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In this study, pepper (Zanthoxylum bungeanum Maxim.) leaf (ZL) extract was added to larou to investigate the improvement in the quality of physicochemical properties, texture, water distribution, and microorganism growth during storage for 20 days. Based on the results, the addition of ZL extract significantly retarded the increase in cooking loss, TBARS value, hardness, and microorganism growth. Moreover, the addition of ZL extract decreased the pH value, lightness, and microorganism counts, and increased the moisture content, total soluble protein content, a* value, b* value, and chewiness. The LF-NMR results showed that the addition of ZL extract shortened the T2 relaxation time and boosted the proportion of immobilized water, facilitating the validation of the improvement in water retention of larou during storage. The FT-IR results indicated that the addition of ZL extract influenced the protein secondary structure by inducing the conversion of α-helices to β-sheet structures. Accordingly, ZL extract has the potential to serve as a natural antioxidant, effectively helping to ameliorate the quality properties of cured meat products during storage. [ABSTRACT FROM AUTHOR]

The use of oil-in-water (O/W) emulsion has drawn increasing attention in the baking industry. Compared with some of the well-recognized functionalities, such as textural improvers and flavor carriers, its cryoprotective behavior in frozen dough has not been extensively investigated. Herein, this study reported a pea-protein (PP)-stabilized O/W emulsion with good freeze–thaw stability and evaluated its effectiveness as a high-performance dough cryoprotectant. Specifically, the emulsions were stabilized by 2, 3, and 4 wt% of PP (PP-2, -3, and -4, respectively) and incorporated into frozen doughs, whose cryoprotective effects were systematically evaluated in terms of dough storage stability and baking performance after 4 weeks of storage. Results showed that the frozen dough with PP-3 emulsion exhibited the most uniform water distribution and reduced content of freezable water as reflected by the results from differential scanning calorimetry and low-field nuclear magnetic resonance analyses. Moreover, the PP emulsion helped to maintain the integrity of the gluten network, thus enhancing the dough elasticity. Accordingly, the emulsion-added bread samples exhibited significantly improved loaf volume and textural properties (e.g., softness) and less baking loss. Our findings highlighted the potential of PP emulsion as a viable and high-performance dough cryoprotectant. [ABSTRACT FROM AUTHOR]

The rising demand for both water and energy has intensified the urgency of addressing the water–energy nexus. Energy is required for water treatment and distribution, and energy production processes require water. The increasing demand for energy requires substantial amounts of water, primarily for cooling. The emergence of new persistent contaminants has necessitated the use of advanced, energy-intensive water treatment methods. Coupled with the energy demands of water distribution, this has significantly strained the already limited energy resources. Regrettably, no straightforward, universal model exists for estimating water usage and energy consumption in power and water treatment plants, respectively. Current approaches rely on data from direct surveys of plant operators, which are often unreliable and incomplete. This has significantly undermined the efficiency of the plants as these surveys often miss out on complex interactions, lack robust predictive power and fail to account for dynamic temporal changes. The study thus aims to evaluate the potential of mathematical modeling and simulation in the water–energy nexus. It formulates a mathematical framework and subsequent simulation in Java programming to estimate the water use in hydroelectric power and geothermal energy, the energy consumption of the advanced water treatment processes focusing on advanced oxidation processes and membrane separation processes and energy demands of water distribution. The importance of mathematical modeling and simulation in the water–energy nexus has been extensively discussed. The paper then addresses the challenges and prospects and provides a way forward. The findings of this study strongly demonstrate the effectiveness of mathematical modeling and simulation in navigating the complexities of the water–energy nexus. [ABSTRACT FROM AUTHOR]

The efficiency of water distribution at primary, secondary, and tertiary levels in the Indus Basin Irrigation System (IBIS) has historically suffered due to poor design, suboptimal operation, and water scarcity. To address these issues, the system has been designed with ungated irrigation outlets to ensure equitable water allocation at secondary and tertiary levels. This research evaluates the hydraulic performance of three irrigation outlets: adjustable proportional module (APM), adjustable orifice semi-module (AOSM), and open flume (OF) using a physical model study. A distributary channel model with these outlets was constructed at the Centre of Excellence in Water Resources Engineering, where discharge coefficients (Cd) were calibrated and measured under various hydraulic and geometric conditions, including free and submerged flow conditions, and with adjustments to flow depths and outlet settings. The results showed variability in Cd values under free flow and submerged flow conditions with APM and AOSM ranging from 6.07 to 8.20 and 0.56 to 0.74, respectively, and OF between 2.46 and 4.31. Additionally, the behavior of outlet tampering on Cd values was also assessed under three scenarios: tampering with the half wing wall (1st), tampering with the full wing wall (2nd), and lowering the bed level downstream of the outlet (3rd). The increase in Cd values for APM and AOSM was + 10.84% and + 14.49% under 1st scenario, + 17.12% and + 22.36% under 2nd scenario, and + 24.25% and + 26.30% under 3rd scenario, respectively. The results reveal that even minor tampering with outlet structures can lead to significant deviations in performance, highlighting the importance of maintaining stringent control over outlet configurations to ensure equitable and efficient water distribution. There is a critical need for rigorous, site-specific calibration of irrigation outlets to optimize their performance under local conditions and redesigning outlet structures to minimize the impacts of tampering, thereby enhancing the overall sustainability of water use in large-scale irrigation systems. The findings from this study provide essential insights for irrigation engineers and policymakers tasked with upgrading and maintaining irrigation infrastructure. By adopting a more customized approach to outlet design and management, it is possible to significantly improve water use efficiency and achieve more sustainable irrigation practices. [ABSTRACT FROM AUTHOR]

Contaminant Source Identification in Water Distribution Network (CSWIDN) is critical for ensuring public health, and optimization algorithms are commonly used to solve this complex problem. However, these algorithms are highly sensitive to the problem's landscape features, which has limited their effectiveness in practice. Despite this, there has been little experimental analysis of the fitness landscape for CSWIDN, particularly given its mixed-encoding nature. This study addresses this gap by conducting a comprehensive fitness landscape analysis of CSWIDN using the Nearest-Better Network (NBN), the only applicable method for mixed-encoding problems. Our analysis reveals for the first time that CSWIDN exhibits the landscape features, including neutrality, ruggedness, modality, dynamic change, and separability. These findings not only deepen our understanding of the problem's inherent landscape features but also provide quantitative insights into how these features influence algorithm performance. Additionally, based on these insights, we propose specific algorithm design recommendations that are better suited to the unique challenges of the CSWIDN problem. This work advances the knowledge of CSWIDN optimization by both qualitatively characterizing its landscape and quantitatively linking these features to algorithms' behaviors. [ABSTRACT FROM AUTHOR]

