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In this paper, a new axial-type magnetic gear with an auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AMGs, it has a higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of the proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterparts in RMGs, the AFS-AMG achieves a small size. Then, the geometrical parameters of the AFS-AMG are optimized to obtain better electromagnetic performance, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical model of the double-stator spoke-type permanent magnet vernier machine (DSSTVM) using the subdomain method (SDM), which can be used to calculate the magnetic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the corresponding magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM. [ABSTRACT FROM AUTHOR]

Simple Summary: The gut microbiota and metabolites play a crucial role in the physiological health of the host and are influenced by various factors, including seasons. We utilized 16s sequencing and untargeted metabolomics to uncover the impact of seasonal change on the gut microbiota and metabolites of Tibetan antelopes. Overall, this study revealed differences in the gut microbiota and metabolites of Tibetan antelopes during the cold and warm seasons. These findings can provide a basis for the conservation of Tibetan antelopes. The Tibetan antelope is an endangered species suffering from poaching and habitat fragmentation. The intestinal flora and metabolites play a crucial role in the physiological homeostasis of hosts, which are influenced by various environmental factors like seasonal variation. In this particular research, our main goal was to explore the alterations in the metabolism and gut microbiota of Tibetan antelopes between the cold season (XB) and warm season (DA), using untargeted metabolomics and 16S rRNA gene-sequencing analyses. The findings indicated that Tibetan antelopes had a higher alpha-diversity of intestinal microbes during the cold season than during the warm season. Principal co-ordinate analysis revealed notable seasonal discrepancies in the function and structure of intestinal microbes in Tibetan antelopes. The relative abundance of Firmicutes was significantly increased during the cold season compared to during the warm season. Furthermore, the Tibetan antelope's primary metabolic functions of the intestinal micro-organisms were significantly higher during the cold season. The untargeted metabolomics analysis results showed a total of 532 metabolites that were significantly different between the cold season and warm season groups. These metabolites were found to be enriched in a total of 62 metabolic pathways. Among the most significant pathways of enrichment were the purine metabolism and pyrimidine metabolism. The levels of related metabolites in those pathways were remarkably higher in the warm season compared to the cold season. The comprehensive analysis of 16S rRNA and the metabolome reveals there is a significant correlation between differential microbiota and differential metabolites. Therefore, the gut microbiota changes caused by seasonal changes influenced the metabolites as well. This research reveals the function of seasonal changes in the intestinal flora and metabolites in the adaptation of Tibetan antelopes to environmental fluctuations and supplies a theoretical basis for instructing the protection management of Tibetan antelopes. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of remote sensing satellites has dramatically increased the demands of Earth observation and observing efficiency. Designing a promising satellite resource scheduling method is a pivotal way to meet the requirements of this scenario. However, with hundreds or more satellites involved, the existing optimization methods struggle to address the NP-hard resource scheduling problem effectively. In this paper, an approach named software-defined satellite observation (SDSO) is proposed. First, adopting the new design ideology, we define a unified specification based on a discrete spatial grid to describe the observation capability of all satellites. The observation resources are virtualized using the virtual resource pool technique and then stored in the database in advance, implementing on-demand acquisition for observation resources. Next, we designed a model of the remote sensing satellite resource scheduling problem based on a virtual resource pool and designed a solution method for searching information within the virtual resource pool. Finally, the experimental results show that the computational efficiency of the proposed SDSO methodology has a substantial advantage over the traditional methods. Meanwhile, with the growing number of satellites involved in scheduling, there is only a slight degradation in the execution performance of our method, while the time complexity of optimization-based approaches increases exponentially. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images (RSIs) has become crucial in recent years. However, researchers often prioritize detecting small objects, neglecting medium- to large-sized ones. Moreover, detecting objects hidden in shadows is challenging. Additionally, most detectors have extensive parameters, leading to higher hardware costs. To address these issues, this paper proposes a multi-scale and high-precision lightweight object detector named MHLDet. Firstly, we integrated the SimAM attention mechanism into the backbone and constructed a new feature-extraction module called validity-neat feature extract (VNFE). This module captures more feature information while simultaneously reducing the number of parameters. Secondly, we propose an improved spatial pyramid pooling model, named SPPE, to integrate multi-scale feature information better, enhancing the model to detect multi-scale objects. Finally, this paper introduces the convolution aggregation crosslayer (CACL) into the network. This module can reduce the size of the feature map and enhance the ability to fuse context information, thereby obtaining a feature map with more semantic information. We performed evaluation experiments on both the SIMD dataset and the UCAS-AOD dataset. Compared to other methods, our approach achieved the highest detection accuracy. Furthermore, it reduced the number of parameters by 12.7% compared to YOLOv7-Tiny. The experimental results illustrated that our proposed method is more lightweight and exhibits superior detection accuracy compared to other lightweight models. [ABSTRACT FROM AUTHOR]

Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently monitoring the dynamics of these lakes are of great significance in predicting and preventing GLOF events. To automatically extract the lake areas, many deep learning (DL) methods capable of capturing the multi-level features of lakes have been proposed in segmentation and classification tasks. However, the portability of these supervised DL methods need to be improved in order to be directly applied to different data sources, as they require laborious effort to collect the labeled lake masks. In this work, we proposed a simple glacial lake extraction model (SimGL) via weakly-supervised contrastive learning to extend and improve the extraction performances in cases that lack the labeled lake masks. In SimGL, a Siamese network was employed to learn similar objects by maximizing the similarity between the input image and its augmentations. Then, a simple Normalized Difference Water Index (NDWI) map was provided as the location cue instead of the labeled lake masks to constrain the model to capture the representations related to the glacial lakes and the segmentations to coincide with the true lake areas. Finally, the experimental results of the glacial lake extraction on the 1540 Landsat-8 image patches showed that our approach, SimGL, offers a competitive effort with some supervised methods (such as Random Forest) and outperforms other unsupervised image segmentation methods in cases that lack true image labels. [ABSTRACT FROM AUTHOR]

Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Feature Fusion Module (FFM) is proposed, which can better fuse features by learning the importance of different scale features and improve the representation ability of multi-scale features by reusing feature maps to help models better detect multi-scale objects. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, we tested the CGMDet on VisDrone and UAVDT datasets. The proposed CGMDet improves mAP0.5 by 1.9% on the VisDrone dataset and 3.0% on the UAVDT dataset. [ABSTRACT FROM AUTHOR]

As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight Iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight Iridium targets, respectively. Compared to similar research works, our method has obvious advantages. [ABSTRACT FROM AUTHOR]

Botryosphaeria dothidea is, globally, one of the most economically important phytopathogenic fungi worldwide, causing the canker and dieback of fruit trees. An increasing number of viruses infecting B. dothidea have lately been reported, several of which could confer hypovirulence. In this study, isolated from strain ZM170285-1 of B. dothidea, a novel double-stranded RNA (dsRNA) mycovirus, tentatively named Botryosphaeria dothidea partitivirus 2 (BdPV2), was identified well. The BdPV2 harbored three dsRNA segments (1–3) with lengths of 1751, 1568, and 1198 bp, which encoded an RNA-dependent RNA polymerase (RdRp), a capsid protein (CP), and a hypothetical protein of unknown function, respectively. BLASTp searches revealed that the predicted protein sequences of dsRNA1 and dsRNA2 had the highest identities (74.95% and 61.01%) with the corresponding dsRNAs of Penicillium stoloniferum virus S (PsV-S), whereas dsRNA3 shared the highest identity (32.95%) with the dsRNA3 of Aspergillus ochraceous virus 1 (AoV1). Phylogenetic analysis indicated that BdPV2 belonged to the Gammapartitivirus genus and Partitiviridae family. To our knowledge, this is the first report of a Gammapartitivirus in B. dothidea. [ABSTRACT FROM AUTHOR]

The salinization of farmland soil is exacerbated during the freeze–thaw (FT) process, endangering agricultural production. The change of soil salt ions results in the formation and development of soil salinization. The objectives of this study were to investigate the migration characteristics of salt ions during the FT process, identify the effects of inconsistencies in ions transport on the development of soil salinization chemical properties. A six-month field observation was conducted from November 2020 to April 2021 in the Hetao Irrigation District, China, a typical seasonally frozen soil area affected by salinization. Soil salt ions, soil moisture content (SMC), soil temperature, and pH were measured. Soil salt content (SSC), sodium adsorption ratio (SAR) were calculated. The ions accumulated in the frozen soil layer during the freezing period in the order of Cl− > Mg2+ > Ca2+ > Na+ > SO42−, and accumulated in the topsoil during the thawing period in the order of Cl− > Na+ > Mg2+ > Ca2+ > SO42−, while the change in HCO3− was mostly the opposite. The FT process changed the main salt anions from sulfate to chloride. After the FT process, the topsoil was endangered by high salinization, excess Cl− toxicity, and a potential alkalization threat. This study has great guiding significance for the management and control of soil salinization before spring sowing in saline areas. [ABSTRACT FROM AUTHOR]

