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Currently pressure sensors utilized for water pressure measurement need batteries for direct power supply. However, batteries are lifespan-limited and not so reliable in the buried water pipe environment. The maintenance work due to battery failures leads to high cost to utility owners. Wireless passive surface acoustic wave (WP-SAW) sensors do not need direct power supply from batteries and can work in harsh environment. They are low-cost and compatible with microelectromechanical systems (MEMS) technologies. This study investigates a temperature-compensated WP-SAW bidirectional reflective delay line (RDL) pressure sensor and its feasibility in improving water pressure measurement. The linear temperature-compensated pressure sensing functional model between the output phase shifts and the pressure change is established theoretically and verified by experiments. An experimental framework for testing the sensor node is built. The water pressure sensing adaptor is proposed. Down conversion and quadrature sampling for the measurement of phase shifts are implemented. Experimental results: the experimental data show good linearities, which fits the established functional relationship; the numerical functional relationship has been derived and expressed; the sensor node has a good performance in the range of pressure difference from 0 to 0.5 MPa, which meets the normal 28-meter water pressure sensing requirements; the accuracy of this sensor is 7.2188 kPa, which can be utilized for the water pressure sensing tasks in water distribution systems.

Objective The purpose of our study was to assess the knowledge and attitudes toward expanded carrier screening (ECS) between the medical staff and general population in China. Study design It was a survey-based cross-sectional study in Chinese. We provided an online survey for the general public nationwide. We classified the population into the medical staff and general population to evaluate the knowledge gap and provide a reference for ECS's education programs. Except for obstetrician-gynecologists and nurses in the department of Obstetrics and Gynecology, other medical staff were not included in our study. A total of 1947 questionnaires were collected from July 11, 2020 to February 10, 2021. Two hundred and eighty-four questionnaires were excluded from further analysis. The remaining 1663 cases were incorporated into the final analysis. Data were analyzed using IBM SPSS Statistics 26. Comparisons between categorical variables were tested by the use of crosstabs and χ2 test. Results The total awareness rates of the knowledge about monogenic diseases and ECS in the respondents were low, with 35.7%, 26.1%, 3.3%, 23.3%, 24.1%, 55.2%, and 23.4% for questions Q1–Q7, respectively. Medical staff had more knowledge than general population. Knowledge about monogenic diseases and ECS was positively correlated with educational level. Most respondents showed a positive attitude toward ECS: 54.4% thought ECS was necessary, and 80.5% wanted to know more about monogenic diseases. Conclusion Although the public had little knowledge about monogenic disease and ECS, most of them showed a positive attitude. Our cross-analysis showed that medical staff had more knowledge compared to general population. Pre-test education for ECS can be carried out by medical staff who are not qualified for genetic counseling. ECS training for medical staff, especially obstetrician-gynecologist and nurse in the department of Obstetrics and Gynecology, can reduce the workload of genetic counseling.

Background Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a rare disorder caused by mutation of the forkhead box protein 3 (FOXP3) gene, often leading to intractable and life-threatening diarrhea. Fecal microbiota transplantation (FMT), has been regarded in recent years as an available approach to reconstruct disrupted gut microbiome and successfully used to attenuates diarrhea induced by different underlying diseases. Therefore, FMT may have curative potential on the symptoms of enteropathy in patients with IPEX syndrome. Methods Physical and laboratory examinations were performed, and clinical data were collected. FMT was administered via frozen fecal microbial solution, and the fecal microbiota composition was analyzed using 16S rDNA sequencing before and after FMT. Results The patient was diagnosed with IPEX syndrome with a mutation detected in the FOXP3 gene, which was identified as c.767T > C (p.M256T). He presented with recurrent watery diarrhea and respiratory infections after birth and developed a significant failure to thrive. Disturbances in the gut microbiota composition and marked decreased bacterial diversity were observed to be involved in the persistent and refractory diarrhea. After receiving FMT treatment, the patient responded with remission of the diarrhea without apparent side effects. His stool output significantly decreased, corresponding to increased microbial diversity and modification of his microbiota composition. The patient finally achieved full recovery after hematopoietic stem cell transplantation (HSCT). Conclusion Our data suggest an association between the gut microbiota and clinical symptoms of patient with IPEX syndrome and demonstrate FMT as an alternative therapy for severe diarrhea unresponsive to routine therapy in these patients.

Objective The purpose of this survey is to evaluate the knowledge of Chinese pregnant women about fetal chromosomal aneuploidy and microdeletion and microduplication syndromes, and Non-invasive prenatal testing (NIPT). Study Design Written questionnaires were distributed to pregnant women who visited the fetal medicine clinic of the third affiliated hospital of Guangzhou medical university. A total of 330 questionnaires were given. Twenty-two questionnaires with incomplete information were excluded from further analysis. The remaining 308 cases were incorporated into the final analysis. Data were analyzed using IBM SPSS Statistics 26. Comparisons between categorical variables were tested by the use of crosstabs and χ 2 test. Results Among pregnant women, the recognition of Down syndrome was the highest (93.5 %), followed by maternal serum screening (74.0 %) and NIPT (69.2 %) for chromosomal aneuploidy. The awareness rates of chromosomal microdeletions and microduplications (18.2 %) and monogenic disorders (13.3 %) were the lowest. There were no significant differences in age, education, and conception way between pregnant women (P > 0.05). When asked the opinion on increasing the testing range of NIPT, more than 50 % of pregnant women chose to follow the provider's advice. Conclusion More than half of pregnant women lacked knowledge of screening methods for fetal chromosomal abnormalities. Less than 20 % of pregnant women knew microdeletion microduplication syndromes. To enhance the understanding of chromosomal abnormalities can ensure that women can actively choose tests rather than passively agreeing to their provider's recommendations so as to avoid missing the optimal prenatal screening time.

