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Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60–1.50 d), Interval from First Service to Conception (FSTC, 0.20–16.20 d), Calving Interval (CI, 4.00–7.00 d), and Number of Services (NUMS, 0.07–0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding.

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy.

Using the aldehyde amine condensation procedure and the triphenylamine group as the skeleton structure, the new triphenylamine-aromatic aldehyde-succinylhydrazone probe molecule DHBYMH was created. A newly created acylhydrazone probe was structurally characterized by mass spectrometry (MS), NMR, and infrared spectroscopy (FTIR). Fluorescence and UV spectroscopy were used to examine DHBYMH’s sensing capabilities for metal ions. Notably, DHBYMH achieved a detection limit of 1.62 × 10−7 M by demonstrating exceptional selectivity and sensitivity towards Cu2+ ions in an optimum sample solvent system (DMSO/H2O, (v/v = 7/3); pH = 7.0; cysteine (Cys) concentration: 1 × 10−4 M). NMR titration, high-resolution mass spectrometry analysis, and DFT computation were used to clarify the response mechanism. Ultimately, predicated on DHBYMH’s reversible identification of Cu2+ ions in the presence of EDTA, a molecular logic gate was successfully designed.

Fusarium sacchari, a key pathogen of sugarcane, is responsible for the Pokkah boeng disease (PBD) in China. The 14-3-3 proteins have been implicated in critical developmental processes, including dimorphic transition, signal transduction, and carbon metabolism in various phytopathogenic fungi. However, their roles are poorly understood in F. sacchari. This study focused on the characterization of two 14-3-3 protein-encoding genes, FsBmh1 and FsBmh2, within F. sacchari. Both genes were found to be expressed during the vegetative growth stage, yet FsBmh1 was repressed at the sporulation stage in vitro. To elucidate the functions of these genes, the deletion mutants ΔFsBmh1 and ΔFsBmh2 were generated. The ΔFsBmh2 exhibited more pronounced phenotypic defects, such as impaired hyphal branching, septation, conidiation, spore germination, and colony growth, compared to the ΔFsBmh1. Notably, both knockout mutants showed a reduction in virulence, with transcriptome analysis revealing changes associated with the observed phenotypes. To further investigate the functional interplay between FsBmh1 and FsBmh2, we constructed and analyzed mutants with combined deletion and silencing (ΔFsBmh/siFsBmh) as well as overexpression (O-FsBmh). The combinations of ΔFsBmh1/siFsBmh2 or ΔFsBmh2/siFsBmh1 displayed more severe phenotypes than those with single allele deletions, suggesting a functional redundancy between the two 14-3-3 proteins. Yeast two-hybrid (Y2H) assays identified 20 proteins with pivotal roles in primary metabolism or diverse biological functions, 12 of which interacted with both FsBmh1 and FsBmh2. Three proteins were specifically associated with FsBmh1, while five interacted exclusively with FsBmh2. In summary, this research provides novel insights into the roles of FsBmh1 and FsBmh2 in F. sacchari and highlights potential targets for PBD management through the modulation of FsBmh functions.

Iodine, primarily in the form of iodide (I−), is the bioavailable form for the thyroid in the human body. Both deficiency and excess intake of iodide can lead to serious health issues, such as thyroid disease. Selecting iodide ions among anions has been a significant challenge for decades due to interference from other anions. In this study, we designed and synthesized a new pincer-type acridine–triazole fluorescent probe (probe 1) with an acridine ring as a spacer and a triazole as a linking arm attached to two naphthol groups. This probe can selectively recognize iodide ions in a mixed solvent of THF/H2O (v/v, 9/1), changing its color from colorless to light yellow, making it suitable for highly sensitive and selective colorimetric and fluorescent detection in water systems. We also synthesized another molecular tweezer-type acridine–triazole fluorescent probe (probe 2) that exhibits uniform detection characteristics for iodide ions in the acetonitrile system. Interestingly, compared to probe 2, probe 1 can be detected by the naked eye due to its circulation effect, providing a simple method for iodine detection. The detection limit of probe 1 is determined to be 10−8 mol·L−1 by spectrometric titration and isothermal titration calorimetry measurements. The binding stoichiometry between probe 1 and iodide ions is calculated to be 1:1 by these methods, and the binding constant is 2 × 105 mol·L−1.

