Aichi Prefectural University

RoboCup consists of multiple leagues, each with distinct rules and tasks, which makes it difficult for general audiences to understand the matches. To address this issue, we propose a method for automatically generating narrated highlight videos that convey match information in an accessible manner. Specifically, we utilize a large multimodal model to extract and summarize match content from videos, generate descriptive texts, and synthesize narration for inclusion in the final video. Subjective evaluations revealed issues such as mismatches between narration and visuals and insufficient extraction of key scenes. However, the proposed method demonstrated appeal in terms of entertainment value.

BackgroundSport injuries are now becoming a major issue affecting training in paddle sports. This study was to investigate the distribution and prevalence of injuries among adolescent flat-water kayak and canoe athletes.MethodsWe performed a retrospective design study to survey one-hundred forty Chinese elite adolescent flat-water kayak and canoe athletes (89 kayakers and 51 canoers; 81 males and 59 females, with an average age of 16 years) using a self-reported questionnaire. The questionnaire investigated basic information and kayak- and canoe-related injuries over the past year. The primary outcome measures were the distribution of kayak- and canoe-related injuries and the injury rate per 1,000 training hours with a 95% confidence interval (CI).ResultsA total of 207 injuries were reported from all the participants, including 138 injuries related to flat-water kayak and 69 injuries related to canoe. The most common injured site in flat-water kayak athletes was the lower back, followed by the shoulder, wrist, and knee. In flat-water canoe athletes, the most common injured site was the shoulder, followed by the lower back, back, and knee. Regarding injury rates, the flat-water kayak athletes showed 0.90 injuries per 1,000 training hours (95% CI = 0.75–1.05). The female kayak athletes had 1.02 injuries per 1,000 training hours (95% CI = 0.81–1.23), while the male kayak athletes had 0.74 injuries per 1,000 training hours (0.53–0.95). Notably, the injury rate per 1,000 training hours was significantly higher in female canoe athletes (1.51, 95% CI = 1.05 to 1.98) than in male canoe athletes (0.47, 95% CI = 0.30 to 0.64).ConclusionThe findings indicated that flat-water kayak- and canoe-related injuries mostly involved in the shoulder and lower back among Chinese elite adolescent athletes, with female athletes, particularly in canoe, being more susceptible to injury. These insights are critical for enhancing athlete injury prevention strategies and providing coaches and athletes with comprehensive reference data on injury.

If scattered X-rays carry information that is independent of that is carried by primary X-rays, the accuracy of attenuation coefficients estimated using both primary and scattered X-rays is expected to be better than that estimated using only primary X-rays. However, because scattered X-rays cannot be easily introduced into conventional X-ray computed tomography (CT), the issue has gained scant attention. This study demonstrates theoretically that the measurement of scattered X-rays improves the accuracy of reconstruction in CT, even in a photoelectric absorption scenario. Here, the CT geometry was simplified for a system that targeted a homogeneous thin cylinder, retaining the necessary configuration. Furthermore, we constructed a mathematical model termed the π-junction model. This model is an extension of the T-junction model used in one of our previous studies. It addresses the photoelectric effect, which was not considered in the T-junction model. The variance in the estimation of the attenuation coefficients of this model from the measurements of both primary and scattered photons was evaluated as the Cramer-Rao lower bound. Both the theory and numerical experiments using Monte Carlo simulation showed that the accuracy of estimating the attenuation coefficient could be improved by measuring the scattered X-rays together with the primary X-rays, even in the presence of photoelectric absorption. This result provides a basis for the superiority of using scattered X-rays.

Anemia is one of the most common social problems, as one in ten people is said to be anemic. There are several methods to diagnose anemia, such as color observation of the eyelid conjunctiva and blood sampling, but both require specialized knowledge and are not easy to perform in terms of both time and cost. <br/> In this paper, we propose a new method for estimating anemia from facial images captured by a hyperspectral camera. First, the spectrum of the eyelid conjunctiva region is obtained from the hyperspectral image. Next, the hemoglobin concentration is estimated by finding the ratio of reflectance in a specific wavelength band. <br/>Experiments with different water contents of blood-like samples confirmed the possibility of concentration estimation.