Copyright of Packaging & Food Machinery is the property of Packaging & Food Machinery Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Groundwater levels vary from region to another and sometimes in different zones in the same country due to different boundary conditions and extraction rates. Therefore, understanding intricate aquifer systems and predicting how they will react to hydrological changes require the use of groundwater models. In Egypt, the groundwater levels in the Nile Delta aquifer decrease causing problems to the delta ecosystem while it is rising in Aswan area due to the presence of Nasser Lake causing several damages to the city's buildings and infrastructures. In order to maximize its benefits and lessen the harm brought on by inadequate groundwater management in the city of Aswan, the height of the groundwater level in that city was examined, appraised, and groundwater management scenarios were established in this study. To achieve the objectives of the study, a simulation of Aswan aquifer's groundwater system is built based on a quasi-three-dimensional transient groundwater flow model using MODFLOW. The model was calibrated and verified. Four management scenarios are tested. The fifth scenario, in this scenario, the four scenarios combined together at the same time and with the same conditions and ratios were proposed to be implemented. The results of the proposal to implement the four scenarios together showed that the rates of decline in groundwater levels in the last stage will be 12.44%. The study results reveal that a better understanding of the simulated long-term average spatial distribution of water balance components is useful for managing and planning the available water resources in the Aswan aquifer. [ABSTRACT FROM AUTHOR]

Resilience-based decision-making for urban water distribution systems (WDSs) is a challenge when WDS sensing data contain incomplete or missing values. This study investigated the impact of missing data imputation on a WDS resilience evaluation depending on missing data percentages. Incomplete datasets for the nodal pressure of the C-town WDS were developed with 10%, 30%, and 50% missing data percentages by manipulating a true dataset for normal operation conditions produced using EPANET. This study employed multiple imputation methods including classification and regression trees, predictive mean matching, linear regression regarding model error, and linear regression using projected values. Then, resilience values were evaluated and compared using unimputed and imputed datasets. An analysis of performance indicators based on NRMSE, NMAE, NR-Square, and N-PBIAS revealed that higher missing-data percentages led to increased deviation between the true and imputed datasets. The resilience evaluation using unimputed datasets produced significant deviations from the true resilience values, which tended to increase as the missing data percentages increased. However, the imputed datasets substantially contributed to reducing the deviations. These findings underscore the contributions of data imputation to enhancing resilience evaluation in WDS decision-making and suggest insights into advancing a resilience evaluation framework for urban WDSs with more reliable data imputation approaches. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of the concepts and assessment processes of water resources in China, focusing on the characteristics of water resources and variations in water cycle fluxes. It reveals that the distribution of water resources in China is uneven, with more south and less north, and human activities have led to a decline in water resources, particularly in northern arid and semi-arid regions. Further analysis shows that duplicated measurements of surface water and groundwater significantly affect water balance calculation and water resource assessments, serving as a crucial factor guiding water resource development and utilization. The study also finds that consistency correction of runoff series is insufficient to meet the requirements of accurate water resource assessment. It is urgent to strengthen fundamental research in hydrology and hydrogeology, and to establish a dynamic assessment system for the efficient management and rational use of surface water and groundwater in the current changing environment. [ABSTRACT FROM AUTHOR]

The study proposes a novel speckle interferometric method for detecting and quantifying microplastic leaching from paper cups, addressing concerns raised by the World Health Organization regarding human health risks. Hot water at varying temperatures is placed in 36 paper cups from different manufacturers, and the specklegrams of the paper cups' interior surface are recorded. The quantity of microplastics leached into water is estimated by the Neubauer chamber method, which increases with rising water temperature. Surface morphology analysis through atomic force microscopic images reveals thermal-induced melting and smearing of microplastics, decreasing roughness parameters. Co-occurrence matrix analysis of specklegrams correlates image parameters—inertia moment, homogeneity, energy, contrast, and entropy—with the microplastics count, showing surface modifications and altered pixel intensity distribution with increasing water temperature. Regression equations based on image parameters establish a strong correlation with the microplastics count, that are validated against the Neubauer chamber method. The study indicates contrast as the potential sensitive specklegram feature for microplastics detection and quantification. [ABSTRACT FROM AUTHOR]

