Physical, chemical and biological characterization of membrane fouling

In this chapter, advanced characterization of membrane fouling as a diagnostic tool has been summarized to prevent membrane fouling. Physical, chemical and biological analyses as membrane autopsies are mainly utilized to better understand membrane foulant. The physical characterization gives structure, roughness, charge effect, strength and hydrophobicity of membrane fouling. The chemical methods provide qualitative and quantitative measurements of different inorganic and organic matter. The biological properties present the spatial biofilm distribution, structure of dominant microorganisms and isolation and identification of microorganisms. In addition, detailed membrane foulant types are reviewed in terms of structure, roughness, hydrophobicity, charge effect, strength, calcium, magnesium, aluminum, iron, silicate, particle, functional group, biopolymer, humic acid, polysaccharide, structural composition, biofilm structure, microorganism and foulant interaction.

Chronic traumatic encephalopathy: historical origins and current perspective

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that is most often identified in postmortem autopsies of individuals exposed to repetitive head impacts, such as boxers and football players. The neuropathology of CTE is characterized by the accumulation of hyperphosphorylated tau protein in a pattern that is unique from that of other neurodegenerative diseases, including Alzheimer's disease. The clinical features of CTE are often progressive, leading to dramatic changes in mood, behavior, and cognition, frequently resulting in debilitating dementia. In some cases, motor features, including parkinsonism, can also be present. In this review, the historical origins of CTE are revealed and an overview of the current state of knowledge of CTE is provided, including the neuropathology, clinical features, proposed clinical and pathological diagnostic criteria, potential in vivo biomarkers, known risk factors, and treatment options.

Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.