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Purpose Lesbian, gay, bisexual, transgender, queer, and other sexual and gender diverse (LGBTQ+) individuals have health needs specific to their identities. However, they face discrimination and cis-heteronormativity in most patient-provider interactions, which often translate into poor healthcare. Evidence suggests doctors are inadequately trained to care for LGBTQ+ patients. Medical students are well-placed as the future workforce to establish affirming behaviours. This study garners LGBTQ+ patients’ healthcare experiences, where limited qualitative evidence exists, and explores whether students have insight into these experiences.Method Thirty LGBTQ+ patients and twenty students, evenly divided between Singapore and the United Kingdom (UK), two legally and culturally different countries, consented to semi-structured interviews in 2022 to evaluate their LGBTQ+ healthcare perceptions. Thematic analysis was conducted using a collaborative, iterative process involving five investigators, with frequent auditing of data interpretation.Results Most patients described implicit biases with a lack of support and professionalism from doctors, hindering health outcomes. Patients experienced misgendering and a lack of recognition of sexual and gender diversity; students appreciated the need to acknowledge patient identity. Although perceptions surrounding certain themes were similar between patients and students in both countries, patients’ voices on the complexity and dissatisfaction of gender-diverse care contrasted with students’ lack of insight on these themes. Singapore patients were more concerned with sociolegal acceptance affecting health needs, whilst UK patients noted more nuanced barriers to healthcare. Although many students were unsure about specific health needs and perceived a lack of training, they expressed willingness to create an equitable healthcare environment.Conclusions LGBTQ+ patients provided powerful narratives on discrimination surrounding their healthcare needs. To address these, medical students must be encouraged by healthcare educators to develop identity-affirming behaviours as future change-makers and challenge cis-heteronormative views. Alongside vital institutional changes tailored to each country, patients’ and students’ collective action would create meaningful educational opportunities to reach culture change.

Enhanced weathering of terrestrial rock material is a promising method for the removal of anthropogenic CO2 emissions from the atmosphere. Herein, we demonstrate that an ameliorated mining waste product can be effectively weathered in the soil environment when used as a soil amendment in conjunction with the cultivation of fast-growing willows (Salix matsudana Koidz. ⨯ S. alba L. “Austree”) in a pot study environment. Utilizing this locally sourced amendment minimizes emissions associated with grinding and transportation of enhanced weathering materials. Results showed that the willows were able to tolerate the relatively high metal concentrations of this amendment and sequester inorganic carbon (C) through the production of bicarbonate in soil solution. During the period of peak plant growth (10 weeks after planting), alkalinity measurements of soil solution from pots with willows and the addition of 25% by mass mine waste product indicated an additional 10 mg of inorganic C sequestration per liter of leached soil solution compared to unamended soils with willows. This represents 4.5 times the inorganic C sequestration rate of unamended soils. The addition of ameliorated mining waste also increased the pH of the soil solution by up to two units (pH of 6 in control vs. pH of 8 with the addition of 25% by mass mineral amendment). In addition to inorganic C sequestration, weathering of the ameliorated mining waste product may also provide base cations (such as calcium and magnesium) which could improve soil fertility. These results are encouraging for future investigation of ameliorated mine waste rock to sequester carbon and enhance the production of willows grown for ecosystem services and phytotechnologies.

Deep learning models based on Transformer and CNN are current research hotspots. However, there are more time complexity and space complexity in extracting global–local features, the feature extraction ability is insufficient for small lesions in CT images. A Re-parameterization Neighborhood Enhancement-based Dual-Stream Network (RNE-DSNet) is proposed for CT image recognition in this paper. This method improves the ability to recognize small lesions while reducing the Params number and FLOPs, which gives a trade-off between performance and cost. The main works are as following: Firstly, a Re-parameterization Neighborhood Enhancement-based Dual-Stream Network is designed to improve the global–local feature extraction ability. Secondly, the Re-parameterization Group Residual Block is designed in the CNN branch, which improves the local feature extraction ability through multi-scale and multi-branch structures, and the number of the Params and FLOPs are reduced effectively. The Neighborhood Enhancement Transformer Block is designed in the Transformer branch, in which the global attention is calculated by parallel Hadamard product and Dot product, and the global attention maps are enhanced by deep convolution operation to embed local features in global features. Thirdly, the Adaptive-random Mask Cropping method is designed to improve the features extraction ability for small lesions in CT images through heat map and random Mask techniques. RNE-DSNet is compared with other SOTA models. The Accuracy (ACC), Precision (PRE), Recall (REC), F1 score (F1), and Area Under Curve (AUC) values of RNE-DSNet are reached 99.22%, 99.23%, 99.08%, 99.23% and 99.38%, respectively, which verified the effectiveness of the RNE-DSNet model. The number Params and FLOPs are reduced by 62.78% and 84.20%, respectively. The effectiveness of RNE-DSNet is verified by heat map visualization. The performance of RNE-DSNet is generally better than the others methods. There is positive significance for computer aided diagnosis.

Twelve compounds, including four undescribed cytochalasins, xylariachalasins A–D (1–4), four undescribed polyketides (5–8), and four known cytochalasins (9–12), were isolated from the mangrove endophytic fungus Xylaria arbuscula QYF. Their structures and absolute configurations were established by extensive spectroscopic analyses (1D and 2D NMR, HRESIMS), electronic circular dichroism (ECD) calculations, 13C NMR calculation and DP4+ analysis, single-crystal X-ray diffraction, and the modified Mosher ester method. Compounds 1 and 2 are rare cytochalasin hydroperoxides. In bioactivity assays, Compound 2 exhibited moderate antimicrobial activities against Staphylococcus aureus and Candida albicans with MIC values of 12.5 μM for both Compound 10 exhibited significant cytotoxic activity against MDA-MB-435 with an IC50 value of 3.61 ± 1.60 μM.

Abstract In recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability.

The melting of particles is a unique characteristic that differentiates ultra-high speed laser cladding (UHSLC) from conventional laser cladding (LC). However, the investigation on the melting behavior of in-flight particles in UHSLC is inadequate and difficult due to the mutual impacts of laser, particles and substrates. Therefore, a smart experiment was designed to explore the melting behavior of in-flight particles, in which the impact of substrates was eliminated. The surface morphology and the cross-sectional views of the molten 316L and Ti6Al4V particles were characterized using a scanning electron microscope (SEM). The interfaces between the particles and substrates were analyzed using a transmission electron microscopy (TEM). The melting model was built using ray tracing method (RTM) and finite element method (FEM). The results show that the in-flight particles presented semi-molten morphology with the laser power of 1800 W. Meanwhile some full-molten particles were observed with the laser power of 3000 W. The simulation results show that the particle temperature was regulated by the particle sizes and velocities instead of the feeding rates. The temperature of the 316L particles with a size of 30 μm decreased from 2718 K to 725 K as the particle velocity increased from 3 m/s to 20 m/s. The temperature of the Ti6Al4V particles with a velocity of 10 m/s decreased from 3859 K to 867 K as the particle size increased from 5 μm to 60 μm. The incident laser is dominant in temperature rising. The sheltering effect cannot be neglected. The temperature of 316L, Ag, Al6061, Cu, Inconel 718 and Ti6Al4V particles in UHSLC was predicted. The results offer a new perspective to understand the melting behavior of in-flight particles in UHSLC.

