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Exercise can effectively slow aging and prevent the onset and reduce the complications of chronic diseases in the elderly. However, roughly one-third of older adults are inactive. Joyful learning is an effective method for promoting physical activity, while using games is a feasible strategy for achieving joyful learning that enables individuals to fully immerse themselves in and enjoy an activity. Therefore, exploring gaming strategies to enhance physical activity among the elderly is worthwhile. In this paper, a set of gaming strategies based on the literature and practical experience is proposed. The name of this strategy, GAME, is an acronym of the following: goal setting and educational content development (G), activity design and game content creation (A), mechanics and dynamics implementation (M), and evaluation of qualitative and quantitative health outcomes (E). This systematic descriptive approach helps clearly demonstrate how gamification strategies can promote physical activity and health in older adults. The aim of this paper is to provide a reference and guide for education, research, and clinical practice in health promotion programs targeting older adults. [ABSTRACT FROM AUTHOR]

Copyright of Digital Education is the property of Haiyan Publishing Co. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Distance Education (1672-0008) is the property of Zhejiang Open University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the scarcity of defect samples in industrial applications, this paper proposed a method for segmenting surface defects in metal workpieces with only a minimal number of required defect samples. The method combined image generation techniques with a semi-supervised learning strategy. It utilized small-sized defect patches, and extracted from a minimal number of defect images to train a defect generation model. Subsequently, the method integrated these generated defect images into normal images to facilitate data augmentation. Additionally, the method applied a semi-supervised learning strategy to train the segmentation network, aiming to mitigate the adverse effects of differences between generated and real data distributions. The experimental phase involved conducting tests on a real-world computer vision detection system for metal workpieces. The results demonstrate that the semi-supervised training strategy significantly enhances the segmentation model's generalization ability to real defects. The method achieves high segmentation accuracy using only five defect sample images. [ABSTRACT FROM AUTHOR]

Path-coverage oriented testing is a crucial method in software testing, and the rapid generation of high-quality test data to satisfy path coverage requirements has been a persistent research challenge. To address issues such as long running times, unstable exploration processes, and the generation of redundant test cases in existing intelligent optimization methods, this paper proposed a selection strategy based on the reinforcement learning paradigm applied to test data generation with path coverage as the criterion. By defining executable paths as the state of the intelligent agent, it defined the data selection after each iteration update as the agent's action. It associated the reward function with state changes, and employed a greedy strategy during the state update process to guide input data towards continuous variations in unexplored states. This iterative selection process aimed to continuously choose data that covered new executable paths, thereby achieving the goal of covering all execution paths of the target program. Experimental results demonstrate that compared to other algorithms, the proposed strategy significantly reduces running times and iteration counts while achieving notable improvements in coverage. Theoretical analysis supports the conclusion that the proposed strategy effectively realizes path coverage and enhances the efficiency of test data generation in practical applications. [ABSTRACT FROM AUTHOR]

The traditional differential evolution（DE） algorithm balanced global exploration and local exploitation inadequately, and had problems with easily falling into local optimal solutions, low solution accuracy and slow convergence speed. Therefore, this paper proposed a differential evolution algorithm based on random neighborhood mutation and optimal opposition-based learning（RNODE） and analyzed for its complexity. Firstly, the algorithm generated a random neighborhood for each individual in the current population, and used the global best individual to guide the neighborhood best individual to generate a composite basis vector, combined with an adaptive update mechanism of the control parameters to constitute a random neighborhood mutation strategy, which enabled the algorithm maintained its exploration ability and guided the population towards the optimal direction. Secondly, to further help the algorithm jump out of the local optimum, the algorithm performed the optimal opposition-based learning strategy on the poorer individuals to expand the search area. Finally, this paper compared RNODE with 9 algorithms to verify the effectiveness and advancement of RNODE. The experimental results on 23 benchmark functions and 2 real-world engineering optimization problems show that the RNODE algorithm has a higher convergence accuracy, faster speed and a greater stability. [ABSTRACT FROM AUTHOR]

