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Abstract The open‐circuit magnetic field of the V‐shaped interior PM (IPM) machine is predicted by the analytical method. The method is implemented following the basic principle of the subdomain method. The machine model is divided into five different regions, including rotor slot, magnetic barrier, air gap, stator slot, and stator slot opening. To perform the analytical prediction process in the polar coordinate system, these regions should be transformed into the appropriate subdomains. The last four regions can be transformed into the subdomains with the required shape based on the existing method. The rotor slot cannot be treated as a single subdomain, for the shape of which is irregular. Herein, the PM and vacuum region in the rotor slot is divided into several fan‐shaped subdomains. The division method is described clearly. Then, the boundary conditions alongside the interface between adjacent subdomains are derived based on function transformation. On this basis, the open‐circuit magnetic field of the IPM machine can be predicted. The magnetic field of the 6p/36 s V‐shaped IPM machine is obtained by the proposed method, finite element method simulation, and experiment, respectively. Additionally, the comparison results verify the accuracy and efficiency of the proposed method.

Abstract Individual optical addressing in chains of trapped atomic ions requires the generation of many small, closely spaced beams with low cross-talk. Furthermore, implementing parallel operations necessitates phase, frequency, and amplitude control of each individual beam. Here, we present a scalable method for achieving all of these capabilities using a high-performance integrated photonic chip coupled to a network of optical fibre components. The chip design results in very low cross-talk between neighbouring channels even at the micrometre-scale spacing by implementing a very high refractive index contrast between the channel core and cladding. Furthermore, the photonic chip manufacturing procedure is highly flexible, allowing for the creation of devices with an arbitrary number of channels as well as non-uniform channel spacing at the chip output. We present the system used to integrate the chip within our ion trap apparatus and characterise the performance of the full individual addressing setup using a single trapped ion as a light-field sensor. Our measurements showed intensity cross-talk below ~10–3 across the chip, with minimum observed cross-talk as low as ~10–5.

Due to their unstructured characteristics, mature convolutional neural network (CNN) models often have difficulty performing spatial analysis with vector data. Current studies used graph neural network (GCN) models to address this problem. However, the definition of cognition factors involves uncertainties, making it challenging to accurately and comprehensively define these factors. In this paper, the road interchange detection task is taken as an example to introduce the MeshCNN, a deep learning model based on triangular mesh data, aiming to provide a new solution for spatial analysis of vector data. A triangular edge classification model is first trained with simple input features. Then, interchanges are detected based on the classification results with an adaptive method. Experiments were conducted on real-world road network data from four cities. The results reveal that the proposed method outperformed the existing methods with precision and recall rate of 89.36% and 79.25% for interchange detection on the total datasets. Furthermore, our proposed method can also detect interchanges in other regions more easily than the GCN method.

As global demand for renewable energy and electric vehicles increases, the need for lithium has surged significantly. Extracting lithium from salt lake brine has become a cutting-edge technology in lithium resource production. In this study, two-dimensional (2D) GO/MXene composite membranes were fabricated using pressure-assisted filtration with a polyethyleneimine (PEI) coating, resulting in positively charged PEI-GO/MXene membranes. These innovative membranes, taking advantage of the synergistic effects of interlayer channel sieving and the Donnan effect, demonstrated excellent performance in Mg2+/Li+ separation with a mass ratio of 20 (Mg2+ rejection = 85.3%, Li+ rejection = 16.7%, SLi,Mg = 5.7) in simulated saline lake brine. Testing on actual salt lake brine in Tibet, China, confirmed the composite membrane’s potential for effective Mg2+/Li+ separation. In the actual brine test with high concentration, Mg2+/Li+ after membrane separation is 2.2, which indicates that the membrane can significantly reduce the concentration of Mg2+ in the brine. Additionally, the PEI-GO/MXene composite membrane demonstrated strong anti-swelling properties and effective divalent ion rejection. This research presents an innovative approach to advance the development of 2D membranes for the selective removal of Mg2+ and Li+ from salt lake brine.

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition.

The Yellow River estuary is one of the three largest estuaries along the coastal waters of China, along with the Yangtze and Pearl River estuaries. Its adjacent seawaters are rich in biological resources, with abundant fish habitat, spawning grounds, and migration channels in the Bohai Sea and the Yellow Sea. However, as the waters are too shallow for a survey ship, data was insufficient for research in the intertidal zone of the salt and freshwater interchange. Instead, by interviewing and analyzing the daily catches by the set net of the local fishermen, this study explored the fishery biodiversity in the intertidal zone of the Yellow River estuary from April to November 2020. Yellow River estuary is rich in fishery resources, especially small fishery organisms and fish recruitment stocks. There were monthly variations in the dominant species composition and biodiversity index. A total of 61 species were recorded, most of which were fish and crustaceans. The diversity, evenness, and species richness index increased in summer (July and August) and decreased in autumn (October and November). By cluster analysis of the species compositions, the surveyed months can be divided into two groups: May to September, and October to November. Lateolabrax maculatus and Liza haematocheila were the most common and dominant species in all surveyed months. The main contributors to the catch of each month were: snails from April to July, snails and fish in August, fish in September, and crabs from October to November. Catches by the set net were mainly composed of individuals with body mass < 2 g from April to September. From body mass-frequency distributions, fish with body mass < 10 g were the main contributors to total catches from April to September. The mean body mass of fish in all surveyed months was 13 g/ind.. The dominant fish species with body mass with < 20 g were: Gobioidei in April; L. maculatus in May; Konosirus punctatus in June; Pennahia argentata, Gobioidei, and L. haematocheila in June to August; and Sardinella zunasi, Thrissa kammalensis, T. mystax, Sillago sihama, Tylosurus melanotus, Gobioidei, and L. haematocheila in September. From the above results, the local set net fishery survey proved to be an effective method of collecting fishery data in the intertidal zone and can be incorporated in studies considering the distribution of fishery resources and breeding grounds in the seawaters adjacent to estuaries. In addition, this study provided clear evidence that set net fishing practices could severely impact the natural recruitment and stock enhancement of fish resources due to the low selectivity of nets and the overlap with larval fish migration channels. The number of larval fish captured by the set net fishery was, roughly estimated, several times higher than the local enhancement released. To minimize the effects of net mesh size, considering the spatial-temporal distributions of fish larvae, set net fishing practices should be concentrated to summer and early autumn, though further research is required to comprehensively account for both the protection of fishery resources and livelihoods.

