A Survey on the Status of Smart Healthcare from the Universal Village Perspective

This survey paper discusses the condition of smart healthcare implementation. It discusses the current healthcare problems and how smart healthcare technologies ease the problems. Our group, Universal Village, realizes that the integration and interaction between parties in a system will maximize the effectiveness and benefit for the system. Based on this idea, this paper considers the smart city system as a whole, and talks about how smart healthcare interacts with infrastructures and functions inside and outside of the smart healthcare field. Then, it analyzes how a more powerful integrated system can be built from the smart healthcare system. In the end, several case studies are listed. Based on our analysis and the case studies, this paper then ended with the future prospects of the smart healthcare.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Electrochemical impacts of sheet-like hafnium phosphide and hafnium disulfide catalysts bonded with reduced graphene oxide sheets for bifunctional oxygen reactions in alkaline electrolytes

Non-noble metal-based catalysts with efficient catalytic activities for the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) are critical for energy conversion devices, including fuel cells and metal–air batteries. In this work, novel hafnium phosphide-reduced graphene oxide nanosheets (HfP-rGO NS) and hafnium disulfide-reduced graphene oxide nanosheets (HfS2-rGO NS) were synthesized and investigated as bifunctional electrocatalysts for OER and ORR. The prepared HfP-rGO NS and HfS2-rGO NS catalysts showed nanosheet structures, where the HfP or HfS2 nanosheet was closely packed with rGO. A unique methodology was adopted to lodge the non-metal oxide catalytic sheets (i.e., HfP and HfS2) over the rGO sheets, which positioned the oxide layer on the catalytic sheet surface for instant oxygen evolution. Low intensity X-ray diffraction patterns and Raman spectra confirmed the sheet-like structure of HfP-rGO NS and HfS2-rGO NS. Scanning electron microscope mapping images revealed that all elements (i.e., Hf, P, C and O for HfP-rGO NS and Hf, S, C and O for HfS2-rGO NS) were equally distributed in the synthesized heteroatomic nanosheets. Moreover, both the HfP-rGO NS and HfS2-rGO NS demonstrated excellent durability for both ORR and OER. This outperforms the most state-of-the-art non-precious-metal-based bifunctional catalysts, which is attributed to the synergistic effect of rGO and Hf-based catalysts. The different ORR and OER reaction potentials in HfP-rGO NS and HfS2-rGO NS likely result from the influence of HfP and HfS2

Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Tools

© 2024 The Author(s). This is an open access article under the Creative Commons Attribution-Non Commercial-No Derivatives CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/Smart manufacturing systems are a new paradigm in Industry 4.0 driven by the emerging information and communication technology and artificial intelligence that converge to digital twin, which are able to perceive, recognize, and handle the changes in demand and production. Reconfigurable machine tools (RMTs) can promote the flexibility of smart manufacturing systems. The fundamental problem lies in dynamically reconfiguring the RMTs in smart manufacturing systems efficiently and accurately by considering the flexibility of production precedence and operation sequences simultaneously. Therefore, in this article, a deep reinforcement learning-based reconfiguration planning method of digital twin-driven smart manufacturing systems with RMT is proposed to seek optimal reconfiguration policy online. The reconfiguration processes of smart manufacturing systems are modeled by considering reconfiguration cost, moving cost, and processing cost. Deep Q-network is adopted to explore the state space and action space to find the optimal reconfiguration scheme with the highest return. An industry case study is presented to demonstrate the effectiveness and efficiency of the proposed method, where the reconfiguration processes of a smart manufacturing system consisting of five RMTs for producing four parts are discussed.Peer reviewe

Transcribing Latin Manuscripts in Respect to Linguistics

Current text detection software, although can transcribe modern languages with high accuracy, has flaws detecting texts and transcribing original Latin manuscripts sufficiently. This paper proposes a general approach for transcribing Latin manuscripts in respect to linguistics and develops a system to transcribe Latin manuscripts containing intricate abbreviations, which combines basic object detection algorithms with linguistics. We used methods from image processing and made changes based on the characteristics of Latin.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. To further improve the model's generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over five target languages. The results show that our approach significantly outperforms existing state-of-the-art methods across the board.Comment: This paper is accepted by AAAI2020. Code is available at https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cros

Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning

ObjectivesThe study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining.MethodsThe data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts.ResultsUp to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years.ConclusionsThe study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future

Kosmos-2.5: A Multimodal Literate Model

We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models