Surface runoff models are essential for designing water and soil protection measures. However, they often exhibit uncertainty in both parameterization and results. Typically, uncertainty is evaluated by comparing model realizations with measured data. However, this approach is constrained by limited data availability, preventing comprehensive uncertainty assessment. To overcome this limitation, we employed the generalized likelihood uncertainty estimation (GLUE) methodology to conduct sensitivity and uncertainty analyses on a series of surface runoff models. These models were based on an ensemble of artificial rainfall experiments comprising 77 scenarios with similar settings. We utilized the rainfall-runoff-erosion model SMODERP2D to simulate the experiments and employed Differential Evolution, a heuristic optimization method, to generate sets of behavioural models for each experiment. Additionally, we evaluated the sensitivity and uncertainty with respect to two variables; water level and surface runoff. Our results indicate similar sensitivity of water level and surface runoff to most parameters, with a generally high equifinality. The ensemble of models revealed high uncertainty in bare soil models, especially under dry initial soil water conditions where the lag time for runoff onset was the largest (e.g. runoff coefficient ranged between 0–0.8). Conversely, models with wet initial soil water conditions exhibited lower uncertainty compared to those with dry initial soil water content (e.g. runoff coefficient ranged between 0.6 – 1). Models with crop cover showed a multimodal distribution in water flow and volume, possibly due to variations in crop type and growth stages. Therefore, distinguishing these crop properties could reduce uncertainty. Utilizing an ensemble of models for sensitivity and uncertainty analysis demonstrated its potential in identifying sources of uncertainty, thereby enhancing the robustness and generalizability of such analyses. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When reservoirs are constructed on rivers, river velocity decreases and the river system gradually evolves into a reservoir system. This phenomenon is particularly pronounced in the case of megareservoirs. Previous studies on this problem mostly relied on physical models, analyzing changes in water temperature. However, traditional physical process models are limited by data availability on hydrology, meteorology, and topography. Conversely, data-driven models offer the advantages of a simple structure, ability to use remote sensing data as primary data, easy acquisition, and efficiency in parameter tuning. This study constructed a hybrid artificial neural network data-driven water temperature model using a mutual information screening model to drive factors and dividing the dataset using the hold-out method. Taking the Xiaowan Reservoir as an example, the vertical distribution of water temperature across 20 layers was simulated and predicted. The results are as follows: (1) The Hybrid Artificial Neural Network (H-ANN) model enhanced the accuracy of simulating vertical water temperature in the reservoir by taking into account the correlation between water temperatures at different depths, effectively overcoming the challenges of traditional physical models (acquisition of experimental data and difficulties in model parameter tuning). (2) The water temperature simulated using the H-ANN model showed good agreement with observed water temperature in the Xiaowan Reservoir. The average Nash-Sutcliffe efficiency (NSE), correlation coefficient (r), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) for water temperature in the 1–200 m layer were 0.94, 0.98, 0.23°C, 0.1°C, and 0.13°C, respectively, during the training period, and 0.9, 0.96, 0.32°C, 0.16°C, and 0.19°C, respectively, during the testing period. Overall, the model showed a high degree of conformity between simulated and observed series, indicating the suitability of the mutual information-based and concatenated multilayer ANN data-driven model for simulating vertical water temperature. (3) The Xiaowan Reservoir is a typical stratified reservoir with evident seasonal thermal stratification, where the epilimnion ranges from 1 to 15 m, the metalimnion ranges from 15 to 80 m, and the hypolimnion lies below 80 m. [ABSTRACT FROM AUTHOR]

Modern irrigation areas often use a combination of channels and pipes for irrigation. Due to changes in terrain, inflow, and pipe diameter, pipelines are often prone to experiencing a state of no pressure. This lack of pressure affects not only the velocity distribution and water surface profile of the main channel but also the velocity distribution and flow distribution of the pipeline. Therefore, in this study, we employed a combination of physical model experiments and theoretical analysis to study the influence of non-pressure pipelines on the hydraulic characteristics of channel–pipe combined irrigation systems filled to different degrees. Through this, the variation laws of the flow velocity distribution, turbulence intensity, water surface, diversion width, and diversion ratio under different filling degrees were obtained. The non-pressure pipeline flow velocity expression was obtained through dimensional analysis, and the calculation formula for the non-pressure pipeline flow velocity coefficient was fitted using linear regression analysis. The relative error between the calculated value and the measured value did not exceed 8.81%. The research results presented in this article can provide technical support for the design and maintenance of channel–pipe combined irrigation systems. [ABSTRACT FROM AUTHOR]

The food–energy–water (FEW) nexus has emerged as an alternative for managing resources in the food, energy, and water systems. However, there are limited case studies applying this approach in the Latin American and Caribbean region. This region stands to benefit significantly from the FEW nexus approach due to its heavy reliance on hydropower for electricity generation and unevenly distributed and poorly managed water resources. In this study, an urban FEW nexus framework was used in the Otun River Watershed (ORW) to evaluate changes in food, energy, and water demand for four scenarios. Additionally, regional climate models (RCMs) were used to forecast water availability in the ORW from 2030–2039. The results show that water demand could increase by 16% and energy demand will increase by roughly 15% for scenario 2, while water demand in scenario 3 will likely remain unchanged in relation to the current conditions (base scenario). Enhancing water resources management in the ORW will involve a variety of measures, including: implementing practices to reduce water losses in distribution systems, developing green infrastructure and decentralized wastewater systems, and embracing urban and peri-urban farming. Successful application of urban FEW nexus solutions requires involvement from stakeholders across the food, energy, and water systems. [ABSTRACT FROM AUTHOR]

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis. [ABSTRACT FROM AUTHOR]

Under environmental influence, newly poured concrete experiences excessive evaporation of surface water, which degrades concrete performance. Therefore, the water change process inside the concrete at construction sites needs to be monitored accurately. However, existing technologies cannot fully meet this requirement. In this study, the theoretical relationship between water and dielectric constant changes inside concrete was analyzed on the basis of the dielectric constant characteristics of various components inside concrete. On the basis of the principle of standing wave ratio, a real-time and in situ electrical measurement technology for water content was proposed. The results show quadratic polynomial correlation between the measured voltage of the sensor and the water content of the concrete, and the experimental verification results show a correlation coefficient greater than 0.95 between the measured values and the theoretical relationship. The results of the application of the method in a water conservancy project site show that it has good value for in situ applications and can accurately and automatically monitor the distribution of the water content of concrete in real time. [ABSTRACT FROM AUTHOR]

Determining the particle size distribution of water mist is necessary for a wide range of industrial applications or experimental measurements. Particle size defines the ability of water mist to absorb heat, absorb or scatter heat radiation, inertize the environment, reduce environmental dust, regulate odor, etc. The paper presents the principle of measuring droplet size distribution using laser diffraction and the measurement procedure for four hollow cone water mist nozzles with orifice 400 µm, 500 µm, 600 µm and 1000 µm. The measurements were performed at distances of 25 mm, 50 mm, 75 mm, 100 mm, 125 mm, 150 mm, 175 mm, 200 mm and 225 mm from the orifice of the individual nozzles in the middle of the water mist cone. Further measurements were made 25 mm, 50 mm and 75 mm from the nozzle orifices on the edge of the fog cones. All measurements were performed at a working pressure of 70 bar in a water mist system. [ABSTRACT FROM AUTHOR]