The mechanical properties and crack propagation behavior of aluminum alloys, both with and without corroded surfaces, were thoroughly investigated through molecular dynamic (MD) simulations. The study delved into the effects of corrosion depth and width on the mechanical properties of corroded aluminum alloys. It was found that as the corrosion depth increases, the yield strength experiences an initial decrease followed by a subsequent increase. This can be attributed to the impact of increased corrosion depth on the healing of surface roughness, which ultimately leads to significant changes in yield strength. Furthermore, the presence of corrosion pits was identified as a key factor in regulating the local microstructure evolution within the material, leading to pronounced differences in stress distribution localization. This, in turn, influenced the path of crack propagation within the material. These findings not only contribute to a deeper understanding of the behavior of aluminum alloys under corrosion, but also provide valuable insights for the development of aluminum alloys with enhanced mechanical properties.

Due to the harsh service environment of military aircraft, aluminum alloy on aircraft is prone to corrosion. Maintenance personnel need better rust removal methods to ensure the smooth progress of the flight mission. In this paper, the rust layer on the surface of 7B04 aluminum alloy was cleaned by nanosecond pulsed laser and mechanical grinding. Then the effects on surface morphology, surface roughness, surface hardness, residual stress and corrosion resistance of aluminum alloy were analyzed to determine an improved derusting method and optimize laser cleaning parameter based on the overlap ratio (η). Results showed that mechanical grinding not only did not remove rust completely but also damaged the matrix. When the laser scanning speed was 5650 mm s−1, η was 80%, the rust of the sample was completely removed without damaging the matrix. At the same time, the hardness and corrosion resistance of surface were improved and the appropriate increase of roughness was conducive to the repair of surface coating. When the scanning speed increased and the overlap ratio decreased, there was rust residue which can not meet the requirements of rust removal. When the scanning speed decreased and the overlap ratio increased, the rust was completely removed and the corrosion resistance of the surface was enhanced, but it caused excessive ablation damage to the matrix. And the above two cases will increase the difficulty of coating repair.

As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronecker basis representation of a given piece of data in the matrix format. Nevertheless, in practical scenarios, much of the data are intrinsically multi-dimensional, and thus, using a matrix format for data representation will inevitably yield sub-optimal outcomes. Tensor decomposition (TD), as a high-order generalization of matrix decomposition, has been widely used to analyze multi-dimensional data. In a direct generalization to the matrix rank, low-rank tensor modeling has been developed for multi-dimensional data analysis and achieved great success. Despite its efficacy, the connection between TD rank and the sparsity of the tensor data is not direct. In this work, we introduce a novel tensor ring sparsity measurement (TRSM) for measuring the sparsity of the tensor. This metric relies on the tensor ring (TR) Kronecker basis representation of the tensor, providing a unified interpretation akin to matrix sparsity measurements, wherein the Kronecker basis serves as the foundational representation component. Moreover, TRSM can be efficiently computed by the product of the ranks of the mode-2 unfolded TR-cores. To enhance the practical performance of TRSM, the folded-concave penalty of the minimax concave penalty is introduced as a nonconvex relaxation. Lastly, we extend the TRSM to the tensor completion problem and use the alternating direction method of the multipliers scheme to solve it. Experiments on image and video data completion demonstrate the effectiveness of the proposed method.

ABSTRACT: Although vaccines play a major role in the prevention of infectious bronchitis (IB), Anti-IB drugs still have great potential in poultry production. Radix Isatidis polysaccharide (RIP) is a crude extract of Banlangen with antioxidant, antibacterial, antiviral, and multiple immunomodulatory functions. The aim of this study was to explore the innate immune mechanisms responsible for RIP-mediated alleviation of infectious bronchitis virus (IBV)-induced kidney lesions in chickens. Specific-pathogen-free (SPF) chicken and chicken embryo kidney (CEK) cells cultures were pretreated with RIP and then infected with the QX-type IBV strain, Sczy3. Morbidity, mortality, and tissue mean lesion scores were calculated for IBV-infected chickens, and the viral loads, inflammatory factor gene mRNA expression levels, and innate immune pathway gene mRNA expression levels in infected chickens and CEK cell cultures were determined. The results show that RIP could alleviate IBV-induced kidney damage, decrease CEK cells susceptibility to IBV infection, and reduce viral loads. Additionally, RIP reduced the mRNA expression levels of the inflammatory factors IL-6, IL-8, and IL-1β by decreasing the mRNA expression level of NF-κB. Conversely, the expression levels of MDA5, TLR3, STING, Myd88, IRF7, and IFN-β were increased, indicating that RIP conferred resistance to QX-type IBV infection via the MDA5, TLR3, IRF7 signaling pathway. These results provide a reference for both further research into the antiviral mechanisms of RIP and the development of preventative and therapeutic drugs for IB.

