Gradual Leak Detection in Water Distribution Networks Based on Multistep Forecasting Strategy

With the availability of real-time monitoring data, leakage detection for water distribution networks (WDNs) based on data-driven methods has received increasing attention in recent years. Accurate forecasts based on historical data could provide valuable information about the condition of the WDN, and abnormal events could be detected if the observed behaviour is substantially different from the typical behaviour. Therefore, an accurate forecast model is essential for prediction-based leakage detection methods. While most data-driven methods focus on burst detection, it is also important to develop an early warning system for gradual leakage events as they will cause more water loss due to a longer time to awareness. Therefore, a real-time early leakage detection technique based on a multistep forecasting strategy is proposed in this study. A multistep flow forecasting model is introduced to capture the diurnal, weekly and seasonal patterns in the historical data. The generated multistep forecasting is further compared with the observed measurements, and residuals are calculated based on cosine distance. Based on the analysis of the residual vector, the gradual leakage event could be detected in a timely manner. The proposed method is applied to the L-town datasets containing one year of real-life flow monitoring data. The results prove the superiority of the proposed multistep prediction model-based method over the traditional one-step prediction model for gradual leakage detection. In addition, the results show that the proposed methodology can detect small gradual leakage events within just a few days while generating no false alarms. The method is further applied to a real-life network and showed consistent results

Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks

This paper explores the use of ‘conditional convolutional generative adversarial networks’ (CDCGAN) for image-based leak detection and localization (LD&L) in water distribution networks (WDNs). The method employs pressure measurements and is based on four pillars: (1) hydraulic model-based generation of leak-free training data by taking into account the demand uncertainty, (2) conversion of hydraulic model input demand-output pressure pairs into images using kriging interpolation, (3) training of a CDCGAN model for image-to-image translation, and (4) using the structural similarity (SSIM) index for LD&L. SSIM, computed over the entire pressure distribution image is used for leak detection, and a local estimate of SSIM is employed for leak localization. The CDCGAN model employed in this paper is based on the pix2pix architecture. The effectiveness of the proposed methodology is demonstrated on leakage datasets under various scenarios. Results show that the method has an accuracy of approximately 70% for real-time leak detection. The proposed method is well-suited for real-time applications due to the low computational cost of CDCGAN predictions compared to WDN hydraulic models, is robust in presence of uncertainty due to the nature of generative adversarial networks, and scales well to large and variable-sized monitoring data due to the use of an image-based approach

Enhancing output performance of surface-modified wood sponge-carbon black ink hygroelectric generator via moisture-triggered galvanic cell

Enhancing output performance of surface-modified wood sponge-carbon black ink hygroelectric generator via moisture-triggered galvanic cel

Balance of Lignin Light-Color and UV-Shielding Properties: Pyruvic Acid Fractionation for Green Sunscreen Formulations

Light-color and UV-shielding properties were both critical factors of lignin-based sunscreens. Lignin’s conjugated structure provides effective UV-shielding characteristics that also impart coloration, making it challenging to achieve a balance between the two. The color requirements of sunscreens often require a compromise in the UV-shielding properties of lignin. To resolve this issue, a sustainable and efficient method was proposed by utilizing biocompatible pyruvic acid to fractionate the Camellia oleifera shell, resulting in the extraction of a light-colored lignin with exceptional UV absorption at 290-400 nm. Incorporating 1% lignin into sunscreens led to a significant increase in the sun protection factor (SPF) of the resulting product (from 18.19 and 32.72 to 29.79 and 49.52, respectively), with no staining on skin. The characteristic of the lignin structure demonstrates that the light-colored lignin can be largely attributed to the G group lignin swiftly dissolving in pyruvic acid without notable condensation. The enhanced UV-shielding properties of lignin arise from the cleavage of the β-O-4 bond, resulting in the production of more phenolic hydroxyl groups and the esterification of pyruvic acid with the hydroxyl groups of the side chains

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes, which has been studied in the issues of potential, triggering and hazard analysis. The strain energy approach is a common method to investigate liquefaction potential. In this study, two Artificial Neural Network (ANN) models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept (W) by using laboratory test data. A large database was collected from the literature. One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model. To investigate the complex influence of fine content (FC) on liquefaction resistance, according to previous studies, the second database was arranged by samples with FC of less than 28% and was used to train the second ANN model. Then, two presented ANN models in this study, in addition to four extra available models, were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein. Furthermore, a parametric sensitivity analysis was performed through Monte Carlo Simulation (MCS) to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils. According to the results, the developed models provide a higher accuracy prediction performance than the previously publishedmodels. The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils

A novel premixing strategy for highly sensitive detection of nitrite on paper-based analytical devices

A novel premixing strategy for highly sensitive detection of nitrite on paper-based analytical device

