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关键词： 交互式健康教育   运动疗法   腹膜透析   运动功能   自护能力  

摘要： 目的探讨交互式健康教育联合运动疗法对腹膜透析患者运动功能、自护能力及生活质量的影响。方法选取2021年6月至2023年6月潜江市中心医院收治的80例行腹膜透析治疗患者为研究对象,采用随机数表法将其分为对照组、观察组,每组各40例,对照组予以常规护理措施,观察组在上述护理基础上加用交互式健康教育联合运动疗法,比较两组患者干预前后运动功能[6 min步行试验(6MWT)、10次坐立秒数]、自护能力(ESCA)及生活质量(SF-36)评分。结果干预后,两组患者6MWT、10次坐立秒数显著改善,且观察组6MWT高于对照组,10次坐立秒数少于对照组,差异有统计学意义(P<0.05)。干预前后,两组ESCA、SF-36量表各维度评分及总分评分显著改善,且观察组上述指标高于对照组,差异有统计学意义(P<0.05)。结论交互式健康教育联合运动疗法有助于改善腹膜透析患者运动功能,提高自我护理能力,提升生活质量,值得临床推广应用。

关键词： 新工科   实验班   计算机科学与技术专业   实践教学改革  

摘要： “新工科”背景下,为解决地方高校计算机科学与技术专业存在的技术快速发展与人才培养模式之间存在的问题,辽东学院该专业以实验班为依托,通过改革教学模式,调动学生主观能动性,探索专业理论知识与实际生活应用的结合、学校教学与行业工作需求相结合、学习内容与工作岗位相结合的三结合模式,有效地提高了学生就业的核心竞争力。

关键词： STEM理念   人工智能   混合式教学   实践教学   多元化与智能评价  

摘要： 随着人工智能等新兴技术与高等教育的深度融合,高校教学改革成了提高人才培养质量的关键。本文结合当前高校计算机专业教学存在的问题,从混合式教学模式、实践教学平台、多元化与智能评价机制等多方面,提出了STEM理念下人工智能技术与计算机专业教学融合的改革策略,为计算机专业教学改革提供了新思路和借鉴价值。

关键词： 立德树人   五育并举   计算机学科   研究生教育   思政教育  

摘要： 新时代背景下,五育并举理念在推进教育现代化、建设教育强国中起到愈发重要的作用。研究生作为高层次储备人才,是国家发展、民族复兴的重要力量和基础保障,思政教育作为其培养中的重要一环,更应坚持以立德树人为根本任务,落实五育并举,促进人才全面发展。为此,构建五育并举视域下思政教育新模式,使五育并举赋能高校研究生思想政治教育模式创新,对焕发时代意义具有尤为重要的意义。天津大学智能与计算学部在工作中坚持强化思想引领,问需学生诉求,把握学科特色,通过分析学生在成长成才中遇到的问题,以学生喜闻乐见的活动形式,拓宽思政教育与智育、体育、美育、劳育间的协同方式,探索符合计算机学科研究生思政教育的新路径,使思政教育更具针对性。

关键词： Task analysis   Encoding   Resource management   Termination of employment   Machine learning algorithms   Vectors   Optimization   Deep reinforcement learning (DRL)   distributed coded machine learning (DCML)   worker selection   workload allocation  

摘要： Machine learning (ML) has been widely applied to successfully address a variety of different problems across diverse domains, such as robotics, healthcare, and finance. However, high-complexity ML algorithms often require overlong computation time, which significantly impacts their feasibility. Distributed ML (DML) has been used to tackle the slow computation problem with high-complexity ML algorithms. Nevertheless, with DML, the computation results from all participating computing devices need to be collected in order to complete an ML task. When part of the participating devices, known as the stragglers, cannot return their results in time, the overall computation time will be extended. Distributed coded ML (DCML) is a promising solution to mitigate the negative impact of the stragglers. With DCML, redundancy is injected into an ML task so that only a subset of the results from participating devices are required to finish the ML task. In DCML, how to select proper participating devices, referred to as workers, and how to allocate appropriate workloads to the selected workers are two challenging problems. In this article, we consider a DCML scenario where numerous computing devices are available for an ML task. These devices are willing to offer their computation capacity in exchange for compensation. To encourage the computing devices to participate in the distributed computation, a reverse auction-based incentive mechanism is employed. With the objective of minimizing both the completion time of the ML task and the compensation for participating devices, we propose a deep-reinforcement-learning-based workload allocation and worker selection scheme for DCML, DAS. To our knowledge, this is the first attempt to simultaneously tackle both the workload allocation and worker selection issues in DCML. Our experimental results indicate that DAS outperforms the state-of-the-art schemes in terms of completion time and compensation.