This paper describes the application of a physically based agrohydrological model (named FLOWS), coupled with a kinematic wave approach model (named KWV) for water and solute runoff routing, for interpreting the fate of water and nutrients coming from cultivated fields to surface drainage network located in 'Piana del Sassu' in the Arborea plain, a hydraulically reclaimed area with shallow groundwater. Modelling was supported by a large complex database on soil, groundwater and surface drainage water, which was used for establishing the boundary conditions for simulations, as well as for calibrating and validating the model. The model FLOWS provided the water and nutrient fluxes to the surface water, which were passed to the KWV model for their routing along the elementary fields in the experimental area and from these to the ditches and finally to the drainage channel. The modelling approach effectively predicted the water and solute distribution along the soil profile, as well as the losses of water and nutrients to the surface water. The results showed a significant amount of water and dissolved nutrients to flow quickly from the soil uppermost layer to the surface drainage network during both the irrigation season and during rainfall events. During irrigation applications, losses were mostly due to rainfall intensity exceeding the maximum infiltration velocity of the shallow soil layer in the case of sprinkler irrigation and to subsurface lateral drainage in the case of exceeding irrigation water provided by drip irrigation. This makes the Sassu plain a significant contributor of nutrients (nitrate and phosphorus) to the surface water. Consequently, even though the agricultural activities might not be an important issue for the groundwater vulnerability, the management of water and nutrients should be significantly improved to avoid ecohydrological threats to the important coastal water bodies present in the area. [ABSTRACT FROM AUTHOR]

The effects of antifreeze proteins (AFP) on the quality changes of pork during freezing were investigated in comparison to those of the commercial antifreeze agent alginate (4 mg/mL). Samples with no added AFP were used as a control. The effects of adding different amounts of AFP (0.05, 0.1, 0.2, and 0.3 mg/mL) on the quality, physicochemical properties of myofibrillar proteins, microstructure, and water distribution of frozen pork were investigated. The results showed that AFP decreased the water loss of frozen pork, which was beneficial to maintaining pork color and microstructure. Addition of AFP at 0.3 mg/mL had the highest cryoprotective effect on the cooking loss, thawing loss and color characteristics of pork during frozen storage, followed by AFP at 0.2 mg/mL and alginate at 4 mg/mL. The shear force and total sulfhydryl content of myofibrillar proteins in the 0.2 and 0.3 mg/mL AFP groups were significantly higher than those of the control group (P < 0.05). nuclear magnetic resonance imaging (NMRI) discovered that the infiltration of AFP reduced the loss of bound and immobile water in pork. Scanning electron microscopy showed that in the presence of 0.2 mg/mL AFP, the arrangement of muscle fibers was orderly, appearing more compact and denser. Overall, AFP addition had a positive effect on improving the quality of frozen pork and might have the potential of being a safe and efficient cryoprotection method.

Abstract The enduring challenge of water distribution between India and Bangladesh persists, impacting bilateral relations significantly. This research elucidates the geopolitical intricacies of this issue and examines its adverse socioeconomic effects on Bangladesh, proposing strategic solutions. Employing qualitative methods, this study conducts a case study analysis incorporating archival research and content analysis. Data is sourced from secondary materials such as academic reports, policies, and working papers, with a theoretical framework rooted in Hydro-Politics. The study reveals Bangladesh’s geographical dependency on India, particularly concerning upstream rivers where India wields hegemonic influence. Several factors contribute to the ongoing impasse, including India’s internal political dynamics and unresolved issues like the Teesta dispute. Furthermore, the manipulation of water flow, such as spillway release during monsoons, presents challenges, impacting flood management and resource availability. These alterations carry significant ramifications for local communities and biodiversity. This study delves into the underexplored realm of the India-Bangladesh water distribution issue, assessing its socioeconomic impacts and proposing strategic solutions. The policy recommendations offer insights for national governments and international bodies grappling with similar challenges.

In water-guided laser technology, the stability of water jets is crucial to ensure the efficient transmission of laser energy. However, the thermal effects generated when high-power lasers are coupled with water jets are bound to impact the stability of the water jets, thus becoming a critical issue that restricts the development of high-power water-guided laser technology. In addressing this issue, this paper establishes a temperature model for coupling high-power lasers with water jets. Subsequently, with validation of the model's effectiveness through experimental data, simulations are conducted to analyze the temperature distribution within the water jet. During the simulation process, lasers with different parameters were coupled with water jets to analyze the temperature variation law of the water jets. Additionally, the maximum laser power coupled with water jets of different lengths was solved. Based on the calculated temperature values of the water jet, simulate the evolution of its profile over time when it has an initial temperature gradient. Simultaneously, perform a three-dimensional reconstruction of the obtained perturbed profile and conduct ray tracing to analyze the laser's transmission losses within the perturbed profile. Finally, high-speed cameras are utilized to capture the profile of the water jet, validating the laser-induced fragmentation behavior in the water jet. The research findings will provide a significant reference value for selecting laser parameters and controlling thermal effects in water-guided high-power technology. [ABSTRACT FROM AUTHOR]

The advancements of the Internet of Things and Low-Power Wide-Area Network technology will accelerate in the next future the adoption of smart meters in water distribution systems, enabling the collection of a huge amount of fine-grained data. How to turn massive smart meter data into actionable knowledge will be the key point to limit water wastage and promote efficient and sustainable distribution. Although the collection of data worldwide is currently limited, the potential future impact of exploiting data-driven and machine learning methods is increasingly recognized in research and industry, as shown by many scientific works published in recent years. In particular, the interest in deep learning for smart water distribution systems is increasing, motivated by the ability to learn intricate patterns from big data. This work aims to provide an overview of the current research and identify challenges for future directions by conducting an application-oriented survey. Specifically, by analysing data characteristics and operational targets, we propose a new taxonomy that helps structure properly the macro-areas of water management into infrastructure analysis, demand analysis and water quality monitoring. Existing methods are discussed for each application under these three stages. In addition, we also discuss potential research directions, such as federated learning, incremental learning, probabilistic modeling and explainability and address broad issues like data availability and implications for privacy. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effect of static magnetic field (SMF) synergized with low temperature (4 °C) on the properties of shrimp gels and to reveal the underlying mechanisms through the biochemical properties, microstructural and rheological properties as well as the water distribution in the shrimp sols and gels. The experimental results showed that exposing shrimp sols to SMF for 16 h followed by heat treatment significantly improved the strength and hardness of the gels. This improvement was attributed to the fact that SMF treatment altered the water distribution in the sol, leading to a decrease in the T2 relaxation time of the gel and the conversion of immobilized water to bound water. Furthermore, FTIR results indicated that the α-helix structure in the secondary structure of the proteins was transformed into β-sheet and random coiling under the influence of SMF. The gels treated with SMF for 16 h were observed to have the densest structure by microstructural measurements. However, continuous extension of the SMF treatment time negatively affected the gel quality. This study provides valuable insights for optimizing the preparation process of shrimp gels and may facilitate further application of magnetic field treatment techniques in food science. [ABSTRACT FROM AUTHOR]