Abstract Background Bone metastasis is the leading cause of death in patients with prostate cancer (PCa) and currently has no effective treatment. Disseminated tumor cells in bone marrow often obtain new characteristics to cause therapy resistance and tumor recurrence. Thus, understanding the status of disseminated prostate cancer cells in bone marrow is crucial for developing a new treatment. Methods We analyzed the transcriptome of disseminated tumor cells from a single cell RNA-sequencing data of PCa bone metastases. We built a bone metastasis model through caudal artery injection of tumor cells, and sorted the tumor hybrid cells by flow cytometry. We performed multi-omics analysis, including transcriptomic, proteomic and phosphoproteomic analysis, to compare the difference between the tumor hybrid cells and parental cells. In vivo experiments were performed to analyze the tumor growth rate, metastatic and tumorigenic potential, drug and radiation sensitivity in hybrid cells. Single cell RNA-sequencing and CyTOF were performed to analyze the impact of hybrid cells on tumor microenvironment. Results Here, we identified a unique cluster of cancer cells in PCa bone metastases, which expressed myeloid cell markers and showed a significant change in pathways related to immune regulation and tumor progression. We found that cell fusion between disseminated tumor cells and bone marrow cells can be source of these myeloid-like tumor cells. Multi-omics showed the pathways related to cell adhesion and proliferation, such as focal adhesion, tight junction, DNA replication, and cell cycle, were most significantly changed in these hybrid cells. In vivo experiment showed hybrid cells had a significantly increased proliferative rate, and metastatic potential. Single cell RNA-sequencing and CyTOF showed tumor-associated neutrophils/monocytes/macrophages were highly enriched in hybrid cells-induced tumor microenvironment with a higher immunosuppressive capacity. Otherwise, the hybrid cells showed an enhanced EMT phenotype with higher tumorigenicity, and were resistant to docetaxel and ferroptosis, but sensitive to radiotherapy. Conclusion Taken together, our data demonstrate that spontaneous cell fusion in bone marrow can generate myeloid-like tumor hybrid cells that promote the progression of bone metastasis, and these unique population of disseminated tumor cells can provide a potential therapeutic target for PCa bone metastasis.

IntroductionLesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) individuals have an increased scope of healthcare needs and face many barriers to accessing healthcare. However, LGBTQ+ healthcare education remains scarce, and students’ understanding of LGBTQ+ healthcare remains largely uncharacterised. This study investigated the knowledge of and attitudes toward LGBTQ+ healthcare among medical students in Singapore and the United Kingdom (UK), two culturally different countries.MethodsMedical students in two medical schools, one in Singapore and the other in the UK, completed self-administered cross-sectional surveys using multiple-choice, Likert scale, and free-text questions to explore their ideas, concerns, and expectations about LGBTQ+ healthcare education within their medical curricula.ResultsFrom 330 responses, students’ knowledge levels were moderate overall, with pronounced gaps in certain areas, including terminology, sexual health, and conversion therapy. Deficiencies in knowledge were significantly greater among students in Singapore compared to the UK (p

Abstract Deep learning (DL) has attracted significant attention in partial discharge (PD) pattern recognition of gas‐insulated switchgear (GIS). However, the existing DL method extracts numerical information and ignores spatial correlation information. In addition, once the sample size is reduced, model performance is severely degraded. To address these issues, a novel capsule deep graph convolutional network (CDGCN) is employed for GIS PD pattern recognition. First, the GIS PD data is transformed to graph data. Then, the CDGCN is established for GIS PD pattern recognition. The introduction of a ChannelSortPooling layer enriches the graph representation information and filters out the feature values with higher information content. In addition, the multi‐view graph representation is converted to a capsule form, and a capsule network with stronger feature extraction capability is introduced to improve the accuracy of the final graph representation. The results reveal that the proposed CDGCN can effectively diagnose the GIS PD and is more accurate than the traditional DL method. The proposed CDGCN can handle small samples pattern recognition problems and provides a reliable reference for GIS PD pattern recognition.

BackgroundConstipation is common gastrointestinal disorder with high prevalence and recurrence, making people suffering. However, the treatment for constipation remains ineffectual. We aimed to the study the effects and mechanisms of postbiotic of hawthorn-probiotic on loperamide modeled old KM mice.MethodsConstipated mice were grouped and treated with 10% lactulose (Y), hawthorn group (S), probiotic group (F) and postbiotic of hawthorn-probiotic (FS). Fecal changes were observed. AQP3 and Enac-γ were measured by RT-qPCR and Western blotting, intestinal barrier by H&E and immunofluorescence staining, cell proliferation and apoptosis by CCK8 and flow cytometry. Gut microbiota was further determined by 16 s rRNA sequence of feces.ResultsPostbiotic of hawthorn-probiotic improved intestinal movement and pathomorphology, elevated AQP3, Enac-γ and mucin-2 expression, accompanied by decreased serum TNF-α and cell apoptosis, but increased proliferation. Furthermore, it modified the gut microbiota of constipated mice, featured by upregulation of Lactobacillaceae.ConclusionPostbiotic of hawthorn-probiotic relieved constipation by combined effects of regulating intestinal water and sodium metabolism, maintain intestinal barrier and gut microflora.Graphical Abstract