关键词： 中华优秀传统文化   高校思政   游戏   融合创新  

摘要： 中华优秀传统文化是中华民族宝贵的精神财富,对高校思想政治教育具有重要意义。游戏作为新兴文化载体,正成为大学生喜闻乐见的娱乐方式。本文通过分析《黑神话:悟空》体现的中华优秀传统文化元素及其育人价值,提出高校思政工作应重视游戏文化的引导,挖掘游戏蕴含的思政资源,创新教学方式方法,增强教育的吸引力和感染力,推动中华优秀传统文化与高校思政工作创新融合。

关键词： Dispatching   Real-time systems   Time factors   Mobile computing   Computer science   Feature extraction   Reinforcement learning   On-demand delivery   time-constrained delivery   concurrent order dispatching   reinforcement learning  

摘要： On-demand delivery has experienced rapid growth in recent years, revolutionizing people's lifestyles with its timeliness and convenience. The order dispatch process in on-demand delivery is concurrent, wherein couriers continuously accept new orders and deliver them to customers within strict time constraints and dynamic demand and supply. Most of the existing order dispatch mechanisms are designed for independent dispatch or concurrent dispatch without strict deadlines, rendering them unsuitable for real-time concurrent dispatch in on-demand delivery. To address the challenge, we propose a Time-Constrained Actor-Critic Reinforcement learning based concurrent dispatch system called TCAC-Dispatch to reduce the overdue rate and enhance the long-term revenue. Specifically, we first design a deep matching network (DMN) with a variable action space, which integrates both states embedding (including route behaviors encoding) and actions' embedding into a long-term value for dispatching decisions. Additionally, we design a time-constrained action pruning module to ensure compliance with time constraints. Then we utilize the Actor-Critic framework to tackle the concurrent dispatch considering strict time constraints and stochastic demand-supply. To further optimize the efficiency and delivery resource utilization, we propose an extension of TCAC (i.e., TCAC+), which consists of (i) a learning-based order service time prediction module to determine whether to relax the deadline of some orders;and (ii) a multi-critic framework to optimize concurrent order dispatch with both tight deadlines and relaxed deadlines using dynamic weighting mechanism. We evaluate the TCAC-Dispatch with one-month data involved with 36.48 million orders and 42,000 couriers collected from Eleme, one of the largest on-demand delivery companies in China. Experiments are conducted on a data-driven emulator deployed on the development environment of Eleme and the results demonstrate that our method outper

关键词： Matrix decomposition   Indexes   Computer science   Task analysis   Silicon   Learning systems   Laplace equations   Graph propagation   incomplete multiview clustering (IMVC)  

摘要： In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://***/CLiu272/TNNLS-PGP.

关键词： Drug resistance   Infectious diseases   Indigenous health   Rural health services  

摘要： Background: Rates of antimicrobial resistance (AMR) for some pathogens in Australia are considerably higher in rural and remote compared to urban regions. The inaugural Hot North Antimicrobial Academy was a 9-month educational programme aimed to build workforce knowledge and capacity in antimicrobial use, audit, stewardship, surveillance and drug resistance in remote primary health care. Methods: The Academy was advertised to Aboriginal and Torres Strait Islander, regional and remote healthcare workers. Participants were Aboriginal health practitioners, nurses, pharmacists and doctors from Queensland, Northern Territory, South Australia and Western Australia working in remote primary health care with a focus on Indigenous health. Due to COVID-19 restrictions, the Academy ran virtually from February-November 2021 using Microsoft Teams. The Academy was evaluated using surveys and yarning circles to assess impact and knowledge gain. Results: Participants and faculty from across Australia attended 19 lectures and mentorship sessions. Eleven participants commenced and eight (73%) completed the Academy. The Academy raised participants awareness of AMR guidelines, governance and generating change;built confidence in advocacy;grew knowledge about drug resistant infections;and created a community of AMR champions in Indigenous health. Conclusion: The evaluation confirmed the Academy met the needs of participants, provided opportunities to move stewardship from tertiary hospitals into Indigenous and remote clinics and developed skills in research, audit, stewardship and advocacy for all involved. All sessions were recorded for future use, with facilitation by the National Aboriginal Community Controlled Health Organisation (NACCHO) in future years. 2024 The Author(s). Published by Elsevier B.V. on behalf of Australasian College for Infection Prevention and Control. This is an open access article under the CC BY-NC-ND license (http:// creativecommo