As the traditional IOAs couldn’t deal with interpolation smoothing RPP problems well and MPA proposed recently had advantages and disadvantages, this paper proposed an improved MPA, named IEMPA, to solve RPP. Firstly, this algorithm used a global best opposition learning strategy to update the position of a predator selected randomly, so as to reduce the probability of falling into local optima. Then it proposed a three-stage best guiding worst strategy to strengthen the worst individual in order to improve the whole group and the search ability. Then, it applied an information sharing strategy in an early preying phase to further improve the search ability. Finally, this paper integrated IEMPA and interpolation smoothing strategy into RPP. A large number of optimization experiments on classical functions and multi-scene RPP problems show that IEMPA has stronger search ability, higher accuracy and faster convergence than MPA and other excellent algorithms, and it can cope with RPP better and will be applied to other complex optimization problems. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of School Administrators is the property of School Administration Research Association, R.O.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Supervised-learning based person re-identification require a large amount of manual labeled data, which is not applicable in practical deployment. This paper proposes a support pairs active learning（SPAL） re-identification framework to lower the manual labeling cost for large-scale person re-identification. Specifically, this paper build a kind of unsupervised active learning framework, and in this framework it designs a dual uncertainty selection strategy to iteratively discover support pairs and requires human annotations. Afterwards, it introduces a constrained clustering algorithm to propagate the relationships of labeled support pairs to other unlabeled samples. Moreover, a hybrid learning strategy consisting of an unsupervised contrastive loss and a supervised support pairs loss is proposed to learn the discriminative feature representation. On large-scale person re-identification dataset MSMT17, compared with the state-of-the-art method, the labeling cost of the proposed method is reduced by 64%, mAP and Rank1 are increased by 11.0% and 14.9% respectively. Extensive experiments demonstrate that it can effectively lower the labeling cost and is superior to state-of-the-art unsupervised active learning person re-identification methods. [ABSTRACT FROM AUTHOR]

Aiming at the hybrid flow shop scheduling problem with the objective of minimizing the makespan, this paper proposed an opposite artificial bee colony algorithm with opposition-based learning to solve the problem. First, according to the characteristics of hybrid flow shop scheduling problem, this paper established the corresponding mathematical model and simulation optimization model. Secondly, in order to avoid the problem of local optima in the process of optimization, this paper added the opposition-based learning strategy in the three stages of population initialization, employment bee and observation bee, used the reverse order strategy between two points and the element exchange strategy to speed up the optimization, then used the elitist preservation strategy to retain the optimal solution. Finally, the simulation experiment selected two examples and 21 benchmark problems of different scales, the results of simulation experiment verified that proposed algorithm is effective in solving such problems by comparison with the experimental of related algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this nursing project is to improve the nursing integrity rate of the patient after oral free flap surgery, and the quality of medical care. Nursing staff in the burn intensive care unit of a medical center had failed to detect and report abnormal changes of the oral flap tissue in time, which caused irreversible damage to the oral free flap. After investigation through observation and examination paper, it was confirmed that the problem was successfully addressed by holding physical on-the-job education courses, drawing cartoon pictures of the monitoring process after oral free flap surgery, designing mnemonic phrase and limericks, recording multi-media teaching videos and uploading them to a digital learning platform, and drawing 2D stereoscopic images for teaching and auditing systems. The correcting rate of the nursing care cognition of oral free flap surgery had increased from 79.9% to 100%, while the care integrity rate has increased from 79.1% to 97.5%. Through effective countermeasures, this project not only improves the care integrity rate, but also improves overall satisfaction. It is expected to be implemented to relevant units to promote overall care quality and maintain patient's safety. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of artificial intelligence (AI) technology, especially the application of deep learning (DL), the detection and quantitative evaluation of typical imaging markers of small cerebral vascular disease (CSVD) has been accelerated and the accuracy has been improved. In recent years, it has attracted much attention in the field of medical imaging. This paper intends to summarize the research progress and problems of deep learning in the imaging markers of CSVD such as cerebral microbleeds (CMBs), white matter hyperintensities (WMH), enlarged perivascular space (EPVS), lacunes, recent small subcortical infarcts (RSSI) and cerebral atrophy, so as to provide support for the precise treatment of CSVD. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the shortcomings of the basic Gravity search algorithm（GSA）, such as prone to premature maturity, easy to fall into local optimum, and lack an effective acceleration mechanism, this paper proposed a GSA（IABHGSA） based on the improved adaptive black hole mechanism. The algorithm used the improved Tent mapping to initialize the population, which makes the distribution of the initial population more random, uniform and traversal, and it enhanced the global exploration capability of the algorithm; The algorithm introduced an improved adaptive black hole mechanism and selected the position update strategy, according to the evolution of the particles, which makes the position update more reasonable, Effectively reduce the possibility of particles falling into local optima; Through the optimal and worst particle update strategy based on learning ideas, enhanced the algorithm’s ability to escape from local optima and improved the algorithm’s optimization speed; The algorithm introduced group migration to provide effective accelerated convergence mechanism for the algorithm. Finally, this paper selected 8 benchmark functions to test IABHGSA and compared with the experimental results of related algorithms, the results show that IABHGSA has better optimization performance. [ABSTRACT FROM AUTHOR]