Remote sensing is a powerful tool that provides flexibility and scalability for monitoring and investigating glacial lakes in High Mountain Asia (HMA). However, existing methods for mapping glacial lakes are designed based on a combination of several spectral features and ancillary data (such as the digital elevation model, DEM) to highlight the lake extent and suppress background information. These methods, however, suffer from either the inevitable requirement of post-processing work or the high costs of additional data acquisition. Signifying a key advancement in the deep learning models, a generative adversarial network (GAN) can capture multi-level features and learn the mapping rules in source and target domains using a minimax game between a generator and discriminator. This provides a new and feasible way to conduct large-scale glacial lake mapping. In this work, a complete glacial lake dataset was first created, containing approximately 4600 patches of Landsat-8 OLI images edited in three ways—random cropping, density cropping, and uniform cropping. Then, a GAN model for glacial lake mapping (GAN-GL) was constructed. The GAN-GL consists of two parts—a generator that incorporates a water attention module and an image segmentation module to produce the glacial lake masks, and a discriminator which employs the ResNet-152 backbone to ascertain whether a given pixel belonged to a glacial lake. The model was evaluated using the created glacial lake dataset, delivering a good performance, with an F1 score of 92.17% and IoU of 86.34%. Moreover, compared to the mapping results derived from the global–local iterative segmentation algorithm and random forest for the entire Eastern Himalayas, our proposed model was superior regarding the segmentation of glacial lakes under complex and diverse environmental conditions, in terms of accuracy (precision = 93.19%) and segmentation efficiency. Our model was also very good at detecting small glacial lakes without assistance from ancillary data or human intervention. [ABSTRACT FROM AUTHOR]

Soil salinization is a major abiotic stress factor that negatively impacts plant growth, development, and crop yield, severely limiting agricultural production and economic development. Cotton, a key cash crop, is commonly cultivated as a pioneer crop in regions with saline-alkali soil due to its relatively strong tolerance to salt. This characteristic renders it a valuable subject for investigating the molecular mechanisms underlying plant salt tolerance and for identifying genes that confer salt tolerance. In this study, focus was placed on examining a salt-tolerant variety, E991, and a salt-sensitive variety, ZM24. A combined analysis of transcriptomic data from these cotton varieties led to the identification of potential salt stress-responsive genes within the glutathione S-transferase (GST) family. These versatile enzyme proteins, prevalent in animals, plants, and microorganisms, were demonstrated to be involved in various abiotic stress responses. Our findings indicate that suppressing GhGSTF9 in cotton led to a notably salt-sensitive phenotype, whereas heterologous overexpression in Arabidopsis plants decreases the accumulation of reactive oxygen species under salt stress, thereby enhancing salt stress tolerance. This suggests that GhGSTF9 serves as a positive regulator in cotton's response to salt stress. These results offer new target genes for developing salt-tolerant cotton varieties. [ABSTRACT FROM AUTHOR]

In this research, SCAPS-1D simulation software (Version: 3.3.10) was employed to enhance the efficiency of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells. By fine-tuning essential parameters like the work function of the conductive glass, the back contact point, defect density, and the thickness of the light absorption layer, we effectively simulated the optimal performance of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells under identical conditions. The effects of different X-site elements on the overall performance of the device were also explored. The theoretical photoelectric conversion efficiency of the device gradually increases with the successive substitution of halogen elements (Cl, Br, I), reaching 6.09%, 17.02%, and 26.74%, respectively. This trend is primarily attributed to the increasing size of the halogen atoms, which leads to better light absorption and charge transport properties, with iodine (I) yielding the highest theoretical conversion efficiency. These findings suggest that optimizing the halogen element in CsSnX3 can significantly enhance device performance, providing valuable theoretical guidance for the development of high-efficiency all-inorganic perovskite solar cells. [ABSTRACT FROM AUTHOR]

Dielectric elastomer (DE) is one type of promising field-activated electroactive polymer. However, its significant electromechanical actuated properties are always obtained under a giant electric voltage, which greatly restricts the potential applications of DE. In the present work, the well-constructed core-shell TiO2@SiO2 nanoparticles were fabricated by using the classical Stöber method. A series of TiO2@SiO2 nano-architectures-filled polydimethylsiloxane (PDMS) composites were prepared via solution blending and compression-molding procedures. Benefiting from the additional SiO2 shell, both the interfacial compatibility between fillers and matrix and core-shell interfacial interaction can be improved. The TiO2@SiO2/PDMS nanocomposites exhibit a significantly enhanced in-plane actuated strain of 6.08% under a low electric field of 30 V · μm−1 at 16 vol.% TiO2@SiO2 addition, which is 180% higher than that of neat PDMS. The experimental results reveal that the well-designed core-shell structure can play an important role in both improving the electromechanical actuated property and maintaining a good flexibility of DE composites. This research provides a promising approach for the design of the novel composites with advanced low-field actuated electromechanical property in next generation DE systems. [ABSTRACT FROM AUTHOR]