Abstract Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.

The availability and stoichiometry ratio of nitrogen (N) and phosphorus (P) play vital roles in plant trophic dynamics and primary production. However, the responses of these plant traits to varying N and P supplies remain largely unclear for supratidal wetland herbs. Here, we conducted a 4-year field manipulation experiment in a supratidal wetland in the Yellow River Delta. The changes in aboveground biomass, leaf N and P concentrations and N:P ratios of two dominant herbs (Suaeda glauca and Phragmites australis) were examined at 3 overall nutrient supply levels (low, medium and high) combined with 3 N:P supply ratios (5:1, 15:1 and 45:1). The results showed that the leaf trophic dynamics of the two dominant species rely on the overall supply level as well as on the N:P supply ratio, while the aboveground biomass of both species was only significantly influenced by the overall supply level. With the increase in supply level, S. glauca gained an advantage over P. australis in aboveground biomass competition. The leaf N and P concentrations of both species raised with the respective increasing nutrient inputs, and N:P improved with the increasing supply ratio. The leaf stoichiometry of S. glauca was more strongly influenced by the various N and P supplies than that of P. australis. Specifically, the gap of nutrient contents between the two species widened as nutrient availability improved, with the dominance of S. glauca increasing while that of P. australis decreasing. This species-specific response may explain the altered aboveground biomass of the two species. Our findings suggested that changing the N and P supply can potentially influence primary productivity by changing leaf nutrient status, indirectly affecting the shifts in plant dominance and community composition in supratidal wetland ecosystems.

Human activities have profoundly affected coastal wetlands. Apart from direct occupation, indirect influences are usually caused by modifications to environmental conditions, which are various and complex. Saltmarsh bare patches might be related to these artificial modifications. They have little or no vegetation cover and lose some important functions. Hence, the mechanisms underlying saltmarsh bare patches and the effects of artificial modifications should be studied. We took the Yellow River Delta as our research object and explored the characteristics of saltmarsh bare patches in the Yellow River Estuary Natural Reserve. Our results show that artificial modifications limit some key plant life stages and thus hinder the natural regeneration process. Once suffering from fatal events, such as long-term inundation or artificial damage, this limitation will lead to the formation of persistent bare patches in saltmarshes. First, a shortage of local seed sources will be induced by the death of local plants when they are affected by a fatal event. Second, the replenishment of external seeds is often constrained by dikes and dams. Third, construction activities and the prolonged high-water inundation events can lead to soil compaction and the lack of microtopographic heterogeneity, which will hinder the retention and anchorage of seeds. Overall, it is essential to realize the underlying mechanisms of persistent bare patches and their potential to be included in cost-effective restoration or management plans.

Burrowing crabs are widely distributed and have large populations in estuarine wetlands. Crab excavation can have potentially significant bioturbation effects on the vertical structure of sediments, and the processes of nutrients deposition and mineralization. However, the effects of crab micro activities on the geochemical cycling processes of the whole estuarine ecosystems are not clear, specifically the contributions of burrowing crabs to sediment and nutrients turnover in coastal ecosystems. Due to the lack of knowledge on crab burrowing behavior and borrow morphology, it is difficult to accurately estimate the excavation and turnover volumes of crabs. Therefore, this study examined the bioturbation activity of the crab Helice tientsinensis in western Pacific estuary ecosystems by analyzing their burrow morphology and local sediment properties. The common burrow shapes of Helice tientsinensis were J- and Y-shaped burrows. Burrow morphological characteristics such as total burrow depth, curve burrow length, burrow volume, and opening diameter significantly differed among tidal zones. Crab carapace size, water depth, soil hardness, and bulk density were the main factors driving burrow morphology. Sediment excavation by crabs was ~50 times greater than the deposition of sediment into crab burrows. The net transported amounts of sediment (31.66–33.18 g·d-1·m-2) and nutrients (total nitrogen: 0.075–0.090 g·d-1·m-2, total carbon: 3.96–4.55 g·d-1·m-2, and organic matter: 0.44–0.77 g·d-1·m-2) were mainly from the belowground sediment to the surface. These results highlighted the important role of crabs in sediment and nutrients cycling within coastal estuary ecosystems.

Citrullus colocynthis (L.) Schrad. (Cucurbitaceae) is widely distributed in the desert areas of the world. The fruit bodies of C. colocynthis are recognized for their wide range of nutraceutical potential, as well as medicinal and pharmaceutical uses. The plant has been reported for various uses, such as asthma, bronchitis, cancer, colic, common cold, cough, diabetes, dysentery, and jaundice. The fruit has been extensively studied for its biological activities, which include insecticide, antitumor, and antidiabetic effects. Numerous bioactive compounds have been reported in its fruit bodies, such as essential oils, fatty acids, glycosides, alkaloids, and flavonoids. Of these, flavonoids or caffeic acid derivatives are the constituents associated with the inhibition of fungal or bacterial growth, whereas eudesmane sesquiterpenes or sesquiterpene lactones are most active against insects, mites, and nematodes. In this review, the scientific evidence for the biological activity of C. colocynthis against insecticide, cytotoxic, and antidiabetic effects is summarized.

