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ABSTRACTMultiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method’s performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies.

In this study, a novel nanocomposite of graphene oxide-zinc oxide-manganese (GO-ZnO-M) was successfully synthesized using a modified Hummers method and hydrothermal technique. Enhanced electrochemical performance was observed in the GO-ZnO-M nanocomposite when utilized as an electrode material for the detection of rutin, a flavonoid known for its significant health benefits. The GO-ZnO-M modified electrode demonstrated excellent sensitivity, selectivity, reproducibility, and stability during rutin detection. Furthermore, the sensor exhibited a wide linear detection range and a low detection limit. The practical applicability of the GO-ZnO-M/GCE sensor was assessed by detecting rutin in pharmaceutical tablet samples, yielding satisfactory results. This study highlights the potential of GO-ZnO-M nanocomposites as promising materials for the electrochemical detection of rutin and other biologically significant compounds.

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm’s ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.

Long noncoding RNA (lncRNAs) are involved in the pathogenesis of ulcerative colitis (UC). Moxibustion, a traditional Chinese medicine, can improve symptoms in patients with UC and reduce intestinal inflammation in rats with UC. However, it remains unclear whether the ameliorative effect of moxibustion on intestinal mucosal inflammation in UC is related to lncRNAs. Thirty-two rats were randomly assigned to four groups: normal control, UC, moxibustion (MOX), and sulfasalazine (SASP). The UC rat model was induced by administering 4% dextran sulfate sodium (DSS) in drinking water. Rats in the moxibustion group underwent bilateral Tianshu (ST25) moxibustion using the herbs-partition moxibustion method. Rats in the sulfasalazine group received SASP solution via gavage twice daily for seven consecutive days. Our results revealed that, compared with the UC group [2.00 (1.00, 2.50)], the DAI score [0.25 (0.00, 0.50)] was significantly lower in the MOX group (P

The occurrence of geohazards entails sudden, unpredictable, and cascading effects, with numerous conceptual frameworks and intricate spatiotemporal relationships existing between hazard events. Presently, the absence of a unified mechanism for describing and expressing geohazard knowledge poses substantial challenges in terms of sharing and reusing domain-specific knowledge pertaining to geohazards. Therefore, it is imperative to address the issue of constructing a cohesive descriptive model that facilitates the sharing and reuse of geohazard knowledge. In this study, we propose a multilayered ontology construction method tailored specifically for the domain of landslide geological hazards. By comparing existing methods, we establish a hierarchical structure and expression framework for the geological hazard ontology. Notably, our approach seamlessly integrates the conceptual and semantic layers in the relationship description at each level, enabling association representation of hazard data across multiple tiers. We define essential concepts and attributes related to landslide geological hazards, along with their respective interrelationships. To achieve effective knowledge sharing and reuse, we model the ontology of the landslide geological disaster domain using the Web Ontology Language (OWL). This modeling approach serves as a powerful tool that facilitates the sharing and reuse of disaster-related knowledge. Finally, we verify the method's validity and reliability by employing illustrative case studies. The results demonstrate that the proposed approach imposes an affordable workload on human resources. Additionally, the foundational domain ontology significantly enhances information retrieval performance, thereby yielding satisfactory outcomes.

The natural convection disappears and the melting rate of phase change material (PCM) decreases in the microgravity environment of spacecraft operation. For the characteristics of low thermal conductivity of phase change materials, a triple casing based on sinusoidal function type corrugated structure as latent heat storage system (TTES) is proposed to improve the melting performance of the latent heat storage system. In this paper, the study model is simplified to a 2D model using commercial CFD software to numerically simulate the phase change heat transfer process, and the heat transfer characteristics of the sinusoidal function type corrugated triple casing and the effect of Reynolds number on the melting process are investigated. The results show that: the joint action mechanism of sinusoidal corrugation structure and phase change material local heat transfer can significantly shorten the heat charging time; reasonable change of corrugation period and crest height can shorten the heat charging time and increase the heat storage; comprehensive analysis of heat charging time, heat storage and average heat flux, through single-factor optimization to obtain a sinusoidal pipe with corrugation period between 100 mm and 150mm and crest height around 15 mm on The comprehensive performance of flow heat exchange has the best effect.