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson’s correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.

In this study, a functional segmentation method for the design of adiabatic devices is presented. This method can be applied to calculate the compact adiabatic waveguide shapes for adiabatic taper waveguides and adiabatic mode converters. We introduce two algorithms, namely the quadratic function segmentation design for the adiabatic taper waveguide and the cubic function segmentation design for the adiabatic mode coupler. The design shows that for each segment, there is no need for separate simulations to obtain the length of each segment, and only the length of each segment can be determined by equations. This approach is much less cumbersome than other numerical design methods for adiabatic devices. The proposed functional segmentation design requires only a device length of 36 μm to achieve 95% power transfer efficiency, while a traditional linear-shape connection requires a device length of 54 μm to achieve the same power transfer efficiency. Therefore, the device designed by the function segmentation method has better compactness compared to the conventional linear-shape connection design of adiabatic devices.

In this study, a numerical method for designing efficient adiabatic devices with multiple structural parameter variations (NAMSP) is developed. This method can be applied to a wide range of devices based on adiabatic mode evolution structures. The numerical design complexity of multiple structural parameter variations will be greatly improved compared to the case of a single parameter variation. Therefore, an efficient domain decomposition scheme was originally introduced into the NAMSP method. The proposed method can help compute compact adiabatic guided-wave shapes for these adiabatic devices with multiple structural parameter variations. Adiabatic devices with multiple structural parameter variations are used to connect different complex waveguides, which are often difficult to design using analytical methods. The design involves tapering the width of the two or more core layers at one time; however, this change in the width typically affects the mode both vertically and horizontally. Our numerical method allows the shape of the width variation for each layer that facilitates compact adiabatic mode transformation to be obtained. The efficiency of the adiabatic device that was designed using the NAMSP method considerably exceeds that obtained using a linear-shaped device. Moreover, our designed adiabatic device enables an ultra-wide operating bandwidth (spans in the wavelength from 1050 nm to 4780 nm).

Ordinary mixers can hardly meet the requirements in terahertz (THz) communications due to the low-power and expensive THz sources. Sensitive harmonic mixers have been widely studied to avoid this problem, owing to the fact that the higher the number of harmonics, the lower the local oscillator (LO) frequency, and the lower the cost. High-Tc superconducting (HTS) Josephson junction (JJ) mixers are performing candidates for THz receiver frontends because of the advantages of excellent sensitivity, wide bandwidth, high harmonic number and low LO power requirement. However, the normal-state resistance of HTS JJ is so low that traditional antennas are difficult to match it. In other words, it is quite a challenge to match the input impedance to a low input impedance for traditional antennas, especially for antennas fed by coplanar striplines (CPSs). In this work, based on the structure of bowtie, two types of stub tuners were integrated to decrease the impedance of the bowtie antenna so as to improve the coupling efficiency between the traditional bowtie antenna and the JJ. Furthermore, HTS YBa2Cu3O7-δ (YBCO) JJ harmonic mixers coupled with the proposed structures and fed by CPSs are fabricated and measured. The measurements show that the JJ mixer coupled with a pair of open-end stubs of the bowtie antenna achieves up to 88 harmonics, with a conversion efficiency of −69.6 dB. In contrast, the JJ mixer coupled with a pair of lumped-element stubs of the bowtie antenna only attains to 30 harmonics, with a conversion efficiency of −73.4 dB. Additional numerical simulations indicate that the coupling efficiency is enhanced when the complex impedance of the antenna is explicitly considered. Compared with other coupled traditional antennas, the JJ mixer with bowtie antenna has the largest harmonic number. This work paves the way for the future application of low-frequency and low-cost LO for THz communications.