Near-alpha titanium alloys are widely used in aeroengine blades due to their excellent specific strength and mechanical properties. The mechanical properties of near-α titanium alloys are closely related to the evolution of the microstructure and precipitates. In this paper, the microstructure and mechanical properties of a new type of multi-component near-α titanium alloy sheet after rolling, 700 °C aging, and 800 °C aging were studied. The results show that the strength of the alloy after aging at 700 °C increases from 1156 MPa to 1304 MPa, respectively, but decreases to 1246 MPa with the aging temperature increasing. The ductility of the alloy aged at 700 °C is lower than that of the rolled state, but the ductility increases slightly with the aging temperature increasing. The effect of aging heat treatment on the microstructure and precipitation behavior of alloy plates has been studied and compared with alloys before aging. After heat treatment, the content of primary α decreases from 25% to 5%, respectively. Two kinds of silicide precipitate at different positions, with the large-size spherical silicide being (Ti, Zr, Nb)5Si3, and the small-size fusiform silicide being (Ti, Zr, Nb)6Si3, respectively. Ti3Al was precipitated in the primary α phase, during the aging process. The silicides exhibit the strengthening effect on the alloy, but the effect weakens when the silicides grow up. The loss in ductility is mainly attributed to the precipitation of the α2 phase after aging treatment. However, ductility is improved after applying higher aging temperatures as the size of the α2 phase becomes smaller, and the distribution of them tends to become dispersed.

Abstract Vortex beams carrying orbital angular momentum (OAM), which featuring helical phase front, have been regarded as an alternative spatial degree of freedom for optical mode coding and multiplexing. For most reported OAM-based mode coding schemes, data information is only encoded by different OAM mode states. In this paper, we introduce a novel design technique to construct vortex array phase grating (VAPGs) for the flexible generation of vortex arrays, and employ the proposed VAPGs to realize multi-dimensional space/mode/amplitude coding/decoding. By designing VAPGs with different parameters and loading them on to a single spatial light modulator (SLM), we successfully generate vortex array with different mode states and relative power in the experiments. Moreover, a 10-bit multi-dimensional space/mode/amplitude data coding/decoding scheme for image transfer in free-space link with a zero bit-error-rate is experimentally demonstrated, which confirm the feasibility of our proposed VAPG-based coding/decoding scheme.

Abstract This study was conducted to investigate the speciation, bioavailability and environmental risk of heavy metals (HMs) in chicken manure (CM) and water-washed swine manure (WSM) and their biochars produced at different pyrolysis temperatures (200 to 800 °C). As the pyrolysis temperature increased, the remaining proportion, toxicity characteristic leaching procedure (TCLP), HCl and diethylenetriamine pentaacetic acid (DTPA) of HMs gradually declined. This result proved that the speciation of HMs in chicken manure biochars (CMB) and water-washed swine manure biochars (WSMB) was influenced by pyrolysis temperature. The proportions of stable fractions were enhanced with increased pyrolysis temperature and weakened the HM validity for vegetation at 800 °C. Finally, the results of the risk assessment showed that the environmental risk of HMs in CMB and WSMB decreased with increasing pyrolysis temperature. Therefore, pyrolysis at 800 °C can provide a practical approach to lessen the initial and underlying heavy metal toxicity of CMB and WSMB to the environment.

Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and become an emerging research topic in computer vision and machine learning very recently. However, focusing on modeling spectral low-rankness, the t-SVD-based models may be insufficient to exploit low-rankness in the original domain, leading to limited performance while learning from tensor data (like videos) that are low-rank in both original and spectral domains. To this point, we define a hybrid tensor norm dubbed the “Tubal + Tucker” Nuclear Norm (T2NN) as the sum of two tensor norms, respectively, induced by t-SVD and Tucker decomposition to simultaneously impose low-rankness in both spectral and original domains. We further utilize the new norm for tensor recovery from linear observations by formulating a penalized least squares estimator. The statistical performance of the proposed estimator is then analyzed by establishing upper bounds on the estimation error in both deterministic and non-asymptotic manners. We also develop an efficient algorithm within the framework of Alternating Direction Method of Multipliers (ADMM). Experimental results on both synthetic and real datasets show the effectiveness of the proposed model.

AbstractHot dry rock (HDR) is an important geothermal resource and the primary direction of future geothermal development. Granite is particularly rich in radiogenic heat-generating elements (e.g., U, Th, and K) and is thus the main target for HDR development worldwide. Southeastern China contains the most abundant granite distribution in the country and accounts for more than 40% of the total granite area. This paper analyzes the rock radiogenic heat production, geothermal flux, crustal thermal structure, and reservoir temperature in the key exploration areas based on a systematic investigation of the distribution of granite resources and their heat generation characteristics. A heat generation and accumulation model of HDR resources is proposed for southeastern China. The results show that southeastern China can be characterized by a quadratic heat gathering model that includes a mantle source heat supply, intracrustal heat generation, fracture heat transfer, and cover heat preservation. Among these, the radiogenic heat production of granite is one of the main factors that affects shallow geothermal fields in this region. Fracture structure and cover conditions also exert a strong control on the gathering and dissipation of underground heat. The widely distributed and high-heat-producing tuffs and fused tuffs in the Zhangzhou area offer promising heat-generating and heat-gathering conditions for future exploration and development of hot dry rock resources in the area. The results provide important insight on the genesis and heat control factors of HDR resources in southeastern China and a theoretical reference for future HDR resource exploration and development practices in the region.

Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images.

The Yanshan area is rich in geothermal resources. However, limited research has been done on the geothermal resources of intermountain basins in the area, which restrict the exploration and development of geothermal resources. In this study, the Yanheying Basin, a typical intermountain basin located in the eastern foothills of Yanshan Mountain, is selected to perform a comprehensive analysis regarding heat flow and lithospheric thermal structure, to have a better understanding of the geothermal background and resource utilization potential of the area. The methods of deep borehole temperature logging, and rock sampling and testing were applied. With geological and geophysical data collected in the surrounding basins, the thermophysical parameters of rock formations, terrestrial heat flow and temperature distribution on a crustal-scale around the basin were analyzed. Results show that the ratio of crust heat flow to that of the mantle in the Yanheying Basin is 0.68, which falls in the range of that of the North China Plain (0.6–0.8), showing an obvious result of lithospheric thinning. The results also show that both crust and mantle heat flows in the Yanheying Basin are far lower than those of the North China Plain. it indicates that the crust of the Yanheying Basin and the entire Yanshan area has experienced a low degree of damage. That provides a piece of good thermodynamic evidence for the spatial variation of North China Craton destruction. The depth of the Curie surface in the study area is estimated to be about 24 km, which is consistent with the results of aeromagnetic data analysis. It confirms the results of lithospheric thermal structure and deep temperature distribution in the Yanheying Basin. The Yanheying Basin has certain potential for geothermal resources utilization in the depth of 2–6 km, where probably exist several carbonate thermal reservoirs with medium-temperature geothermal water. The above results can provide new insights into the geothermal research and exploration of intermountain basins in the Yanshan area.

Leonurine has been shown to have excellent anti-myocardial ischemia effects. Our previous studies suggested that cardiac protection by leonurine during myocardial ischemia appeared to be inextricably linked to its regulation of the liver. At present, however, there are few mechanistic studies of leonurine and its regulation of hepatic metabolism against ischemic injury. In this study, a metabolomics approach was developed to give a global view of the metabolic profiles of the heart and liver during myocardial ischemia. Principal component analysis and orthogonal partial least squares discrimination analysis were applied to filter differential metabolites, and a debiased sparse partial correlation analysis was used to analyze the correlation of the differential metabolites between heart and liver. As a result, a total of thirty-one differential metabolites were identified, six in the myocardial tissue and twenty-five in the hepatic tissue, involving multiple metabolic pathways including glycine, serine and threonine, purine, fatty acid, and amino acid metabolic pathways. Correlation analysis revealed a net of these differential metabolites, suggesting an interaction between hepatic and myocardial metabolism. These results suggest that leonurine may reduce myocardial injury during myocardial ischemia by regulating the metabolism of glycine, serine and threonine, purine, fatty acids, and amino acids in the liver and heart.

Orthogonal matching pursuit (OMP for short) is a classical method for sparse signal recovery in compressed sensing. In this paper, we consider the application of OMP to reconstruct sparse polynomials generated by uniformly bounded orthonormal systems, which is an extension of the work on OMP to reconstruct sparse trigonometric polynomials. Firstly, in both cases of sampled data with and without noise, sufficient conditions for OMP to recover the coefficient vector of a sparse polynomial are given, which are more loose than the existing results. Then, based on a more accurate estimation of the mutual coherence of a structured random matrix, the recovery guarantees and success probabilities for OMP to reconstruct sparse polynomials are obtained with the help of those sufficient conditions. In addition, the error estimation for the recovered coefficient vector is gained when the sampled data contain noise. Finally, the validity and correctness of the theoretical conclusions are verified by numerical experiments.

With abundant resources in the wetland, the Yellow River Delta provides important habitats for various kinds of rare bird species. However, due to climate change and human water withdrawal activities, no-flow events of the Yellow River occurred frequently since the 1970s, resulting in serious wetland degradation and biodiversity losses. Ecological water supplement has been regarded as one of the most effective measures to cope with these environmental problems. Accordingly, how much water is required for the ecological restoration becomes an essential question to be answered. Since the primary function of the Yellow River Delta Natural Reserve (YRDNR), the typical wetland in the Yellow River Delta, is to provide suitable habitats for various rare birds, the purpose of this paper is to propose a method based on water balance analysis to specify monthly ecological water supplement for the habitats of typical birds in the YRDNR. Specifically, three typical birds were selected as the protection objects, including Grus japonensis, Ciconia boyciana, and Larus saundersi. Monthly water supplement was determined by monthly long-term time series data (2003–2018) of evapotranspiration product derived from remote sensing and meteorological data (precipitation and pan evaporation) acquired from National Meteorological Information Center. The results showed that the annual ecological water supplement for 10 km2 ideal habitats was 666.17×104 m3, 1165.22×104 m3, and 664.48×104 m3 for G. japonensis, C. boyciana, and L. saundersi, respectively. Besides, it was found that annual precipitation generally had a significant negative relation with annual ecological water supplement, which suggested that local precipitation should be considered as a key factor in determining the amount of annual water supplement. The frequency analysis was then implemented to provide reasonable scheme of actual ecological water supplement under different precipitation scenarios. The results indicated that annual ecological water supplement for the whole YRDNR was 1.81×108 m3, 1.94×108 m3, 2.10×108 m3 and 2.25×108 m3 respectively under wet, average, dry and extreme dry conditions. If water storage measurements are implemented to store surplus precipitation under rainy season, the average volume would reduce by nearly 10%.