BackgroundStudies on the efficacy of acupuncture and auricular acupressure on sleep disturbances in cancer patients have been growing, but there is no specific and comprehensive systematic review and meta-analysis. This review aims to evaluate the efficacy and safety of acupuncture and auricular acupressure on sleep disturbances in cancer survivors based on existing randomized clinical trials (RCTs).MethodsFour English-language and four Chinese-language biomedical databases were searched for RCTs published from database inception to July 30, 2021. RCTs comparing acupuncture and auricular acupressure with sham control, drug therapy, behavior therapy, or usual care for managing cancer were included. The quality of RCTs was appraised with the Cochrane Collaboration risk of bias (ROB) tool. Mean differences (MDs) and 95% confidence intervals (CIs) were calculated for the effect sizes.ResultsThirteen RCTs with 961 patients were included. The risk of performance bias or reporting bias for most of the included trials was high or unclear. Evidence was not found for short-term effects on sleep scales compared to sham control (MD, 1.98; 95% CI, 0.33–3.64; p = 0.02; I2 = 36%), wait list control (MD, 0.40; 95% CI, −0.87–1.68; p = 0.54; I2 = 49%), drug therapy (MD, 1.18; 95% CI, −3.09–5.46; p = 0.59; I2 = 98%). For long-term effect, two sham-controlled RCTs showed no significance of acupuncture on insomnia scale scores (MD, 1.71; 95% CI, −2.38–5.81; p = 0.41; I2 = 89%). Subgroup analyses suggested no evidence that auricular acupressure (MD, 3.14; 95% CI=1.52, 4.76; p = 0.0001; I2 = 0%) or acupuncture (MD, 0.54; 95% CI=−1.27, 2.34; p = 0.56; I2 = 0%) was associated with the reduction in insomnia scale scores.ConclusionsThis systematic review and meta-analysis found no evidence about acupuncture or auricular acupressure in the improvement of sleep disturbances in cancer survivors in terms of short- or long-term effect. Adverse events were minor. The finding was inconsistent with previous research and suggested that more well-designed and large-scale randomized controlled trials are needed to identify the efficacy of acupuncture and auricular acupressure for sleep disturbances in cancer survivors.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, CRD42020171612.

China is considered to be a nation with the largest number of netizens and online fandom throughout the world. The power of netizens cannot be overlooked, particularly with regard to the digital mobilization of nationalism. In a recent event regarding a geographic map controversy spawned by the TV series of “Go Go Squid!” many netizens became fan patriots and activists for territorial integrity. This TV series caused tremendous resistance due to its showing an incomplete Map of China in one of its episodes on 31st July 2019. This research deploys three research methods of netnography (digital scraping of fan expression), a qualitative interview and commentary analysis to examine fans’ engagement upon this sensitive issue in China. The fans of the series and its main character naturally divided into different groups to show their opposing standpoints. With the help of Chinese featured cyberspace, Chinese netizens were hierarchically grouped as either loyal fans of the series, general/ordinary fans of the actress, or fan patriots and fan activists of the nation. Ambivalent attitudes from state media intensified the debates to call for a much more robust mechanism to guide cyber fandom.

To ensure the equity of exclusive bus lane (EBL) allocation under multimodal traffic conditions, a bi-level programming model is first constructed. The upper-level model is the minimum total system cost considering the Gini coefficient and the lower-level model constructed a stochastic user equilibrium (SUE) model based on logit loading. Secondly, a heuristic algorithm combining an improved genetic algorithm (GA) and a method of the successive average method (MSA) is designed. Finally, the Nguyen and Dupuis networks are used as examples to verify and analyze the effectiveness, superiority and sensitivity of the model and algorithm. The results show that the method can effectively obtain the optimal solution of the upper-level model as 15,004 RMB, the Gini coefficient is 0.31, and the equity is at a relatively reasonable level. Compared with the different allocation schemes, the proposed scheme has a higher bus sharing rate and lower Gini coefficient. At the same time, when the actual demand is twice the basic demand, the bus share rate is the largest, 65%, and the Gini coefficient is the smallest at 0.3. The bus share rate decreases with the increase in the proportion of high time value travelers, which fully verifies the sensitivity of the model to the type of traveler.

