Bioinspired Adaptive,Elastic,and Conductive Graphene Structured Thin-Films Achieving High-Efficiency Underwater Detection and Vibration Perception

Underwater exploration has been an attractive topic for understanding the very nature of the lakes and even deep oceans.In recent years,extensive efforts have been devoted to developing functional materials and their integrated devices for underwater information capturing.However,there still remains a great challenge for water depth detection and vibration monitoring in a high-efficient,controllable,and scalable way.Inspired by the lateral line of fish that can sensitively sense the water depth and environmental stimuli,an ultrathin,elastic,and adaptive underwater sensor based on Ecoflex matrix with embedded assembled graphene sheets is fabricated.The graphene structured thin film is endowed with favourable adaptive and morphable features,which can conformally adhere to the structural surface and transform to a bulged state driven by water pressure.Owing to the introduction of the graphene-based layer,the integrated sensing system can actively detect the water depth with a wide range of 0.3-1.8 m.Furthermore,similar to the fish,the mechanical stimuli from land(e.g.knocking,stomping)and water(e.g.wind blowing,raining,fishing)can also be sensitively captured in real time.This graphene structured thin-film system is expected to demonstrate significant potentials in underwater monitoring,communication,and risk avoidance.

Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

Fluorescence Quenching of a Novel Eu(Ⅲ) Coordination Compound for the Detection of Trace Amounts of Water： Synthesis, Structure and Photoluminescent Property

A novel luminescent coordination compound Eu（TTA）3（DEDAF）（1, TTA = 1,1,1-trifluoro-3-（2-thenoyl）acetone, DEDAF = 9,9-diethyl-4,5-diazafluoren） has been synthesized and fully characterized by infrared spectrum, elemental analysis, UV-vis spectrum, etc. X-ray single-crystal diffraction analysis reveals that compound 1 shows a mononuclear structure with the europium atom in coordinating to one DAF and three TTA ligands. The mononuclear structure units are assembled into a 3-D polymer by hydrogen bonds and π-π interactions. Photoluminescent property of 1 was investigated in detail at room temperature. Complex 1 emits strong red luminescence. However, it could be quenched even by small amount of water. The fluorescence intensity at 614 nm decreases linearly with the water content increasing（vol% in acetonitrile） in the range of 0.025~0.2% under 278 nm excitation. Thermogravimetric analysis has also been studied, which demonstrates good thermal stability of 1.

Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China

Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas.This study took Gansu Province,China,a typical area with complex terrain and variable climate,as the research subject.Based on Google Earth Engine,we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022,and quantitatively analyzed the main causes of regional differences in surface water area.The findings revealed that surface water area in Gansu Province expanded by 406.88 km2 from 1985 to 2022.Seasonal surface water area exhibited significant fluctuations,while permanent surface water area showed a steady increase.Notably,terrestrial water storage exhibited a trend of first decreasing and then increasing,correlated with the dynamics of surface water area.Climate change and human activities jointly affected surface hydrological processes,with the impact of climate change being slightly higher than that of human activities.Spatially,climate change affected the'source'of surface water to a greater extent,while human activities tended to affect the'destination'of surface water.Challenges of surface water resources faced by inland arid and semi-arid areas like Gansu Province are multifaceted.Therefore,we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water'supply-demand'balance strategies.The study not only sheds light on the dynamics of surface water area in Gansu Province,but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.

Detection and treatment of water inflow in karst tunnel：A case study in Daba tunnel

In a karst tunnel, fissures or cracks that are filled with weathered materials are a type of potential water outlet as they are easily triggered and converted into groundwater outlets under the influence of high groundwater pressure. A terrible water inrush caused by potential water outlets can seriously hinder the project construction. Potential water outlets and water sources that surrounding the tunnel must be detected before water inflow can be treated. This paper provides a successful case of the detection and treatment of water inflow in a karst tunnel and proposes a potential water outlet detection(PWOD) method in which heavy rainfall(>50 mm/d) is considered a trigger for a potential water outlet. The Daba tunnel located in Hunan province, China, has been constructed in a karst stratum where the rock mass has been weathered intensely by the influence of two faults. Heavy rain triggered some potential water outlets, causing a serious water inrush. The PWOD method was applied in this project for the treatment of water inflow, and six potential water outlets in total were identified through three heavy rains. Meanwhile, a geophysical prospecting technique was also used to detect water sources. The connections between water outlets and water sources were identified with a 3-D graphic that included all of them. According to the distribution of water outlets and water sources, the detection area was divided into three sections and separately treated by curtain grouting.

Leakage Detection in Water Distribution Network Based on a New Heuristic Genetic Algorithm Model

The leakage control is an important task, because it is associated with some problems such as economic loss, safety concerns, and environmental damages. The pervious methods which have already been devised for leakage detection are not only expensive and time consuming, but also have a low efficient. As a result, the global leakage detection methods such as leak detection based on simulation and calibration of the network have been considered recently. In this research, leak detection based on calibration in two hypothetical and a laboratorial networks is considered. Additionally a novel optimization method called step-by-step elimination method (SSEM) combining with a genetic algorithm (GA) is introduced to calibration and leakage detection in networks. This method step-by-step detects and eliminates the nodes that provide no contribution in leakage among uncertain parameters of calibration of a network. The proposed method initiates with an ordinary calibration for a studied network, follow by elimination of suspicious nodes among adjusted parameters, then, the network is re-calibrated. Finally the process is repeated until the numbers of unknown demands are equal to the desired numbers or the exact leakage locations and values are determined. These investigations illustrate the capability of this method for detecting the locations and sizes of leakages.

