Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

With the wide application of the Internet and smart systems, data centers (DCs) have become a hot spot of global concern. The energy saving for data centers is at the core of the related works. The thermal performance of a data center directly affects its total energy consumption, as cooling consumption accounts for nearly 50% of total energy consumption. Superior power distribution is a reliable method to improve the thermal performance of DCs. Therefore, analyzing the effects of different power distribution on thermal performance is a challenge for DCs. This paper analyzes the thermal performance numerically and experimentally in DCs with different power distribution. First, it uses Fluent simulate the temperature distribution and flow field distribution in the room, taking the cloud computing room as the research object. Then, it summarizes a formula based on the computing power distribution in a certain range by the numerical and experimental analysis. Finally, it calculates an optimal cooling power by analyzing the cooling power distribution. The results shows that it reduces the maximum temperature difference between the highest temperature of the cabinet from 5-7k to within 1.2k. In addition, the cooling energy consumption is reduced by more than 5%.

Global navigation satellite system and its application fields are constantly expanding and deepening. This paper mainly introduces the current situation of global satellite navigation system and its application technology, development trend and application prospect. At the same time, this paper makes a comprehensive comparison of these navigation systems, analyzes the opportunities and challenges faced by China’s BeiDou satellite navigation system in the global context, and puts forward some suggestions for future work.

The nylon 66/nano-CaCO3 composites were prepared by melt blending on a twin-screw extruder. Scanning electron microscopy (SEM), polarized light microscopy (PLM), thermal loss (TGA) and differential scanning calorimetry (DSC) The effects of nanometer calcium carbonate on the polycrystalline behavior and thermal properties of nylon 66/nano CaCO3 composites were investigated. The results show that the nanometer calcium carbonate particles are dispersed in the nylon 66 matrix and exist in the form of aggregates. The nanometer calcium carbonate has the effect of heterogeneous nucleation, which can reduce the size of the spherules. The decomposition temperature of the nylon 66 is 400 ℃, the addition of nano-CaCO3 to reduce the decomposition temperature. At the same time, DSC test showed that the β-crystalline form in the material reduced the melting temperature of the material. The addition of nano-CaCO3 in the nylon 66 matrix resulted in the decrease of the crystallization temperature and the increase of the half-height width of the endothermic peak. The lower the crystallization temperature, the wider the crystallization temperature range.

Knowledge of the state of fragmentation and transformation of a forested landscape is crucial for proper planning and biodiversity conservation. Chile is one of the world’s biodiversity hotspots; within it is the Nahuelbuta mountain range, which is considered an area of high biodiversity value and intense anthropic pressure. Despite this, there is no precise information on the degree of transformation of its landscape and its conservation status. The objective of this work was to evaluate the state of the landscape and the spatio-temporal changes of the native forests in this mountain range. Using Landsat images from 1986 and 2011, thematic maps of land use were generated. A 33% loss of native forest in 25 years was observed, mainly associated to the substitution by forest plantations. Changes in the spatial patterns of land cover and land use reveal a profound transformation of the landscape and advanced fragmentation of forests. We discuss how these patterns of change threaten the persistence of several endemic species at high risk of extinction. If these anthropogenic processes continue, these species could face an increased risk of extinction.