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Multiple 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. [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]

• A new sub-domain model and overall model for the static analysis of doubly-curved arch (DCA) structure is established by using the spline subdomain method (SSW) method, and the numerical examples of this model match well with the results of the numerical examples of Abaqus finite element software. • Numerical formulas for SSM analysis of DCA structures were derived. • The effects of different structural dimensions and material properties on the static performance of DCA were investigated. A static computational study of a new type of Doubly-Curved Arch (DCA) structure using the Spline Subdomain Method (SSM) has been conducted and a new subdomain and generalized model for the static analysis of compression DCA structures have been developed. The equations for the numerical calculations by SSM are derived. The model is based on the SSM for the subdomain division of DCA. For the discretization of the flat shell subdomain, two sets of uniformly distributed spline nodes are used along the neighboring edges of the rectangular projection of the shallow shell subdomain. The linear combination of the product of these two specially designed cubic B-spline interpolation functions is the displacement field. This model generates its overall structural stiffness matrix and load vector of the DCA by direct assembly following the discretization of the spline in the shallow shell subdomain. This method is characterized by high modeling efficiency, low computational cost, and a wide range of applications for the analysis of irregular shell structures. Abaqus software is used to verify the performance of the suggested method through numerical examples. The findings demonstrate that non-regular DCA structures subjected to static compression can have their mechanical properties analyzed using the suggested method. [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]