As space missions are needed in the future, the assembly volume of the space truss will become larger and larger, and the advancing path of the on-orbit cellular robot to the mission target will become more and more complicated. If the shortest moving path cannot be found in the truss environment, the climbing time of the robot on the truss will be greatly increased. To improve the speed of the cellular robot moving to the target point on the large space truss, this paper designs a cellular robot structure and configuration suitable for climbing on the truss and uses the improved sparrow algorithm to solve the problem of robot motion path planning. By establishing a mathematical model of the space truss, the improved sparrow algorithm is used to find the shortest path between the starting point and the end point in the truss environment. Finally, the data of this algorithm are compared with the data of other algorithms. The data results show that the improved sparrow algorithm is very effective in solving the optimal path of the space truss. The improved sparrow algorithm keeps the same optimal path compared with the standard sparrow algorithm, and the overall reaction time is increased by 51.60%, and the number of effective iterations is increased by about 13.87%.

The lack of excellent wheat germplasm resources on the Qinghai-Tibet Plateau has led to a gradual decrease in genetic diversity and an increasingly narrow genetic background in wheat grown in this region. Rational use of excellent genes from wheat relatives is important to increase genetic diversity, broaden the genetic base and achieve high yield and quality in common wheat. The objective of this study was to use principal component and cluster analyses of 13 important agronomic traits of 44 Polish wheat varieties over 3 years and comprehensively evaluate them to screen for excellent germplasm resources, thus providing the basic material for broadening the genetic base of Qinghai-Tibet Plateau wheat germplasm resources.

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9$ \% $ to 62.8$ \% $$ (p<0.001) $; the average shortest path length decreased from 1.53 to 1.14 $ (p<0.001) $; the average betweenness reduced from 0.65 to 0.11$ (p<0.001) $; the average cluster size dropped from 4.05 to 2.72 $ (p=0.004) $; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 $ (p=0.099). $ Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

The new mineral tennantite-(Ni), Cu6(Cu4Ni2)As4S13, has been discovered from the Luobusa Chromitite, Tibet, southwestern China. Tennantite-(Ni) occurs as anhedral grains ranging from 2 to 20 μm in size. In reflected light microscopy, tennantite-(Ni) is isotropic and appears yellow-greenish grey. Reflectance data for Commission on Ore Mineralogy wavelengths in air for tennantite-(Ni) are: 31.0 (470 nm), 29.6 (546 nm), 29.6 (589 nm) and 29.3 (650 nm). Electron microprobe analysis for holotype material gave the empirical formula (on basis of total cations = 16 apfu): M(2)Cu6 M(1)[Cu4.00(Ni0.97Cu0.53Fe0.50)Σ2.00]Σ6.00X(3)(As2.94Sb1.06)Σ4S12.77. Tennantite-(Ni) is cubic, with space group I$\bar{ 4}$3m (#217), a =10.2957(9) Å, V = 1091.4(3) Å3 and Z = 2. By using single-crystal X-ray diffraction, the crystal structure has been determined and refined to a final R1 = 0.0423 on the basis of 163 independent reflections [Fo > 4σ (Fo)]. The calculated seven strongest powder X-ray diffraction lines [d in Å (I) (hkl)] are: 2.972 (100) (222), 1.820 (83) (440), 2.574 (28) (400), 1.552 (18) (622), 3.640 (10) (220), 1.880 (10) (521) and 1.287 (7) (800). Tennantite-(Ni) is isostructural with other tetrahedrite-group minerals, and nickel is hosted at the tetrahedrally coordinated M(1) site, along with Cu and minor Fe. The mineral and its name have been approved by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association (IMA2021-018).

In this paper, flexible substrate integrated waveguide (SIW) resonators have been designed and fabricated on polyimide substrates for humidity sensing applications. The proposed SIW resonant cavity allows the resonator to obtain the maximum humidity sensitivity and meet the demand for flexible microwave sensing detection. Meanwhile, the humidity response performance can be further significantly enhanced by introducing nanodiamond (ND) sensing material. Three prototypes of ND-coated SIW sensors with different bending radii are measured to analyze their humidity sensing performance. The experimental results demonstrate that the proposed ND-coated SIW sensor with the minimum bending radius can achieve a maximum humidity sensitivity of 1.09 MHz/% relative humidity (RH) in the high RH region (>75.3% RH) and a low humidity hysteresis of 1.8% in the range of 11.3–97.3% RH. This study provides a promising candidate to realize flexible microwave sensors with excellent sensing performance.

Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are convolutional neural network (CNN)-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: (a) their high dimension and (b) lack of generalization, leading to low efficiency and poor performance in real robotic applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in four challenging real-world datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. The code of our work is publicly available at https://github.com/MedlarTea/CAE-VPR.

