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Contagious Eczema (CE), caused by ORFV, impacts sheep and goats globally, with severe symptoms and economic losses. The ORFV situation in Yunnan, China, was unclear before 2021–2023 study. Eleven scab samples from goats on small farms in three Yunnan municipalities were collected. Four ORFV strains were isolated and characterized using scanning electron microscopy, cytopathic effect observation, and PCR. Phylogenetic analyses of ORFV011 and ORFV059 genes showed significant results. For ORFV011, the nucleotide similarity of the four strains to D1701 strain was 98.4–99%. For ORFV059, it was 97.2–97.9% with OV-SA00 strain. These findings suggest gene rearrangements and interactions among strains during Yunnan’s ORFV outbreak, forming a unique evolutionary lineage. Our study is the first comprehensive one on Yunnan’s ORFV prevalence with in-depth phylogenetic analysis. It has important implications. In vaccine development, understanding genetic variances helps create better vaccines. For disease control, customized strategies like targeted quarantine and disinfection can be designed based on strain characteristics. From a public health aspect, as CE is zoonotic, closely monitoring ORFV in goats aids in predicting and preventing human infections, thus being significant for protecting goats against CE in Yunnan.

The paper focused on the distributed tracking problem for a specific class of multi-agent systems, characterized by bandwidth constraint and dead zone actuators, where the bandwidth limitations exist in neighbor agents and the dead zone nonlinearity refers to a generalized mathematical model. Initially, a series of event-triggered mechanisms with relative thresholds were established for neighbor agents, ensuring that control signals were transmitted only when necessary. Next, the generalized dead zone models were decomposed into two parts: indefinite terms with control coefficients and disturbance-like terms, resulting in unpredictability and damaging effects. Subsequently, based on the backstepping procedure, final consensus controllers with multiple polynomial compensators were constructed. These controllers offset the coupling coefficients caused by event-triggered mechanisms and dead zone non-smooth. Stability analysis was given to substantiate the theoretical correctness of this method and support the claim of Zeno behavior avoidance. Finally, simulation studies were performed for the feasibility of our proposed methodology.

To describe the hysteresis nonlinearities in smart actuators, numerous models have been presented in the literature, among which the Preisach operator is the most effective due to its capability to capture multi-loop or sophisticated hysteresis curves. When such an operator is coupled with uncertain nonlinear dynamics, especially in noncanonical form, it is a challenging problem to develop techniques to cancel out the hysteresis effects and, at the same time, achieve asymptotic tracking performance. To address this problem, in this paper, we investigate the problem of iterative inverse-based adaptive control for uncertain noncanonical nonlinear systems with unknown input Preisach hysteresis, and a new adaptive version of the closest-match algorithm is proposed to compensate for the Preisach hysteresis. With our scheme, the stability and convergence of the closed-loop system can be established. The effectiveness of the proposed control scheme is illustrated through simulation and experimental results.

In this paper, the tracking control problem of a class of fuzzy approximation-based noncanonical nonlinear systems with hysteresis inputs is investigated, where the fuzzy weight matrix is not available for measurement, and the hysteresis nonlinearities are modeled by the Prandtl–Ishlinskii operator. Due to the coupling effects, the plant input containing hysteresis is unknown. To solve the problem, two adaptive control schemes are developed. The first is a Lyapunov-based scheme, and the second is a gradient-based scheme. For convenience, only the relative-degree-one case is taken into account in design and analysis. With the proposed schemes, it can be proved that all signals in the closed-loop system are bounded, and the tracking error converges to a small region around zero. Simulation results show that the maximum steady-state error converges to [−0.0131,0.0183]μm and [−0.0139,0.0161]μm with two control schemes, which confirms the obtained results.

Vision is an important way for unmanned mobile platforms to understand surrounding environmental information. For an unmanned mobile platform, quickly and accurately obtaining environmental information is a basic requirement for its subsequent visual tasks. Based on this, a unique convolution module called Multi-Scale Depthwise Separable Convolution module is proposed for real-time semantic segmentation. This module mainly consists of concatenation pointwise convolution and multi-scale depthwise convolution. Not only does the concatenation pointwise convolution change the number of channels, but it also combines the spatial features from the multi-scale depthwise convolution operations to produce additional features. The Multi-Scale Depthwise Separable Convolution module can strengthen the non-linear relationship between input and output. Specifically, the multi-scale depthwise convolution module extracts multi-scale spatial features while remaining lightweight. This fully uses multi-scale information to describe objects despite their different sizes. Here, Mean Intersection over Union (MIoU), parameters, and inference speed were used to describe the performance of the proposed network. On the Camvid, KITTI, and Cityscapes datasets, the proposed algorithm compromised between accuracy and memory in comparison to widely used and cutting-edge algorithms. In particular, the proposed algorithm acquired 61.02 MIoU with 2.68 M parameters on the Camvid test dataset.

Alpelisib, an oral selective and small-molecule phosphoinositide 3-kinase inhibitor, was lately approved in the United States to treat breast cancer. A sensitive method to quantify alpelisib levels in rat plasma on the basis of ultra-performance liquid chromatography–tandem mass spectrometry technique was established and validated, which was successfully employed to explore the effects of CYP3A4 inhibitors on alpelisib pharmacokinetics in rats. A C18 column named Acquity UPLC BEH C18 was applied to achieve the separation of alpelisib and internal standard duvelisib after protein precipitation with acetonitrile. The mobile phase in this study had two components, namely, acetonitrile and water having 0.1% formic acid, and a program with gradient elution method was used at a flow rate of 0.40 ml/min. Mass spectrometry in a positive multiple reaction monitoring mode was operated. In the scope of 1–5,000 ng/ml, this assay had excellent linearity. Our newly developed assay was verified in all aspects of bioanalytical method validation, involving lower limit of quantification, selectivity, accuracy and precision, calibration curve, extraction recovery, matrix effect, and stability. Then, this assay was used to detect the plasma levels of alpelisib from a drug-drug interaction investigation, where ketoconazole remarkably increased the plasma concentration of alpelisib and changed alpelisib pharmacokinetics more than itraconazole. This study will help better understand the pharmacokinetic properties of alpelisib, and further clinical studies should be done to confirm this result in patients.

