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The key to restoring arid and semi-arid ecosystems is maintaining soil water and organic carbon contents. Alfalfa (Medicago sativa L.) is a high-yield perennial forage crop and performs ecological functions as a drought-resistance leguminous herb. It has been widely planted for reconstruction of degraded soils in the Loess Plateau in northwestern China, but long-term planting may affect soil carbon–water coupling and lead to crop yield reduction. To maximize the benefits of reconstructed grassland, this study explored the couplings of soil water, organic carbon, and alfalfa productivity along a reconstruction chronosequence in a semi-arid area of the Loess Plateau. Space-for-time substitution approach was used to select different-aged stands of reconstructed grassland (1, 5, 7, 10, 15, 20, 30 years old). Alfalfa above-ground biomass (AGB), soil water storage (SWS), organic carbon storage (SOCS), and carbon–water coupling coordination degree (D) were measured in the 0–100 cm soil profile. Alfalfa AGB reached a peak in the 7th year, and the degradation began in the 10th year. Both SWS and SOCS varied nonlinearly with stand age. The greatest loss of SWS occurred in the 15th year (80–100 cm depth), whereas the largest increase of SOCS occurred in the 30th year (0–20 cm depth). There was a negative feedback relationship between AGB and SWS over the 30-year study period (Pearson r = −0.835, P = 0.098). AGB and SOCS initially showed a trade-off within the first 10 years (Pearson r = −0.7431, P = 0.2569), in contrast to their positive feedback in the 20–30th years (Pearson r = 0.9978, P = 0.0421). A decoupling between SWS and SOCS (D < 0.6) was observed after 12 years of alfalfa planting. For agricultural production, a greater supply of water and organic fertilizer is required from the 7th year of alfalfa planting, and reseeding may be needed around the 10th year to prolong the life of alfalfa community. Alfalfa should be planted for no more than 12 consecutive years in the study area for ecological protection.

Chloro-haloacetonitrile (Cl-HAN), belongs to a group of nitrogenous disinfection by-products (N-DBPs) found in surface water, and are known to pose a major risk to the safety of human drinking water. However, the exact biological toxicity mechanism and the extent of the stress response caused by Cl-HAN remain unclear, resulting in a lack of effective measures to control its presence. Thus, the quantitative toxicological genomics and bioinformatics methods were applied to explore the effects of three chloro-haloacetonitriles (Cl-HANs) on the transcription of fusion genes under varying concentrations of stress in E. coli over 2-hour period. The initial stress response and their toxic mechanism were analyzed. The study also identified the molecular toxicity endpoint, and the core genes that are responsible for the specific toxicity of different Cl-HANs. Cl-HANs exhibited concentration-dependent characteristics of toxic effects, and caused changes in gene expression related oxidative and membrane stress. The stress response results showed that dichloroacetonitrile (dCAN) still caused significant DNA damage under the lowest concentration stress. Chloroacetonitrile (CAN) and trichloroacetonitrile (tCAN) exhibited lower genetic toxicity levels at 513 μg/L and 10.7 μg/L, respectively. The toxic effects of tCAN were widespread. And there was a good correlation between the molecular endpoint (EC-TELI1.5) and the phenotypic endpoint (LD50) with rp=-0.8634 (P=0.0593). In all concentrations of stress in CAN, dCAN, and tCAN, the number of overexpressed genes shared was 15, 2, and 14, respectively. Furthermore, bioinformatics analysis demonstrated that Cl-HANs affected genes associated with general stress pathways, such as cell biochemistry and physical homeostasis, resulting in changes in biological processes. And for CAN-induced DNA damage, polA played a dominant role, while katG, oxyR, and ahpC were the core genes involved in oxidative stress induced by dCAN and tCAN, respectively. These findings provide valuable data for the toxic effect of Cl-HANs.

Abstract Since the impressive superior performance demonstrated by deep learning methods is widely used in histopathological image analysis and diagnosis, existing work cannot fully extract the information in the breast cancer images due to the limited high resolution of histopathological images. In this study, we construct a novel intermediate layer structure that fully extracts feature information and name it DMBANet, which can extract as much feature information as possible from the input image by up-dimensioning the intermediate convolutional layers to improve the performance of the network. Furthermore, we employ the depth-separable convolution method on the Spindle Structure by decoupling the intermediate convolutional layers and convolving them separately, to significantly reduce the number of parameters and computation of the Spindle Structure and improve the overall network operation speed. We also design the Spindle Structure as a multi-branch model and add different attention mechanisms to different branches. Spindle Structure can effectively improve the performance of the network, the branches with added attention can extract richer and more focused feature information, and the branch with residual connections can minimize the degradation phenomenon in our network and speed up network optimization. The comprehensive experiment shows the superior performance of DMBANet compared to the state-of-the-art method, achieving about 98% classification accuracy, which is better than existing methods. The code is available at https://github.com/Nagi-Dr/DMBANet-main .

High-energy gas fracturing of shale is a novel, high efficacy and eco-friendly mining technique, which is a typical dynamic perturbing behavior. To effectively extract shale gas, it is important to understand the dynamic mechanical properties of shale. Dynamic experiments on shale subjected to true triaxial compression at different strain rates are first conducted in this research. The dynamic stress-strain curves, peak strain, peak stress and failure modes of shale are investigated. The results of the study indicate that the intermediate principal stress and the minor principal stress have the significant influence on the dynamic mechanical behaviors, although this effect decreases as the strain rate increases. The characteristics of compression-shear failure primarily occur in shale subjected to triaxial compression at high strain rates, which distinguishes it from the fragmentation characteristics observed in shale under dynamic uniaxial compression. Additionally, a numerical three-dimensional Split Hopkinson Pressure Bar (3D-SHPB), which is established by coupling PFC3D and FLAC3D methods, is validated to replicate the laboratory characteristics of shale. The dynamic mechanical characteristics of shale subjected to different confining stresses are systematically investigated by the coupling PFC3D and FLAC3D method. The numerical results are in good agreement with the experimental data.

