Ultrasound-Assisted Extraction of Paeonol from Moutan Cortex: Purification and Component Identification of Extract.

Moutan Cortex (MC) is a traditional Chinese medicine that contains abundant medicinal components, such as paeonol, paeoniflorin, etc. Paeonol is the main active component of MC. In this study, paeonol was extracted from MC through an ultrasound-assisted extraction process, which is based on single-factor experiments and response surface methodology (RSM). Subsequently, eight macroporous resins of different properties were used to purify paeonol from MC. The main components of the purified extract were identified by ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS/MS). The results indicate the optimal parameters are as follows: liquid-to-material ratio 21:1 mL/g, ethanol concentration 62%, ultrasonic time 31 min, ultrasonic temperature 36 °C, ultrasonic power 420 W. Under these extraction conditions, the actual yield of paeonol was 14.01 mg/g. Among the eight tested macroporous resins, HPD-300 macroporous resin was verified to possess the highest adsorption and desorption qualities. The content of paeonol increased from 6.93% (crude extract) to 41.40% (purified extract) after the HPD-300 macroporous resin treatment. A total of five major phenolic compounds and two principal monoterpene glycosides were characterized by comparison with reference compounds. These findings will make a contribution to the isolation and utilization of the active components from MC.

Hundred-Fold Enhancement in the Anomalous Hall Effect Induced by Hydrogenation.

The anomalous Hall effect (AHE) is one of the most fascinating transport properties in condensed matter physics. However, the AHE magnitude, which mainly depends on net spin polarization and band topology, is generally small in oxides and thus limits potential applications. Here, we demonstrate a giant enhancement of AHE in a LaCoO3-induced 5d itinerant ferromagnet SrIrO3 by hydrogenation. The anomalous Hall resistivity and anomalous Hall angle, which are two of the most critical parameters in AHE-based devices, are found to increase to 62.2 µΩ·cm and 3%, respectively, showing an unprecedentedly large enhancement ratio of ∼10000%. Theoretical analysis suggests the key roles of Berry curvature in enhancing AHE. Furthermore, the hydrogenation concomitantly induces the significant elevation of Curie temperature from 75 to 160 K and 40-fold reinforcement of coercivity. Such giant regulation and very large AHE magnitude observed in SrIrO3 could pave the path for 5d oxide devices.

Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data.

Accurate PM2.5 concentrations predicting is critical for public health and wellness as well as pollution control. However, traditional methods are difficult to accurately predict PM2.5. An adaptive model coupled with artificial neural network (ANN) and wavelet analysis (WANN) is utilized to predict daily PM2.5 concentrations with remote sensing and surface observation data. The four evaluation metrics, namely Pearson correlation coefficient (R), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), are utilized to evaluate the performances of the artificial neural network (ANN) and WANN methods. From the predicting results, The WANN model has a higher R (R = 0.9990) during the testing period compared with R (R = 0.6844) based on the ANN model. Similarly, the WANN model has a lower MAPE (3.6988%), RMSE (1.0145 µg/m3), MAE (1.3864 µg/m3), compared with MAPE (80.0086%), RMSE (16.5838 µg/m3), MAE (12.2420 µg/m3) of the ANN. In addition, comparing the outcomes of the proposed WANN method with the ANN method, it was observed that the error during the training and verification period has decreased significantly. Furthermore, the statistical methods are used to analyze WANN and ANN, showing that WANN has higher training accuracy and better stability. Therefore, it is feasible to establish WANN to predict PM2.5 concentrations (1 day in advance) by using remote sensing and surface observation data.

High field magnetic transport measurements of FeGe thin plates.

Magnetic skyrmions have garnered considerable attention due to their topological properties and potential applications in information storage. These unique structures can be found in chiral magnets, including well-known compounds like MnSi and FeGe with a B20-type crystal structure. In this study, we utilized Lorentz transmission electron microscopy to investigate the influence of magnetic skyrmions on the Hall effect in FeGe under low magnetic fields. Additionally, we examined the magnetoresistance (MR) and Hall effect of FeGe under a high magnetic field of 28 T. Our findings reveal distinct mechanisms governing the MR at low and high temperatures. Notably, the anomalous Hall effect plays a significant role in the Hall resistivity observed at low magnetic fields. Meanwhile, the contribution of the skyrmion-induced topological Hall signal in the FeGe is ignorable. Furthermore, by employing a two-carrier model and fitting the carrier concentration of FeGe under high magnetic fields, we demonstrate a transition in the dominant carrier type from electrons to holes as the temperature increases. These results contribute to a deeper understanding of the intrinsic magnetic properties of FeGe.

Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem.

China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO2 to mitigate climate change caused by the increase of anthropogenic CO2. It is essential to evaluate the carbon sink potential (CSP) of China's forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China's forest ecosystem in historical (1982-2021) and future (2022-2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index (CSindex) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO2, which is approximately 52.03% of the cumulative increased CO2 emissions in China from 1959 to 2018. In the next 60 years, China's forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO2. Compared with the carbon stock in the historical period, the cumulative absorption of CO2 by China's forest ecosystem in 2032-2036, 2062-2066, and 2077-2081 are approximately 11.25-39.68, 110.66-121.49 and 101.31-111.11 Bt CO2, respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical CSindex covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83-72.22 Bt CO2, respectively. In the future, China's forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.

Extraction, Purification, and Component Identification of Monoterpene Glycosides from Paeonia suffruticosa Seed Meal.

Paeonia suffruticosa (P. suffruticosa) seed meal is a byproduct of P. suffruticosa seed processing, which contains bioactive substances such as monoterpene glycosides, and has not been effectively utilized at present. In this study, monoterpene glycosides were extracted from P. suffruticosa seed meal using an ultrasound-assisted ethanol extraction process. The monoterpene glycoside extract was then purified by macroporous resin and identified using HPLC-Q-TOF-MS/MS. The results indicated the following optimal extraction conditions: ethanol concentration, 33%; ultrasound temperature, 55 °C; ultrasound power, 400 W; liquid-material ratio, 33:1; and ultrasound time, 44 min. Under these conditions, the yield of monoterpene glycosides was 121.03 mg/g. The purity of the monoterpene glycosides increased from 20.5% (crude extract) to 71.2% (purified extract) when using LSA-900C macroporous resin. Six monoterpene glycosides (oxy paeoniflorin, isomaltose paeoniflorin, albiflorin, 6'-O-ß-D-glucopyranoside albiflorin, paeoniflorin, and Mudanpioside i) were identified from the extract using HPLC-Q-TOF-MS/MS. The main substances were albiflorin and paeoniflorin, and the contents were 15.24 mg/g and 14.12 mg/g, respectively. The results of this study can provide a theoretical basis for the effective utilization of P. suffruticosa seed meal.

Disorder-tuned conductivity in amorphous monolayer carbon.

Correlating atomic configurations-specifically, degree of disorder (DOD)-of an amorphous solid with properties is a long-standing riddle in materials science and condensed matter physics, owing to difficulties in determining precise atomic positions in 3D structures1-5. To this end, 2D systems provide insight to the puzzle by allowing straightforward imaging of all atoms6,7. Direct imaging of amorphous monolayer carbon (AMC) grown by laser-assisted depositions has resolved atomic configurations, supporting the modern crystallite view of vitreous solids over random network theory8. Nevertheless, a causal link between atomic-scale structures and macroscopic properties remains elusive. Here we report facile tuning of DOD and electrical conductivity in AMC films by varying growth temperatures. Specifically, the pyrolysis threshold temperature is the key to growing variable-range-hopping conductive AMC with medium-range order (MRO), whereas increasing the temperature by 25 °C results in AMC losing MRO and becoming electrically insulating, with an increase in sheet resistance of 109 times. Beyond visualizing highly distorted nanocrystallites embedded in a continuous random network, atomic-resolution electron microscopy shows the absence/presence of MRO and temperature-dependent densities of nanocrystallites, two order parameters proposed to fully describe DOD. Numerical calculations establish the conductivity diagram as a function of these two parameters, directly linking microstructures to electrical properties. Our work represents an important step towards understanding the structure-property relationship of amorphous materials at the fundamental level and paves the way to electronic devices using 2D amorphous materials.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors.

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

Unintended consequences of combating desertification in China.

Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region.

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China.

Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenarios.

