Foreign Object Research Articles

ANALYSIS OF ACCIDENTS DURING DRILLING WITH FLOATING DEVICES IN THE CASPIAN SEA

At drilling of chinks on areas Gunashli, May 28, Bahar, Neft Dashları, Bulla with density of a chisel solution above (1.9-2,0)·104 N/m3 on depth 2000÷4500 m with balance in system the chink-layer is observed decrease in density of a chisel solution to 1,25÷1,30 g/sm3 with the subsequent open gas fountains. The gas getting in chink by diffusion basically comes to light at washing after lifting and descent of the boring tool. In trenches there are elements of the aerated solution, which density a little above before the circulating. Diffusion is the phenomenon caused by movement of molecules. A principal cause of diffusion of gas is presence of a gradient of concentration. Receipt of gas with diffusion from a layer in a chink will take place until gas molecules goes from a zone with high concentration in zones with low concentration, i.e. in chink. Getting to a chisel solution, diffusion gas, go up together with solutions and passing from zones of high pressures in zones low, extends, therefore its density decreases, that, in turn, conducts to decrease in hydrostatic pressure in a face of a chink and to serious complications. The article examines and analyzes the main causes of complications and accidents that arise during the construction, operation and repair of oil and gas wells, and leading to gas and oil shows, open fountains and fires in the fields of Azerbaijan. The ways in which complications arise during the extraction of hydrocarbons are briefly discussed. The conditions and nature of the occurrence of complications leading to accidents, gas manifestations, open fountains and fires are analyzed. Based on an analysis of the complications that arise during the construction, operation and repair of oil and gas wells, technologies and technical means are presented that make it possible to improve the quality of repair work carried out on oil and gas wells during their construction and operation. Equipment and tools for emergency response are considered. Technologies are proposed for eliminating accidents associated with the breakage and fall of downhole tool equipment onto the bottom, with cleaning the bottom of foreign objects, and with the elimination of stuck pipes. Modern technologies for eliminating gas shows and open fountains complicated by a fire at the mouth of a gushing well are proposed. In a chink it is required to make washing of a chisel solution for prevention of complications with closed preventer with dispersing of a boring column. Keywords: well, manifestation, gas, diffusion, pressure, washing solution.

Preserving differential privacy in neural networks for foreign object detection with heterogeneity-based noising among distributed devices

Preserving differential privacy in neural networks for foreign object detection with heterogeneity-based noising among distributed devices

Few incidentally found interesting foreign objects in human body: a case series

Foreign bodies are objects that do not typically belong in the human body but can be ingested, inserted, or entered due to injuries. This article presents various cases and examples of foreign bodies, including objects swallowed, objects inserted into the rectum, vagina, urethra, ear, and nose, or due to injuries caused by falls, puncture wounds, and gunshot wounds. Foreign bodies can be difficult to detect, particularly if they are not inherently radio-opaque, and may be overlooked by patients who cannot provide an adequate history. These foreign bodies may cause harm to the patient. Interpretation is done on radiographs, computed tomography (CT), Ultrasonography (USG), and magnetic resonance imaging (MRI) studies. Most foreign objects pass through the gastrointestinal tract without problem; sharp and elongated objects can cause significant injury, and even if they only partially perforate the bowel wall, they can produce chronic inflammatory processes that produce symptoms months or years later. Hence, searching for foreign bodies should be done throughout the gastrointestinal tract, particularly in children and people with mental illness who are more likely to swallow multiple items more than once. Although rare, various materials can be left behind in the body of a patient after surgery, including large and small wire sutures, surgical drains, and retained sponges, which can cause potential complications and foreign body reactions. This article highlights the importance of being aware of the presence of foreign bodies in clinical practice, and a thorough search should be carried out using different modalities, especially CT. Great suspicion and early diagnosis of foreign bodies can avoid potential complications and morbidity. In general, it provides information on the diagnosis and treatment of various types of foreign bodies.

Nondestructive Inspection and Quantification of Select Interface Defects in Honeycomb Sandwich Panels.

Honeycomb sandwich panels are utilized in many industrial applications due to their high bending resistance relative to their weight. Defects between the core and the facesheet compromise their integrity and efficiency due to the inability to transfer loads. The material system studied in the present paper is a unidirectional carbon fiber composite facesheet with a honeycomb core with a variety of defects at the interface between the two material systems. Current nondestructive techniques focus on defect detectability, whereas the presented method uses high-frequency ultrasound testing (UT) to detect and quantify the defect geometry and defect type. Testing is performed using two approaches, a laboratory scale immersion tank and a novel portable UT system, both of which utilize only single-side access to the part. Coupons are presented with defects spanning from 5 to 40 mm in diameter, whereas defects in the range of 15-25 mm and smaller are considered below the detectability limits of existing inspection methods. Defect types studied include missing adhesive, unintentional foreign objects that occur during the manufacturing process, damaged core, and removed core sections. An algorithm is presented to quantify the defect perimeter. The provided results demonstrate successful defect detection, with an average defect diameter error of 0.6 mm across all coupons studied in the immersion system and 1.1 mm for the portable system. The best accuracy comes from the missing adhesive coupons, with an average error of 0.3 mm. Conversely, the worst results come from the missing or damaged honeycomb coupons, with an error average of 0.7 mm, well below the standard detectability levels of 15-25 mm.