Abstract The toxicity of lead ions in halide perovskite absorbing materials is the main bottleneck for practical application. To replace the traditional lead halide perovskites by environmental friendly double perovskite, computational tools based on density functional theory were employed to predict the intrinsic properties of potential double perovskites to efficiently and rapidly find more double perovskites with properties suitable for optoelectronic applications. Screening homovalent alternatives for B and X‐site ions in vacancy‐ordered double perovskite Cs2BX6 for solar cell applications and photocatalyst was done using Perdew–Burke–Ernzerhof and Heyd–Scuseria–Ernzerhof functional with spin‐orbit coupling. Three empirical factors and formation enthalpy were used to evaluate the stability of 30 materials at different temperatures. Finally, the Cs‐based vacancy‐ordered double perovskites with suitable bandgap for optoelectronic applications can thus be obtained. Using computational techniques, this study can also provide theoretical guidance for the rational design of possible double perovskite materials with improved photocatalytic characteristics.

According to the reality of China, we divide Internet finance into four parts as Internet payment services, Internet financing services, Internet financial management services, and online banking business of traditional commercial banks, to discuss the impact of Internet finance on the profit efficiency of commercial banks. Our theoretical analysis shows that the external impact of Internet finance will reduce the profit of commercial banks, but Internet financing and Internet financial management have less influence, and the financial Internet business can improve the profit efficiency of commercial banks to some extent. An empirical test of Chinese data provides support for these predictions, it suggests that the estimated impact is significant and robust. Furthermore, differences in ownerships and profit efficiency of commercial banks will be at work.

In order to make the identification and utilization of nutritional quality components in Chinese cabbage more predictive, to obtain ideal raw materials, and to help screen functional Chinese cabbage varieties that have high nutritional value, we conducted a comprehensive evaluation of the nutritional quality of different Chinese cabbage varieties. In this study, 17 nutritional quality indexes of 35 Chinese cabbage varieties, including crude fiber (CF), crude protein (CP), vitamin C (VC), glucose (Glc), fructose (Fru), malic acid (MA), citric acid (CA), oxalic acid (OA), total amino acid (TAA) and CA, K, Mg, P, Cu, Fe, Mn and Zn, were analyzed using diversity analysis, correlation analysis, principal component analysis, membership function analysis and cluster analysis. The results showed that there were different degrees of variation in the 17 nutritional quality indexes, and the coefficients of variation ranged from 11.45% to 91.47%. The correlation analysis found that there were significant or extremely significant correlations between different nutrient elements of Chinese cabbage, which indicated that principal component analysis could be carried out, and the comprehensive score (D value) of different materials could be obtained using principal component analysis and the membership function method. The nutritional quality of Chinese cabbage was classified into five grades by cluster analysis. Finally, a mathematical model for evaluating the nutritional quality of Chinese cabbage was established using the D value and multiple stepwise regression methods, and 10 key indexes were selected from the 17 indexes, which could be used for the rapid identification of the nutritional quality of Chinese cabbage. This study provided a theoretical basis for the nutritional quality evaluation and variety breeding of Chinese cabbage.

The credibility of information is known as a major cause of a wide range of issues, such as: altered product information in food supply chains; fake transactions on E-commerce platforms; lengthy claim settlement time in agricultural insurance; and costly borrowings in agricultural financing. For a more specific example in food supply chains, end customers want to check the product information, but either doubt the authenticity of information, or simply do not have access to the information. The reason is that upstream suppliers and downstream retailers are often reluctant to share data, fearing privacy loss or business secret leakage. The consequence is that regulatory departments may face enormous challenges to identify accurate contamination sources, if there is scarce information or falsely recorded information at any stage of the food supply chain. In this paper, we focus on four common scenarios demanding information credibility in the agricultural supply chain: product traceability, E-commerce platforms, agricultural insurance, and agricultural financing. We review some high-profile smart credibility applications with emphasis on how blockchain related technologies can provide the information credibility by examining extant issues and relevant frameworks.

