This study explores the neural correlates of critical thinking by analyzing electroencephalogram (EEG) patterns across different brain lobes during resting and cognitive task conditions. Specifically, the research investigates variations in alpha, beta, and gamma frequency bands to identify EEG signatures associated with higher-order cognitive processes. Ten healthy participants (aged 20–27 years, both male and female) completed four activities: a resting task with eyes closed and minimal movement, followed by three critical thinking tasks (Sets A, B, and C), each comprising ten brain teaser questions. EEG data were acquired using a 14-channel mobile EEG system (EMOTIV EPOC +). Signal preprocessing involved applying a Butterworth bandpass filter. Feature extraction utilized both linear (Power Spectral Density via Welch and Burg methods) and nonlinear (convolutional operations) approaches to compute statistical features including maximum, minimum, mean, median, mode, standard deviation, and variance. Subsequently, classification was performed using Decision Tree (DT), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) algorithms. The findings reveal that the combination of the Welch method with KNN and a subset of statistical features yields the highest classification accuracy, particularly in the beta and gamma bands. Notably, gamma band activity from the frontal lobe achieved the highest classification accuracy of 89.7
This research addresses the critical global health issue of limited access to safe drinking water by presenting a novel solar-powered water purification module. Focusing on affordable and sustainable solutions, the study details the development and evaluation of a prototype utilizing maifan stone filtration, achieving a significant 95
Subconcussive impacts, characterized by cranial impacts that result in rapid head acceleration without immediate clinical symptoms of concussion, are a growing concern in sports, including sepak takraw. This mini-review explores current evaluation strategies for these impacts in related sports to sepak takraw, highlighting the usage of biomechanical sensors, neuroimaging modalities, and biofluid biomarkers. Mouthguard and skin-mounted sensors are biomechanical sensors that widely used in capturing head kinematics in non-helmeted sports, whilst; diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS) frequently detect white-matter and metabolic alterations. Among biomarkers, neurofilament light (NfL) consistently correlates with cumulative axonal stress, while tau-PET offers pathology-specific insight into chronic tauopathy. This review highlights the need for multimodal, longitudinal approaches and field-deployable technologies, and advocates broader inclusion of diverse athletic populations especially for sepak takraw players. The directions can inform protocols that monitor, mitigate, and ultimately prevent long-term brain-health consequences in sepak takraw and comparable sports.
Walking on inclined and declined surfaces introduces distinct biomechanical challenges that alter lower-limb joint loading and stability. This study aimed to investigate the effects of surface inclination on ankle joint reaction forces and to determine the safest walking angle based on the minimum forces exerted. Ten healthy male participants from Universiti Malaysia Perlis walked at self-selected speeds across slope angles of 0°, ±5°, ±7.5°, and ± 10°. Motion capture data were collected using a Qualisys Track Manager system integrated with embedded force plates, and joint reaction forces were analyzed through Visual3D software using inverse dynamics. Data were filtered with a fourth-order Butterworth low-pass filter at 4 Hz to obtain accurate kinematic and kinetic outcomes. The findings revealed that the mean maximum ankle joint reaction force remained nearly constant between level walking and 5° inclination but decreased noticeably at 7.5° and 10° slopes in both uphill and downhill conditions. Statistical analysis showed no significant difference between level and uphill walking, whereas a significant reduction in joint reaction force was observed at −7.5° and − 10° declinations. The ankle joint consistently exhibited the highest load compared to the knee and hip joints across all slope angles. Decline walking demonstrated lower mechanical demands on the ankle, indicating a safer condition compared to incline walking. The study provides valuable insight into joint behavior on sloped terrains and underscores the importance of biomechanical understanding and clinical awareness to minimize joint stress and potential musculoskeletal injury during locomotion on inclined and declined surfaces.
This study explores the effect of glove design on goalkeeper performance through a comparative analysis between Malaysian and international glove brands. A mixed-method approach was employed, involving performance testing under dry and wet conditions, questionnaire surveys, and a design development phase consisting of ideation sketches and 3D modeling using SolidWorks software. A total of 20 male goalkeepers aged 18 to 35 participated in the study. Quantitative data were analyzed using SPSS version 26.0 to evaluate grip, comfort, durability, and climate suitability. Results showed that international gloves performed better in terms of grip (mean = 4.56) and durability (mean = 4.42) due to advanced materials such as German latex, PU foam, and finger spines. In contrast, Malaysian gloves demonstrated higher comfort (mean = 4.60) and breathability (mean = 4.10), making them more appropriate for tropical weather. Based on user feedback, a new glove concept was designed combining contact latex for grip, neoprene for breathability, and an ergonomic fit suitable for Southeast Asian climates. The findings suggest that climate-responsive glove design plays a critical role in enhancing both the physical performance and psychological comfort of goalkeepers.