For the university course timetable problem (UCTP), this paper proposes a parallel iterative local search algorithm based on multi-view learning strategy to solve it. The algorithm designs a multi-neighborhood set with eight base neighborhoods according to the characteristics of UCTP, and formulates the base neighborhood selection probability setting rules based on the increase speed ratio. In the iterative local search process, a multi-view learning strategy with view pooling is presented to fuse the search results of different local search and the search direction is adjusted in time to improve the search efficiency. The algorithm is optimized through the idea of parallel computing to improve the convergence speed of multi-view search. Experimental results show that the proposed algorithm has better solution accuracy, and has excellent scalability and parallel efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the existing multi-label learning techniques consider the correlation learning problem but ignore the inconsistency feature of structural properties, which can change the structural property of the label space obtained by function mapping and it can directly affect the classification performance. In order to deal with this problem, this paper proposed a multi-label classification algorithm based on structural property preserving policy and correlation learning policies. Firstly, the algorithm constructed a mapping function to realize the mapping between the feature space and the label space. Secondly, it used structural property preserving strategy based on feature data to reduce the difference between feature data caused by linear transformation. Finally, this paper introduced the correlation learning strategy based on label data to further optimize the parameters of the proposed algorithm to improve the classification performance. The experimental results through a group of benchmark instances show that compared with the other classical multi-label classification algorithm, the proposed algorithm is much superior in classification performance, and the algorithm has great effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Facial local detail information plays an important role in facial expression recognition（FER）.However, most of the existing methods only focus on the high-level semantic information of facial expressions, while ignoring the fine-grained information of local facial regions. To solve this problem, this paper proposed a deep convolutional neural network fusing local feature and two-stage attention weight learning（FLF-TAWL）,which could adaptively capture important facial regions to improve the effectiveness of facial expression recognition. The FLF-TAWL model was composed of a dual-branch framework, one branch extracted local features from image blocks, and the other branch extracted global features from the entire expression image. Firstly, this paper proposed a two-stage attention weight learning strategy. In the first stage, it roughly learned the importance weights of global and local features, in the second stage, it further refined the attention weight, and fused the local and global features. Secondly, the model used a region-biased loss function to encourage the most important regions to obtain higher attention weights. Finally, this paper carried out extensive experiments on FERPlus, Cohn-Kanada（CK+） and JAFFE datasets to obtain accuracy rates of 90.92%,98.90% and 97.39% respectively. The experimental results verify the effectiveness and feasibility of the FLF-TAWL model. [ABSTRACT FROM AUTHOR]