Hollow-core fibers provide an ideal environment for the interaction of a laser and gases; however, achieving stable and efficient coupling of a high-power laser is one of the key problems to be solved. Here, we study stable and efficient coupling of a high-power continuous-wave fiber laser with anti-resonant hollow-core fibers spliced with end caps. By optimizing the splicing process, a maximum laser power of 460 W was injected and 356.5 W was outputted under uncooled conditions, realizing a total transmission efficiency of approximately 77.5%. A test of more than 1 h showed the stability of this a coupling method. A few other important characteristics, such as the Fresnel reflection of the end caps and the influence of the end caps on the transmission beam quality, were measured and examined. This study opens new opportunities for stable and highly efficient coupling of high-power lasers with hollow-core fibers, which is significant for their applications in many other fields besides high-power fiber gas lasers, such as high-power laser delivery and liquid lasers in hollow-core fibers.

AbstractTo evaluate the stability of a prestressed railway embankment (PRE), approximate solutions for the additional normal and shear stresses on an arbitrary potential sliding surface of a PRE are first derived using the elastic theory, by which the propagation effect of additional stresses due to the prestressing loads is appropriately considered. Then, a limit equilibrium slice method incorporating the additional stress effect, equivalent interslice forces, track weight, and train axle load but avoiding repeated iteration calculations is developed to analyze the PRE stability. The additional stress solutions are also introduced to modify three commonly used conventional slope stability analysis methods, and comparative analyses were performed to investigate the applicability of the proposed method. The results showed that the differences between the safety factor (Fs) of a PRE evaluated using the proposed and modified methods were within 2% in this study and the values were less than those yielded by the conventional methods because of the partial negative effect of the additional normal and shear stresses on a few slices around the lateral pressure plate (LPP). Besides, the adaptive genetic algorithm was introduced to search the critical sliding surface of the PRE. Finally, the effect of reinforcement pressure on the stability of a PRE was investigated, where the results indicated that the Fs of the PRE increased from 1.323 to 1.626 with an increase of the reinforcement pressure from 0 to 200 kPa, while the PRE gradually varies from potential integral instability to potential local instability. The accurate and efficient determination of the additional stresses on the sliding surface is important in analyzing the PRE stability, and the developed method can provide an easy-to-use and relatively safer stability analysis method for the design of a PRE.

Peasants are producers and the direct stakeholders of rural environmental problems, and also the core subject of the comprehensive rural ecological improvement. However, in the current mode of rural environmental governance, the government takes the dominant role, with low participation of peasants. Based on the representative micro-survey data of China, and from the perspective of the psychosocial effects of the peasants’ self-identification, this research made use of instrumental variables and generalized structural equation mode (GSEM) to explore the reason behand this phenomenon. The research shows that, first, at present Chinese peasants have a low degree of self-identification, and self-identification has a remarkable and positive stimulating effect on the participation of peasants in rural ecological improvement; second, heterogeneity analysis shows that self-identification is a stronger incentive for peasant groups with female offspring and rural families with relatively higher incomes in the participation of rural environmental improvement, and is a less remarkable incentive for rural families with relatively low incomes; and third, the mediation model results indicate that social fairness and social capital are important mediating paths with which self-identification influences the participation of peasants in rural environmental improvement, and the exertion of the stimulating effect of self-identification on the participation in rural environmental improvement is inseparable from the coordination and integration of the perception effect of peasants as the behavioral subject in terms of social fairness, and is still more inseparable from the support of corresponding social capital. What we’ve discovered means that the enhancement of self-identification of peasants by strengthening the construction of public cultural services and cultural guidance in rural area is the crucial element to motivate the peasants to participate in rural environmental improvement so as to realize sustainable rural development.