The myometrium is the outer layer of the uterus. Its contraction and steroidogenic activities are required for embryo implantation. However, the molecular mechanisms underlying its functions remain unknown in pigs. The myometrium includes the inner circular muscle (CM) and the outer longitudinal muscle (LM) layers. In this study, we collected the CM and LM samples from the mesometrial side (named M) of the uterus on days 12 (pre-implantation stage) and 15 (implantation stage) of pregnancy and day 15 of the estrous cycle. The transcriptomic results revealed distinct differences between the uterine CM and LM layers in early pregnancy: the genes expressed in the LM layer were mainly related to contraction pathways, whereas the transcriptional signatures in the CM layer on day 15 of pregnancy were primarily involved in the immune response processes. Subsequent comparisons in the CM layer between pregnant and cyclic gilts show that the transcriptional signatures of the CM layer are implantation-dependent. Next, we investigated the genome-wide profiling of histone H3 lysine 27 acetylation (H3K27ac) and histone H3 lysine 4 trimethylation (H3K4me3) in pig uterine CM and LM layers. The genomic regions that had transcriptional activity and were associated with the expression of genes in the two layers were characterized. Taken together, the regulatory regions identified in the study may contribute to modulating the gene expression in pig uterine CM and LM layers during implantation.

Pine wilt disease is extremely ruinous to forests. It is an important to hold back the transmission of the disease in order to detect diseased trees on UAV imagery, by using a detection algorithm. However, most of the existing detection algorithms for diseased trees ignore the interference of complex backgrounds to the diseased tree feature extraction in drone images. Moreover, the sampling range of the positive sample does not match the circular shape of the diseased tree in the existing sampling methods, resulting in a poor-quality positive sample of the sampled diseased tree. This paper proposes a Global Multi-Scale Channel Adaptation Network to solve these problems. Specifically, a global multi-scale channel attention module is developed, which alleviates the negative impact of background regions on the model. In addition, a center circle sampling method is proposed to make the sampling range of the positive sample fit the shape of a circular disease tree target, enhancing the positive sample’s sampling quality significantly. The experimental results show that our algorithm exceeds the seven mainstream algorithms on the diseased tree dataset, and achieves the best detection effect. The average precision (AP) and the recall are 79.8% and 86.6%, respectively.

The eastern Beijing plain has been suffering severe subsidence for the last decades, mainly associated with the long-term excessive extraction of groundwater resource. Since the end of 2014, the annual water supply in Beijing plain has reached several hundred million cubic meters because of the South-to-North Water Diversion (SNWD) Project, which has reduced the groundwater exploitation and changed the status of land subsidence. In this work, we first obtain the current spatiotemporal variations of land subsidence in the eastern Beijing plain by using progressive small baseline subsets (SBAS) InSAR time series analysis method with Sentinel-1 SAR data acquired from July 2015 to December 2021. Then, we analyze the correlations between InSAR-derived subsidence and groundwater level change by applying the cross wavelet method. The results show that two major subsidence zones are successfully detected with the maximum deformation rate of −150 mm/yr and maximum cumulative deformation of −950 mm. Besides, the ground deformation at different stages from 2016 to 2021 reveal that the area and magnitude of major deformation significantly slow down, even in the regions with severe subsidence, especially in the year of 2017, which is about two years later than the start time of SNWD Project in Beijing. Further, we find the InSAR-derived subsidence lags groundwater level change with about 1–2-month lagging time, indicating that the dynamic variation of groundwater level fluctuation may be the main factor affecting the uneven subsidence in the severe subsiding zones. Last, differential subsidence rates are identified at both sides of geological faults, such as Nankou-Sunhe fault and Nanyuan-Tongxian fault, from the observed deformation map, which could be explained that the groundwater flow is blocked when a fault is encountered. These findings can provide significant information to reveal the deformation mechanisms of land subsidence, establish the hydrogeological models and assist decision-making, early warning and hazard relief in Beijing, China.

