Hainan Normal University

As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼ 40,000 and ∼ 53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼ 19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼ 4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

This study examines the relationship between environmental attitudes as measured by the new environmental paradigm (NEP) and nature-based tourism (NBT) motivations based on data gathered from a national forest park in China. The results indicate that tourists' environmental attitudes are heterogeneous across all four tourism motivations. Specifically, those who are more supportive of limits to growth and who are more concerned about ecocrisis tend to have a higher desire to be close to nature, to learn about nature, and to escape from routine and issues associated with cities. In addition, those whose motivations are oriented to develop skills and abilities or seek to experience new things, environments, and social contact tend to be more supportive of the notion of human over nature. Thus, it can be concluded that environmental attitudes and NBT motivations are closely and positively related. Research limits, theoretical and practical implications, and future research needs of the study are discussed.

High-performance microwave absorbers with special features are desired to meet the requirements of more complex modern service environments, especially corrosive environments. Therefore, high-efficiency microwave absorbers with corrosion resistance should be developed urgently. Herein, a 3D NiAl-layered double hydroxide/graphene (NiAl-LDH/G) composite synthesized by atomic-layer-deposition-assisted in situ growth is presented as an anticorrosive microwave absorber. The content of NiAl-LDH in the composite is optimized to achieve impedance matching. Furthermore, under the cooperative effects of the interface polarization loss, conduction loss, and 3D porous sandwich-like structure, the optimal NiAl-LDH/G shows excellent microwave absorption performance with a minimum reflection loss of -41.5 dB and a maximum effective absorption bandwidth of 4.4 GHz at a loading of only 7 wt% in epoxy. Remarkably, the encapsulation effect of NiAl-LDH can restrain the galvanic corrosion owing to graphene. The epoxy coating with the NiAl-LDH/G microwave absorber on carbon steel exhibits long-term corrosion resistance, owing to the synergetic effect of the superior impermeability of graphene and the chloridion-capture capacity of the NiAl-LDH. The NiAl-LDH/G composite is a promising anticorrosive microwave absorber, and the findings of this study may motivate the development of functional microwave absorbers that meet the demands of anticorrosive performance of coatings.

Three-component reactions can directly convert three reactants into the desired products in one pot and thus greatly shorten the synthetic path. Recently, transition-metal catalysis has been applied in the difunctionalization of alkenes and remarkable progress has been achieved to facilitate the construction of a wide range of functional molecules with high atom- and step-economic efficiency. This Focus Review highlights recent advances in this field.

This paper reports a study that investigated the dimensions underneath the construct of foreign language classroom enjoyment and explored the pattern in which these enjoyment dimensions affect foreign language achievement. To this end, 320 first language Chinese senior high school students, who were learning English as a foreign language (EFL), were invited to complete an adapted version of the Foreign Language Enjoyment Scale. These students’ mid-term English examination scores were collected as a measure of foreign language achievement. Factorial analysis revealed a 3-factor solution for the adapted scale, including Enjoyment of Teacher Support, Enjoyment of Student Support, and Enjoyment of Foreign Language Learning. Path analysis showed that Enjoyment of Foreign Language Learning had a direct effect on mid-term scores. Enjoyment of Teacher Support and Enjoyment of Student Support indirectly affected language achievement via Enjoyment of Foreign Language Learning. There was no significant difference in the indirect effect of enjoyment of teacher and student support on English achievement. The findings are discussed and pedagogical implications are suggested.

