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Background: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. Methods: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. Results: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. Conclusions: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.

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In most fabric industries fabric quality is assessed through manual inspection, which depends on an individual judgment. It is necessary to design an automatic fabric defect performance inspection system for the industry. This study aimed to develop a real-time, low-cost, and high-performance home textile fabric defect inspection machine system. The proposed system uses the Haar wavelet transform to reduce the information content of the fabric image. The brightness of the fabric image is compensated and the camera luminance is corrected in order to filter the image texture for fabric images with the Gaussian filter after correction. After that, the fabric defect classification was performed by using the random forest classifier. The designed system capability can detect and verify 10 kinds of fabrics with different colors. Moreover, the hardware cost of the machine is low and the average true defect recognition detection rate is more than 98.70%, with good adaptability. Meanwhile, the average processing detection time for a single image is 70 ms with a fabric defect inspection speed of 30 m/min. The efficiency of the machine is increased by five times compared with the traditional inspection. The designed inspection machine can also replace manual grading, cutting, and finishing in the processes of labeling defects. Eventually, it can reduced man power and overall mass production cost, so even small-scale home textile industries can afford a machine with high-precision defect detection.

Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

Introduction:Post-anaesthetic sore throat (PAST) is a well-recognized consequence of tracheal intubation; however, quantitative morphometric measurements remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on laryngeal mucosa change, pre and post-surgery under general anesthesia with intubation. Materials and methods: The laryngeal images were captured and divided into the control group and the intubation group. Image processing techniques were used to quantify the post-extubation laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, and maximum cross-sectional area of the glottic space were determined and calculated. These parameters were analyzed and compared pre and post-surgery. Results: A total of 69 subjects were enrolled in this study, comprising 32 subjects in the healthy group and 37 subjects in the intubation group. The color space and texture analysis with contrast and correlation profiles all shows trend toward higher measures in the intubation group than in the healthy group, with statistical significance and outstanding discrimination ability, especially in the interarytenoid region. The incidence of PAST was approximately 46% (17 patients). The gender difference, type of surgery, and the fixation position of the tube were not significantly related to the PAST occurrence. All the eigenvalues showed significant differences pre and post-surgery in the interarytenoid region and a significant trend toward red and increased contrast texture profiles was revealed. Furthermore, the glottic area showed a significant decrease of 25.29%, while the vocal width showed a significant increase post extubation. Conclusion: Our equipment and processing can measure subtle laryngeal changes that would allow a clinician to diagnose postoperative laryngeal inflammation in a simpler and less invasive way. The trend toward red, the increased contrast texture and vocal width, and the reduced glottic space were all compatible with post-intubation inflammatory response, especially in the interarytenoid region. This is important to know so that one can take appropriate steps to alleviate PAST in the future.

The symbiotic photovoltaic (PV) electrofarming system introduced in this study is developed for the PV setup in an agriculture farming land. The study discusses the effect of different PV system design conditions influenced by annual sunhours on agricultural farm land. The aim is to increase the sunhours on the PV panel for optimized electricity generation. Therefore, this study combines the Taguchi method with Grey Relational Analysis (GRA) to optimize the two quality characteristics of the symbiotic electrofarming PV system with the best design parameter combination. The selected multiple quality characteristics are PV power generation and sunhours on farm land. The control factors include location, upright column height, module tilt angle, and PV panel width. First, the Taguchi method is used to populate a L9(34) orthogonal array with the settings of the experimental plan. After the experimental results are obtained, signal-to-noise ratios are calculated, factor response tables and response graphs are drawn up, and analysis of variance is performed to obtain those significant factors which have great impact on the quality characteristics. The experiments show that the parameters which effects power generation are: location, upright column height, module tilt angle, and PV panel width. The ranking of the degree of influence of the control factors on the quality characteristics is location > PV panel width > module tilt angle > upright column height. By controlling these factors, the quality characteristics of the system can be effectively estimated. The results for PV power generation and sunhours on farm land both fall within the 95% CI (confidence interval), which shows that they are reliable and reproducible. The optimal design parameter realized in this research obtains a power generation of 26,497 kWh and a sunshine time of 1963 h. The finding showed that it can help to build a sustainable PV system combined with agriculture cultivation.