In order to optimize the performance of the multi-verse algorithm to solve the optimal value of function, this paper proposed a global optimization multiverse algorithm（G-MVO） with an improved search mechanism. Aiming at the defects of the standard algorithm, which was easy to fall into local optimum and premature convergence due to the single search mechanism, this paper proposed three learning strategies to enhance the algorithm performance: through multi-strategy interaction and cooperation to reduce the complexity of the algorithm and improve the accuracy of the solution, designing adaptive parameters to dynamically choose the best strategy and optimizing the performance of the algorithm globally. In order to verify the effectiveness of the G-MVO algorithm, simulation experiments were carried out on 8 benchmark functions of different dimensions. Experimental results show that the proposed algorithm has better solution precision and convergence speed. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing graph auto-encoders can't capture the context information between nodes of the graph, this paper proposed adaptive graph auto-encoder based on restarted random walk. It firstly constructed a two-layer graph convolutional network to encode the topology and features of the graph. At the same time, it carried out the restarted random walk to capture the context information between nodes. Next, it designed the adaptive learning strategy to aggregate the representations obtained by restarted random walk and graph convolutional network. It can adaptively assign weights according to the importance of the two representations. To prove the effectiveness of the proposed method, this paper applied the final representations of the graph to the task of node clustering and link prediction. The experimental results show that the proposed method achieves more advanced performance compared with the baseline methods. [ABSTRACT FROM AUTHOR]

Critical node detection plays an important role in understanding and controlling complex network systems, thus its application potential is enormous and has received extensive attention. However, the NP-hardness of the critical node detection problem (CNDP) makes precise solutions extremely challenging. To address this issue, this paper proposes a critical node detection algorithm based on the moth-flame optimization algorithm (MFOCNP) . The algorithm introduces strategies such as Opposition-Based Learning (OBL) to accelerate convergence speed, and proposes methods such as fast population evolution (FPE) and Hybrid Gaussian evolution (HGE) to improve solution quality and overcome local optima traps, thereby enhancing solution space exploration capabilities. To verify the algorithm’s performance, numerous comparative experiments were conducted, and in 100% of synthetic network experiments and 95% of real network experiments, the MFOCNP algorithm outperformed all other algorithms,demonstrating the robustness of the MFOCNP algorithm. The effectiveness of the FPE and HGE mechanisms of the algorithm was also validated. [ABSTRACT FROM AUTHOR]

Unsupervised commonsense question answering is a question answering model that uses the machine to automatically generate question-answering data. There are some problems in the question-answering data generated by current methods, such as noise data and random difficulty of questions. This paper proposed an unsupervised commonsense question-answering model based on curriculum learning. Firstly, it generated a question-answering dataset according to knowledge, then evaluated the diversity and fluency of the question answering dataset, and filtered the data by combining the two evaluation results to remove noise data. Finally, according to the course learning strategy, it used the similarity between the interference item and the correct answer as the difficulty evaluation standard to train the model according to the difficulty level. The accuracy of the test tasks is improved by 1.5%～3.5%, which proves that the model is effective in unsupervised commonsense question-answering tasks. [ABSTRACT FROM AUTHOR]

Unsupervised commonsense question answering is a question answering model that uses the machine to automatically generate question-answering data. There are some problems in the question-answering data generated by current methods, such as noise data and random difficulty of questions. This paper proposed an unsupervised commonsense question-answering model based on curriculum learning. Firstly, it generated a question-answering dataset according to knowledge, then evaluated the diversity and fluency of the question answering dataset, and filtered the data by combining the two evaluation results to remove noise data. Finally, according to the course learning strategy, it used the similarity between the interference item and the correct answer as the difficulty evaluation standard to train the model according to the difficulty level. The accuracy of the test tasks is improved by 1.5%～3.5%, which proves that the model is effective in unsupervised commonsense question-answering tasks. [ABSTRACT FROM AUTHOR]