Due to the instability of the characteristics of materials, fabrication processes and user handling, newly designed and fabricated wireless passive surface acoustic wave (WP-SAW) sensor nodes have inconsistent sensing performance. Furthermore, ambient environmental interferences aggravate inconsistences under complex working conditions. In this paper, a multi-iteration enhanced two-point simple moving average (MI-2P-SMA) method is proposed for sensing error reduction of a WP-SAW reflective delay line water temperature and pressure sensor. This method is improved from the traditional 2P-SMA method for better performance on error reduction. The results show: the MI-2P-SMA method does not change the original characteristics of experimental data; it can reduce relative errors of the WP-SAW reflective delay line water temperature and pressure sensor and has better performance than a traditional 2P-SMA method; it reduces the number of data points and the extent of this reduction is dependent on iteration time.

Indoor air quality (IAQ) monitoring has attracted increasing attention with the rapid development of industrialization and urbanization in the modern society as people typically spend more than 80% of their time in indoor environments. A novel IAQ detector (IAQD) integrated with multiple communication interfaces has been designed, built, programmed, deployed, and tested in order to meet the requirements of wide variety of scenarios. The IAQD measures the IAQ data, including temperature, humidity, CO2, dust, and formaldehyde timely. With state-of-the-art Internet of Things (IoT) technologies, the IAQD is integrated with Modbus, LoRa, WiFi, general packet radio service, and NB-IoT communication interfaces, which enables to be applied to wired communications, short-range wireless communications, and remote transmission to the cloud. The designed software in cloud allows users to track the IAQ of their home or office or industries everywhere. The performance IAQD is evaluated in terms of packet loss rate and time delay. The evaluation of IAQD are demonstrated and analyzed within the office environment over a week. Experimental results show that the proposed system is effectiveness in measuring the air-quality status and provide excellent consistency and stability.

In the practical implementation of wireless powered communication network (WPCN) project, the energy consumption is an important factor to evaluate the performance and efficiency of the communication. In this project, a UAV enabled WPCN acts as a hybrid access point to handle multiple ground terminals (GT’s) in an uneven plane. The ground terminals will harvest radio frequency (RF) energy from the RF signals directed by the UAV and these terminal uses this harvested energy to send information to the uplink. The objective of the paper is to find an optimal position of UAV using a proposed dome packing method to navigate the UAV, thus UAV will be able to charge the GTs within the cluster using 3D beamforming method where the beam will be focusing to a particular terminal rather than broadcasting the signals everywhere and thereby minimize the total energy consumption and mission completion time.

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary...

With the rapid development of new technologies such as artificial intelligence, big data, and the Internet of Things, many researchers have probed into the study of learning analysis, trying to solve the problems of teaching by analyzing the learning behavior data from learning process. And in many learning behavior research, the sensor network usually consists of a host of mutually independent data sources, which can be used to monitor measured objects from multiple dimensions thereby obtaining the multi-source multi-modal sensory data. However, there still exist false negative readings, false positive readings and environmental interference, etc. Therefore, we propose a multi-source multimode sensory data acquisition method based on Date Quality(DQ). We first define the data quality in terms of four aspects-accuracy, integrity, consistency and instantaneity. Then, by the modeling there aspects respectively, we propose metrics to estimate the comprehensive data quality method of multi-source multi-mode sensory data. Finally, a data acquisition method is presented based on data quality, which selects a part of data sources for data transmission according to the given precision. This method aims at reducing the consumption of the sensory network on the premise of the data quality guarantee. An extensive experimental evaluation demonstrates the efficiency and effectiveness of the algorithm.

Objective This study sought to investigate the role of human papillomavirus (HPV) in the carcinogenesis of oral leukoplakia (OLK), with the oral cavity as the site of interest. Study Design A total of 76 patients (152 specimens) were enrolled in the study. The patients were divided into 2 groups: the malignant transformation of OLK (OLK-MT) group and the non–malignant transformation of OLK (OLK-non-MT) group. HPV reverse dot blot analysis, HPV DNA polymerase chain reaction (PCR), and p16INK4A immunohistochemistry (IHC) were used to determine HPV infection status. Results Transformation of OLK commonly occurred in the lateral/ventral tongue, buccal mucosa, and gingiva. On the basis of the initial analysis of specimens, only 5.3% (4 of 76) of patients were found to be HPV-16 positive, and these patients’ final specimens yielded negative results. Overexpression of p16INK4A in the dysplastic stage was associated with the transformation of OLK (P = .013; odds ratio = 3.544). Conclusions Transformation of OLK was common in patients who are elderly, in females, and in nonsmokers/nondrinkers; lesions were located in the lateral/ventral tongue, with dysplasia and overexpressed p16INK4A seen during the initial stage. HPV may be an opportunistic infection in the oral cavity and may not be a cause of malignant transformation of OLK. p16INK4A expression, which initially increases and then diminishes or disappears, may be an early predictor of malignant transformation.