Scanning electron microscopy coupled with energydispersive X-ray spectrometry for quick detection of sulfuroxidizing bacteria in environmental water samples

Detection of sulfur-oxidizing bacteria has largely been dependent on targeted gene sequencing technology or traditional cell cultivation, which usually takes from days to months to carry out. This clearly does not meet the requirements of analysis for time-sensitive samples and/or complicated environmental samples. Since energy-dispersive X-ray spectrometry(EDS) can be used to simultaneously detect multiple elements in a sample, including sulfur, with minimal sample treatment, this technology was applied to detect sulfur-oxidizing bacteria using their high sulfur content within the cell. This article describes the application of scanning electron microscopy imaging coupled with EDS mapping for quick detection of sulfur oxidizers in contaminated environmental water samples, with minimal sample handling. Scanning electron microscopy imaging revealed the existence of dense granules within the bacterial cells, while EDS identified large amounts of sulfur within them. EDS mapping localized the sulfur to these granules. Subsequent 16S rRNA gene sequencing showed that the bacteria detected in our samples belonged to the genus Chromatium, which are sulfur oxidizers. Thus, EDS mapping made it possible to identify sulfur oxidizers in environmental samples based on localized sulfur within their cells, within a short time(within 24 h of sampling). This technique has wide ranging applications for detection of sulfur bacteria in environmental water samples.

A method for detecting the breaking of wind-generated waves in deep water

The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engineering. The first problem encountered in both its theoretical study and practical measurement is how to detect the breaking of waves.

Rapid detection of coliforms in drinking water of Arak city using multiplex PCR method in comparison with the standard method of culture(Most probably Number)

Objective:To analyse molecular detection of coliforms and shorten the time of PCR.Methods:Rapid detection of coliforms by amplification of lacZ and uidA genes in a multiplex PCR reaction was designed and performed in comparison with most probably number(MPN)method for 16 artificial and 101 field samples.The molecular method was also conducted on isolated coliforms from positive MPN samples;standard sample for verification of microbial method certificated reference material;isolated strains from certificated reference material and standard bacteria.The PCR and electrophoresis parameters were changed for reducing the operation time.Results:Results of PCR for lacZ and uidA genes were similar in all of standard,operational and artificial samples and showed the 876 bp and 147 bp bands of lacZ and uidA genes by multiplex PCR.PCR results were confirmed by MPN culture method by sensitivity 86%(95%CI:0.71-0.93).Also the total execution time,with a successful change of factors,was reduced to less than two and a half hour.Conclusions:Multiplex PCR method with shortened operation time was used for the simultaneous detection of total coliforms and Escherichia coli in distribution system of Arak city.It's recommended to be used at least as an initial screening test,and then the positive samples could be randomly tested by MPN.

Simultaneous Detection of Phenols and Anilines in Oilfield Waste Water

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Different Detection and Treatment Methods for <i>Entamoeba histolytica</i>and <i>Entamoeba dispar</i>in Water/Wastewater: A Review

Entamoeba histolytica is an anaerobic parasitic protozoan and well known as a human pathogen, while its close relative, Entamoeba dispar, also possesses similar characteristics as an infectious agent. These microorganisms are generally transmitted in fecal-contaminated water. However, E. dispar present in industrial wastewater is also capable of creating biofilms that can cause adverse impacts in piping networks. Therefore, it is important to detect both of these protozoan species in water and to find a cost-effective technique for inactivation or management control. This review article summarizes the available detection methods in water and wastewater matrices along with feasible disinfection techniques.

Sensitive Determination of Metal Ions in Drinking Water by Capillary Electrophoresis Coupled with Contactless Conductivity Detection Using 18-Crown-6 Ether and Hexadecyltrimethylammonium Bromide as Complexing Reagents

A simple, economical, and sensitive capillary electrophoresis (CE) method integrated with capacitively coupled contactless conductivity detection was developed for the determination of metal ions such as K+, Na+, Mg2+, Sr2+, Ca2+ in drinking water. 18-Crown-6 ether and Hexadecyltrimethylammonium Bromide (CTAB) were employed as complexing reagents. The effects of electrolyte additives, citric acid buffer solution, and other separation conditions of CE were comprehensively investigated and carefully optimized. The best results were obtained in a running buffer solution composed of citric acid (12 mM), 18-crown-6 ether (0.2 mM), and CTAB (0.015 mM) at pH 3.5. Under these conditions, a complete separation of five metal ions was successfully achieved in less than 12 min. The limits of detection for the optimal procedure were determined to be in the range of 0.02 - 0.2 mg·L-1. The repeatability with respect to migration times and peak areas, expressed as relative standard deviations, was better than 2.3% and 5.1%, respectively. Evaluation of the efficiency of the methodology indicated that it was reliable for the determination of metal ions in six different brands of drinking water samples.

