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A novel biosensor using poly(3,4-ethylene dioxythiophene) (PEDOT) modified Pt electrode was developed for selective determination of dopamine (DA) in presence of high concentrations of ascorbic acid (AA) and uric acid (UA) with a maximum molar ratio of 1/1000, and 1/100 in the presence of sodium dodecyl sulfate (SDS). SDS forms a monolayer on PEDOT surface with a high density of negatively charged end directed outside the electrode. The electrochemical response of dopamine was improved by SDS due to the enhanced accumulation of protonated dopamine via electrostatic interactions. The common overlapped oxidation peaks of AA, UA and DA can be resolved by using SDS as the DA current

Conducting polymers (CPs) are organic polymers that exhibit electronic, magnetic and optical properties of metals and semiconductors while retaining the attractive mechanical properties and processing advantages of polymers. These features, along with chemical sensitivity, room temperature operation, and tunable charge transport properties, have launched conducting polymers as a major class of chemical transducers, creating powerful thin/thick film sensors for over two decades. Additionally, their synthetic routes are compatible with several functionalization schemes, including covalent bonding to the monomer, entrapment during synthesis, and surface chemistry, which can impart a high degree of selectivity. CPs can be used as the selective layer in sensors (sensor receptor) or as the transducer itself. Physical transducers varying from optical, mass sensitive and electrochemical are all discussed in this chapter. In comparison with most of the commercially available sensors, based usually on metal oxides and operated at high temperatures, the sensors made of CPs have many improved characteristics. They have high sensitivities and short response time and more interestingly they work at room temperature. As a result, more and more attentions have been paid to the sensors fabricated from CPs.

With the widespread use of variable speed drives, a robust scheme that can detect and diagnose bearing faults under fixed and variable speed conditions becomes essential for reliable operation. Unfortunately, most of the reported methods in the literature are dedicated to working under fixed speed and will face challenges under variable speed conditions. Besides, most of them require detailed bearing information that may be unavailable in the real world. Therefore, in this paper, a new scheme is proposed for bearing faults detection and diagnosis under fixed and time varying speed conditions. The proposed scheme is based on the analysis of vibration signals using the persistence spectrum that can provide images rich with health-related features largely independent from rotating speed. Then, the produced image is compared with priorly stored images of the persistence spectrum of a healthy case. This comparison is performed using the multi-scale structural similarity index, which is a robust basis for images comparison without the need for training or expert knowledge. The obtained index is compared against an adaptive threshold for fault detection. Upon detecting a fault, the persistence spectrum image is compared with that of stored different fault types for fault diagnosis. The proposed scheme is extensively validated using three experimental datasets under different speed conditions. The results show that it can detect bearing faults in an earlier stage without the need for bearing specifications or shaft speed. Moreover, it can successfully diagnose bearing faults severity with accuracy reaching 100% with the minimum required data.

Most of the published research studies for detecting induction motor broken bar faults (BBFs) use a time–frequency (t − f ) decomposition tool to characterize the fault-related components. However, the identification and the assessment of these components in (t − f ) domain require skilled user or powerful pattern recognition technique. Moreover, a relatively long starting duration is necessary. This article introduces an automated scheme to detect BBFs and distinguish fault severity in induction motors under startup conditions regardless of the user experience and even under short starting duration and in a noisy environment. This scheme is based on the analysis of the starting current using optimized Stockwell transform (ST). An active set algorithm is applied to maximize the energy concentration of the left-side harmonic (LSH) component. Then, an adaptive time–frequency filter is applied to extract the LSH component from the (t − f ) domain, where the energy of the right part of LSH (RLSH) is utilized as an effective index for BBFs detection and for discriminating BBFs severity. Both real experimental data and simulation-based tests on 0.746- and 11-kW motors are used to extensively verify the performance of the proposed scheme. The achieved results have ensured that the proposed scheme can achieve a high accuracy with the minimum data and shortest acquisition time in comparison with some recent methods in the literature.

Cancer is a global health challenge that remains to be a field of extensive research aiming to find new anticancer therapeutics. The 20S proteasome complex is one of the targets of anticancerdrugs, as it is correlated with several cancer types. Herein, we aim to discuss the 20S proteasome subunits and investigatethe currently studied proteasome inhibitors targeting the catalytically active proteasome subunits. In this review, we summarize the proteindegradation mechanism of the 20S proteasome complex and compareit with the 26S proteasome complex. Afterwards, the localization of the 20S proteasome is summarized as well as its use as a diagnosticandprognostic marker. The FDA-approved proteasome inhibitors (PIs) under clinical trials are summarized and their current limited use in solid tumors is also reviewed in addition to the expression of theβ5 subunit in differentcell lines. The review discusses in-silico analysis of the active subunit of the 20S proteasome complex. For development of new proteasome inhibitor drugs, the natural products inhibiting the 20S proteasome are summarized, as well as novel methodologies and challenges for the natural product discovery and current information about the biosynthetic gene clusters encoding them. We herein briefly summarize some resistancemechanismsto the proteasomeinhibitors. Additionally, we focus on the three main classes of proteasome inhibitors: 1] boronic acid, 2] beta-lactone and 3] epoxide inhibitor classes, as well as other PI classes, and their IC50 values and their structure–activity relationship (SAR). Lastly,we summarize several future prospects of developing new proteasome inhibitors towards the treatment of tumors, especially solid tumors.

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