In recent decades, the invasion of saltmarsh plant Spartina alterniflora (S. alterniflora) over a large part of coastal wetlands in China, including the Yellow River Estuary (YRE) as a regional economic hub and global ecosystem services hotspot, has caused increasing concern because of its serious threats to native ecosystems. During the same period, local authorities have implemented a Water-Sediment Regulation Scheme (WSRS) in the Yellow River for flood mitigation and delta restoration purposes. The altered hydrological regime has resulted in unintended changes to estuarine ecosystem. However, the direct consequence of the WSRS on the expansion of S. alterniflora remains unclear. In this study, quantitative relationship between the inter- and intra-annual expansion patterns of S. alterniflora represented by relevant landscape metrics and indicators that quantify the concurrent variations of river and sediment discharges as the proxy of the WSRS impacts were analysed over the period of Year 2011–2018, and the analyses were performed on the YRE as a whole and on five different zones subdivided based on the invasion sequence. The results showed that there was no significant difference in the inter-annual area variation of S. alterniflora between the years with and without WSRS. Compared with the years without WSRS (2016–2017), the intra-annual (monthly) increment of the various landscape metrics [i.e., NP (number of patches), CA (class area), LPI (largest patch index) and AI (aggregation index] were found to be significantly higher in the initial stage of peak growing season (June-July) than in the mid- and late stages (July-September) in the years with WSRS (2011–2015, 2018) in the subregion located close to the south bank of YRE as the most prominent impact zone. In addition, F (mean flow), Ff (number of high flow pulses), Tf (Julian date of maximum flow) and D (duration of WSRS) were identified as the explanatory variables for the intra-annual vegetation landscape pattern changes, and their relative contributions to resultant changes were also assessed. Our results broaden the understanding of estuarine hydrological disturbance as a potential driver regulating the saltmarsh vegetation, and also have implications for S. alterniflora invasion control at estuaries under changing environment.

There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to significantly reduce the network complexity, and then we embed a group normalization (GN) layer in the inverted residual structure to alleviate network performance degradation. Furthermore, we extract multiscale building features through an atrous spatial pyramid pooling (ASPP) module and apply long skip connections to establish long-distance dependence to fuse features at different levels of the given image. Finally, we add a deformable convolution network layer before the pixel classification step to enhance the feature extraction capability of MSL-Net for buildings with irregular shapes. The experimental results obtained on three publicly available datasets demonstrate that our proposed method achieves state-of-the-art accuracy with a faster inference speed than that of competing approaches. Specifically, the proposed MSL-Net achieves 90.4%, 81.1% and 70.9% intersection over union (IoU) values on the WHU Building Aerial Imagery dataset, Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset, respectively, with an inference speed of 101.4 frames per second (FPS) for an input image of size 3 × 512 × 512 on an NVIDIA RTX 3090 GPU. With an excellent tradeoff between accuracy and speed, our proposed MSL-Net may hold great promise for use in building extraction tasks.

Temperature sensitivity (Q10) is important to reveal carbon decomposition responding to climate change. It’s remains limited to understand how Q10 of anaerobic soil organic matter (SOM) decomposition is regulated by soil property in various wetlands with distinct hydrological characteristics. In the present study, samples of soil at the depths of 0–10 cm, 10–20 cm and 20–30 cm were collected in three typical Phragmites australis marshes, including a freshwater marsh (FPa), a no-tidal salt marsh (NTPa) and a tidal salt marsh (TPa), in the Yellow River estuary. The soil samples were incubated at 10 °C, 20 °C and 30 °C, respectively, to determine the rates of anaerobic CO2 production, Q10 values, and their relations to soil properties. Over 70-d incubation, temperature rise significantly increased the mean rates by 70%–136% (from 10 °C to 20 °C) and 64%–142% (from 20 °C to 30 °C) among the marshes, with the mean Q10 values ranging from 1.61 to 2.66. The rates of CO2 production and Q10 values were significantly affected by wetland type and soil depth. Among marshes, the Q10 of the FPa and NTPa soils was higher than that of the TPa soil (for 0–10 cm and 10–20 cm but for 20–30 cm); along soil depths, the Q10 of the top soil was higher than that of the subsoil (for FPa and NTPa but for TPa). Q10 was affected by total organic carbon (TOC), salinity and pH, and the effects were different with marshes. TOC was a main factor regulating Q10 for the freshwater and the no-tidal salt marshes, while salinity and pH were main factors for the tidal salt marsh. The findings highlight an interactive mechanism of soil property and tidal underlying the response of SOM decomposition to temperature change in estuarine wetlands.

