Computational modelling and application of mechanical waves to detect arterial network anomalies: Diagnosis of common carotid stenosis.

BACKGROUND AND OBJECTIVE: This paper proposes a novel strategy to localize anomalies in the arterial network based on its response to controlled transient waves. The idea is borrowed from system identification theories in which wave reflections can render significant information about a target system. Cardiovascular system studies often focus on the waves originating from the heart pulsations, which are of low bandwidth and, hence, can hardly carry information about the arteries with the desired resolution. METHODS: Our strategy uses a relatively higher bandwidth transient signal to characterize healthy and unhealthy arterial networks through a frequency response function (FRF). We tested our novel approach on data simulated using a one-dimensional cardiovascular model that produced pulse waves in the larger arteries of the arterial network. Specifically, we excited the blood flow from the brachial artery with a relatively high bandwidth flow disturbance and collected the subsequent pressure waveform at peripheral positions. To better differentiate FRFs of healthy and unhealthy networks, we used a FRF that removes the effects of heart pulsations. RESULTS: Results demonstrate the ability of the proposed FRF to detect and follow-up on the development of a common carotid artery (CCA) stenosis. We tested distinct geometrical variations of the stenosis (size, length and position) and observed differences between the FRFs of healthy and unhealthy networks in all cases; such differences were mainly due to geometrical variations determined by the stenosis. CONCLUSIONS: We have provided a theoretical proof of concept that demonstrates the ability of our novel strategy to detect and track the development of CCA stenosis by using peripheral pressure waves that can be measured non-invasively in clinical practice.

Foul sewer model development using geotagged information and smart water meter data.

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Machine learning based marine water quality prediction for coastal hydro-environment management.

During the past three decades, harmful algal blooms (HAB) events have been frequently observed in marine waters around many coastal cities in the world including Hong Kong. The increasing occurrence of HAB has caused acute influences and damages on water environment and marine aquaculture with millions of monetary losses. For example, the Tolo Harbour is one of the most affected areas in Hong Kong, where more than 30% HAB occurred. In order to forewarn the potential HAB incidents, the machine learning (ML) methods have been increasingly resorted in modelling and forecasting water quality issues. In this study, two different ML methods - artificial neural networks (ANN) and support vector machine (SVM) - are implemented and improved by introducing different hybrid learning algorithms for the simulations and comparative analysis of more than 30-year measured data, so as to accurately forecast algal growth and eutrophication in Tolo Harbour in Hong Kong. The application results show the good applicability and accuracy of these two ML methods for the predictions of both trend and magnitude of the algal growth. Specifically, the results reveal that ANN is preferable to achieve satisfactory results with quick response, while the SVM is suitable to accurately identify the optimal model but taking longer training time. Moreover, it is demonstrated that the used ML methods could ensure robustness to learn complicated relationship between algal dynamics and different coastal environmental variables and thereby to identify significant variables accurately. The results analysis and discussion of this study also indicate the potentials and advantages of the applied ML models to provide useful information and implications for understanding the mechanism and process of HAB outbreak and evolution that is helpful to improving the water quality prediction for coastal hydro-environment management.

Morphological environment survey and hydrodynamic modeling of a large bifurcation-confluence complex in Yangtze River, China.

Since the establishment of the world-class Three Gorges Dam (TGD) across the Yangtze River, China, the downstream reach has experienced a long-term adjustment with regard to the river morphology and hydrodynamics, imposing a profound impact on the environmental conditions of human living and aquatic ecosystem. This study presents an investigation on the river channel morphological characteristics and hydrodynamic environment of a large bifurcation-confluence complex downstream of the TGD through detailed field survey and numerical modeling. Results show that the main stem, before being bifurcated into two sub-channels (the North Channel and the South Channel), experiences a meander, leading to the severe bed scouring near the outer bank (pools) resulted from a high flow mass flux and bed shear stress. Because of being bifurcated, the river width with largely growing may result in the reduction of flow velocity and sediment deposition (riffles), and thereby two plugbars are formed near the entrance of two sub-channels. In the meantime, the velocity-reversal phenomenon (flow velocity and friction velocity) is identified when low flows are transited into high flows. The flow mass flux, however, is always larger in pool regions, which is highly related to water depth. As a result, the topographic steering of flows by riffles, bars and floodplains may have more impact on flow path under low flow conditions, while the bankline shape would become more important under high flows. Furthermore, the topographic steering could play a key role in the pattern of flow separations near the confluence. More interestingly, the confluence flow separation only occurs under low flow conditions and its occurring location shifts upwards the tributary (the North Channel), which differs from observations in previous studies. The visualized numerical results of friction velocity distribution indicate that sediment is more likely to deposit in the North Channel (entrance) with lower friction velocity, implying the potential closure of the sub-channel.