Over the years, research in the field of cultural heritage preservation and document analysis has exponentially grown. In this study, we propose an advanced approach for non-destructive estimation of paper fibers using macro images. Expanding on studies that implemented EfficientNet-B0, we explore the effectiveness of six other deep learning networks, including DenseNet-201, DarkNet-53, Inception-v3, Xception, Inception-ResNet-v2, and NASNet-Large, in conjunction with enlarged patch sizes. We experimentally classified three types of paper fibers, namely, kozo, mitsumata, and gampi. During the experiments, patch sizes of 500, 750, and 1000 pixels were evaluated and their impact on classification accuracy was analyzed. The experiments demonstrated that Inception-ResNet-v2 with 1000-pixel patches achieved the highest patch classification accuracy of 82.7%, whereas Xception with 750-pixel patches exhibited the best macro-image-based fiber estimation performance at 84.9%. Additionally, we assessed the efficacy of the method for images containing text, observing consistent improvements in the case of larger patch sizes. However, limitations exist in background patch availability for text-heavy images. This comprehensive evaluation of network architectures and patch sizes can significantly advance the field of non-destructive paper analysis, offering valuable insights into future developments in historical document examination and conservation science.

This paper focuses on traditional deep learning-based no-reference (or reference-based) image quality assessment (IQA) methods, enhancing them from the perspective of image feature extraction. It replaces the VGG16 network with the ResNet50 network for feature extraction and uses the Global Average Pooling (GAP) layer instead of FC512. Subsequently, it computes the weighted average of quality scores for different parts of the image to obtain the overall image quality. Specifically, the paper first preprocesses images by cropping, flipping, mirroring, tilting, and other methods to expand the image dataset and make it more reflective of real-world scenarios. Then, it utilizes the ResNet50 network for feature extraction, showing superior performance compared to the VGG network. Finally, a weighted pooling method is employed to derive the ultimate image score. On the TID2013 and CLIVE datasets, the Pearson Linear Correlation Coefficient (PLCC) values are 0.877 and 0.7095, respectively, while the Spearman Rank Order Correlation Coefficient (SROCC) values are 0.8510 and 0.6956. These values surpass those obtained using traditional algorithms like SSIM and GSMD, indicating the superior predictive performance of the new algorithm. Moreover, the proposed algorithm demonstrates advantages in speed and accuracy, meeting real-time application requirements more effectively.

BackgroundFrailty is one of the most significant issues related to human aging. Although studies have confirmed the association of mental and cognitive disorders with frailty, the association might be influenced by age, since oldest-old adults are more likely to have adverse health outcomes. Thus, this study aimed to examine independent and combined associations of mental health and cognitive function with frailty in oldest-old adults using data from the Chinese Longitudinal Healthy Longevity Survey in 2018.MethodsA sum of 6,891 and 3,171 older adults aged 80 and older were included in this study when analyzing the association of depression and cognitive impairment with frailty, respectively. Frailty was measured by the Study of Osteoporotic Fractures frailty index, depression was assessed by the Center for Epidemiologic Studies Depression Scale, and cognitive impairment was evaluated by the Chinese version of modified Mini-Mental State Examination. Independent sample t-test, Chi-square tests, and logistic regression analyses were used to examine the associations of depression and cognitive impairment with frailty.ResultsOlder persons with depression or cognitive impairment had a higher chance of frailty. The adjusted odds ratio (OR) of frailty was 1.27 (95% CI: 1.01, 1.59, p = 0.044) in those with depression, and 1.85 (95% CI: 1.14, 3.01, p = 0.013) in those with cognitive impairment. Compared to adults who had neither depression nor cognitive impairment, those with either depression or cognitive impairment, and those with both depression and cognitive impairment had a significantly higher likelihood of frailty (adjusted OR: 1.61, 95% CI: 1.07, 2.41; and adjusted OR: 4.03, 95% CI: 2.05, 7.94).ConclusionsThe findings suggest that depression and cognitive impairment are associated with frailty. The concurrence of depression and cognitive impairment has an additive effect on frailty in oldest-old population.