Lots of research efforts have been devoted to increase the transmission capacity in optical communications using orbital angular momentum (OAM) multiplexing. To enable long-haul OAM mode transmission, an in-line OAM fiber amplifier is desired. A ring-core fiber (RCF) is considered to be a preferable design for stable OAM mode propagation in the fiber. Here, we demonstrate an OAM fiber amplifier based on a fabricated ring-core erbium-doped fiber (RC-EDF). We characterize the performance of the RC-EDF-assisted OAM fiber amplifier and demonstrate its use in OAM multiplexing communications with OAM modes carrying quadrature phase-shift keying (QPSK) and quadrature amplitude modulation (QAM) signals. The amplification of two OAM modes over four wavelengths is demonstrated in a data-carrying OAM-division multiplexing and wavelength-division multiplexing system. The obtained results show favorable performance of the RC-EDF-assisted OAM fiber amplifier. These demonstrations may open up new perspectives for long-haul transmission in capacity scaling fiber-optic communications employing OAM modes.

Three-dimensional laser writing inside silicon remains today inaccessible with the shortest infrared light pulses unless complex schemes are used to circumvent screening propagation nonlinearities. Here, we explore a new approach irradiating silicon with trains of femtosecond laser pulses at repetition rates up to 5.6 THz that is order of magnitude higher than any source used for laser processing so far. This extremely high repetition rate is faster than laser energy dissipation from microvolume inside silicon, thus enabling unique capabilities for pulse-to-pulse accumulation of free carriers generated by nonlinear ionization, as well as progressive thermal bandgap closure before any diffusion process comes into play. By space-resolved measurements of energy delivery inside silicon, we evidence changes in the interplay between detrimental nonlinearities and accumulation-based effects. This leads to a net increase on the level of space-time energy localization. The improvement is also supported by experiments demonstrating high performance for 3D laser writing inside silicon. In comparison to repeated single pulses, irradiation with trains of only four-picosecond pulses with the same total energy leads to an apparent decrease of the energy threshold for modification and drastic improvements on the repeatability, uniformity, and symmetricity of the produced features. The unique benefits of THz bursts can provide a new route to meet the challenge of 3D inscription inside narrow bandgap materials.

In this paper, the goal is to reconstruct a tensor, i.e., a multi-dimensional array, when only subsets of its entries are observed. For well-posedness, the tensor is assumed to have a low-Tucker-rank structure. To estimate the underlying tensor from its partial observations, we first propose an estimator based on a newly defined balanced spectral (k, p)-support norm. To efficiently compute the estimator, we come up with a scalable algorithm for the minimization of the spectral (k, p)-support norm. Instead of directly solving the primal problem which involves full SVD in each iteration, the proposed algorithm benefits from the Lagrangian dual through minimizing the dual norm of the (k, p)-support norm which only computes the first k leading singular values and singular vectors in each iteration. To explore the statistical performance of the proposed estimator, upper bounds on the sample complexity and estimation error are then established. Simulation studies confirm that the error bounds can predict the scalable behavior of the estimation error. Experimental results on synthetic and real datasets demonstrate that the spectral (k, p)-support norm based method outperforms the nuclear norm based ones.

In many applications, data organized in tensor form contains noise and missing entries. In this paper, the goal is to complete a tensor from its partial noisy observations. Specifically, we consider low-tubal-rank tensors sampled by element-wise Bernoulli sampling with additive sub-exponential noise. Algorithmically, a soft-impute-like algorithm, namely iterative singular tube thresholding (ISTT), is proposed. Statistically, bound on the estimation error of ISTT is explored. First, the estimation error is upper bounded non-asymptotically. Then, the minimax optimal lower bound of the estimation error for tensors with limited tubal-rank and bounded l∞-norm is established, indicating that the proposed upper bound is order optimal up to a logarithm factor. Numerical simulations show that the proposed upper bound can precisely predict the scaling behavior of the estimation error of ISTT. Compared with several state-of-the-art convex algorithms, the effectiveness of ISTT is demonstrated through experiments on real-world data sets.

A tuned mass rocking wall (TMRW)-frame structure system is proposed to improve the energy dissipation ability of the traditional rocking wall-frame system. Based on the energy dissipation principle of the traditional tuned mass damper (TMD), a TMRW is designed with proper mass and stiffness according to the dynamic characteristic of the host structure. Firstly, considering the presence of inherent structural damping, the dynamic amplification factor of the main mass was derived from the dynamic equations of the TMRW mechanism. A practical design table was then obtained after parameter study. Secondly, by taking a six-story frame structure as an example, the dynamic time-history analysis was conducted to study TMRW’s seismic performance. The inter-story drift ratios of the TMRW-frame, the traditional rocking wall-frame, and the frame structures were compared, and the seismic responses of the controlled and uncontrolled structures were also compared. The results demonstrate that the TMRW can effectively reduce the inter-story displacement of the host structure, and the lateral deformation mode of the host structure tends to be more uniform. However, compared with the traditional rocking wall-frame system, the proposed TMRW has less ability on coordinating deformation.

Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes much larger than that of the roof, providing improved potential for photovoltaic equipment installation. Conventional urban solar potential evaluation methods are usually based on light detection and ranging (LiDAR). However, LiDAR can only be used in existing buildings, and the lack of semantic information in the point cloud data generated by LiDAR makes it impossible to evaluate the photovoltaic potential of facades (including details such as windows) in detail and with accuracy. In this study, we developed a method to accurately extract facades and roofs in order to evaluate photovoltaic potential based on the Industry Foundation Classes. To verify the feasibility of this approach, we used a building from Xuzhou city, Jiangsu province, China. The simulation results indicate that, out of the total building photovoltaic installable area (8995 m2), that of the facade is 8240 m2. The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings.

