Search

Atmospheric deposition is an important source of heavy metal in soil and the use of dust collection cylinders is a traditional monitoring method. This method has limitations in agricultural areas because polluted soil particles may become resuspended and eventually deposited into these cylinders, leading to overestimates in the amount of atmospheric deposition in soil. To address this concern, we propose that frequent snowfall can help suppress local soil dust resuspension and that fresh snow can serve as an efficient surrogate surface when collecting atmospheric deposition samples. To investigate the rationality of this method, 52 snow samples were collected from sites surrounding smelting plants in Anyang, an industrial region of North China. The results revealed that the concentration of cadmium in the melted snow ranged between 0.03 and 41.09 μg/L, with mean values three times higher around the industrial sites (5.31 μg/L) than background farmlands (1.54 μg/L). In addition, the cadmium concentration in the snow from sites surrounding the factories was higher in the north than in the south because of prevailing winds blowing from the southwest. Moreover, snow samples from sites with high concentrations of cadmium and sulfate can be categorized into different groups via the clustering method, conforming to the spatial distribution of particulate matter emissions and sulfur dioxide satellite column concentrations. Finally, a positive correlation was found between the cadmium content in the snow and the production capacity (R2 = 0.90, p < 0.05) and total permitted emissions (R2 = 0.69, p > 0.05) of the nearby factories. These findings demonstrate that snow is a reliable medium for documenting atmospheric dry deposition associated with specific industrial emissions.

State estimation of batteries is crucial in battery management systems (BMSs), particularly for accurately predicting the state of charge (SOC), which ensures safe and efficient battery operation. This paper proposes a joint SOC estimation method based on a fractional-order model, utilizing a multi-innovation full-tracking adaptive unscented Kalman filter (FOMIST-AUKF-EKF) combined with an extended Kalman filter (EKF) for online parameter updates. The fractional-order model more effectively represents the battery’s dynamic characteristics compared to traditional integer-order models, providing a more precise depiction of electrochemical processes and nonlinear behaviors. It offers superior modeling for long-memory effects, complex dynamics, and aging processes, enhancing adaptability to aging and nonlinear characteristics. Comparative results indicate a maximum end-voltage error reduction of 0.002 V with the fractional-order model compared to the integer-order model. The multi-innovation technology increases filter robustness against noise by incorporating multiple historical observations, while the full-tracking adaptive strategy dynamically adjusts the noise covariance matrix based on real-time data, thus enhancing estimation accuracy. Furthermore, EKF updates battery parameters (e.g., resistance and capacitance) in real time, correcting model errors and improving SOC prediction accuracy. Simulation and experimental validation show that the proposed method significantly outperforms traditional UKF-based SOC estimation techniques in accuracy, stability, and adaptability. Specifically, under varying conditions such as NEDC and DST, the method demonstrates excellent robustness and practicality, with maximum SOC estimation errors of 0.27% and 0.67%, respectively.

The identification of supply and demand areas for ecosystem services (ES) and the simulation of ES flows are essential for optimizing ESs to achieve socio-economic sustainable development. However, the selection of investigation methods and simulation model remains a persistent challenge. This study selected the Yellow River Delta High-Efficiency Eco-Economic Zone in China as the case study area and assessed the habitat quality and carbon sequestration services for 2000, 2010, and 2020. The quantile regression method was employed to quantify the impacts of land use structure on balancing the supply and demand of ESs. The minimum cumulative resistance model, circuit corridor model, and wind direction model were utilized to analyze changes in flux and flow direction of ESs’ supply and demand. The results demonstrated that the following: (1) the supply of ESs generally increased, with a significant rise in demand for carbon sequestration service and a declining trend in habitat quality service demand. (2) A clear spatial mismatch existed between the supply and demand of ESs. (3) The impact of land use structure on the balance of ES supply and demand is complex. (4) Habitat quality and carbon sequestration services exhibited distinct spatial clustering patterns. (5) The flow patterns of habitat quality service were characterized by specific supply and demand areas, with corridors and pinch points indicating the flow paths and potential barriers; not all demand areas for carbon sequestration service can be satisfied due to variations in service levels and geographical distance. The innovation of this study lies in the following aspects: (1) it acknowledges the uniqueness of ecosystem services, with a focus on assessing habitat quality and carbon sequestration services; (2) it precisely quantifies the flow of ecosystem services, analyzes the spatial dynamics of service flows, and investigates the impact of changes in land-use structure on these flows; (3) it strengthens the correlation between the supply and demand of ecosystem services and socio-economic activities, uncovers the contradictions between supply and demand along with their underlying causes, and proposes effective strategies for resolution. The findings can provide theoretical and methodological references for the optimization of ES.

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys.

Quantum entanglement detection is one of the fundamental tasks in quantum information science. Conventional methods for quantum state tomography exhibit limitations in scalability as the number of qubits increases, leading to exponential growth in the number of unknown parameters and required measurements. Consequently, the accuracy enhancement achieved by these methods is constrained. In response to this challenge, we developed a tailored convolutional neural network (CNN) model capable of effectively detecting entanglement in two-qubit quantum states, achieving an accuracy exceeding 97.5%. Notably, even in the presence of noise, this model retains its robust performance, displaying resilience against a tolerable level of noise contamination. Furthermore, the inherent generalization power of CNNs allows our model, which was initially trained on a specific spectrum of quantum states, to extend its applicability to wider states, positioning it as an outstanding tool for the further application of machine learning in the field of quantum computing, opening up new pathways for solving entanglement detection problems in quantum information.