Facial landmark detection aims at locating a sparse set of fiducial facial key-points. Two significant issues (i.e., Intra-Dataset Variation and Inter-Dataset Variation) remain in datasets which dramatically lead to performance degradation. Specifically, dataset variations will lead to severe over-fitting easily and perform poor generalization in recent in-the-wild datasets which severely harm the robustness of facial landmark detection algorithm. In this study, we show that model robustness can be significantly improved by leveraging rich variations within and between different datasets. This is non-trivial because of the serious data bias within one certain dataset and inconsistent landmark definitions between different datasets, which make it an extraordinarily tough task. To address the mentioned problems, we proposed a novel Deep Coupling Neural Network (DCNN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). In particular, DA-Net takes advantage of different characteristics and distributions across different datasets, while CD-Net makes a final decision on candidate hypotheses given by DA-Net to leverage variations within one certain dataset. Extensive evaluations show that our approach dramatically outperforms state-of-the-art methods on the challenging 300-W and WFLW dataset.

Objective This study was performed to compare the clinical value of the second-generation Shikani optical stylet with that of the Macintosh laryngoscope for tracheal intubation of patients undergoing cerebral aneurysm embolization. Methods Thirty-six patients who underwent cerebral aneurysm embolization were included. The intubation time, intubation success rate, blood oxygen saturation, heart rate, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured. Adverse reactions during tracheal intubation and the local tissue injury rate were recorded. Comparisons between the groups were performed with one-way analysis of variance. Results The heart rate, SBP, and DBP upon tracheal intubation and at 1 and 3 minutes were significantly higher in the Macintosh laryngoscope group than in the Shikani optical stylet group. The time to completion of tracheal intubation was significantly shorter and the tissue injury rate was significantly lower in the Shikani optimal stylet group than in the Macintosh laryngoscope group. Conclusions The second-generation Shikani optical stylet is a simple, safe, and reliable tool for tracheal intubation in critically ill patients undergoing cerebral aneurysm embolization.

Background: Monogenic diseases affect about 10 in 1000 live births globally and account for 20% of infant mortality and 18% of pediatric hospitalizations. Many monogenic disorders could be prevented by offering expanded carrier screening (ECS) to the general population. The purpose of our study was to assess knowledge about monogenic diseases and attitudes toward ECS in China.Methods: Chinese individuals were invited to complete an online survey from July 11 to September 10, 2020. A total of 1663 questionnaires were collected. Statistical analysis was performed using IBM SPSS Statistics 26. Descriptive statistics were computed for all items. Categorical data were reported as frequencies and percentages with differences assessed by chi-square test.Results: The respondents’ total awareness rate ranged from 3.3% to 55.2%. Medical practitioners had more knowledge than non-medical practitioners. Knowledge about monogenic diseases was positively correlated with educational level. Most respondents showed a positive attitude toward ECS: 54.4% of respondents thought ECS was necessary, and 80.5% wanted to know more about monogenic diseases.Conclusions: Although the public had little knowledge about monogenic disease and ECS, most of them showed a positive attitude. Lack of knowledge is a barrier to application of ECS. Since pre-test counseling by a genetic counselor before ECS may not always be feasible, pre-test information may not be delivered by a trained genetic counselor. If clinicians are going to be the primary counselors, further education is necessary to improve their comfort and competence with this role.

This paper aims to assess and analyze the health impact of consuming contaminated drinking water in a water distributed system (WDS). The analysis was based on qualitative simulation performed in two different models named hydraulic and water quality in a WDS. The computation focuses on quantitative analysis for chemically contaminated water impacts by analyzing the dose level in various locations in the water network and the mass of the substance that entered the human body. Several numerical experiments have been applied to evaluate the impact of water pollution on human life. They analyzed the impact on human life according to various factors, including the location of the injected node (pollution occurrence) and the ingested dose level. The results show a significant impact of water contaminant on human life in multiple areas in the water network, and the level of this impact changed from one location to another in WDSs based on several factors such as the location of the pollution occurrence, the contaminant concentration, and the dose level. In order to reduce the impact of this contaminant, water quality sensors have been used and deployed on the water network to help detect this contaminant. The sensors were optimally deployed based on the time-detection of water contamination and the volume of polluted water consumed. Numerical experiments were carried out to compare water pollution’s impact with and without using water quality sensors. The results show that the health impact was reduced by up to 98.37% by using water quality sensors.

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non‐stationary multivariate process. For instance, future mean sea‐level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep‐landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors., Plain Language Summary: Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high‐impact events is crucial for an accurate assessment of climate‐related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still limited. In practice, studying compound events is a challenging task, which often requires interaction between experts across multiple disciplines. Recently, compound events were divided into four types to aid the framing of research on this topic, but guidelines on how to study these four types are missing. Here, we take a pragmatic approach and—focusing on case studies of different compound event types—illustrate how to address specific research questions that could be of interest to users. The results allow identifying recommendations for compound event analyses. Furthermore, through the case studies, we highlight the relevance that compounding effects have for the occurrence of landslides, flooding, vegetation impacts, and crop failures. The guidelines emerged from this work will assist the development of compound event analysis across disciplines and sectors., Key Points: Using case studies representative of four main compound event types we show how compound event‐related research questions can be tackled. We present user‐friendly guidelines for compound event analysis applicable to different compound event types. We demonstrate that compound events cause vegetation impacts, coastal flooding, landslides, and continental‐scale crop yield failures., European COST action DAMOCLES, NERC, Swiss National Science Foundation, Helmholtz Initiative and Networking Fund, Netherlands Organisation for Scientific Research (NWO), Fundação para a Ciência e a Tecnologia, Scientific Employment Stimulus 2017, Italian Ministry of University and Research, European Union's Horizon 2020 research and innovation programme, AXA Research Fund for support, Portuguese Foundation for Science and Technology

Maintaining high water quality is the main goal for water management planning and iterative evaluation of operating policies. For effective water monitoring, it is crucial to test a vast number of drinking water samples that is time-consuming and labour-intensive. The primary objective of this study is to determine, with high accuracy, the quality of drinking water samples by machine learning classification models while keeping computation time low. This paper aims to investigate and evaluate the performance of two supervised classification algorithms, including artificial neural network (ANN) and support vector machine (SVM) for multiclass water classification. The evaluation uses the confusion matrix that includes all metrics ratios, such as true positive, true negative, false positive, and false negative. Moreover, the overall accuracy and f1-score of the models are evaluated. The results demonstrate that ANN outperformed the SVM with an overall accuracy of 94% in comparison to SVM, which shows an overall accuracy of 89%.

