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.
การเปลี่ยนแปลงสภาพอากาศของโลกทวีความรุนแรงขึ้นอย่างต่อเนื่อง สถานการณ์ดังกล่าวส่งผลกระทบ โดยตรงต่อภาคการเกษตร โดยเฉพาะในประเทศไทยที่มีแนวโน้มเผชิญกับปัญหาการขาดแคลนน้ำและความ ผันผวนของปริมาณน้ำฝน ซึ่งส่งผลต่อทั้งปริมาณและคุณภาพของผลผลิตทางการเกษตรโดยตรง ทั้งนี้ การบริหารจัดการน้ำในภาคเกษตรกรรมของประเทศไทยยังคงเผชิญกับข้อจำกัดหลายประการ เกษตรกรส่วนใหญ่ยังคงพึ่งพาประสบการณ์ส่วนตัวในการให้น้ำพืช ซึ่งอาจนำไปสู่การใช้น้ำที่ไม่มีประสิทธิภาพ เช่น การให้น้ำมากเกินความจำเป็นหรือน้อยเกินไปจนส่งผลกระทบต่อผลผลิต หรืออาจนำไปสู่ปัญหา เช่น การแตกใบอ่อน การร่วงของดอก และมีผลผลิตที่ไม่ได้คุณภาพ (Togneri et al., 2023) ในขณะที่ข้อมูลทาง วิชาการที่สามารถช่วยให้การบริหารจัดการน้ำมีความแม่นยำขึ้น เช่น ค่าอัตราการใช้น้ำของพืชอ้างอิง (Evapotranspiration: ETo) และค่าสัมประสิทธิ์พืช (Kc) กลับเข้าถึงได้ยาก เนื่องจากมีความซับซ้อนในการ คำนวณ อีกทั้งข้อมูลที่มีอยู่มักเป็นข้อมูลเฉลี่ยรายจังหวัดซึ่งไม่สามารถนำไปใช้ได้อย่างมีประสิทธิภาพในระดับ ฟาร์ม โครงการนี้จึงมุ่งเน้นไปที่การพัฒนาระบบปัญญาประดิษฐ์สำหรับการติดตามและควบคุมการให้น้ำพืชอัจฉริยะ โดยอิงข้อมูลสภาพอากาศซึ่งจะช่วยแก้ไขข้อจำกัดของเกษตรกรไทยในการเข้าถึงข้อมูลที่ ถูกต้องและการบริหารจัดการน้ำที่แม่นยำ
คณะวิศวกรรมศาสตร์
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.
คณะแพทยศาสตร์
Background: The RGL3 gene plays a role in key signal transduction pathways and has been implicated in hypertension risk through the identification of a copy number variant deletion in exon 6. Genome-wide association studies have highlighted RGL3 as associated with hypertension, providing insights into the genetic underpinnings of the condition and its protective effects on cardiovascular health. Despite these findings, there is a lack of data that confirms the precise role of RGL3 in hypertension. Additionally, the functional impact of certain variants, particularly those classified as variants of uncertain significance, remains poorly understood. Objectives: This study aims to analyze alterations in the RGL3 protein structure caused by mutations and validate the location of the ligand binding sites. Methods: Clinical variants of the RGL3 gene were obtained from NCBI ClinVar. Variants of uncertain significance and likely benign were analyzed. Multiple sequence alignment was conducted using BioEdit v7.7.1. AlphaFold 2 predicted the wild-type and mutant 3D structures, followed by quality assessment via PROCHECK. Functional domain analysis of RasGEF, RASGEF_NTER, and RA domains was performed, and BIOVIA Discovery Studio Visualizer 2024 was used to evaluate structural and physicochemical changes. Results: The analysis of 81 RGL3 variants identified 5 likely benign and 76 variants of uncertain significance (VUS), all of which were missense mutations. Structural modeling using AlphaFold 2 revealed three key domains: RasGEF_NTER, RasGEF, and RA, where mutations induced conformational changes. Ramachandran plot validation confirmed 79.7% of residues in favored regions, indicating an overall reliable structure. Moreover, mutations within RasGEF and RA domains altered polarity, charge, and stability, suggesting potential functional disruptions. These findings provide insight into the structural consequences of RGL3 mutations, contributing to further functional assessments. Discussion & Conclusion: The identified RGL3 mutations induced physicochemical alterations in key domains, affecting charge, polarity, hydrophobicity, and flexibility. These changes likely disrupt interactions with Ras-like GTPases, impairing GDP-GTP exchange and cellular signaling. Structural analysis highlighted mutations in RasGEF and RA domains that may interfere with activation states, potentially affecting protein function and stability. These findings suggest that mutations in RGL3 could have functional consequences, emphasizing the need for further molecular and functional studies to explore their pathogenic potential.
คณะเทคโนโลยีสารสนเทศ
This research presents the development of an AI-powered system designed to automate the identification and quantification of dental surgical instruments. By leveraging deep learning-based object detection, the system ensures the completeness of instrument sets post-procedure. The system's ability to process multiple images simultaneously streamlines the inventory process, reducing manual effort and potential errors. The extracted data on instrument quantity and type can be seamlessly integrated into a database for various downstream applications.