Digital non-Foster-inspired electronics for broadband impedance matching.

Narrow bandwidths are a general bottleneck for applications relying on passive, linear, subwavelength resonators. In the past decades, several efforts have been devoted to overcoming this challenge, broadening the bandwidth of small resonators by the means of analog non-Foster matching networks for radiators, antennas and metamaterials. However, most non-Foster approaches present challenges in terms of tunability, stability and power limitations. Here, by tuning a subwavelength acoustic transducer with digital non-Foster-inspired electronics, we demonstrate five-fold bandwidth enhancement compared to conventional analog non-Foster matching. Long-distance transmission over airborne acoustic channels, with approximately three orders of magnitude increase in power level, validates the performance of the proposed approach. We also demonstrate convenient reconfigurability of our non-Foster-inspired electronics. This implementation provides a viable solution to enhance the bandwidth of sub-wavelength resonance-based systems, extendable to the electromagnetic domain, and enables the practical implementation of airborne and underwater acoustic radiators.

Probing H_{2} Double Ionization with Bicircular Laser Fields.

We present a kinematically complete study on strong-field double ionization of H_{2} molecules in two-color bicircular laser fields. The releasing times of electrons and protons are recorded with the double-hand attoclock. We observe the relative emission angles of two electrons oscillate with the kinetic energy release of protons, indicating the internal concerted four-body fragmentation. Using a three-dimensional molecular semiclassical ensemble model, we have disentangled the attosecond correlated electron emission in H_{2} double ionization. This work reveals the strong electron-nuclear coupling in the molecular bond breaking and may open up a new approach to experimentally accessing the intramolecular electron and bond dynamics with bicircular fields.

Hematological and biochemical parameters of giant pandas (Ailuropoda melanoleuca) in captive and semi-natural environments.

Physiological indexes like blood parameters have been widely used to monitor the health of free-roaming animals. Attempts to reintroduce one of China's most endangered species, the giant panda (Ailuropoda melanoleuca), have been hampered by a lack of data on its ecology and physiology. We examined three giant pandas' hematological and blood chemistry parameters in a soft release program and 30 captive giant pandas as controls and determined the reference intervals (RIs) for those blood parameters in the captive animals. Elevation, captivity status and the interaction of those factors were statistically significant for hematologic measures. Release pandas had significantly higher hemoglobin and hematocrit values after they moved to high elevation locations. We also found significant difference in the enzyme parameters between high and low elevation pandas such as higher aspartate aminotransferase, alanine aminotransferase, creatinine kinase, amylase and lower lactate dehydrogenase and alkaline phosphatase. Release pandas also had higher nutrition parameter values such as higher albumin, globulin and creatinine. The RI for blood parameters in our study provides a baseline to monitor the health of captive animals and forms the basis for assessing the health of free-roaming giant pandas in future reintroduction efforts.

Automatic recognition of giant panda vocalizations using wide spectrum features and deep neural network.

The goal of this study is to present an automatic vocalization recognition system of giant pandas (GPs). Over 12800 vocal samples of GPs were recorded at Chengdu Research Base of Giant Panda Breeding (CRBGPB) and labeled by CRBGPB animal husbandry staff. These vocal samples were divided into 16 categories, each with 800 samples. A novel deep neural network (DNN) named 3Fbank-GRU was proposed to automatically give labels to GP's vocalizations. Unlike existing human vocalization recognition frameworks based on Mel filter bank (Fbank) which used low-frequency features of voice only, we extracted the high, medium and low frequency features by Fbank and two self-deduced filter banks, named Medium Mel Filter bank (MFbank) and Reversed Mel Filter bank (RFbank). The three frequency features were sent into the 3Fbank-GRU to train and test. By training models using datasets labeled by CRBGPB animal husbandry staff and subsequent testing of trained models on recognizing tasks, the proposed method achieved recognition accuracy over 95%, which means that the automatic system can be used to accurately label large data sets of GP vocalizations collected by camera traps or other recording methods.

Does location of the households' matters? Identifying the households' willingness to pay and preference heterogeneity in advancement of vulnerable ecosystem services: An approach of choice experiment.

