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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Oct 13, 2025

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System.

Jian Zhou1,2, Jian Wang1,2, Yang Chen1,2

  • 1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary

This study introduces a novel water quality prediction method using multi-source transfer learning for water environmental Internet of Things (IoT) systems. The approach leverages data from nearby monitoring points to enhance prediction accuracy.

Keywords:
adjacency effectdistributed computingecho state networkenvironmental IoT systemmulti-source transfer learningwater quality prediction

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Area of Science:

  • Environmental Science
  • Data Science
  • Sensor Networks

Background:

  • Water quality monitoring systems generate vast amounts of data, enabling prediction.
  • Traditional methods often overlook spatial correlations between monitoring points.
  • Water flow causes adjacency effects in water quality data.

Purpose of the Study:

  • To develop an accurate water quality prediction method for water environmental Internet of Things (IoT) systems.
  • To effectively utilize water quality data from adjacent monitoring points.
  • To improve prediction accuracy by addressing limitations of traditional methods.

Main Methods:

  • A multi-source transfer learning framework was constructed for water quality prediction.
  • Common features were extracted and aligned from multiple nearby and target monitoring points.
  • Water quality prediction models were built using echo state networks with distributed computing and integrated.
  • Prediction parameters were optimized through iterative backpropagation of population deviation.

Main Results:

  • The proposed method effectively uses water quality information from multiple nearby monitoring points.
  • Prediction bias was reduced by aligning features and models.
  • Experimental application on a Hong Kong water quality dataset validated the method's efficacy.

Conclusions:

  • Multi-source transfer learning significantly enhances water quality prediction in IoT systems.
  • Integrating data from adjacent sites improves model accuracy and reduces bias.
  • The developed framework offers a robust solution for real-time water quality management.