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes, which has been studied in the issues of potential, triggering and hazard analysis. The strain energy approach is a common method to investigate liquefaction potential. In this study, two Artificial Neural Network (ANN) models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept (W) by using laboratory test data. A large database was collected from the literature. One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model. To investigate the complex influence of fine content (FC) on liquefaction resistance, according to previous studies, the second database was arranged by samples with FC of less than 28% and was used to train the second ANN model. Then, two presented ANN models in this study, in addition to four extra available models, were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein. Furthermore, a parametric sensitivity analysis was performed through Monte Carlo Simulation (MCS) to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils. According to the results, the developed models provide a higher accuracy prediction performance than the previously publishedmodels. The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils

Differential diagnosis of patients with persistent β-hCG elevation and myometrial invasion following nonmolar gestation

OBJECTIVE: To identify preoperative predictors of gestational trophoblastic neoplasia (GTN) in patients with persistent β–human chorionic gonadotropin (β-hCG) elevation and myometrial invasion following nonmolar gestation and to evaluate the safety of uterine mass laparoscopic resection in the diagnosis of GTN. STUDY DESIGN: Patients with persistent β-hCG eleva- tion following nonmolar pregnancy, ultrasound showing myometrial invasion, and having undergone laparoscopic resection of the uterine mass were retrospectively included from a database of endoscopic surgeries. The 38 patients identified were divided into 2 groups based on histologic outcomes: GTN (n=12) and non-GTN (n=26). Preoperative variables of the 2 groups were compared by univariate analysis. Response to chemotherapy and prognosis were also analyzed. RESULTS: There was no statistical difference between the 2 groups in the variables analyzed. All GTN patients received postoperative chemotherapy, and the rate of complete remission was 100%. The mean follow-up time was 23±15.1 months, and no patient showed signs of relapse. CONCLUSION: No preoperative variable analyzed here can accurately confirm the diagnosis of GTN in patients with persistent β-hCG elevation and myome- trial invasion following nonmolar pregnancy. Laparoscopic resection of the uterine mass followed by chemotherapy may be an effective and safe way to obtain histologic tissues in these patients

Fertility-sparing uterine lesion resection for young women with gestational trophoblastic neoplasias: single institution experience

Purpose: To evaluate the oncological safety and pregnant outcomes of fertilitysparing uterine lesion resection in treating gestational trophoblastic neoplasias. Results: After the treatment of surgery and chemotherapy, all the patients achieved complete remission. With a median follow-up time of 44 months (range, 6-188), 3 patients (3.85%) relapsed within 3-26 months. Multivariate analysis showed that tumor size was the independent risk factor of recurrence and the cutoff value was 4.2cm. Among 37 patients who attempted to conceive, 31 achieved clinical pregnancy. The rate of pregnancy and live birth were 83.8% and 77.4%. Uterine rupture did not occurred no matter in cesarean section or vaginal delivery. No congenital abnormalities were reported among the live births. Methods: From January 1995 to December 2014, 78 patients with gestational trophoblastic neoplasias who underwent fertility-sparing uterine lesion resection at Peking Union Medical College Hospital were reviewed. The complete remission rate, fertility rate, pregnant outcomes and risk factors of recurrence were analyzed. Conclusions: Fertility-sparing uterine lesion resection might be considered as a safe and reasonable alternative for high-selected young women to remove uterine lesion in the treatment of gestational trophoblastic neoplasias

Suppressing infiltration and coffee-ring effects of colorimetric reagents on paper for trace-level detection of Ni(II)

A PVA matrix was used to suppress infiltration and coffee-ring effects during colorimetric analysis on a paper substrate and improve the trace-level detection of nickel ions (Ni2+) in environmental samples. To improve the color response, a cross-linked PVA matrix was used to anchor the indicator reagent of Ni2+ on the surface of the porous paper substrate as well as reduce evaporation flow by increasing hydrogen bonding in the sample droplet. These phenomena mitigated the loss of color signal by suppressing infiltration and coffee-ring effects. Under optimized conditions, including crosslinker concentration and addition order, type of commercial filter paper, and loading volume of the PVA/indicator mixture, the sensor obtained a limit of detection (LOD) as low as 0.92 ppm and a notable linear behavior of R2 > 0.97 at a linear range of 0.5–50 ppm, which is sufficient for the detection of legal maximum residue limit (MRL) of Ni2+ in wastewaters in China. The PVA-assisted sensor showed good selectivity to most metal ions and could work at different pH levels ranging from 3 to 9. The sensor also exhibited highly sensitive and stable performance in repetitive measurements and had a shelf-life of more than 3 months. These results suggest that our work provides a facile approach to improving the sensitivity and reliability of paper-based sensors for monitoring trace-level Ni2+ concentration in aqueous samples with good selectivity and prolonged shelf-life. Graphical abstract: [Figure not available: see fulltext.