The turbidity degree of surface water in the rainy season will change with the change in rainfall, and the predictability and flexibility of water sources lead to the difficulty of purifying suspended solids. Therefore, the key technologies for purifying suspended solids in turbid surface water under the influence of the rainy season are studied. Select a small pool in a certain area as the research object, test the basic water quality indicators of the water source, measure the particle size distribution of the raw water according to the characteristics of suspended particles in the raw water, prepare the flocculant organic solution with a concentration of inorganic to organic ratio of 10:1, measure the turbidity of the water sample, flocculate the suspended solids in the raw water, and use ultrafiltration technology to purify the suspended solids in the raw water in the study area. The Daphnia magna was separated and purified, and the purification test of suspended solids in turbid surface water in the study area was carried out according to the prepared flocculant. The test results show that the use of PAM-PAM composite flocculant can achieve a better purification effect of suspended solids in turbid surface water under the influence of the rainy season, and the flocculant is combined with ultrafiltration. When the dosage of flocculant is 4mg/L, the removal rate of suspended solids is better. For suspended solids with smaller particle sizes, the combination of flocculant ultrafiltration and Daphnia magna can purify a large number of suspended solids in surface water bodies. A good purification effect is achieved. [ABSTRACT FROM AUTHOR]

Low efficiency affects energy-demanding systems such as water distribution networks (WDNs). In these systems, the pressure typically is maintained below the switch using regulators to minimize water loss from leaks. Utilizing energy production equipment may be an effective way to reduce water waste, while also producing energy, although its viability depends on how much energy can be recovered. A water distribution system's design is a combinatorial issue, which typically has a larger number of local optima. Consequently, hybrid metaheuristic and heuristic processes can explore the solution with less computational time requirements to gain deep understanding of the problem structure and specific characteristics of the problem. However, these outcomes have an enormous computational burden because of the relatively large number of hydraulic simulations. This investigated where pressure-reducing valves (PRVs) and pumps as turbines (PATs) should be placed in a network that distributes water. The study suggests a deterministic mathematical optimization technique for minimizing the price of WDNs utilizing recognized recognized pipe distances and a defined range of commercially obtainable sizes. A novel heuristic mixed-integer nonlinear programming technique (H-MINLP) with a beleaguered path search process is used to perform optimization. Based on the evaluation of the ideal trajectories in which water flows in a WDN. Two distinct subroutines work together to decrease the sizes of network pipes methodically and sequentially, and effectively exploit the search space. There are no parameters to adjust in the new method, and therefore it does not require a consequential purpose. In addition, a graph clustering technique is utilized to increase the heuristic approach's performance by simplifying its convergence. Compared with other methods in the literature, this hybrid optimization ensures good solutions in terms of energy and water savings. According to the findings, compared with other research on the same network, the proposed optimization decreased leakage by 21%. [ABSTRACT FROM AUTHOR]

The water distribution network (WDN) constitutes a pivotal and valuable asset in municipal infrastructure, yet aging pipelines often contend with leaks. Wireless noise loggers have emerged as a promising technology for early leak detection in WDNs. However, prevalent high false alarms within these systems impede resource efficiency and hinder urban infrastructure development. Prior noise logger models, derived from lab-scale experiments, cast doubt on their accuracy in real networks, contributing to elevated false alarm rates. This paper endeavors to introduce an innovative leak detection model by harnessing data from acoustic noise loggers deployed in a Hong Kong WDN. Utilizing authentic acoustic data and augmenting features for model development, along with employing a multiclassifier ensemble learning algorithm, constitute the principal contributions of this research. Fourier transform was applied to analyze sound signals, and convolutional neural network (CNN), naïve Bayes (NB), support vector machine (SVM), and decision tree (DT) algorithms were employed to develop four distinct models. An ensemble technique effectively amalgamated the strengths of the top three models, resulting in an impressive accuracy of 99.38%. The outcomes underscore the potential of noise logger–based models for real-time monitoring, offering a reduced false alarm solution. Leak detection companies stand to benefit significantly from this model because it harnesses machine learning techniques for precise leak detection in WDNs. Moreover, municipalities equipped with leak detection sensors can leverage this model to optimize their systems, contributing to improved urban infrastructure development, particularly in densely populated cities. Practical Applications: The research presented in this paper offers advancements in the field of leak detection in water pipeline systems using machine learning models. By developing and validating models such as SVM, DT, NB, and CNN, practical tools for detecting pipeline leaks with high accuracy were provided. These models can be integrated into existing pipeline monitoring systems to enhance real-time leak detection, reduce water loss, and prevent potential environmental damage. The findings demonstrate that CNN models offer superior accuracy and reliability, making them suitable for rapid deployment in critical infrastructure. This research contributes to improving the safety and efficiency of water distribution networks, and aids in cost savings and better resource management for utility companies and municipalities. [ABSTRACT FROM AUTHOR]

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Water distribution networks (WDNs) are essential for urban water supply and postearthquake recovery. Conducting a seismic resilience assessment of a WDN and taking corresponding improvement measures can improve the disaster resistance capacity of the water system. A number of previous studies have evaluated the seismic resilience of WDNs, but seldom have taken into account the impact of aftershock disturbances. Aftershock disturbances may aggravate the damage to water distribution networks, prolong the repair time and reduce the recovery ability of WDNs. This study presents an improved framework for assessing WDN resilience considering aftershock disturbances. The stochastic principle and the elliptic ground vibration attenuation model were utilized to determine the spatial distribution of site ground vibration under the mainshock and aftershock. A superposition damage assumption was applied to incorporate aftershock effects in Monte Carlo simulations, and the pressure-driven analysis (PDA) and discrete-event simulation (DES) models were used to analyze the postearthquake hydraulic balance and model the restoration process. By constructing various scenarios, the probabilistic characteristic parameters of resilience indexes and the relationship between WDN resilience metrics and parameters related to aftershocks were investigated. A case study of a real WDN in China demonstrated that there is a correlation between aftershock-related parameters and resilience indexes. Moreover, relying solely on one resilience metric is insufficient for comprehensively understanding the seismic capacity of WDNs. [ABSTRACT FROM AUTHOR]

