Development of novel methods for municipal water main infrastructure integrity management

Water Distribution Network (WDN) is an important component of municipal infrastructure. Many municipal water distribution systems are exposed to harsh environment and subjected to corrosion with age. Many of the water mains in North America are close to or have exceeded their design life and are experiencing a number of issues associated with leaks and breakage of the water mains. Maintaining structural integrity of the water infrastructure with the limited municipal budget has been a challenge. Under this circumstance, the municipalities are focusing on prioritizing their infrastructure for maintenance with optimum utilization of the resources. In this regard, an effective method for prioritizing is required for optimally maintaining the infrastructure integrity. The proposed research focuses on developing risk/reliability based prioritizing methods for water main infrastructure maintenance. Historic water main break data (i.e. number of breaks per km) is often used to identify breakage patterns in the attempts to reliability assessments of deteriorating water mains. This statistical modelling approach is unable to identify the failure mechanism and have limited use. Physical/mechanistic models are therefore desired for better understanding of the failure mechanisms and reliability assessment of WDN. In the proposed research, mechanics-based model is developed for the reliability assessment of water mains. Existing models for remaining strength assessment of the deteriorating pipelines are first examined to develop improved models. Pipe stress analysis is then performed for the reliability assessment of the pipes based on a stochastic analysis using Monte Carlo simulation. For prioritizing water mains, system reliability and risk assessment methods are employed. For small WDN, the system failure of the pipeline network is modeled using Fault-Tree Analysis (FTA). The FTA is however tedious for large complex network. For large WDN, a complex network analysis method is employed to determine the potential of network disconnection due to water main break. Algebraic Connectivity (AC) of a complex network analysis is found to effectively represent the robustness and redundancy of WDN. The fluctuation in AC due to water main break could be used to assess the criticality of each pipe segment to the overall structure of the network. The AC then used as a part of overall consequence of the network due to water main breaks. A Fuzzy Inference System is proposed to combine network consequence with other consequence for risk assessment of complex WDN. In summary, a novel risk/reliability-based method for maintenance of water distribution system is developed in this thesis. In developing this method, mechanics-based failure is considered for reliability assessment and AC from graph theory is used for the consequence assessment of water main break on the overall network. A framework is developed for risk assessment considering the reliability and various consequences

Tumor Segmentation and Classification Using Machine Learning Approaches

Medical image processing has recently developed progressively in terms of methodologies and applications to increase serviceability in health care management. Modern medical image processing employs various methods to diagnose tumors due to the burgeoning demand in the related industry. This study uses the PG-DBCWMF, the HV area method, and CTSIFT extraction to identify brain tumors that have been combined with pancreatic tumors. In terms of efficiency, precision, creativity, and other factors, these strategies offer improved performance in therapeutic settings. The three techniques, PG-DBCWMF, HV region algorithm, and CTSIFT extraction, are combined in the suggested method. The PG-DBCWMF (Patch Group Decision Couple Window Median Filter) works well in the preprocessing stage and eliminates noise. The HV region technique precisely calculates the vertical and horizontal angles of the known images. CTSIFT is a feature extraction method that recognizes the area of tumor images that is impacted. The brain tumor and pancreatic tumor databases, which produce the best PNSR, MSE, and other results, were used for the experimental evaluation

Assistive technologies for the older people: Physical activity monitoring and fall detection

The advancements in information and communications technologies (ICT) and micro-nano manufacturing lead to innovative developments of smart sensors and intelligent devices as well as related assistive technologies which have been directly contributing to improving the life quality, from early detection of diseases to assisting daily living activities. Physical activity monitoring and fall detection are two specific examples where assistive technologies with the use of smart sensors and intelligent devices may play a key role in enhancing the life quality, especially improving the musculoskeletal health which is an essential aspect of health and wellbeing; and it is more important for the older people. This paper presents and dis-cusses about how sensors and wearable devices, such as accelerometers and mobile phones, may be employed to promote the musculoskeletal health. Assistive technologies and methods for physical activity monitoring and fall detection are discussed, with the focus on the fall detection using mobile phone technology, and assessments of the loading intensity of physical activity in a non-laboratory environment. The possible research directions, challenges and potential collaborations in the areas of assistive technologies and ICT solutions for the older populations are proposed and addressed

