The COVID-19 pandemic led to an initial increase in the incidence of carbapenem-resistant Enterobacterales (CRE) from clinical cultures in South-East Asia hospitals, which was unsustained as the pandemic progressed. Conversely, there was a decrease in CRE incidence from surveillance cultures and overall combined incidence. Further studies are needed for future pandemic preparedness.

This paper studies the synchronization control of the blanket remote maintenance robot (BRMR) of the China fusion engineering test reactor (CFETR). First, the general state space mathematical model of BRMR was established by using a physical-based method. Second, based on the receding horizon optimization of model predictive control (MPC) and cross-coupling error reduction in cross-coupling control (CCC), the innovative MPC-CCC controller was proposed to realize the single-system and multisystem error convergence and high accuracy transportation of blanket through the high accuracy synchronization control of BRMR. Third, to verify the control effectiveness of the MPC-CCC controller, two types of simulations and experiments were implied compared with the original proportional-integral (PI) controller in Mover. Results showed that simulation and experiments were highly consistent. It is found that the use of an MPC-CCC controller can result in up to a 70% reduction in displacement error and up to a 59% reduction in synchronization error compared to the PI controller. And the accuracy of the MPC-CCC controller satisfies the real requirement of the maintenance process of the blanket. This work provides the theoretical basis and practical experience for the highly stable, safe, and efficient maintenance of blankets in the future.

In this paper, we provide a new approach to prove some weighted-blowup formulae for genus zero orbifold Gromov–Witten invariants. As a consequence, we show the invariance of symplectically rational connectedness with respect to weighted-blowup along positive centers. Furthermore, we use this method to give a new proof to the genus zero relative-orbifold correspondence of Gromov–Witten invariants.

The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global public health crisis. The objectives of this study were to analyse the dynamic trend in specific antibodies in the serum of patients infected with SARS-CoV-2 within 12 months after recovery and to make a preliminary assessment of the protective effect of vaccination. Eighty-seven patients with confirmed COVID-19 who were admitted to our hospital from January to February 2020 were followed after recovery. Three-millilitre blood samples were collected for specific antibody detection at four time points: 1, 6 and 12 months after recovery and 1 month after vaccination. The changes in specific immunoglobulin G (IgG) antibody and total antibody levels over 12 months were analysed. Moreover, an independent comparison of the neutralising antibody levels of patients after vaccination with those of healthy medical staff after vaccination was performed to compare the inhibition rates of the neutralising antibody to the virus. No statistically significant difference in the sex distribution between groups was observed (P > 0.05). Older patients had a greater risk of developing severe and critical COVID-19 (P < 0.05). The percentages of subjects positive for IgG antibodies at 1, 6 and 12 months after recovery were 88.5%, 75.9% and 50.6%, respectively. The rate of IgG antibody conversion from positive to negative was not uniform across time points: the change was slow in the first 6 months but increased significantly in the last 6 months (P < 0.05). The positive rate of critically ill patients in the first 6 months was 100.0%. The trend over time in total antibody levels was similar to that of IgG antibody levels. Over 12 months, the sample/cut off value of total antibodies continued to decrease, while that of different disease severities was significantly different (P < 0.05). After vaccine administration, the total antibody level exceeded the detection level in the first month, which was independent of disease severity (P > 0.05). Significant differences were observed in the inhibition rate of the neutralising antibody against the virus in the disease group and the control group (P < 0.05). IgG antibody produced by patients naturally infected with SARS-CoV-2 has a duration of no less than 1 year, and the change trend graph of total antibody levels was the same as that of IgG antibody levels. Under vaccine stimulation, the positive rate of IgG antibody was as high as 100%, and the total antibody concentration reached the highest level, which was independent of disease severity. Neutralising antibodies following vaccination in patients who recovered from COVID-19 had a higher inhibition rate against SARS-CoV-2 than those of vaccinated healthy controls, indicating that these COVID-19 patients had a lower risk of reinfection and were better protected.

This study presents an under-actuated snake arm maintainer (SAM) for complex and extreme environments such as nuclear power plants. The structure adopts the layered cable drive principle, whereby a single drive layer drives multiple joints. This design significantly reduces the complexity of the control system while increasing the spatial curvature. The traction of multiple wire ropes with a composite capstan drives the synchronous angular motion of several adjacent joints. By changing the number of joints in the single driver layer of the snake arm, the arm can be adapted to various complex environments. The trajectory planning and trajectory tracking motion control methods of the under-actuated SAM are established based on the improved backbone method and the variable rod length algorithm. Finally, a 10-joint prototype with an arm length of 2300 mm is designed for nuclear reactor maintenance. Trajectory experiments confirmed the rationality of the under-actuated SAM, the correctness of the inverse kinematics, and the effectiveness of the motion control methods.