Stromal-cellular remodeling of breast tissue after silicone implant damage

One of the current vectors of modern reconstructive and cosmetic surgery is augmentation mammoplasty - an operation to enlarge the mammary glands. The breast implant is well tolerated by the body, as the immunogenicity of its material is extremely low. At the same time, the silicone gel of the implant is a foreign object and the surrounding breast tissues give a stereotypical response in the form of growth of the connective tissue stroma with the formation of a capsule around the implant and the appearance of focal cellular infiltrates. Modern breast implants, unlike implants of previous decades, are more sophisticated in their structure, and the level of protection against damage is much higher. A complication associated with the long-term use (up to 20 years) of implants, where low-quality silicone or industrial silicone was used as a filler, is the penetration of low-molecular-weight particles of the implant through its shell to the surrounding tissues with the subsequent formation of silicone granules. In the vast majority, such changes develop after mechanical traumatization of the implant. The purpose of the study is to establish the morphological criteria of the reaction of breast tissue after silicone implant damage. The material for research was histological micropreparations of breast tissue from the archive of the "Grace Clinic" (Kyiv), with the mutual consent of patients who applied for repeated mammoplasty. The duration of the implants in the breast gland was from 6 to 20 years. The age range of the women was from 36 to 53 years. On preparations stained with hematoxylin and eosin, tissue biopsies of the glands of 9 patients with a diagnosis of deformation of silicone implants and violation of the integrity of their wall were presented. The micropreparations were visualized using a UlabXSP-137TLED light microscope at various magnifications and photographed with an XCAM 1080P camera. The study of histological preparations of soft tissues of the breast glands, from which silicone implants were removed, established changes in stromal-cellular elements, namely: decorated fibrous structures accumulated in the form of dense capsules with elements of local contractures; from the side of cellular elements, the most demonstrative were cellular infiltrates, which included macrophages, lymphocytes, and plasma cells, which indicated a high probability of a chronic inflammatory process. A large number of giant multinucleated cells of foreign bodies were present in practically all examined sections, which indicated the impossibility of macrophages to complete phagocytosis with lysis of the phagocytosed material, which by its chemical composition is the silicone of the implant. All the described phenomena can be interpreted as a natural reaction of the tissues to the implant, the contents of which got into the soft tissues of the breast. Thus, the presence of numerous giant multinucleated cells of foreign bodies, dense capsules with elements of local contractures, and a significant predominance of macrophages, lymphocytes, and plasma cells in cellular infiltrates over other cellular elements can be considered a morphological criterion for the reaction of breast tissue to silicone implant damage.

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly.

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

Foreign object damage characteristics of a thin nickel-based superalloy plate at room and high temperatures

Turbine blades with thin-walled structures usually works in harsh environments, and foreign object damage (FOD) is one of the conditions of special concern. In this paper, the FOD characteristics of thin nickel-based superalloy plates are studied by a combination of experimental, numerical and analytical methods, considering room and high temperatures, different impact conditions and plate thicknesses. An easy-to-use test system is developed to realize high speed impact of the thin nickel-based superalloy plate under elevated temperature. Crater morphologies, internal microstructure, and residual stress are analyzed after impact with different conditions. Numerical simulation of the impact process is performed by using Johnson-Cook (J-C) constitutive model. Based on Hertz theory, an analytical method for calculating the crater length and depth is proposed considering the deformation of the impact steel sphere. Results shows that the FOD characteristics at high temperature is significantly different from that at room temperature. The crater has lager dimensions under high speed and elevated temperature. Moreover, significant grain refinement is obvious and the dislocation layer is also thicker at higher speed and higher temperature. Due to the effect of high temperature softening, hardness and residual stress after impact with elevated temperature is lower than that at room temperature. Besides, non-normal impact mainly influences Goss texture and distribution of residual stress after high temperature impact. In addition, it is found that thickness have a significant effect on the FOD characteristics especially when the plate is thinner. The validity of the numerical model and analytical method is proved by comparing with the experimental results. The present study can provide data foundation and numerical analysis support for the damage assessment and maintenance of turbine blades.