Soil pollution is a serious threat to human life and development. Different remedial measures are applied to soils with different levels of contamination. The degree of soil contamination in different areas is generally evaluated and categorised based on the analysis of samples. Regional soil sampling sites are generally sparse because of the cost of sampling and other factors, which makes it difficult to accurately assess the extent of regional soil contamination. In this study, a spatial classification model was established for the Di(2-ethylhexyl) phthalate (DEHP) pollution level using a Bi-directional Long Short-Term Memory (Bi-LSTM) neural network considering that the sampling information gradually diminishes with increasing distance between the sampling and prediction points. In this study, a method is proposed for the prediction of the spatial distribution of soil pollution categories based on sparse samples. We also established a model for the spatial distribution of organic pollution categories. The analysis of an actual contaminated area shows that the DEHP concentrations at different locations can be effectively predicted with the proposed method by categorising the contamination levels of specific DEHP samples. The results show that the method can be used to classify the degree of light/severe DEHP contamination. The results are in good agreement with the actual situation, verifying the validity of the method. This method is important for the rapid assessment of the spatial distribution of soil contamination levels based on sparse sampling.

Abstract Background Acupuncture, as an important component of traditional Chinese medicine (TCM), has been widely applied in the treatment of chronic insomnia in China, while there is no clinical study related to its therapeutic mechanism. Methods/design A single-center, single-blind, randomized, placebo-controlled trial will be conducted at Jiangsu Hospital of Traditional Chinese Medicine. A total of 60 patients will be registered. Eligible participants will be randomly divided into acupuncture group and sham acupuncture group (n = 30 cases in each group). Patients in both groups will be treated once every other day, three times per week for 4 weeks. The primary outcome measures are Pittsburgh Sleep Quality Index (PSQI) and concentrations of adrenocorticotropic hormone (ATCH), corticotrophin-releasing hormone (CRH), and cortisol (CORT). Secondary outcome measures are Insomnia Severity Index (ISI) and Fatigue Severity Scale (FSS). Discussion This study aims to evaluate the therapeutic effects of acupuncture on chronic insomnia by using PSQI, ISI, and FSS. The mechanism of acupuncture on CIPs will be preliminarily discussed by analyzing the changes in concentrations of CRH, ACTH, and CORT before and after treatment. Trial registration Chinese Clinical Trials Register, ChiCTR1800020298.

Objectives To develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.Design A nationally representative retrospective study.Setting Representative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.Participants Representative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measures In-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).Results A total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.Conclusions The models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.

The rhizosphere bacterium Bacillus cereus AR156 is capable of forming biofilms, killing nematodes, and protecting plants. However, the underlying molecular mechanisms of these processes are not well understood. In this study, we found that the isogenic mutants ΔBcspo0A and ΔBcsinI have significantly reduced colonization and nematicidal activity in vitro and biological control efficacy on the tomato plant under greenhouse conditions. We further investigated the role of the spo0A-sinI-sinR regulatory circuit in biofilm formation, killing against nematodes, and biological control in AR156. Results from mutagenesis of those regulatory genes in AR156 and their heterologous expression in B. subtilis suggested that the spo0A-sinI-sinR genetic circuit is not only essential for biofilm formation and cell differentiation in AR156 but also able to functionally replace their counterparts in B. subtilis in a nearly indistinguishable fashion. Genome-wide transcriptional profiling in the wild type and the ΔBcspo0A and ΔBcsinI mutants further revealed hundreds of differentially expressed genes, likely positively regulated by both Spo0A and SinI (via SinR) in AR156. Among them, 29 genes are predicted to be directly controlled by SinR, whose counterpart in B. subtilis is a biofilm master repressor. Collectively, our studies demonstrated the essential role of the spo0A-sinI-sinR regulatory circuit in biofilm formation, cell differentiation, and bacteria–host interactions in B. cereus AR156.

Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.

Under the background of the rapid development of The Times, the material conditions, living standards and education level of teenagers have been greatly improved, so the healthy development of teenagers is easily affected by the external environment. In a rapidly developing society, adolescents are affected by academic burden, changes in lifestyle, and stress from family patterns, which bring major challenges to the development of adolescent mental health. Psychological problems such as depression, anxiety and social phobia are on the rise among teenagers. Early educators pay more attention to the development of adolescent mental health, but with the increasing prominence of adolescent mental health problems, mental health is a special aspect of today's society and the country pays more attention to. To this end, promoting the development of adolescent mental health has become the research direction of many experts and scholars, many of which will focus on the field of sports, because sports have better operability, mass and fun, experts and scholars expect to improve the mental health problems of adolescents through sports. Cuju is a traditional Chinese health sport, which consists of kicking, touching and kicking. Based on the integration of systematic review and metaanalysis, this paper reviewed the research on the influence of Cuju on adolescents' mental health, and confirmed the intervention effect of traditional Chinese ball games on adolescents' mental health through research. Objective: To explore the influencing factors of the effect of ball game intervention on adolescent mental health. [ABSTRACT FROM AUTHOR]

Introduction: The incidence of kidney cancer (KCa) in Canada is rising. Despite this, there is a shortage of research assessing KCa care experiences. This study aims to explore the current experiences of KCa survivors related to treatment and management, information provision, and barriers to care. Methods: A cross-sectional, descriptive study of KCa patients was conducted online and through various cancer centers across Canada. English- and French-speaking adults who received a KCa diagnosis and were currently undergoing treatment or had completed treatment in Canada were eligible to participate. Results: In total, 368 surveys were completed. Ten percent of respondents had not yet received treatment, 29% were receiving treatment, and 56% had completed treatment. Most respondents (72%) had localized KCa (stage 0-3) at diagnosis. Sixty-one percent of respondents reported that their doctors discussed various treatment options with them and 24% reported discussing applicable clinical trials. Most (85%) respondents received information about their KCa and 36% discussed where to get information about their disease and support. The most commonly reported barriers to care were side effects (26%), system delays (26%), not having access to certain treatments (25%), and financial burden (24%). More participants in Central Region and Quebec (p=0.004) and rural/suburban (p=0.014) areas reported lacking access to certain treatments and KCa experts. Conclusions: This was the first large-scale study to explore access to care experiences of Canadian KCa survivors. Results show examples of good patient-centered care and provide new practical information that can inform efforts to improve patient-centered care for KCa patients. [ABSTRACT FROM AUTHOR]

In this work, a new finite element (FE) model calibration method of concrete dams based on strong-motion records and multivariate relevant vector machines (MRVM) is proposed. The modal features of a dam are extracted using second order blind identification (SOBI) based method at first. For some selected combinations of uncertain parameters of the FE model using the Latin hypercube design, the corresponding structural modal features are calculated using the finite element method (FEM). With these data, a procedure to calibrate the uncertain parameters of a dam's dynamic FE model is developed. By taking the uncertain parameters as inputs and the calculated structural modal features using FEM as outputs, the MRVM model is trained to record the complex relationship between them. Then, the genetic algorithm (GA) is adopted to solve the optimization problem corresponding to the dynamic FE model calibration problem, and the trained MRVM model, instead of FEM, is used to obtain the modal parameters of a dam for different feasible solutions during the optimization search process to improve the computational efficiency. Using the simulated seismic response records of a numerical example the accuracy, robustness and computation efficiency of the proposed dynamic FE model calibration method is verified. The analysis result using the strong-motion records of a realistic concrete dam indicates that the proposed dynamic FE model calibration method has good performance. [ABSTRACT FROM AUTHOR]