Visual Examination and Photography in Failure Analysis

Failure analysis is an investigative process in which the visual observations of features present on a failed component and the surrounding environment are essential in determining the root cause of a failure. This article reviews the basic photographic principles and techniques that are applied to failure analysis, both in the field and in the laboratory. It discusses the processes involved in visual examination, field photographic documentation, and laboratory photographic documentation of failed components. The article describes the operating principles of each part of a professional digital camera. It covers basic photographic principles and manipulation of settings that assist in producing high-quality images. The need for accurate photographic documentation in failure analysis is also presented.

Hybrid laser-arc welding of laser- and plasma-cut 20-mm-thick structural steels

It is already known that the laser beam welding (LBW) or hybrid laser-arc welding (HLAW) processes are sensitive to manufacturing tolerances such as gaps and misalignment of the edges, especially at welding of thick-walled steels due to its narrow beam diameter. Therefore, the joining parts preferably have to be milled. The study deals with the influence of the edge quality, the gap and the misalignment of edges on the weld seam quality of hybrid laser-arc welded 20-mm-thick structural steel plates which were prepared by laser and plasma cutting. Single-pass welds were conducted in butt joint configuration. An AC magnet was used as a contactless backing. It was positioned under the workpiece during the welding process to prevent sagging. The profile of the edges and the gap between the workpieces were measured before welding by a profile scanner or a digital camera, respectively. With a laser beam power of just 13.7 kW, the single-pass welds could be performed. A gap bridgeability up to 1 mm at laser-cut and 2 mm at plasma-cut samples could be reached respectively. Furthermore, a misalignment of the edges up to 2 mm could be welded in a single pass. The new findings may eliminate the need for cost and time-consuming preparation of the edges.

Validation of a Human Pose Tracking Algorithm for Measuring Upper Limb Joints: Comparison With Photography-based Goniometry

Background: Range of motion (ROM) measurements are essential for diagnosing and evaluating upper extremity conditions. Clinical goniometry is the most commonly used methods but it is time-consuming and skill-demanding. Recent advances in human tracking algorithm suggest potential for automatic angle measuring from RGB images. It provides an attractive alternative for at-distance measuring. However, the reliability of this method has not been fully established. The purpose of this study is to evaluate if the results of algorithm are as reliable as human raters in upper limb movements.Methods: Thirty healthy young adults (20 males, 10 females) participated in this study. Participants were asked to performed a 6-motion task including movement of shoulder, elbow and wrist. Images of movements were capture by commercial digital camera. Each movement was measured by a pose tracking algorithm and compared with the surgeon-measurement results. The mean differences between the two measurements were compared. Pearson correlation coefficients were used to determine the relationship. Reliability was investigated by the intra-class correlation coefficients.Results: Comparing this algorithm-based method with manual measurement, the mean differences were less than 3 degrees in 5 motions (shoulder abduction: 0.51; shoulder elevation: 2.87; elbow flexion:0.38; elbow extension:0.65; wrist extension: 0.78) except wrist flexion. All the intra-class correlation coefficients were larger than 0.60. The Pearson coefficients also showed high correlations between the two measurements (p<0.001). Conclusions: Our results indicated that pose estimation is a reliable method to measure the shoulder and elbow angles, supporting RGB images for measuring joint ROM. Our results proved the possibility that patients can assess their ROM by photos taken by a digital camera.Trial registration: This study was registered in the Clinical Trials Center of The First Affiliated Hospital, Sun Yat-sen University (2021-387).

Assessment of Deep Convolutional Neural Network Models for Species Identification of Forensically-Important Fly Maggots Based on Images of Posterior Spiracles

Forensic entomology is the branch of forensic science that is related to using arthropod specimens found in legal issues. Fly maggots are one of crucial pieces of evidence that can be used for estimating post-mortem intervals worldwide. However, the species-level identification of fly maggots is difficult, time consuming, and requires specialized taxonomic training. In this work, a novel method for the identification of different forensically-important fly species is proposed using convolutional neural networks (CNNs). The data used for the experiment were obtained from a digital camera connected to a compound microscope. We compared the performance of four widely used models that vary in complexity of architecture to evaluate tradeoffs in accuracy and speed for species classification including ResNet-101, Densenet161, Vgg19_bn, and AlexNet. In the validation step, all of the studied models provided 100% accuracy for identifying maggots of 4 species including Chrysomya megacephala (Diptera: Calliphoridae), Chrysomya (Achoetandrus) rufifacies (Diptera: Calliphoridae), Lucilia cuprina (Diptera: Calliphoridae), and Musca domestica (Diptera: Muscidae) based on images of posterior spiracles. However, AlexNet showed the fastest speed to process the identification model and presented a good balance between performance and speed. Therefore, the AlexNet model was selected for the testing step. The results of the confusion matrix of AlexNet showed that misclassification was found between C. megacephala and C. (Achoetandrus) rufifacies as well as between C. megacephala and L. cuprina. No misclassification was found for M. domestica. In addition, we created a web-application platform called thefly.ai to help users identify species of fly maggots in their own images using our classification model. The results from this study can be applied to identify further species by using other types of images. This model can also be used in the development of identification features in mobile applications. This study is a crucial step for integrating information from biology and AI-technology to develop a novel platform for use in forensic investigation.