Few-shot classification addresses the challenge of swiftly enabling a deep learning model to comprehend new classes based on minimal supporting image samples. Despite recent research predominantly focusing on devising increasingly intricate classifiers for gauging similarities between query and support images, there is room for improvement in the critical role of feature embeddings and relational measurements. In this paper, a novel technique called Progressive Multi-Scale Relation Network (PMRNet), is designed for few-shot classification tasks. Our work introduces a feature extraction module that adeptly extracts and interactively fuses multi-scale features to address the problem of insufficient feature extraction. Furthermore, an enhanced attention mechanism is integrated, which amplifies critical local and global features for increased discriminative power. A novel bilinear relation module is also designed specifically to accurately quantify the similarities between the extracted features. The PMRNet has been rigorously evaluated on three benchmark datasets. PMRNet’s empirical performance in few-shot image classification tasks exhibits commendable accuracy, substantiating its efficacy as a robust architectural framework for significantly boosting the precision of such tasks.

Abstract Objectives A genome sequence of a threatened species can provide valuable genetic information that is important for improving the conservation strategies. The white-eared night heron (Gorsachius magnificus) is an endangered and poorly known ardeid bird. In order to support future studies on conservation genetics and evolutionary adaptation of this species, we have reported a de novo assembled and annotated whole-genome sequence of the G. magnificus. Data description The final draft genome assembly of the G. magnificus was 1.19 Gb in size, with a contig N50 of 187.69 kb and a scaffold N50 of 7,338.28 kb. According to BUSCO analysis, the genome assembly contained 97.49% of the 8,338 genes in the Aves (odb10) dataset. Approximately 10.52% of the genome assembly was composed of repetitive sequences. A total of 14,613 protein-coding genes were predicted in the genome assembly, with functional annotations available for 14,611 genes. The genome assembly exhibited a heterozygosity rate of 0.49 heterozygosity per kilobase pair. This draft genome of G. magnificus provides valuable genomic resources for future studies on conservation and evolution.

Line commutated converter based high-voltage direct-current (LCC-HVDC) transmissions are prone to harmonic oscillation under weak grids. Impedance modeling is an effective method for assessing interaction stability. Firstly, this paper proposes an improved calculation method for the DC voltage and AC currents of commutation stations to address the complex linearization of the commutation process and constructs an overall harmonic state-space (HSS) model of an LCC-HVDC. Based on the HSS model, the closed-loop AC impedances on the LCC-HVDC sending and receiving ends are then derived and verified. The impedance characteristics of the LCC-HVDC are then analyzed to provide a physical explanation for the harmonic oscillation of the system. The effects of the grid strength and control parameters on system stability are also analyzed. To improve the impedance characteristics and operating stability of the LCC-HVDC system, a virtual impedance based stability enhancement control is proposed, and a parameter design method is considered to ensure satisfactory phase margins at both the sending and receiving ends. Finally, simulation results are presented to verify the validity of the impedance model and virtual impedance based stability enhancement control.

The growth and biological decline of alfalfa may be linked to the rhizosphere microbiome. However, plant–microbe interactions in the rhizosphere of alfalfa and associated microbial community variations with stand age remain elusive. This study explored the successional pattern of rhizosphere microbial communities across different aged alfalfa stands and its relationship with alfalfa decline. Rhizosphere soils were collected from 2- and 6-year-old alfalfa stands. Control soils were collected from interspaces between alfalfa plants in the same stands. Soil bacterial and fungal communities were characterized by 16S and ITS rRNA gene sequencing, respectively. Specific microbial taxa colonized the rhizosphere soils, but not the control soils. The rhizosphere-specific taxa mainly included potentially beneficial genera (e.g., Dechloromonas, Verrucomicrobium) in the young stand and harmful genera (e.g., Peziza, Campylocarpon) in the old stand. Alfalfa roots regulated soil microbial communities by selective promotion or inhibition of distinct taxa. The majority of time-enriched taxa were reported as harmful fungi, whose relative abundances were negatively correlated with plant traits. Time-depleted taxa were mostly known as beneficial bacteria, which had relative abundances positively correlated with plant traits. The relative abundances of functional bacterial genes associated with vancomycin biosynthesis, zeatin biosynthesis, and amino acid metabolism trended lower in rhizosphere soils from the old stand. An upward trend was observed for fungal pathogens and wood saprotrophs with increasing stand age. The results suggest that root activity drives the negative succession of rhizosphere microbial communities during alfalfa decline in old stands.

Abstract To achieve a green and sustainable wireless communication network, the reconfigurable intelligent surface (RIS) technology has emerged as an emerging technology. The RIS-assisted mmWave massive MIMO vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of RIS-assisted mmWave V2X communications is though not exempt of challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted mmWave massive MIMO system to improving the safety and traffic efficiency of cooperative automated driving. The received signal of the vehicle is decomposed into four-dimensional tensors and study the channel, frequency and time attention (CFTA) algorithm using the low order tensor characteristics of mmWave channels. This algorithm can extract the frequency and time characteristics of the channel, and obtain the millimeter wave characteristic channel between the corresponding base station (BS) and the vehicles. The problem of jointly optimization of beamforming matrix at the BS and the phase shift matrix on the RIS is constructed and solved using deep reinforcement learning (DRL). Unlike traditional alternating optimization methods, the integration of DRL techniques into the optimal design of the system enables the observation of immediate rewards, learning from the environment and improving it to obtain optimal solutions to high-dimensional jointly beamforming optimization problems ultimately. The simulation results show that the proposed algorithm has meaningful performance compared with other methods.

ABSTRACTUsing a continuous laser to soften glass locally is a novel way of selective glass forming. Due to gaussian distribution of laser power density, a temperature gradient will be generated on glass treated with localized irradiation by laser, which brings about the coexistence of a glass state and a viscoelastic state. This situation creates a bulge on the surface of the softened area. Based on this phenomenon, we propose a measurement approach and a calculation model for the softening zone width of glass irradiated locally by laser and verify the calculation model through simulations and experiments. The maximum temperature deviation of the proposed temperature field prediction model is 4.7°C, while the minimum deviation is 0.53°C; making the deviation between the calculation model of the softening zone width and the experimental measurement result less than 0.319%.

Abstract Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) that was found dead in the mountainous region in Gansu, China. We generate a near-complete genome assembly containing only 18 gaps scattering in 8 out of the 40 assembled chromosomes. We characterize the DNA methylation landscape which is correlated with GC content and gene expression. Our phylogenomic analysis suggests Otidiformes and Musophagiformes are sister groups that diverged from each other 46.3 million years ago. The genetic diversity of great bustard is found the lowest among the four available Otidiformes genomes, possibly due to population declines during past glacial periods. As one of the heaviest migratory birds, great bustard possesses several expanded gene families related to cardiac contraction, actin contraction, calcium ion signaling transduction, as well as positively selected genes enriched for metabolism. Finally, we identify an extremely young evolutionary stratum on the sex chromosome, a rare case among birds. Together, our study provides insights into the conservation genomics, adaption and chromosome evolution of the great bustard.