Climate change affects air quality and people's health. Therefore, accurate prediction of future climate change is of great significance for human beings to better adapt and mitigate climate change. Using the projection simulation dataset of the CMIP6 multi-model ensemble, the future climate change in the Sahara region under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) is analyzed. The results show that annual and seasonal average surface air temperature in the Sahara region will continue to rise throughout the twenty-first century relative to the baseline period 1995-2014 if greenhouse gas (GHG) concentrations continue increasing. Under the four SSPs scenarios, the warming in the Sahara region will be more pronounced than in the whole world through the twenty-first century. The annual maximum temperature (TX), the annual minimum temperature (TN), the annual count of days with maximum temperature above 35 °C (TX 35), and the annual count of days with maximum temperature above 40 °C (TX 40) in the Sahara region will continue to increase until the end of the twenty-first century under the four scenarios. The results of climate change prediction can provide scientific reference for climate policy-making.

Controllable Itinerant Ferromagnetism in Weakly Correlated 5d SrIrO3.

The weakly correlated nature of 5d oxide SrIrO3 determines its rare ferromagnetism, and the control of its magnetic order is even less studied. Tailoring structure distortion is currently a main route to tune the magnetic order of 5d iridates, but only for the spatially confined insulating counterparts. Here, we have realized ferromagnetic order in metallic SrIrO3 by construction of SrIrO3/ferromagnetic-insulator (LaCoO3) superlattices, which reveal a giant coercivity of ∼10 T and saturation field of ∼25 T with strong perpendicular magnetic anisotropy. The Curie temperature of SrIrO3 can be controlled by engineering interface charge transfer, which is confirmed by Hall effect measurements collaborating with EELS and XAS. Besides, the noncoplanar spin texture is captured, which is caused by interfacial Dzyaloshinskii-Moriya interactions as well. These results indicate controllable itinerant ferromagnetism and an emergent topological magnetic state in strong spin-orbit coupled semimetal SrIrO3, showing great potential to develop efficient spintronic devices.

Scaling of Berry-curvature monopole dominated large linear positive magnetoresistance.

The linear positive magnetoresistance (LPMR) is a widely observed phenomenon in topological materials, which is promising for potential applications on topological spintronics. However, its mechanism remains ambiguous yet, and the effect is thus uncontrollable. Here, we report a quantitative scaling model that correlates the LPMR with the Berry curvature, based on a ferromagnetic Weyl semimetal CoS2 that bears the largest LPMR of over 500% at 2 K and 9 T, among known magnetic topological semimetals. In this system, masses of Weyl nodes existing near the Fermi level, revealed by theoretical calculations, serve as Berry-curvature monopoles and low-effective-mass carriers. Based on the Weyl picture, we propose a relation [Formula: see text], with B being the applied magnetic field and [Formula: see text] the average Berry curvature near the Fermi surface, and further introduce temperature factor to both MR/B slope (MR per unit field) and anomalous Hall conductivity, which establishes the connection between the model and experimental measurements. A clear picture of the linearly slowing down of carriers, i.e., the LPMR effect, is demonstrated under the cooperation of the k-space Berry curvature and real-space magnetic field. Our study not only provides experimental evidence of Berry curvature-induced LPMR but also promotes the common understanding and functional designing of the large Berry-curvature MR in topological Dirac/Weyl systems for magnetic sensing or information storage.

Surfactant-Mediated Ultrasonic-Assisted Extraction and Purification of Antioxidants from Chaenomeles speciosa (Sweet) Nakai for Chemical- and Cell-Based Antioxidant Capacity Evaluation.

In this study, a surfactant-mediated ultrasonic-assisted process was used for the first time to produce an antioxidant-enriched extract from Chaenomeles speciosa (Sweet) Nakai (C. speciosa, a popular fruit grown widely in the temperate regions of China). Ultrasonic treatment at 51 °C and 200 W for 30 min with sodium dodecyl sulfate as the surfactant led to a phenolic yield of 32.42 mg/g from dried C. speciosa powder, based on single-factor experiments, the Plackett-Burman design and the Box-Behnken design. The phenolic content increased from 6.5% (the crude extract) to 57% (the purified extract) after the purification, using LSA-900C macroporous resin. Both the crude and purified extracts exhibited a significant total reducing power and DPPH/ABTS scavenging abilities, with the purified extract being more potent. The purified extract exerted significant antioxidant actions in the tert-butyl hydroperoxide-stimulated HepG2 cells, e.g., increasing the activities of superoxide dismutase and catalase, while decreasing the reactive oxygen species and malondialdehyde levels, through the regulation of the genes and proteins of the Nrf2/Keap1 signaling pathway. Therefore, the extract from C. speciosa is a desirable antioxidant agent for the oxidative damage of the body to meet the rising demand for natural therapeutics.