The Talent Identification System is a machine-learning-based system that makes suitable sports recommendations to students. This study develops a Sport Talent Identification System using a Support Vector Machine (SVM) trained on physical and physiological performance data collected from the Sports Council (MSN) of Perlis athletes. To improve prediction reliability and reduce class imbalance, the dataset was grouped into five major categories: ‘Sukan Ke- cepatan/Kekuatan’ (Power), ‘Sukan Combat’, ‘Sukan Berkumpulan’, ‘Sukan Raket’, and ‘Sukan Skill’. The results show that the SVM model achieved 63.03
This study presents a novel approach to classify five skateboarding tricks (Kickflip, Frontside-180, Nollie Frontside Shove-it, Pop Shove-it, and Ollie) using transfer learning models integrated with Support Vector Machine (SVM) classification. As skateboarding continues to gain prominence in competitive sports, including its Olympic debut, there is increasing demand for objective evaluation systems. The methodology captures skateboarding trick sequences using a YI action camera positioned 1.26m from the performance area and extracts image frames at 30fps. By overall of approximately 750 images were extracted and then would proceed through a train, validation, and test split of 60:20:20 ratio, respectively. Four pre-trained CNN architectures (NasNetLarge, NasNetMobile, MobileNetV2, and MobileNet) were evaluated as feature extractors coupled with SVM classification. Comprehensive evaluation revealed that NasNetLarge achieved the highest classification accuracy of 93
Biometric authentication using EEG signals, specifically auditory evoked potentials (AEP), offers robust security advantages over traditional methods. This paper evaluates the performance of various classifiers—Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest (RF) in EEG-based biometric systems. Employing Shannon entropy and Linear Discriminant Analysis (LDA) for feature extraction and selection, respectively, we found SVM achieved the highest classification accuracy (96.15
This study investigates how three different thumb positions—Shallow, Deep, and Deepest Single Hook—affect upper limb muscle activation and shooting accuracy in traditional archery. Using surface electromyography (sEMG), the root mean square (RMS) values of five key muscles were recorded and normalized against maximum voluntary contraction (MVC). Results showed that the Deepest Single Hook produced the lowest muscle activation while achieving the highest shooting accuracy. The Posterior Deltoid was consistently the most activated muscle across all positions. Findings suggest that the Deepest Single Hook offers biomechanical advantages and may enhance performance while reducing fatigue and injury risk.
Golf swing analysis is traditionally limited by high costs and complex equipment such as high-speed cameras and launch monitors, making it inaccessible to most amateur players. This lack of affordable, real-time feedback creates challenges in effectively improving performance. The objective of this project is to design a low-cost, portable system that provides immediate swing feedback using simple hardware and a user-friendly mobile application. The proposed system integrates motion sensors (MPU6050 and ADXL345) with an ESP32 microcontroller to capture swing metrics, including speed, impact force, and face angle. Data is transmitted via Bluetooth to an Android application developed in Android Studio, which processes, visualizes, and stores results locally with optional cloud synchronization through Firebase. The methodology involved hardware prototyping, software development, and testing for accuracy, connectivity, and usability. Results show that the system reliably captures swing data at a 10 Hz sampling rate, with Bluetooth communication providing a delay of less than 300 ms, ensuring near real-time feedback. The Android app successfully displayed metrics, stored swing history, and offered a clean Material3-based interface. Overall, the system achieved its goal of delivering an affordable and accessible swing analysis tool suitable for both amateur golfers and coaches.
This systematic review examines the role of chatbots in biomechanics research, highlighting their potential to improve data collection, patient engagement, and educational outreach. Chatbots, defined as conversational AI programs, have increasingly been used in biomechanics to assist with data management, rehabilitation support, and patient education. The review synthesizes findings across studies, noting the benefits of chatbots in enhancing thequality and efficiency of biomechanicsresearch, aswell as thechallenges of data accuracy, user adherence, and privacy concerns. With advancements in AI and natural language processing, chatbots hold promise for transforming biomechanics research by facilitating communication, automating data processes, and providing personalized support. However, further research is needed to address existing limitations and fully realize their potential within biomechanics applications.