Background Lung adenocarcinoma (LUAD) is a major pathological subtype of malignant lung cancer with a poor prognosis. Necroptosis is a caspase-independent programmed cell death mode that plays a pivotal role in cancer oncogenesis and metastasis. Here, we explore the prognostic values of different necroptosis-related genes (NRGs) in LUAD. Methods mRNA expression data and related clinical information for LUAD samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. NRGs were identified using the GeneCards database. Least absolute shrinkage and selection operator Cox regression and multivariate Cox analysis were used to construct a prognostic risk model. Time-dependent receiver-operating characteristic curves and a nomogram were constructed to validate the predictive values of the prognostic signatures. A necroptosis-related protein–protein interaction network was visualised using the STRING database and Cytoscape software. Functional analyses, including Gene Ontology, Kyoto Encyclopaedia of Genes and Genomes pathway enrichment, gene set enrichment, and gene set variation analyses, were conducted to explore the underlying molecular mechanisms. Finally, the mRNA expression of the prognostic signatures in LUAD cell lines was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. Results A prognostic model was established for eight NRGs (CALM1, DDX17, FPR1, OGT, PGLYRP1, PRDX1, TUFM, and CPSF3) based on TCGA-cohort data and validated with the GSE68465 cohort. Patients with low-risk scores had better survival outcomes than those with high-risk scores (p = 0.00013). The nomogram was used to predict the prognosis of patients with LUAD. The prediction curves for 1-, 3-, and 5-year OS showed good predictive performance and the accuracy of the nomograms increased over time. RT-qPCR results demonstrated that these eight genes, especially CALM1, PRDX1, and PGLYRP1, were differentially expressed in LUAD cells. Conclusion We constructed a reliable eight-NRG signature that provides new insights for guiding clinical practice in the prognosis and treatment of LUAD.

Cracking behaviors of rocks significantly affect the safety and stability of the explorations of underground space and deep resources. To understand deeply the microscopic cracking process and mechanical property of rocks, X-ray micro-computed tomography (X-μCT) is applied to capture the rock microstructures. The digital color difference UNet (DCD-UNet)-based deep learning algorithm with 3D reconstruction is proposed to reconstruct the multiphase heterogeneity microstructure models of rocks. The microscopic cracking and mechanical properties are studied based on the proposed microstructure-based peridynamic model. Results show that the DCD-UNet algorithm is more effective to recognize and to represent the microscopic multiphase heterogeneity of rocks. As damage characteristic index of multiphase rocks increases, transgranular cracks in the same grain phase, transgranular and intergranular cracks of pore-grain phase, intergranular and secondary transgranular cracks and transgranular crack between different grains propagate. The ultimate microscopic failure modes of rocks are mainly controlled by the transgranular cracks-based T1-shear, T3-shear, T1-tension, T2-tension and T3-tension failures, and the intergranular cracks-based T1-tension, T1-shear and T3-shear failures under uniaxial compression.

Abstract Objective Purpose To observe the safety and effect of the C-pupilloplasty for the treatment of iris coloboma and traumatic iris defects. Methods A total of 21 cases (21 eyes) with iris coloboma or traumatic iris defects who underwent C-pupilloplasty (a single-pass three-throw technique) from Feb. 2016 to Mar. 2020 were analyzed retrospectively. Uncorrected visual acuity, refraction, corneal topographic keratometry and endothelial cell density were examined. Results All the patients were successfully treated, and a central and round pupil was restored. The mean follow-up duration was 8.76 ± 3.58 months (ranging from 2 to 14 months). All patients had round or round-like pupils with a diameter less than or equal to 3 mm after the C-pupilloplasty. Very slightly endothelial loss, negligible symptoms such as glare, distortion, dizziness and photophobia were observed. Conclusion We introduced a new technique of pupilloplasty (C-pupilloplasty) which could be a more straight forward and more effective treatment for iris coloboma and traumatic iris defect.

Rural collectively owned commercial construction land marketization (RCCCLM), a land policy that is still being piloted despite being allowed by law, has been regarded as an indispensable policy tool that can meet the land demand for rural development. This study aims to analyze the typical modes and key paths of RCCCLM for rural transformation theoretically and practically. We developed an innovative theoretical framework of “Situation-Structure-Implementation-Outcome” to illustrate the relationship between RCCCLM and rural transformation. By tracing the process of the impact of RCCCLM on rural transformation in typical areas, this study fills the gap in existing research that has not yet explored the theoretical mechanisms between RCCCLM and rural transformation. The results show that a combination of such factors as geographical location and rural land use status brings about three major ways of RCCCLM, namely, local marketization, adjustive marketization and consolidation-based marketization, to promote rural transformation. The influencing mechanism of RCCCLM on rural transformation mainly concerns rights granting, rural land use structure reshaping, the transformation of farmers’ livelihood and improvement in rural governance capacity. Rural transformation follows a basic rule and phased characteristics of evolving from single function to multiple functions, and RCCCLM displays a differentiated driving effect on the evolution of rural functions in different stages of rural development. These findings will provide new insights into rural land use and rural transformation in China and other developing countries and help solve the bottlenecks in rural transformation.

Earlier studies have reported some calculation methods for commutation failure fault level (CFFL) in line-commutated-converter based high-voltage direct current (LCC-HVDC) system under single-line-to-ground (SLG) faults. The accuracy of earlier methods is limited because they only consider the commutating voltage drop and phase shift, while neglecting the DC current variation. Hence, this paper proposes a CFFL calculation method under SLG faults considering DC current variation, for better planning and designing of LCC-HVDC systems. First, the fault commutating voltage magnitude and phase shift are calculated. Then, the fault DC voltage during different commutation processes is deduced. Based on the commutating voltage magnitude and phase shift, and DC voltage during different commutation processes under SLG faults, the characteristics of CFFL with different fault time are demonstrated and analyzed. Next, the transient time-domain response of the DC current after the fault is obtained based on the DC transmission line model. Discrete commutation processes are constructed based on the commutation voltage-time area rule to solve the extinction angle under different fault levels and fault time. Finally, the CFFL is calculated considering the fault time, commutating voltage drop, phase shift, and DC current variation. The accuracy of the proposed method compared with the traditional method is validated based on the CIGRE benchmark model in PSCAD/EMTDC.