Peste des petits ruminants (PPR) is a transboundary viral disease that threatens more than 1.74 billion goats and sheep in approximately 70 countries globally. In 2015, the international community set the goal of eradicating PPR by 2030, and, since then, Food and Agriculture Organization of the United Nations (FAO) and World Organization for Animal Health (OIE) have jointly developed and implemented the Global Control and Eradication Strategy for PPR. Here, data from the United Nations Food and Agriculture Organization Statistical Database (FAOSTAT), the OIE World Animal Health Information System (WAHIS), Regional Roadmap Meetings, and countries' responses to PPR Monitoring and Assessment Tool (PMAT) questionnaires were analyzed to inform on current progress towards PPR eradication. OIE recorded the use of over 333 million doses of vaccine in 12 countries from 2015 to 2018, 41.8% of which were used in Asia and 58.2% in Africa. Between 2015 and 2019, a total of 12,757 PPR outbreaks were reported to OIE: 75.1% in Asia, 24.8% in Africa, and 0.1% in Europe. The number of global outbreaks in 2019 fell to 1218, compared with 3688 in 2015. Analysis of vaccine use and PPR outbreaks in countries indicates that disease control strategies, particularly vaccination campaigns and vaccine distribution strategies, still require scientific evaluation. It is imperative that vaccination is undertaken based on the epidemiology of the disease in a region and is coordinated between neighboring countries to restrict transboundary movements. Strengthening surveillance and post-vaccination sero-monitoring at the national level is also essential. The PPR vaccine stock/bank established by FAO, OIE, and other partners have improved the quality assurance and supply of vaccines. However, to achieve PPR eradication, filling the funding gap for vaccination campaigns and other program activities will be critical. [ABSTRACT FROM AUTHOR]

The exact mathematical modeling of electric machines has always been an effective tool for scholars to understand the working principles and structure requirements of novel machine topologies. This paper provides an analytical modeling method—the harmonic modeling method (HMM)—for two types of consequent-pole magnetic-geared machines, namely the single consequent-pole magnetic-geared machine (SCP-MGM) and the dual consequent-pole magnetic-geared machine (DCP-MGM). By dividing the whole machine domain into different ring-like subdomains and solving the Maxwell equations, the magnetic field distribution and electromagnetic parameters of the two machines can be obtained, respectively. The two machines were applied in the propulsion systems of hybrid electric vehicles (HEVs). The electromagnetic performances of two machines under different operating conditions were also compared. It turns out that the DCP-MGM can reach a larger electromagnetic torque compared to that of the SCP-MGM under the same conditions. Finally, the predicted results were verified by the finite element analysis (FEA). A good agreement can be observed between HMM and FEA. Furthermore, HMM can also be applied to the mathematical modeling of other consequent-pole electric machines in further study. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT)-based near-Earth remote sensing technology, the problem of network intrusion for near-Earth remote sensing systems has become more complex and large-scale. Therefore, seeking an intelligent, automated, and robust network intrusion detection method is essential. Many researchers have researched network intrusion detection methods, such as traditional feature-based and machine learning methods. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-Earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the factors of existing near-Earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes and applies them to the network intrusion detection of near-Earth remote sensing systems. Finally, the proposed method in this article is validated using the latest flow-based datasets NF-BoT-IoT-v2 and NF-ToN-IoT-v2. The results demonstrate that the binary classification accuracy for network intrusion detection exceeds 99%, while the multi-classification accuracy exceeds 93%. These findings provide substantial evidence that the proposed method outperforms existing intrusion detection techniques. [ABSTRACT FROM AUTHOR]

All-inorganic perovskite solar cells are attractive photovoltaic devices because of their excellent optoelectronic performance and thermal stability. Unfortunately, the currently used efficient inorganic perovskite materials can spontaneously transform into undesirable phases without light-absorption properties. Studies have been carried out to stabilize all-inorganic perovskite by mixing low-dimensional perovskite. Compared with organic two-dimensional (2D) perovskite, inorganic 2D Cs2PbI2Cl2 shows superior thermal stability. Our group has successfully fabricated 2D/3D mixed-dimensional Cs2PbI2Cl2/CsPbI2.5Br0.5 films with increasing phase stability. The high boiling point of dimethyl sulfoxide (DMSO) makes it a preferred solvent in the preparation of Cs2PbI2Cl2/CsPbI2.5Br0.5 inorganic perovskite. When the perovskite films are prepared by the one-step solution method, it is difficult to evaporate the residual solvent molecules from the prefabricated films, resulting in films with rough surface morphology and high defect density. This study used the rapid precipitation method to control the formation of perovskite by treating it with methanol/isopropanol (MT/IPA) mixed solvent to produce densely packed, smooth, and high-crystallized perovskite films. The bulk defects and the carrier transport barrier of the interface were effectively reduced, which decreased the recombination of the carriers in the device. As a result, this effectively improved photoelectric performance. Through treatment with MT/IPA, the photoelectric conversion efficiency (PCE) of solar cells prepared in the N2 atmosphere increased from 13.44% to 14.10%, and the PCE of the device prepared in the air increased from 3.52% to 8.91%. [ABSTRACT FROM AUTHOR]

