Search

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study employed the use of a self-built AI platform (Ladder) for students to systematically learn and apply AI learning model established by the partial least squares (PLS) method to investigate the influence between variables (learning attitudes, self-regulated learning, AI anxiety, individual impact, computational thinking abilities, cognitive styles). This study was particularly conducted in the Department of Computer Science and Information Engineering of a top national university in Southern Taiwan. The valid data were collected from 65 students (55 male students; 10 female students). Furthermore, this study demonstrated the relationship between cognitive style, self-regulated learning and computational thinking. For the first time, it explored the impact of AI anxiety and completed existing research on it. The results of this study show that interest in learning positively affects learning attitudes. In addition, learning attitudes have a positive influence on each individual's performance. Based on multiple theories and the artificial intelligence learning platform, the model proposed in this study effectively understood students' learning status.

Artificial intelligence (AI) education is becoming an advanced learning trend in programming education. However, AI subjects can be difficult to understand because they require high programming skills and complex knowledge. This makes it challenging to determine how different departments of students are affected by them. This study draws on research in programming education and STEM education to explore the different factors that affect students in AI learning. Therefore, the purpose of this study is to investigate the impact of AI learning platforms on information undergraduate and non-information undergraduate by using a research model. The course was implemented for 65 students in the information undergraduate group and 39 students in the non-information undergraduate group. The findings showed that the two groups had different learning effects under different variables. Students with different cognitive styles may use different skills to positively influence self-regulated learning. This study provides important evidence to understand the learning impact of artificial intelligence among university students from different disciplines.

This work proposes a novel pixel circuit using analog pulse width modulation (PWM) for micro light-emitting diode (micro-LED) displays. The proposed circuit generates uniform sweep waveforms and driving currents to control micro-LED emission precisely by compensating for threshold voltage (VTH) variations of p-type low-temperature polycrystalline silicon thin-film transistors (LTPS TFTs) and VDD current-resistance drops (I-R drops). Driven by the PWM method, micro-LED can operate with high wall-plug efficiency (WPE), leading to low power consumption. To analyze the circuit performance, the electrical characteristics of a micro-LED and LTPS TFTs are measured to establish the simulated models. The HSPICE simulation results present that the current error rates and time shift rates of emission currents are below 1.65% and 2.75%, respectively, with ±0.3 V variations in VTH and 1.2 V drops in VDD. Furthermore, the power improvement relative to a 7T1C pulse amplitude modulation (PAM) circuit is higher than 19.05% at all gray levels, confirming that the proposed circuit is suitable for use in micro-LED displays.

This paper proposes a mini-light-emitting diode (mini-LED) driving circuit that is driven by pulse width modulation (PWM) for the backlights of active-matrix (AM) liquid crystal displays (LCDs). The proposed circuit compensates for threshold voltage (VTH) variations of low-temperature poly-crystalline silicon thin-film transistors (LTPS TFTs) and the current-resistance (I-R) rise in VSS lines to supply a stable driving current. Operating the mini-LED at the high luminous efficacy by the PWM driving method and setting only the driving TFT on the path of the driving current reduce the power consumption of the backlight. Based on a 2.89-inch LCD panel with an AMLED $48\times48$ backlit module, the TFTs are fabricated, measured, and fitted. Simulation results show that the relative current error rates are all below 4.67% when the VTH of the driving TFT varies by ±0.3 V and the VSS rises by 0.5 V. The voltage across VDD and VSS of the proposed circuit is 4.5 V lower than that of the 6T1C compensating driving circuit, so the power consumption of the circuit is at least 27.05% lower. Therefore, the proposed driving circuit is well suitable to use in mini-LED backlit LCDs.

Visual programming language is a crucial part of learning programming. On this basis, it is essential to use visual programming to lower the learning threshold for students to learn about artificial intelligence (AI) to meet current demands in higher education. Therefore, a 3-h AI course with an RGB-to-HSL learning task was implemented; the results of which were used to analyze university students from two different disciplines. Valid data were collected for 65 students (55 men, 10 women) in the Science (Sci)-student group and 39 students (20 men, 19 women) in the Humanities (Hum)-student group. Independent sample t-tests were conducted to analyze the difference between cognitive styles and computational thinking. No significant differences in either cognitive style or computational thinking ability were found after the AI course, indicating that taking visual AI courses lowers the learning threshold for students and makes it possible for them to take more difficult AI courses, which in turn effectively helping them acquire AI knowledge, which is crucial for cultivating talent in the field of AI.

This work proposes a new pixel circuit for active-matrix organic light-emitting (AMOLED) smartwatch displays with a low frame rate. Within the long emission period, the leakage current of a low-temperature polycrystalline silicon thin-film transistor (LTPS TFT) is reduced to suppress the distortion of the driving voltage at the gate node of the driving TFT. Based on the measured electrical characteristics of a fabricated p-type LTPS TFT, the HSPICE model is established to verify the feasibility of the proposed circuit. The analytical results indicate that the relative OLED current error rates are all below 4.73%, as the threshold voltage of TFT varies by ±0.5 V. Notably, the OLED current varies by only 2.94% during the emission period of 66.7 ms at a medium gray level, demonstrating the effectiveness of the leakage prevention scheme.