Amorphous titanium dioxide (a-TiO2) is widely used as a coating material in applications such as electrochemistry and self-cleaning surfaces where its interface with water has a central role. However, little is known about the structures of the a-TiO2 surface and aqueous interface, particularly at the microscopic level. In this work, we construct a model of the a-TiO2 surface via a cut-melt-and-quench procedure based on molecular dynamics simulations with deep neural network potentials (DPs) trained on density functional theory data. After interfacing the a-TiO2 surface with water, we investigate the structure and dynamics of the resulting system using a combination of DP-based molecular dynamics (DPMD) and ab initio molecular dynamics (AIMD) simulations. Both AIMD and DPMD simulations reveal that the distribution of water on the a-TiO2 surface lacks distinct layers normally found at the aqueous interface of crystalline TiO2, leading to an ∼10 times faster diffusion of water at the interface. Bridging hydroxyls (Ti2–ObH) resulting from water dissociation decay several times more slowly than terminal hydroxyls (Ti–OwH) due to fast Ti–OwH2 → Ti–OwH proton exchange events. These results provide a basis for a detailed understanding of the properties of a-TiO2 in electrochemical environments. Moreover, the procedure of generating the a-TiO2-interface employed here is generally applicable to studying the aqueous interfaces of amorphous metal oxides. [ABSTRACT FROM AUTHOR]

[Objective] To determine the effect of biological soil crusts (biocrusts) on water evapotranspiration of the shrub-grass ecosystem. [Methods] The effect of biocrusts on water evapotranspiration of the shrub-grass ecosystem in water-wind erosion crisscross region of Loess Plateau was investigated by using original soil columns of field condition. In the study, the bare soil column was used as control, and three treatments were set up. The three treatments were shrub-grass (single plant shrub-20% grass), shrub-grass-30% biocrusts and shrub-grass-50% biocrusts. [Results] The results showed that after 12 hours of simulated 30 mm precipitation infiltration, water content of 0—20 cm soil layer in the shrub-grass-biotic crust treatment was 15.4% higher than that in the bare soil treatment and 12.8% higher than that in the shrub-grass treatment. Compared with bare soil, soil water evapotranspiration of shrub-grass and shrub-grass with two level of biocrusts’ coverage significantly increased. Among them, soil water evapotranspiration of shrub-grass treatment increased by 120.3% compared to bare soil, while shrub-grass-biocrusts increased by 116.5% on average. Biocrusts reduced soil water evapotranspiration of the shrub-grass ecosystem. Compared with the shrub-grass treatment, soil water evapotranspiration of the shrub-grass-biocrusts decreased by 1.7% on average. The degree of reduction of evapotranspiration was related to the coverage of biocrusts, period and soil depth. Among them, the water evapotranspiration of shrub-grass-50% biocrusts was 13.4% lower than that of shrub-grass-30% biocrusts. The reduction of water evapotranspiration of shrub-grass ecosystem caused by biocrusts was obvious during the first 1~4 days after rain. The reduction of water evapotranspiration of shrub-grass ecosystem caused by biocrusts mainly related to the evapotranspiration of 20—45 cm soil layer. Compared with the shrub-grass treatment, the water evapotranspiration loss of the 0—20 cm soil layer of the shrub-grass-biocrust treatment increased by 14.4% on average, while the water dissipation below 20 cm decreased by 15.5% on average. [Conclusion] Under the condition of 30 mm rainfall, biocrusts reduced the water evapotranspiration of shrub-grass ecosystem, which may beneficial the water deficit of shrub-grass vegetation in the wind-water erosion crisscross area. The study provides a reference for revealing the effect of biocrusts on water evapotranspiration in shrub-grass ecosystems.

Abstract Underground rocks are generally subjected to water erosion and dynamic disturbances. To exclude the effect of clay minerals on the water–rock interaction mechanism, clay-free artificial porous rock (APR) was used to investigate the coupling effect of water saturation and loading rate on tensile behavior. Dynamic Brazilian disc experiments were performed on APR with six water saturation levels (100, 80, 60, 40, 20, and 0%) using a split Hopkinson pressure bar (SHPB) combined with a high-speed camera. The results indicated that the number of cracks in the APR specimens increased with the water saturation level during the dynamic damage process, with more radial cracks at the water saturation of 80–100%. The dynamic tensile strength (DTS) and dissipated energy density exhibited different change trends with water saturation levels under different loading rates and the rate dependence was most significant in the fully saturated state (100%). At lower loading rates, DTS initially decreased rapidly and then more slowly with increasing water saturation. At higher loading rates, DTS exhibited a ‘decrease then increase’ trend. Moreover, the water-affecting factor exhibited an overall decreasing trend with the loading rate and gradually converged to 1.0. These phenomena can be attributed to the interplay between the weakening and strengthening water-effect mechanisms based on the microstructure and water distribution in the APR specimens.

To investigate the water-binding mechanism of fresh sweet potato residue, the water distribution in starch processing residues from 10 varieties of sweet potato was determined by low-field nuclear magnetic resonance (LF-NMR), and the correlation between water states and major organic components was analyzed. Then, the causal relationship was verified through enzymatic hydrolysis experiments. The results showed that three or four states of water were found in the 10 varieties of fresh sweet potato residues, namely compound (T21, 0.658–2.310 ms), immobilized (T22, 7.055–21.550 ms), entrapped (T23, 377.410–705.480 ms) and free flowing water (T24, 705.480–3 764.936 ms). The percentage of entrapped water in sweet potato residues was the highest (93.59% on average), followed by immobilized water (5.15% on average). The percentages of compound water and free flowing water were only 0.85% and 0.41%, respectively. The percentage of entrapped water was significantly positively correlated with the contents of pectin, dietary fiber, and insoluble dietary fiber. The amount of water discharged from sweet potato residues significantly increase after being hydrolyzed by pectinase, but did not significantly change after being hydrolyzed by cellulase, hemicellulase or lignin peroxidase. Under scanning electron microscopy (SEM), it was observed that most of starch particles were encapsulated or bound to cell wall debris. This study can provide a theoretical basis for the development of an efficient dehydration technology for sweet potato residues.

