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Because of their mortality rate, diabetes and COVID-19 are serious diseases. Moreover, people with diabetes are at a higher risk of developing COVID-19 complications. This article therefore proposes a single, non-invasive system that can help people with diabetes and COVID-19 to monitor their health parameters by measuring oxygen saturation (SPO2), heart rate, and body temperature. This is in contrast to other pulse oximeters and previous work reported in the literature. A Max30102 sensor, consisting of two light-emitting diodes (LEDs), can serve as a transmission spectrum to enable three synchronous parameter measurements. Hence, the Max30102 sensor facilitates identification of the relationship between COVID-19 and diabetes in a cost-effective manner. Fifty subjects (20 healthy, 20 diabetic, and 10 with COVID-19), aged 18-61 years, were recruited to provide data on heart rate, body temperature, and oxygen saturation, measured in a variety of activities and scenarios. The results showed accuracy of ±97% for heart rate, ±98% for body temperature, and ±99% for oxygen saturation with an enhanced time efficiency of 5-7 seconds in contrast to a commercialized pulse oximeter, which took 10-12 seconds. The results were then compared with those of commercially available pulse oximetry (Oxitech Pulse Oximeter) and a thermometer (Medisana Infrared Thermometer). These results revealed that uncontrolled diabetes can be as dangerous as COVID-19 in terms of high resting heart rate and low oxygen saturation. Doi: 10.28991/esj-2022-SPER-04 Full Text: PDF

Leg orthosis is one of medical device for fracture bone treatment. Several complications have been reported when patients are wearing the conventional Plaster of Paris or fibreglass leg orthosis. Its including numbness due to tight application, swelling, venous congestion and skin irritation. Therefore, the aim of this study is to develop and biomechanical analyse a customized leg cast that could tackle those complications. First, the leg was scanned using a 3D scanner. The leg cast was constructed from a 3D scanned leg model and designed using 3-Matic software which later simulated in finite element analysis using Marc software. The study focuses on three gait cycles; midstance, heel strike and toe off where stress and deformation were predicted. From the results, the cast only supported the ankle in midstance phase with only 7.297MPa stress and not capable to support the ankle in heel strike and toe off phases with maximum stress of 106.99 MPa and 213.12 MPa, respectively, which are beyond its yield strength of ABS, 23MPa. The average of overall displacement occurred for both skin and cast were less than 1.5mm. In short, the cast could only support the ankle in midstance phase and cannot be used for heel strike.

Adalimumab and secukinumab are commonly used for moderate to severe psoriasis vulgaris (PV). Although distinct individual responses to and impaired effectiveness of these biological agents occur occasionally, little is known about the underlying reasons. Here, we report a proteomic analysis of psoriatic lesions from patients treated with these drugs using data-independent acquisition mass spectrometry (DIA-MS). Thousands of differentially expressed proteins (DEPs) changed over 12 weeks of treatment. Network analysis showed that DEPs could interact and induce transformation in matrix components, metabolic regulation, and immune response. The results of parallel reaction monitoring (PRM) analysis suggested that S100s, STAT1, KRT2, TYMP, SOD2, HSP90AB1, TFRC, and COL5A1 were the most significantly changed proteins in both groups. There was a positive association between the Psoriasis Area and Severity Index (PASI) score and three proteins (TFRC, IMPDH2, KRT2). Our study findings suggest that inhibition of IL-17A and TNF-α can induce changes in multiple molecules in psoriatic lesions and have an overlapping influence on the immune response and process through direct or indirect effects.

Diagnosis and treatment of hepatocellular carcinoma or metastases rely heavily on accurate segmentation and classification of liver tumours. However, due to the liver tumor’s hazy borders and wide range of possible shapes, sizes, and positions, accurate and automatic tumour segmentation and classification remains a difficult challenge. With the advancement of computing, new models in artificial intelligence have evolved. Following its success in Natural language processing (NLP), the transformer paradigm has been adopted by the computer vision (CV) community of the NLP. While there are already accepted approaches to classifying the liver, especially in clinical settings, there is room for advancement in terms of their precision. This paper makes an effort to apply a novel model for segmenting and classifying liver tumours built on deep learning. In order to accomplish this, the created model follows a three-stage procedure consisting of (a) pre-processing, (b) liver segmentation, and (c) classification. In the first phase, the collected Computed Tomography (CT) images undergo three stages of pre-processing, including contrast improvement via histogram equalization and noise reduction via the median filter. Next, an enhanced mask region-based convolutional neural networks (Mask R-CNN) model is used to separate the liver from the CT abdominal image. To prevent overfitting, the segmented picture is fed onto an Enhanced Swin Transformer Network with Adversarial Propagation (APESTNet). The experimental results prove the superior performance of the proposed perfect on a wide variety of CT images, as well as its efficiency and low sensitivity to noise.

In 1998, "The Community of the Future" was published by the Drucker Foundation, which was composed of Peter Drucker and 39 other world-class masters. This book collected a total of 28 articles regarding the concept of "future community." In the book, the Drucker Foundation conveys the idea of what the community of the future should be by constantly exploring the new rules of urban civilization. The book indicates that the community of the future is an ideal place for everyone. We struggle to get infinitely closer to the community of the future, and everything we do is about building the community of the future. The prophetic concepts in the book point out the direction for our community governance and guide us to keep working and innovating with a view to creating an ideal future community.

Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d=1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.

To study the dynamic recrystallization and texture evolution of GH4169 alloy during the cross wedge rolling processing, the microstructure, crystal orientation and texture in the surface and core of the GH4169 alloy with the area reduction of 30% and 50% were analyzed by the metallographic microscope (OM) and electron backscatter diffraction (EBSD), respectively.The results show that the crystal orientation gradually tends to be random with the occurrence of dynamic recrystallization during the cross wedge rolling process of GH4169 alloy. There are more high-angle grain boundaries in the rolled surface than those in the core. No evident change of the texture intensity in the rolled surface can be observed, while the core texture intensity increases obviously. The textures have rotated after the cross wedge rolling and the previous types of {001}〈110〉,{111}〈110〉,{111}〈011〉 change to the types of {001}〈010〉,{112}〈110〉,{110}〈111〉,{110}〈112〉. The dynamic recrystallization and texture evolution of GH4169 alloy are dominated by the special deformation characteristics of the cross wedge rolling.

To study the dynamic recrystallization and texture evolution of GH4169 alloy during the cross wedge rolling processing, the microstructure, crystal orientation and texture in the surface and core of the GH4169 alloy with the area reduction of 30% and 50% were analyzed by the metallographic microscope (OM) and electron backscatter diffraction (EBSD), respectively.The results show that the crystal orientation gradually tends to be random with the occurrence of dynamic recrystallization during the cross wedge rolling process of GH4169 alloy. There are more high-angle grain boundaries in the rolled surface than those in the core. No evident change of the texture intensity in the rolled surface can be observed, while the core texture intensity increases obviously. The textures have rotated after the cross wedge rolling and the previous types of {001}〈110〉,{111}〈110〉,{111}〈011〉 change to the types of {001}〈010〉,{112}〈110〉,{110}〈111〉,{110}〈112〉. The dynamic recrystallization and texture evolution of GH4169 alloy are dominated by the special deformation characteristics of the cross wedge rolling.

In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination ( R 2 ), and mean average error (MAE), the results are 1.191621 × 10−5, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management. [ABSTRACT FROM AUTHOR]