The study presents an adaptive robust control method for the Pendubot subjects to matched and mismatched uncertainty. First, the control task is formatted as a reduced-dimension equality constraint of the system states. To handle the matched and mismatched uncertainties, an orthogonal decomposition method is employed to make the mismatched part disappear after decomposition. Based on the above, an adaptive robust control law based on constraint-following is devised. By the Lyapunov approach, it is rigorously proven that the proposed approach ensures the uniform boundedness and uniform ultimate boundedness of the closed-loop control system and thus renders approximate constraint-following, regardless of uncertainty. Simulation and experimental results are provided and discussed, demonstrating the good performance of the proposed approach.

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

Let Q be a quiver of type $\tilde {A}_n$ . Let $\alpha =\alpha _1+\alpha _2+\cdots +\alpha _s$ be the canonical decomposition. For the polynomials $M_Q(\alpha ,q)$ that count the number of isoclasses of representations of Q over ${\mathbb F}_q$ with dimension vector $\alpha $ , we obtain a precise relation between the degree of $M_Q(\alpha ,q)$ and that of $\prod _{i=1}^{s} M_Q(\alpha _i,q)$ for an arbitrary dimension vector $\alpha $ .

This study is performed to figure out how the presence of diabetes affects the infection, progression and prognosis of 2019 novel coronavirus disease (COVID-19), and the effective therapy that can treat the diabetes-complicated patients with COVID-19. A multicentre study was performed in four hospitals. COVID-19 patients with diabetes mellitus (DM) or hyperglycaemia were compared with those without these conditions and matched by propensity score matching for their clinical progress and outcome. Totally, 2444 confirmed COVID-19 patients were recruited, from whom 336 had DM. Compared to 1344 non-DM patients with age and sex matched, DM-COVID-19 patients had significantly higher rates of intensive care unit entrance (12.43% vs. 6.58%, P = 0.014), kidney failure (9.20% vs. 4.05%, P = 0.027) and mortality (25.00% vs. 18.15%, P < 0.001). Age and sex-stratified comparison revealed increased susceptibility to COVID-19 only from females with DM. For either non-DM or DM group, hyperglycaemia was associated with adverse outcomes, featured by higher rates of severe pneumonia and mortality, in comparison with non-hyperglycaemia. This was accompanied by significantly altered laboratory indicators including lymphocyte and neutrophil percentage, C-reactive protein and urea nitrogen level, all with correlation coefficients >0.35. Both diabetes and hyperglycaemia were independently associated with adverse prognosis of COVID-19, with hazard ratios of 10.41 and 3.58, respectively.

A droplet impinging on a superhydrophobic substrate in an electric field is an important process in droplet manipulation and electrostatic spraying. Here, the entire impinging dynamic of the droplet in a vertical electric field is studied by a visualization experiment and numerical simulation with OpenFOAM. We investigate the effect of an electrostatic force on droplet impact in depth, where four ejection modes and three rebound modes are found experimentally. In particular, the filamentous ejecting phenomenon occurs after a droplet impinging on a superhydrophobic substrate is first discovered. In the numerical simulation, the strong coupling between the dynamic distribution of the interface electric charge and the evolution of the droplet profile can lead to different ejection modes, and the different ejection behaviours are caused by the combined effects of electrostatic pressure, capillary pressure, dynamic pressure and static pressure on the droplet apex. A charge scaling law for the ejection droplets is proposed. Furthermore, a set of theoretical models is established, which can successfully predict the threshold electric capillary number for different droplet ejection modes. The results reveal some important characteristics for a droplet impinging on a superhydrophobic surface in an electric field, which could facilitate the design of electrically operated droplet equipment and guide the safe and stable operation of the device.

Big data has been reported widely to facilitate epidemic prevention and control in health care during the coronavirus disease 2019 (COVID-19) pandemic. However, there is still a lack of practical experience in applying it to hospital prevention and control. This study is devoted to the practical experience of design and implementation as well as the preliminary results of an innovative big data-driven COVID-19 risk personnel screening management system in a hospital. Our screening system integrates data sources in four dimensions, which includes Health Quick Response (QR) code, abroad travelling history, transportation close contact personnel and key surveillance personnel. Its screening targets cover all patients, care partner and staff who come to the hospital. As of November 2021, nearly 690 000 people and 5.79 million person-time had used automated COVID-19 risk screening and monitoring. A total of 10 376 person-time (0.18%) with abnormal QR code were identified, 242 person-time with abroad travelling history were identified, 925 person-time were marked based on the data of key surveillance personnel, no transportation history personnel been reported and no COVID-19 nosocomial infection occurred in the hospital. Through the application of this system, the hospital's expenditure on manpower and material resources for epidemic prevention and control has also been significantly reduced. Collectively, this study has proved to be an effective and efficient model for the use of digital health technology in response to the COVID-19 pandemic. Based on the data from multiple sources, this system has an irreplaceable role in identifying close contacts or suspicious person, and can significantly reduce the social burden caused by COVID-19, especially the human resources and economic costs of hospital prevention and control. It may provide guidance for clinical epidemic prevention and control in hospitals, as well as for future public health emergencies.