A Novel TLC-IA Approach for Forensic Differentiation of Fountain Pen Inks Using RGB Profiling | Chapter 9 | Science and Technology: Developments and Applications Vol. 10

Forensic science uses scientific methods or expertise to investigate criminal activities or analyse evidence that may be presented in a court of law. This article intends to present a novel and rapid technique for distinguishing inks on disputed papers. Ten blue fountain pen inks of two different types(bottle and refill) in differentbrands were acquired from the local market to create the collection and to beanalysed in the study. The separation of ink components was conducted using thin-layer chromatography (TLC). TLC investigations were conducted with Merck (Darmstadt, Germany) 20 cm x 20 cm silica gel 60 TLC plates without fluorescent indicators. The data was acquired by developing specialised image analysis software to assess thin-layer chromatograms (TLC-IA). This study marks Turkey’s inaugural application of TLC-IA to analyse fountain pen inks, employing RGB profiles for ink differentiation. This work represents the inaugural investigation into fountain pen inks, employing TLC-IA with RGB analysis for ink differentiation. The ink spot was removed from the document with methanol and isolated by thin-layer chromatography utilising a plastic sheet coated with silica gel 60, devoid of fluorescent indicator, employing a mobile phase composed of ethyl acetate, ethanol, and water in a ratio of 70:35:30 (v/v/v). A novel software was developed to differentiate fountain pen inks based following the development of the chromatogram, the chromatograms were digitised using a standard office scanner, and the intensity profiles of RGB attributes along the development line of each sample were generated and analysed with bespoke software. The RGB profiles of fountain pen inks from different manufacturers exhibited markedly varied patterns in most instances. Normalising RGB profiles facilitates a uniform comparison of colour values among various ink samples. Standardising RGB values to a range of 0–1 enhances the distinction among ink formulations.. In this analysis, darker colours on the heatmap represented higher similarity values between the samples. The study revealed that b 06, b 07, and b 08 were highly like one another across all visual methods—similarity matrix, normalised RGB profiles, and 3D scatter plots—while b 10 exhibited consistently low similarity to other samples, particularly b 04 and b 05. In conclusion, the importance of thin-layer chromatography (TLC) with RGB detection in forensic science lies in its ability to efficiently separate and visually analyse complex mixtures, such as inks or drugs, providing valuable evidence in investigations.

 

This pioneering work in Turkey facilitates the forensic utilisation of TLC-IA for ink

analysis, notably focussing on fountain pen inks for the first time.

 

 

Author (s) Details

Ozlem Simsek
Chemical Engineering Department, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey and Graduate School of Addiction and Forensic Sciences, Uskudar University, Istanbul, Turkey.

 

S. Selim Seker
Electrical-Electronics Engineering Department, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey and Electrical and Electronics Engineering Department, Bogazici University, Istanbul, Turkey.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v10/5132

 

Artificial Intelligence in Radiology and Pathology: Transforming Medical Imaging Interpretation | chapter 5 | Medical Science: Recent Advances and Applications Vol. 4

Artificial intelligence (AI) has revolutionised the fields of radiology and pathology, significantly enhancing the accuracy, efficiency, and accessibility of medical imaging interpretation. AI-driven algorithms, particularly deep learning and machine learning models, have demonstrated remarkable capabilities in detecting, classifying, and segmenting pathological findings in medical images, including X-rays, CT scans, MRIs, and histopathological slides. These advancements not only aid in early disease detection and diagnosis but also facilitate workflow optimisation, reducing radiologists' and pathologists' workload. Furthermore, AI-driven predictive models contribute to precision medicine by enabling personalised treatment plans. However, challenges such as data privacy, ethical concerns, and the need for robust validation limit widespread clinical adoption. This review explores the current applications of AI in radiology and pathology, its impact on diagnostic accuracy, and the challenges that must be addressed for seamless integration into clinical practice.

 

Author (s) Details

Divyesh Goswami

Department of Pathology, Nootan Medical College and Research Center, Visnagar, Gujarat, India.