For the safe and fast recovery of line commutated converter based high-voltage direct current (LCC-HVDC) transmission systems after faults, a DC current order optimization based strategy is proposed. Considering the constraint of electric and control quantities, the DC current order with the maximum active power transfer is calculated by Thevenin equivalent parameters (TEPs) and quasi-state equations of LCC-HVDC transmission systems. Meanwhile, to mitigate the subsequent commutation failures (SCFs) that may come with the fault recovery process, the maximum DC current order that avoids SCFs is calculated through imaginary commutation process. Finally, the minimum value of the two DC current orders is sent to the control system. Simulation results based on PSCAD/EMT-DC show that the proposed strategy mitigates SCFs effectively and exhibits good performance in recovery.

Abstract Background The black-crowned night heron (Nycticorax nycticorax) is an ardeid bird successfully adapted to the nocturnal environment. Previous studies had indicated that the eyes of the night herons have evolved several specialized morphological traits favoring nocturnal vision. However, the molecular mechanisms of the nocturnal vision adaptation of night herons remained inattentions. In this study, the whole genome of N. nycticorax was sequenced and comparative analyses were performed on the vision-related and olfactory receptor (OR) genes to understand the molecular mechanisms of the visual and olfactory adaptation of night herons. Results The results indicated that a number of vision genes were under positive or relaxed selection in N. nycticorax, whereas a number of other vision genes were under relaxed or intensified selection in the boat-billed heron (Cochlearius cochlearius), which suggested that the two species adapt to nocturnality with different genetic mechanisms. The different selections acting on vision genes are probably associated with the enlargement of eye size and the enhancement of visual sensitivity in night herons. The analyses on olfactory receptor (OR) genes indicated that the total number of OR genes in the genomes of N. nycticorax and C. cochlearius were about half those in the little egret (Egretta garzetta), whereas the diversity of their OR genes was not remarkably different. Additionally, the number of expressed OR genes in the transcriptomes of N. nycticorax was also fewer than that in E. garzetta. These results suggest a reduced olfactory capability in night herons compared with E. garzetta. Conclusions Our results provided evidence that several vision genes of the night herons were subjected to different natural selections, which can contribute to a better understanding of the genetic mechanisms of visual adaptions of the night heron. In addition, the finding of the reduced number of total and expressed OR genes in night herons may reflect a trade-off between olfaction and vision.

BackgroundOsteoporosis is a common chronic disease in older persons. Physical activity can prevent chronic diseases as well as many diseases associated with aging. Older persons often suffer from depression and other psychological problems. However, there are few studies on the correlation between depression and physical activity in older persons with osteoporosis in China.MethodsThis cross-sectional study was conducted from June 1 to December 28, 2021. A total of 207 subjects who met the inclusion and were exclusion criteria were selected from the outpatient department of West China Hospital and evaluated using a self-designed demographic data questionnaire, the Self-rating Depression Scale (SDS), and the Physical Activity Scale for the Elderly (PASE). Multivariate linear regression was performed to analyze the factors affecting physical activity, and multivariate logistic regression analysis was performed to analyze the factors affecting depression. Spearman’s correlation coefficients were calculated to analyze the correlation between depression and physical activity in older persons with osteoporosis.ResultsA total of 173 valid questionnaires were statistically analyzed. A total of 122 (70.5%) participants were identified as having depression (SDS ≥ 50 points). The median depression score was 62.5 (24.38), and the median PASE score was 69.29 (116.64). Multivariate logistic regression model results showed that physical activity and pain were the main risk factors for depression in older persons with osteoporosis (p

Eliminating poverty and achieving social justice are global concerns. China has focused on common prosperity. Comprehensive land consolidation is a potential policy tool that can contribute to common prosperity, but its effectiveness and implementation methods are yet to be verified and discussed. Therefore, we construct an analytical framework to understand how comprehensive land consolidation promotes common prosperity. The pilot area for comprehensive land consolidation in Ningbo City is used as a qualitative case study. The research results indicate that comprehensive land consolidation focuses on development and sharing to promote high-quality socio-economic development, social fairness, and justice. The paths for achieving development included the following: (1) a network governance structure consisting of multiple entities to enhance land governance; (2) various consolidation activities were conducted at the town scale to optimize the functionality and spatial pattern of public spaces; and (3) enabling the flow of urban and rural factors for improved resource allocation efficiency and providing an impetus for industrial development. The paths for achieving sharing included clarifying collective land ownership and promoting land transactions to provide diverse land values and ensure a shared distribution. This research provides new insights applicable to other Chinese cities and numerous developing countries engaged in land consolidation to address social distribution issues.

Against the backdrop of an urban-rural dual structure, governance structure significantly influences the fairness and sharing of land value increment profits from land consolidation in various regions. This paper develops a spatial-institutional analytical framework, reflects on the traditional government-led land quota planning management mechanism and advocates optimizing governance structures in land consolidation to facilitate urban-rural element flows and the sharing of land value increment profits. This paper selects two comprehensive land consolidation cases in the Fenghua District and Cixi City of Ningbo, representing government-led and village collective self-organized governance structures. The research shows that the Fenghua case adopts a government-led state-owned enterprise coordinated governance structure, which state-owned enterprises bear operational risks and the majority of profit sharing, while village collectives, as landowners, are insufficiently protected in benefit sharing. The Cixi case employs a government-embedded village-town governance structure, with the village collectives as the main operating body, allowing collective construction land to enter the market to ensure the sharing of community profits. Both governance structures promote the bidirectional flow of urban-rural land resource elements; the former significantly drives urban development, while the latter ensures better benefit sharing for village collectives. The research proposes that optimizing governance structures and improving land quota allocation mechanisms can further stimulate various entities’ participation in land consolidation and land value activation.

