Preparation of silver-based metal-organic framework and chitosan hybrid material for removing drug and dye.

Pharmaceuticals and personal care products and dyes have low biodegradability and high toxicity, seriously threaten the human health and ecological environment. Therefore, seeking effective removal methods has become the focus of research. In this study, silver-based metal-organic framework (Ag-MOF) and chitosan (CS) hybrid adsorbent (Ag-MOF-CS) was synthesized via solvothermal one-pot synthesis to remove diclofenac sodium (DCF) and acid Red 1 (AR1) from water for the first time.

KGSCS-a smart care system for elderly with geriatric chronic diseases: a knowledge graph approach.

Background: The increasing aging population has led to a shortage of geriatric chronic disease caregiver, resulting in inadequate care for elderly people. In this global context, many older people rely on nonprofessional family care. The credibility of existing health websites cannot meet the needs of care.

Photocatalysis-PMS oxidation system based on CQDs-doped carbon nitride nanosheets for degradation of residual drugs in water.

Graphite-like carbon nitride (g-CN) is favored for its excellent physicochemical properties. However, the high complexation rate of photogenerated carriers greatly limits its practical applications. Based on this, a novel CQDs-doped carbon nitride nanosheets composite (CNS/CQDs) was prepared and applied to the visible light-induced activation of peroxymonosulfate (PMS) for meloxicam (Mel) and tetracycline (TC) degradation.

Quality evaluation of ground improvement by deep cement mixing piles via ground-penetrating radar.

Deep cement mixing piles are a key technology for treating settlement distress of soft soil subgrade. However, it is very challenging to accurately evaluate the quality of pile construction due to the limitations of pile material, large number of piles and small pile spacing. Here, we propose the idea of transforming defect detection of piles into quality evaluation of ground improvement.

A canonical correlation analysis based method for contamination event detection in water sources.

In this study, a general framework integrating a data-driven estimation model is employed for contamination event detection in water sources. Sequential canonical correlation coefficients are updated in the model using multivariate water quality time series. The proposed method utilizes canonical correlation analysis for studying the interplay between two sets of water quality parameters.

Why conventional detection methods fail in identifying the existence of contamination events.

Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation.

Performance evaluation for three pollution detection methods using data from a real contamination accident.

Early warning systems have been widely deployed to safeguard water security. Many contamination detection methods have been developed and evaluated in the past decades. Although encouraging detection performance has been obtained and reported, these evaluations mainly used artificial or laboratory data.

A multivariate based event detection method and performance comparison with two baseline methods.

Early warning systems have been widely deployed to protect water systems from accidental and intentional contamination events. Conventional detection algorithms are often criticized for having high false positive rates and low true positive rates. This mainly stems from the inability of these methods to determine whether variation in sensor measurements is caused by equipment noise or the presence of contamination.

A real time method of contaminant classification using conventional water quality sensors.

Early warning systems are often used to detect deliberate and accidental contamination events in a water source. After contamination detection, it is important to classify the type of contaminant quickly to provide support for implementation of remediation attempts. Conventional methods commonly rely on laboratory-based analysis or qualitative geometry analysis, which require long analysis time or suffer low true positive rate.

Contaminant classification using cosine distances based on multiple conventional sensors.

Emergent contamination events have a significant impact on water systems. After contamination detection, it is important to classify the type of contaminant quickly to provide support for remediation attempts. Conventional methods generally either rely on laboratory-based analysis, which requires a long analysis time, or on multivariable-based geometry analysis and sequence analysis, which is prone to being affected by the contaminant concentration.

A method of detecting contamination events using multiple conventional water quality sensors.

Early warning systems are often used for detecting contamination accidents. Traditional event detection methods suffer from high false negative and false positive errors. This paper proposes a detection method using multiple conventional water quality sensors and introduces a method to determine the values of parameters, which was configured as a multiple optimization problem and solved using a non-dominated sorting genetic algorithm (NSGA-II).

Contamination event detection using multiple types of conventional water quality sensors in source water.

Early warning systems are often used to detect deliberate and accidental contamination events in a water system. Conventional methods normally detect a contamination event by comparing the predicted and observed water quality values from one sensor. This paper proposes a new method for event detection by exploring the correlative relationships between multiple types of conventional water quality sensors.