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models. Plain Language Summary: This paper focuses on the observational analysis of how soil moisture (SM) influences the vertical cloud‐water distribution throughout the day. By analyzing data from Soil Moisture Active Passive (SMAP) and Dual‐frequency Precipitation Radar (DPR), we gain insights into how antecedent SM affects cloud‐water reflectivity at different heights in the lower atmosphere. Our data‐driven approach produces spatial maps of SM's contribution to cloud reflectivity and rainfall in the central US as a function of cloud height and SM time lag. Our method will help diagnose weather and climate model biases. Key Points: Functional decomposition of satellite‐measured soil moisture (SM) and cloud vertical profiles (CVP) provides insights into SM‐CVP feedbacksThe sign and strength of this feedback varies with height, time lag, and geographic location, in agreement with qualitative studiesOur approach can be used to diagnose weather and climate model biases as they relate to land‐atmosphere coupling [ABSTRACT FROM AUTHOR]

AbstractThe development of heat transfer models for proton exchange membrane water electrolysis (PEMWE) cells is necessary to enhance their efficiency, durability, and safety. Although modeling efforts have indicated that the heat transfer to fluids is significant, the magnitude of the heat transfer and mechanism have not been adequately studied. To fill this knowledge gap, we quantified the amounts of heat transferred in a thermally insulated cell and associated system. We found that heat transfer to the inflow water accounted for the majority of heat transfer to the fluids, and the rise of the membrane (CCM + PTL) temperature was due mainly to deterioration of the distribution of water at high current density. The sensible heat transfer and the resistive heat were directly proportional to each other, and the quantity of latent heat transfer could be influenced by resistive heating and kinetic factors. This study was the first to provide detailed experimental insights into the factors that mainly affect heat transfer to fluids in PEM water electrolysis cells. [ABSTRACT FROM AUTHOR]

Background: DPANN archaea, including Woesearchaeota, encompass a large fraction of the archaeal diversity, yet their genomic diversity, lifestyle, and role in natural microbiomes remain elusive. With an archaeal assemblage naturally enriched in Woesearchaeota and steep vertical geochemical gradients, Lake Dziani Dzaha (Mayotte) provides an ideal model to decipher their in-situ activity and ecology. Results: Using genome-resolved metagenomics and phylogenomics, we identified highly diversified Woesearchaeota populations and defined novel halophilic clades. Depth distribution of these populations in the water column showed an unusual double peak of abundance, located at two distinct chemoclines that are hotspots of microbial diversity in the water column. Genome-centric metatranscriptomics confirmed this vertical distribution and revealed a fermentative activity, with acetate and lactate as end products, and active cell-to-cell processes, supporting strong interactions with other community members at chemoclines. Our results also revealed distinct Woesearchaeota ecotypes, with different transcriptional patterns, contrasted lifestyles, and ecological strategies, depending on environmental/host conditions. Conclusions: This work provides novel insights into Woesearchaeota in situ activity and metabolism, revealing invariant, bimodal, and adaptative lifestyles among halophilic Woesearchaeota. This challenges our precepts of an invariable host-dependent metabolism for all the members of this taxa and revises our understanding of their contributions to ecosystem functioning and microbiome assemblage. Bo26ccZX9sY8C76pvHyDey Video Abstract [ABSTRACT FROM AUTHOR]

Water hyacinth (Pontederia crassipes) is an invasive weed that covers a significant portion of Lake Tana. The infestation has an impact on the lake's ecological and socioeconomic systems. Early detection of the spread of water hyacinth using geospatial techniques is crucial for its effective management and control. The main objective of this study was to examine the spatiotemporal distribution of water hyacinth from 2016 to 2022 using a random forest machine learning model. The study used 16 variables obtained from Sentinel-2A, Sentinel-1 SAR, and SRTM DEM, and a random forest supervised classification model was applied. Seven spectral indices, five spectral bands, two Sentinel-1 SAR bands, and two topographic variables were used in combination to model the spatial distribution of water hyacinth. The model was evaluated using the overall accuracy and kappa coefficient. The findings demonstrated that the overall accuracy ranged from 0.91 to 0.94 and kappa coefficient from 0.88 to 0.92 in the wet season and 0.93 to 0.95 and 0.90 to 0.93 in the dry season, respectively. B11 and B5 (2022), VH, soil adjusted vegetation index (SAVI), and normalized difference water index (NDWI) (2020), B5 and B12 (2018), and VH and slope (2016) are the highly important variables in the classification. The study found that the spatial coverage of water hyacinth was 686.5 and 650.4 ha (2016), 1,851 and 1,259 ha (2018), 1,396.7 and 1,305.7 ha (2020), and 1,436.5 and 1,216.5 ha (2022) in the wet and dry seasons, respectively. The research findings indicate that variables derived from optical (Sentinel-2A and SRTM) and non-optical (Sentinel-1 SAR) satellite imagery effectively identify water hyacinth and display its spatiotemporal spread using the random forest machine learning algorithm. [ABSTRACT FROM AUTHOR]

Biofilm is a structurally‐connected microbial community, covered by a self‐produced polymeric matrix and adhered to biotic or abiotic surfaces. This study aimed to evaluate the occurrence of biofilm‐producing antibiotic‐resistant bacteria in water from distribution systems. Water samples were taken from 32 tanks across Ondo City and Akure metropolis, Nigeria. Information regarding the sanitation status of the tanks was gathered by observation and oral interviews. The physicochemical properties were determined using standard methods. Using the pour plate technique. Agars included serially diluted water samples were inoculated onto plate count agar, mannitol salt agar, Salmonella-Shigella agar, MacConkey agar, and cetrimide nutrient agar to assess total viable bacteria, Staphylococcus aureus, Salmonella and Shigella, coliforms, and Pseudomonas aeruginosa, respectively. Eosin‐methylene blue agar was used to cultivate Escherichia coli and Enterobacter aerogenes. Pure isolates were characterised using API kits and assessed for antibiotic resistance and biofilm production employing the Kirby‐Bauer and tissue culture plate techniques, respectively. The ages of the water tanks ranged from 1 to 25 years old; all tanks had cover‐lids; 13 (40.63%) had water guards while 12 (37.5%) underwent water treatment. The physicochemical properties chiefly fell within WHO standards for drinking water. One hundred and eighty‐seven isolates were obtained. S. aureus (15.51%) had the highest frequency while Salmonella enterica (3.2%) had the lowest frequency. Thirty‐six percent of the isolates were strong biofilm producers, while 20.67% Gram‐negative and 18.69% Gram‐positive bacterial isolates were antibiotic‐resistant. This study revealed a high occurrence of biofilm‐forming bacteria and prevalence of antibiotic‐resistant bacteria in water distribution systems, emphasizing the urgency of improving water quality for public health protection. [ABSTRACT FROM AUTHOR]

