Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading t...
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Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deeplearning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learningalgorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model's performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of th
Due to the huge impact of COVID-19, the world is currently facing a medical emergency and shortage of vaccine. Many countries do not have enough medical equipment and infrastructure to tackle this challenge. Due to th...
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Due to the huge impact of COVID-19, the world is currently facing a medical emergency and shortage of vaccine. Many countries do not have enough medical equipment and infrastructure to tackle this challenge. Due to the lack of a central administration to guide the countries to take the necessary precautions, they do not proactively identify the cases in advance. This has caused Covid-19 cases to be on the increase, with the number of cases increasing at a geometric progression. Rapid testing, RT-PCR testing, and a CT-Scan/X-Ray of the chest are the primary procedures in identifying the covid-19 disease. Proper immunization is delivered on a priority basis based on the instances discovered in order to preserve human lives. In this research paper, we suggest a technique for identifying covid-19 positive cases and determine the most affected locations of covid-19 cases for vaccine distribution in order to limit the disease's impact. To handle the aforementioned issues, we propose a cloud based image analysis approach for using a COVID-19 vaccination distribution (CIA-CVD) model. The model uses a deeplearning, machine learning, digital image processing and cloud solution to deal with the increasing cases of COVID-19 and its priority wise distribution of the vaccination.
A comprehensive strategy for effective identification of total constituents in Chinese patent medicine has been advanced applying full scan-preferred parent ions capture-static and active exclusion (FS-PIC-SAE) acquis...
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A comprehensive strategy for effective identification of total constituents in Chinese patent medicine has been advanced applying full scan-preferred parent ions capture-static and active exclusion (FS-PIC-SAE) acquisition coupled with intelligent deep-learning supported mass defect filter (MDF) process, with Naoxintong capsule (NXT) as a case. Online comprehensive two-dimensional liquid chromatography (2DLC) coupled with Q-TOFMS/MS system was established for obtaining the excellent separation and detection performance of total components, which could exhibit excellent peak capacity with 1052 and orthogonality with 0.69. In addition, a total of 901 unknown compounds could be classified into nine chemical classes rapidly and effectively, based on the intelligent deep-learning algorithm supported MDF model with 96.4% accuracy. Consequently, 276 compounds were successfully identified from NXT, especially including 44 flavonoids, 27 phenolic acids, 25 fatty acids, 17 saponins, 21 phthalocyanines, 20 triterpenes, 10 monoterpenes, 13 diterpenoid ketones, 14 amino acids, and others. It is concluded that the proposed program is an effective and practical strategy enabling the in-depth chemical profiling of complex herbal and biological samples.
Wearable touch panels, a typical flexible electronic device, can recognize and feed back the information of finger touch and movement. Excellent wearable touch panels are required to accurately and quickly monitor the...
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Wearable touch panels, a typical flexible electronic device, can recognize and feed back the information of finger touch and movement. Excellent wearable touch panels are required to accurately and quickly monitor the signals of finger movement as well as the capacity of bearing various types of deformation. High-performance thermistor materials are one of the key functional components, but to date, a long-standing bottleneck is that inorganic semiconductors are typically brittle while the electrical properties of organic semiconductors are quite low. Herein, a high-performance flexible temperature sensor is reported by using plastic Ag2S with ultrahigh temperature coefficient of resistance of -4.7% K-1 and resolution of 0.05 K, and rapid response/recovery time of 0.11/0.11 s. Moreover, the temperature sensor shows excellent durability without performance damage or loss during force stimuli tests. In addition, a fully flexible intelligent touch panel composed of a 16 x 10 Ag2S-film-based temperature sensor array, as well as a flexible printed circuit board and a deep-learning algorithm is designed for perceiving finger touch signals in real-time, and intelligent feedback of Chinese characters and letters on an app. These results strongly show that high-performance flexible inorganic semiconductors can be widely used in flexible electronics.
An image analysis method was developed based on deep-learning algorithms to extract phase fractions quantitatively in a rectangular trickle bed, and the average identification error was lower than 5%. Furthermore, the...
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An image analysis method was developed based on deep-learning algorithms to extract phase fractions quantitatively in a rectangular trickle bed, and the average identification error was lower than 5%. Furthermore, the flow regime transition in the trickle bed was studied. In trickle-to-pulse flow transition, the trickle flow could be further classified into the stable trickle flow and accelerated one. The SD of liquid fractions and the peak width at half-height of the probability density curve of liquid fractions were close to zero in stable trickle flow, increased rapidly in accelerated trickle flow, and remained approximately constant in pulse flow. In bubble-to-pulse flow transition, dispersed bubbles in bubble flow induced the outliers outside the upper boundary of the boxplot of gas fraction, while alternative appearance of gas-rich zone and liquid-rich zone in pulse flow induced outliers outside both the upper and lower boundaries of the boxplot of gas fraction.
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