Abstract Background Trauma patients are often in a state of psychological stress, experiencing helplessness, sadness, frustration, irritation, avoidance, irritability and other adverse emotions. Doctors and nurses are at the forefront of caring trauma patients and they play a crucial role in psychological supports and mental health care. However, few qualitative studies had based on the framework of the Theory of Planned Behavior (TPB) to explore the experiences in providing psychological care for trauma patients. We examined attitudinal, normative, and control beliefs underpinning medical staffs’ decisions to perform psychological care. Method A qualitative study of in-depth semi-structured interviews was conducted among 14 doctors and nurses engaging in trauma care. The participants came from six tertiary hospitals in Chongqing, China. Data analysis was performed using the approach of Colaizzi. According to the framework of TPB, the researchers identified and summarized the themes. Results Important advantages (mutual trust, patients' adherence and recovery), disadvantages (workload, short-term ineffective, practice unconfidently), referents (supportive: managers, patients, kinsfolk, nursing culture; unsupportive: some colleagues and patients), barriers (insufficient time or energy, resources situations), and facilitators (access to psychologist, training/education, reminders) were identified. Some demands, such as training diversity, multidisciplinary cooperation and families' support, reflected by doctors and nurses were important for them to carry out psychological care. Conclusion According to the TPB, this article explored the internal and external promotion and hindrance factors that affecting the intentions and behaviors of doctors and nurses in implementing psychological care for trauma patients. We also focused on the experience and demands of health professionals in conducting psychological care, which could provide references for managers to formulate corresponding psychological care procedures and norms.

Abstract Background The incidence of osteoporosis increases with age. Frailty is a distinct characteristic seen in older osteoporosis patients. Poor sleep quality is common in elderly individuals. However, there are few studies on the correlation between frailty and sleep quality in elderly patients with osteoporosis in China. Methods This cross-sectional study was conducted from December 8, 2020, to April 30, 2021. A total of 247 patients who met the inclusion and exclusion criteria were recruited in outpatient departments at West China Hospital. A self-designed demographic data questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the Frailty Phenotype scale were used to evaluate the sleep quality and frailty of the participants. Multivariate logistic regression was performed to evaluate the factors affecting frailty and sleep quality in osteoporosis patients. Results A total of 231 valid questionnaires were statistically analysed. The mean frailty score was 3.2 ± 1.6 and a total of 152 (65.8%) were frailty. The mean PSQI score was 11.9 ± 4.5 and a total of 183 (79.2%) patients had poor sleep quality (PSQI > 7). Multiple logistic regression showed that female, pain, polypharmacy, activities of daily living status (ADLs), and sleep quality were independent influencing factors for frailty, while comorbidity, ADLs and frailty status were independent influencing factors for sleep quality. Conclusion We found that frailty and sleep quality were prevalent and that frailty was positively correlated with the PSQI score in elderly patients with osteoporosis in China. The higher the frailty score is, the worse the sleep quality. It means the frailer old patients were, the worse their sleep quality, and poor sleep quality may increase the risk of frailty in elderly patients with osteoporosis. To manage elderly patients with osteoporosis effectively, medical staff should pay attention to frailty, sleep quality and its influencing factors.

The advent of big data technologies makes a profound impact on various facets of our lives, which also presents an opportunity for Chinese audits. However, the heterogeneity of multi-source audit data, the intricacy of converting Chinese into SQL, and the inefficiency of data processing methods present significant obstacles to the growth of Chinese audits. In this article, we proposed BDMCA, a big data management system designed for Chinese audits. We developed a hybrid management architecture for handling Chinese audit big data, that can alleviate the heterogeneity of multi-mode data. Moreover, we defined an R-HBase spatio-temporal meta-structure for auditing purposes, which exhibits almost linear response time and excellent scalability. Compared to MD-HBase, R-HBase performs 4.5× and 3× better in range query and kNN query, respectively. In addition, we leveraged the slot value filling method to generate templates and build a multi-topic presentation learning model MRo-SQL. MRo-SQL outperforms the state-of-the-art X-SQL parsing model with improvements in logical-form accuracy of up to 5.2%, and execution accuracy of up to 5.9%.

Unmanned aerial vehicles (UAVs) can support low-cost, highly mobile communications while making it possible to establish dedicated terrestrial networks. To overcome the pointing error (PE) and beam misalignment of millimeter-wave large-scale multiple-input multiple-output/free-space optics (MIMO/FSO) caused by UAV jitter, a millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is proposed. This approach is used for reconfigurable intelligent surface (RIS) network-assisted UAV millimeter-wave massive MIMO/FSO to improve system spectral efficiency. Firstly, the high-dimensional channel of RIS-assisted millimeter-wave massive MIMO/FSO in UAV is represented as a low-dimensional channel by tensor training decomposition. Secondly, the two-way gated recursive unit attention neural network model can effectively solve the FSO PE caused by UAV jitter, and the fast fading channel and Doppler frequency shift are estimated by the Fast Cyclic Tensor Power Method (FCTPM) based on tensor training decomposition. Finally, the RIS phase shift matrix is optimized by singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were estimated by using the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation results show that by using the proposed method, the spectrum utilization rate is improved by 23.6% compared with other methods.

With the rise of social networks, more and more users share their location on social networks. This gives us a new perspective on the study of user movement patterns. In this paper, we solve the trajectory re-identification task by identifying human movement patterns and then linking unknown trajectories to the user who generated them. Existing solutions generally focus on the location point and the location point information, or a single trajectory, and few studies pay attention to the information between the trajectory and the trajectory. For this reason, in this paper, we propose a new model based on a contrastive distillation network, which uses a contrastive distillation model and attention mechanisms to capture latent semantic information for trajectory sequences and focuses on common key information between pairs of trajectories. Combined with the trajectory library composed of historical trajectories, it not only reduces the number of candidate trajectories but also improves the accuracy of trajectory re-identification. Our extensive experiments on three real-world location-based social network (LBSN) datasets show that our method outperforms existing methods.

Fire checking is indispensable for guaranteeing the fire safety of buildings as it reviews the compliance of the building with fire codes and regulations. Automated Compliance Checking (ACC) systems that check building data utilizing Building Information Modeling (BIM) against fire codes have emerged as an active field of research. Substantial efforts have focused on analyzing the properties of the building components. However, the analysis of the spatial geometric relationships of building components has received inadequate attention. The present study proposes a novel ACC system leveraging Natural Language Processing (NLP) techniques to review the spatial geometric relationships of building components in BIM models. First, a framework for a BIM-based ACC system is delineated and decomposes ACC into three constituent subtasks: building model parsing, code knowledge translation, and compliance check result reporting. Second, an approach for structured processing of spatial geometric stipulations in fire codes using NLP is presented to review the geometric relationships between components in building models. Finally, the system’s performance is assessed by testing fire code compliance across various building types utilizing BIM models. The empirical findings demonstrate that the system achieves superior recall compared with the manually formulated gold standard, with the ACC system enabling quick, accurate, and comprehensive automated compliance checking.