The present research underlines the need to expand far outside bundling or hierarchical providing strategy that often focuses on a specific habitat or ecosystem and creates a location-based strategy that considers how dependency in other parts of the region with ecosystem functions and processes leads to complements and resources' trade-offs. Thus, for assessment of spatial heterogeneity based on willingness to pay (WTP) for upgrading environmental attributes across Heihe River Basin (HRB), a choice experiment survey was carried out in the entire river basin. The HRB is one of the big inland river in the Northwestern region of China and is selected on basis of its geomorphological and geographical significance. A sum of 1679 individuals were interviewed through choice experiment technique from whole river basin consisting of five main cities and 33 adjoining rural areas. The Random Parameter logit model, Krinsky-Robb technique as well as delta method were applied for the evaluation of spatial heterogeneity and estimation of individual specific WTP, respectively. Spatial heterogeneity is verified among sampled individuals' preferences about upgradation of environmental attributes, such as, observed preferences of individuals' and their varying corresponding WTP amounts for per unit's upgradation in agriculture product quality, greenhouse gases reduction, farmland landscape, and biodiversity, which reflects heterogeneous tastes and preferences of the selected individuals. In addition, the assessed outcomes for identifying the impacts of distance decay through random parameter logit model depicted the vital role of distance influence on respondents' WTP for restoring the degraded environmental attributes, such that among 3 ad hoc distance bands, WTP of those sampled individuals who are in proximity of ≤ 10 km to HRB is more than the rest of the individuals, i.e., individuals living in the range of ≤ 20 km and > 20 km. For instance, WTP for agriculture product quality is 119.147 CNY/year in ≤ 10 km and is higher than the remainders.

Cascading impacts of global metal mining on climate change and human health caused by COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has significantly disrupted global metal mining and associated supply chains. Here we analyse the cascading effects of the metal mining disruption associated with the COVID-19 pandemic on the economy, climate change, and human health. We find that the pandemic reduced global metal mining by 10-20% in 2020. This reduction subsequently led to losses in global economic output of approximately 117 billion US dollars, reduced CO2 emissions by approximately 33 million tonnes (exceeding Hungary's emissions in 2015), and reduced human health damage by 78,192 disability-adjusted life years. In particular, copper and iron mining made the most significant contribution to these effects. China and rest-of-the-world America were the most affected. The cascading effects of the metal mining disruption associated with the pandemic on the economy, climate change, and human health should be simultaneously considered in designing green economic stimulus policies.

Giant panda age recognition based on a facial image deep learning system.

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.

Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network.

The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.

Research on equivalent thermal network modeling for rare-earth giant magnetostrictive transducer.

Of crucial importance for giant magnetostrictive transducers (GMTs) design is to quickly and accurately analysis the temperature distribution. With the advantages of low calculation cost and high accuracy, thermal network modelling has been developed for thermal analysis of GMT. However, the existing thermal models have their limits to describe these complicated thermal behaviors in the GMTs: most of researches focused on steady-state which is incapable of capturing temperature variances;  the temperature distribution of giant magnetostrictive (GMM) rods is generally assumed to be uniform whereas the temperature gradient on the GMM rod is remarkable due to its poor thermal conductivity;  the non-uniform distribution of GMM's losses is seldom introduced into thermal model. Therefore, a transient equivalent thermal network (TETN) model of GMT is established in this paper, considering the aforementioned three aspects. Firstly, based on the structure and working principle of a longitudinal vibration GMT, thermal analysis was carried out. Following this, according to the heat transfer process of GMT, the TETN model was established and the corresponding model parameters were calculated. Finally, the accuracy of the TETN model for the temporal and spatial analysis of the transducer temperature are verified by simulation and experiment.

The complete mitochondrial genome sequence of Sinomicrurus peinani (Serpentes: Elapidae).

Sinomicrurus peinani is a new species of the genus Sinomicrurus (Serpentes: Elapidae) from China and Vietnam in 2020. In this study, we successfully sequenced mitochondrial genome of an individual S. peinani. The complete mitochondrial genome of S. peinani is a circular molecule with the entire length of 19,477 bp. The base composition is T (28.1%), G (11.9%), and GC (38.5%), which contains two ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, 13 protein-coding genes, one origin of replication gene (D-loop), and two non-coding control regions, an origin of light-strand replication, and a 2346 bp non-coding region between tRNA-N and tRNA-Y. A maximum-likelihood (ML) tree of S. peinani and 13 other related species was constructed. The DNA data presented here will be useful to study the evolutionary relationships and genetic diversity of S. peinani.

Modeling of High-Power Tonpilz Terfenol-D Transducer Using Complex Material Parameters.