Water and nitrogen losses from farmland during the snowmelt process play a vital role in water and nitrogen management in cold regions. To explore the mechanisms and factors contributing to water and nitrogen loss from different sloping farmlands during the snowmelt period, field experiments were conducted under two slope treatments (8° and 15°), two soil water content (SWC) treatments, and two snow water equivalent (SWE) (5 mm and 10 mm) treatments in a seasonal freezing agricultural watershed of Northeast China. The results showed that during the snowmelt process, SWE was the most important factor affecting water and nitrogen production through the surface and total runoff of the sloping farmland, followed by the slope. The water and nitrogen yield in the high snow (HS) treatments ranged from 1.76 to 8.15 and 1.65 to 12.62 times higher than those in the low snow (LS) treatments. The generation of nitrogen was advanced compared with that of water induced by the preferential production of nitrogen. A higher slope promoted this preferential production function of nitrogen. Enhanced infiltration combined with the preferential yield of nitrogen resulted in a greatly decreased yield of water and nitrogen in the gentle slope and LS (GS_LS) treatments. These findings are valuable for accurately describing the water and nitrogen cycling in seasonally freezing sloping farmland. [ABSTRACT FROM AUTHOR]

Silicon-derived biomaterials are conducive to regulating the fate of osteo-related stem cells, while their effects on the osteogenic differentiation of human adipose-derived stem cells (hADSCs) remain inconclusive. Mesoporous silica (mSiO2) is synthesized in a facile route that exhibited the capability of promoting osteogenic differentiation of hADSCs. The metabolism of SiO2 in cells is proposed according to the colocalization fluorescence analysis between lysosomes and nanoparticles. The released silicon elements promote osteogenic differentiation. The detection of secretory proteins through numerous parallel experiments performed via a microfluidic chip confirms the positive effect of SiO2 on the osteogenic differentiation of hADSCs. Moreover, constructed with superparamagnetic iron oxide (Fe3O4), the magnetic nanoparticles (MNPs) of Fe3O4@mSiO2 endow the cells with magnetic resonance imaging (MRI) properties. The MNP-regulated osteogenic differentiation of autologous adipose-derived stem cells provides considerable clinical application prospects for stem cell therapy of bone tissue repair with an effective reduction in immune rejection. [ABSTRACT FROM AUTHOR]

Nitrogen is an indispensable and limiting element for plant and microbial growth. To investigate the combined effects of salinity and freezing–thawing (FT) processes on soil inorganic nitrogen (SIN) transformation in seasonally freezing salinized farmland, laboratory incubation experiments were conducted under five soil salt content (SSC) treatments (0.08%, 0.25%, 0.35%, 0.50%, and 0.70%), four FT temperature treatments (C (5 °C), FT (−5 + 5 °C), FT (−10 + 5 °C), and FT (−15 + 5 °C)), and two soil water content (SWC) treatments (40% and 80% of maximum water holding capacity (WHC)). Ammonium (NH4+-N) and nitrate (NO3−-N) nitrogen were monitored at the first, second, fifth, and eighth incubation days. The FT processes increased relative NH4+-N content by 13%, 39%, and 77% with the decreasing of freezing temperature from −5 °C to −15 °C compared with C (5 °C) treatments, respectively. FT (−5 + 5 °C) and FT (−15 + 5 °C) treatments decreased the relative NO3--N contents by 4% and 6% compared with C (5 °C) treatments, respectively. Under FT treatments, the increment of relative NH4+-N content was higher in low-SSC treatments and lower in high-SSC treatments. The relationship between relative NO3–-N content and SSC gradually changed from a decrease in C (5 °C) to an increase in FT (−15+5 °C) treatments. SWC decreased NH4+-N content in high-SSC and low-freezing temperature treatments (SSC × freezing temperature < −2.5%· °C), while NH4+-N increased in low-SSC and unfrozen treatments. The variations of SIN/Rmin (nitrogen mineralization rate) were mostly affected by NO3–-N/Rnit (net nitrification rate) and NH4+-N/Ra (net ammonification rate) in C (5 °C) and FT treatments, respectively. Overall, the results suggested that enhanced salinity inhibited the effects of freezing temperature on NH4+-N and NO3−-N formation, respectively. The increase in SWC weakened the NH4+-N formation induced by the decrease in freezing temperature, and this function increased with the increase in salinity. [ABSTRACT FROM AUTHOR]