A guidance and control algorithm for the heading control of a large underwater vehicle in hover state is proposed. The complexity of the flow field makes it difficult to build an accurate mathematical model for heading control under hover condition. Furthermore, the turning motion in the hover state causes additional vertical hydrodynamic and pitching moments which may affect the control performance of depth and pitching, and even bring safety problems. An H∞ synthesis method is proposed, in which guidance weighting function is designed for the heading control constraints, sensitivity weighting function for the tracking performance requirements, and uncertainty weighting function for modeling error. Firstly, the hydrodynamic characteristics and constraints of hover and steering are analyzed, and the mathematical model is established. Secondly, based on the analysis of the affection of steering motion on depth and trim state, a control strategy is proposed to suppress interference by setting steering speed and limiting acceleration, which improves the safety of the control algorithm. And then, a H∞ robust control algorithm based on three kinds of weighting function is designed. Finally, the method is simulated by using submarine model and validated by underwater vehicle experiment.The research in this article has potential application value for safety and low noise performance for the control of large underwater platform and manned submarines.

Abstract Background Wucai (Brassica campestris L. ssp. chinensis var. rosularis Tsen) is a variant of nonheading Chinese cabbage (Brassica campestris L.), which is one of the major vegetables in China. Cytoplasmic male sterility (CMS) has been used for Wucai breeding in recent years. However, the underlying molecular mechanism of Wucai CMS remains unclear. In this study, the phenotypic and cytological features of Wucai CMS were observed by anatomical analysis, and a comparative transcriptome analysis was carried out to identify genes related to male sterility using Illumina RNA sequencing technology (RNA-Seq). Results Microscopic observation demonstrated that tapetum development was abnormal in the CMS line, which failed to produce fertile pollen. Bioinformatics analysis detected 4430 differentially expressed genes (DEGs) between the fertile and sterile flower buds. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to better understand the functions of these DEGs. Among the DEGs, 35 genes (53 DEGS) were implicated in anther and pollen development, and 11 genes were involved in pollen cell wall formation and modification; most of these showed downregulated expression in sterile buds. In addition, several genes related to tapetum development (A6, AMS, MS1, MYB39, and TSM1) and a few genes annotated to flowering (CO, AP3, VIN3, FLC, FT, and AGL) were detected and confirmed by qRT-PCR as being expressed at the meiosis, tetrad, and uninucleate microspore stages, thus implying possible roles in specifying or determining the fate and development of the tapetum, male gametophyte and stamen. Moreover, the top four largest transcription factor families (MYB, bHLH, NAC and WRKY) were analyzed, and most showed reduced expression in sterile buds. These differentially expressed transcription factors might result in abortion of pollen development in Wucai. Conclusion The present comparative transcriptome analysis suggested that many key genes and transcription factors involved in anther development show reduced gene expression patterns in the CMS line, which might contribute to male sterility in Wucai. This study provides valuable information for a better understanding of CMS molecular mechanisms and functional genome studies in Wucai.

A series of tests for water transfer and ice accumulation were conducted under different soil types and conditions of water supply method, temperature gradient, and initial water content; the influence of the above parameters on the efficiency of water and vapor transfer was investigated and discussed. The main conclusions drawn are as follows. First, due to the difference in permeability for different soil type, water (i.e., liquid water and vapor) transfers differently. The water (or ice) accumulated in the soils of calcareous sand, silty soil in Lanzhou (SSL), red clay in Changsha, and silty soil in Hohhot (SSH) under the top plate is 34.5%, 21.0%, 11.33%, and 26.7%, respectively. In addition, the water (or ice) accumulation is determined by the holding capacity of water. Second, the supply method of liquid water is more efficiently compared with that of vapor supply, with the water contents increasing to 60.5% and 57.3% for liquid water and vapor supply. Third, the larger the temperature gradient, the greater the water accumulation in the frozen area. The increased amount of water mass under different temperature boundary conditions is 227.9 g, 253.3 g, and 273.8 g, respectively. Finally, the initial water content in silty soil has a significant influence on water and vapor transfer. The increased amounts of water for the tests of the initial water content of 5%, 10%, and 15% are 282.6 g, 253.3 g, and 132.5 g, respectively. The smaller the initial water content, the greater the water transfer in the unfrozen zone and vapor transfer in the frozen zone.

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the "black box" problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.

Pleyers, G. (2018). Movimientos sociales en el siglo XXI: Perspectivas y herramientas analíticas. Ciudad de Buenos Aires, Argentina: CLACSO.