The role of technological innovation and cleaner energy towards the environment in ASEAN countries: proposing a policy for sustainable development goals

The association between economic growth (EG) and environmental degradation (ED) has been highlighted extensively in prior studies. However, investigation regarding ‘technological innovation and clean energy role’ in dealing with environmental concerns has comprised limited context while considering the ASEAN economies under sustainable development goals. Therefore, the study attempts to investigate the phenomenon by using CS-ARDL analysis under short as well as long run. The findings through CSARDL in long- and short-run indicate that REN have impact carbon emission and ecological footprints negatively. Additionally, the EG in targeted economies is causing a higher level of CE and ecological footprints. Whereas, GDP2ofund to be significant in lowering the ED in the form of CE and ecological footprints. It is suggested that policies related to CE through EG should be developed in order to control the environmental issues in the future

Mitigating effect of embankment to adjacent pipe with CDM columns

Pipelines are valuable infrastructures that covering a large area or expanding to long distance for the transporting function. This leads to the variety of loads and effects applied on such buried structures. A thread to pipeline integrity is the construction of the embankment on the soft soil which leads to the displacement of the pipe adjacent to the slope. This displacement will effect to the increase of internal force or causing failure of the near-by pipes. The use of concrete pile to improve the soil properties may be a solution; however, the cost for this is expensive. To propose an alternative solution for the problem, this study uses a system of cement deep mixing, CDM, columns as the solution for protecting the pipe. A simple 2D Finite Element, FE, model using Plaxis software has been established based on the equivalent soil approach which considering the CDM columns and their surrounding soil as an unified soil. The effectiveness of the proposed solution has been numerically investigated and proven. The lateral displacement of pipe and the maximum ring bending moment and other internal forces are significantly reduced with the appearance of the CDM columns. The selective parametric study has been implemented revealing the critical input variables are the distance of pipe to the slope and the length of the CDM column

Cross-cultural adaptation and psychometric validation of the Vietnamese version of the evidence-based practice competency questionnaire for registered nurses (EBP-COQ Prof©)

Background & Aim: Establishing strategies to enhance evidence-based practice (EBP) requires a reliable instrument for assessing EBP competency. This study focused on translating and validating the Evidence-Based Practice Competency Questionnaire for Registered Nurses (EBP-COQ Prof©) in the Vietnamese context. Methods & Materials: Through a methodological approach, this study performed cross-cultural adaptation and psychometric validation. The study involved 372 nurses selected through convenience sampling. Content validity was established using the Content Validity Index for Items (I-CVI) and the Content Validity Index for Scales (S-CVI). Construct validity was assessed via exploratory (EFA) and confirmatory factor analysis (CFA). Reliability was determined using Cronbach's alpha and the intra-class correlation coefficient (ICC). Criterion validity was examined by comparing EBP-COQ Prof© competency between nurses with and without prior EBP education. Results: The Vietnamese version of EBP-COQ Prof© maintained consistency with the original version following cross-cultural adaptation. Content validity was confirmed with I-CVI> 0.78 and S-CVI/AVE= 0.97. EFA and CFA revealed consistent components with the original version: attitude (8 items), knowledge (11 items), skills (6 items), and utilization (10 items). Cronbach's alpha values were high: attitudes (0.965), knowledge (0.962), skills (0.909), and utilization (0.926). ICC values were also significant: attitudes (0.754), knowledge (0.895), skills (0.823), and utilization (0.966). Nurses with prior EBP education demonstrated higher EBP-COQ Prof© competency. Conclusion: The translated and validated EBP-COQ Prof© provides a robust tool for assessing EBP competency among Vietnamese nurses. Its reliability, validity, and sensitivity to educational effects underscore its potential for promoting EBP in nursing