This paper investigates a novel Efficient Dual-branch Bottleneck Network (EDBNet) to perform real-time semantic segmentation tasks on mobile robot systems based on CCD camera. To remedy the non-linear connection between the input and the output, a small-scale and shallow module called the Efficient Dual-branch Bottleneck (EDB) module is established. The EDB unit consists of two branches with different dilation rates, and each branch widens the non-linear layers. This module helps to simultaneously extract local and situational information while maintaining a minimal set of parameters. Moreover, the EDBNet, which is built on the EDB unit, is intended to enhance accuracy, inference speed, and parameter flexibility. It employs dilated convolution with a high dilation rate to increase the receptive field and three downsampling procedures to maintain feature maps with superior spatial resolution. Additionally, the EDBNet uses effective convolutions and compresses the network layer to reduce computational complexity, which is an efficient technique to capture a great deal of information while keeping a rapid computing speed. Finally, using the CamVid and Cityscapes datasets, we obtain Mean Intersection over Union (MIoU) results of 68.58 percent and 71.21 percent, respectively, with just 1.03 million parameters and faster performance on a single GTX 1070Ti card. These results also demonstrate the effectiveness of the practical mobile robot system.

Waterlogged archaeological wood of shipwrecks has been preserved under seawater for centuries, such that microbial erosion has caused severe bacterial degradation and acidification. These wooden cultural relics are of great significance for understanding the shipbuilding technology, trade activities, and environmental ecology of centuries ago. From the perspective of structure and composition, these waterlogged archeological woods have the characteristics of high water content and a large loss of lignin and cellulose, which makes the hull prone to collapse during preservation. Therefore, it is urgent to apply conservation and preservation treatments for deacidification and consolidation. Due to the fragility of wood and the complexity of repair work, the current development of conservation and preservation technology has multiple aims, such as antibacterial, deacidification, and reinforcement effects. In this editorial, the current challenges and conservation treatments with antimicrobial or deacidification utilities will be introduced.

Cellulose nanocrystals, as well as hydroxypropyl cellulose, can form lyotropic liquid crystals, which can be processed into pigments or glitter products for sustainable coloration. Some stimuli-responsive polymers or nanoparticles are expected to form colorimetric sensors via co-assembly with these cellulosic photonic crystals. The co-assembly behavior of CNCs with polymers is determined by the hydrogen bonds and physical adsorption. Thus, adjusting the molecular chain structure, hydrophilicity, and electrostatic interaction of co-assembled polymers can lead to flexible and tunable colorimetric cellulosic sensors. Despite the advantages of cellulose-based amorphous photonic crystal (APC) pigments or glitters as sustainable and visually captivating sensors, there are still problems in efficient preparation and co-assembly conditions. This editorial will provide a brief discussion of the benefits, applications, and challenges of cellulose-based APCs.

In this work, aluminum nitride particles (AlN) and boron nitride whiskers (BNw) were surface-functionalized by polydopamine, and then novel composites were prepared by co-doping. By observing with transmission electron microscopy, we clearly saw that the surfaces of AlN and BNw were successfully coated with a thin polydopamine coating. This is beneficial to enhance the filler-matrix interface interaction and the filler dispersion. The co-doping of the two thermally conductive fillers increases the chance of contact between the filler and the filler, and reduces the interfacial thermal resistance, thereby forming a thermal conduction network more efficiently. The results show that at a frequency of 1 MHz at room temperature, the dielectric constant of 30 wt%AlN@PDA/6 wt%BNw@PDA/EP composites is 4.74 and the dielectric loss tangent is 0.0171. Its thermal conductivity is 0.40 W/(m·K), the glass transition temperature reaches 250.2°C, the impact strength reaches 6.89 kJ m−2, and the breakdown field is 10.93 kV/mm.

This letter presents a new triple-band four-way filtering power divider (FPD) with greatly improved frequency selectivity and in-band isolation. By elaborately developing a multi-port multi-mode topology between four identical multi-mode resonators and feedlines, a triple-band four-way FPD is attained. In order to validate the feasibility of the proposal, one prototype is designed, fabricated, and measured. Both the simulated and measured results of the designed FPD are provided with a good agreement. Results indicate that this triple-band four-way FPD exhibits not only sharp selective filtering performance, but also satisfactory port-to-port isolations.

Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult. Feature selection is a technical approach that can effectively reduce the data dimension. In the study of feature selection, the multi-objective optimization algorithm has shown an excellent global optimization performance. The Pareto relationship can handle contradictory objectives in the multi-objective problem well. Therefore, a Shapley value-fused feature selection algorithm for multi-label learning (SHAPFS-ML) is proposed. The method takes multi-label criteria as the optimization objectives and the proposed crossover and mutation operators based on Shapley value are conducive to identifying relevant, redundant and irrelevant features. The comparison of experimental results on real-world datasets reveals that SHAPFS-ML is an effective feature selection method for multi-label classification, which can reduce the classification algorithm’s computational complexity and improve the classification accuracy.