This work proposes a hydrogenated amorphous silicon (a-Si:H) thin-film transistor (TFT) gate driver with a low-leakage capacitive coupling structure to reduce the delay of the clock signal. The proposed circuit suppresses the fluctuation in the gate node of the driving TFT induced by clock-feedthrough effect, reducing the leakage current that flows from the clock signal line to the power source line with a low voltage level. This reduction in the leakage current can improve the degradation in the voltage of the clock signal and thus avoid the increase in the rising time of the scan pulse. Measurements for extracting the drain current versus gate-to-source voltage (ID-VGS) curves of a fabricated a-Si:H TFT are made to establish a simulation TFT model by fitting the obtained curves. For the same parameters of the TFTs and capacitances, the leakage current and the rising time of the output waveform of the proposed circuit are 45.45% and 21.36% lower, respectively, than those of previously developed gate driver.

Background/purpose: We assessed the mobility of single-root teeth by using Miller's mobility index (MMI) and to analyze the validity of MMI for the diagnosis of periodontitis. Materials and methods: A total of 30 patients were included and the Spearman correlation coefficient was used to assess the correlation between MMI, clinical attachment level (CAL), and probing depth (PD). The validity of MMI for the diagnosis of the severity of periodontitis was evaluated using the receiver operating characteristic (ROC) curve, area under curve (AUC) value, positive predictive value (PPV). Results: Strong correlations were observed between MMI and CAL (r = 0.92) and between MMI and PD (r = 0.76). When the CAL = 3–4 mm and CAL ≥5 mm groups were pooled together, the AUC value was 0.81. The AUC was 0.86 for diagnosis with MMI in the CAL ≥5 mm group. A PPV of 100% was achieved for all grades when MMI >1. When the teeth with PD ≥ 5 to

A new capacitive coupling method to enhance the driving performance of hydrogenated amorphous silicon thin-film transistors (a-Si:H TFTs) in high-resolution applications is presented. The gate voltage can be enlarged by the entirely transmitted high voltage of a global direct current power source line (VDD). Established models that are based on the measured electrical characteristics of fabricated a-Si:H TFTs with different aspect ratios are used to evaluate the feasibility of this proposed method in the gate driver. The maximum voltage of the gate voltage can be increased from 37.3 V to 47.6 V when VDD is set to 20 V, improving the driving capability of the gate driver by more than 17%, based on the specifications of a 5.99 inch HD + (720 × 1440) panel at a frame rate of 120 Hz.

The present work deals with the modeling of multi-defected solids under the action of large deformation. A micromechanics constitutive model, formulated in terms of the compressible anisotropic NeoHookean strain energy density function, is presented to characterize the corresponding nonlinear effective elastic behavior. By employing a scalar energy parameter, a correspondence relation between the effective hyperelastic model and this energy parameter is established. The corresponding effective material coefficients are then evaluated through combined use of the “direct difference approach” and the extended “modified compliance contribution tensor” method. The proposed material constitutive model can be further used to estimate the effective mechanical properties for engineering structures with complicated geometry and mechanics and appears to be an efficient computational homogenization tool in practice.

With the advances in mobile network technology, the use of portable devices and mobile networks for learning is not limited by time and space. Such use, in combination with appropriate learning strategies, can achieve a better effect. Despite the effectiveness of mobile learning, students’ learning direction, progress, and achievement may differ. Thus, the enhancement of learners’ opinions on the usability and interactivity during mobile learning are challenging issues to overcome. This study developed a sharing mind map-oriented mobile learning system integrated with valuable information preserved in a digital archiving system. In addition to the functions of traditional mind maps, this system also enabled students to complete and record relevant information that they had found onto the mind map and further improve the integrity of their own knowledge. To investigate the effectiveness of this teaching approach, this study added digital archive data and used mind map sharing to help learners develop knowledge. By using the proposed approach, students were able to perform self-assessment on learning content, choose appropriate learning directions, and progress according to their level of learning. At the same time, they could collaboratively learn with peers to engage themselves more deeply in their learning. That is, their learning motivation could be constantly triggered through the observations and sharing of mind maps from one to another. This study selected sixth graders as its research subjects in two classes at the school where one researcher works. There were 31 and 30 valid samples in the experimental group and control group, respectively, with a total of 61 students. The experimental group was conducted by using sharing mind map with corresponding geographical archived information to investigate the effectiveness of sharing mind map (SMM) in mobile learning; on the other hand, the control group was conducted by using a traditional learning approach. The outcomes indicate that students’ learning performance could be enhanced by using archived information SMM mobile learning.