We present a review and analysis of the conservation status and International Union for Conservation of Nature (IUCN) threat categories of all 360 currently recognized species of extant and recently extinct turtles and tortoises (Order Testudines). Our analysis is based on the 2018 IUCN Red List status of 251 listed species, augmented by provisional Red List assessments by the IUCN Tortoise and Freshwater Turtle Specialist Group (TFTSG) of 109 currently unlisted species of tortoises and freshwater turtles, as well as re-assessments of several outdated IUCN Red List assessments. Of all recognized species of turtles and tortoises, this combined analysis indicates that 20.0% are Critically Endangered (CR), 35.3% are Critically Endangered or Endangered (CR+EN), and 51.9% are Threatened (CR+EN+Vulnerable). Adjusting for the potential threat levels of Data Deficient (DD) species indicates that 56.3% of all data-sufficient species are Threatened. We calculated percentages of imperiled species and modified Average Threat Levels (ATL; ranging from Least Concern = 1 to Extinct = 8) for various taxonomic and geographic groupings. Proportionally more species in the subfamily Geoemydinae (Asian members of the family Geoemydidae) are imperiled (74.2% CR+EN, 79.0% Threatened, 3.89 ATL) compared to other taxonomic groupings, but the families Podocnemididae, Testudinidae, and Trionychidae and the superfamily Chelonioidea (marine turtles of the families Cheloniidae and Dermochelyidae) also have high percentages of imperiled species and ATLs (42.9-50.0% CR+EN, 73.8-100.0% Threatened, 3.44-4.06 ATL). The subfamily Rhinoclemmydinae (Neotropical turtles of the family Geoemydidae) and the families Kinosternidae and Pelomedusidae have the lowest percentages of imperiled species and ATLs (0%-7.4% CR+EN, 7.4%-13.3% Threatened, 1.65-1.87 ATL). Turtles from Asia have the highest percentages of imperiled species (75.0% CR+EN, 83.0% Threatened, 3.98 ATL), significantly higher than predicted based on the regional species richness, due to much higher levels of exploitation in that geographic region. The family Testudinidae has the highest ATL (4.06) of all Testudines due to the extinction of several species of giant tortoises from Indian and Pacific Ocean islands since 1500 CE. The family Testudinidae also has an ATL higher than all other larger polytypic families (≥ 5 species) of Reptilia or Amphibia. The order Testudines is, on average, more imperiled than all other larger orders (≥ 20 species) of Reptilia, Amphibia, Mammalia, or Aves, but has percentages of CR+EN and Threatened species and an ATL (2.96) similar to those of Primates and Caudata (salamanders).

The selectivity for sulfur removal from oils is an important topic. In this work, the selectivity for different sulfur removal methods has been studied by conceptual density functional theory (CDFT) at the B3LYP/6‐311++G(3df,2p) level of theory. In principle, the selectivity is directly related to the mechanisms of sulfur removal. It cannot be precisely elucidated until the mechanisms are totally known. However, current work shows that relationships can be constructed between CDFT and the selectivity. That is, for hydrodesulfurization, good descriptors will be ionization energy, hardness, and bond lengths of SC; for adsorptive desulfurization, the hardness is a good descriptor; for oxidative desulfurization, good descriptors are electron density and Fukui function. And for extractive desulfurization (nonmetal‐based ionic liquids), electron affinity and electrophilicity may be good descriptors. In addition, structures and frontier orbitals of various sulfides have also been discussed. It is hoped that these relationships between CDFT and selectivity can give useful information to develop highly efficient sulfur removal methods for specific sulfides, like 4,6‐dimethyldibenzothiophene, and 4‐methyldibenzothiophene. © 2016 American Institute of Chemical Engineers AIChE J , 62: 2087–2100, 2016

This review systematically discusses the whole process of solar-driven interfacial desalination and the critical issues involved from the perspective of energy flow.

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article.

This article presents a review of recent developments on various aspects of the quantum Rabi model. Particular emphasis is given on the exact analytic solution obtained in terms of confluent Heun functions. The analytic solutions for various generalisations of the quantum Rabi model are also discussed. Results are also reviewed on the level statistics and the dynamics of the quantum Rabi model. The article concludes with an introductory overview of several experimental realisations of the quantum Rabi model. An outlook towards future developments is also given.

Protecting the environment and enhancing food security are among the world's Sustainable Development Goals and greatest challenges. International food trade is an important mechanism to enhance food security worldwide. Nonetheless, it is widely concluded that in international food trade importing countries gain environmental benefits, while exporting countries suffer environmental problems by using land and other resources to produce food for exports. Our study shows that international food trade can also lead to environmental pollution in importing countries. At the global level, our metaanalysis indicates that there was increased nitrogen (N) pollution after much farmland for domestically cultivated N-fixing soybeans in importing countries was converted to grow high N-demanding crops (wheat, corn, rice, and vegetables). The findings were further verified by an intensive study at the regional level in China, the largest soybean-importing country, where the conversion of soybean lands to corn fields and rice paddies has also led to N pollution. Our study provides a sharp contrast to the conventional wisdom that only exports contribute substantially to environmental woes. Our results suggest the need to evaluate environmental consequences of international trade of all other major goods and products in all importing countries, which have significant implications for fundamental rethinking in global policy-making and debates on environmental responsibilities among consumers, producers, and traders across the world.