In this study, self-made cat food attractant was prepared by Maillard reaction using hydrolysate of grass carp waste as raw material and glucose and cysteine hydrochloride as substrate. Its volatile compounds, antioxidant capacity, and pet palatability were investigated. The volatile compounds of attractants were analyzed using gas chromatography–mass spectrometry (GC-MS) which showed that alcohols and aldehydes were the most volatile in self-made attractants, accounting for 34.29% and 33.52%, respectively. Furthermore, Maillard reaction could significantly increase the antioxidant activity of self-made attractant, including scavenging activity on OH and DPPH free radicals as well as the chelating ability of Fe2+. The acceptance and palatability of two kinds of cat food were studied by adding 3% self-made or commercial attractants. The results of this study also found that both attractants could remarkably improve the intake rate of cat food. However, the self-made group was significantly less than the commercial group in first smell, first bite, and feeding rate, which might be because of the absence of umami ingredients and spices in self-made attractants. [ABSTRACT FROM AUTHOR]

Cardiovascular failure is one of the most relevant causes of death in pulmonary hypertension (PH). With progressive increases of right ventricular (RV) afterload in PH patients, both RV and left ventricular (LV) function impair and RV–LV dyssynchrony develop in parallel. We aimed to analyze the balance between the left and right ventricular deformation to assess the outcome of patients with pulmonary hypertension by means of speckle tracking echocardiography. In this prospective study, 54 patients with invasively diagnosed pulmonary hypertension, and 26 healthy volunteers were included and underwent a broad panel of noninvasive assessment including 2D-echocardiography, 2D speckle tracking, 6-minute walking test and BNP. Patients were followed up for 338.7 ± 131.1 (range 60 to 572) days. There were significant differences in |LVGLS/RVFLS-1| and |LASc/RASc-1| between PH patients and the control group. During the follow up, 13 patients experienced MACEs, which included 7 patients with cardiac death and 6 patients with re-admitted hospital due to right ventricular dysfunction. In the multivariate Cox model analysis, |LVGLS/RVFLS-1| remained independent prognosis of markers (HR = 4.03). Our study findings show that |LVGLS/RVFLS-1| is of high clinical and prognostic relevance in pulmonary hypertension patients and reveal the importance of the balance between the left and right ventricular deformation. [ABSTRACT FROM AUTHOR]

Smoke-induced acute lung injury (ALI) is a grievous disease with high mortality. Despite advances in medical intervention, no drug has yet been approved by the Food and Drug Administration (FDA) for ALI. In this study, we reported that pretreatment with high-molecular-weight hyaluronan (1600 kDa, HA1600) alleviated pulmonary inflammation and injury in mice exposed to smoke and also upregulated long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), as well as suppressor of cytokine signaling-1 (SOCS-1), in the lung tissues. Next, we overexpressed MALAT1 in the lungs by intratracheal administration of adenovirus cloned with MALAT1 cDNA and found that the survival of mice after smoke exposure was improved. Moreover, pulmonary overexpression of MALAT1 ameliorated smoke-induced ALI in mice and elevated the level of SOCS-1 in the lungs. In conclusion, the results pointed out that HA1600 exerted a protective effect against smoke-induced ALI through increasing the MALAT1 level and the subsequent SOCS-1 expression. Our study provides a potential therapeutic approach to smoke-induced ALI and a novel insight into the mechanism of action of HA1600. [ABSTRACT FROM AUTHOR]

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km2 and 1577.38 ± 288.82 km2; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region. [ABSTRACT FROM AUTHOR]

Matched filtering is widely used in active sonar because of its simplicity and ease of implementation. However, the resolution performance generally depends on the transmitted waveform. Moreover, its detection performance is limited by the high-level sidelobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. This paper proposed a method named iterative deconvolution-time reversal (ID-TR), on which the energy of the cross-ambiguity function is modeled, as a convolution of the energy of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density. Similarly, the generalized target reflectivity density is a convolution of the spread function of channel with the reflectivity density of target as well. The ambiguity caused by the transmitted signal and the spread function of channel are removed by Richardson-Lucy iterative deconvolution and the time reversal processing, respectively. Moreover, this is a special case of the Richardson-Lucy algorithm that the blur function is one-dimensional and time-invariant. Therefore, the iteration deconvolution is actually implemented by the iterative temporal time reversal processing. Due to the iterative time reversal method can focus more and more energy on the strongest target with the iterative number increasing and then the peak-signal power increases, the simulated result shows that the noise reduction can achieve 250 dB in the "ideal" free field environment and 100 dB in a strong multipaths waveguide environment if a 1-ms linear frequency modulation with a 4-kHz frequency bandwidth is transmitted and the number of iteration is 10. Moreover, the range resolution is approximately a delta function. The results of the experiment in a tank show that the noise level is suppressed by more than 70 dB and the reverberation level is suppressed by 3 dB in the case of a single target and the iteration number being 8. [ABSTRACT FROM AUTHOR]