The loss effect in smart materials, the active part of a transducer, is of significant importance to acoustic transducer designers, as it directly affects the important characteristics of the transducer, such as the impedance spectra, frequency response, and the amount of heat generated. It is therefore beneficial to be able to incorporate energy losses in the design phase. For high-power low-frequency transducers requiring more smart materials, losses become even more appreciable. In this paper, similar to piezoelectric materials, three losses in Terfenol-D are considered by introducing complex quantities, representing the elastic loss, piezomagnetic loss, and magnetic loss. The frequency-dependent eddy current loss is also considered and incorporated into the complex permeability of giant magnetostrictive materials. These complex material parameters are then successfully applied to improve the popular plane-wave method (PWM) circuit model and finite element method (FEM) model. To verify the accuracy and effectiveness of the proposed methods, a high-power Tonpilz Terfenol-D transducer with a resonance frequency of around 1 kHz and a maximum transmitting current response (TCR) of 187 dB/1A/µPa is manufactured and tested. The good agreement between the simulation and experimental results validates the improved PWM circuit model and FEA model, which may shed light on the more predictable design of high-power giant magnetostrictive transducers in the future.

Evaluating a potential model to analyze the function of the gut microbiota of the giant panda.

To contribute to the conservation of endangered animals, the utilization of model systems is critical to analyze the function of their gut microbiota. In this study, the results of a fecal microbial transplantation (FMT) experiment with germ-free (GF) mice receiving giant panda or horse fecal microbiota showed a clear clustering by donor microbial communities in GF mice, which was consistent with the results of blood metabolites from these mice. At the genus level, FMT re-established approximately 9% of the giant panda donor microbiota in GF mice compared to about 32% for the horse donor microbiota. In line with this, the difference between the panda donor microbiota and panda-mice microbiota on whole-community level was significantly larger than that between the horse donor microbiota and the horse-mice microbiota. These results were consistent with source tracking analysis that found a significantly higher retention rate of the horse donor microbiota (30.9%) than the giant panda donor microbiota (4.0%) in GF mice where the microbiota remained stable after FMT. Further analyzes indicated that the possible reason for the low retention rate of the panda donor microbiota in GF mice was a low relative abundance of Clostridiaceae in the panda donor microbiota. Our results indicate that the donor microbiota has a large effect on GF mice microbiota after FMT.

SPICE Modeling of a High-Power Terfenol-D Transducer Considering Losses and Magnetic Flux Leakage.

Of great importance is modeling for transducer design and application to predict its performance and simulate key characteristics. The equivalent circuit modeling (ECM), one of the most powerful tools, has been widely used in the transducer industry and academia due to its outstanding merits of low simulation cost and easy usage for multi-field simulation in both time and frequency domains. Nevertheless, most of the existing equivalent circuit models for Terfenol-D transducers normally ignore three material losses, namely elastic loss, piezomagnetic loss, and magnetic loss. Additionally, the magnetic leakage due to the intrinsic poor magnetic permeability of Terfenol-D is rarely considered into the piezomagnetic coupling. Both loss effects will produce substantial errors. Therefore, an improved SPICE model for a high-power Terfenol-D transducer considering the aforementioned three losses and magnetic flux leakage (MFL) is proposed in this article, which is implemented on the platform of LTspice software. To verify the usefulness and effectiveness of the proposed technique, a high-power Terfenol-D tonpilz transducer prototype with a resonance frequency of around 1 kHz and a maximum transmitting current response (TCR) of 187.1 dB/1A/ µ Pa is built and tested. The experimental results, both in the air and water of the transducer, are in excellent agreement with the simulated results, which well validates our proposed modeling methods.

Field metabolic rates of giant pandas reveal energetic adaptations.

Knowledge of energy expenditure informs conservation managers for long term plans for endangered species health and habitat suitability. We measured field metabolic rate (FMR) of free-roaming giant pandas in large enclosures in a nature reserve using the doubly labeled water method. Giant pandas in zoo like enclosures had a similar FMR (14,182 kJ/day) to giant pandas in larger field enclosures (13,280 kJ/day). In winter, giant pandas raised their metabolic rates when living at - 2.4 °C (36,108 kJ/day) indicating that they were below their thermal neutral zone. The lower critical temperature for thermoregulation was about 8.0 °C and the upper critical temperature was about 28 °C. Giant panda FMRs were somewhat lower than active metabolic rates of sloth bears, lower than FMRs of grizzly bears and polar bears and 69 and 81% of predicted values based on a regression of FMR versus body mass of mammals. That is probably due to their lower levels of activity since other bears actively forage for food over a larger home range and pandas often sit in a patch of bamboo and eat bamboo for hours at a time. The low metabolic rates of giant pandas in summer, their inability to acquire fat stores to hibernate in winter, and their ability to raise their metabolic rate to thermoregulate in winter are energetic adaptations related to eating a diet composed almost exclusively of bamboo. Differences in FMR of giant pandas between our study and previous studies (one similar and one lower) appear to be due to differences in activity of the giant pandas in those studies.