Protein nanocapsules have exhibited promising potential applications in the field of protein drug delivery. A major issue with various promising nano-sized biotherapeutics including protein nanocapsules is that owing to their particle size they are subject to cellular uptake via endocytosis, and become entrapped and then degraded within endolysosomes, which can significantly impair their therapeutic efficacy. In addition, many nano-sized biotherapeutics could be also sequestered by autophagosomes and degraded through the autolysosomal pathway. Thus, a limiting step in achieving an effective protein therapy is to facilitate the endosomal escape and auto-lysosomal escape to ensure cytosolic delivery of the protein drugs. Here, we prepared a protein nanocapsule based on BSA (nBSA) and the BSA nanocapsules modified with a bilayer of lauric acid (LA-nBSA) to investigate the escape effects from the endosome and autophagosome. The size distribution of nBSA and LA-nBSA analyzed using DLS presents a uniform diameter centered at 10 nm and 16 nm. The data also showed that FITC-labeled nBSA and LA-nBSA were taken up by the cells mainly through Arf-6-dependent endocytosis and Rab34-mediated macropinocytosis. In addition, LA-nBSA could efficiently escape from endosomal before the degradation in endo-lysosomes. Autophagy could also sequester the LA-nBSA through p62 autophagosome vesicles. These two types of nanocapsules underwent different intracellular destinies and lauric acid (LA) coating played a vital role in intracellular particle retention. In conclusion, the protein nanocapsules modified with LA could enhance the protein nanocapsules escape from intercellular trafficking vesicles, and protect the protein from degradation by the lysosomes.

In recent years, fiber gas lasers have obtained a rapid development, however, efficient and stable pump coupling is a key limitation for their applications in the future. Here, we report an all-fiber gas cavity based on anti-resonant hollow-core fibers which have the beneficial properties of adjustable broad transmission bands and potential low transmission attenuation, especially in the mid-infrared. This kind of all-fiber gas cavity is fabricated by directly splicing with end caps at both ends for the first time. The high-power laser transmission characteristics were studied, and the experimental results show that the all-fiber gas cavities have a very stable performance. The maximum input laser power at 1080 nm is about 260 W, and the output power is 203 W, giving a total transmission efficiency of 78.1%. This work opens a new opportunity for the development of high-power all-fiber structured fiber gas lasers.

Current numerical methods for simulating biophysical processes in aquatic environments are typically constructed in a grid-based Eulerian framework or as an individual-based model in a particle-based Lagrangian framework. Often, the biogeochemical processes and physical (hydrodynamic) processes occur at different time and space scales, and changes in biological processes do not affect the hydrodynamic conditions. Therefore, it is possible to develop an alternative strategy to grid-based approaches for linking hydrodynamic and biogeochemical models that can significantly improve computational efficiency for this type of linked biophysical model. In this work, we utilize a new technique that links hydrodynamic effects and biological processes through a property-carrying particle model (PCPM) in a Lagrangian/Eulerian framework. The model is tested in idealized cases and its utility is demonstrated in a practical application to Sandusky Bay. Results show the integration of Lagrangian and Eulerian approaches allows for a natural coupling of mass transport (represented by particle movements and random walk) and biological processes in water columns which is described by a nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model. This method is far more efficient than traditional tracer-based Eulerian biophysical models for 3-D simulation, particularly for a large domain and/or ensemble simulations.

The presence of adhesins is arguably an important determinant of pathogenicity for Uropathogenic Escherichia coli (UPEC). Antimicrobial susceptibilities were tested by agar dilution method, fifteen adhesin genes were detected by polymerase chain reaction, and multilocus sequence typing (MLST) was analyzed in 70 UPEC isolates and 41 commensal E. coli strains. Extended-spectrum β-lactamase (ESBL) was determined with confirmatory test. The prevalence of ESBL-producers in UPEC (53%, 37/70) was higher than the commensal intestinal isolates (7%, 3/41), and 97% (36/37) of the ESBL-producing UPEC harbored bla CTX-M genes. afa was present in 36% (10/28) UPEC isolates from recurrent lower urinary tract infection (UTI), and none in the acute pyelonephritis, acute uncomplicated cystitis or commensal strains (P