This paper presents a method on improving water pressure monitoring using wireless passive surface acoustic wave (WP-SAW) sensor. An experimental framework acting as water pipe simulator is built. Signal processing methods such as down-conversion, quadrature sampling, average, and multi-iterative two-point simple moving average (MI-2P-SMA) are utilized for improving sensing accuracy. The results show the relative errors of temperature at all testing points are less than 1% and the relative errors of pressure at all testing points are less than 2%.

Toll roads constructed under public-private-partnerships (PPP) are very common around the world. Due to the high capital cost invested and high risks associated with subsequent returns, investors are concerned about future revenue. Charging high tolls from freight vehicles is a common practice, especially in urban areas. As a result, freight vehicles tend to divert from freeways (toll roads) to highways and arterial roads to minimize their costs, but this may cause more damage to infrastructure and environment, thus more costs for society and the environment. Consequently, developing an effective solution for toll charges for freight vehicles is a complex City Logistics problem involving multiple stakeholders with multi-objectives. This research investigates total costs (economic, social and environmental) associated with freight transportation based on existing toll structures for a toll road in Melbourne, Australia. Using a real case study, a step by step process was developed to illustrate the inefficiencies that exist in the present rigid toll charging mechanisms and how these inefficiencies can be corrected considering multi-stakeholder objectives and the overall impacts. This research identifies an optimal set of solutions that can be considered by decision-makers for implementation considering trade-offs between multiple management objectives. By considering subsidies, this research also identifies a good practical solution for minimizing the total cost of urban freight transportation while satisfying the investor needs. This policy decision is illustrated and related issues are discussed.

Traditional approaches to inundation modelling are computationally intensive and thus not well suited to assessing the uncertainty involved in estimating flood inundation surfaces for planning, design and forecasting purposes. In this study, a rapid flood inundation modelling framework is developed, consisting of a novel spatial reduction and reconstruction (SRR) approach and a deep learning (DL) modelling component. The SRR approach is developed to reduce computational cost by identifying representative locations of inundation surfaces where water levels are simulated using DL models, and to efficiently reconstruct inundation surfaces based on simulated water level information. The DL model includes a built-in input selection layer to simplify the model development process, and a Long Short-Term Memory layer for time series modelling. The accuracy and efficiency of the SRR-DL framework is assessed by application to a real-world river system where the inundation of over 3 million grid cells can be simulated in 4 s.

The emerge of the Internet of Things (IoT) paradigm in various smart utilizations such as; smart farming and water system monitoring, has been the highlight of this era. Innovative solutions providing better coverage and minimized power consumption by end nodes such as Low Power Wide Area Networks (LP-WAN) has facilitated the advances towards improved IoT connectivity. Long Range Wide Area Network (LoRaWAN) technology stands out as one leading platform of LP-WANs receiving vast attention from both the industries and the academia. Performance evaluation of LoRaWAN is promising, even in the field of localization of end node devices in outdoor low-density platforms. Considering harsh environmental sensor network, where there is a need to deploy a higher dense WSN (i.e sensor every 10 m2 ) and there is unexpected infrequent random displacement of sensor location due to environmental conditions. The accuracy of LoRaWAN in updating the network with revised sensors' coordinates might be unfulfilled. Given the acoustic range of localization to be around few meters with fine precision, in this work, we propose the use of hybrid ultrasonic and LoRa signals to enhance the localization of a high density outdoor WSN.

The study aim was to investigate the risk factors for the progression of oral leukoplakia (OLK) to malignancy.The data from 2,628 patients with OLK were retrospectively reviewed. Of these 2,628 patients, 192 had undergone sequential biopsies and were separated into 4 groups according to their final diagnosis. The risk factors were analyzed using Kaplan-Meier univariate survival analysis and Cox multivariate analysis.In 41 of the 2,628 patients (1.7%), the OLK had progressed to cancer, with a mean interval to malignancy of 26.7 months. Of the 192 patients with sequential biopsies, OLK was maintained or had progressed to mild, moderate, or severe dysplasia or carcinoma in 50, 66, 35, and 41 patients, respectively. The 3- and 5-year oral cancer-free survival (OCFS) was 78.9 and 72.5%, respectively. The factors associated with worse overall survival were lesions located in the ventral tongue (P = .04), alcohol use (P = .025), nonhomogeneous lesions (P .01), and high-risk dysplasia (P .01). Cox regression analyses indicated that nonhomogeneous lesions (P = .03) and high-risk dysplasia (P .01) were independent prognostic factors for the progression of OLK to malignancy.High-risk dysplasia and nonhomogeneous lesions were shown to be important factors for progression to malignancy in patients with OLK. Thus, such patients should receive close follow-up and undergo sequential biopsies in the first 2 to 3 years for early screening of OLK evolving into a malignancy.

Water distribution systems are seeing the increased deployment of new technologies that use Internet of Things (IoT) to gather, analyse and extract useful information from data; further enabling Smart Water Networks (SWNs). IoT type technologies have a huge potential to enable more efficient water resources management. Heterogeneous IoT sensors/devices/technologies from different vendors are starting to be employed in SWNs. The deployment of IoT sensors is a critical issue that significantly affects a wireless sensors network’s real-time monitoring performance. The aim of the deployment of IoT sensors (i.e. nodes) for wireless networks is to find the most efficient number, type and locations of sensors that enable satisfying a SWN’s hydraulic and water quality requirements (among the others) while considering issues such as energy consumption of the nodes and coverage of the target monitoring area, to mention just a few. This paper provides an overview of the SWN architecture and its components and scrutinises emerging sensor and communication technologies for developing SWNs focussing on the sensing and control, and collection and communication layers of the SWN architecture. Additionally, it presents a selective review of recent literature on issues related to the intelligent deployment of sensors in wireless sensors networks and on sensors deployment for water quality and leak detection/localisation applications in SWNs. The main aim of the literature review carried out and presented here has been to highlight the main challenges of developing IoT-enabled SWNs, so that interesting future research directions for achieving a context-aware IoT framework for SWNs can be identified.

Data-driven models can achieve high accuracy and low computational cost without a priori knowledge of hydrological system, which have been successfully applied in streamflow forecasting for decades. However, it is still a challenging task to improve the performance of these models, especially for the rivers with dramatic flow changes. In this paper, a new integrated framework for daily streamflow forecasting based on different flow regimes is developed. The framework integrates a Fuzzy C-means (FCM) clustering for streamflow regime identification, a partial mutual information (PMI) for input selection, Deep Belief Networks (DBN) for mapping the nonlinear relationships between the selected inputs and streamflow during different streamflow dynamics processes, and a rigorous validation process considering structure validity to interpret the physical processes simulated using the DBN models. The framework was applied to three streamflow stations with different climate conditions in USA, and the results show that the framework has significantly improved modelling performance (approximately 12%) compared to single data-driven models. The integration of data-driven models and physical process classification also leads to improved integration of physical understanding of the complex characteristic of different flow regimes into the modelling process, leading to overall improved confidence in the developed daily streamflow forecasting models.

Flood inundation models are important tools in flood management. Commonly used flood inundation models, such as hydrodynamic or simplified conceptual models, are either computationally intensive or cannot simulate the temporal behavior of floods. Therefore, emulation models based on data-driven methods, such as artificial neural networks (ANNs), have been developed. However, the performance of ANN models, like any other data-driven models, is limited by available data and will not perform well in data-sparse regions. In this study, we developed an ANN-based hybrid modeling approach to improve model performance in data-sparse regions by leveraging better model performance in data-rich regions. We applied our proposed hybrid modeling approach with three ANN models, including the traditional point-based ANN and two newly proposed block-based ANN models. The results demonstrate that all three ANN models have better performance in data-rich regions compared to data-sparse regions as expected, with the block-based ANN with the most complicated model structure performing better in data-rich regions and the simplest point-based ANN performing better in data-sparse regions. The hybrid modeling approach can significantly improve model performance in data-sparse regions, with the hybrid model based on the most complex block-based ANN performing the best. Our results show the importance of considering the trade-offs between data availability and model complexity in developing data-driven models, and demonstrate the potential for improving performance in data-sparse regions by using a hybrid modeling approach that optimizes model complexity based on data availability.

Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%., Graphical abstract Image, graphical abstract

Multi-objective evolutionary algorithms (MOEAs) are becoming increasingly popular for solving formal environmental and water resources optimisation problems. In the past, the focus of these studies has generally been on methodological issues related to the optimisation algorithm. However, in recent years, there has been increased recognition of the need to apply these approaches to real-world problems to facilitate the realisation of their full potential. In order to assist with this, a framework for including stakeholder input in real-world optimisation problems is introduced in this paper, including a conceptual framework and a procedure for implementing it. The framework is applied to an urban water supply security study for Adelaide, South Australia. This study highlights the role of stakeholder input at the various stages of the optimisation process, as well as the resulting changes in the formulation, analysis and results. A discussion of the lessons learnt from the case study is also provided. Framework for including stakeholder input in evolutionary algorithm optimisation.Stakeholder input included at all stages of optimisation process.Framework applied to urban water supply security study in Adelaide, South Australia.

Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI.

Background Colorectal cancer (CRC) is the 3rd most common cancer type in the world. The correlation between immune repertoire and prognosis of CRC has been well studied in the last decades. The diversity and stability of the immune cells can be measured by hypervariable complementarity‐determining region 3 (CDR3) segments of the T‐cell receptor (TCR). Methods In this study, we collected five healthy controls and 19 CRC patients’ peripheral blood mononuclear cells (PBMCs) in three stages, namely 1 day preoperative, 3 days’ postoperative, and 7 days’ postoperative, respectively. Simultaneously, we have also done the comparative analysis of these two different anesthesia methods, namely TIVA and CEGA. Sequencing of the TCR segments has been performed by multiplex PCR and high‐throughput next‐generation sequencing. We also analyzed the distribution of CDR3 length, highly expansion clones (HECs), TRBV, and TRBJ gene usage. Results Our result showed a significant difference between TCR CDR3 length distribution and HEC distribution between CRC patients and healthy controls. We also found that TRBV11‐2, TRBV12‐1, TRBV16, TRBV3‐2, TRBV4‐2, TRBV4‐3, TRBV5‐4, TRBV6‐8, TRBV7‐8, TRBV7‐9 and RBV11‐2, TRBV12‐1, TRBV16, TRBV3‐2, TRBV4‐2, TRBV4‐3, TRBV5‐4, TRBV6‐8, TRBV7‐8, and TRBV7‐9 usages are different between CRC patients and healthy controls. Conclusion In conclusion, CRC patients were presented with different immune repertoire in comparison with healthy controls. In this study, significant difference in TRBV and TRBJ gene usage in between case and control group could provide some potential biomarker for the diagnosis and the treatment of the patients with CRC.