Land surface albedo (LSA) directly affects the radiation balance and the surface heat budget. LSA is a key variable for local and global climate research. The complexity of LSA variations and the driving factors highlight the importance of continuous spatial and temporal monitoring. Snow, vegetation and soil are the main underlying surface factors affecting LSA dynamics. In this study, we combined Global Land Surface Satellite (GLASS) products and ERA5 reanalysis products to analyze the spatiotemporal variation and drivers of annual mean blue-sky albedo for stable land cover types in the middle-high latitudes of the Northern Hemisphere (30~90°N) from 1982 to 2015. Snow cover (SC) exhibited a decreasing trend in 99.59% of all pixels (23.73% significant), with a rate of −0.0813. Soil moisture (SM) exhibited a decreasing trend in 85.66% of all pixels (22.27% significant), with a rate of −0.0002. The leaf area index (LAI) exhibited a greening trend in 74.38% of all pixels (25.23% significant), with a rate of 0.0014. Blue-sky albedo exhibited a decreasing trend in 98.97% of all pixels (65.12% significant), with a rate of −0.0008 (OLS slope). Approximately 98.16% of all pixels (57.01% significant) exhibited a positive correlation between blue-sky albedo and SC. Approximately 47.78% and 67.38% of all pixels (17.13% and 25.3% significant, respectively) exhibited a negative correlation between blue-sky albedo and SM and LAI, respectively. Approximately 10.31%, 20.81% and 68.88% of the pixel blue-sky albedo reduction was mainly controlled by SC, SM and LAI, respectively. The decrease in blue-sky albedo north of 40°N was mainly caused by the decrease in SC. The decrease in blue-sky albedo south of 40°N was mainly caused by SM reduction and vegetation greening. The decrease in blue-sky albedo in the western Tibetan Plateau was caused by vegetation greening, SM increase and SC reduction. The results have important scientific significance for the study of surface processes and global climate change. [ABSTRACT FROM AUTHOR]

Electric machines with high torque density are needed in many applications, such as electric vehicles, electric robotics, electric ships, electric aircraft, etc. and they can avoid planetary gears thus reducing manufacturing costs. This paper presents a novel axial-radial flux permanent magnet (ARFPM) machine with high torque density. The proposed ARFPM machine integrates both axial-flux and radial-flux machine topologies in a compact space, which effectively improves the copper utilization of the machine. First, the radial rotor can balance the large axial forces on axial rotors and prevent them from deforming due to the forces. On the other hand, the machine adopts Halbach-array permanent magnets (PMs) on the rotors to suppress air-gap flux density harmonics. Also, the Halbach-array PMs can reduce the total attracted force on axial rotors. The operational principle of the ARFPM machine was investigated and analyzed. Then, 3D finite-element analysis (FEA) was conducted to show the merits of the ARFPM machine. Demonstration results with different parameters are compared to obtain an optimal structure. These indicated that the proposed ARFPM machine with Halbach-array PMs can achieve a more sinusoidal back electromotive force (EMF). In addition, a comparative analysis was conducted for the proposed ARFPM machine. The machine was compared with a conventional axial-flux permanent magnet (AFPM) machine and a radial-flux permanent magnet (RFPM) machine based on the same dimensions. This showed that the proposed ARFPM machine had the highest torque density and relatively small torque ripple. [ABSTRACT FROM AUTHOR]

Gender differences in pulmonary inflammation have been well documented. Although low molecular mass hyaluronan (LMMHA) is known to trigger pulmonary lung inflammation, sex differences in susceptibility to LMMHA are still unknown. In this study, we test the hypothesis that mice may display sex-specific differences after LMMHA administration. After LMMHA administration, male mice have higher neutrophil, cytokine, and chemokine counts compared to that of their female counterparts. Additionally, Ovariectomized (OVX) mice show greater LMMHA-induced inflammation compared to that of mice with intact ovaries. Injections of OVX mice with 17β-estradiol can decrease inflammatory responses in the OVX mice. These results show that ovarian hormones regulate LMMHA induced lung inflammation. [ABSTRACT FROM AUTHOR]