Wheat nutrition and processing-quality are primarily based on the endosperm ingredients. However, the effect of embryos on grain traits and components remains unclear. In this study, we found that in the cross-pollinated and self-pollinated progenies of the four wheat mtl mutants (mtl-A, mtl-AD, mtl-BD, and mtl-ABD) the haploid induction rates were 0–15.6% and 0–14.1%, and the embryo abortion rates were 0–27.4% and 0–24.1%, respectively, in which mtl-A had no effect on haploid induction and embryo development. The embryoless grains (ELG) were comparable to the normal grains (NG) from mtl-AD, mtl-BD, and mtl-ABD in grain length, grain width and thousand-kernel weight, but the grain traits were significantly less than those in NG from mutant mtl-A. During grain filling period, mtl-ABD had similar ELG ratio and amount of starch granule (SG) and protein body (PB) in ELG and NG. At maturity stage, the morphological features of A-type and B-type SG in ELG were similar to those in NG in mtl mutants; however, amylose, gliadin, and glutenin contents were higher in ELG, and total starch, albumin and globulin contents were higher in NG. Our results clarified the effect of the wheat mtl mutants on haploid induction and grain traits and nutrition composition in this crop, and provided new clues for studying the development of embryo and endosperm and their interaction in plants.

Knowledge representation learning achieves the automatic completion of knowledge graphs (KGs) by embedding entities into continuous low-dimensional vector space. In knowledge graph completion (KGC) tasks, the inter-dependencies and hierarchical information in KGs have gained attention. Existing methods do not well capture the latent dependencies between all components of entities and relations. To address this, we introduce the mathematical theories of octonion, a more expressive generalized form of complex number and quaternion, and propose a translation-based KGC model with octonion (TransO). TransO models entities as octonion coordinate vectors, relations as the combination of octonion component matrices and coordinate vectors, and uses specific grouping calculation rules to interact between entities and relations. In addition, since hyperbolic Poincaré space in non-Euclidean mathematics can represent hierarchical data more accurately and effectively than traditional Euclidean space, we propose a Poincaré-extended TransO model (PTransO). PTransO transforms octonion coordinate vectors into hyperbolic embeddings by exponential mapping, and integrates the Euclidean-based calculations into hyperbolic space by operations such as Möbius addition and hyperbolic distance. The experimental results of link prediction indicate that TransO outperforms other translation-based models on the WN18 benchmark, and PTransO further achieves state-of-the-art performance in low-dimensional space on the well-established WN18RR and FB15k-237 benchmarks.

Mangrove forests have unique ecosystem functions and services, yet the coastal mangroves in tropics are often disturbed by tropical cyclones. Hurricane Maria swept Puerto Rico and nearby Caribbean islands in September 2017 and caused tremendous damage to the coastal mangrove systems. Understanding the vulnerability and resistance of mangrove forests to disturbances is pivotal for future restoration and conservation. In this study, we used LiDAR point clouds to derive the canopy height of five major mangrove forests, including true mangroves and mangrove associates, along the coast of Puerto Rico before and after the hurricanes, which allowed us to detect the spatial variations of canopy height reduction. We then spatially regressed the pre-hurricane canopy height and the canopy height reduction on biophysical factors such as the elevation, the distance to rivers/canals within and nearby, the distance to coast, tree density, and canopy unevenness. The analyses resulted in the following findings. The pre-hurricane canopy height increased with elevation when elevation was low and moderate but decreased with elevation when elevation was high. The canopy height reduction increased quadratically with the pre-hurricane canopy height, but decreased with elevation for the four sites dominated by true mangroves. The site of Palma del Mar dominated by Pterocarpus, a mangrove associate, experienced the strongest wind, and the canopy height reduction increased with elevation. The canopy height reduction decreased with the distance to rivers/canals only for sites with low to moderate mean elevation of 0.36–0.39 m. In addition to the hurricane winds, the rainfall during hurricanes is an important factor causing canopy damage by inundating the aerial roots. In summary, the pre-hurricane canopy structures, physical environment, and external forces brought by hurricanes interplayed to affect the vulnerability of coastal mangroves to major hurricanes.