The decode-and-forward (DF) based free-space optical-radio frequency (FSO-RF) hybrid system combines the advantages of both FSO links and RF links, which makes the system easy to be deployed and enables the extended transmission coverage. In the case of multiple users, the unreasonable dynamic antenna selection algorithm will lead to multi-user interference (MUI) and reduce the spectral efficiency of the system. Therefore, we propose hybrid precoding based on manifold learning with the antenna partitioning algorithm for dual-hop hybrid FSO-RF systems. In the hybrid precoding of dynamic subarray structure, the high-dimensional channels are embedded into the low-dimensional manifolds by the optimized multidimensional scaling (MDS). The potential spatial correlations of the high-dimensional channel are preserved by the scaling by majorizing a complicated function (SMACOF) algorithm. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. Meanwhile, an antenna subarray partitioning algorithm is proposed, so that each antenna unit can be assigned to an RF chain based on the increment of the user’s maximum signal to interference noise ratio (SINR). By calculating the simulated equivalent channel SINR for the selected users, the antenna division can greatly reduce the computational complexity and the size of the search space, and ensure fairness among users. Simulation results show that this method can obtain almost the best summation rate and higher spectral efficiency compared with the conventional method.

The commutation failure (CF) mitigation effectiveness is normally restricted by the delay of extinction angle (EA) measurement or the errors of existing prediction methods for EA or firing angle (FA). For this purpose, this paper proposes a CF mitigation method based on the imaginary commutation process. For each sample point, an imaginary commutation process is constructed to simulate the actual commutation process. Then, the imaginary EA is calculated by comparing the imaginary supply voltage-time area and the imaginary demand voltage-time area, which can update the imaginary EA earlier than the measured EA. In addition, the proposed method considers the impacts of commutation voltage variation, DC current vari-ation, and phase angle shift of commutation voltage on the commutation process, which can ensure a more accurate EA calculation. Moreover, the DC current prediction is proposed to improve the CF mitigation performance under the single-phase AC faults. Finally, the simulation results based on CIGRE model prove that the proposed method has a good performance in CF mitigation.

Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multi-user channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.

The mitigation of commutation failure (CF) depends on the accuracy of CF prediction. In terms of the large error of the existing extinction angle (EA) calculation during the fault transient period, a method for CF prediction and mitigation is proposed. Variations in both DC current and overlap angle (OA) are considered in the proposed method to predict the EA rapidly. In addition, variations in critical EA and the effect of firing angle (FA) on both DC current and OA are considered in the proposed method to obtain the accurate FA order for the control system. The proposed method can achieve good performance in terms of CF mitigation and reduce reactive consumption at the inverter side when a fault occurs. Simulation results based on the PSCAD/EMTDC show that the proposed method predicts CF rapidly and exhibits good performance in terms of CF mitigation.

ObjectiveCranioplasty (CP) and ventriculoperitoneal shunt (VPS) are procedures required after decompression of the flap (DC) to protect the cranial frame and prevent hydrocephalus. This study evaluated the safety and efficacy of different surgical sequences of CP and VPS after DC and identified risk factors for necessary permanent VPS.MethodsFrom January 2017 to December 2021, valid follow-up data were collected in 192 cases. The observation group preferred CP, and then evaluated whether to receive VPS according to the progress of hydrocephalus. the control group was prioritized for VPS and continued with CP after 1 week. The improvement of hydrocephalus symptoms, follow-up outcomes, and post-operative complications before and after surgery were compared between the two groups, and univariate analysis was used to determine the risk factors for necessary permanent risk factors for VPS.ResultsThere were 86 cases (44.8%) in the observation group, who received CP first, while 106 cases (55.2%) in the control group received VPS and CP, respectively. There was no significant difference between the two groups according to Barthel index, FMAS, Mrs, GCS, and Evans index, and there was no statistical difference in complications between the two groups. However, in the observation group, hydrocephalus disappeared after CP operation in 29 cases (33.7%), and finally avoided VPS. Univariate analysis showed that the main etiology was related to the size of the skull defect, the distance of the talus margin relative to the flap to the midline, and lumbar puncture pressure was a predictor of the need for permanent VPS.ConclusionThis study provides detailed information on the efficacy and complications of different sequences of preferential CP or VPS after DC surgery. We found that priority CP reduced the incidence of VPS surgery without affecting surgical outcomes and complications.

In this study, to confirm the effect of confining pressure on dynamic mechanical behavior and failure modes of concrete, a split Hopkinson pressure bar dynamic loading device was utilized to perform dynamic compressive experiments under confined and unconfined conditions. The confining pressure was achieved by applying a lateral metal sleeve on the testing specimen which was loaded in the axial direction. The experimental results prove that dynamic peak axial stress, dynamic peak lateral stress, and peak axial strain of concrete are strongly sensitive to the strain rate under confined conditions. Moreover, the failure patterns are significantly affected by the stress-loading rate and confining pressure. Concrete shows stronger strain rate effects under an unconfined condition than that under a confined condition. More cracks are created in concrete subjected to uniaxial dynamic compression at a higher strain rate, which can be explained by a thermal-activated mechanism. By contrast, crack generation is prevented by confinement. Fitting formulas of the dynamic peak stress and dynamic peak axial strain are established by considering strain rate effects (50–250 s−1) as well as the dynamic confining increase factor (DIFc).

Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and with low power consumption. Tensor-based joint beamforming with low-cost ultrasonic and RIS-assisted Dual-Hop Hybrid free space optical (FSO) mm Wave massive Multiple Input Multiple Output (MIMO) of vehicle-to-everything (V2X) is proposed. To address the occlusion problem for high-speed mobility of the vehicle, an RIS-assisted mixed FSO-MIMO V2X system is proposed. The low-cost ultrasonic array signal model is developed to solve the accurate direction-of-arrival (DOA) estimation. The ultrasonic-assisted RIS phase shift matrix based on subspace self-organizing iterations is designed to track the beam direction between RIS and vehicle. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized to maximize each subarray’s rate. The vehicle motion state is transformed into a two-dimensional model for prior distribution to calculate the particle weights of the RIS phase. Multi-vehicle Tucker tensor decomposition is used to describe the high-dimensional beam space. We conceive a multi-vehicle joint optimization method for designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. A cascaded channel decomposition method based on Singular Value Decomposition (SVD) is used to obtain the combined matrix beamforming of BS and vehicle. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can reflect signals and transmissive signals simultaneously and can extend the coverage of signals. A conventional RIS mainly focuses on the case where the signal source and the target are on the same side. In this paper, a STAR-RIS-assisted non-orthogonal multiple access (NOMA) downlink communication system is considered to maximize the achievable rate for users by jointly optimizing the power-allocation coefficients, active beamforming, and STAR-RIS beamforming under the mode-switching (MS) protocol. The critical information of the channel is first extracted using the Uniform Manifold Approximation and Projection (UMAP) method. Based on the key extracted channel features, STAR-RIS elements and users are clustered individually using the fuzzy C-mean clustering (FCM) method. The alternating optimization method decomposes the original optimization problem into three sub-optimization problems. Finally, the sub-problems are converted to unconstrained optimization methods using penalty functions for the solution. Simulation results show that when the number of elements of RIS is 60, the achievable rate of the STAR-RIS–NOMA system is about 18% higher than that of the RIS–NOMA system.