The early detection and treatment of brain tumours are essential, but diagnosing brain tumours accurately remains a significant challenge. This study presented a brain tumour classification model that utilized three convolutional neural network (CNN) models, namely VGG-16, SqueezeNet, and Inception-ResNet-V2, to classify brain tumour magnetic resonance images (MRI). The models were evaluated using established performance metrics such as precision, recall, F1 score, Matthew’s correlation coefficient (MCC), and accuracy. The models were executed using images from a Kaggle dataset, which included three types of brain tumours and one class of healthy brain images. The study identified the optimal hyperparameters, including a training time of 50 epochs, an SGD optimizer and a learning rate of 0.001, based on accuracy, loss, and other performance metrics. The VGG-16 and Inception-ResNet-V2 models achieved an accuracy of 99
A digital arterial disease in the upper extremity is uncommon compared to arterial disease in the lower extremity. A microvascular anastomosis is performed as a vascular reconstruction. However, mismatching in size between end-to-end anastomosis of venules-arterioles internal diameter. might cause blockage in the new vascular reconstruction region. In a previous study, internal diameter discrepancy in vessel size (small-large or vice versa) caused abnormal blood flow behavior. Then it will initiate the thrombosis formations and be supported by clinical theory. This study aims to analyze the blood flow behavior through mismatching in size anastomosis models and their flow patterns that might affect the initiation of thrombus formation in venule models. A three-dimensional computational fluid dynamic (3-D CFD) method is employed to investigate blood flow velocity, vascular resistance, and wall shear stress (WSS) on ideal straight (well matched between the internal diameter of the venule and recipient arteriole) and internal diameter mismatched anastomosis models. In this experiment, we expect that steady-state laminar blood flow demonstrates abnormal flow patterns in mismatched internal diameter anastomosis models compared to an ideal matched model. In conclusion, any abnormal blood flow pattern will initiate the formation of a thrombus and reduce the anastomosed venule-arterioles survival.
The rise of drug-resistant bacteria in recent years has drastically increased. The alternative antimicrobial agents have heightened interest in plant-derived natural active compounds. This study examines the phytochemical composition and assesses the antimicrobial efficacy of Camellia sinensis, Glycyrrhiza glabra (Licorice), Morus alba (Mulberry), and Zingiber officinale (Ginger). The leaves can be mostly used as traditional medicine which includes reducing inflammation, stress alleviation, immune system support and more. However, there is lacked information about the phytochemical content and antibacterial activity of herbal tea. The Soxhlet extraction method using methanol as its solvent was used to create an extract from the leaves of C. sinensis, G. glabra, M. alba, and Z. officinale. Total Phenolic Contents (TPC), Total Flavonoid Contents (TFC), and Antioxidants Activity (AOA) are expected to attain elevated levels with significant antibacterial efficacy. A finding of this study shown C. sinensis, Chinese Herbal tea (CHT or black tea), Z. officiniale, M. alba, G. glabra had shown a TPC values at 180 μg GAE/g < 160 μg GAE/g < 65 μg GAE/g < 50 μg GAE/g < 40 μg GAE/g. The Total Phenolic Contents (TPC) of C. sinensis extract at 0.58 μg GAE/g more than mixed Chinese herbal tea. However, Z. officinale and CHT potent antibacterial activity compared with other extracts. The future investigation will involve the encapsulation of samples, extraction of crude using various techniques, and manipulation of raw material ratio to provide a more precise evaluation of this Chinese Herbal Tea.
Euphorbia tirucalli L. is a medicinal plant known for its biological properties, such as antioxidant and antibacterial, attributed to its rich polyphenolic content. The extraction process may determine the recovery of the bioactive compounds in the plant, such as extraction methods and their solvents. This work focused on the comparison of extraction methods, employing maceration and ultrasonic-assisted extraction (UAE) for polyphenolic extraction from E. tirucalli. It was revealed that UAE was the most effective method with TPC, TFC, and antioxidant activity corresponding to 29.26 ± 0.15 mgGAE/100g, 152.85 ± 9.04 mgQE/100g, and 80.37 ± 0.59
This study investigates the encapsulation of Malaysia honey (Tualang, Gelam and Kelulut) with maltodextrin using freeze drying to preserve bioactive compounds and enhance stability. Maltodextrin is a polysaccharide commonly used for encapsulation which provide a protective matrix that preserve honey from environmental degradation factors like moisture and oxygen. The freeze-drying process conducted at low temperatures, minimize thermal degradation and maintain the structural of honey component. The crystallinity and phase structure of the encapsulated honey are analyzed using X-ray diffraction (XRD) which is important for assessing the stability and release properties of the bioactive compounds in honey. Kelulut honey demonstrates the highest crystallinity index at 0.37
The Ti-6Al-4V alloy is widely used in implant applications due to its excellent mechanical properties, including high strength for load-bearing, elasticity comparable to human bone, non-toxicity, and biocompatibility. However, its bioactivity is limited because titanium is biologically inert, making it less effective in promoting bone tissue growth and achieving strong osseointegration. To address this, mesoporous carbonated hydroxyapatite (Meso-CHA) was selected as a coating material for Ti-6Al-4V due to its superior biocompatibility and bioactivity. This study demonstrates that combining the mechanical advantages of Ti-6Al-4V with the bioactivity of Meso-CHA can result in a more effective implant. Meso-CHA powder was successfully synthesized using the precipitation method. It was observed that increasing the concentration of nitric acid roughened the alloy surface, enhancing the adhesion of Meso-CHA. Optimal deposition conditions were identified as 10 V for 10 min. While higher voltages and longer deposition times produced thicker Meso-CHA coatings, these coatings exhibited surface cracks. In vitro bioactivity tests confirmed the formation of apatite, indicating that the Meso-CHA coating significantly enhances the alloy’s bioactivity and promotes improved osseointegration.
