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Clinical diagnostics is a fundamental course required for clinical medical students and serves as a prerequisite for several advanced clinical subjects. However, recent observations indicate a decline in interest among eight-year clinical medicine students at Peking Union Medical College regarding clinical diagnostics courses. Instead, these students seem to prioritize the publication of high-impact articles and involvement in scientific research over their medical coursework, leading to a lack of sufficient attention to clinical diagnostics. In the clinical diagnostics course conducted in the first half of 2024, our objective was to engage medical students by presenting the subject matter in an interesting and relevant manner. We curated textual information regarding the health condition of Lin Daiyu, the protagonist from the Chinese literary classic "The Dream of the Red Chamber," and encouraged students to deduce potential diseases she may have experienced based on the original text. Additionally, we sourced historical photographs of Empress Dowager Cixi from the Qing Dynasty, which facilitated the hypothesis that she likely suffered from goiter. These images were employed as a practical examination question during the mid-semester assessment to evaluate the students' proficiency in conducting neck physical examinations. Furthermore, we shared an inspiring anecdote about healthcare professionals who repurposed potato chip packaging into stethoscopes during the COVID-19 pandemic, underscoring the critical role of physical diagnosis and examination in urgent situations. Following the mid-term exam in clinical diagnosis, a questionnaire survey was administered to the medical students who participated in the examination. The results indicated that 93% of the students found the question regarding Lin Daiyu to be highly engaging, while 89% found the question about Empress Dowager Cixi equally captivating. These innovative teaching strategies significantly enhanced the medical students' enthusiasm for learning clinical diagnostics.

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The colorization of grayscale images can, nowadays, take advantage of recent progress and the automation of deep-learning techniques. From the media industry to medical or geospatial applications, image colorization is an attractive and investigated image processing practice, and it is also helpful for revitalizing historical photographs. After exploring some of the existing fully automatic learning methods, the article presents a new neural network architecture, Hyper-U-NET, which combines a U-NET-like architecture and HyperConnections to handle the colorization of historical black and white aerial images. The training dataset (about 10,000 colored aerial image patches) and the realized neural network are available on our GitHub page to boost further research investigations in this field.

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Curling disease in long historical photos significantly affects the presentation of cultural heritage information. However, people lack attention to the formation process and microstructural changes of photo curling. In this article, a long historical photo (1912-1949 AD) collected by the Second Historical Archives of China was taken as the research object, and the formation process and cause of the curling were further explored. Firstly, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), X-ray energy disperse spectrometer (EDS), and other instruments were used to analyze the material composition of the long historical photo. It was found that the photographic paper was made of gelatin, barium sulfate, and plant fiber layers. Then, the effects of hygrothermal environments on curling and contraction in the gelatin layer and simulated photographic paper were explored. Meanwhile, the formation process and main influence factors of the curling were preliminarily revealed. The morphological analysis by SEM was carried out to identify the inner correlation between the microstructure and curling of photos. Finally, the possible formation cause of photo curling was analyzed. This study provides a scientific basis and experimental data for the preservation and restoration of long historical photos based on gelatin.

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Hundreds of oil wells were drilled along Oil Creek in Pennsylvania in the mid-1800s, birthing the modern oil industry. No longer in operation, many wells are now classified as abandoned, and, due to their age, their locations are either unknown or inaccurately recorded. These historic-well sites present environmental, safety, and economic concerns in the form of possible methane leaks and physical hazards. Airborne magnetic and LiDAR surveys were conducted in the Pioneer Run watershed in Oil Creek State Park to find abandoned wells in a historically significant but physically challenging location. Wells were drilled in this area prior to modern geolocation and legal documentation. Although a large number of old wells were abandoned summarily without remediation of the site, much of the land area within Oil Creek State Park is now covered in trees and dense underbrush, which can obscure wellheads. The thick vegetation and steep terrain limited the possibility of ground-based surveys to easily find well sites for methane emissions studies. The data from remote sensing surveys were used to corroborate potential well locations from historic maps and photographs. Potential well sites were verified in a ground-based field survey and monitored for methane emissions. Two historic photographs documenting oil activity in the late 1800s were georeferenced using a combination of magnetic and LiDAR data. LiDAR data, which were more useful in georeferencing and in field verification, identified 290 field locations in the Pioneer Run watershed, 86% of which were possible well sites. Sixty-two percent of the ground-verified wells remained unplugged and comprised the majority of leaking wells. The mean methane emissions factor for unplugged wells was 0.027 ± 0.099 kg/day, lower than other Appalachian Basin methane emissions estimates. LiDAR was used for the first time, in combination with an airborne magnetic survey, to reveal underground oil industry features and inform well identification and remediation efforts in difficult-to-navigate regions. In the oldest oil fields, where well casing has been removed or wood conductor casing was installed, historic photographs provide additional lines of evidence for oil wells where ground disturbances have concealed surface features. Identification of well sites is necessary for mitigation efforts, as unplugged wells emit methane, a potent greenhouse gas.

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RESUMEN

BACKGROUND: Hundreds of oil wells were drilled along Oil Creek in Pennsylvania in the mid-1800s, birthing the modern oil industry. No longer in operation, many wells are now classified as abandoned, and, due to their age, their locations are either unknown or inaccurately recorded. These historic well sites present environmental, safety, and economic concerns in the form of possible methane leaks and physical hazards. METHODS: Airborne magnetic and LiDAR surveys were conducted in the Pioneer Run watershed in Oil Creek State Park to find abandoned wells in a historically significant but physically challenging location. Wells were drilled in this area prior to modern geolocation and legal documentation. Although a large number of old wells were abandoned summarily without remediation of the site, much of the land area within Oil Creek State Park is now covered in trees and dense underbrush, which can obscure wellheads. The thick vegetation and steep terrain limited the possibility of ground-based surveys to easily find well sites for methane emissions studies. The data from remote sensing surveys were used to corroborate potential well locations from historic maps and photographs. Potential well sites were verified in a ground-based field survey and monitored for methane emissions. RESULTS: Two historic photographs documenting oil activity in the late 1800s were georeferenced using a combination of magnetic and LiDAR data. LiDAR data, which were more useful in georeferencing and in field verification, identified 290 field locations in the Pioneer Run watershed, 86% of which were possible well sites. Sixty-two percent of the ground-verified wells remained unplugged and comprised the majority of leaking wells. The mean methane emissions factor for unplugged wells was 0.027 ± 0.099 kg/day, lower than other Appalachian Basin methane emissions estimates. CONCLUSIONS: LiDAR was used for the first time, in combination with an airborne magnetic survey, to reveal underground oil industry features and inform well identification and remediation efforts in difficult-to-navigate regions. In the oldest oil fields, where well casing has been removed or wood conductor casing was installed, historic photographs provide additional lines of evidence for oil wells where ground disturbances have concealed surface features. Identification of well sites is necessary for mitigation efforts, as unplugged wells emit methane, a potent greenhouse gas.

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Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+.