The coastal mangrove forest bears important ecosystem functions and services, including the protection of shorelines and coastal communities. While coastal mangroves often suffer severe damage during storms, understanding the vulnerability and resistance of mangroves to the damage at a landscape scale is crucial for coastal mangrove management and conservation. In September 2017, two consecutive major hurricanes caused tremendous damage to the coastal mangroves in the Caribbean. By utilizing LiDAR data taken before and after the hurricanes in a basin mangrove forest in Northeast Puerto Rico, we analyzed the spatial variation of a canopy structure before the hurricanes and hurricane-induced canopy height reduction and explored possible drivers by means of spatial regressions. Regarding the canopy structure, we found that the pre-hurricane canopy height of the mangrove forest decreased with elevation and distance to the freshwater/sewage canals within the forest, and these two drivers explained 82% of variations in the mangrove canopy height. The model, thus, implies that freshwater and nutrient inputs brought by the canals tend to promote the canopy height, and mangrove trees at lower elevation are especially more advantageous. Similarly, tree densities decreased with the canopy height but increased with the elevation and the distance to the canals. We also found that this mangrove forest suffered on average a 53% canopy height reduction, reflecting mostly heavy crown defoliation and the rupture of branches. The regression, which explains 88% of spatial variation in the canopy height reduction, showed that mangroves with a higher canopy or lower density, or growing in lower elevation, or being closer to the canals suffered more damage. Our findings indicate that delivered freshwater/sewage by means of human-made canals has a strong impact on the canopy structure as well as its resistance to tropical storms. Freshwater and sewage tend to release the salinity stress and nutrient deficit and, thus, to promote the mangrove canopy height. However, the addition of freshwater and nutrients might also increase the risk of mangrove damage during the storms probably because of an altered allometry of assimilates.

Tropical dry forest is vulnerable to increased climate variability with more frequent and severe storms. Studies of hurricane impact on tropical dry forest often focused on individual tree traits. How trees in tropical dry forests work together to combat wind damage is still unclear. To address this, we integrated ground-observed ecosystem structure from National Ecological Observation Network (NEON) with airborne-LiDAR images and analyzed resistance in forest structure of Guánica dry forest in Puerto Rico to major hurricanes in 2017 at the forest-stand level. Using each plot instead of the individual tree as the base unit, we regressed mean changes in stem height and fractions of lost or damaged stems at 15 plots on mean stem diameter, mean and standard deviation of stem height, stem density, and topography. Meanwhile, using the LiDAR-derived canopy heights, we compared the changes in canopy height before and after the hurricanes and regressed spatially the canopy height change on prior-hurricane tree cover, canopy height, and rugosity. We found that the damage was small in places with high stem density or high tree cover. Ground-observed damage in terms of height reduction significantly increased with the standard deviation of stem height, an index of roughness, but decreased with the mean stem diameter of the plots. LiDAR-detected damage in terms of reduction in canopy height was also found to decrease with tree cover and mean canopy height when the canopy height was small or moderate but increase with the rugosity. The fraction of lost stems significantly decreased with the stem density, and the fraction of damaged stems significantly increased with the roughness and the plot elevation. The collective parameters of forest stand quantified from ground-observation and LiDAR, such as stem density, tree cover, and canopy roughness or rugosity, highlighted mutual supports of trees and played important roles in resisting damages to the tropical dry forest during major hurricanes.

Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.

The article investigated the effects of solution and ging temperatures on microstructure and mechanical properties of ultra-high strength stainless steel 10Cr13Co13Mo5Ni3W1VE(S280). Higher solution temperatures can improve impact toughness because of the quantity reduction of submicron-sized particles which act as microporous nucleation sites. S280 has the best mechanical properties at 1080 °C solution temperature. After quenching, the steel is completely martensite with almost no retained austenite. Aging at 560 °C results in peak strength due to the precipitation of fine carbides coherent zones. The loss of precipitates/matrix coherency and precipitates coarsening cause a decrease in strength at higher aging temperatures. Good strength and toughness obtained at 540 °C aging temperature are attributed to fine and dispersed strengthening phases such as Cr2C and Fe2Mo, and the recovery of austenite in high-density dislocation martensite matrix. The details of electron microscopy research, strengthening and toughening mechanisms are discussed.