We attained wurtzite ScxAl1−xN (0.16 ≤ x ≤ 0.37) thin films by varying the Sc and Al fluxes at a fixed active nitrogen flux during plasma-assisted molecular beam epitaxy. Atomic fluxes of Sc and Al sources via measured Sc percentage in as-grown ScxAl1−xN thin films were derived as the feedback for precise determination of the ScxAl1−xN growth diagram. We identified an optimal III/N atomic flux ratio of 0.78 for smooth Sc0.18Al0.82N thin films. Further increasing the III/N ratio led to phase separation under N-rich conditions, validated by the observation of high-Sc-content hillocks with energy-dispersive X-ray spectroscopy mapping. At the fixed III/N ratio of 0.78, we found that phase separation with high-Al-content hillocks occurs for x > 0.37, which is substantially lower than the thermodynamically dictated threshold Sc content of ~0.55 in wurtzite ScxAl1−xN. We postulate that these wurtzite-phase purity degradation scenarios are correlated with adatom diffusion and the competitive incorporation process of Sc and Al. Therefore, the ScxAl1−xN growth window is severely restricted by the adatom kinetics. We obtained single crystalline Sc0.37Al0.63N thin films with X-ray diffraction (002)/(102) ω rocking curve full-width at half-maximums of 2156 arcsec and 209 arcsec and surface roughness of 1.70 nm. Piezoelectric force microscopy probing of the Sc0.37Al0.63N epilayer validates unambiguous polarization flipping by 180°.

An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance.

Flexible cages are widely used in marine aquaculture, yet their mechanical features in extreme seas are still unclear. This study proposes a numerical algorithm to solve the coupled response of the multiple cage systems. The net and mooring lines are modeled using the lumped-mass model, while the flexible floating collar system is assessed with the large-deformation FEM model, and the two models are coupled through an iterative scheme. Sea trials are conducted, and the motion of the cage is obtained using an image processing technique, which validates the numerical algorithm. Using the proposed numerical algorithm, a series of simulations are performed to investigate the response of flexible cages in extreme seas. Motions, line tensions, and structural sectional forces are studied, and the effects of factors such as the wavelength of incident waves and the diameter of collar pipes are investigated.

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method.

This study aimed to determine the effects of different doses of Acremonium terricola culture (ATC) on lactation performance, immune function, antioxidant capacity, and intestinal flora of sows. Forty-five Landrace sows (3–6 parity) were randomly assigned to the following three treatments from 85 days of gestation to 21 days after farrowing: a control diet (CON, basal diet), a low-dose Acremonium terricola culture diet (0.2% ATC, basal diet + 0.2% ATC), and a high-dose Acremonium terricola culture diet (0.4% ATC, basal diet + 0.4% ATC). Compared with the CON group, the supplementation of 0.2% ATC increased the average daily milk yield of sows by 4.98%, increased milk fat, total solids, and freezing point depression on day 1 postpartum (p < 0.05), increased serum concentration of Triiodothyronine, Thyroxin, and Estradiol on day 21 postpartum (p < 0.05). Compared with the CON group, the supplementation of 0.4% ATC increased the average daily milk yield of sows by 9.38% (p < 0.05). Furthermore, the supplementation of 0.2% ATC increased serum concentration of IgG, IgM, and IFN-γ, CD4 on day 1 postpartum (p < 0.05) and increased serum concentration of immunoglobulin A ( IgA), immunoglobulin G (IgG), immunoglobulin M ( IgM), complement 3 (C3), cluster of differentiation 4 (CD4), cluster of differentiation 8 (CD8), interferon-γ (IFN-γ) on day 21 postpartum (p < 0.05), while the supplementation of 0.4% ATC reduced serum concentration of IL-2 on day 21 postpartum (p < 0.05). Moreover, the supplementation of 0.4% ATC significantly increased serum concentration of catalase (CAT) (p < 0.05). Additionally, the supplementation of ATC affected the relative abundance of the intestinal flora at different taxonomic levels in sows and increased the abundance of beneficial bacteria such as in the norank_f__Eubacterium_coprostanoligenes group, Eubacterium_coprostanoligenes group, and Lachnospiraceae_XPB1014 group of sows, while reducing the abundance of harmful bacteria such as Phascolarctobacterium and Clostridium_sensu_stricto_1. These data revealed that the supplementation of ATC during late gestation and lactation can improve lactation performance, immune function, antioxidant capacity, and the gut microbiota. Compared with supplementation of 0.4% ATC, 0.2% ATC enhances the levels of thyroid-related hormones, specific antibodies, and cytokines in serum, promotes the diversity of beneficial gut microbiota, beneficial bacteria in the intestine, reduces the population of harmful bacteria, and thereby bolsters the immunity of sows. Hence, 0.2% ATC is deemed a more optimal concentration.

In this paper, a physics-based ANN assisting method for extracting transient properties of extrinsically triggering photoconductive semiconductor switches (ET-PCSSs) is proposed. It exploits the nonlinear mapping of ANN between transient current (input) and doping concentration (output). According to the basic laws of photoelectric device operating, two types of ANN models are constructed by gaussian and polynomial fitting. The mean absolute error (MAE) of forecasting transient photocurrent can be less than 10 A under low triggering optical powers, which verifies the feasibility of ANN assisting TCAD applied to PCSSs. The results are comparable to computation by Mixed-Mode simulation, yet even thousands of seconds of CPU runtime cost are saved in every period. To improve the robustness of the Poly-ANN predictor, Bayesian optimization (BO) is implemented for minimizing the curl deviation of photocurrent-time curves.