To achieve accurate and stable contactless heart rate estimation under lighting changes and head motion conditions, this paper proposes an end-to-end heart rate estimation model called rPPG-UNet, which is based on the UNet model and incorporates a multi-head self-attention mechanism. The model realizes the extraction and reconstruction of physiological features by using the U-shaped encoder-decoder network structure, and uses Skip Connection to connect the encoder and decoder to realize the multiplexing of shallow temporal features. The model also incorporates a multi-head self-attention mechanism to capture the temporal dependencies of physiological features. Finally, the model adopts a multi-task learning strategy to improve the accuracy of heart rate estimation and accelerate network training. Experimental results on public datasets show that rPPG-UNet outperforms other baseline models and can achieve more accurate contactless heart rate estimation. [ABSTRACT FROM AUTHOR]

Aiming at the problem of "over-exploitation" in dimensional learning strategy (DLS), this paper proposed a multiswarm collaborative particle swarm optimization algorithm with comprehensive dimension learning (CDL-MCPSO). The cluster structure of master-slave paradigm divides the population into a master group and four slave groups, in which the master group executes a comprehensive learning strategy to conduct a large-scale exploration in the search space, and the slave groups execute a comprehensive dimensional learning strategy (CDL) to exploit high-precision solutions near the local optimal. The master-slave groups can effectively achieve the balance between exploration and exploitation by executing algorithms with different functions. At the same time, in order to maintain the diversity of the population, the CDL-MCPSO proposed a new solution exchange mechanism (SEM), By doing this, the algorithm can accomplish information exchange and cooperation after the master-slave groups running their respective algorithms for several generations independently, so as to guide the particles to conduct more accurate searches in the later stage. Finally, for the high randomness of the initialization process, the algorithm adopted latin hypercube sampling to reconstruct the input distribution. In order to verify the effectiveness of CDL-MCPSO, compared with 5 kinds of PSO variants in 10 test functions. the results show that the algorithm can always find better or equivalent solutions. It is feasible and efficient in solving complex functions. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of COA, such as slow convergence speed, low solution accuracy and being easy to fall into local optimum, this paper proposed an improved coyote optimization algorithm based on dual strategy learning mechanism and adaptive chaotic mutation strategy ( DCSCOA ). Firstly, it adopted an oscillatory decline factor to generate diverse individuals for enhancing the global search ability. Secondly, it proposed a dual strategy learning mechanism to appropriately increase the influence of the group head wolf, so as to balance the local search ability and global search ability of the algorithm, and to improve the solution accuracy and convergence speed of the algorithm. Finally,it used an adaptive chaotic mutation mechanism to generate new individuals when the algorithm stagnates, so as to make the algorithm jump out of the local optimum. Through simulation experiments on 20 basic test functions and 11 CEC2017 test functions, the results show that the improved algorithm has higher solution accuracy,faster convergence speed and stronger stability. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of crow search algorithm ( CSA), this paper proposed a crow search algorithm using multi-mode flight ( MFCSA) . Based on the strength of foraging ability, the algorithm divided the population into two groups: strong and weak foraging ability. Those with strong foraging ability adopted the strategy of trailing and tracking the optimal target of the current group, and flied to the vicinity of the current optimal position of the group under the guidance of the group information to carry out search activities, which enhanced the local exploitation ability of the algorithm. Those with weaker foraging ability adopted the two strategies of observing and learning the foraging methods of the strong, and flying away quickly when encountering danger, the former could improve the global exploration ability of the algorithm, and the latter could maintain the diversity of the population. Through the numerical experiment simulation of 15 benchmark test functions and 2 engineering application problems, the results show that the MFCSA has better performance in optimization accuracy, convergence speed, etc., enhances the ability to avoid falling into the local optimum, and has better stability. [ABSTRACT FROM AUTHOR]