A serious drop in ensuring the water quality in the distribution system is a factor that affects public health. This could lead to increase in biological and non-biological contents, change in colour and odour of the water. These contaminants cause a serious threat to the whole water ecosystem. The conventional methods of analyzing the water quality require much time and labour. So there is a need to monitor and protect the water with a real-time water quality monitoring system in order to make active measurements to reduce contamination. The growth of the technology had helped in developing efficient methods to solve many serious issues in real time. Internet of things (IoT) has achieved a great focus due to its faster processing and intelligence. This paper focuses on discussing the architecture, applications and need of IoT in water management system.

Wireless Passive Surface Acoustic Wave (SAW) sensors have attracted great attention in numerous applications. They are powered by interrogating Radio Frequency (RF) pulses rather than batteries. In this study, a wireless passive SAW temperature and pressure delay line sensor was adapted in a designed framework which can control the ambient liquid temperature and pressure and characterised. The experimental results meet the theoretical analysis that the related phase delay of the response signal depends linearly on the temperature (pressure) when the pressure (temperature) keeps constant.

Sorghum grains with different applications had different phenolic profiles, which were corresponded to various antioxidant capacities. In this study, total phenolic, proanthocyanidins and flavonoids contents, as well as contents of individual phenolic compounds from sorghum grains with various applications were determined, and their antioxidant capacities were evaluated. Total phenolic contents (TPC) and total proanthocyanidins contents (TPAC) showed strong correlation with antioxidant activities (r > 0.95, p < 0.01). Hongyingzi (S-1), one of the brewing sorghums, showed the highest level of TPC and TPAC, while white grain sorghum (S-8) had the lowest. Except for black grain sorghum (S-7), that contained the highest contents of ferulic acid, brewing sorghum grains contained the higher contents of the most individual phenolic compounds, especially the variety S-1. The correlation among individual phenolic compounds and antioxidant activities indicated that the free forms of protocatechuic acid (r = 0.982 of FRAPassay, p < 0.01) and taxifolin (r = 0.826 of FRAP assay, p < 0.01) may be the main functional compounds. These results indicate that brewing sorghum grains can also be utilized as effective materials for functional foods.

The present study assessed the expression of the DNA doublestrand repair (DDR) proteins ATM serine/threonine kinase (ATM), checkpoint kinase 2 (CHEK2) and γH2A histone family member X (γH2AFX) in oral leukoplakia (OL) and evaluated their clinical significance and usefulness as biomarkers for predicting OL transformation. Retrospectively, ATM, CHEK2 and γH2AFX protein levels were evaluated using immunohistochemical analysis in 61 OL, 33 oral squamous cell carcinoma (OSCC) and 15 normal oral mucosa tissues. OL tissues were classified into two groups according to the epithelial dysplasia pathology: The low risk dysplasia group (n=41) and the high-risk dysplasia group (n=20). The results of the present study revealed that the expression of ATM and γH2AFX in OSCC was significantly increased compared with that in OL with low-risk dysplasia and normal oral mucosa tissues. There was no statistically significant difference in CHEK2 expression among the groups. ATM expression was correlated with that of γH2AFX in OSCC tissue. The prognostic values of the DDR proteins and their correlation with clinical and pathological parameters were evaluated further in 99 OL patients with low risk dysplasia. Multivariate analysis revealed that increased expression of ATM and γH2AFX was significantly associated with an increased risk of malignant transformation. Immunohistochemical analysis of ATM and γH2AFX protein expression provided useful prognostic information on the carcinogenesis of OL. Increased ATM and γH2AFX expression may indicate a poor prognosis.

Wireless passive surface acoustic wave (SAW) sensor has been widely used in many applications. This paper presents a feasibility study on a designed wireless passive SAW delay line temperature and pressure sensor for monitoring water distribution systems. The substrate of the sensor node is a Y-Z orientation cut LiNbO 3 crystalline with 0.5 mm thickness. The Interdigital transducer (IDT) was fabricated centrally on the surface of the substrate with an antenna connected. There are three reflectors fabricated on the same surface of the IDT. One reflector is on one side and the other two are on the other side of the IDT. A simulated water pipe platform with testing devices was assembled to test the feasibility of the sensor node working in water pipe environment. The experimental result showed that the designed sensor worked properly but 4/5 amplitude of signals is lost compared to the experimental results in the open-air environment.

This paper has completed the hardware design of STM32F103 system circuit, infrared code learning module, human body infrared sensor module, indoor temperature and humidity sensor module, completes the software design of learning air conditioning remote control code, indoor temperature and humidity sensor module, also completes the energy-saving controller hardware and software debugging. Finally, this paper takes split type air conditioning units in a conference room as the control object (including indoor lighting system) to complete the energy-saving controller in air conditioner electrical system design, installation and debugging, completes the air conditioning start-stop and infrared temperature control code learning test. The test results show that energy-saving controller has realized infrared automatic perception of human activity, machine start-stop code self-learning and temperature settings and other functions.

Subsequent to the least cost design problem of water distribution systems (WDSs), optimal operation is probably the most explored topic. Since 1970 a variety of methods have been developed to address this problem. Although the proposed methods give theoretic ways for solving optimal operation problems of WDSs, there are little successful application cases in practice, especially when the methods were used to large scale WDSs due to too many decision variables. In consideration of using fewer decision variables, the two-level optimal method is adopted in this paper. By abstracting pump stations into high level reservoirs, the water distribution system hydraulic model can be modified into a modality, which can be used in first optimal level of two-level optimal method. Also, by building on feasible pump combination database, a new optimal method in the second optimal level will be proposed, and the proposed new method in the second optimal level will be embedded into the first optimal level, so that the problem of results discordant in different levels will be solved. By introducing new concepts and improving present optimal methods, a more practical optimal operation method of water distribution system will be established. By applying in a large scale practical water distribution system, the proposed method has been evaluated.

This paper focuses on implementation of a generic OGC compliant framework to integrate data in Water Supply Distribution Systems using interoperable standards. The architecture of this framework was generic to merge clients’ data irrespective of the data format. The Integration Manager (IM) of the architecture processes the data and transforms it into WaterML 2.0 format and exchange observations using a standard web service (OGC SOS).The contribution of this paper in water industry is the use of OGC Standards. Then, an implementation of a generic framework for real case studies to integrate water data.

The pipe burst accidents in water distribution systems occur frequently nowadays, affecting the safety of people and causing enormous economic losses. Therefore, it has a great significance to analyze the main factors that contributed to pipe burst. Based on the historical data of pipe burst, the traditional statistical method and improved analytical hierarchy process (IAHP) were adopted to conduct a qualitative and quantitative analysis between these factors and pipe burst in Baocheng district. This research focuses on the non-hydraulic factors of pipe burst, which provides a premise for a multi-factor comprehensive analysis and network maintenance.

The conventional water pressure measurement method requiresbatteries as power sources for pressure sensors. This methodis unreliable because the risk of damaging batteries is high especially in harsh environment. This paper provides a novel method that wireless passive surface acoustic wave (SAW) pressure sensoris introduced for water pressure measurement. Wireless passive SAW sensors do not need direct power supply. Therefore, batteries are not requiredby using this new technology. The main types of wireless passive SAW sensors are reviewed. The performance of wireless passive SAW sensor is tested. The water pressure sensing framework is proposed.

Integrated urban water management (IUWM) considering alternative water supply options is attracting increasing attention from water resources managers due to its efficiency and flexibility ...

Face alignment is a critical topic in the computer vision community. Numerous efforts have been made and various benchmark datasets have been released in recent decades. However, two significant issues remain in recent datasets, e.g., Intra-Dataset Variation and Inter-Dataset Variation. Inter-Dataset Variation refers to bias on expression, head pose, etc. inside one certain dataset, while Intra-Dataset Variation refers to different bias across different datasets. To address the mentioned problems, we proposed a novel Deep Variation Leveraging Network (DVLN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). Extensive evaluations show that our approach demonstrates real-time performance and dramatically outperforms state-of-the-art methods on the challenging 300-W dataset.,,,,,, To address the mentioned problems, we proposed a novel Deep Variation Leveraging Network (DVLN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). In particular, DA-Net takes advantage of different characteristics and distributions across different datasets, while CD-Net makes a final decision on candidate hypotheses given by DA-Net to leverage variations within one certain dataset. Extensive evaluations show that our approach demonstrates real-time performance and dramatically outperforms state-of-the-art methods on the challenging 300-W dataset.

Internet of Things (IoT) technology has recently been widely utilized into a variety of industrial applications. Wireless Passive Surface Acoustic Wave (SAW) sensors have attracted great attention in numerous IoT enabled applications. The sensor nodes are not directly supplied by the power supply as it absorbs the energy from the interrogating Radio Frequency (RF) pulses to excite the SAW. Temperature and pressure are two imperative parameters to monitor whether the pipelines in water distribution system are properly functioning. In this study, the wired and wireless response signals of the passive SAW delay line temperature and pressure sensor is compared. There is a 1/5 attenuation on the response signal when wireless interrogation and response signal receiving process is applied. A wireless passive SAW delay line temperature and pressure sensor was planted in a framework simulating the environment of the water pipelines, and the response signals of the sensor node in different ambient environments are analysed and compared. Compared to the air environment, there is only a sight attenuation of the response signal when the sensor node is planted in the simulating water pipeline environment framework.

Devices used in Internet of Things (IoT) technology are interconnected through internet and continuously send data for storage in disparate databases and files. This poses heterogeneity in data syntax and semantics. In the past few years, researches have been carried out to handle these problems in various information systems such as water and healthcare by applying semantic and syntactic uniformity. This research presents an Information and Communication Technology (ICT) framework to sustain the interoperability between stakeholders within an information system by applying Model Driven Software Development (MDSD) paradigm and ontology. The major contribution of this research is building software model which is annotated with semantic model to maintain the comprehension between the modelers and the programmers allowing sustainability by adapting to the changing environment. Moreover, the framework offers two types of semantic validation i.e. model and data of the documents to integrate sensor data which we claim as a novelty in this study. The validated IoT data is integrated through RESTful web service interface of SensorThings API. The aim of this paper is to apply the methodology of the framework, on IoT based healthcare system, that has been successful in water information system.