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10−3 and 4.8 ± 0.116 × 10−3 yr−1, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.

High dynamic range (HDR) images give a strong disposition to capture all parts of natural scene information due to their wider brightness range than traditional low dynamic range (LDR) images. However, to visualize HDR images on common LDR displays, tone mapping operations (TMOs) are extra required, which inevitably lead to visual quality degradation, especially in the bright and dark regions. To evaluate the performance of different TMOs accurately, this paper proposes a blind tone-mapped image quality assessment method based on regional sparse response and aesthetics (RSRA-BTMI) by considering the influences of detail information and color on the human visual system. Specifically, for the detail loss in a tone-mapped image (TMI), multi-dictionaries are first designed for different brightness regions and whole TMI. Then regional sparse atoms aggregated by local entropy and global reconstruction residuals are presented to characterize the regional and global detail distortion in TMI, respectively. Besides, a few efficient aesthetic features are extracted to measure the color unnaturalness of TMI. Finally, all extracted features are linked with relevant subjective scores to conduct quality regression via random forest. Experimental results on the ESPL-LIVE HDR database demonstrate that the proposed RSRA-BTMI method is superior to the existing state-of-the-art blind TMI quality assessment methods.

With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities and other fields, more and more processing techniques are being developed for 3D meshes, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment. In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with Graph Fourier transform (GFT), and then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Experiments are performed successfully on the public databases and the obtained results show that the proposed BMQA-GSES method provides good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.

In order to overcome the poor robustness of traditional image registration algorithms in illuminating and solving the problem of low accuracy of a learning-based image homography matrix estimation algorithm, an image registration algorithm based on convolutional neural network (CNN) and local homography transformation is proposed. Firstly, to ensure the diversity of samples, a sample and label generation method based on moving direct linear transformation (MDLT) is designed. The generated samples and labels can effectively reflect the local characteristics of images and are suitable for training the CNN model with which multiple pairs of local matching points between two images to be registered can be calculated. Then, the local homography matrices between the two images are estimated by using the MDLT and finally the image registration can be realized. The experimental results show that the proposed image registration algorithm achieves higher accuracy than other commonly used algorithms such as the SIFT, ORB, ECC, and APAP algorithms, as well as another two learning-based algorithms, and it has good robustness for different types of illumination imaging.

Because perceptual video coding (PVC) can reduce bitrates with negligible visual quality loss in video compression, a PVC scheme based on just noticeable distortion (JND) model is proposed for ultra-high definition video. Firstly, the proposed JND model is designed, considering the spatial JND characteristics such as contrast sensitivity, luminance adaptation and saliency weight factor. Secondly, in order to perform precise JND suppression, the Gauss differential entropy (GDE) filter is designed to divide the image into smooth and complex texture region. Thirdly, through incorporating the proposed JND model into the encoding process, the transform coefficients are suppressed in harmonization with the transform/quantization process of high efficiency video coding (HEVC). In order to achieve the JND suppression effectively, a distortion compensation factor and distortion compensation control factor are incorporated to control the extent of distortion in the rate distortion optimization process. The experimental results show that the proposed PVC scheme can achieve a remarkable bitrate reduction of 32.98% for low delay (LD) configuration and 28.61% for random access (RA) configuration with a negligible subjective quality loss. Meanwhile, the proposed method only causes about average 12.94% and 22.45% encoding time increase under LD and RA configuration compared with an HEVC reference software, respectively.