Differences in inhibitory control in two species of Tanganyikan bower‐building cichlids contrasting in building flexibility

AbstractA central challenge in understanding the evolution of cognition is the ability to compare a set of species differing in a trait of interest while being ecologically and phylogenetically close. Here, we examine whether differences in bower‐building flexibility are related to differences in cognitive flexibility between two Tanganyikan cichlids. Cognitive flexibility enables animals to modify their decision rules when faced with new situations, and inhibitory control, the ability to inhibit a normally favoured response, is an essential component of this capacity. We tested male Aulonocranus dewindti and Cyathopharynx furcifer in a choice‐against‐preference paradigm. Both species clean their bowers of foreign objects and we found that both preferred to remove a snail shell over a stone. We tested their ability to modify this preference and learned to preferably select the stone instead of the shell. Although neither species showed clear learning of the new preference rule, both demonstrated inhibitory control through increased decision times and manipulations of the objects when selecting the stone. Specifically, A. dewindti, the species exhibiting greater behavioural flexibility in the construction of their bowers, selected the stone in fewer trials than C. furcifer, providing support for a link between behavioural flexibility in bower construction and cognitive flexibility.

Button battery in the nose: dangerous foreign object

Button battery in the nose: dangerous foreign object

Safety and Security-Specific Application of Multiple Drone Sensors at Movement Areas of an Aerodrome

Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.

Complicated magnetic foreign objects of the gastrointestinal tract: two clinical cases

Complicated magnetic foreign objects of the gastrointestinal tract: two clinical cases

A visual foreign object detection system for wireless charging of electric vehicles

Wireless charging of electric vehicles can be achieved by installing a transmitter coil into the ground and a receiver coil at the underbody of a vehicle. In order to charge efficiently, accurate alignment of the charging components must be accomplished, which can be achieved with a camera-based positioning system. Due to an air gap between both charging components, foreign objects can interfere with the charging process and pose potential hazards to the environment. Various foreign object detection systems have been developed with the motivation to increase the safety of wireless charging. In this paper, we propose a foreign object detection technique that utilizes the integrated camera of an embedded positioning system. Due to operation in an outdoor environment, we cannot determine the types of objects that may occur in advance. Accordingly, our approach achieves object-type independence by learning the features of the charging surface, to then classify anomalous regions as foreign objects. To examine the capability of detecting foreign objects, we evaluate our approach by conducting experiments with images depicting known and unknown object types. For the experiments, we use an image dataset recorded by a positioning camera of an operating wireless charging station in an outdoor environment, which we published alongside our research. As a benchmark system, we employ YOLOv8 (Jocher et al. in Ultralytics YOLO, 2023), a state-of-the-art neural network that has been used in various contexts for foreign object detection. While we acknowledge the performance of YOLOv8 for known object types, our approach achieves up to 18% higher precision and 46% higher detection success for unknown objects.

A System of Image Recognition-Based Railway Foreign Object Intrusion Monitoring Design

A System of Image Recognition-Based Railway Foreign Object Intrusion Monitoring Design

Analisis Kecelakaan Kerja Menggunakan Metode Hazard and Operability Study (HAZOP) Pada UKM Peuyeum Bandung 1

The process of making peuyeum carried out by UKM Peuyeum Bandung 1 is still done manually, where it is done for a long time and in the same position and the equipment used by UKM Peuyeum Bandung 1 does not meet occupational health and safety standards. In carrying out this work there may be risks. The aim of this research is to minimize risks in making peuyeum by UKM Peyeum Bandung 1. The type of research carried out is quantitative research. The conclusion of the research on risk identification and work safety at UKM Peuyeum Bandung 1 is that the results of hazard identification using HAZOP analysis at UKM Peuyeum Bandung 1 show that there are 5 sources of danger, namely worker attitudes, foreign objects in the work area, stove, peeler and washing area. One of the processes at the source of worker attitude hazards is getting an "Extreme" rating so that it must be prioritized to get recommendations or suggestions for improvement first. By using the HAZOP analysis method, you can socialize and create banners on the importance of K3, adding occupational health and safety equipment to SMEs, especially gloves, safety shoes and masks.