Facial emotion recognition (FER) serves as a significant link that connects human-to-computer interaction and concerns affective computing and mental health since it is possible for the machine to understand and react to human feelings in real-time. A deep convolutional neural network was constructed as part of this research to distinguish anger, contempt, disgust, fear, happy, sadness, and surprise which are the seven target feelings present in pictures from the CK + dataset. Specifically, the original and final implementation of the Convolution Neural-Network (CNN) architecture can be described as comprising a sequence of multiple convolutional layers and a sequence of dense layers that are used for high-reliability emotion class predictions. During model training, 48 by 48 pixels were used on the train test validation images. Various forms of dropout regularization were used to reduce overfitting and enhance model robustness. F1 scores, precision and recall metrics along with other evaluation metrics were deployed to assess model performance with all the categories achieving a reasonable classification success ratio. Therefore, the CNN model can be regarded as a quite dependable tool for emotion recognition with also extending to practical FER primary operations.
Objective: This study investigated whether incorporating upper-body isometric resistance exercises targeting the posterior oblique sling (POS) in a standard ham-string training programme would enhance hamstring reaction times, strength, and flexibility in moderately active adults. Methods: Thirty participants (18 males, 12 females; aged 18–40 years) were randomly assigned to intervention (ULC) or control (CON) groups. Both groups completed a five-week, home-based ham-string strengthening and stretching programme. The ULC group performed additional upper-body isometric resistance exercises involving shoulder flexion, abduction, and diagonal patterns. Reaction time of the biceps femoris was measured using surface electromyography (sEMG) in response to visual cues. Secondary outcomes included isometric hamstring strength using a hand-held dynamometer, while flexibility was measured using the Active Knee Extension test. A two-way mixed-design ANOVA was used to assess time × group effects. Results: A significant group × time interaction was found for reaction time (p = 0.003), with greater improvements in the ULC (−5.73 ms) compared to the CON (−2.89 ms). Strength also improved more in the ULC (+1.06 kg vs. +0.56 kg; p < 0.001). Flexibility gains were negligible between groups (p = 0.411). Conclusion: Upper-body isometric priming enhanced hamstring reaction time and strength be-yond traditional training, suggesting practical value for improving lower-limb neuromotor performance in sport settings. These findings point to the potential of integrating POS activation into preparatory routines to optimise intersegmental coordination and force transfer before lower-limb tasks.
Introduction: The forehand topspin is a fundamental stroke in table tennis, yet limited research has explored how physical conditioning affects its accuracy. This study addresses a gap in the literature by investigating whether a six-week resistance band training program, integrated with table tennis drills, can enhance forehand topspin accuracy and control in adolescent athletes during a critical period of neuromuscular development. Objective: To investigate the effects of a six-week resistance band training program integrated with sport-specific drills on forehand topspin accuracy in Malaysian adolescent table tennis players, and to assess changes in upper and lower limb strength, muscular endurance, and core stability. Results: The experimental group demonstrated significantly greater improvements in forehand topspin accuracy total score (p = .036), grip strength (p = .015), push-up repetitions (p = .014), core stability (p < .001), and burpee endurance (p = .035) compared to the control group. Conclusions: Resistance band training effectively enhances stroke accuracy and physical performance metrics in adolescent table tennis players. Integrating such training with regular practice may support neuromuscular development and improve competitive performance during a critical stage of motor learning.