Iron-based shape memory alloys (Fe-SMAs), traditionally manufactured, are favored in engineering applications owing to their cost-effectiveness and ease of fabrication. However, the conventional manufacturing process of Fe-SMAs is time-consuming and raw-material-wasting. In contrast, additive manufacturing (AM) technology offers a streamlined approach to the integral molding of materials, significantly reducing raw material usage and fabrication time. Despite its potential, research on AMed Fe-SMAs remains in its early stages. This review provides updated information on current AM technologies utilized for Fe-SMAs and their applications. It provides an in-depth discussion on how printing parameters, defects, and post-printing microstructure control affect the mechanical properties and shape memory effect (SME) of AMed Fe-SMAs. Furthermore, this review identifies existing challenges in the AMed Fe-SMA approach and proposes future research directions, highlighting potential areas for development. The insights presented aim to guide improvements in the material properties of AMed Fe-SMAs by optimizing printing parameters and enhancing the SME through microstructure adjustment.

A substantial body of evidence indicates that neurological, psychological, and behavioral health issues are profoundly linked to interoceptive sensitivity. The present study aimed to identify the effects of interoceptive sensitivity on the relationship between physical activity and symptoms of depression in Chinese college students. This study employed a cross-sectional design using convenience sampling. An online self-reported survey was distributed to college students in China. The participants’ interoceptive sensitivity, physical activity levels, and depressive symptoms were measured using the MAIA-2, IPAQ-SF, and PHQ-9, respectively. The mediating effect was tested via regression analysis and a parallel mediation model, with bootstrap confidence intervals for indirect effects. The results showed a significant negative correlation between physical activity and depression. A significant positive correlation was observed between physical activity and seven dimensions of interoceptive sensitivity. Conversely, interoceptive sensitivity exhibited a negative correlation with depression. The bootstrap mediation analysis showed that the “not distracting” and “trusting” dimensions of interoceptive sensitivity had significant indirect effects on the relationship between physical activity and depression, suggesting that physical activity might reduce depressive symptoms via these two interoceptive sensitivity dimensions. The findings suggest that interoceptive sensitivity should be integrated into therapeutic interventions, such as physical activity interventions, in the treatment of mental illnesses, particularly depression. Increasing physical activity levels, with a specific focus on enhancing interoceptive modulation, appears to be a promising approach for addressing depression in college students.

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries.

Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer’s multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance.

In this study, an overview of two years of research findings concerning the 2022 Hunga Tonga–Hunga Ha’apai (HTHH) volcanic eruption in the stratospheric environment is provided, focusing on water vapor, aerosols, and ozone. Additionally, the potential impacts of these changes on aviation equipment materials are discussed. The HTHH volcanic eruption released a large amount of particles (e.g., ash and ice) and gases (e.g., H2O, SO2, and HCl), significantly affecting the redistribution of stratospheric water vapor and aerosols. Stratospheric water vapor increased by approximately 140–150 Tg (8–10%), with a concentration peak observed in the height range of 22.2–27 km (38–17 hPa). Satellite measurements indicate that the HTHH volcano injected approximately 0.2–0.5 Tg of sulfur dioxide into the stratosphere, which was partially converted into sulfate aerosols. In-situ observations revealed that the volcanic aerosols exhibit hygroscopic characteristics, with particle sizes ranging from 0.22–0.42 μm under background conditions to 0.42–1.27 μm. The moist stratospheric conditions increased the aerosol surface area, inducing heterogeneous chlorine chemical reactions on the aerosol surface, resulting in stratospheric ozone depletion in the HTHH plume within one week. In addition, atmospheric disturbances and ionospheric disruptions triggered by volcanic eruptions may adversely affect aircraft and communication systems. Further research is required to understand the evolution of volcanic aerosols and the impact of volcanic activity on aviation equipment materials.

Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the small scale of medical datasets, a limitation directly stemming from current medical data acquisition capabilities. To this end, we introduce AttEUnet, a medical cell segmentation network enhanced by edge attention, based on the Attention U-Net architecture. It incorporates a detection branch enhanced with edge attention and a learnable fusion gate unit to improve segmentation accuracy and convergence speed on small medical datasets. The AttEUnet allows for the integration of various types of prior information into the backbone network according to different tasks, offering notable flexibility and generalization ability. This method was trained and validated on two public datasets, MoNuSeg and PanNuke. The results show that AttEUnet significantly improves segmentation performance on small medical datasets, especially in capturing edge details, with F1 scores of 0.859 and 0.888 and Intersection over Union (IoU) scores of 0.758 and 0.794 on the respective datasets, outperforming both convolutional neural networks (CNNs) and transformer-based baseline networks. Furthermore, the proposed method demonstrated a convergence speed over 10.6 times faster than that of the baseline networks. The edge attention branch proposed in this study can also be added as an independent module to other classic network structures and can integrate more attention priors based on the task at hand, offering considerable scalability.

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.

Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this paper, we proposed an Attention-Interactive Dual-Branch Convolutional Neural Network (AIDB-Net) for HS pansharpening. Our model purely consists of convolutional layers and simultaneously inherits the strengths of both convolution and self-attention, especially the modeling of short- and long-range dependencies. Specially, we first extract, tokenize, and align the hyperspectral image (HSI) and panchromatic image (PAN) by Overlapping Patch Embedding Blocks. Then, we specialize a novel Spectral-Spatial Interactive Attention which is able to globally interact and fuse the cross-modality features. The resultant token-global similarity scores can guide the refinement and renewal of the textural details and spectral characteristics within HSI features. By deeply combined these two paradigms, our AIDB-Net significantly improve the pansharpening performance. Moreover, with the acceleration by the convolution inductive bias, our interactive attention can be trained without large scale dataset and achieves competitive time cost with its counterparts. Compared with the state-of-the-art methods, our AIDB-Net makes 5.2%, 3.1%, and 2.2% improvement on PSNR metric on three public datasets, respectively. Comprehensive experiments quantitatively and qualitatively demonstrate the effectiveness and superiority of our AIDB-Net.

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved.

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation.

