Kalasin University Journal of Science Technology and Innovation

A Causal Model of Factors Affecting Government Nurses’ Intentions to Use Artificial Intelligence for Counseling in Thailand

2026-06-12T10:43:35+07:00

This study aimed to develop and validate a causal model and to examine the direct and indirect effects of factors influencing nurses’ intention to use artificial intelligence (AI) for counseling. The research instrument was a questionnaire whose content validity was evaluated by three experts. The Item–Objective Congruence (IOC) indices for all items ranged from 0.67 to 1.00. The reliability of the instrument was assessed using Cronbach’s alpha coefficients ranging from .935 to .968. Data were collected through an online questionnaire administered to 396 public-sector nurses selected using a proportionate multistage sampling technique. Descriptive statistics, including mean scores, were used for data analysis. Structural Equation Modeling (SEM) was conducted using LISREL software to test the proposed causal model.

The findings indicated that the proposed causal model fit the empirical data well (χ² = 22.18, df = 19, p = .28, χ²/df = 1.17, RMSEA = .02, RMR = .01, and CFI = 1.00). Regarding total effects, environmental factors exhibited the strongest influence on nurses’ intention to use AI for counseling (TE = .94), followed by personal factors (TE = .13), both of which were statistically significant at the .01 level. In terms of direct effects, environmental factors exerted the greatest direct influence (DE = .85), followed by personal factors (DE = .67), with both effects reaching statistical significance at the .01 level. Regarding indirect effects, environmental factors had the strongest indirect influence through personal factors, with an effect size of .09, which was statistically significant at the .01 level. Together, environmental factors (ENV) and personal factors (PEF) explained 90% of the variance in public-sector nurses’ intention to use AI for counseling (OBI).

Effects of Dried Azolla Meal as a Substitute for Soybean Meal in Diets of Labeo rohita

2026-05-21T13:43:00+07:00

This study aimed to evaluate the effectiveness of dried Azolla (Azolla pinnata) as a replacement for soybean meal in the diets of rohu fish (Labeo rohita). The experiment consisted of four dietary treatments with three replicates per treatment. Experimental diets were formulated by replacing soybean meal protein with dried Azolla at levels of 0, 20, 40, and 60%, resulting in crude protein contents of 29.42, 29.37, 27.77, and 27.15%, respectively. Rohu juveniles with an average initial weight of 4.91 g were reared for 90 days in cages (2 × 2 × 1 m) at a stocking density of 100 fish per cage. At the end of the experiment, the average weight gain was 20.91 ± 3.76, 19.54 ± 2.69, 21.37 ± 2.74, and 21.95 ± 1.78 g for the 0, 20, 40, and 60% replacement groups, respectively. The average daily gain was 0.23 ± 0.06, 0.20 ± 0.02, 0.28 ± 0.07, and 0.25 ± 0.05 g day⁻¹, while the specific growth rate was 1.82 ± 0.25, 1.70 ± 0.13, 2.01 ± 0.27, and 1.90 ± 0.19% day⁻¹, respectively. Survival rates were 41.00 ± 3.06, 46.67 ± 2.51, 56.00 ± 19.67, and 47.33 ± 4.10%, respectively. Feed conversion ratios were 2.40 ± 0.37, 2.61 ± 0.25, 2.18 ± 0.34, and 2.29 ± 0.25, while feed efficiency values were 0.42 ± 0.06, 0.38 ± 0.03, 0.46 ± 0.07, and 0.43 ± 0.05, respectively. The protein efficiency ratio was 1.36 ± 0.19, 1.31 ± 0.13, 1.67 ± 0.26, and 1.66 ± 0.19, respectively No significant differences were observed among treatments for growth performance, feed utilization, or survival (p > 0.05). However, fish fed the diet containing 40% dried Azolla exhibited a tendency toward higher survival compared with the other treatments without any adverse effects on growth performance. The findings indicate that dried Azolla can be used as an alternative protein source to replace soybean meal in rohu diets at levels up to 40% without negatively affecting growth performance or feed utilization, despite having a lower dietary protein content than some of the other experimental diets.

Response Behavior of a Control System for a Single-Beam Twin-Propeller Vertical Balancing Flight Model Using a P, PI, PD, and PID Controller

2026-06-13T11:41:42+07:00

This research aims to develop a single-beam twin-propeller vertical balancing flight model and compare the response behaviors of the system controlled by four controller types: P, PI, PD, and PID controllers. In the experiments, the beam equilibrium position was set to 90°. The response tests were then conducted at reference angles of 110°, 120°, and 130°. The system performance was evaluated in terms of overshoot, settling time, and steady-state error. At the reference angle of 110 °, the PD controller provided the best overshoot performance, with an overshoot of 0.50%, a settling time of 2.01 s, and a steady-state error of 0.55 °. Meanwhile, the PID controller produced an overshoot of 1.86%, a settling time of 2.43 s, and a steady-state error of 1.90 °. At the reference angle of 120 °, the PD controller achieved the lowest overshoot of 0.41%, with a settling time of 4.02 s and a steady-state error of 0.45 °. At the reference angle of 130 °, the PD controller produced an overshoot of 3.73%, a settling time of 1.15 s, and a steady-state error of 4.10 °. The PID controller produced an overshoot of 6.04%, a settling time of 2.97 s, and a steady-state error of 3.20 °. The experimental results show that the PD controller is the most suitable for reducing overshoot and achieving a fast response, while the PID controller can compensate for steady-state error.