Fluorescent Aptasensor and Colorimetric Aptablot for p-tau231 Detection: Toward Early Diagnosis of Alzheimer’s Disease

The pathology of Alzheimer’s disease (AD), the most common cause of dementia, is considered to be mainly driven by two major hallmarks (tau and amyloid beta). It is highly desirable to develop an affordable medicinal diagnostic that can be utilized worldwide for the early diagnosis of AD. Hence, p-tau231 was selected as a specific target, which appears both in AD serum and cerebrospinal fluid, for the development of a sensing platform for the diagnosis of AD. To the best of our knowledge, these are the first aptamer-mediated biosensors that rely on sensitive fluorescent and colorimetric aptasensors for the rapid monitoring of p-tau231. The nitrogen-doped carbon dot-based turn-on fluorescent aptasensor could rapidly analyze p-tau231 down to 3.64 ng/mL within 40 min, and the colorimetric Cu-enhanced-Au aptablot displayed high sensitivity at 4.71 pg/mL through a digital camera, with visibility to the naked eye down to 8 ng/mL p-tau231 within 140 min. Owing to their advantages, which include affordability, rapidity, high sensitivity, and dependence on complicated instruments, these aptamer-based biosensors offer significant potential for the early diagnosis of AD worldwide.

Topical Therapy of Skin Scabs in Psoriasis with Salicylic Acid: Case Report

The objective was to evaluate the keratolytic and anti-inflammatory action of salicylic acid in the affected area in the case reported. Methods. Clinical photography a digital camera was used Olympus SP570UZ with master software 2.0. The images obtained both in the initial phase without treatment and at the 72 hours of treatment were transferred and stored on a 4-core Sansung computer. The auto-dial adjustment option was selected. In this way, the camera selects the optimal way to take the photo shot. Analysis of the affected skin with scabs. Image J software was used to measure the area selected for the study in both cases. This measurement was made in pixels for the photographic region under study. Results. The image j software program measures the areas in pixels, to decrease in measurement error there was no calibration. That is, the measurement of the areas in pixels was maintained. The percentage ratios of the affected or non-affected areas for both left and right legs are presented in Tables. It is possible to observe the reduction of the affected area. The doubtful areas represent for the left leg 5.21% and for the right leg 30.08% after treatment. Only a clearly visible area with crusts and scabs of 1.60% was observed after treatment.

Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery

An effective crop management practice is very important to the sustenance of crop production. With the emergence of Industrial Revolution 4.0 (IR 4.0), precision farming has become the key element in modern agriculture to help farmers in maintaining the sustainability of crop production. Unmanned aerial vehicle (UAV) also known as drone was widely used in agriculture as one of the potential technologies to collect the data and monitor the crop condition. Managing and monitoring the paddy field especially at the bigger scale is one of the biggest challenges for farmers. Traditionally, the paddy field and crop condition are only monitored and observed manually by the farmers which may sometimes lead to inaccurate observation of the plot due the large area. Therefore, this study proposes the application of unmanned aerial vehicles and RGB imagery for monitoring rice crop development and paddy field condition. The integration of UAV with RGB digital camera were used to collect the data in the paddy field. Result shows that the early monitoring of rice crops is important to identify the crop condition. Therefore, with the use of aerial imagery analysis from UAV, it can help to improve rice crop management and eventually is expected to increase rice crop production.

INFLUENCE OF ALIMENTARY DEPRIVATION ON MORPHOLOGICAL CHANGES OF RAT'S THYROID GLAND

Introduction: Despite the well-studied effect of alimentary deprivation on the body, the literature data on its effect on functional activity and, in particular, on morphological changes in the thyroid gland are single and often contradictory, which does not allow unambiguous conclusions. All this requires a more detailed study of the role and mechanisms of the impact of restricted nutrition on the thyroid gland. Aim: To investigate the effect of alimentary deprivation on morphological changes in the thyroid gland of young rats. Methods: The study was conducted on 24 male Wistar rats aged 3 months. Rats of all groups were in uniform conditions, on a standard diet. Animals of the experimental group, for 28 days, received a diet reduced by 30 %. Work with rats was carried out in accordance with the principles of the Declaration of Helsinki. Histological preparations were made from the central areas of the thyroid tissue according to the standard method. Using a digital camera, the micropreparations were photographed under a Nikon Eclipse E 100 microscope (Japan). Morphometry was performed using a computer program "Image J". Results: Histological analysis of the rat's thyroid gland affected by alimentary deprivation revealed that it had an unchanged physiological structure. The follicles were mostly of oval shape and of various sizes. Colloid in the follicles of experimental animals is of moderate density and contains numerous resorption vacuoles. Thyroid cells are of prismatic and cubic shape. It was found that in the thyroid gland of experimental rats the area of ​​follicles, colloid, their inner diameter decreases, the height of thyrocytes increases, the stereological resorption index increases and the colloid accumulation index decreases, the number of interfollicular islands increases. Also in experimental animals there was a decrease in the width of the interlobar and interfollicular connective tissue. Conclusion: In rats fed on a reduced diet, morphological signs of increased functional activity of the thyroid gland were found.