scholarly journals Metastatic Spinal Lesions: State-of-the-Art Treatment Options and Future Trends

2008 ◽  
Vol 29 (9) ◽  
pp. 1605-1611 ◽  
Author(s):  
B.A. Georgy
Keyword(s):  
State Of The Art ◽  
Treatment Options ◽  
Future Trends ◽  
Spinal Lesions

scholarly journals Application of Microextraction-Based Techniques for Screening-Controlled Drugs in Forensic Context—A Review

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2168
Author(s):  
Samir M. Ahmad ◽  
Oriana C. Gonçalves ◽  
Mariana N. Oliveira ◽  
Nuno R. Neng ◽  
José M. F. Nogueira
Keyword(s):  
Environmental Sustainability ◽  
Illicit Drugs ◽  
State Of The Art ◽  
Cost Benefit ◽  
Future Trends ◽  
Present Contribution ◽  
Controlled Drugs ◽  
Related Compounds ◽  
Innovative Techniques ◽  
Great Simplicity

The analysis of controlled drugs in forensic matrices, i.e., urine, blood, plasma, saliva, and hair, is one of the current hot topics in the clinical and toxicological context. The use of microextraction-based approaches has gained considerable notoriety, mainly due to the great simplicity, cost-benefit, and environmental sustainability. For this reason, the application of these innovative techniques has become more relevant than ever in programs for monitoring priority substances such as the main illicit drugs, e.g., opioids, stimulants, cannabinoids, hallucinogens, dissociative drugs, and related compounds. The present contribution aims to make a comprehensive review on the state-of-the art advantages and future trends on the application of microextraction-based techniques for screening-controlled drugs in the forensic context.

scholarly journals State of the Art and Future Trends of Unconventional PD-Measurement at Power Transformers

2014 ◽  
Vol 8 (6) ◽  
Author(s):  
Jürgen Fabian ◽  
Martin Neuwersch ◽  
Christof Sumereder ◽  
Michael Muhr ◽  
Robert Schwarz
Keyword(s):  
State Of The Art ◽  
Power Transformers ◽  
Future Trends

scholarly journals Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Composite Structures ◽  
Structural Damage ◽  
Structural Integrity ◽  
State Of The Art ◽  
Future Trends ◽  
Learning Models ◽  
Damage Diagnosis ◽  
Comprehensive Review ◽  
Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

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