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In this work, we experimentally achieved a real-time video optical wireless transmission which can broadcast two users in different positions, simultaneously. Firstly, a single-pixel 50-μm micro-LED is designed and fabricated on a GaN-based wafer and then two micro-LED chips are connected as parallel form in a single bulb. The transceiver based on two FPGA boards is used to generate quadrature amplitude modulation-orthogonal frequency division multiplexed (QAM-OFDM) signals and measure the communication performance of the VLC system. The low-power micro-LEDs bulb emits 0.8 mW optical power through two optical paths at 20 mA driving current, and the modulation bandwidths for two users’ access are 90 MHz and 73 MHz, respectively. In this two-user real-time video transmission system, the total data rate exceeds 105.54 Mbps without naked eye discernable distortion over 2-m free-space distance. Different from traditional point-to-point links, this work demonstrates the possibility of flexible high-speed multi-user real-time video optical wireless transmission based on a parallel micro-LEDs bulb.

At present, the research on the physical composition and properties of pineapple plants is scarce, and the uncertainty of fruit picking method is the key bottleneck factor hindering the research and development of pineapple harvesting machinery. Based on the statistics of survey data from many places, this paper analyzes the fruit-stem fracture mechanism and the theoretical conditions for optimal separation through structural modeling, mechanical behavior analysis and function judgment. On this basis, the "pineapple plant fixation bench" and "fruit-stem bending separation torque test equipment" were developed, and large-size, small-size tests and random optimization tests were carried out successively. The test results showed that the larger of the stem-stalk fixation distance, the more torque and fracture starting angle required for fruit fracture would increase, and the change range was small when the stem-stalk fixation distance was within 50 mm, and the probability of brittle fracture and complete separation was very high. When the space between the fracture section and the fruit-stem connecting point is about 5mm, the range of bending moment value required for the fruit-stem fracture is 1.88 to 2.77 N·m, the range of fracture starting angle is 12.2° to 18.1°, and the angular travel range during the separation process is 82.9° to 87.5°. When the stem-stalk fixation distance is about 15 mm, it is the best fruit-stem separation condition and the breaking torque measured in the verification test is about 2.76 N·m. The fracture starting angle is about 13.8°, the maximum prediction error is 13.1%, and the elastic modulus near the fruit-stem joint ranges from 16.1 to 23.9 MPa. This conclusion can provide an important design basis for the research and development of pineapple field picking robot and harvesting equipment. [ABSTRACT FROM AUTHOR]

The complex terrain environment in the hilly land directly affects the operational reliability of agricultural robots. In order to study the impact of road irregularity on walking chassis vibration, the 3CYLZ-750 remote-controlled weeding machine which is applied to orchards was taken as the object of study, and the rear roller was selected as the object of observation to reveal the rules under which the vibration of the track chassis changes as there is a sudden change in road surface elevation. A column-type test-to-pass method based on unit excitation was proposed in this study. The excitation behavior and action process were analyzed by category. A critical acceleration prediction model was built and verified by virtual simulation and hard road surface excitation testing. The results showed that at the forward velocity of 0-2.5 km/h and exciter height of 20-100 mm, the vertical vibration acceleration of the target roller was significantly affected by Track Contact Point Centrifugal Acceleration (TCPCA). As TCPCA increased, the change rate of vertical vibration acceleration decreased, reaching a minimum of [-13.8, 28.8]; as TCPCA decreased, the vertical vibration acceleration tended to increase positively at a maximum variation range of [-13.3, 42.2]. The measured and simulated macroscopic change rules were consistent with the theoretical analysis, further verifying the correctness of variable extraction, and providing a research basis for the accurate modification and improvement of the model. The research conclusions can lay a theoretical foundation for analyzing the walking reliability of the track chassis, and provide a design basis and technical support for the development of a tracked agricultural robot chassis for the hilly land in the future. [ABSTRACT FROM AUTHOR]

Isothermal constant-strain-rate compression experiments of S280 ultrahigh-strength stainless steel were conducted at 800–1150 °C, 0.001–10 s−1, and 70% height reduction. The flow stress behaviors were analyzed based on the compression data. The strain compensation Arrhenius constitutive relation, multiple linear regression constitutive relation, and back-propagation (BP) neural network constitutive relation of this alloy were established for the first time. The S280 ultrahigh-strength stainless steel is characterized by a positive strain rate and negative temperature sensitivity. Its flow stress at high temperature (1000–1150 °C) and low temperature (800–950 °C) is generally at the steady state and the softening state, respectively. The three new flow stress constitutive relations all meet the requirements for engineering applications in terms of predictive precision. The BP neural network constitutive relation shows the highest predictive precision, with correlation coefficient R of 0.999 and average absolute relative error AARE of 1.04%. The strain compensation Arrhenius constitutive relation shows the lowest predictive precision, with R of 0.994 and AARE of 14.748%. The multiple linear regression constitutive relation shows the modest predictive precision, with R of 0.994 and AARE of 6.24%.

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.

Abstract Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning. Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available. Results The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level. Conclusions The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions. Graphical Abstract

The S280 ultra-high strength stainless steel was subjected to isothermal constant strain rate compression experiments under the conditions of deformation temperature 1000–1150 °C and strain rate 0.001–10 s−1 by Thermecmaster-Z thermal simulation testing machine. The flow behavior of S280 ultra-high strength stainless steel was analyzed; the dynamic recrystallization critical strain model, dynamic recrystallization volume fraction model, dynamic recrystallization grain size and average grain size model of the steel were established, and its dynamic recrystallization behavior was numerically simulated. The results show that the flow stress of S280 ultra-high strength stainless steel increases with the increase of strain rate and the decrease of deformation temperature. The distribution of dynamic recrystallization volume fraction and average grain size in each deformation region after thermal compression is not uniform, and the dynamic recrystallization volume fraction increases with the increase of deformation temperature and decrease of strain rate; the average grain size increases with the increase of deformation temperature and strain rate. The numerical simulation results are in good agreement with the experimental results, which can provide a theoretical basis for the microstructure control of the steel during thermal deformation.

Abstract Background The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks? Methods We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity. Results We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management (including case detection, quarantine, and further treatment), i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy (case number reduces from 20,860 to 2689) and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system. Conclusions Computationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions. Graphical Abstract

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.

Isothermal and constant-strain-rate compression experiments for S280 ultra-high-strength stainless steel were carried out under deformation temperatures of 1000–1150 °C and strain rates of 0.001–10 s−1 with a Thermecmaster-Z thermal simulator. The flow–stress behavior of the alloy was studied and the hot deformation activation energy was calculated. A critical strain model of the dynamic recrystallization (DRX) of the alloy was established using the work hardening rate for the first time. The results show that S280 ultra-high-strength stainless steel was positively sensitive to the strain rate and negatively sensitive to temperature, and its flow–stress curve showed characteristics of flow softening. The hot deformation activation energy corresponding to the peak strain was 519.064 kJ/mol. The DRX critical strain of the steel was determined from the minimum value of the −∂(lnθ)/∂ε − ε curve. The relationship between the DRX critical strain and peak strain could be characterized as εc=0.599εp and the relationship between the DRX critical stress and peak stress could be characterized as σc= 0.959σp The critical strain model of DRX could be expressed as εc=0.010Z0.062. The research results can provide theoretical support for avoiding the generation of actual thermal processing microstructure defects such as coarse grains and for obtaining products with excellent microstructure and properties.