NaY zeolite was synthesized from kaolin/dimethyl sulfoxide (DMSO) intercalation composites using an in situ crystallization technique. The effects of the intercalation ratios and the amounts of the kaolin/DMSO intercalation composite on the synthesis of an NaY zeolite molecular sieve were studied. The samples were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, differential thermal analysis, N2 adsorption–desorption and scanning electron microscopy. In the in situ synthesis system, when the kaolin/DMSO intercalation composite was added, pure NaY zeolite was formed. By increasing the amount of kaolin/DMSO intercalation composite added, the crystallinity of the samples increased, and after reaching the maximum amount of kaolin/DMSO intercalation composite added, the crystallinity decreased with further increases of the amount of kaolin/DMSO intercalation composite added. To higher intercalation ratio, the crystallinity can be greatly improved at the lower addition content. At an intercalation ratio of 84%, the added amount of kaolin/DMSO intercalation composite was 2.5% and the crystallinity of the NaY zeolite molecular sieve reached a maximum value of 45%. At intercalation ratios of 55% and 22%, the amount of kaolin/DMSO intercalation composite added was 15% and the crystallinities of the NaY zeolite molecular sieves were 44% and 47%, respectively. The NaY zeolite has good thermal stability and a particle diameter of ~0.5 μm. The Brunauer–Emmett–Teller (BET) specific surface area and pore volume of the sample were 519 m2 g–1 and 0.355 cm3 g–1, respectively.

Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms.

A fever clinic within a hospital plays a vital role in pandemic control because it serves as an outpost for pandemic discovery, monitoring and handling. As the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan was gradually brought under control, the fever clinic in the West Campus of Wuhan Union Hospital introduced a new model for construction and management of temporary mobile isolation wards. A traditional battlefield hospital model was combined with pandemic control regulations, to build a complex of mobile isolation wards that used adaptive design and construction for medical operational, medical waste management and water drainage systems. The mobile isolation wards allowed for the sharing of medical resources with the fever clinic. This increased the capacity and efficiency of receiving, screening, triaging and isolation and observation of patients with fever. The innovative mobile isolation wards also controlled new sudden outbreaks of COVID-19. We document the adaptive design and construction model of the novel complex of mobile isolation wards and explain its characteristics, functions and use.

We give a survey of current research on Gödel’s incompleteness theorems from the following three aspects: classifications of different proofs of Gödel’s incompleteness theorems, the limit of the applicability of Gödel’s first incompleteness theorem, and the limit of the applicability of Gödel’s second incompleteness theorem.

In this paper, we examine the limit of applicability of Gödel’s first incompleteness theorem ($\textsf {G1}$ for short). We first define the notion “$\textsf {G1}$ holds for the theory $T$”. This paper is motivated by the following question: can we find a theory with a minimal degree of interpretation for which $\textsf {G1}$ holds. To approach this question, we first examine the following question: is there a theory T such that Robinson’s $\mathbf {R}$ interprets T but T does not interpret $\mathbf {R}$ (i.e., T is weaker than $\mathbf {R}$ w.r.t. interpretation) and $\textsf {G1}$ holds for T? In this paper, we show that there are many such theories based on Jeřábek’s work using some model theory. We prove that for each recursively inseparable pair $\langle A,B\rangle $, we can construct a r.e. theory $U_{\langle A,B\rangle }$ such that $U_{\langle A,B\rangle }$ is weaker than $\mathbf {R}$ w.r.t. interpretation and $\textsf {G1}$ holds for $U_{\langle A,B\rangle }$. As a corollary, we answer a question from Albert Visser. Moreover, we prove that for any Turing degree $\mathbf {0}< \mathbf {d}<\mathbf {0}^{\prime }$, there is a theory T with Turing degree $\mathbf {d}$ such that $\textsf {G1}$ holds for T and T is weaker than $\mathbf {R}$ w.r.t. Turing reducibility. As a corollary, based on Shoenfield’s work using some recursion theory, we show that there is no theory with a minimal degree of Turing reducibility for which $\textsf {G1}$ holds.