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Urban Farming Bridges Community, Technology and Sustainability
Get PDFSERDANG, October 18 – Urban farming has emerged as an innovative approach that connects food security needs, community well-being, and environmental sustainability
FSTM UPM Strengthens Food Security Awareness through Open Day @FoodTech 2025
Get PDFSERDANG, October 15 – The Faculty of Food Science and Technology (FSTM), Universiti Putra Malaysia (UPM) strengthened its role in raising public awareness on the importance of food security through the organisation of Open Day @FoodTech UPM 2025, themed “Transforming Our Plates, Strengthening Food Security.
Deputy Prime Minister Proposes UPM’s Faculty of Modern Languages and Communication as National Narrative Lab
Get PDFSERDANG, October 4 – Deputy Prime Minister, Dato’ Seri Dr. Ahmad Zahid Hamidi, has proposed that Universiti Putra Malaysia’s Faculty of Modern Languages and Communication (FBMK) be developed as a “national narrative laboratory” to strengthen public discourse through expertise in communication, languages and research
A thematic review on interpretation perspective of UNESCO Global Geoparks over the past 5 years (2019-2024): analysis of trends for future studies
Get PDFThe interpretation of the geological, geomorphological, and archeological significance of these geosites will assist visitors in comprehending and appreciating their importance to our heritage and the significance of their protection for future generations. The apparent lack of attention to interpretation in the geopark field has raised concerns among researchers and park authorities about how to promote the most effective interpretation principles when interpretive design and practice are popularized, despite the promising development of interpretive research. This study evaluates articles published between 2019 and 2024 to explore how interpretation is addressed in the context of the UNESCO Global Geoparks. Using a thematic review methodology, this research incorporates keywords from the SCOPUS and WoS databases. The thematic review analysis revealed four key themes: effective interpretation, geoconservation and SDGs, geoeducation and awareness, and the promotion of geotourism. The findings provide valuable insights for park policymakers, visitors, practitioners, and researchers interested in the interpretation field related to UNESCO Global Geoparks
Data uncertainty (DU)-former: an episodic memory electroencephalography classification model for pre- and post-training assessment
Get PDFEpisodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal–spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model’s robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former’s performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment
Impact of image preprocessing and crack type distribution on YOLOv8-based road crack detection
Get PDFRoad crack detection is crucial for ensuring pavement safety and optimizing maintenance strategies. This study investigated the impact of image preprocessing methods and dataset balance on the performance of YOLOv8s-based crack detection. Four datasets (CFD, Crack500, CrackTree200, and CrackVariety) were evaluated using three image formats: RGB, grayscale (five conversion methods), and binarized images. The experimental results indicate that RGB images consistently achieved the highest detection accuracy, confirming that preserving color-based contrast and texture information benefits YOLOv8’s feature extraction. Grayscale conversion showed dataset-dependent variations, with different methods performing best on different datasets, while binarization generally degraded detection accuracy, except in the balanced CrackVariety dataset. Furthermore, this study highlights that dataset balance significantly impacts model performance, as imbalanced datasets (CFD, Crack500, CrackTree200) led to biased predictions favoring dominant crack classes. In contrast, CrackVariety’s balanced distribution resulted in more stable and generalized detection. These findings suggest that dataset balance has a greater influence on detection accuracy than preprocessing methods. Future research should focus on data augmentation and resampling strategies to mitigate class imbalance, as well as explore multi-modal fusion approaches for further performance enhancements
The distribution of dissolved copper and natural organic ligands in tropical coastal waters under seasonal variation
Get PDFThe bioavailability of dissolved copper (Cu) in seawater is influenced by the presence of natural organic matter. Changes in physicochemical conditions, such as pH, temperature, and salinity, can significantly affect the solubility and speciation of copper, thereby impacting the complexation of Cu(II)-binding organic ligands. The concentration of dissolved Cu in the coastal water of Mersing, Malaysia, was detected by anodic stripping voltammetry (ASV). The natural organic copper(II)-binding ligands (CuL) and their conditional stability constants (log K′) were determined by using the competitive ligand exchange–adsorptive cathodic stripping voltammetry method (CLE–AdCSV) in our samples. The in situ parameters, such as pH, temperature, salinity, and dissolved oxygen (DO), were found to be significantly different between sampling periods and indicated the different physical chemical conditions between the sampling periods. However, we found a consistent concentration of dissolved Cu throughout the water column between sampling periods. This suggests that the presence of a strong class of natural organic ligands (L1) in Mersing’s coastal water maintains the dissolved Cu(II) ions in the water column and prevents the scavenging and precipitation processes under the seasonal variations
Assessment of potential dominant factors for brownfield landscape regeneration: a case study in Xi'an, China
Get PDFRapid global urbanization has made brownfield reuse a vital issue for sustainable urban development. However, the regeneration of brownfield landscapes is a complex and lengthy process that requires a combination of factors to be considered. Their landscape regeneration must be planned and prioritized to utilize brownfield sites and achieve positive social benefits. Therefore, an urgent need must be established to establish an assessment framework and system for various types of brownfield landscape regeneration dominant factors to find different brownfield landscape regeneration dominant factors. This research developed an assessment model using the Analytic Hierarchy Process (AHP), covering five brownfield types: industrial, mining, military, transportation, and landfill in Xi’an, China. The potential assessment factors in three levels were analyzed for weighting to explore the dominant factors for the potential regeneration of brownfield landscapes in Xi’an. The results showed that, firstly, among the five first-level assessment factors, the physicality factor was the most important. Secondly, among the 16 second-level factors, the spatial and physical features of the visual landscape were the most critical. Finally, among the 40 three-level factors, spatial features were the primary factor. Therefore, the purpose of this research is to provide a specific assessment system and data analysis methods and ideas for the dominant factors of urban brownfield landscape regeneration in China and other regions based on the assessment framework with strong adaptability proposed by the AHP method, which can be flexibly adapted in the different areas and countries, to realize the sustainable development of cities in various regions
Dari benih cili ayah, lahirlah usahawan muda agro
No full textBINTULU, 1 Nov – Dari sekadar menyemai biji cili milik ayahnya, seorang pelajar Diploma Perniagaantani, Fakulti Perhutanan dan Sains Pertanian, Universiti Putra Malaysia Sarawak (UPMS), Yvena Twomie Picturesques Anak Miro kini muncul sebagai usahawan muda agro yang berjaya menarik perhatian penduduk setempat melalui jualan anak pokok cili