Height Detection and Analysis of Water Flowing Fractured Zone of Coal Face

Taking 91105 working face as the research object, the observation method of water flowing fractured zone and the layout of mining holes were determined by analyzing the field geological structure. It was shown that the fractured zone height and the ratio given by the measured method were 52.33 and 12.46, respectively. By the numerical simulation method with the software of UDEC, the fractured zone height and the ratio were 42.5 and 10.12. By comparison of measured height data and UDEC numerical simulation, there were some differences between the measured height and the calculated results of UDEC numerical simulation method. The method of simulation can be used as the technical basis for the design of waterproof coal pillar in the future.

基于视觉感知与V-detector的水质异常检测方法

为了快速准确地对水质进行评价分析,提出了一种新的水质异常检测方法.该方法将视觉感知技术与人工免疫系统中的否定选择原理相结合.首先采用视觉驱动的生物监测技术对水质指标进行采集,然后利用基于否定选择原理的可变V-detector算法对水质指标进行评价分析.由于视觉感知技术具有敏感性、综合性以及人工免疫系统具有自组织、自学习、自识别等特点,故该方法能够快速准确地对水质进行实时检测评价.实验结果表明该方法是一种实用的水质检测评价方法.

融入注意力机制的水位检测算法

传统图像处理的水位检测算法易受环境因素的影响,因此提出一种融入注意力机制的水位检测算法。首先,利用融入CBAM注意力机制的YOLO v5目标检测模型获取尺身数字类别及其坐标信息;其次,通过融入ECA注意力机制的DeepLabv3+语义分割模型实现水尺和背景的分割;再次,使用边缘检测算法得出水位线信息;最后,根据尺身数字信息和水位线信息将像素值换算为真实水位值。实验结果表明,优化水位检测算法与人工读数误差在2 cm以内,满足水位检测标准要求。

瞬变电磁探测技术在小纪汗煤矿的应用与研究

煤炭作为我国国民经济的重要支柱,其深部开采面临复杂多变的地层特征,包括高地应力、构造应力变化及梯度差异,导致井下环境充满危险因素,水害尤为突出。瞬变电磁探测技术在近20年来取得显著进步,成为煤矿防治水工作的关键手段。本研究采用矿井瞬变电磁法,针对小纪汗煤矿特定煤层区域,详细探查了煤层顶底板含水情况。通过对瞬变电磁视电阻率数据的综合分析,我们成功绘制了等值线断面图和平面图,进而进行了地质推断,明确了煤层与上下含水层的分布情况。研究结果显示,煤矿充水强度较大,主要水源为顶板砂岩裂隙承压水。瞬变电磁法的应用,不仅精确界定了顶底板承压水聚集区,还为矿井开采与疏放水工程的科学布置提供了坚实依据,展现了该技术在实际应用中的高效与准确性。

复杂场景下无水尺水位的影像水位反演智能检测方法

实现精细化水务管控和洪涝灾害预警,需要实时、准确感知水位突变事件。现有技术不能满足夜晚、雾霾、雨天、雪天、漂浮物遮挡及阴影等复杂恶劣环境下的水位识别需求。为此,本文提出一种融合改进YOLOv5与卡尔曼滤波原理的无水尺水位智能检测技术:①引入YOLOv5对水位线(水岸分界线)进行检测,并利用线性拟合方法获得实际水位线;②针对水位线在延伸方向无限大而在其法向无限小特点,提出强化中尺度特征的多层级特征融合方法改进原YOLOv5算法;③利用卡尔曼滤波引入水位历史信息作为先验知识,提高本技术对复杂恶劣环境的泛化性能;④将图像中事先标定的固定的标志物加入到深度学习网络中训练,根据标志位真实尺寸解算实际水位高程,实现无水尺检测方案。相关试验和实践表明,改进的YOLOv5更加轻量化;本文所述水位智能检测技术斜率准确性为97.3%,较原算法提高了2.4%;截距准确性为99.3%,较原算法提高了0.5%;在夜晚、雾霾、雨天、雪天、漂浮物遮挡及阴影等复杂恶劣环境下可以自动、准确识别出水位高程,误差小于0.1 m。

Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study

Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.

Advances in Water Quality Monitoring of Inorganics: Current Trends

New methods of analysis for water quality monitoring to detect inorganic substances are required to meet the demands of determining concentration, particularly at low detection limits, analysing speciation and even identifying the pollution source. Such information is essential to inform public health decisions and to comply with more stringent legislation. This paper concentrates on two case studies, reviewing the development in monitoring methods, and predicting future trends. Arsenic and nitrates detection was selected as these pollutants are particularly problematic from a human health perspective. Additionally, the challenges faced in developing monitoring methods for these chemicals are relevant to a wide range of other inorganics. The current state of the art in detection approaches for these chemicals are discussed along with recommendations for future research to further improve the methods.