Wheat stem rust, caused by the fungus Puccinia graminis f. sp. tritici (Pgt), poses a substantial threat to global wheat production. Utilizing stem rust resistance (Sr) genes represents an economically viable, effective, and environmentally friendly approach to disease control. In this study, gene postulation, molecular testing, and pedigree analysis were used to identify the presence of Sr genes in 45 wheat cultivars. In addition, the resistance of these cultivars was evaluated against two predominant Pgt races, 34MRGQM and 21C3CTHTM, at the adult-plant stage during 2021–2022. The results identify seven Sr genes (Sr31, Sr38, Sr30, SrTmp, Sr22, Sr19, and Sr5) within 35 wheat cultivars. Among these, 23 cultivars contained Sr31, whereas Sr5 and SrTmp were present in four cultivars each. Han 5316, Shimai 15, Shiyou 20, and Kenong 1006 exhibited the presence of Sr19, Sr22, Sr30, and Sr38, respectively. Molecular studies confirmed the absence of Sr25 and Sr26 in any of the wheat cultivars. During field evaluation, 37 (82.2%) and 39 (86.7%) wheat cultivars demonstrated resistance to races 34MRGQM and 21C3CTHTM, respectively. Moreover, 33 wheat cultivars (73.3%) exhibited resistance to all the tested races. These study findings will significantly contribute to future research in wheat pre-breeding and abiotic stress tolerance.

Apicomplexan protozoa, which are a group of specialized intracellular parasitic protozoa, infect humans and other animals and cause a variety of diseases. The lack of research on the interaction mechanism between Apicomplexan protozoa and their hosts is a key factor restricting the development of new drugs and vaccines. In the early stages of infection, cell apoptosis is inhibited by Apicomplexan protozoa through their interaction with the host cells; thereby, the survival and reproduction of Apicomplexan protozoa in host cells is promoted. In this review, the key virulence proteins and pathways are introduced regarding the inhibition of cell apoptosis by the interaction between the protozoa and their host during the early stage of Apicomplexan protozoa infection. It provides a theoretical basis for the development of drugs or vaccines for protozoal diseases.

Hot-deformation pre-bonding is a newly developed method to avoid smelting defects in large stainless steel billets, in which the high-quality interfacial connection needs to be realized as a key guarantee. In this work, a novel electromagnetic shocking treatment (EST) method, as a special way to apply an external physical field, was proposed to introduce the hot-deformation pre-bonded austenitic stainless steel. It is demonstrated that EST can efficiently optimize the hot-deformation pre-bonded interface and restore the tensile properties of pre-bonded stainless steel within several seconds. The interface healing behaviors induced by EST were analyzed via the in situ observation of an optical micrograph, scanning electron micrograph, as well as electron backscattering diffraction. The optimization of the interface connection is mainly attributed to the fact that EST can act on the interface locally, leading to the healing of voids at the interface accompanied by a reduction in stress and the decomposition of oxide. In addition, EST plays another important role in adjusting the texture difference at both ends of the interface, which can further improve the mechanical properties. The results suggest that EST exhibits great potential in promoting the hot-deformation pre-bonding of large billets.

Cigarettes contain various chemicals that cause damage to nerve cells. Exposure to cigarette smoke (CS) causes insulin resistance (IR) in nerve cells. However, the mechanisms for a disorder in the cigarette-induced insulin signaling pathway and in neurotoxicity remain unclear. Therefore, we evaluated, by a series of pathology analyses and behavioral tests, the neurotoxic effects of chronic exposure to CS on C57BL/6 mice. Mice exposed to CS with more than 200 mg/m3 total particulate matter (TPM) exhibited memory deficits and cognitive impairment. Pathological staining of paraffin sections of mouse brain tissue revealed that CS-exposed mice had, in the brain, neuronal damage characterized by thinner pyramidal and granular cell layers and fewer neurons. Further, the exposure of SH-SY5Y cells to cigarette smoke extract (CSE) resulted in diminished insulin sensitivity and reduced glucose uptake in a dose-dependent fashion. The PI3K/GSK3 insulin signaling pathway is particularly relevant to neurotoxicity. microRNAs are involved in the PI3K/GSK3β/p-Tau pathway, and we found that cigarette exposure activates miR-153-3p, decreases PI3K regulatory subunits PIK3R1, and induces Tau hyperphosphorylation. Exposure to an miR-153 inhibitor or to a PI3K inhibitor alleviated the reduced insulin sensitivity caused by CS. Therefore, our results indicate that miR-153-3p, via PIK3R1, causes insulin resistance in the brain, and is involved in CS-induced neurotoxicity.

Online surface inspection systems have gradually found applications in industrial settings. However, the manual effort required to sift through a vast amount of data to identify defect images remains costly. This study delves into a self-supervised binary classification algorithm for addressing the task of defect image classification within ductile cast iron pipe (DCIP) images. Leveraging the CutPaste-Mix data augmentation strategy, we combine defect-free data with enhanced data to input into a deep convolutional neural network. Through Gaussian Density Estimation, we compute anomaly scores to achieve the classification of abnormal regions. Our approach has been implemented in real-world scenarios, involving equipment installation, data collection, and experimentation. The results demonstrate the robust performance of our method, in both the DCIP image dataset and practical field application, achieving an impressive 99.5 AUC (Area Under Curve). This presents a cost-effective means of providing data support for subsequent DCIP surface inspection model training.