EVALUATION OF GENETIC DIVERSITY OF THE BLACK GLUTINOUS RICE BASED ON AGRO-MORPHOLOGICAL CHARACTERS

The study assessed the variations in nine agro-morphological characters among and within the black glutinous rice (Oryza sativa) population from Chau Thanh District, Tra Vinh Province. The nine quantitative agromorphological characters that were measured include culm length, leaf length, leaf width, number of panicles, panicle length, grain length, grain width, number of firm grain, and number of grain per panicle. The unweighted pair group method with arithmetic mean method and principal coordinate analysis by the NTSYS program were applied in this study to classify the nine agro-morphological characters. In addition, to compare the variations in quantitative characters between O. sativa populations, one-way analysis of variance (ANOVA) was used. The results showed significant differences between the black glutinous rice populations for all quantitative agro-morphological characters. Moreover, some agro-morphological characters showed positive correlations to each other. The dendrogram generated from the analysis process of the agromorphological data divided the O. sativa populations into two groups with unfamiliar features. However, the O. sativa populations assessed exhibited a wide range of variations in morphological characteristics, both within the same population and among other populations with the same strains

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

The ultimate compressive load of concrete-filled steel tubular (CFST) structural members is recognized as one of the most important engineering parameters for the design of such composite structures. Therefore, this paper deals with the prediction of ultimate load of rectangular CFST structural members using the adaptive neurofuzzy inference system (ANFIS) surrogate model. To this end, compression test data on CFST members were extracted from the available literature, including: (i) the mechanical properties of the constituent materials (i.e., steel’s yield strength and concrete’s compressive strength) and (ii) the geometric parameters (i.e., column length, width and height of cross section, and steel tube thickness). The ultimate load is the output response of the problem. The ANFIS model was trained using a hybrid of the least-squares and backpropagation gradient descent method. Quality assessment criteria such as coefficient of determination (R2), root mean square error (RMSE), and slope of linear regression were used for error measurements. A 11-fold cross-validation technique was employed to evaluate the performance of the model. Results showed that for the training process, the average performance was as follows: R2, RMSE, and slope were 0.9861, 89.83 kN, and 0.9861, respectively. For the validating process, the average performance was as follows: R2, RMSE, and slope were 0.9637, 140.242 kN, and 0.9806, respectively. Therefore, the ANFIS model may be considered valid because it performs well in predicting ultimate load using the validated data. Moreover, partial dependence (PD) analysis was employed to interpret the “black-box” ANFIS model. It is observed that PD enabled us to locally track the influence of each input variable on the output response. Besides reliable prediction of ultimate load, ANFIS can also provide maps of ultimate load. Finally, the ANFIS model developed in this study was compared with other works in the literature, showing that the ANFIS model could improve the accuracy of ultimate load prediction, in comparison to previously published results

An improved state machine-based energy management strategy for renewable energy microgrid with hydrogen storage system

Renewable energy (solar and wind) sources have evolved dramatically in recent years around the globe, primarily because they have the potential to generate environmentally friendly energy. However, operating systems with high renewable energy penetration remain challenging due to the stochastic nature of these energy sources. To tackle these problems, the authors propose a state machine-based energy management strategy combined with a hysteresis band control strategy for renewable energy hybrid microgrids that integrates hydrogen storage systems. By considering the power difference between the renewable energy source, and the demand, the battery’s state of charge, and the hydrogen storage level, the proposed energy management strategy can control the power of fuel cells, electrolyzers, and batteries in a microgrid and the power imported into/exported from the main grid. The results showed that the energy management strategy provides the following advantages: (1) the power supply and demand balance in the microgrid was balanced, (2) the lifespans of the electrolyzer and fuel cell were extended, and (3) the state of charge of the battery and the stored level of the hydrogen were appropriately ensured