 

Amar C Sajjan
Department of Microbiology, Chalmeda Anand Rao Institute of Medical Sciences, Karimnagar, Telangana, India.

 

Ruchi Yadav
Department of Pathology, Maharishi Chyawan Government Medical College Koriyawas, Mahendergarh, Narnaul, Haryana, India.

 

Nipa Hathila
Department of Radiodiagnosis, Pacific Medical College and Hospital, Udaipur, Rajasthan, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/msraa/v4/5423

Comparison of Image Analysis Techniques for the Determination of the Influence of Melt Flow on the Solidification Microstructure of Technical Aluminum Alloys | Chapter 5 | | Proceedings of the 8th International Conference on Solidification and Gravity

This work aims to contribute to the improvement of automated analysis of 2D microstructures in technical aluminum alloys by evaluating the effectiveness of various microstructural image analysis methods: Traditional analysis techniques for 2D images and cutting-edge computer vision deep learning-based methods (Mask Region-based Convolutional Neural Networks or Mask R-CNN [1], a model used to detect the position and exact shape of objects) are compared for detecting, classifying, and quantifying microstructural features in technical iron containing aluminum alloys. Determining that deep learning-based methods lead to comparable or better results would allow for such models to be used for analysis of the chosen microstructural features. This could improve detection accuracy and speed, as well as measurement reproducibility.

The focus is on detecting and characterizing Fe-containing intermetallic phases that precipitate during solidification, with the aim of examining the effects of increased iron content on the microstructure of these alloys. Therefore, the microstructure of directionally solidified hypoeutectic AlSi6Cu4Fe1 and hypereutectic AlSi6Cu4Fe2 alloys is analyzed with regards to area fraction and size of the intermetallic precipitates within two distinct sample processing zones: the first half of the samples was solidified under diffusive mass transport conditions and the second under forced convective conditions, both in microgravity. This dual approach allows for the evaluation of the impact of convection on the microstructure of aluminum alloys with elevated iron content.

 

Author (s) Details

 

Golo Zimmermann
RWTH Aachen University, Foundry Institute, Intzestraße 5, 52072 Aachen, Germany.

 

Sonja Steinbach
Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany.

Laszlo Sturz
ACCESS e.V., Intzestraße 5, 52072 Aachen, Germany.

 

Alexandre Viardin
ACCESS e.V., Intzestraße 5, 52072 Aachen, Germany.

 

Angelos Theofilatos
ACCESS e.V., Intzestraße 5, 52072 Aachen, Germany.

 

Florian Kargl
RWTH Aachen University, Foundry Institute, Intzestraße 5, 52072 Aachen, Germany and Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49473-95-9/CH5

Monitoring the Respiratory Rate in the Premature Newborn by Analyzing Thermal Images| Chapter 10 | Novel Perspectives of Engineering Research Vol. 5

 The prospect of using a non-invasive approach to monitor respiratory rate in premature neonates is investigated in this work. The technologies used to monitor new-born breathing are now difficult to operate and do not produce good findings. The application of thermographic approach in non-invasive respiration monitoring represents the goals of this paper. Premature newborns' respiration rates could be monitored by analysing recorded thermal pictures. Because it is a non-invasive procedure, the thermographic technique was used. The determination of temperature variations in the nostrils induced by breathing (inhale/exhale) is utilised to monitor in this scenario. The rate of inspiration/expiration was calculated based on temperature differences from video images centred on the region of the nostrils with a period of 1 minute. Based on the findings, we believe that IR thermography is a technology that can be combined into a complicated system that can be used to monitor a newborn's respiration rate.

Author(S) Details

Catalina Luca
Department of Biomedical, “Grigore T. Popa” University of Medicine and Pharmacy, Romania.

Doru Andritoi
Department of Biomedical, “Grigore T. Popa” University of Medicine and Pharmacy, Romania.

Calin Corciova
Department of Biomedical, “Grigore T. Popa” University of Medicine and Pharmacy, Romania.

View Book:- https://stm.bookpi.org/NPER-V5/article/view/5326