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The smart orchard tournament of the robot developer competition requires participants to utilise a robotic arm equipped with a monocular camera to grasp ping-pong balls bearing fruit patterns. Common issues include low object recognition accuracy, inefficient performance, and grasping failures due to collisions with obstacles. This paper proposes a novel object grasping framework, EPSO-TPPP-YOLOv8, based on a rigid robotic arm for the task of fruit picking. In the safe distance analysis section, the two obstacles, cylinder and sphere, are first modelled. The robotic arm is then considered as an object consisting of several cylinders. The formula for calculating the safe distance between the robot arm and the obstacle is provided, and the design of the obstacle avoidance path is guided according to the calculation results. In the robot arm path planning section, the hyper-parameters of the angles of each robot arm joint are first determined, and then particle swarm optimization based on a novel set of strategies and evaluation criteria is used to generate the desired paths. The findings reveal that, in the absence of human intervention, the proposed EPSO algorithm reliably identifies paths that adhere to safety boundary limits within 50 iterations. The EPSO-TPPP framework, when operated under human guidance, has been demonstrated to achieve this within 10 iterations. In the subsequent phase of vision-based object recognition phase, a distinctive dataset is constructed based on the specified task. It is an established fact that simulation and field experiments have demonstrated the efficacy of the robot arm’s obstacle-avoidance grasping functionality.

Relativistic reflection features are commonly observed in the X-ray spectra of accreting black holes. In the presence of high quality data and with the correct astrophysical model, X-ray reflection spectroscopy can be quite a powerful tool to probe the strong gravity region, study the morphology of the accreting matter, measure black hole spins, and possibly test Einstein’s theory of general relativity in the strong field regime. In the last decade, there has been significant progress in the development of the analysis of these features, thanks to more sophisticated astrophysical models and new observational facilities. Here we review the state-of-the-art in relativistic reflection modeling, listing assumptions and simplifications that may affect, at some level, the final measurements and may be investigated better in the future. We review black hole spin measurements and the most recent efforts to use X-ray reflection spectroscopy for testing fundamental physics.

Almost all the neutralizing antibodies targeting the receptor-binding domain (RBD) of spike (S) protein show weakened or lost efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged or emerging variants, such as Omicron and its sub-variants. This suggests that highly conserved epitopes are crucial for the development of neutralizing antibodies. Here, we present one nanobody, N235, displaying broad neutralization against the SARS-CoV-2 prototype and multiple variants, including the newly emerged Omicron and its sub-variants. Cryo-electron microscopy demonstrates N235 binds a novel, conserved, cryptic epitope in the N-terminal domain (NTD) of the S protein, which interferes with the RBD in the neighboring S protein. The neutralization mechanism interpreted via flow cytometry and Western blot shows that N235 appears to induce the S1 subunit shedding from the trimeric S complex. Furthermore, a nano-IgM construct (MN235), engineered by fusing N235 with the human IgM Fc region, displays prevention via inducing S1 shedding and cross-linking virus particles. Compared to N235, MN235 exhibits varied enhancement in neutralization against pseudotyped and authentic viruses in vitro. The intranasal administration of MN235 in low doses can effectively prevent the infection of Omicron sub-variant BA.1 and XBB in vivo, suggesting that it can be developed as a promising prophylactic antibody to cope with the ongoing and future infection.

Acute pancreatitis (AP) is an inflammatory disease of the pancreas. Despite of a steadily increasing in morbidity and mortality, there is still no effective therapy. Gut microbial dysbiosis and its derived-metabolites disorder have been shown to play an important role in the development of AP, however, little is known regarding the crosstalk between gut microbiota and metabolites. In this study, we assessed the alterations in gut microbiota and metabolites by constructing three AP mouse models by means of metagenomic and metabolomic sequencing, and further clarified their relationship by correlation analysis. The results revealed that each model exhibited unique flora and metabolite profiles. KEGG analysis showed that the differential flora and metabolite-enriched pathway functions were correlated with lipid metabolism and amino acid metabolism. Moreover, two core differential bacterial species on Burkholderiales bacterium YL45 and Bifidobacterium pseudolongum along with eleven differential metabolites appeared to exert certain effects during the course of AP. In conclusion, further exploration of the crosstalk between microbiota and derived metabolites may provide novel insights and strategies into the diagnosis and treatment of AP.

The progression in integrated circuit technology has necessitated advanced solutions for the storage and rapid transmission of extensive data generated by electronic modules. Compared to traditional signal compression and transmission techniques, compressed sensing (CS) transcends the limitations of the Shannon–Nyquist sampling theorem by enabling low-frequency signal sampling, thereby becoming a extensively utilized approach in the signal processing. Recent advancements in Artificial Intelligence have further propelled the reconstruction efficacy of deep learning-based CS methods, mitigating certain constraints inherent in traditional CS approaches. Nonetheless, the existing deep learning-based CS methods are not optimally efficient for electronic data processing. In this regard, this study proposes a novel double-path multiscale adaptive compressed sensing network (DMA-CS). This network is structured around four key modules: signal compression, preprocessing, initial reconstruction, and secondary reconstruction. The signal compression module samples the signal for compression, the preprocessing module prepares the sampled signal for subsequent processing, the initial reconstruction module employs a double-path complementary network comprising a multiscale residual module fused with a multihead attention module and an inverse residual module for the initial reconstruction. Then, the secondary reconstruction module uses the adaptive dilated convolution residual module to adaptively adjust the size of the convolution kernel to ensure the high-quality reconstruction of different signals and combines it with the tree-like structure residual block for enhanced reconstruction. Our experimental evaluation on the P2020 module fault signal dataset and NASA Lithium Battery dataset demonstrates that our scheme attains the lowest percentage root-mean-square difference and the highest signal-to-noise ratio, demonstrating substantial enhancement in reconstruction performance and robustness.