Effect of impact-corrosion coupling damage on fatigue properties of 2198-T8 aluminum-lithium alloy

The peeling of aircraft surface paint due to foreign object impact can expose the aluminum–lithium alloy substrate to atmospheric corrosion, resulting in the fatigue performance being affected by the combined effect of impact damage and corrosion damage. In this work, the effects of impact damage and corrosion damage on the fatigue life of 2198-T8 aluminum–lithium alloy sheet were investigated. The strain field distribution of specimens with coupled impact and corrosion damage under cyclic loading was analyzed by Digital Image Correlation (DIC) technique, and the coupling relationship between impact and corrosion damage was analyzed based on the crack propagation path and fracture morphology. The results show that surface corrosion pits depth distribution of impact dents follows the same trend as residual stress distribution. For the coupling damage of the hemispherical punch, the coupled effect between impact damage and corrosion damage is most evident at low impact energy. At higher impact energies, the impact damage dominates the coupling damage. As the salt spray corrosion time increases, the fatigue life decrease rate gradually slows with increasing impact energy, and the impact damage has a greater effect on the fatigue life in the early corrosion stage. Compared to the salt spray corrosion environment at 35 °C, the fatigue life of the specimen at 25 °C increased by a maximum of 50.48 % as the impact energy increased from 10 J to 25 J.

AdvancingTire Safety: Explainable Artificial Intelligence-Powered Foreign Object Defect Detection with Xception Networks and Grad-CAM Interpretation

Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.

Automated Foreign Object Detection for Carbon Fiber Laminates Using High-Resolution Ultrasound Testing.

Carbon fiber laminates have become popular in the manufacturing industry for their many desirable properties, like good vibration damping, high strength-to-weight ratio, toughness, high dimensional stability, and low coefficient of thermal expansion. During the manufacturing process, undesirable foreign objects, such as peel-ply strips, gloving material, and Kapton film, can be introduced into the part which can lead to a localized weakness. These manufacturing defects can function as stress concentration points and oftentimes cause a premature catastrophic failure. In this study, a method using high-resolution pulse-echo ultrasound testing is employed for the detection and quantification of the dimensions of foreign object debris (FOD) embedded within carbon fiber laminates. This research presents a method to create high-resolution C-scans using an out of immersion tank portable housing ultrasound scanning system, with similar capabilities to that of a full immersion system. From the full-waveform dataset, we extract the FOD depth and planar dimensions with an automatic edge detection technique. Results from several carbon fiber laminates are investigated with embedded foreign objects that are often considered undetectable. Results are presented for FOD identification for two different shapes: circles with diameters ranging from 7.62 mm to 12.7 mm, and 3-4-5 triangles with hypotenuses ranging from 7.6 mm to 12.7 mm. CT imaging is used to confirm proper FOD placement and that the FOD was not damaged or altered during manufacturing. Of importance for the ultrasound inspection results, in every single case studied, the FOD is detected, the layer depth is properly identified, and the typical error is less than 1.5 mm for the primary dimension.

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

A Deep Neural Network for Road Extraction with the Capability to Remove Foreign Objects with Similar Spectra

Abstract. Existing road extraction methods based on deep learning often struggle with distinguishing ground objects that share similar spectral information, such as roads and buildings. Consequently, this study proposes a dual encoder-decoder deep neural network to address road extraction in complex backgrounds. In the feature extraction stage, the first encoder-decoder designed for extracting road features. The second encoder-decoder utilized for extracting building features. During the feature fusion stage, road features and building features are integrated using a subtraction method. The resultant road features, constrained by building features, enhance the preservation of accurate road feature information. Within the feature fusion stage, road feature maps and building feature maps designated for fusion are input into the convolutional block attention module. This step aims to amplify the features of different channels and extract key information from diverse spatial positions. Subsequently, feature fusion is executed using the element-by-element subtraction method. The outcome is road features constrained by building features, thus preserving more precise road feature information. Experimental results demonstrate that the model successfully learns both road and building features concurrently. It effectively distinguishes between easily confused roads and buildings with similar spectral information, ultimately enhancing the accuracy of road extraction.

Diagnosis of post-traumatic arteriovenous fistula on the back of the tongue: the importance of ultrasonography

Arteriovenous fistulas (AVF) are vascular anomalies that can occur after trauma in different parts of the body, including the oral cavity, these abnormalities can trigger several consequences for the affected individual. Such abnormality can be classified as acquired, usually after some trauma or hemodialysis, and congenital. Its diagnosis, in turn, is essential for the management of the patient, avoiding clinical complications. In the oral cavity, although rare, it can affect, especially, the dorsum of the tongue. Ultrasonography is an imaging technique that can be useful in diagnosing AVF in the tongue. This technique allows visualizing vascular structures in real time, assessing blood flow and identifying anomalies such as AVFs. The present study aims to report a case of a 39-year-old female patient who presented a post-traumatic AVF caused by a foreign object in the region of the right dorsum of the tongue. During the professional evaluation, it was possible to use color Doppler ultrasonography to elucidate the presence of venous and arterial flow in the abnormal region, corroborating the clinical findings and being essential to close the diagnosis, denoting the important role of this imaging exam.