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Background The primary care facilities in a region play a vital role in maintaining the health of the local residents as the gatekeeper. At present, there is a lack of research on the allocation of primary care resources in Tibet, a remote autonomous region in northwestern China. Objective To analyze the current situation and equity of the allocation of primary care resources in Tibet, to provide a scientific basis for optimizing the allocation of primary care resources in Tibet. Methods Data used in this study were from six volumes (2015—2020) of Tibetan Health Statistics Yearbook and China Health Statistics Yearbook, as well as the human resource information in the Primary Care Direct Reporting System of Tibet Health Commission from 2015 to 2020. Descriptive analysis was used to analyze the allocation of primary care resources in Tibet from 2015 to 2020. Gini coefficient and concentration index were used to evaluate the equity of primary care resource allocation in Tibet in 2020. Results The number of beds in primary hospitals in Tibet increased from 3 393 in 2015 to 3 867 in 2020. The number of (assistant) practicing physicians per 1 000 people increased from 0.37 in 2015 to 0.61 in 2020. The utilization rate of beds in primary hospitals decreased from 30.9% in 2015 to 11.5% in 2020. The daily visits per primary care physician decreased from 13.80 in 2015 to 9.95 in 2020. The daily number of hospital bed days of care per primary care physician decreased from 0.44 in 2015 to 0.10 in 2020. In 2020, the Gini coefficients of health resources allocated in primary hospitals according to population in Tibet were greater than 0.3, which were better than those allocated according to geography. The difference in the concentration degree between number of beds, number of health technicians, number of (assistant) practicing physicians or number of registered nurses and population, was -6.93, -4.50, -2.50, -6.15, respectively, in Lhasa, and 0.05, -0.21, -0.80, -0.22, respectively, in Changdu, and -0.88, 0.10, 0.47, -0.05, respectively, in Shannan, but was all greater than 0 in other cities. Conclusion The primary health workers in Tibet showed a trend of "low growth in number, low capability and low efficiency". It is suggested to pay attention to regional characteristics to improve the allocation standards of primary care resources, promote the equity of the allocation of primary care resources based on geographical classification, reform the employment mechanism and human resource management system in primary care, innovate the system and mechanism of aiding Tibet and establish a new model of financial investment at the primary level.

In Australia, 28% of the population live in remote and rural areas, where they face many health service utilization challenges due to geographical conditions, and generally have lower health status than those living in coastal metropolitan areas. To address the challenges of accessing health services in remote and rural areas, the Australian Government Department of Health and Aged Care has introduced a geographical classification system to help healthcare providers to improve healthcare services since 1994, and regularly updated the system to adapt to the latest sociodemographic and healthcare status, as well as formulated a range of complementary health policies to support rural and remote areas. From 2018, Australian Government Department of Health and Aged Care has adopted a new classification standard, the Modified Monash Model. We reviewed Australia's practices and concluded that, to better deliver high-quality and accessible healthcare services to areas with weak healthcare resources in China, Australia's experience could be used for reference, during taking actions to promote and refine the geographic classification system for healthcare services in a timely manner, formulating policies supporting the enhancement of access to health workers systematically, and taking advantage of modern, high and new technologies.

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design. [ABSTRACT FROM AUTHOR]

With the rapid growth of global urbanization and the rising demand for sustainable development, it is essential to study the performance and durability of building materials. As a traditional and widely employed building material, Chinese clay tiles play a significant role in traditional Chinese architecture. However, traditional methods for assessing structural surface damage necessitate the time-consuming and labor-intensive assessment and judgment of trained professionals. Consequently, it is crucial to employ machine learning techniques for automatic damage type identification. This study identifies the types of damage to Chinese clay tiles in Macau by employing machine learning techniques and the YOLOv4 object detection model. A total of 363 photographs of on-site Chinese clay tiles were used as training samples, and 200 epochs of the model training were performed. The primary findings of this study are as follows: (1) The machine learning method, based on the YOLOv4 model, provides an effective and precise solution for the automatic identification of Chinese clay tiles damage types, overcoming the human and time-cost constraints of conventional evaluation methods. (2) The detection accuracy of the detection model in this study is 95.42% for the detection of Chinese clay tiles cracks, 80.91% for the detection of stains, and 89.34% for the detection of surface wear, with an overall accuracy of 88.98%, which meets the basic detection requirements. (3) The experimental results demonstrate the viability and efficacy of the proposed method for identifying clay tile damage types and provide a method reference for the preservation and sustainable development of historical buildings. [ABSTRACT FROM AUTHOR]

Q fever is an important worldwide zoonosis with nonspecific symptoms, making diagnosis challenging (1-3). Humans become infected mainly by inhalation of Coxiella burnetii-contaminated aerosols from animal waste or contaminated soil [...]

Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)–disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease–SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy. [ABSTRACT FROM AUTHOR]

Low-rate denial of service (LDoS) attack is a new type of network attack, which is characterized by low attack cost and strong concealment. As a new type of network architecture, software defined network (SDN) is also threatened by LDoS attacks. The control and forwarding separation of the SDN network and the programmable net-work behavior provide new ideas for the detection and defense of LDoS attacks. This paper proposes a new LDoS attack detection and defense method based on OpenFlow protocol. The rate of each OpenFlow data stream is separately counted, and the double-sliding-window method in signal detection is used to detect the attack traffic. Once the attack traffic is detected, the controller can implement real-time defense against the attack behavior by sending a flow table. Experi-ments show that this method can effectively detect LDoS attacks and can defend against LDoS attacks in a short time.