The Qinghai-Tibet Plateau (QTP) is among the most sensitive ecosystems to changes in global climate and human activities, and quantifying its consequent change in land-cover land-use (LCLU) is vital for assessing the responses and feedbacks of alpine ecosystems to global climate changes. In this study, we first classified annual LCLU maps from 2001−2015 in QTP from MODIS satellite images, then analyzed the patterns of regional hotspots with significant land changes across QTP, and finally, associated these trends in land change with climate forcing and human activities. The pattern of land changes suggested that forests and closed shrublands experienced substantial expansions in the southeastern mountainous region during 2001−2015 with the expansion of massive meadow loss. Agricultural land abandonment and the conversion by conservation policies existed in QTP, and the newly-reclaimed agricultural land partially offset the loss with the resulting net change of −5.1%. Although the urban area only expanded 586 km2, mainly at the expense of agricultural land, its rate of change was the largest (41.2%). Surface water exhibited a large expansion of 5866 km2 (10.2%) in the endorheic basins, while mountain glaciers retreated 8894 km2 (−3.4%) mainly in the southern and southeastern QTP. Warming and the implementation of conservation policies might promote the shrub encroachment into grasslands and forest recovery in the southeastern plateau. While increased precipitation might contribute to the expansion of surface water in the endorheic basins, warming melts the glaciers in the south and southeast and complicates the hydrological service in the region. The substantial changes in land-cover reveal the high sensitivity of QTP to changes in climate and human activities. Rational policies for conservation might mitigate the adverse impacts to maintain essential services provided by the important alpine ecosystems.

Image matching is an outstanding issue because of the existing of geometric and radiometric distortion in stereo remote sensing images. Weighted α-shape (WαSH) local invariant features are tolerant to image rotation, scale change, affine deformation, illumination change, and blurring. However, since the number of WαSH features is small, it is difficult to get enough matches to estimate the satisfactory homography matrix or fundamental matrix. In addition, the WαSH detector is extremely sensitive to image noise because it is built on sampled edges. Considering the shortcomings of the WαSH detector, this paper improves the WαSH feature matching method based on the 2D discrete wavelet transform (2D-DWT). The method firstly performs 2D-DWT on the image, and then detects WαSH features on the transformed images. According to the methods of descriptor construction for WαSH features, three matching methods on the basis of wavelet transform WαSH features (WWF), improved wavelet transform WαSH features (IWWF), and layered IWWF (LIWWF) are distinguished with respect to the character of the sub-images. The experimental results on the dataset containing affine distortion, scale distortion, illumination change, and noise images, showed that the proposed methods acquired more matches and better stableness than WαSH. Experimentation on remote sensing images with less affine distortion and slight noise showed that the proposed methods obtained the correct matching rate greater than 90%. For images containing severe distortion, KAZE obtained a 35.71% correct matching rate, which is unacceptable for calculating the homography matrix, while IWWF achieved a 71.42% correct matching rate. IWWF was the only method that achieved the correct matching rate of no less than 50% for all four test stereo remote sensing image pairs and was the most stable compared to MSER, DWT-MSER, WαSH, DWT-WαSH, KAZE, WWF, and LIWWF.

Due to sharp changes in local brightness in high dynamic range scenes, fused images obtained by the traditional multi-exposure fusion methods usually have an unnatural appearance resulting from halo artifacts. In this paper, we propose a halo-free multi-exposure fusion method based on sparse representation of gradient features for high dynamic range imaging. First, we analyze the cause of halo artifacts. Since the range of local brightness changes in high dynamic scenes may be far wider than the dynamic range of an ordinary camera, there are some invalid, large-amplitude gradients in the multi-exposure source images, so halo artifacts are produced in the fused image. Subsequently, by analyzing the significance of the local sparse coefficient in a luminance gradient map, we construct a local gradient sparse descriptor to extract local details of source images. Then, as an activity level measurement in the fusion method, the local gradient sparse descriptor is used to extract image features and remove halo artifacts when the source images have sharp local changes in brightness. Experimental results show that the proposed method obtains state-of-the-art performance in subjective and objective evaluation, particularly in terms of effectively eliminating halo artifacts.

