood susceptibility. The validation results indicate that a satisfactory result, with a correlation coefficient of 0.814, was obtained. A comparison of the GANN model with those from the SOBEK model was conducted. The comparative results demonstrated that the proposed method can provide good accuracy in predicting flood susceptibility. The results of flood susceptibility are categorized into five classes: Very low, low, moderate, high, and very high, with coverage areas of 60.5%, 27.4%, 8.6%, 2.5%, and 1%, respectively. The results demonstrate that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area including the financial center of the city, can be categorized as high to very high flood susceptibility zones.
Journal Article
Modeling of land subsidence using GIS-based artificial neural network in Yunlin County, Taiwan
In this study, the land subsidence in Yunlin County, Taiwan, was modeled using an artificial neural network (ANN). Maps of the fine-grained soil percentage, average maximum drainage path length, agricultural land use percentage, electricity consumption of wells, and accumulated land subsidence depth were produced through geographic information system spatial analysis for 5607 cells in the study area. An ANN model based on a backpropagation neural network was developed to predict the accumulated land subsidence depth. A comparison of the model predictions with ground-truth leveling survey data indicated that the developed model had high accuracy. Moreover, the developed model was used to investigate the relationship of electricity consumption reduction with reductions in the total area of land with severe subsidence (> 4 cm per year); the relationship was approximately linear. In particular, the optimal results were obtained when decreasing the electricity consumption from 80 to 70% of the current value, with the area of severe land subsidence decreasing by 13.66%.
Journal Article
Deep learning time-series modeling for assessing land subsidence under reduced groundwater use
Intensive groundwater extraction and a severe 2021 drought have worsened land subsidence in Taiwan’s Choshui Delta, highlighting the need for effective predictive modeling to guide mitigation. In this study, we develop a machine learning framework for subsidence analysis using electricity consumption data from pumping wells as a proxy for groundwater extraction. A long short-term memory (LSTM) neural network is trained to reconstruct missing subsidence records and forecast subsidence trends, while an artificial neural network links well electricity usage to groundwater level fluctuations. Using these tools, we identify groundwater-level decline from pumping as a key driver of subsidence. The LSTM model achieves high accuracy in reproducing historical subsidence and provides reliable predictions of subsidence behavior. Scenario simulations indicate that reducing groundwater pumping, simulated by lowering well electricity use, allows groundwater levels to recover and significantly slows the rate of land subsidence. To assess the effectiveness of pumping reduction strategies, two artificial scenarios were simulated. The average subsidence rate at the Xiutan Elementary School multi-layer compression monitoring well (MLCW) decreased from 2.23 cm/year (observed) to 1.94 cm/year in first scenario and 1.34 cm/year in second scenario, demonstrating the potential of groundwater control in mitigating land subsidence. These findings underscore the importance of integrating groundwater-use indicators into subsidence models and demonstrate that curtailing groundwater extraction can effectively mitigate land subsidence in vulnerable deltaic regions.
Journal Article
Autophagy activators rescue and alleviate pathogenesis of a mouse model with proteinopathies of the TAR DNA-binding protein 43
TDP-43 is a multifunctional DNA/RNA-binding protein that has been identified as the major component of the cytoplasmic ubiquitin (+) inclusions (UBIs) in diseased cells of frontotemporal lobar dementia (FTLD-U) and amyotrophic lateral sclerosis (ALS). Unfortunately, effective drugs for these neurodegenerative diseases are yet to be developed. We have tested the therapeutic potential of rapamycin, an inhibitor of the mammalian target of rapamycin (mTOR) and three other autophagy activators (spermidine, carbamazepine, and tamoxifen) in a FTLD-U mouse model with TDP-43 proteinopathies. Rapamycin treatment has been reported to be beneficial in some animal models of neurodegenerative diseases but not others. Furthermore, the effects of rapamycin treatment in FTLD-U have not been investigated. We show that rapamycin treatment effectively rescues the learning/memory impairment of these mice at 3 mo of age, and it significantly slows down the age-dependent loss of their motor function. These behavioral improvements upon rapamycin treatment are accompanied by a decreased level of caspase-3 and a reduction of neuron loss in the forebrain of FTLD-U mice. Furthermore, the number of cells with cytosolic TDP-43 (+) inclusions and the amounts of full-length TDP-43 as well as its cleavage products (35 kDa and 25 kDa) in the urea-soluble fraction of the cellular extract are significantly decreased upon rapamycin treatment. These changes in TDP-43 metabolism are accompanied by rapamycin-induced decreases in mTOR-regulated phospho-p70 S6 kinase (P-p70) and the p62 protein, as well as increases in the autophagic marker LC3. Finally, rapamycin as well as spermidine, carbamazepine, and tamoxifen could also rescue the motor dysfunction of 7-mo-old FTLD-U mice. These data suggest that autophagy activation is a potentially useful route for the therapy of neurodegenerative diseases with TDP-43 proteinopathies.
Journal Article
Reconstructing missing time-varying land subsidence data using back propagation neural network with principal component analysis
Land subsidence, a complex geophysical phenomenon, necessitates comprehensive time-varying data to understand regional subsidence patterns over time. This article focuses on the crucial task of reconstructing missing time-varying land subsidence data in the Choshui Delta, Taiwan. We propose a novel algorithm that leverages a multi-factorial perspective to accurately reconstruct the missing time-varying land subsidence data. By considering eight influential factors, our method seeks to capture the intricate interplay among these variables in the land subsidence process. Utilizing Principal Component Analysis (PCA), we ascertain the significance of these influencing factors and their principal components in relation to land subsidence. To reconstruct the absent time-dependent land subsidence data using PCA-derived principal components, we employ the backpropagation neural network. We illustrate the approach using data from three multi-layer compaction monitoring wells from 2008 to 2021 in a highly subsiding region within the study area. The proposed model is validated, and the resulting network is used to reconstruct the missing time-varying subsidence data. The accuracy of the reconstructed data is evaluated using metrics such as root mean square error and coefficient of determination. The results demonstrate the high accuracy of the proposed neural network model, which obviates the need for a sophisticated hydrogeological numerical model involving corresponding soil compaction parameters.