The Ministry of Education launched the University Social Responsibility (USR) Program in 2018. Since then, universities have actively promoted inter-disciplinary cooperation and implemented highend knowledge to meet community needs. Universities are using social responsibility to enhance the level of social participation engaged in by their students to achieve the sustainable development goals set by the United Nations. The aim of this paper was to share the experience of the Taipei Medical University (TMU) system in practicing social responsibility by integrating TMU resources with those of TMU-affiliated hospitals. The USR program at Taipei Medical University promotes the reform of school governance through encouraging university faculties to collaborate with hospital physicians to develop a specific curriculum that implements an innovative social-prescribing model. This curriculum is designed to provide opportunities for students to step out the classroom and into the community. Through clinical observations, home visits, and community service, students gain a deep understanding of community health issues from residents in the surrounding neighborhoods. Medical humanities education is achieved through students' involvement in both patient individual care and community health issues. In addition, faculties guide students in reflective learning from holistic healthcare perspectives. Students are encouraged to use evidence-based methods to develop innovative models of care in the community. Medical staff participation in this program helps ensure that social prescribing to community residents accurately reflects professional assessments. These prescriptions refer patients to local health promotion units involved in community collaboration. The medical university students build a bridge between the medical system and the community to implement the goals of health education. The impact of social prescribing on the social determinants of health is known to be significantly greater than that of medicine prescriptions. The expected outcomes of this project are to reduce the health inequalities experienced by vulnerable groups in the community and to share Taiwan's experiences with the academic community worldwide. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies, including machine learning, deep learning, natural language processing, generative AI, the metaverse and other iterations, are rapidly changing the landscape of education. Related technologies not only enhance the teaching and learning process but also improve the quality and availability of educational content. AI facilitates educational transformation, reshapes teaching models, and helps students achieve their personalized learning needs, thus improving learning outcomes, learning efficiencies, and teaching practices. Despite the many AI application cases in nursing management and clinical practice, the application of AI in nursing education remains in its infancy. Machine learning has been used to predict the academic performance and graduation results of nursing students, thereby facilitating the early identification of additional support needs. Natural language processing technology has been used to develop chatbots and virtual teachers to assist learning, providing personalized learning support to help students overcome learning obstacles. Also, generative AI technologies such as ChatGPT (chat generative pre-trained transformer) have been used to create simulated patient cases and as a tool for grading academic writing automatically. Moreover, the combination of generative AI technology and the metaverse has introduced new possibilities to nursing education, allowing students to learn in a more-immersive virtual environment. Despite the significant benefits brought by AI to nursing education, its implementation and integration still face multiple challenges, including high costs, the need to provide technical training to teachers, and the need to address issues such as academic integrity and data privacy. The authors hope this article will help promote interdisciplinary cooperation between nursing educators and information and communication experts and the development of AI-assisted teaching to open a new chapter in nursing education. [ABSTRACT FROM AUTHOR]

In order to solve the problems of easily fall into the local optimal value, low convergence accuracy and slow convergence speed in the basic slime mould algorithm（SMA）,this paper put forward an improved ISMA based on elite opposition-based learning and quadratic interpolation. Elite opposition-based learning strategy was conducive to improve the diversity and quality of population, improved the global optimization performance and convergence accuracy of the algorithm. It used quadratic interpolation to generate new individuals and fitness evaluation to update the global optimal solution was propitious to enhance the local development ability, reduced the convergence time and made the algorithm jump out of the local extremum. By solving multiple unimodal, multi-modal and high-dimensional test functions, the results show that the ISMA combined with the two strategies has higher optimization accuracy, optimization speed as well as robustness. [ABSTRACT FROM AUTHOR]

By analyzing the performance of biogeography-based optimization (BBO), this paper proposed a dual biogeographybased optimization based on hybrid convex migration and optimal Cauchy mutation (DuBBO). The algorithm used the dynamic mixed convex migration operator made it converge to the optimal solution quickly. In the mutation mechanism, the algorithm used optimal mutation and Cauchy distribution random number to help the algorithm jump out of the local optimal solution. Finally, the algorithm added the dual learning strategy to speed up the convergence speed and improve ability. The experimental results on 23 benchmark functions show the effectiveness and necessity of the three strategies. Compared DuBBO with BBO and other six excellent improved algorithms. The experimental results show that DuBBO has the best overall performance, faster convergence speed and higher convergence accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)