This paper systematically investigates the properties of six kinds of entropy-based risk measures: Information Entropy and Cumulative Residual Entropy in the probability space, Fuzzy Entropy, Credibility Entropy and Sine Entropy in the fuzzy space, and Hybrid Entropy in the hybridized uncertainty of both fuzziness and randomness. We discover that none of the risk measures satisfy all six of the following properties, which various scholars have associated with effective risk measures: Monotonicity, Translation Invariance, Sub-additivity, Positive Homogeneity, Consistency and Convexity. Measures based on Fuzzy Entropy, Credibility Entropy, and Sine Entropy all exhibit the same properties: Sub-additivity, Positive Homogeneity, Consistency, and Convexity. These measures based on Information Entropy and Hybrid Entropy, meanwhile, only exhibit Sub-additivity and Consistency. Cumulative Residual Entropy satisfies just Sub-additivity, Positive Homogeneity, and Convexity. After identifying these properties, we develop seven portfolio models based on different risk measures and made empirical comparisons using samples from both the Shenzhen Stock Exchange of China and the New York Stock Exchange of America. The comparisons show that the Mean Fuzzy Entropy Model performs the best among the seven models with respect to both daily returns and relative cumulative returns. Overall, these results could provide an important reference for both constructing effective risk measures and rationally selecting the appropriate risk measure under different portfolio selection conditions.

Accurate and timely monitoring of tropical land cover/use (LCLU) changes is urgent due to the rapid deforestation/reforestation and its impact on global land-atmosphere interaction. However, persistent cloud cover in the tropics imposes the greatest challenge and retards LCLU mapping in mountainous areas such as the tropic island of Puerto Rico, where forest transition changed from deforestation to reforestation due to the economy shift from agriculture to industry and service after the 1940s. To improve the LCLU mapping in the tropics and to evaluate the trend of forest transition of Puerto Rico in the new century, we integrated the optical Landsat images with the L-band SAR to map LC in 2010 by taking advantage of the cloud-penetrating ability of the SAR signals. The results showed that the incorporation of SAR data with the Landsat data significantly, although not substantially, enhanced the accuracy of LCLU mapping of Puerto Rico, and the Kappa statistic reached 90.5% from 88.4% without SAR data. The enhancement of mapping by SAR is important for urban and forest, as well as locations with limited optical data caused by cloud cover. We found both forests and urban lands continued expanding in the new century despite the declining population. However, the forest cover change slowed down in 2000–2010 compared to that in 1991–2000. The deforestation rate reduced by 42.1% in 2000–2010, and the reforestation was mostly located in the east and southeast of the island where Hurricane Georges landed and caused severe vegetation damage in 1998. We also found that reforestation increased, but deforestation decreased along the topography slope. Reforestation was much higher within the protected area compared to that in the surroundings in the wet and moist forest zones.

Shrubs have been reported to expand into grassland and polar regions in the world, which causes complex changes in ecosystem carbon, nutrients, and resilience. Given the projected global drying trend, shrubs with their superior drought resistance and tolerance may play more important roles in global ecosystem function. Shrubland exists in all of the climate zones in China, from subtropical to temperate and high cold regions, and they occupy more than 20% of the land area. In this paper, we analyzed the spatiotemporal trend of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) for six shrubland types in China from 2001 to 2013 and its relationship to intra- and inter-annual regional climate dynamics. Existing literature reported that the vegetation index did not change significantly in China during 2000–2012. However, we found that the shrubland EVI in China increased significantly at a rate of 1.01 × 10−3 EVI·a−1 from 2001 to 2013. Two major shrubland types (subtropical evergreen and temperate deciduous) and two desert types (high-cold desert and temperate desert) increased significantly, whereas subalpine evergreen shrubland decreased at a rate of −0.64 × 10−3 EVI·a−1. We also detected a significantly lengthened growing season of temperate deciduous shrubland. The growing season length contributed significantly to the annual averaged EVI for temperate deciduous, subalpine deciduous and subtropical evergreen shrublands. Furthermore, the precipitation variation contributed more to the annual averaged EVI than the temperature. The year-round decrease in rainfall and the increase in temperature led to a significant reduction in the subalpine evergreen shrubland EVI. The enhancement of countrywide shrubland EVI may promote its contribution to the regional ecosystem function and its potential to invade grasslands.