The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2-25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.

National parks provide a considerable number of co-benefits to society, including the balance of ecosystems, conservation of heritage values, and tourism. However, studies on zoning approaches for the management of national parks are lacking. The landscape characterization approach is a holistic method for identifying regional landscapes and helps improve zoning management, thus promoting sustainable planning. Here, we propose a landscape character classification (LCC) approach for national parks by integrating a k-means clustering algorithm and geographic information system (GIS). We used Laoshan National Park (LNP) as a case study and aimed to (1) quantify the major landscape factors (altitude, topography relief, soil type, and heritage impact intensity) that influence the landscape classification of mountainous protected areas; (2) create a map of landscape character types and areas to guide a zoning boundary; and (3) further examine how decision makers assign different conservation strategies to each landscape character area. Our results indicate that different landscape character areas reflect distinct ecological environments and heritage values and that differentiated zoning management can effectively mitigate the impact of natural disasters and human activities. Our study suggests that national parks require scientific landscape character zoning, rational descriptions of landscape character types, and targeted management measures to achieve the dual objectives of zoning and landscape conservation.

The COVID-19 has been spreading globally since 2019 and causes serious damage to the whole society. A macro perspective study to explore the changes of some social-related indexes of different countries is meaningful.
We collected nine social-related indexes and the score of COVID-safety-assessment. Data analysis is carried out using three time series models. In particular, a prediction-correction procedure was employed to explore the impact of the pandemic on the indexes of developed and developing countries.
It shows that COVID-19 epidemic has an impact on the life of residents in various aspects, specifically in quality of life, purchasing power, and safety. Cluster analysis and bivariate statistical analysis further indicate that indexes affected by the pandemic in developed and developing countries are different.
This pandemic has altered the lives of residents in many ways. Our further research shows that the impacts of social-related indexes in developed and developing countries are different, which is bounded up with their epidemic severity and control measures. On the other hand, the climate is crucial for the control of COVID-19. Consequently, exploring the changes of social-related indexes is significative, and it is conducive to provide targeted governance strategies for various countries. Our article will contribute to countries with different levels of development pay more attention to social changes and take timely and effective measures to adjust social changes while trying to control this pandemic.

The concept of haze habituation was proposed based on haze perception and behavior in this paper. This study employed factor analysis and Potential Conflict Index (PCI) to analyze the dimensions, degrees, and internal differences of the public\'s haze habituation. Then, K-means clustering algorithm was applied to classify the public into four categories. The entropy method was used to quantitatively evaluate the public\'s haze habituation, and the natural breakpoint method was used to grade it into five levels. Finally, an ordered logistic regression model was chosen to analyze the influencing factors of the public\'s haze habituation. The results indicate that: (1) The public\'s haze habituation can be measured from five dimensions: protective behavior, haze reduction behavior, haze attention, life impact perception, and health impact perception. The public had the same views on protective behavior, haze reduction behavior, life impact perception, and health impact perception. However, there is a wide divergence among the public on the haze attention; (2) Based on the above five dimensions, the public can be divided into the protective sensitive group, attention sensitive group, health sensitive group, and environmental protection sensitive group; (3) Generally, the public has a low haze habituation where the protective behavior, haze reduction behavior, and health impact perception are the crucial elements; (4) Gender, self-health assessment, and travel mode have a significant positive impact on the public\'s haze habituation, respectively. Age, the family with elders or children, and annual family income have a significant negative impact on the public\'s haze habituation, respectively.

Data of the commercial parameters of Pleurotus ostreatus and Pleurotus djamor were analyzed using the data mining technique: K-means clustering algorithm. The parameters evaluated were: biological efficiency, crop yield ratio, productivity rate, nutritional composition, antioxidant and antimicrobial activities in the production of fruit bodies of 50 strains of Pleurotus ostreatus and 50 strains of Pleurotus djamor, cultivated on the most representative agricultural wastes from the province of Guayas: 80% sugarcane bagasse and 20% wheat straw (M1), and 60% wheat straw and 40% sugarcane bagasse (M2). The database of the parameters obtained in experimental procedures was grouped into three clusters, providing a visualization of the strains with a higher relation to each parameter (vector) measured.

Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.

Based on the relevant data of construction waste (CW) in the People\'s Republic of China (PRC) from 2010 to 2018, this study applied K-means clustering algorithm and grey prediction methods to systematically investigate the spatiotemporal characteristic distribution and provincial clustering of CW in the PRC, and predicted the annual output of CW in the next five years from the scientific perspective. Results showed that the annual output of CW in the PRC displayed an overall trend of \"rising first and then falling\" and \"being high in the middle east and low in the northwest,\" and the areas with obvious agglomeration gradually spread from the west to the middle and eastern regions. The law of development was consistent with the goals of the Chinese government to promulgate urban agglomeration development policies, prefabricated building encouragement policies, and CW management regulations. In the next five years, the annual output of CW in the PRC will increase by a small margin. Thus, all aspects of CW resource management should be conducted in a planned and step-by-step manner.

OBJECTIVE: Breast cancer is a popular well-known tumor in women globally and the subsequent driving reason for malignancy death. The purpose of the present study is to develop Low cost, commercial, and affordable system that discriminates malignant from normal breast tissues by exploiting the unique properties of Hyperspectral (HS) Imaging.
METHODS: The difference in the optical properties of the investigated breast tissues gives various reactions to light transmission, absorption, and especially the reflection over the spectral range. A custom optical imaging system (COIS) was designed to assess variable responses to monochromatic LEDs (415, 565, 660 nm) to highlight the differences in the reflectance properties of malignant/normal tissue. Statistical analysis was computed for determining the ideal wavelength to differentiate between normal and malignant regions. The experiment was repeated using the same LEDs, and low-cost CCD camera to examine the capability of such a system to discriminate between normal and malignant tissue.
RESULTS: Spectral images obtained by Hyperspectral camera, have been analyzed to reveal the difference of reflectance malignant and normal breast tissue. Superficial spectral reflection image with blue LED (415 nm) showed high variance (10.11). However, a more-depth reflection image with red LED (660 nm) showed low variance (4.44). So the optimum contrast image was produced by combining the three spectral information images from blue, green, and red LED. The COIS using a commercial CCD camera was in agreement with the HS camera.
CONCLUSIONS: The novel COIS of the commercial Low-cost CCD Camera is reliable and can be used with endoscopy technique as an assistant tool for surgical doctor to make decision and assess the resection edges in real time during surgery.

Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions \"in vivo\" and correlate these to an individual\'s clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm2 in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a k-means clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P < 0.001). The number of A and B type lesions was similar, although in juxtacortical areas B-type lesions predominated (60%, P < 0.001). Also, the percentage of B-type lesion volume was higher (64%, P < 0.001), indicating that these lesions were larger. The number and volume of B-type lesions was related to the severity of disease evolution, clinical disability and cognitive decline (P = 0.004, Bonferroni correction). Specifically, more and larger B-type lesions were correlated with a worse Multiple Sclerosis Severity Score, cerebellar function and cognitive performance. Thus, by combining several microscopic and macroscopic diffusion properties, the severity of damage within focal lesions can be characterized, further contributing to our understanding of the mechanisms that drive disease evolution. Accordingly, the classification of lesion types has the potential to permit more specific and better-targeted treatment of patients with multiple sclerosis.