Current understanding of amyloid-β (Aβ) metabolism and toxicity provides an extensive list of potential targets for developing drugs for treating Alzheimer's disease. We took two independent approaches, including synaptic-plasticity-based analysis and behavioral screening of synthetic compounds, for identifying single compounds that are capable of rescuing the Aβ-induced memory loss in both transgenic fruit fly and transgenic mouse models. Two clinically available drugs and three synthetic compounds not only showed positive effects in behavioral tests but also antagonized the Aβ oligomers-induced activation of the epidermal growth factor receptor (EGFR). Such surprising converging outcomes from two parallel approaches lead us to conclude that EGFR is a preferred target for treating Aβ-induced memory loss.

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.

What kind of genetic variation contributes the most to adaptation is a fundamental question in evolutionary biology. By resequencing genomes of 80 individuals, we inferred the origin of genomic variants associated with a complex adaptive syndrome involving multiple quantitative traits, namely, adaptation between high and low altitudes, in the vinous-throated parrotbill ( Sinosuthora webbiana ) in Taiwan. By comparing these variants with those in the Asian mainland population, we revealed standing variation in 24 noncoding genomic regions to be the predominant genetic source of adaptation. Parrotbills at both high and low altitudes exhibited signatures of recent selection, suggesting that not only the front but also the trailing edges of postglacial expanding populations could be subjected to environmental stresses. This study verifies and quantifies the importance of standing variation in adaptation in a cohort of genes, illustrating that the evolutionary potential of a population depends significantly on its preexisting genetic diversity. These findings provide important context for understanding adaptation and conservation of species in the Anthropocene.

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

A series of fluorene-based Ln-MOFs with 3D neutral network are reported. Eu-MOF can act as the highly sensitive sensor to Pb²⁺ and Fe³⁺ ions in aqueous solution by fluorescence enhancement and quenching, respectively. The dynamic and static quenching constants of Fe³⁺ are obtained.

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1<56.63 Mg ha−1). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R2 = 0.62, RMSE = 51.03 Mg ha−1). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.

Medical devices and surgical implants are necessary for tissue engineering and regenerative medicines. However, the biofouling and microbial colonization on the implant surface continues to be a major concern, which is difficult to eradicate and typically necessitates either antibiotic therapy or implant removal. As a result, efficient and eco-friendly bioinspired coating strategies for tethering functional materials or molecules on different medical substrates are highly desirable, especially for endowing versatile surface functionalities. Tannic acid (TA), a well-known tea stain polyphenol, has a good affinity for various substrates and actively inhibits the adhesion and colonization of microbes. Thus, functionalization of polymers, nanomaterials, metal-phenolic networks (MPNs), and proteins using TA bestows the end-products with unique binding or anchoring abilities on various implantable surfaces. This review addresses the recent advancements in the essential biomedical perspective of TA-based bioinspired universal surface coating technologies by focusing on their intrinsic features and ability to produce engineered functional composites. Further, the possible contributions of TA-based composites in antifouling and antibacterial applications on various biomedical substrates are outlined.

A heteropolyanion-based ionic liquid, [(3-sulfonic acid) propylpyridine]3PW12O40·2H2O, [PSPy]3PW12O40·2H2O, abbreviated [PSPy]3PW, was synthesized and approved as an effective catalyst for desulfurization of fuels in [omim]PF6 by using aqueous H2O2 as oxidant. The catalysis was fulfilled with advantages of high activity, simplified workup, and flexible recyclability. The catalytic oxidation reactivity of sulfur-containing compounds was in the order dibenzothiophene (DBT) > 4,6-dimethyldibenzothiophene (4,6-DMDBT) > benzothiophene (BT). The effects of the amount of [PSPy]3PW, H2O2, and reaction time and temperature were investigated in detail. Under the optimal conditions, the removal of DBT achieved 99.4%. Especially, we found that the removal of 4,6-DMDBT could be up to 98.8%, and the system could be recycled at least 9 times without significant decrease in activity. The sulfur level of FCC gasoline could be reduced from 360 to 70 ppm in the extraction and catalytic oxidation system.