LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization

With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face identification. Most of animal face recognition study focuses on face detection and face identification. Animals are often uncooperative when taking photos, so the collected animal face images are often in arbitrary directions. The use of non-standard images may significantly reduce the performance of face recognition system. However, there is no study on normalizing of the animal face image with arbitrary directions. In this study, we developed a light-weight angle detection and region-based convolutional network (LAD-RCNN) containing a new rotation angle coding method that can detect the rotation angle and the location of animal face in one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset including goat dataset and gaot infrared image. Evaluation result show that the AP of face detection was more than 95% and the deviation between the detected rotation angle and the ground-truth rotation angle were less than 0.036 (i.e. 6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent performance on livestock face and its direction detection, and therefore it is very suitable for livestock face detection and Normalizing. Code is available at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/Comment: 8 figures, 5 table

A quasi real-time approach to investigating the damage and fracture process in plain concrete by X-ray tomography

In most concrete-related computer tomography (CT) experiments, detailed information on the damage and fracture process is obtained using nonreal-time approaches, with the CT method constantly regarded as a qualitative method. This study develops a quasi real-time method with the use of experimental instruments. The average CT number is used to analyse the damage and fracture process in concrete specimens and the theory that underlies concrete damage and fracture is improved. Various characteristics of the fracture form in different loading cases are investigated at the macro and micro levels. This study provides a convenient and fast method for qualitatively and quantitatively analysing concrete. First published online: 01 Jun 201

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules

Geographic Variations in the Incidence of Glioblastoma and Prognostic Factors Predictive of Overall Survival in US Adults from 2004–2013

Objective: The purpose of this study was to evaluate variations in the regional incidence of glioblastoma in US adults in 2004–2013.Study Design and Setting: We evaluated 24,262 patients with primary glioblastoma. Data were categorized based on geographic regions that included different SEER registry sites as follows: (1) Northeast: Connecticut, New Jersey (3,977 patients); (2) South: Kentucky, Louisiana, Metropolitan Atlanta, Rural Georgia, Greater Georgia (excluding AT and RG) (5,212 patients); (3) North Central: Metropolitan Detroit, Iowa (2,320 patients); (4) West: Hawaii, New Mexico, Seattle (Puget Sound), Utah, San Francisco-Oakland SMSA, San Jose-Monterey, Los Angeles, Greater California (excluding SF, LA, and SJ), Alaska (12,753 patients).Results: Statistically significant differences in the rates of overall patient survival (P < 0.001) and the incidence of glioblastoma (24.31, 22.6, 20.35, 15.03 per 100,000/year in the South, Northeast, West, North Central regions, respectively) were identified between geographic regions. Multivariate Cox regression analysis demonstrated that overall survival was better in patients of Asian or Pacific Islander race. In addition, age, registry site, marital status, tumor laterality, histological classification, the extent of disease, tumor size, tumor extension, and treatment methods were identified as significant prognostic factors.Conclusion: Glioblastoma incidence is geographic region and race/ethnicity–dependent