Hydrologic-induced concentrated soil nutrients and improved plant growth increased carbon storage in a floodplain wetland over wet-dry alternating zones.

Hydrological gradient variations in wetlands have a vital impact on wetland carbon storage. However, the mechanisms by which hydrological gradient variations affect biomass and carbon storage by regulating the soil nutrient contents and plant diversity remain unclear. This study attempted to explore these influencing mechanisms by studying the relationships between hydrological gradient variations and carbon storage in wetlands. The results showed that the average nutrient content, plant biomass and soil carbon content values in the high-frequency wet-dry alternating zones (HFWA, zones where the frequency of water level occurs between -25 cm and 25 cm greater than 0.5) were 1.4 times, 2.3 times and 0.43 higher, respectively, than those in the low-frequency wet-dry alternating zones (LFWA, zones where the frequency of water level occurs between -25 cm and 25 cm less than 0.3). These results indicated that the HFWA zones had higher soil nutrients, higher plant dominance, higher biomass and higher soil carbon contents than the LFWA zones. The structural equation model revealed a significant positive correlation between wet-dry alternations and the soil nutrient-plant biomass-soil carbon relation in wetlands. Moreover, there was also a significant positive correlation between wet-dry alternations and the plant dominance-plant biomass-soil carbon relation in wetlands. This implied that the concentrated effect of HFWA on soil nutrients promotes plant growth, enhances plant dominance, promotes plant productivity, and enhances the capacities of plants to input carbon to the soil, thereby increasing the soil carbon content. This study closely linked wetland hydrological gradients, plant biodiversity and wetland carbon sequestration and profoundly revealed the mechanisms by which hydrological gradients in wetlands regulate the concentrations of nutrient elements, thereby affecting vegetation growth and carbon sequestration; these results could provide a new cognitive basis for understanding the coupling of carbon and water.

Increased tropical vegetation respiration is dually induced by El Niño and upper atmospheric warm anomalies.

Tropical vegetation respiration (TVR) is affected by extreme climate change. As it is very difficult to directly observe TVR, our understanding of the land-ocean-atmosphere carbon cycle, and particularly the regulatory effect of El Niño-Southern Oscillation (ENSO) on TVR and the land-atmosphere carbon balance, is very limited. Therefore, usingModerate Resolution Imaging Spectroradiometer (MODIS) products and meteorological data, we investigated the response of TVR to changes in ENSO during 2000-2015. The influence of El Niño on TVR was approximately 10.8% higher than that of La Niña. During El Niño years, a significant and anomalous increase in thermal measures related to TVR and ENSO and a significant and anomalous decrease in related hydrological measures favor the formation of warmer and drier climate conditions. Furthermore, the zonal distributions of air temperature and vertical velocity at 200-1000 hPa during El Niño years show that a stronger atmospheric inversion over tropical regions causes an increase in the surface temperature. Moreover, anomalous atmospheric subsidence inhibits the upward transport of water vapor, leading to a decrease in the cloud formation probability and reduced precipitation. In summary, increased surface temperatures caused by increased solar radiation and enhanced atmospheric inversion and decreased precipitation cause warmer and drier climate conditions, which forces TVR to increase. As TVR constitutes the key node of the land-atmosphere­carbon cycle process, we focus on TVR and its close linkage with ENSO events and further establish a knowledge framework for understanding the land-atmosphere-ocean carbon cycle. This study deepens our understanding of not only the mechanism of the land-atmosphere carbon balance but also the ocean-induced terrestrial ecosystem processes spurred by ENSO-involved climate change. PLAIN LANGUAGE SUMMARY: Vegetation respiration regulates the carbon balance of the land and atmosphere. As it is very difficult to directly observe vegetation respiration, our understanding of the land-ocean-atmosphere carbon cycle involved and the roles of vegetation respiration and El Niño-Southern Oscillation (ENSO) in regulating the land-atmosphere carbon balance is very limited. Therefore, using MODIS products and meteorological data, we investigated the response of tropical vegetation respiration to changes in ENSO during 2000-2015. We found that during El Niño years, warmer and drier climate conditions over tropical regions increased vegetation respiration. Exacerbating the warmer and drier climate conditions, upper atmospheric warm anomalies further caused a remarkable increase in tropical vegetation respiration. Based on the land-atmosphere­carbon cycle process, we establish a knowledge framework for understanding the land-atmosphere-ocean carbon cycle. This knowledge deepens our understanding of not only the mechanism of the land-atmosphere carbon balance but also the ocean-induced terrestrial ecosystem processes spurred by ENSO-involved climate change.

