Motor control is a critical process for muscle contraction, which is initiated by nerve impulses governed by the motor cortex. This process is vital for performing activities of daily living (ADLs). Consequently, a disruption in communication between the brain and muscles, as seen in various chronic conditions and diseases, can impair bodily movement and ADLs. Evaluating the interaction between brain function and motor control is significant for the diagnosis and treatment of motor control disorders; moreover, it can contribute to the development of brain-computer interfaces (BCIs). The purpose of this study is to investigate brain activation in designed upper extremity motor control tasks in regulating the pushing force in different brain regions; and develop investigation methods to assess motor control tasks and brain activation using a robotic arm to guide upper extremity force and motor control. Eighteen healthy young adults were asked to perform upper extremity motor control tasks and recorded the hemodynamic signals. Functional Near-Infrared Spectroscopy (fNIRs) and robotic arms were used to assess brain activation and the regulation of pushing force and extremity motor control. Two types of motion, static and dynamic, move along a designated trajectory in both forward and backward directions, and three different force levels selected from a range of ADLs, including 4, 12, and 20 N, were used as force-regulating upper extremity motor control tasks. The hemodynamic responses were measured in specific regions of interest, namely the primary motor cortex (M1), premotor cortex (PMC), supplementary motor area (SMA), and prefrontal cortex (PFC). Utilizing a two-way repeated measures ANOVA with Bonferroni correction (p < 0.00625) across all regions, we observed no significant interaction effect between force levels and movement types on oxygenated hemoglobin (HbO) levels. However, in both contralateral (c) and ipsilateral (i) PFC, movement type—static versus dynamic—significantly affected brain activation. Additionally, cM1, iPFC, and PMC showed a significant effect of force level on brain activation.
This observational study aims to investigate the relationship between brain activation in specific regions and various motor tasks involving upper extremity movement with force control. Utilizing fNIRs, the research will monitor hemodynamic changes in four key brain areas: the prefrontal cortex (PFC), premotor cortex (PMC), supplementary motor area (SMA), and primary motor cortex (M1) during task performance. The primary population for this investigation consists of healthy young adults, allowing for a clearer understanding of how force control affects brain activation. The scope of the study includes assessing brain activation measured by fNIRs during upper extremity motor and force control tasks, as well as examining how upper extremity movements and force control influence brain activation.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components: 1) Weather Station – This component includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration, among others, to collect real-time weather data. 2) Controller Unit – This unit is equipped with machine learning algorithms or models to estimate the reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data. This enables the system to determine the optimal irrigation amount based on plant needs automatically. 3) User Interface (UI) and Display – This section allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages. It also provides a display for monitoring and interaction with the system. 4) Irrigation Unit – This component is responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.
คณะเทคโนโลยีการเกษตร
Rice is a salt-sensitive crop. The objective of this study was to evaluate the effect of salinity at flowering stage on physiological traits and yield of landrace rice. The experiment design was 4*10 Factorial in RCBD with 4 replications. Factor A was four salinity levels: control, 6, 12 and 16 dS/m; Factor B was 10 rice varieties. Data were collected on physiological traits and grain yield. The results showed that increasing salinity level decreased rice yield. The highest yield reduction was found when the rice received salt stress at 16 dS/m. In addition, rice varieties showed different yield performance when exposed to salt stress. In this found that Hom Yai variety had the lowest yield reduction when grown at 16 dS/m salinity level and did not differ from salt tolerant check variety.
คณะวิศวกรรมศาสตร์
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRRlayers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.