This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, communication loss, electromagnetic interferences, cloud shadows, etc. To estimate the underlying tensor, a new penalized least squares estimator is first formulated by exploiting the low rankness of the signal tensor within the framework of tensor ∗L-Singular Value Decomposition (∗L-SVD) and leveraging the sparse structure of the outlier tensor. Then, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) is designed to compute the estimator in an efficient way. Statistically, the non-asymptotic upper bound on the estimation error is established and further proved to be optimal (up to a log factor) in a minimax sense. Simulation studies on synthetic data demonstrate that the proposed error bound can predict the scaling behavior of the estimation error with problem parameters (i.e., tubal rank of the underlying tensor, sparsity of the outliers, and the number of uncorrupted observations). Both the effectiveness and efficiency of the proposed algorithm are evaluated through experiments for robust completion on seven different types of remote sensing data.

A tuned mass rocking wall (TMRW) is a passive control device that combines the merits of a traditional tuned mass damper (TMD) and a traditional rocking wall (RW). TMRWs not only help avoid weak story failure of the host structure but can also be regarded as a largely tuned mass substructure in the building structure. Through the appropriate design of the frequency ratio, the host structure can dissipate much more energy under earthquake excitations. In this paper, the basic equations of motion for the mechanical model of an SDOF structure-rigid rocking wall are established, and the optimization formulas of frequency ratio and damping ratio of TMRW are derived. Through the dynamic elastoplastic analysis of a six-story TMRW-frame model, the applicability of the derived parameter optimization formulas and the effectiveness of the TMRW in seismic performance control are investigated. The results demonstrate that the TMRW can coordinate the uneven displacement angle between stories of the host structure. Additionally, the TMRW is found to possess the merit of reducing both the peak and root-mean-square (RMS) structural responses when subjected to different types of earthquake excitations.

The hot deformation behavior of Ti-5322 alloy are researched at compression temperatures range of 750–1050 °C and strain rate range of 0.01–10 s ^−1 , to optimize its hot workability. Processing map analysis and microstructure observations reveal that the optimal processing parameters of Ti-5322 alloy are temperatures of 750–825 °C and strain rates of 0.01–0.05 s ^−1 , and temperatures of 925–975 °C and strain rates of 0.01–1 s ^−1 . The peak efficiency of power dissipation can reach 40% owing to the transformation from α phase to β phase, spheroidization behavior and dynamic recrystallization of the β phase. The dynamic recrystallization was the primary form of microstructure evolution above 900 °C, while the spheroidization of α phase below 900 °C. The spheroidization of α lamellae can be attributed to the instability of subgrain boundaries appeared in the α phase during hot deformation. The β phase wadges into the α / α subgrain boundary and α / β interface migration induced the α phase spheroidization. In addition, three instability domains are detected in the processing maps, which confirmed by the presence of microstructures with wedge cracking and adiabatic shear bands.

Despite recent successes in three-dimensional ultrafast laser writing inside silicon and other narrow gap materials, strong discrepancies remain in the identified narrow experimental windows to exceed material breakdown thresholds. In an experiment in which we irradiate silicon with perfectly synchronized femtosecond, picosecond, and nanosecond pulses, we show that the temporal contrast of the ultrashort pulses is a critical driving parameter, even more than the achievable peak vacuum intensity. We identify by appropriate pulse combinations breakdown channels seeded by pre-ionization and thermal runaway from the simulated contrast imperfections. The introduction of this multi-timescale control of the interactions allows bulk writing inaccessible otherwise. It also permits a comprehensive review of previous reports using infrared laser technologies at different characteristic contrast performances. The quantitative analysis of the problem reveals an extremely high sensitivity with modification ignition at intensity contrasts down to 10^{−6} and below. This sensitivity level is common for high-intensity physics experiments but has no equivalent in any other laser material processing application.

Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting in a circulant way, TNN only exploits tube (often carrying temporal/channel information) redundancy in a circulant way while preserving the row and column (often carrying spatial information) relationship. In this paper, a new tensor norm named the triple tubal nuclear norm (TriTNN) is proposed to simultaneously exploit tube, row and column redundancy in a circulant way by using a weighted sum of three TNNs. Thus, more spatial-temporal information can be mined. Further, a TriTNN-based tensor completion model with an ADMM solver is developed. Experiments on color images, videos and LiDAR datasets show the superiority of the proposed TriTNN against state-of-the-art nuclear norm-based tensor norms.

Flexible piezoelectric PZT/fabric composite material is pliable and tough in nature which is in a lack of traditional PZT patches. It has great application prospect in improving the sensitivity of sensor/actuator made by piezoelectric materials especially when they are used for curved surfaces or complicated conditions. In this paper, glass fiber cloth was adopted as carrier to grow PZT piezoelectric crystal particles by hydrothermal method, and the optimum conditions were studied. The results showed that the soft glass fiber cloth was an ideal kind of carrier. A large number of cubic-shaped PZT nanocrystallines grew firmly in the carrier with a dense and uniform distribution. The best hydrothermal condition was found to be pH 13, reaction time 24 h, and reaction temperature 200°C.