β-Ga2O3 nanostructures are attractive wide-band-gap semiconductor materials as they exhibit promising photoelectric properties and potential applications. Despite the extensive efforts on β-Ga2O3 nanowires, investigations into β-Ga2O3 nanotubes are rare since the tubular structures are hard to synthesize. In this paper, we report a facile method for fabricating β-Ga2O3 nanotubes using pre-synthesized GaSb nanowires as sacrificial templates. Through a two-step heating-treatment strategy, the GaSb nanowires are partially oxidized to form β-Ga2O3 shells, and then, the residual inner parts are removed subsequently in vacuum conditions, yielding delicate hollow β-Ga2O3 nanotubes. The length, diameter, and thickness of the nanotubes can be customized by using different GaSb nanowires and heating parameters. In situ transmission electron microscopic heating experiments are performed to reveal the transformation dynamics of the β-Ga2O3 nanotubes, while the Kirkendall effect and the sublimation process are found to be critical. Moreover, photoelectric tests are carried out on the obtained β-Ga2O3 nanotubes. A photoresponsivity of ~25.9 A/W and a detectivity of ~5.6 × 1011 Jones have been achieved with a single-β-Ga2O3-nanotube device under an excitation wavelength of 254 nm.

Background: Psychological distress is an important mental health problem in college students. Insomnia may be a major factor contributing to psychological distress. This study aimed to explore the indirect relationship between insomnia and psychological distress through the five PERMA wellbeing variables (i.e., positive emotions, engagement, relationships, meaning in life, and achievement) among nursing college students. Methods: A cross-sectional study was conducted in China using an online survey design. Mediation analyses were examined using the PROCESS macro version 4.1 for SPSS 27.0. A total of 1741 nursing college students completed the online survey. Results: Insomnia was positively associated with psychological distress (p < 0.01, r = 0.673), while negative associations were detected between PERMA wellbeing variables and insomnia (p < 0.01, r range = −0.176 and −0.272), as well as psychological distress (p < 0.01, r range = −0.196 and −0.386). The association between insomnia and psychological distress was partially mediated by the participants’ positive emotions (indirect effect = 0.137, SE = 0.024, 95% CI boot = [0.094, 0.188]), engagement (indirect effect = −0.033, SE = 0.010, 95% CI boot = [−0.054, −0.017]), and meaning in life (indirect effect = 0.027, SE = 0.014, 95% CI boot = [0.001, 0.055]) but not their relationships or achievement of the PERMA wellbeing variables. Conclusions: The findings of this study suggest that the PERMA wellbeing variables, especially positive emotions, engagement, and meaning in life, could be potential mechanisms by which insomnia is associated with psychological distress. The mediating roles of PERMA wellbeing variables between insomnia and psychological distress could be incorporated into the health management of university administrations to promote the health and wellbeing of nursing college students.

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Over the last several decades, China has continuously introduced Duroc boars and used them as breeding boars. Although this crossbreeding method has increased pork production, it has affected pork quality. Nowadays, one of the primary goals of industrial breeding and production systems is to enhance the quality of meat. This research analyzed the molecular mechanisms that control the quality of pork and may be used as a guide for future efforts to enhance meat quality. The genetic mechanisms of cross-breeding for meat quality improvement were investigated by combining transcriptome and metabolome analysis, using Chinese native Jiaxing black (JXB) pigs and crossbred Duroc × Duroc × Berkshire × JXB (DDBJ) pigs. In the longissimus Dorsi muscle, the content of inosine monophosphate, polyunsaturated fatty acid, and amino acids were considerably higher in JXB pigs in contrast with that of DDBJ pigs, whereas DDBJ pigs have remarkably greater levels of polyunsaturated fatty acids than JXB pigs. Differentially expressed genes (DEGs) and differential metabolites were identified using transcriptomic and metabolomic KEGG enrichment analyses. Differential metabolites mainly include amino acids, fatty acids, and phospholipids. In addition, several DEGs that may explain differences in meat quality between the two pig types were found, including genes associated with the metabolism of lipids (e.g., DGKA, LIPG, and LPINI), fatty acid (e.g., ELOVL5, ELOVL4, and ACAT2), and amino acid (e.g., SLC7A2, SLC7A4). Combined with the DEGS-enriched signaling pathways, the regulatory mechanisms related to amino acids, fatty acids, and phospholipids were mapped. The abundant metabolic pathways and DEGs may provide insight into the specific molecular mechanism that regulates meat quality. Optimizing the composition of fatty acids, phospholipids, amino acids, and other compounds in pork is conducive to improving meat quality. Overall, these findings will provide useful information and further groundwork for enhancing the meat quality that may be achieved via hybrid breeding.

A novel ultra-compact four-port multiple-input-multiple-output (MIMO) cylindrical dielectric resonator antenna (DRA) with improved isolation is proposed for WLAN applications in this paper. The antenna is originally radiated with the assistance of two different excitation mechanisms to generate decoupled orthogonal modes. To further diminish the coupling field and improve the isolation, a suitable U-shaped slot is created on the common ground plane. Two additional rectangular slits are also etched to adjust the impedance matching of other ports. To better reveal the operating mechanism of the decoupling scheme, the common mode (CM) and differential mode (DM) impedance analysis methods between DRA ports are presented. The etched U-shaped slot can tune the impedance of CM and DM to be consistent to realize the decoupling. The antenna is simulated, fabricated, and tested to verify the decoupling mechanism. The results demonstrate that the isolation between ports 1 and 2 is enhanced from 5 dB to 23 dB, and other ports exhibit low coupling of better than 12 dB. Moreover, the antenna with the full size of 30 × 30 × 8.1 mm3 can be used either as a four-port DRA with a bandwidth of 300 MHz or as a two-port DRA with a bandwidth of 700 MHz, at a center frequency of 5.6 GHz.