Journal Article
Evaluation of liquefaction potential in central Taiwan using random forest method
Liquefaction is a significant geotechnical hazard in seismically active regions like Taiwan, threatening infrastructure and public safety. Accurate prediction models are essential for assessing soil susceptibility to liquefaction during seismic events. This study evaluates liquefaction potential in central Taiwan using the random forest (RF) method. The RF models were developed with a dataset of 540 soil and seismic parameter sets, including depth, effective and total overburden stresses, SPT-N values, fine soil content, earthquake magnitude, peak ground acceleration, and historical liquefaction occurrences. Rigorous validation techniques, such as cross-validation and comparisons with observed liquefaction events, confirm the RF model’s effectiveness, achieving an accuracy of 98.89%. The model also quantifies predictor importance, revealing that the SPT-N value is the most critical soil factor, while peak ground acceleration is the key seismic factor for liquefaction prediction. Notably, the RF model outperforms simplified procedures in accuracy, even with fewer input factors. Our case studies show that an accuracy of over 95% can still be achieved, highlighting the RF model’s superior performance compared to conventional methods, which struggle to reach similar levels.
Journal Article
Prevalence, clinical reasons and associated factors of extended treatment duration for drug susceptible tuberculosis – a real-world experience
Limited research has been conducted on the prevalence and factors associated with extended drug-susceptible tuberculosis (TB) treatment. A retrospective study enrolled drug-susceptible TB patients (January 2018 to December 2020) from a hospital’s registry to analyze prevalence, reasons, and factors for extended treatment (≥ 9 months) compared with standard course. Analyzing 221 TB patients, 80 patients received extended treatment. The extended group showed higher hepatitis B infection rates (12.5% vs. 5%,
p
= 0.043), recent cancer treatment (18.8% vs. 8.5%,
p
= 0.025), more adverse drug events (ADEs) (grade 3 or more severe ADEs 27.5% vs. 11.3%,
p
= 0.003), and treatment interruptions (46.3% vs. 18.4%,
p
< 0.001). Logistic regression highlighted hepatitis B infection (AOR 3.10,
p
= 0.039), recent cancer treatment (AOR 3.09,
p
= 0.013), and post-treatment elevated aminotransferase (AOR 2.40,
p
= 0.014) as independent factors for extended treatment. Extended anti-TB treatment affects 28.7% of patients, with host characteristics and adverse drug effects playing a role in treatment duration. Integrating these factors into treatment strategies is vital for optimal patient care.
Journal Article
Predictive Modeling of Fire Incidence Using Deep Neural Networks
To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model’s predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning.
Journal Article
HDAC1 dysregulation induces aberrant cell cycle and DNA damage in progress of TDP‐43 proteinopathies
TAR DNA‐binding protein 43 (TDP‐43) has been implicated in frontotemporal lobar degeneration with ubiquitin‐positive inclusions (FTLD‐TDP) and amyotrophic lateral sclerosis. Histone deacetylase 1 (HDAC1) is involved in DNA repair and neuroprotection in numerous neurodegenerative diseases. However, the pathological mechanisms of FTLD‐TDP underlying TDP‐43 proteinopathies are unclear, and the role of HDAC1 is also poorly understood. Here, we found that aberrant cell cycle activity and DNA damage are important pathogenic factors in FTLD‐TDP transgenic (Tg) mice, and we further identified these pathological features in the frontal cortices of patients with FTLD‐TDP. TDP‐43 proteinopathies contributed to pathogenesis by inducing cytosolic mislocalization of HDAC1 and reducing its activity. Pharmacological recovery of HDAC1 activity in FTLD‐TDP Tg mice ameliorated their cognitive and motor impairments, normalized their aberrant cell cycle activity, and attenuated their DNA damage and neuronal loss. Thus, HDAC1 deregulation is involved in the pathogenesis of TDP‐43 proteinopathies, and HDAC1 is a potential target for therapeutic interventions in FTLD‐TDP.
Synopsis
TDP‐43 proteinopathies cause pathogenesis through inducing cytosolic mislocalization of HDAC1. Pharmacological recovery of HDAC1 activity in FTLD‐TDP transgenic (Tg) mice improves cognitive and motor functions, also attenuates aberrant cell cycle activity, DNA damage and neuronal death.
Aberrant cell cycle activity and DNA damage are found in frontal cortices of both FTLD‐TDP Tg mice and FTLD‐patients.
TDP‐43 interacts with HDAC1 and traps it in cytosolic inclusions during the pathogenesis of TDP‐43 proteinopathies.
TDP‐43 proteinopathies may play an essential role in reducing nuclear levels and activity of HDAC1.
Increased HDAC1 activity ameliorates the cognitive and motor function of Tg mice, also reduces DNA damage and neuronal loss.
Graphical Abstract
TDP‐43 proteinopathies cause pathogenesis through inducing cytosolic mislocalization of HDAC1. Pharmacological recovery of HDAC1 activity in FTLD‐TDP transgenic (Tg) mice improves cognitive and motor functions, also attenuates aberrant cell cycle activity, DNA damage and neuronal death.
Journal Article