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Conventional nursing courses have solely adopted lecture-based instruction for knowledge delivery, which tends to lack interaction, rehearsal, and personalized feedback. The development of chatbot technologies and their broad application have provided an opportunity to solve the abovementioned problems. Some knowledge-based chatbot systems have been developed; however, it is still a challenging issue for researchers to determine exactly how to effectively apply these chatbot technologies in nursing training courses. Intending to explore the application mode of chatbot technologies and their effectiveness in nursing education, this study integrated a knowledge-based chatbot system into the teaching activities of a physical examination course, using smartphones as the learning devices, and guiding students to practice their anatomy knowledge in addition to analyzing their learning efficacy and pleasure. A quasi-experiment was conducted by recruiting two classes of university students with nursing majors. One class was the experimental group learning with the knowledge-based chatbot system, while the other class was the control group learning with the traditional instruction. Based on the experimental results, the knowledge-based chatbot system effectively enhanced students' academic performance, critical thinking, and learning satisfaction. The results indicate that the application of chatbots has great potential in nursing education.

Purpose: This study aimed to examine determinants of undernutrition among children under 2 years of age. Design and Methods: A matched case‐control study design was conducted to recruit 300 mothers comprising 100 mothers with an undernourished child (case group) and 200 mothers with a healthy child (control group). Measurements consisted of demographic characteristics of children data, mother's data, household data, mother's knowledge of child undernutrition, mother's knowledge of nutrition, complementary feeding practices, and undernutrition parameters of the children. A conditional logistic regression was used to identify determinants of undernutrition. The risk of undernutrition was estimated by odds ratios (ORs) and 95% confidence intervals (CIs). Statistical significance was defined as any p value of <.05. Results: Findings showed that mother's knowledge of undernutrition (AOR: 0.95; 95% CI: 0.91−0.98), mother's knowledge of nutrition (AOR: 0.90; 95% CI: 0.85−0.96), and mother's knowledge (AOR: 0.78; 95% CI: 0.67−0.91) and behavior of complementary feeding practices (AOR: 0.97; 95% CI: 0.94−0.99) were significant determinants of undernutrition (p <.05). Practice Implications: Mothers with high scores on knowledge of undernutrition, knowledge of nutrition, and knowledge and behaviors of complementary feeding practices would benefit the children under 2 years to reduce the risk of undernutrition. Healthcare professionals (i.e., pediatric nurses and community health nurses) should provide early assessment of knowledge related to undernutrition, nutrition, and complementary feeding practices for mothers with children under 2 years. [ABSTRACT FROM AUTHOR]

Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators. [ABSTRACT FROM AUTHOR]