Heteroepitaxial growth of high Al-content AlGaN often results in a high density of threading dislocations and surface hexagonal hillocks, which degrade the performance and reliability of AlGaN-based UVC light emitting diodes (LEDs). In this study, the degradation mechanism and impurity/defect behavior of UVC LEDs in relation to the hexagonal hillocks have been studied in detail. It was found that the early degradation of UVC LEDs is primarily caused by electron leakage. The prominent contribution of the hillock edges to the electron leakage is unambiguously evidenced by the transmission electron microscopy measurements, time-of-flight secondary ion mass spectrometry, and conductive atomic force microscopy. Dislocations bunching and segregation of impurities, including C, O, and Si, at the hillock edges are clearly observed, which facilitate the trap-assisted carrier tunneling in the multiple quantum wells and subsequent recombination in the p-AlGaN. This work sheds light on one possible degradation mechanism of AlGaN-based UVC LEDs.

Amorphous fluorine resin is a promising material that can be used for the encapsulation of deep-ultraviolet light-emitting diodes (DUV LEDs) to promote the light output, due to its light characteristics which mean it shows no absorption in the DUV wavelength region. However, obvious decay and high cost are the problems faced by fluorine resin. In this study, thin-fluorine-resin-film-packaged DUV LEDs were fabricated by the drip-coating method and the characteristics were tested. The results show that the light output power increased from 4.95 mW to 5.44 mW at 40 mA, and the cost of fluorine resin can be reduced to ~10%. In addition, no degradation during the aging was observed. However, when the light output power reached 12 mW or higher, >10% decay was observed after aging for 1000 h. In conclusion, thin-fluorine-resin-film-packaged DUV LEDs can achieve 10% light output power enhancement by using less fluorine resin material, and the material is more applicable to low-power DUV LEDs.

Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents’ demands during different periods, compared to the actual layout of NAT sites in the city. The study’s findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.

Strong earthquakes can not only trigger many landslides in a short period of time but can also change the stability of slopes in the earthquake area, causing them to be active for a long time after the earthquake. Research on the variation of slow-motion slopes before and after earthquakes can help us to better understand the mechanism of earthquake-affected landslides, which is also crucial for assessing the long-term landslide risk in seismically active areas. Here, L-band ALOS-2 PALSAR-2 images are utilized with the SBAS-InSAR algorithm to monitor and assess the location and activity changes of slow-moving landslides in the Iburi region (Hokkaido, Japan) before and after an earthquake occurred on 6 September 2018. Unlike previous studies, which focused on single typical landslides, we tracked all the landslides within a 33 × 55 km region close to the epicenter. According to the results, the majority of the co-seismic landslides that quickly failed during the earthquake are now stable, and a few of them are still moving. In contrast, due to near-field seismic shaking, certain slopes that did not show substantial surface changes during the earthquake period continued to move and eventually developed into slow-moving landslides. In addition, it can be seen from the spatial distribution of slow-moving landslides after the earthquake that this distribution is not only dependent on strong earthquake seismic vibration or the hanging-wall effect. Far-field weak vibrations can also accelerate landslides. Additionally, we discovered that the earthquake made the unstable slopes move more quickly but also tended to stabilize the slopes that were already in motion before the earthquake. The various response modes of slow-moving landslides to seismic events are related not only to the intensity of seismic vibration but also to the geological conditions of the region and to the size of the landslide itself. These findings are extremely valuable for studying the mechanism of earthquake-affected landslides.

Soil organic matter (SOM) is a critical indicator of soil nutrient levels, and the precise mapping of its spatial distribution through remote sensing is essential for soil regulation, precise fertilization, and scientific management and protection. This information can offer decision support to agricultural management departments and various agricultural producers. In this paper, two new soil indices, NLIrededge2 and GDVIrededge2, were proposed based on the sensitive spectral response characteristics of SOM in Northeast China. Nine parameters suitable for SOM mapping and modeling were determined using the competitive adaptive reweighted sampling (CARS) method, combined with spectrum reflectance, mathematical transformations of reflectance, vegetation indices, and so on. Then, utilizing unmanned aerial vehicle (UAV)-based multispectral images with centimeter-level resolution, a random forest machine learning algorithm was used to construct the inversion model of SOM and mapping SOM in the study area. The results showed that the random forest algorithm performed best for estimating SOM (R2 = 0.91, RMSE = 0.95, MBE = 0.49, and RPIQ = 3.25) when compared with other machine learning algorithms such as support vector regression (SVR), elastic net, Bayesian ridge, and linear regression. The findings indicated a negative correlation between SOM content and altitude. The study concluded that the SOM modeling and mapping results could meet the needs of farmers to obtain basic information and provide a reference for UAVs to monitor SOM.

We report on the epitaxial lateral overgrowth (ELO) of high-quality AlN on stripe-patterned Si(111) substrates with various trench widths. By narrowing down the trench and ridge widths of patterned Si substrates, crack-free, 6-micrometer-thick, high-quality AlN films on Si substrates were produced. The full-width-at-half-maximum values of the X-ray-diffraction rocking curves for the AlN (0002) and (101¯2) planes were as low as 260 and 374 arcsec, respectively, corresponding to a record low dislocation density of 1.3 × 109 cm−2. Through the combination of a micro-Raman study and the X-ray diffraction analysis, it was found that narrowing the stripe width from 5 μm to 3 μm can reduce the vertical growth thickness before coalescence, resulting in a large decrease in the internal tensile stress and tilt angle, and, therefore, better suppression in the cracks and dislocations of the ELO–AlN. This work paves the way for the fabrication of high-performance Al(Ga)N-based thin-film devices such as ultraviolet light-emitting diodes and AlN bulk acoustic resonators grown on Si.