Abstract Endocrine therapy is the standard treatment for estrogen receptor (ER)-positive breast cancer, but tumors eventually develop resistance. However, endocrine therapy resistance mechanisms mediated through interactions between breast cancer cells and tumor-associated macrophages (TAMs) are still unclear. Here, we characterized sodium/glucose cotransporter 1 (SGLT1) overexpression drives the highly glycolytic phenotype of tamoxifen-resistant breast cancer cells where enhanced lactic acid secretion promotes M2-like TAM polarization via the hypoxia-inducible factor-1α/signal transducer and activator of transcription-3 pathway. In turn, M2-like TAMs activate breast cancer cells through EGFR/PI3K/Akt signaling, providing feedback to upregulate SGLT1 and promote tamoxifen resistance and accelerate tumor growth in vitro and in vivo. Higher expression of SGLT1 and CD163+ TAMs was associated with endocrine-resistant ER-positive breast cancers. Our study identifies a novel vicious cycle of metabolic reprogramming, M2-like TAM polarization, and endocrine therapy resistance, which involves SGLT1, proposing SGLT1 as a therapeutic target to overcome endocrine therapy resistance in breast cancer.

The intercropping patterns of protected cultivations have been widely used to increase productivity and sustainability in modern agriculture. However, there have been few studies of wolfberry intercropping cultivated by clean tillage. We introduced 10 forages into wolfberry cultivation through land productivity and an interspecific competitiveness analysis, and we screened out the appropriate intercropping modes to provide a scientific basis for wolfberry green cultivation and pasture production. The results showed that the wolfberry–forage intercropping land equivalent ratio (LER) of greenhouse and field tests increased from 29% to 59% and from 62% to 170%, respectively, when compared with the monoculture weighted mean, showing significant yield advantages (p < 0.05), particularly in wolfberry–mangold, wolfberry–ryegrass, wolfberry–alfalfa, and wolfberry–clover. The aggressivity of interspecific competitiveness analysis showed that the forage introduction did not affect the dominant competitive position of wolfberry. In addition, wolfberry–forage intercropping could promote the monetary advantage index (MAI). Wolfberry–mangold, wolfberry–ryegrass, and wolfberry–alfalfa performed well, with MAI values of 827.63, 994.18, and 1918.57 for fruit and 2106.54, 1706.27, and 3103.13 for biomass, respectively. Finally, wolfberry–mangold, wolfberry–ryegrass, and wolfberry–alfalfa were screened out, which can form a new mode of wolfberry and forage production.

Capital outflow during industrialization and urbanization is a primary reason for global rural recession, and China is no exception. Since China focuses on the integrated development of urban and rural areas, urban-rural capital flow affects the transformation and sustainable development of rural areas. However, few studies have focused on this issue. Based on long-term field observations of Wufang Village in Shanghai, we established an analytical framework to describe how urban-rural capital flow promotes rural reconstruction. The research results show that the influx of urban industrial and commercial capital results in market-oriented organization and reconstruction focusing on land, industry, and capital: (1) Land-use optimization changes the land ownership and spatial structure of rural areas and improves the spatial value of rural areas. (2) Industrial development is focused on diverse development and the integration of primary, secondary, and tertiary industries in rural areas. (3) Capital investment is performed by a consortium of state-owned enterprises, private enterprises, and rural collective enterprises—which jointly invest, obtain revenue, and share profits—while considering the balance between attracting capital to rural areas and achieving independent development. The experience of Wufang Village has implications for the rural transformation policies of other large cities in China and other countries in Asia and Africa during urbanization.

In this paper, we propose TSFE-Net, two stream feature extraction networks for active stereo matching. First, we perform extra local contrast normalization (LCN) for dataset due to dependency between speckle intensity and distance. Second, we construct two stream feature extraction layers which consist of convolutional layers and deconvolutional layers in different scales to simultaneously learn the features of the original images and LCN images and aggregate context information to form the left and right features. Third, we convert the obtained depth map into disparity map in virtue of camera parameters to construct a supervised learning model. The TSFE-Net not only solves illumination effects between speckle intensity and distance but also reserves details of the original image. Our dataset are captured by RealSense D435 camera. We research extensive quantitative and qualitative evaluations based on a series of scenes, and achieve the end point error (EPE) accuracy of 0.335 on the TITAN XP platform only for valid pixel. The assessment results show that our network has the ability of real-time deep reconstruction for active pattern.

Massive high-resolution spaceborne synthetic aperture radar (SAR) data are available nowadays, effecting rapid development in the urban remote sensing technology. In this study, we use a set of 27 scenes from TerraSAR-X to reconstruct the three-dimensional (3D) point clouds of a skyscraper-North Star Times Tower in Beijing, China. We study in detail the derivation process of a 3D model of tomographic SAR (TomoSAR) from the two-dimensional (2D) imaging mathematical model of SAR. We observe the presence of a spatial shift in the elevation direction of TomoSAR, which yields poor and even incorrect results when greedy algorithms such as orthogonal matching pursuit (OMP) or regularized orthogonal matching pursuit (ROMP) algorithms are applied. Then, we propose a phase compensation method to eliminate the spatial shift. Both OMP and ROMP algorithms are used for TomoSAR imaging on high-resolution TerraSAR-X data. The experimental results with North Star Times Tower demonstrate the accuracy of the theoretical analysis and the effectiveness of the algorithms. Finally, we compare the ROMP and OMP algorithms and conclude that both algorithms can yield similar reconstruction results in TomoSAR imaging. The ROMP algorithm does not depend on sparsity and can suppress noise through regularization selection at the cost of time. The OMP algorithm introduces noise when the sparsity is inaccurate.

