Phase field study of the tip operating state of a freely growing dendrite against convection using a novel parallel multigrid approach

Alloy dendrite growth during solidification with coupled thermal-solute-convection fields has been studied by phase field modeling and simulation. The coupled transport equations were solved using a novel parallel-multigrid numerical approach with high computational efficiency that has enabled the investigation of dendrite growth with realistic alloy values of Lewis number ∼104 and Prandtl number ∼10−2. The detailed dendrite tip shape and character were compared with widely recognized analytical approaches to show validity, and shown to be highly dependent on undercooling, solute concentration and Lewis number. In a relatively low flow velocity regime, variations in the ratio of growth selection parameter with and without convection agreed well with theory

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.Comment: 10 pages. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, United Kingdom, ACM, 201

KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.Comment: 9 page

A deep learning framework for multi-scale models based on physics-informed neural networks

Physics-informed neural networks (PINN) combine deep neural networks with the solution of partial differential equations (PDEs), creating a new and promising research area for numerically solving PDEs. Faced with a class of multi-scale problems that include loss terms of different orders of magnitude in the loss function, it is challenging for standard PINN methods to obtain an available prediction. In this paper, we propose a new framework for solving multi-scale problems by reconstructing the loss function. The framework is based on the standard PINN method, and it modifies the loss function of the standard PINN method by applying different numbers of power operations to the loss terms of different magnitudes, so that the individual loss terms composing the loss function have approximately the same order of magnitude among themselves. In addition, we give a grouping regularization strategy, and this strategy can deal well with the problem which varies significantly in different subdomains. The proposed method enables loss terms with different magnitudes to be optimized simultaneously, and it advances the application of PINN for multi-scale problems

Assessment of Urban Community Emergency Preparedness and Response Capacity Using Entropy Weight Method and Multilayer Fuzzy Comprehensive Model

Public emergencies are occurring with increasing frequency worldwide, leading to what is often referred to as a high-risk society. Amid various public emergencies, urban communities take on a more prominent role. However, studies on assessing the emergency preparedness and response capacity of urban communities are currently limited. To evaluate the essential factors, both inherent and external, that affect the emergency management capacities of urban communities, an index system for assessing community emergency preparedness and response capacity was developed by using the theory of the emergency management cycle. Acknowledging the complexity and ambiguity of emergency preparedness and response capacity within urban communities, a multilayer fuzzy comprehensive evaluation model was established using the entropy weight method. This model was then applied to assess the emergency preparedness and response capacity of communities against the backdrop of the COVID-19 public emergency. Results show that the assessment outcomes generated by the multilayer fuzzy comprehensive evaluation model, employing the entropy weight method, align with the real-world situation, signifying the soundness and effectiveness of the selected methods and index system. The conclusion offers a novel basis and methodology for evaluating the emergency preparedness and response capacity of urban communities, holding considerable practical value

Abordaje fisioterapéutico según guía apta 3.0 en paciente con mielitis transversa, Ibarra, provincia de Imbabura 2022-2023

Desarrollar una propuesta de abordaje fisioterapéutico según guía APTA 3.0 en paciente con Mielitis Transversa en Ibarra, Provincia de Imbabura.La Mielitis Transversa (MT) es una enfermedad inflamatoria y desmielinizante aguda o subaguda de la médula espinal, que los casos reportados apenas representan el 7,86 por cada 100.000 personas, siendo de muy baja incidencia pero genera en muchos casos una discapacidad con secuelas. Por lo que, varios artículos de evidencia actual muestran a la fisioterapia como una herramienta crucial para mejorar el cuadro de pronóstico. El objetivo de la investigación fue desarrollar una propuesta de abordaje fisioterapéutico según Guía APTA 3.0 en paciente con mielitis transversa en base a las necesidades integrales y específicas. El siguiente caso clínico, se realizó en la ciudad de Ibarra, Provincia de Imbabura, Ecuador; en una paciente con mielitis transversa con diversas disfunciones física provocadas por enfermedad, se evaluó los Sistemas Neuromuscular, Musculoesquelética, Cardiovascular y Pulmonar, Integumentaria y Comunicación, afecto, cognición, lenguaje y estilo de aprendizaje. Matizando principalmente al Sistema Neuromuscular, siendo este de mayor predominancia de afección que, como resultado de diagnóstico presenta un Patrón H según la Guía APTA 3.0, dando así un pronóstico una discapacidad en retroceso y funcionalidad en progreso. Por lo que, en base a sus necesidades el objetivo del plan de tratamiento fisioterapéutico lograr una activación muscular de cintura pélvica para mejorar la bipedestación mediante el apoyo de miembros superiores en barras de sujeción.Licenciatur

Reliability analysis of elastic graphite packer in heat injection well during oil shale in-situ conversion

Heat injection well reaches temperatures above 400 ◦C during the process of heat injection, Heat injection is essential for oil shale in-situ conversion technology. The downhole of the and part of the high-temperature gas dissipates through the wellbore annulus. Consequently, in addition to causing energy loss, the dissipation causes thermal damage to the casing and wellhead. To avoid dissipation, components that are suitable for high-temperature environments should be sealed and used during heat injection while mining. Therefore, this study presents the design of a packer composed of elastic graphite rubber and a high-temperature-resistant material. The influence of numerous factors, such as downhole temperature, working load, and height of rubber, on the reliability of the packer was analyzed. Subsequently, the numerical simulation analysis of the packer reliability in in-situ conversion mining under high temperature and pressure environments was performed. The results indicate that when the operating temperature is stable, the operating load has the most obvious influence on the sealing reliability of the packer, whereas the change in the height of the rubber has the least significant effect on the maximum contact stress between the casing and rubber. The change in the operating temperature has the least significant effect on the overall sealing performance of the packer. Moreover, the rise of the temperature will increase the sealing reliability of the packer, and on the contrary, the drop in the temperature will decrease it.Cited as: Guo, W., Shui, H., Liu, Z., Wang, Y., Tu, J. Reliability analysis of elastic graphite packer in heat injection well during oil shale in-situ conversion. Advances in Geo-Energy Research, 2023, 7(1): 28-38. https://doi.org/10.46690/ager.2023.01.0