Flat nozzles are widely used in the continuous casting process due to their stable spraying effect, wide spraying area, and strong applicability. The type and arrangement of nozzles have a direct effect on cooling efficiency and blank quality. Herein, the spray characteristics of flat nozzles in slab continuous casting are investigated with a detecting apparatus developed by University of Science and Technology Beijing and Jiangsu Boji Company (named USTB‐BOJI DA), and a prediction model for water flux distribution under different flat nozzle arrangements is established. The results show that the nozzles in a foot roller zone have good spray performance and water flow distribution, while the nozzles in bending zones are poor. Comparing the predicted and measured results of water flux distributions, the average error is less than 0.24%, indicating that the predictive model is reliable. In addition, the evaluation index is defined for water flux distribution under multinozzle arrangement, and the solution equations are given by applying the predictive model. Combining the above research, a new design and optimization strategy/method for flat nozzle arrangements is proposed, which can significantly improve the uniformity of the water flux distribution of the spray in the secondary cooling zones during continuous casting. [ABSTRACT FROM AUTHOR]

Detecting and mitigating leaks in water distribution networks are vital for water conservation. Machine-learning-based (ML) acoustic leak detection models were introduced as effective alternatives for leak management. However, ML model training requires sufficient labeled data, which hinders related development. To address this challenge, this study employed contrastive learning (CL) for leak detection using limited labeled signals. Experimental results indicate that flip-x and amplitude scaling are optimal combinations for contrastive learning. Besides, ablation and t-distributed stochastic neighbor embedding (t-SNE) results reveal that increasing the model depth does not always yield performance improvement, and five convolutional blocks are more suitable for the leak detection problem in this study. Comparison experiments demonstrate that contrastive learning outperforms supervised learning (SL) when trained with insufficient labeled data. The out-of-sample validation results indicate that the proposed leak detection model is robust and effective in unexplored pipelines. The proposed framework significantly advances ML-based leak detection research and supports sustainable water management practices. [ABSTRACT FROM AUTHOR]

Irrigation requirements are instrumental in planning water distribution and reservoir management. The Markonahalli Dam is constructed across the Shimsha River and has a command area of 7000 ha in Kunigal Taluk of Tumkur District, Karnataka. It is a semiarid region that depends on canal irrigation from the reservoir. The Soil and Water Assessment Tool (SWAT) is employed to estimate potential evapotranspiration (PET) and crop evapotranspiration (ETc) for the command area. The model is calibrated and validated using evapotranspiration data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration and validation are performed using the Sequential Uncertainty Fitting‐2 (SUFI‐2) algorithm within the SWAT Calibration and Uncertainty Programs (SWAT‐CUP). The results indicated reasonably good ETc estimations, with the coefficient of correlation (r) and Nash–Sutcliffe model efficiency (NSE) coefficient being 0.57 and 0.549 for calibration, and 0.165 and 0.105 for validation, respectively. The simulated ETc values are further used to calculate the gross irrigation requirement (GIR) for the command area. The irrigation requirement is further optimized for maximum crop yield using the Nondominated Sorting Genetic Algorithm II (NSGA‐II) and Pareto curves. The optimization results show that 37.85% of the irrigation requirement can be conserved while maximizing crop yield. [ABSTRACT FROM AUTHOR]

Friction-induced vibration in the wiper system of passenger cars not only causes impaired visibility as a result of uneven water film distribution on the windscreen surface, but also leads to noise problems by transmitting vibrations as sound to the passengers. In this study, a novel experimental apparatus was developed to simulate the wiper system, enabling the change in normal support stiffness and the precise adjustment of the yaw angles of a shortened rubber blade and its flexible base relative to the drive direction of a glass plate. The use of fluorescence observation provided a significant advantage, enabling precise measurements of the position of the rubber blade tip and the water film thickness in the contact area of the rubber blade and the glass plate during operation. In conjunction with this, an accelerometer and a gap sensor were employed to measure the normal and tangential motions of the support structures, respectively. This comprehensive setup allows for precise control of blade support conditions, and provides accurate measurement for motions of rubber blade and support structures, making it a powerful tool for investigating friction-induced vibration in the wiper system. The experimental results clearly demonstrated the effectiveness of applying large yaw angles to both the rubber blade and the flexible base in suppressing friction-induced vibration, resulting in smooth motion at any drive speed. These findings have the potential to improve wiper performances under actual operating conditions. [ABSTRACT FROM AUTHOR]

The pressure sensor placement in the water distribution networks (WDNs) is a typical NP‐Hard problem. The goal is to monitor the running state of the whole network area by placing a small number of pressure sensors. This letter proposes an enhanced Rafflesia optimization algorithm (EROA) to solve this problem. The simulation results show that compared with the original ROA algorithm and the traditional intelligent optimization algorithms, the proposed EROA algorithm exhibits superior optimization capabilities and solution efficiency, and can provide a more reasonable sensor placement scheme. [ABSTRACT FROM AUTHOR]

Microplastics are tiny plastic particles with a usual diameter ranging from ~ 1 μ to 5 µm. Recently, microplastic pollution has raised the attention of the worldwide environmental and human concerns. In human beings, digestive system illness, respiratory system disorders, sleep disturbances, obesity, diabetes, and even cancer have been reported after microplastic exposure either through food, air, or skin. Similarly, microplastics are also having negative impacts on the plant health, soil microorganisms, aquatic lives, and other animals. Policies and initiatives have already been in the pipeline to address this problem to deal with microplastic pollution. However, many obstacles are also being observed such as lack of knowledge, lack of research, and also absence of regulatory frameworks. This article has covered the distribution of microplastics in water, soil, food and air. Application of multimodel strategies including fewer plastic item consumption, developing low-cost novel technologies using microorganisms, biofilm, and genetic modified microorganisms has been used to reduce microplastics from the environment. Researchers, academician, policy-makers, and environmentalists should work jointly to cope up with microplastic contamination and their effect on the ecosystem as a whole which can be reduced in the coming years and also to make earth clean. [ABSTRACT FROM AUTHOR]