This study describes the synthesis and vacuole-inducing activity of 5-((4-(pyridin-3-yl)pyrimidin-2-yl)amino)-1H-indole-2-carbohydrazide derivatives, including five potent derivatives 12c, 12 g, 12i, 12n, and 12A that exhibit excellent vacuole-inducing activity. Remarkably, 12A effectively induces methuosis in tested cancer cells but not human normal cells. In addition, 12A exhibits high pan-cytotoxicity against different cancer cell lines but is hardly toxic to normal cells. It is found that the 12A-induced vacuoles are derived from macropinosomes but not autophagosomes. The 12A-induced cytoplasmic vacuoles may originate from the endoplasmic reticulum (ER) and be accompanied by ER stress. The MAPK/JNK signalling pathway is involved in the 12A-induced methuotic cell death. Moreover, 12A exhibits significant inhibition of tumour growth in the MDA-MB-231 xenograft mouse model. The excellent potency and selectivity of 12A prompt us to select it as a good lead compound for further development of methuosis inducers and investigation of the molecular and cellular mechanisms underlying methuosis.

Abstract Background Using fluorescence in situ hybridisation (FISH) to detect any gain of chromosomes 3, 7, or 17 and loss of the 9p21 locus has been proven to be sensitive in the diagnosis of pancreatobiliary tumors. However, both genetic and environmental factors contribute to the pathogenesis of pancreatobiliary tumors. Therefore, it is unknown whether this method is suitable for Chinese patients with pancreatobiliary tumors. This study aims to compare the sensitivity, specificity, predictive values and accuracy of cytology, ERCP/MRCP and FISH based on Chinese patients with pancreatobiliary tumors,and to analyze differences between brushing-based and formalin-fixed paraffin-embedded (FFPE)-based FISH. Methods A total of 66 brush cytology specimens obtained during ERCP were detected by FISH and cytology test respectively to compare the sensitivity, specificity, predictive values and accuracy. Besides, FFPE-based FISH was performed on 46 corresponding paraffin sections of pancreatobiliary tumors obtained by surgical resection. Results Our findings demonstrate that FISH greatly improves diagnostic sensitivity and negative predictive value compared to ERCP/MRCP and cytology without much reduction in specificity and positive predictive value. However, our results also indicate that FFPE-based FISH could not effectively identify the false-negative of brushing-based FISH. Conclusions We believe that FISH can effectively distinguish true positive and false positive results of cytological or radiological suspicions of malignancy. However, FFPE-based FISH still does not precisely recognize the false-negative of brushing-based FISH. Both cytology-based and PPFE-based FISH had limitation in some specimens.

This paper proposes an intelligent approach (fuzzy logic) for the design of external-cavity semiconductor lasers (ECLs) for better disturbance rejection. As a kind of nonlinear optical system, ECL output frequency is difficult to be stabilized by conventional methods. The fuzzy logic algorithm is preferred for ECLs stabilization, which could update parameters automatically. Suddenability to reject nonlinear disturbances arch occur in the fuzzy logic systemduring operation, speed of operation and PID gains are alteredonline in accordance with the disturbances to reject. To showthe efficacy of the proposed method, a fuzzy logic ECL stabilization system is designed. The results prove that our method is effective and robust.

Feld's friendship paradox is a widely accepted observation that states that the friends of any particular individual tend to have more friends on average than the individual has. Due to its implications for information transmission, the friendship paradox has become a generalized paradigm in many disciplines. However, how many of an individual's friends have more friends than the individual does is still unknown, yet interesting. In this paper, we revisited the Feld's friendship paradox and we found that only a limited number of a person's friends have more friends than the person herself has. This conclusion was reached from empirical studies using real-world networks and is true regardless of the number of one's neighbours, which contradicts the intuitive deduction from the friendship paradox. For one thing, if a person is unpopular, the number of her friends that have more friends than she does grows with the number of friends that she makes. This observation crystallizes the tenable margin of the friendship paradox. In another case, if a person is popular, as she acquires more friends, fewer of her friends have more friends than she does. This finding suggests an observation bias in the friendship paradox, which makes individuals feel less popular than their friends. Besides enriching our knowledge of the friendship paradox in psychological science, the findings reported here are also beneficial for technical areas such as large-scale triangle discovery. Although the friendship paradox was proposed from the perspective of the number of neighbours, the findings reported here can shed light on theories and applications from different disciplines like information, happiness, obesity, and extroversion, to name a few.

Two-tier massive multiple-input multiple-output (MIMO) systems achieve high sum spectral efficiency by simultaneously serving large numbers of users. However, as the number of service antennas and users tend to infinity, the performance is limited by directed inter-cell and intra-cell interferences. Handling these interferences are challenging due to the large channel dimensionality and the high complexity associated with implementing large precoding/combining matrices. Moreover, two-tier massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO hybrid precoding is applied. In this paper, a manifold learning two-tier beamforming (MLTB) scheme is proposed to enable efficient and low-complexity operation in large scale dimensional MIMO systems. Users of multi-cell are clustered into several regions of user groups by manifold learning. Most of the high-dimensional channels are embedded in the low-dimensional subspace by manifold learning, while retaining the potential spatial correlation of the high-dimensional channels. The nonlinearity of high-dimensional channel is transformed into local linearity to achieve dimensionality reduction. Through proper user clustering, the beamformers are split into outer beamformers and inner beamformers. The outer beamformers can minimize inter-cell interference and the inner beamformers can minimize multi-user interference of intra-groups. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed MLTB scheme can obtain near-optimal sum-rate and considerably higher energy efficiency than the conventional schemes.

Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers' comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages.

Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.

A successive interference cancellation (SIC)-based weighted least-squares (WLS) estimation for the carrier frequency offset (CFO) and the sampling frequency offset (SFO) is presented for wireless local area networks (WLANs) based on orthogonal frequency division multiplexing (OFDM). The proposed SIC-based WLS performs the estimation by exploiting the phase rotation in the frequency domain caused by the CFO and the SFO. SIC-based WLS estimates the CFO and the SFO successively instead of by traditional simultaneous estimation. The estimation of CFO based on the Taylor series is performed first, and then the WLS estimation of SFO based on successive cancellation of the CFO is carried out. The simulation results show that the SIC-based WLS can estimate the CFO and the SFO effectively. Compared to the WLS scheme, a performance improvement of more than 0.6 dB is achieved by SIC-based WLS, and nearly 10 percent of the complexity is reduced.