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According to the public data collected from the Health Commission of Gansu Province, China, regarding the COVID-19 pandemic during the summer epidemic cycle in 2022, the epidemiological analysis showed that the pandemic spread stability and the symptom rate (the number of confirmed cases divided by the sum of the number of asymptomatic cases and the number of confirmed cases) of COVID-19 were different among 3 main epidemic regions, Lanzhou, Linxia, and Gannan; both the symptom rate and the daily instantaneous symptom rate (daily number of confirmed cases divided by the sum of daily number of asymptomatic cases and daily number of confirmed cases) in Lanzhou were substantially higher than those in Linxia and Gannan. The difference in the food sources due to the high difference of the population ethnic composition in the 3 regions was probably the main driver for the difference of the symptom rates among the 3 regions. This work provides potential values for prevention and control of COVID-19 in different regions.
Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.
Change mode and effects analysis (CMEA) is a powerful technique for measuring product flexibility toward future changes and diminishing the cost of redesign as well as shortening time to market. As a systematic methodology, it provides an in-depth view for the investigation of potential changes, causes, and effects in designs, products, and processes. Traditional CMEA determines the risk priorities of change modes by using change potential number, which requires the risk factors of design flexibility, occurrence, and readiness to be precisely evaluated. However, this is not always possible in real applications due to the uncertainty and subjectivity involved in the early design stages. It has been criticized much for its deficiencies in criteria weighting of the risk factors, change potential number calculation, and risk priorities determination of the change modes. This paper presents a systematic evaluation approach for determining a more rational rank of change modes by combining with the entropy weight method, rough number, and grey relational analysis. In this study, the entropy weight method is adopted to calculate the relative importance of risk factors. Rough number is presented to aggregate individual weights and preferences, and to manipulate the vagueness in the evaluation process. Then a rough number enhanced grey relational analysis is proposed to evaluate the risk ranking of change modes. Finally, a practical example is put forward to validate the performance of the proposed method. The result shows that the proposed change mode evaluation method can effectively overcome the shortcomings of traditional CMEA and strengthen the objectivity of product flexibility measurement.
The late Quaternary sedimentary sequence in the northwestern part of the Sichuan Basin consists of five lithological units and with increasing depth include the: Chengdu Clay; Brown Clay; Red Clay; Sandy Silt; and basal Muddy Gravel. The genesis, provenance and age of the sediments, as well as the possible presence of hiatuses within this sequence are debated. Measurements of grain-size, magnetic susceptibility, quartz content, quartz δ18O values, element composition, and Sr–Nd isotopic concentrations of samples from a typical sedimentary sequence in the area provides new insights into the genesis and history of the sequence. The new data confirm that the sediments in study site are alluvial–aeolian in origin, with basal alluvial deposits overlain by aeolian deposits. Like the uppermost Chengdu Clay, the underlying Brown Clay and Red Clay are aeolian in origin. In contrast, the Silty Sand, like the basal Muddy Gravel, is an alluvial deposit and not an aeolian deposit as previously thought. Moreover, the succession of the aeolian deposits very likely contains two significant sedimentary hiatuses. Sedimentological analysis demonstrates that the source materials for the aeolian deposits in the northwestern part of the Sichuan Basin and those on the eastern Tibetan Plateau are different. Furthermore, the loess deposits on the eastern Tibetan Plateau are derived from heterogeneous local sources.
Creative conceptual design requires significant previous design knowledge. Case-based reasoning enables learning from previous design experience and has a great potential in supporting creative conceptual design by means of seeking to retrieve, reuse, and revise most appropriate cases to generate inspired solutions. However, traditional case-based reasoning based creative conceptual design models focus on design strategies research, pay little attention to defining a consistent knowledge representation model, and neglect the research to make various types of knowledge retrieval tractable. Faced with such drawbacks, the expected design knowledge cannot be retrieved properly, especially in cases where multidisciplinary knowledge is concerned or exact query terms are absent. In order to solve these issues, this paper presents a combined approach to support creative conceptual design process. First, function–behavior–structure knowledge cell is introduced as a unified consistent design knowledge representation model. Second, a hybrid similarity measure is proposed to increase the overall possibility of obtaining useful design knowledge by considering semantic understanding ability. Third, an intelligent creative conceptual design system has been developed with a case study of a novel insulin pump design to demonstrate its usage, and two experiments are conducted to evaluate the performance of the proposed approach. The results show that the proposed approach outperforms other case-based reasoning based creative conceptual design models.
Neuromyelitis optica (NMO) and multiple sclerosis (MS) are chronic neuro-inflammatory diseases believed to arise from complex interactions between environmental and genetic factors. Recently, single nucleotide polymorphisms (SNPs) in interleukin (IL)-2 and -7 receptor alpha genes have been identified as novel susceptibility alleles for MS in genome-wide association studies. However, similar research on NMO is limited. We aimed to investigate the association of IL2RA SNPs rs2104286 and rs12722489 and IL7RA SNP rs6897932 with Southern Han Chinese NMO and MS patients.
Frequencies of the three SNPs were examined in Southern Han Chinese mS cases (n=78), NMS cases (n=67) and controls (n=133) using sequencing-based typing.
The rs2104286G frequency in the IL2RA gene was significantly higher in NMO patients than in controls (puncorr=0.013, pcorr=0.026, OR:1.942, 95%CI:1.146-3.291).
The rs2104286 G allele in IL2RA is present at higher frequencies in NMO patients than in healthy controls within a Southern Han Chinese population.
Spinal surgery is considered a high-risk surgery. To improve the accuracy, stability, and safety of such operations, we report the development of a novel six-degrees-of-freedom Robotic Spinal Surgical System that can assist surgeons in performing transpedicular surgery, one of the most common spinal surgeries. After optimization performed using Response Surface Methodology, the largest available workspace of the robot is determined and is found to easily cover the entire operation area. Cooperative control and navigation-based active control are implemented for different processes of the operation. We propose a hybrid control approach based on the speed and torque interface at the joint level. In this mode, the robot is compliant in Cartesian space, benefitting both the accuracy and efficiency of the operation. A comprehensive assessment index, combining the subjective and objective criteria in terms of positioning and operation efficiency, is proposed to compare the performance of cooperative control in speed mode, torque mode, and hybrid control mode. Active fine adjustment experiments are carried out to verify the positioning accuracy, and the results are found to satisfy the requirements of operation. As an application example, a pedicle screw insertion experiment is performed on a pig vertebral bone, demonstrating the effectiveness of our system.
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