Termites are eusocial insects that live in organized colonies consisting of reproductives, workers, and soldiers. Soldiers are specialized for defense but are expensive to maintain, as they are incapable of husbandry and must be fed and groomed by workers. The soldiers of several species influence foraging behavior by acting as scouts that initiate foraging or by mediating worker behavioral plasticity during food exploration. These behaviors imply that soldiers may play a keystone role in termite colony function, apart from defense. Subterranean termite workers tunnel through soil in search of food while accompanied by varying proportions of soldiers, depending on the species and colony conditions. Previous studies have shown that soldiers accelerate worker exploratory tunneling behavior in two Reticulitermes species, the colonies of which contain fewer than 2% soldiers. This effect, however, is unknown in other subterranean species with different soldier proportions. In this study, we examined the influence of soldiers on exploratory foraging behavior in the Formosan subterranean termite, Coptotermes formosanus Shiraki, which is an economically devastating invasive species that maintains a relatively high soldier proportion (about 10%). When 100 foraging workers were grouped with 0, 2, 10, or 30 soldiers in two-dimensional foraging arenas, we found no significant effect of soldiers on the tunnel length, branch pattern, food source interception, or food collected within 96 h. These results suggest that C. formosanus colonies maintain food exploration efficiency regardless of soldier proportion variation.

This paper aims to seek soliton solutions for the nonlocal generalized Sasa–Satsuma (gSS) equation by constructing the Darboux transformation (DT). We obtain soliton solutions for the nonlocal gSS equation, including double-periodic wave, breather-like, KM-breather solution, dark-soliton, W-shaped soliton, M-shaped soliton, W-shaped periodic wave, M-shaped periodic wave, double-peak dark-breather, double-peak bright-breather, and M-shaped double-peak breather solutions. Furthermore, interaction of these solitons, as well as their dynamical properties and asymptotic analysis, are analyzed. It will be shown that soliton solutions of the nonlocal gSS equation can be reduced into those of the nonlocal Sasa–Satsuma equation. However, several of these properties for the nonlocal Sasa–Satsuma equation are not found in the literature.

Under the influence of variable sea conditions, a ship will have an oscillating motion comprising six degrees of freedom, all of which are connected to each other. Among these degrees of freedom, rolling and pitching motions have a severe impact on a ship’s maritime operations. An accurate and effective ship motion attitude prediction method that makes the prediction in a short period of time is required to guarantee the safety and stability of the ship’s maritime operations. Traditional methods are based on time domain analysis, such as the autoregressive moving average (ARMA) models. However, these models have limitations when it comes to predicting the nonlinear and nonstationary characteristics of real ship motion attitude data. Many intelligent algorithms continue to be applied in nonlinear and nonstationary ship attitude prediction, such as extreme learning machines (ELMs) and the long short-term memory (LSTM) neural network, as well as other deep learning methods, showing promising results. By using the sliding window approach, the time-varying dynamic characteristics of the ship’s motion attitude can be preserved better. The simulation results demonstrate that the proposed model performs well in terms of predicting the nonlinear and nonstationary ship motion attitude.

As a function of a circular polarization beam splitter (CPBS), combining a linear polarization beam splitter with a quarter-wave plate results in a polarization error in a circular polarization fiber-optic circuit. To relieve the error, chiral dual-core photonic crystal fiber (DC-PCF) is investigated as a kind of an efficient circular polarization beam splitter by using the chiral plane-wave expansion (PWE) method. On the basis of the competitive effect in polarization and coupling length between the circular asymmetry of the structure and the chirality of the medium, the effects of the structure and the chirality are analyzed. The numerical results demonstrate that a CPBS needs the weak circular asymmetry in its structure and a relatively stronger chirality of the medium. Then, a kind of CPBS based on chiral DC-PCF is designed with weaker chirality, with a central wavelength of 1.55 μm. The simulation shows the superior performance of having a shorter coupling length and a higher extinction ratio. Furthermore, the dual-wavelength of 1.55 μm and 1.30 μm with left-circular polarization can further be separated by the corresponding chiral DC-PCF. The results show promising applications for the circular polarized multiplexer/demultiplexer in fiber laser communication systems.

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 (p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale.

Neosporosis is caused by Neospora caninum (N. caninum), which mainly infects cattle and goats and severely threatens the animal industry. In this study, the inhibitory effects of polyclonal antiserum anti-NcSRS17, NcSRS2 and NcSRS52 were explored. Cytokines in mice or goat serum were detected after immunization. After infection, the survival of mice was recorded. The pathological changes and parasite loads were observed and detected in tissues. The results showed that anti-NcSRS2, NcSRS17 and NcSRS52 antibodies all inhibit the invasion and proliferation of N. caninum. The IFN-γ level in the NcSRS17 group was higher than that in the NcSRS2 and NcSRS52 groups, and higher in the NcSRS2-mIL-18 group than in the NcSRS2 group. The survival rates of mice were 16% in the positive control group, 67% in the SRS52 group, 83% in the SRS2 and mIL-18 groups and 100% in the SRS17 and SRS2-mIL-18 groups. Goats immunized with NcSRS17-gIL-18 developed high levels of IL-4, IL-12 and IFN-γ compared with those immunized with NcSRS-17. Parasite loads in the brains of animals in the NcSRS17 and NcSRS17-gIL-18 groups were significantly reduced, and were significantly lower in the NcSRS17-gIL-18 group (p ≤ 0.01). This study indicates that SRS17 may be an antigen candidate for vaccine development against neosporosis, and IL-18 can enhance the immune protective efficiency of antigen candidates.

It is of great significance to reduce welding hot cracks and improve the corrosion resistance of nuclear power components by controlling the dilution ratio of the cladding weld bead formed by gas tungsten arc welding (GTAW). This paper presents a method to predict the dilution ratio by using the ratio of the thermal power of forming a unit length weld to the cross-section of the fuse, that is, the linear energy of forming a unit volume of deposited metal, which is characterized by the “Heat Equivalent for Melting Welding Wire” (HEMW). It is found that there is a linear positive correlation between the HEMW and the dilution ratio. With the increase in HEMW, the proportion of deposition heat energy in the whole effective heat input energy decreases, the proportion of base metal melting heat energy increases, and the proportion of non-melting heat energy diffused to the base metal remains basically unchanged. The HEMW is used to predict and control the dilution rate under the condition of pulse welding. By increasing the wire feeding speed under the condition of pulse peak current and reducing the welding speed, a high deposition efficiency, low dilution rate and low heat input is realized, which plays an important guiding role in improving the deposition quality.

