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Vocational nursing educators are regularly challenged to broaden the global perspective of students who have neither studied abroad nor experienced different cultures and to integrate international health care elements into English as a medium of instruction (EMI) nursing classes to increase student transcultural literacy and enhance their learning motivation. In this study, a course developed based on the Comprehensive Learning System was used to plan an EMI perinatal bereavement class, which was subsequently taught using a team-based cooperative learning approach. EMI course content should focus on topics closely related to the life experiences of students and transcultural themes to promote resonance through the interactive process of team-based cooperative learning and increase learning motivation and interest. In the developed EMI class, the students expressed a desire to improve their English-language proficiency, gained a preliminary understanding of transcultural perinatal bereavement care, and gave p

對於技職護理教育者而言，如何讓世界走進尚未有機會進行海外學習，體驗不同文化的學生之生活，增加這群學生對世界的認識，在英語授課的護理課程中融入國際健康照護元素，藉以增加學生的跨文化素養，並提升學生願意使用英語學習護理專業知識的風氣與動機，這是值得思考與努力的方向。本課程的學習者為技職護理系的學生，這群學生已經具有產科護理學的先備知識，但大部分尚未學習周產期傷慟關懷之相關內容。本課程是運用綜合學習系統進行英語授課（English as a medium of instruction, EMI）周產期傷慟關懷課程之規劃，以小組合作學習方式進行。建議在課程內容設計上，可選擇與學生生活歷練有關或是跨文化的主題，透過小組合作學習的互動產生共鳴，進而引發學習動機與興趣，未來有機會能運用在護理專業中。在此次EMI教學過程中，學生們表達希望提升自身英文程度的渴望，同時也習得對不同文化的周產期傷慟關懷之初步認識，對這堂課則有正向的肯定與回饋，此皆可做為EMI護理課程之參考。

A Learning Management System (LMS) facilitates the implementation and effectiveness of online learning by providing sufficient tools for the organization, delivery, and administration of online courses. Traditional learning platforms lack diverse learning styles and limit the ability to provide personalized attention and support. However, the factors affecting the online learning experience are fuzziness and uncertainty. The research articles propose a Fuzzy Analytic Hierarchy Evaluation System (FAHES) that integrates an Analytic Hierarchy Process (AHP) and a Fuzzy Comprehensive Evaluation System (FCES) to analyze the efficiency of online courses over existing challenges. A key component of FAHES is its decision-support facilities that incorporate fuzzy Logic, which allows educational institutions to make well-informed decisions when providing recommendations. AHP aims to create a structured hierarchy out of complex criteria and sub-criteria, allowing for pairwise comparisons and combining expert opinions, resulting in weights that reflect these priorities. Then, the FCES aggregates weighted inputs from multiple criteria and analyzes fuzzy data to evaluate the performance management of online courses. Thus, the model provides a clear and actionable evaluation by methodically assessing complex online learning systems while including and managing subjectivity and ambiguity. This approach provides a clear understanding of different factors influencing learning outcomes, interaction efficiency, personalized recommendations, and learner satisfaction through a multi-level evaluation framework. Hence, online course design and delivery improvement is achieved by providing practical insights and suggestions derived from the evaluation findings.

This paper presents an optimal active power dispatch (OAPD) problem that, unlike common economic dispatch problems, precludes unwanted mismatches on realistic power systems. The OAPD is formulated by considering the unified power flow controller (UPFC), a versatile device from the flexible AC transmission systems. However, the resultant turns into a highly nonlinear and complex optimization problem, which requires a powerful evolutionary algorithm to determine the optimal solutions. Toward this end, this paper explores the use of comprehensive learning particle swarm optimization and differential evolution as a hybrid configuration in a fuzzy framework, called hybrid fuzzy-based improved comprehensive learning particle swarm optimization-differential evolution, to address the proposed problem. To demonstrate the performance of the proposed algorithm, a set of benchmark problems, including real-world constrained optimization problems as well as a profound analysis of Schwefel problem 2.26 are provided. Moreover, to authenticate its effectiveness in solving power and energy-related problems with quite a few decision variables, four different power systems, 3-unit, 6-unit IEEE 30-bus, 10-unit, and 40-unit systems, are implemented. The IEEE 30-bus system is opted for profoundly analyzing the performance of the proposed algorithm in handling the optimal power dispatch problem considering security constraints and UPFC device, where an enhancement, at least $74,000 saving in a 365-day horizon, in total generation cost is obtained. Simulation results also validate that evolutionary algorithms need to be improved/hybridized to achieve better equilibrium between exploration and exploitation processes in a timely manner while solving power and energy-related problems.

The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word “ASSISTments” blends tutoring “assistance” with “assessment” reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013–4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra “Put the teacher in charge, not the computer.” This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about what the platform supports and does not support. In conclusion, goals for the future will be presented.

Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of artificial intelligence (AI) because of its versatility and effectiveness. As an emerging force in the revolutionary trend of deep learning, Siamese networks shine in UAV-based object tracking with their promising balance of accuracy, robustness, and speed. Thanks to the development of embedded processors and the gradual optimization of deep neural networks, Siamese trackers receive extensive research and realize preliminary combinations with UAVs. However, due to the UAV’s limited onboard computational resources and the complex real-world circumstances, aerial tracking with Siamese networks still faces severe obstacles in many aspects. To further explore the deployment of Siamese networks in UAV-based tracking, this work presents a comprehensive review of leading-edge Siamese trackers, along with an exhaustive UAV-specific analysis based on the evaluation using a typical UAV onboard processor. Then, the onboard tests are conducted to validate the feasibility and efficacy of representative Siamese trackers in real-world UAV deployment. Furthermore, to better promote the development of the tracking community, this work analyzes the limitations of existing Siamese trackers and conducts additional experiments represented by low-illumination evaluations. In the end, prospects for the development of Siamese tracking for UAV-based AI systems are deeply discussed. The unified framework of leading-edge Siamese trackers, i.e., code library, and the results of their experimental evaluations are available at https://github.com/vision4robotics/SiameseTracking4UAV.

Deep neural networks (DNNs) have made significant achievements in a wide variety of domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient solutions, including graphics processing units (GPUs), central processing units (CPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuit (ASIC). Nonetheless, CPUs outperform other solutions including GPUs in many cases for the inference workload of DNNs with the support of various techniques, such as the high-performance libraries being the basic building blocks for DNNs. Thus, CPUs have been a preferred choice for DNN inference applications, particularly in the low-latency demand scenarios. However, the DNN inference efficiency remains a critical issue, especially when low latency is required under conditions with limited hardware resources, such as embedded systems. At the same time, the hardware features have not been fully exploited for DNNs and there is much room for improvement. To this end, this paper conducts a series of experiments to make a thorough study for the inference workload of prominent state-of-the-art DNN architectures on a single-instruction-multiple-data (SIMD) CPU platform, as well as with widely applicable scopes for multiple hardware platforms. The study goes into depth in DNNs: the CPU kernel-instruction level performance characteristics of DNNs including branches, branch prediction misses, cache misses, etc, and the underlying convolutional computing mechanism at the SIMD level; The thorough layer-wise time consumption details with potential time-cost bottlenecks; And the exhaustive dynamic activation sparsity with exact details on the redundancy of DNNs. The research provides researchers with comprehensive and insightful details, as well as crucial target areas for optimising and improving the efficiency of DNNs at both the hardware and software levels.

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the exploration of rain removal from videos or a single image has drawn considerable attention in the field of image processing. Recently, various deraining methodologies have been proposed. However, no comprehensive survey work has yet been conducted to summarize existing deraining algorithms and quantitatively compare their generalization ability, and especially, no off-the-shelf toolkit exists for accumulating and categorizing recent representative methods for easy performance reproduction and deraining capability evaluation. In this regard, herein, we present a comprehensive overview of existing video and single image deraining methods as well as reproduce and evaluate current state-of-the-art deraining methods. In particular, these approaches are mainly classified into model- and deep-learning-based methods, and more elaborate branches of each method are presented. Inherent abilities, especially generalization performance, of the state-of-the-art methods have been both quantitatively and visually analyzed through thorough experiments conducted on synthetic and real benchmark datasets. Moreover, to facilitate the reproduction of existing deraining methods for general users, we present a comprehensive repository with detailed classification, including direct links to 85 deraining papers, 24 relevant project pages, source codes of 12 and 25 algorithms for video and single image deraining, respectively, 5 and 10 real and synthesized datasets, respectively, and 7 frequently used image quality evaluation metrics, along with the corresponding computation codes. Research limitations worthy of further exploration have also been discussed for future research along this direction.

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGIewm) and FCE (CGIfce) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGIfce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R2) values of ELM-CGIewm and ELM- CGIfce increased by 20.83% and 20.37%, respectively. (3) The CGIfce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGIfce), with R2 reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGIfce (PSO-ELM-CGIfce), and the precision is significantly improved compared with that before optimization, with R2 reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.

This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students’ mathematics performance. We define progress in three areas: improved student cognition , engagement, and affect , and we attribute this improvement to specific components and interventions that are inherently affective , cognitive , and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students’ metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and student self-reports, and supported through affective learning companions and progress reports. Collectively, these studies elucidate a suite of effective strategies to support advanced personalized learning via an intelligent adaptive tutor that can be tailored to the individual needs, emotions, cognitive states, and metacognitive skills of learners.