Assessing and predicting soil carbon density in China using CMIP5 earth system models.

Soil carbon (SC) is a key component of the carbon cycle and plays an important role in climate change; however, quantitatively assessing SC dynamics at the regional scale remains challenging. Earth system model (ESM) that considers multiple environmental factors and spatial heterogeneity has become a powerful tool to explore carbon cycle-climate feedbacks, although the performance of the ESM is diverse and highly uncertain. Thus, identifying reliable ESMs is a prerequisite for better understanding the response of SC dynamics to human activity and climate change. The 16 ESMs that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were employed to evaluate the skill performance of SC density simulation by comparison with reference data from the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). Although ESMs generally reflect spatial patterns with lower SC in northwest China and higher SC in southeast China, 11 of 16 ESMs underestimated the SC in China, and 5 of 16 ESMs overestimated the SC density as most ESMs had large discrepancies in capturing the SC density in the northern high latitudes of China and the Qinghai-Tibet Plateau. According to a series of model performance statistics, SC simulated by Institute Pierre Simon Laplace (IPSL) Coupled Model had a close spatial pattern with IGBP-DIS and showed higher skills for SC predictions in China relative to other CMIP5 ESMs. The multimodel ensemble average obtained by IPSL family ESMs showed that SC density exhibited increasing trends under both the RCP4.5 scenario and RCP8.5 scenario. The SC density increased slowly under RCP8.5 compared with that under RCP4.5 and even displayed a decreasing trend in the late 21st century. The findings of this study can provide a reference for identifying the shortcomings of SC predictions in China and guide SC parameterization improvement in ESMs.

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks.

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

Two types of magnetic shape-memory effects from twinned microstructure and magneto-structural coupling in Fe1+y Te.

A detailed experimental investigation of Fe1+y Te (y = 0.11, 0.12) using pulsed magnetic fields up to 60 T confirms remarkable magnetic shape-memory (MSM) effects. These effects result from magnetoelastic transformation processes in the low-temperature antiferromagnetic state of these materials. The observation of modulated and finely twinned microstructure at the nanoscale through scanning tunneling microscopy establishes a behavior similar to that of thermoelastic martensite. We identified the observed, elegant hierarchical twinning pattern of monoclinic crystallographic domains as an ideal realization of crossing twin bands. The antiferromagnetism of the monoclinic ground state allows for a magnetic-field-induced reorientation of these twin variants by the motion of one type of twin boundaries. At sufficiently high magnetic fields, we observed a second isothermal transformation process with large hysteresis for different directions of applied field. This gives rise to a second MSM effect caused by a phase transition back to the field-polarized tetragonal lattice state.

Global vegetation productivity responses to the West Pacific Warm Pool.

Sea surface temperatures (SSTs) strongly influence atmospheric circulation and the Earth's climate, which in turn significantly affects vegetation productivity. Most of the previous studies on the subject have focused on links between the El Niño-Southern Oscillation (ENSO) and vegetation productivity, but few studies have addressed the effects of West Pacific Warm Pool (WPWP) on that although the early stages of the ENSO phenomenon may first develop there. In this paper, we use the mean SST values in the WPWP to construct a climate index, known as the WPWP index (WPI), and study the impacts of the WPWP on global vegetation productivity. We provide evidence for a robust link among the alternating warm and cool WPI pattern, terrestrial vegetation productivity and carbon balance. The analysis is based on both satellite observations and model simulations. The results of this study show that the warm and cool WPWP phases have inverse effects on land surface temperature and precipitation. A warm (cool) WPWP is associated with a warmer (cooler) climate on global land surfaces as well as a drier (wetter) climate in southern hemisphere, and hence enhances (suppresses) vegetation productivity in the latitudes of approximately 10-70°N and suppresses (enhances) vegetation growth in the latitudes of approximately 10-30°S. The underlying mechanism is also discussed. The WPI serves as a meaningful climate index for studying the ocean-vegetation teleconnections.