Non-public networks (NPNs) have drawn much attention due to their flexibility and efficiency with B5G. According to the requirements of various application scenarios, NPNs can be tailored to a number of deployments, sharing the B5G system totally or in part. The isolation of NPNs is a critical issue. This paper provides a survey of isolation schemes for B5G NPNs. First, we present an overview of various deployments and the corresponding isolation demands for B5G NPNs. To meet these isolation demands, three kinds of NPN isolation—i.e., spectrum isolation, RAN isolation and CN isolation—are discussed. Then, the corresponding isolation technologies are introduced and analyzed. Finally, open research challenges, such as wireless throughput capacity with spectrum isolation, operation with isolation requirements and data isolation of software-defined CN for B5G NPNs, are discussed.

Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.

Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RGB and multispectral images were analyzed. The VGG-16, Inception-V3 and ResNet-50 algorithms were trained and compared depending on classification accuracy and Kappa coefficient. The results showed that the more severe the lodging, the higher the intensity value and spectral reflectance of RGB and multispectral image. The reflectance variation in red edge band were more evident than that in visible band with different lodging extents. The classification performance using multispectral images was better than that of RGB images in various lodging extents. The test accuracies of three deep learning algorithms in non-lodging based on RGB images were high, i.e., over 90%, but the classification performance between moderate lodging and severe lodging needed to be improved. The test accuracy of ResNet-50 was 96.32% with Kappa coefficients of 0.9551 by using multispectral images, which was superior to VGG-16 and Inception-V3, and the accuracies of ResNet-50 on each lodging subdivision category all reached 96%. The ResNet-50 algorithm of deep learning combined with multispectral images can realize accurate lodging classification to promote post-stress field management and production assessment.

Myriophyllum aquaticum (M. aquaticum) is a commonly used aquatic macrophyte for water purification and could be utilized as animal food. However, the high water content of M. aquaticum makes it difficult for long-term preservation, which leads to challenges as an ideal animal feed ingredient. The storage of Silage for long periods may be a proper method to solve the problem. In the present paper, we assess the effects of lactic acid bacteria Lactobacillus buchneri (LB), Lactobacillus plantarum (LP), or their combination on fermentation and microbial communities during the ensiling of M. aquaticum silage. The results show that the LP-treated silage displays a higher lactic acid concentration than that in the control silage. Both LB and LP increased the abundance of Lactobacillus, but decreased the abundance of Serratia and Prevotella_9 in M. aquaticum silage after 60 days of ensiling. Both LB and LP increased the diversity and richness of fungi. Therefore, the inoculation of LP improved silage fermentation during ensiling. These results show that the inoculation of lactic acid bacteria improves the fermentation quality of M. aquaticum silage, which makes it possible for the application of M. aquaticum to animal forage in the future.

The excessive extraction and recharge of groundwater lead to long-time seasonal land subsidence in Los Angeles, USA, and especially in the Santa Ana basin. The rate of land subsidence in the Santa Ana basin has been rising, which could pose a danger to infrastructure and human lives. However, the most recent research on land surface deformation in the area was conducted using the traditional parameter estimation method, resulting in little understanding of the regional spatiotemporal characteristics. The parametric method consists of a least square linear inversion, using the pre-defined mathematical geometric or geophysical theoretical models to describe groundwater deformation, and it requires precise external environmental variables and accurate geophysical parameters, which are more difficult to implement. In this study, multitemporal InSAR-derived deformation time series are analyzed by using 69 descending C-band Sentinel-1A SAR scenes acquired from 2015 to 2018. A method based on independent component analysis (ICA) is applied to characterize the spatial pattern and temporal evolution of land subsidence in the Santa Ana basin. The results reveal two different spatial and temporal deformation patterns in the basin. First, a widespread seasonal deformation is identified by the first component, related to annual seasonal groundwater level changes, and the overall deformation shows a concentrated spatial pattern. The second component captures a long-term signal with a large-scale spatial pattern. For quantitative assessment, the obtained deformation time series are compared with the GNSS data, validating an accuracy of millimeters. We further calculate the cross-correlation coefficient and the elastic skeletal storage coefficient from the ICA-derived seasonal deformation and groundwater level, which reveals that the deformation responds quickly (i.e., a lag of 8 days) to the change in groundwater and the Santa Ana aquifer retains almost the same elasticity for at least 15 years. Quantifying the spatiotemporal characteristics of the deformation in the Santa Ana basin can provide a reference for the monitoring and managing of groundwater.

Waterway traffic monitoring is an important content in waterway traffic management. Taking into account that the number of monitored water areas is growing and that waterway traffic management capabilities are insufficient in the current situation in China, this paper investigates the location optimization of the vessel traffic service (VTS) radar station. During the research process, radar attenuation and environmental occlusion, as well as variable coverage radius and multiple covering are all considered. In terms of the radar attenuation phenomenon in the propagation process and obstacles such as mountains and islands in the real world, judgment and evaluation methods in a three-dimensional space are proposed. Moreover, a bi-objective mathematical model is then developed, as well as a modified adaptive strategy particle swarm optimization algorithm. Finally, a numerical example and a case are given to verify the effectiveness of the proposed methods, model, and algorithm. The results show the methods, model, and algorithm proposed in this paper can solve the model efficiently and provide a method to optimize the VTS radar station location in practice.