The development of Internet of Things (IoT) and latest Information and Communication Technologies (ICT) have changed the nature of healthcare monitoring and health behaviour intervention in many applications. Water hygiene and water conservation behaviour intervention as important influence factors to human health are gaining much attentions for improving sustained sanitation practice. Based on face-to-face delivery, typical behaviour intervention method is costly and hardly to provide all day access to personalised intervention guidance and feedbacks. In this study, we presented a behavioural information system and water use behaviour model using IoT platform. Using Expanded Theory of Planned Behaviour (ETPB) and adopted structure equation model, this study offers a solution for understanding the behaviour intervention mechanism and methodology for developing empirical model. A case study of behaviour intervention model is presented by utilising residential water conservation behaviour data collected in China. Results suggested that cultural differences have significant influences on the understanding of intervention drivers, promoting projects and increasing awareness, which could improve the behaviour intervention efficiency and further facilitate the improvement of water hygiene practice. The performance evaluation of water saving dimension is discussed as well in the paper.

To quantitatively examine the DNA content and nuclear morphometric status of oral leukoplakia (OL) and investigate its association with the degree of dysplasia in a cytologic study.Oral cytobrush biopsy was carried out to obtain exfoliative epithelial cells from lesions before scalpel biopsy at the same location in a blinded series of 70 patients with OL. Analysis of nuclear morphometry and DNA content status using image cytometry was performed with oral smears stained with the Feulgen-thionin method.Nuclear morphometric analysis revealed significant differences in DNA content amount, DNA index, nuclear area, nuclear radius, nuclear intensity, sphericity, entropy, and fractal dimension (all P .01) between low-grade and high-grade dysplasia. DNA content analysis identified 34 patients with OL (48.6%) with DNA content abnormality. Nonhomogeneous lesion (P = .018) and high-grade dysplasia (P = .008) were significantly associated with abnormal DNA content. Importantly, the positive correlation between the degree of oral dysplasia and DNA content status was significant (P = .004, correlation coefficient = 0.342).Cytology analysis of DNA content and nuclear morphometric status using image cytometry may support their use as a screening and monitoring tool for OL progression.

The means of distributing dense passive radio-frequency identification (RFID) tags has been widely utilized for accurate indoor localization. However, they suffer a disadvantage on low localization precision due to the increasing interference of RFID tag collisions and the variation of behavior of tags. Current localization algorithms used in passive RFID location systems are mostly deterministic and have a limited capability on improving localization precision in a dynamic environment with uncertain sensor measurement. This paper investigates the feasibility of using particle filter technique as an efficient localization approach to deliver both relatively good accuracy and precision in dense passive RFID tag distribution applications. A position feature-based system model is first built to apply the typical particle filter technique in passive RFID location applications. Then, a new particle filter algorithm by using a moving direction estimation-based feature improvement scheme is proposed to enhance localization precision in a dense passive RFID tag environment. Experimental results show that the proposed method can provide relatively good accuracy and precision for passive RFID location applications, with an improved performance over the typical particle filter algorithm and a state-of-the-art deterministic method.

The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.

Research suggests that patient-centered culturally sensitive health care may be an important precursor to patient satisfaction and treatment adherence. Data from this study are a subset from the data collected for the Patient-Centered Culturally Sensitive Health Care and Health Promotion Research Project. The present study was designed to (a) explore the relationship between patients’ perceived patient-centered cultural sensitivity of their health care sites (ie, the physical and social environment and clinic policies) and their self-reported adherence to treatment and (b) investigate whether this relationship is mediated by satisfaction with health care experienced. Participants consisted of a low-income, culturally diverse sample of 1581 patients from 67 health care sites across the United States. A significant positive relationship between patients’ perceived patient-centered cultural sensitivity of their health care sites and their self-reported treatment adherence to a prescribed regimen was observed. Patient satisfaction with care partially mediated the relationship between these 2 variables. Assessing the level of patient-centered cultural sensitivity patients perceive in their health care sites might provide guidance to health care administrators as to how to make their sites more culturally sensitive from the perspective of patients. This, in turn, might increase patients’ treatment adherence, leading to improved health outcomes.

In this paper, we introduce smart sensors for monitoring water distribution systems. To improve the quality of monitoring and prolong the lifetime of the sensor node, there is a need to reduce the amount of data transmitted by predicting the future behaviour of the data while detecting and classifying important events locally in the sensor node, transferring only important data. We propose to reduce the amount of data transmitted between the sensor node and end user by using Principle Component Analysis (PCA) and dual prediction models for the monitoring of hydraulic data based on an Autoregressive Moving Average (ARMA) model.

The main aim of optimal schedule of traditional water distribution system is to reduce energy consumption, without consideration of minimizing network leakage. This research is on the premise of meeting water demand, pressure and quality, at same time taking the minimization of network leakage and power consumption as the optimization objectives, taking pump and valve opening and closing as decision variables, to build leakage control model of multi-source water distribution system by optimal pump schedule. On the basis of this model, the partition scheme and pump scheduling of water supply system are determined; the NSGA-II algorithm is used for solving the optimisation problem. By applying it to practical water supply network in HD city, the results showed thatthis method can simultaneously reduce power consumption and network leakage.

In the research, fractal theory was adopted in water supply network. K-means algorithm was adopted to make the nodes multistage cluster zoning, and prioritize nodes was determined according to the actual situation of each zone level division. In each zone, prim algorithm completed each sub-region of water supply pipe network based on the use of fractal growth of self-organization. The established water supply network could be operated and managed according to the form of partition. The fractal self-growth model was applied to the water distribution system planning of one north city in China, the results clearly show the economic effectiveness.