Smoke inhalation causes acute lung injury (ALI), a severe clinical disease with high mortality. Accumulating evidence indicates that microRNA-155 (miR-155) and suppressor of cytokine signaling 1 (SOCS-1), as mediators of inflammatory response, are involved in the pathogenesis of ALI. In this paper, we explored the proinflammatory mechanism of miR-155 in smoke-inhalation-induced ALI. Our data revealed that smoke inhalation induces miR-155 expression, and miR-155 knockout (KO) significantly ameliorates smoke-inhalation-induced lung injury in mice. Neutrophil infiltration and myeloperoxidase (MPO), macrophage inflammatory protein 2 (MIP-2) and keratinocyte chemoattractant (KC) expressions were decreased in miR-155–/– mice after smoke inhalation as well. Real-time RT-PCR and immunoblotting results showed that SOCS-1 level was remarkably increased in miR-155–/– mice after smoke exposure. Furthermore, the experiments performed in isolated miR-155 KO pulmonary neutrophils demonstrated that the lack of SOCS-1 enhanced inflammatory cytokines (MIP-2 and KC) secretion in response to smoke stimulation. In conclusion, smoke induces increased expression of miR-155, and miR-155 is involved in inflammatory response to smoke-inhalation-induced lung injury by inhibiting the expression of SOCS-1. [ABSTRACT FROM AUTHOR]

To investigate the influence of the change of tip clearance size on the control effect of the endwall suction, the effects of endwall suction on the aerodynamic performance of the axial compressor cascade were studied numerically. Three tip clearance sizes of 0.5% h, 1.0% h, and 2.0% h (h is the blade height) were mainly considered. The results show that the endwall suction scheme whose coverage range was 8–33% axial chord can reduce the leakage flow and improve the aerodynamic performance by directly influencing the structure of tip leakage vortex. The overall total pressure loss coefficients of the three clearance size schemes at 0° angle of incidence with 0.4 inlet Mach number are reduced by about 10.3%, 10.8%, and 6.0%, respectively, at the suction flow rate of 0.7%. Under the same suction flow rate, the onset position of the tip leakage vortex of the cascade with small clearance is shifted from the 15% of the axial chord length of original to the 48% of the axial chord length, which with large clearance is nearly no changed. The leakage flow rate and the distance from the leakage vortex to the suction slot are the main reasons for the different control effect of the endwall suction under different tip clearance sizes. The difference of the spanwise distribution of flow field parameters may also cause the difference of flow control effect. [ABSTRACT FROM AUTHOR]

PM2.5 is particulate matter with a diameter of 2.5 μm or less. Airway macrophages are the key players regulating PM2.5-induced inflammation. High molecular weight hyaluronan (HMW-HA) has previously been shown to exert protective effects on PM2.5-induced acute lung injury and inflammation. However, little is known about the detailed mechanism. In this study, we aimed to determine whether HMW-HA alleviates PM2.5-induced pulmonary inflammation by modulating macrophage polarization. The levels of M1 biomarkers TNF-α, IL-1β, IL-6, CXCL1, CXCL2, NOS2 and CD86, as well as M2 biomarkers IL-10, MRC1, and Arg-1 produced by macrophages were measured by ELISA, qPCR, and flow cytometry. In addition, the amount of M1 macrophages in lung tissues was examined by immunofluorescence of CD68 and NOS2. We observed a decline in PM2.5-induced M1 polarization both in macrophages and lung tissues when HMW-HA was administered simultaneously. Meanwhile, western blot analysis revealed that PM2.5-induced JNK and p38 phosphorylation was suppressed by HMW-HA. Furthermore, in vitro and in vivo studies showed that co-stimulation with HMW-HA and PM2.5 promoted the expression and release of IL-10, but exhibited limited effects on the transcription of MRC1 and ARG1. In conclusion, our results demonstrated that HMW-HA ameliorates PM2.5-induced lung inflammation by repressing M1 polarization through JNK and p38 pathways and promoting the production of pro-resolving cytokine IL-10. [ABSTRACT FROM AUTHOR]

Semen Allii Fistulosi (PSAF) is the seed of Allium fistulosum L. of the Liliaceae family. The purpose of this study was to extract, characterize, and evaluate the antioxidant activity in vitro of proteins. Using single factor and orthogonal design, the optimum conditions of extraction were determined to be as follows: extraction time 150 min, pH 8.5, temperature 60 °C, and ratio (v/w, mL/g) of extraction solvent to raw material 35. The isoelectric point of the pH was determined to be about 4.4 and 10.2, by measuring the protein content of PSAF solutions at different pH values. The amino acid composition of PSAF was determined by high performance liquid chromatography (HPLC), and the results suggested that the species of amino acids contained in the PSAF was complete. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS–PAGE) analysis showed the molecular weight was mainly between 40 and 55 kDa, and Fourier-transform infrared spectroscopy (FTIR) characterized prevalent protein absorption peaks. PSAF exhibited potent scavenging activities against DPPH assays, via targeting of hydroxyl and superoxide radicals, while chelating Fe2+ activity and demonstrating weak reducing power. This work revealed that PSAF possessed potential antioxidant activity in vitro, suggesting potential for use of PSAF as a natural antioxidant. [ABSTRACT FROM AUTHOR]