Free-roaming dogs limit habitat use of giant pandas in nature reserves.

Giant pandas (Ailuropoda melanoleuca) were historically hunted using dogs and are currently threatened by free-roaming dogs and their associated diseases. To better understand the spatial magnitude of this threat, we used a GIS approach to investigate edge effects of dogs on giant panda habitat. We first examined two nature reserves with contrasting free-roaming dog populations: Liziping, with many dogs (~0.44/km2), and Daxiangling, with few dogs (~0.14/km2). Spatial analysis indicated that giant pandas at Liziping (but not Daxiangling) showed a shift in habitat use away from populated areas consistent with a risk response to the foray distance of free-roaming dogs (10.9 km path-distance). Most giant panda locations (86%) from the 2014 census in Liziping were clustered around remote "dog-free zones." Expanding this analysis across the entire giant panda range revealed that 40% of panda habitat is within the foray distance of dogs. Our assessment will inform dog control programs including monitoring, education, veterinary care, and other measures. We recommend that reserves designated for the release of translocated pandas receive priority consideration for dog control efforts. Only by understanding and managing complex interactions between humans, domestic animals, and wild animals can we sustain natural systems in a world increasingly dominated by humans.

A study on giant panda recognition based on images of a large proportion of captive pandas.

As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image-based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost-effective than the approaches used in the previous panda surveys.

Effects of extender type on the quality of post-thaw giant panda (Ailuropoda melanoleuca) semen.

Sperm cryopreservation is an essential approach for assisted reproduction and genetic resources conservation in captive giant pandas. Cryopreservation, however, leads to a significant decrease in sperm quality and, consequently, a low fertilization rate. Therefore, it is mandatory to disclose more suitable and efficient freezing strategies for sperm cryopreservation. In the present study, we compared for the first time the performance of two commercial freeze extender (INRA96 versus TEST) freezing methods on post-thawed semen quality. Semen cryopreserved with the INRA96 showed better total motility (73.00 ± 4.84% vs 57.56 ± 3.60%, P < 0.001), membrane integrity (60.92 ± 2.27% vs 40.53 ± 2.97%, P < 0.001) and acrosome integrity (90.39 ± 2.74% vs 84.26 ± 4.27%, P < 0.05) than stored with TEST. There was no significant difference in DNA integrity after thawing between the two extenders (95.69 ± 3.60% vs 94.26 ± 4.84%). In conclusion, the INRA96 method showed to be better for giant panda sperm cryopreservation and should therefore be recommended for use in order to increase success of artificial insemination.

Sorting Out the Genetic Background of the Last Surviving South China Tigers.

The South China tiger (Panthera tigris amoyensis) is endemic to China and also the most critically endangered subspecies of living tigers. It is considered extinct in the wild and only about 150 individuals survive in captivity to date, whose genetic heritage, however, is ambiguous and controversial. Here, we conducted an explicit genetic assessment of 92 studbook-registered South China tigers from 14 captive facilities using a subspecies-diagnostic system in the context of comparison with other voucher specimens to evaluate the genetic ancestry and level of distinctiveness of the last surviving P. t. amoyensis. Three mtDNA haplotypes were identified from South China tigers sampled in this study, including a unique P. t. amoyensis AMO1 haplotype not found in other subspecies, a COR1 haplotype that is widespread in Indochinese tigers (P. t. corbetti), and an ALT haplotype that is characteristic of Amur tigers (P. t. altaica). Bayesian STRUCTURE analysis and parentage verification confirmed the verified subspecies ancestry (VSA) as the South China tiger in 74 individuals. Genetic introgression from other tigers was detected in 18 tigers, and subsequent exclusion of these and their offspring from the breeding program is recommended. Both STRUCTURE clustering and microsatellite-based phylogenetic analyses demonstrated a close genetic association of the VSA South China tigers to Indochinese tigers, an issue that could only be elucidated by analysis of historical South China tiger specimens with wild origin. Our results also indicated a moderate level of genetic diversity in the captive South China tiger population, suggesting a potential for genetic restoration.

Author Correction: Giant pandas can discriminate the emotions of human facial pictures.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.