A key point in building a contemporary energy system is the search for sustainable and green energy. Many green energy sources exist in the road or pavement domain, such as solar, thermal, wind, and mechanical energy, etc. Under the repeated vehicle loads, stresses and strains are generated in the pavement, which can generate substantial mechanical energy. In recent two decades, there has been a growing scholarly preference for utilizing the piezoelectric effect to convert mechanical energy from pavement into electricity to supply low-power transportation facilities, pavement sensors, etc. This paper provides an in-depth review of state-of-the-art advances in road piezoelectric energy harvesters. The basic principle of piezoelectric energy harvesting and common piezoelectric electric materials were briefly introduced. The piezoelectric energy harvesters suitable for roads are thoroughly reviewed from five perspectives: structure, finite element analyses, protective packaging, management circuit, and application. Finally, the challenges faced by piezoelectric energy harvesters for pavements were summarized, and the potential research directions were also proposed. This review serves as a valuable reference for advancing road piezoelectric harvesting technology development.

Optimizing the power allocation and capacity configuration of hybrid energy storage systems (HESS) is crucial for enhancing grid stability, power quality and renewable energy utilization in wind–solar complementary microgrids. However, the conventional configuration methods exhibit inaccuracy and low reliability. To achieve the optimal capacity configuration of HESS in wind–solar complementary microgrids, a power allocation strategy and a capacity optimization configuration model for HESS consisting of vanadium redox flow batteries (VRBs) and supercapacitors (SCs) were proposed based on parameter-optimized variational mode decomposition (VMD). Firstly, the number of mode decomposition (K) and the penalty factor (α) of VMD were optimized using the random walk grey wolf optimizer (RW-GWO) algorithm, and the HESS power signal was decomposed by RW-GWO-VMD. Secondly, an optimal capacity configuration model was formulated, taking into account the whole life cycle cost of HESS, and particle swarm optimization (PSO) algorithm was applied to optimize HESS capacity while satisfying operational constraints on charge/discharge power, state of charge (SOC) range, and permissible rates of load deficit and energy loss. Thirdly, the optimal capacity allocation was obtained by minimizing the whole life cycle cost of HESS, with the frequency division threshold N serving as the optimization parameter. Finally, comprehensive comparison and analysis of proposed methods were conducted through simulation experiments. The results demonstrated that the whole life cycle cost of RW-GWO-VMD was 7.44% lower than that of EMD, 1.00% lower than that of PSO-VMD, 0.72% lower than that of AOA-VMD, and 0.27% lower than that of GWO-VMD.

In this article, we analyze a second-order stochastic SEIR epidemic model with latent infectious and susceptible populations isolated at home. Firstly, by putting forward a novel inequality, we provide a criterion for the presence of an ergodic stationary distribution of the model. Secondly, we establish sufficient conditions for extinction. Thirdly, by solving the corresponding Fokker–Plank equation, we derive the probability density function around the quasi-endemic equilibrium of the stochastic model. Finally, by using the epidemic data of the corresponding deterministic model, two numerical tests are presented to illustrate the validity of the theoretical results. Our conclusions demonstrate that nations should persevere in their quarantine policies to curb viral transmission when the COVID-19 pandemic proceeds to spread internationally.

Human epidermal growth factor receptor-3 (HER-3) plays a key role in the growth and metastasis of cancer cells. The detection of HER-3 is very important for early screening and treatment of cancer. The AlGaN/GaN-based ion-sensitive heterostructure field effect transistor (ISHFET) is sensitive to surface charges. This makes it a promising candidate for the detection of HER-3. In this paper, we developed a biosensor for the detection of HER-3 with AlGaN/GaN-based ISHFET. The AlGaN/GaN-based ISHFET biosensor exhibits a sensitivity of 0.53 ± 0.04 mA/dec in 0.01 M phosphate buffer saline (1× PBS) (pH = 7.4) solution with 4% bovine serum albumin (BSA) at a source and drain voltage of 2 V. The detection limit is 2 ng/mL. A higher sensitivity (2.20 ± 0.15 mA/dec) can be achieved in 1× PBS buffer solution at a source and drain voltage of 2 V. The AlGaN/GaN-based ISHFET biosensor can be used for micro-liter (5 μL) solution measurements and the measurement can be performed after incubation of 5 min.

Objective Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors. Methods We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics. Results In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics. Conclusion The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.