Integrating Multiculturalism Into Artificial Intelligence-Assisted Programming Lessons: Examining Inter-Ethnicity Differences in Learning Expectancy, Motivation, and Effectiveness.

Given the current popularization of computer programming and the trends of informatization and digitization, colleges have actively responded by making programming lessons compulsory for students of all disciplines. However, students from different ethnic groups often have different learning responses to such lessons due to their respective cultural backgrounds, the environment in which they grew up, and their consideration for future employment. In this study, an AI-assisted programming module was developed and used to compare the differences between multi-ethnic college students in terms of their theoretical and actual learning expectancy, motivation, and effectiveness. The module conducted analysis through the deep learning network and examined the relevant processes that the students underwent during programming lessons, as well as the types of errors they had committed. Their learning motivation for and actual learning performance in programming were then examined based on the cognitive learning theory. The results of the experiment, which involved 96 multi-ethnic college students, indicated that the two groups had dissimilar theoretical performance in terms of their expectancy and motivation for learning programming. The indigenous students' main concern was whether programming would affect their families or tribes, and this concern affected and was reflected in their learning outcomes. In contrast, the learning motivation and goals of Han Chinese students were driven by the cognition of the value of programming to themselves. The research findings can contribute toward the cognition and understanding of multi-ethnic students when learning computer programming and development of the appropriate teaching methods, and serve as a reference for subsequent research on integrating multiculturalism into computer programming lessons.

Design and Analysis of a Pitch Fatigue Detection System for Adaptive Baseball Learning.

Owing to the rapid development of information and communication technologies, such as the Internet of Things, artificial intelligence, and computer vision, in recent years, the concept of smart sports has been proposed. A pitch fatigue detection method that includes acquisition, analysis, quantification, aggregation, learning, and public layers for adaptive baseball learning is proposed herein. The learning determines the fatigue index of the pitcher based on the angle of the pitcher's elbow and back as the number of pitches increases. The coach uses this auxiliary information to avoid baseball injuries during baseball learning. Results show a test accuracy rate of 89.1%, indicating that the proposed method effectively provides reference information for adaptive baseball learning.

Cultivation of Chlorella vulgaris on anaerobically digested swine manure with daily recycling of the post-harvest culture broth.

In this work, a cultivation system with daily recycling of the post-harvest culture broth was set up and performed in order to reuse the water and nutrients in pretreated anaerobically digested swine manure, which was used as media to cultivate Chlorella vulgaris (UTEX 2714) at different recycling ratios. Results showed that the alga grew well in the system with an accumulative algal biomass and productivity of 1.68-3.47g/L and 234.1-532.2mg/L/d, respectively, at the end of the cultivation. Additionally, chemical compositions in this alga varied with the change of recycling ratios, and the highest productivities of carbohydrate, protein and lipids (76.4, 257.2 and 183.7mg/L/d, respectively) were obtained in the system with a recycling ratio of 1/4 or 1/6. Fatty acid profiles indicated that this alga could be used as a good-quality biodiesel feedstock with a biodiesel productivity of 9.65-40.1mg/L/d.

Improvement on the modified Lowry method against interference of divalent cations in soluble protein measurement.

This paper systematically investigated the interference of calcium and magnesium in protein measurement with a modified Lowry method first proposed by Frølund et al. (Appl Microbiol Biotechnol 43:755-761, 1995). This interference has in the past been largely ignored resulting in variable and unreliable results when applied to natural water matrices. We discovered significant formation of calcium and magnesium precipitates that lead to a decline in light absorbance at 750 nm during protein determination. Underestimation of protein concentration (sometimes even yielding negative concentrations) and low experiment reproducibility were demonstrated at high concentrations of divalent cations (e.g., [Ca(2+)] over 1 mmol L(-1)). To eliminate interference from calcium and magnesium, two pretreatment strategies were established based on cation exchange and dialysis. These pretreatments were convenient and were found to be highly effective in removing calcium and magnesium in protein samples. By using the modified Lowry method with these pretreatments, proteins in standard solutions and in wastewater samples were successfully quantified with good reliability and reproducibility. In addition, we demonstrated that simultaneous quantification of humic substances with the modified Lowry method was not affected by the two pretreatments. These approaches are expected to be applicable to protein and humic substance determination in different research fields, in cases where the modified Lowry method is sensitive to divalent cation concentrations.

An efficient management system for wireless sensor networks.

Wireless sensor networks have garnered considerable attention recently. Networks typically have many sensor nodes, and are used in commercial, medical, scientific, and military applications for sensing and monitoring the physical world. Many researchers have attempted to improve wireless sensor network management efficiency. A Simple Network Management Protocol (SNMP)-based sensor network management system was developed that is a convenient and effective way for managers to monitor and control sensor network operations. This paper proposes a novel WSNManagement system that can show the connections stated of relationships among sensor nodes and can be used for monitoring, collecting, and analyzing information obtained by wireless sensor networks. The proposed network management system uses collected information for system configuration. The function of performance analysis facilitates convenient management of sensors. Experimental results show that the proposed method enhances the alive rate of an overall sensor node system, reduces the packet lost rate by roughly 5%, and reduces delay time by roughly 0.2 seconds. Performance analysis demonstrates that the proposed system is effective for wireless sensor network management.

Multi-hop routing mechanism for reliable sensor computing.

Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms.