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A Culturally-Sensitive Approach to Sexual and Reproductive Health Education : A Critical Review of a Parenting Sexuality Book
Get PDFSexual and Reproductive Health (SRH) education has become increasingly critical in contemporary society, with reports of child sexual and physical abuse appearing almost weekly in mainstream media. The current generation of parents, who were raised in environments where discussions about sexuality were relatively private and infrequent, now face the challenge of addressing these issues with their teenage children. Consequently, there is a pressing need to raise awareness and equip parents with the necessary skills for effective communication about SRH. This review examines the book "Berbual-bual dengan Anak tentang Seksualiti (BBDATS)" by Wan Nur Syakira Wan Musa, analyzing its content, purpose, and relevance. The book uniquely integrates Islamic perspectives, which is essential for addressing the needs of Malaysia's predominantly Muslim population. It ensures the content respects cultural and religious norms while promoting effective SRH education. The book features 18 engaging chapters, employing various techniques such as dialogue, highlights, Q&A sections, checklists, and step-by-step guides, making it relevant and accessible. By skillfully linking Islamic knowledge sources, such as the Quran and Hadith, with practical storytelling methods, the book helps readers connect fundamental life principles with the technical skills needed for effective communication. This review highlights the valuable insights provided by BBDATS, suggesting that benchmarking its best practices can enhance SRH education initiatives
Preparation, bacteriostatic, and in silico analysis of halogenated 4-methoxyphenyl-triazene derivatives
Get PDFA series of diazoamino compounds, also known as triazene derivatives were successfully synthesised through the N-coupling diazotisation of p-anisidine with various halogenated substituted anilines at ortho, meta, and para positions. The series was obtained in low to moderate yields of 13-56%. Structural elucidation was performed via FTIR, 1H, and 13C NMR techniques. With ampicillin served as a positive control, the antibacterial efficacy of the synthesised compounds against Escherichia coli
ATCC 25922 and Staphylococcus aureus ATCC 25923 via the Kirby-Bauer disk diffusion resulted in moderate to good
inhibition measuring 6.6 ± 0.25 to 10.0 ± 0.00 mm, respectively. The minimum inhibitory concentration (MIC) values indicating inhibition of ≥ 8.8 mm yielded promising results, with MICs ranging from 82 to 121 ppm for E. coli and from 87 to 101 ppm for S. aureus. These values exceeded the standard ampicillin range of 96 to 127 ppm, suggesting potential for broader clinical applications. Molecular docking analysis of the compounds against CrtM and MurE protein receptors further supported these findings with binding scores of -7.48 to -8.27 kcal/mol and -7.26 to -7.94 kcal/mol, respectively. Notably, these scores surpassed those of ampicillin, which scored only -7.23 kcal/mol and -6.92 kcal/mol, respectively. This enhanced activity is believed to be attributed to the presence of hydrogen bonding and other hydrophobic interactions. Additionally, the bioavailability competencies of all compounds tested via ADMET analysis demonstrated compliance of each compound with
Lipinski’s rule of five with zero violations
Challenges Faced by Lecturers in Teaching Interpersonal Communication Skills Online to Medical Students at University Malaysia Sarawak
Get PDFThe COVID-19 pandemic has brought about changes in the teaching and learning process at overall educational institutions and higher educational institution such as universities also affected. The pandemic period had an impact on the implementation of online teaching and learning with medical students at the Faculty of Health and Medical Sciences, University of Malaysia Sarawak. It is important
to develop medical students' interpersonal communication skills by shifting theoretical and practical courses to virtual courses, which encourage the students to adapt early in their medical student careers. Interpersonal communication is needed in the context of building an educational civilization in shaping the medical student personality. Due to COVID-19 pandemic, educational institutions have been required to conduct teaching and learning activities from face-to-face classes to fully online classes. The challenge today is the loss of interpersonal communication caused by online teaching and
learning. This study examines the challenges faced by the lecturers at the Faculty of Health and Medical Sciences, University of Malaysia Sarawak. This study adapted the qualitative method through a phenomenological approach. Semi-structured In-depth interviews were conducted with lecturers who were experienced in teaching medical students. The data transcribed verbatim and analysed using thematically to encode the main themes and subthemes. There are several challenges identified in online learning such as limited non-verbal cues, reduced student engagement and emotional disconnect, technological barriers and assessment difficulties for lecturers in teaching using interpersonal communication skills to medical students at University Malaysia Sarawak
Sonic Heritage and Acoustic Profiling of the Gendang Kecapi: A Bamboo-Based Instrument from Kelantan, Malaysia
Get PDFThis study considered the ‘Gendang Kecapi’ (GK) musical instrument, the sounds of which were recorded in an anechoic chamber. The Fast Fourier Transform (FFT) data was obtained using a Picoscope oscilloscope. The GK is an idiochord bamboo tube zither from Kelantan, Malaysia. The GK have two strings (called canang), two gongs,and a gendang(drum). The instrument produced unique and innovative sounds. The time frequency analysis (TFA) used Adobe Audition to produce the spectrograms. The fundamental frequency (f0) of string 1 (canang ibu) and string 2 (canang anak) are 0.888 kHz (A5) and 1.054 kHz (C6), respectively. The f0 of gong 1 (gong ibu) and gong 2 (gong anak) are0.230 kHz (A3#) and 0.246 kHz (B3),respectively. The f0 of gendang is 0.380 kHz (F4#). The frequency spectrum showed less distinct fundamental frequency with several lower partial frequencies at 0.017, 0.100, and 0.200 kH
ENGAGING MINDS THROUGH ANIMATION: THE EFFECTIVENESS OF 2D CONTENT IN MEDICAL HEALTH EDUCATION
Get PDFThe article examines the effectiveness of 2D animated content as an engaging and innovative educational
tool for mental health education. The study explores how such animations can enhance understanding and
raise awareness of mental health disorders among diverse audiences. Among the ongoing difficulties mental
health education faces are stigma, misunderstandings, and a dearth of interesting materials that successfully
explain difficult disorders like bipolar disease. To this end, six user-friendly animated modules were
developed, focusing on key aspects of Bipolar Disorder, including an introduction to the condition, its
symptoms, causes, prevalence, types, and available treatments. A structured evaluation process was
employed, combining usability testing to measure knowledge retention with participant feedback to capture
perceptions and experiences. The study engaged three target groups: healthcare professionals, multimedia
experts involved in content creation, and members of the general public with varying levels of familiarity
with Bipolar Disorder. By integrating insights from these groups, the research highlights the transformative
potential of 2D animation in mental health education. Preliminary findings demonstrate that animated content
significantly improves comprehension and awareness, presenting it as a dynamic, engaging, and accessible
medium for disseminating information. These results underscore the promise of 2D animation in reducing
stigma and misconceptions about mental health disorders, paving the way for the development of future
educational resources and interventions
Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications
Get PDFPurpose: This study employs bibliometric analysis to evaluate research trends, key contributors, and applications
of artificial intelligence (AI) models in orthodontic imaging. It highlights the impact and evolution of AI in this field from 1991 to 2024.
Materials and Methods: A total of 130 documents were extracted from the Scopus database, spanning 33 years
of research. The analysis examined annual growth rates, citation metrics, AI model adoption, and international
collaborations. Network visualization was performed using VOSviewer to map research trends and co-authorship
networks.
Results: The study analyzed 96 publications from 47 sources, revealing exponential growth in AI researchparticularly after 2010, with a peak in 2023. The findings show a steady annual growth rate of 9.66% and a maximum citation count of 138 for an AI-based cephalometric analysis study. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) dominate AI applications in orthodontic image analysis. An h-index of 23 and a g-index of 38 reflect the field’s significant research impact. Strong international collaborations were observed, with
28.12% of studies involving cross-border research.
Conclusion: This analysis highlights the growing influence of AI in orthodontic imaging and emphasizes the need
for larger datasets, improved model interpretability, and seamless clinical integration. Addressing these challenges
will further enhance AI-driven diagnostics and treatment planning, guiding future research and broader clinical
applications.(Imaging Sci Dent 20240237)
Weed control efficiency of unmanned aerial vehicle spray in replanting oil palm plantation areas
Get PDFEfficient chemical weed management considers precise application of herbicides, maximizing herbicide retention and absorption, reducing the impact of abiotic factors, and mitigating offtarget movement in order to optimize herbicide efficacy. Hence, this study assessed the employability and cost-efficiency of an unmanned aerial vehicle (UAV) for preplanting application and postemergence selective weed control of grasses infesting legume cover crops (LCCs) in an immature oil palm (Elaeis guineensis Jacq.) plantation. Field experiments were conducted in 2020 and 2021 at a research center and an oil palm replanting area in Jerantut,
Pahang, Malaysia. Droplet deposition and distribution analyses revealed that the pressure at 0.25 MPa yielded better spray coverage and increased droplet counts compared with 0.15 MPa. For preplanting application, both the UAV and mist blower resulted in total weed control. Meanwhile for selective grass control in the LCCs, conventional knapsack sprayer (CKS) application provided slightly better weed control than the UAV over the 12-wk observation.
However, a cost-efficiency analysis revealed that UAV spraying yielded economically favorable results for areas greater than 3,000 ha, with potential savings ranging from 4% to 28%. Furthermore, UAV spraying demonstrated superior operational efficiency and reduced working hours by 37%, water consumption by 91%, and human labor expenses by 81%
compared with both conventional methods. These findings underscore the potential of UAVbased spraying for large-scale weed control in oil palm plantations and highlight its efficiency, comparable effectiveness, and cost-saving benefits
Ti-ZEL in Action : A Mix-Method Study Redefining English-Speaking Abilities for Academic Staff
No full textThis study highlights the effectiveness of the Time Zone in English Language (Ti-ZEL) program in improving speaking abilities among academic staff at one of the public
universities in Pontianak, Indonesia. This was a mixed-method study that included some participants with provoked tests and treatments. Other forms of observation and interviews were conducted to evaluate the overall obstacles and success factors of the Ti-ZEL program.
The program significantly improved speaking abilities with measurable progress (p < 0.001). The findings underscore the importance of providing opportunities and supportive
environments for English practice within habituation programs. Success factors included respondents' basic English skills, an engaging and practical approach, and consistent commitment. Challenges included technical and non-technical barriers, limited proficiency, internal factors (job exhaustion, mental strain), and external distractions (workload, other activities). This cultivated approach greatly governs the level of skill improvement in speaking English. This strongly concludes the necessity of enhancing the English language skill among the academic staff of the university using the Ti-ZEL habituation model. Implementing this approach to the academic staff was new and at first unfamiliar but it proved to be successful
Development of Deep Learning-based Approach for Robust Fault Diagnosis and Failure Prediction of Bearings
Get PDFBearings play a crucial role in rotating machinery. Once a bearing failure occurs, it may lead to severe casualties and economic losses. Therefore, diagnosing bearing faults and ensuring their smooth operation are essential steps in ensuring the safe and stable operation of modern mechanical equipment. However, traditional methods for diagnosing bearing faults typically involve complex manual denoising, filtering, and feature extraction processes, which are both tedious and lack intelligence, making them particularly challenging when dealing with large volumes of data. The operating conditions of bearings are complex and involve various factors, making manual extraction of fault features even more difficult. Thus, this motivates research in improvements of end-to-end bearing fault diagnosis and life prediction. Furthermore, the rapid development of information technology has made deep learning a powerful tool for automatically learning and diagnosing bearing faults under complex conditions. This research aimed to investigate the end-to-end bearing fault diagnosis and life prediction under complex scenarios such as high noise and different operating conditions, explore various deep neural networks for effective bearing degradation prediction and fault diagnosis. To achieve the aim, this research was segmented to 3 parts: (i) Firstly, to achieve end-to-end diagnosis of bearing fault types and severity, this thesis proposed a Multi-scale processing, Channel attention, Feature enhancement, Convolutional Neural Network (MCFCNN) network architecture. The MCFCNN network combines multi- channel parallel convolution, effectively capturing spatial information, and introduces channel attention mechanisms to adaptively recalibrate channel-level feature responses. Moreover, secondary neurons are introduced to enhance the model’s ability to capture complex nonlinear patterns related to bearing faults. In a high-noise environment, the proposed model outperforms existing models in fault diagnosis, with accuracy greater than 80%. (ii) Secondly, a bearing fault test platform was built to validate the effectiveness of the MCFCNN model. Rolling bearings vibration signals were generated using the test platform. In the experiments, the performance of the MCFCNN model in high-noise conditions and the results of different network structures and data augmentation strategies on the final results were verified. In the cross operating scenario, the average resolution in the Bearing Fault Test System (BFTS) dataset is 0.7595, compared to WDCNN with 0.6492 and QCNN with 0.7266. (iii) In the third and final part of this research, the problem of predicting the remaining life of bearings was approached as a classification problem. Rather than predicting the remaining life, the priority was to identify the stage of a bearing’s life cycle for better maintenance and management. Bearing life was divided into four different stages and used the bearing data to predict the current stage of the bearing’s life. Based on this, a method for early prediction of bearing degradation, referred to as Fourier Transform Autoencoder with K-means (FAEK) automatic labeling and Multi-scale Processing Channel Attention Classification (MCC) classification prediction model, was proposed. This method combines a convolutional autoencoder segmentation network with a high-performance classifier, effectively distinguishing bearing degradation processes and achieving accurate classification recognition. In the degradation stage prediction task, our model architecture achieves an accuracy of 0.9665. This thesis focuses on deep learning research applied to bearing fault diagnosis and prediction, which helps to reduce the workload of manual maintenance personnel, enhance the efficiency and accuracy of fault detection, and mitigate losses caused by equipment failures. Indirectly, this reduces maintenance costs, enhances equipment reliability and improves production efficiency, through timely maintenance and fault prevention of equipment
CONSTRUCTING AN INDEX FOR CROSS-BORDER ECOMMERCE DEVELOPMENT LEVELS BASED ON ECOSYSTEM THEORY : EMPIRICAL INSIGHTS FROM RCEP COUNTRIES
Get PDFThis study assesses the development levels of cross-border e-commerce among Regional Comprehensive Economic Partnership (RCEP) nations from 2013 to 2022. The study synthesizes global literature on ecommerce ecosystem theories, key influencing factors, and measurement approaches to construct a multidimensional indicator system. This system integrates the TIMG index to provide a comprehensive analysis of the digital economy's impacts. By employing principal component analysis in Stata, the study formulates a scoring methodology to evaluate e-commerce development across RCEP nations, highlighting trends over the decade. The results identify China, Japan, and Singapore as leading nations, followed closely New Zealand, Malaysia, Thailand, Indonesia, Vietnam, and the Philippines comprise the third tier, while Laos ranks at the lowest development level. The study emphasizes the critical roles of logistics and information flow in e-commerce development, providing valuable insights for future explorations within the RCEP framework