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Kinetic study of biomass activated carbon impregnated with copper (AC/Cu) as a photocatalyst in degradation of ammoniacal nitrogen
Get PDFGeneration of wastewater including leachate that contains ammoniacal nitrogen (AN) is increasing and requires urgent solution. The current status of the photocatalyst development for the degradation of this pollutant is promising but further improvement is desired. This study
explored the possibility of using biomass activated carbon as a catalyst support to provide more surface area. Activated carbon from coconut shell was used to support 30 wt% copper catalyst in a wet impregnation procedure for the catalyst synthesis. Characterization of the photocatalyst
(AC/Cu) was performed using scanning electron microscopy and energy dispersive X-ray (SEMEDX), BET surface area analysis, thermogravimetric and differential thermal analysis (TG/DTA), and Fourier-Transform Infra-Red spectroscopy (FTIR). It was revealed that the catalyst has BET surface area of 430 m2 /g, 0.254 cc/g total pore volume, 60% carbon content, and surface functional groups that include nitriles, alkenes and aromatics at low intensity. The batch experiment on AN removal showed the best catalyst dosage in this system is 0.5 g while 90 spm shaker speed resulted
in the highest AN removal. The initial AN concentration adversely affected the performance of the catalyst where the higher the concentration, the lower the removal. Between pseudo-first and pseudo-second order kinetic models, the latter exhibited the best fit to the experimental data
explaining the mechanism of AN removal is attributable to chemisorption. The photocatalyst supported over biomass carbon has larger BET surface area compared to the conventional titaniabased photocatalyst and the system could be further improved to cater higher concentration of AN
for practical applications
The Impact of COVID-19 Pandemic on Selected Sectoral Stock Returns in Malaysia
No full textThe COVID-19 pandemic that happen in year 2020 brought massive impact in worldwide including Malaysia. The global stock market was affected due to the COVID-19 pandemic. The purpose of this study is to determine the impact of the COVID-19 pandemic on sectoral stock return in Malaysia by using time series analysis. This study employed Ordinary Least Squares (OLS) estimation to determine the impact of the COVID-19 pandemic. The data collected are daily data. The Kuala Lumpur Composite Index (KLCI), financial sector (KLFI), healthcare sector (KLHC) and telecommunication sector (KLTC) are the dependent variable in this study. The independent variables are divided into two categatories which are the COVID-19 variables and the control variables. The COVID-19 variables include the confirmed cases, death cases, recovery cases, lockdown policy, and vaccination while the control variables include the gold price, the oil price, the exchange rate, the volatility index and the NASDAQ composite index. The Granger causality test was developed to determine the short-run causality between those surveyed variables. The result showed that the COVID-19 variables significantly impacted the selected sectoral stock indices and major indices in Malaysia. Therefore, a suitable policy and lockdown measure is essential to minimize the negative impact of COVID-19 pandemic on Malaysian stock market
The role of related humor in facilitating instructional information processing in video lectures : insight from EEG
Get PDFRelated humor is a well-established pedagogical strategy in traditional education; however, its impact on learning from video lectures has been understudied. This study investigated whether related humor, as predicted by Instructional Humor Processing Theory (IHPT), enhances learning in a video-based educational context. Employing a within-subjects design, 25 undergraduate students viewed two video lectures presenting distinct sets of English vocabulary words, each illustrated with either related humorous or related neutral instructional information. Electroencephalography (EEG) signals were recorded while participants viewed the illustrative information, and self-reported learning experiences (motivation, elaboration) and learning performance (recall) were assessed post-lecture. Paired samples t-tests revealed that video lectures incorporating related humorous information outperformed those with related neutral information, as evidenced by increased EEG beta and theta band oscillations, higher motivation scores, and superior recall performance. While related humor elicited greater neural activation associated with memory encoding, no differences were observed in self-reported elaboration between conditions. Taken together, these findings highlight the unique benefits of related humor in facilitating learners’ processing of instructional information within the context of video lectures. Future research should continue to explore the effects and boundaries of related humor, considering variations in humor format and delivery method
Keywords Related humor · Video lectures · Motivation · Elaboration · EE
Ethnic Disparities and Demographic Shifts in Sarawak's Aging Population: A Comprehensive Longitudinal Analysis (1980-2020)
Get PDFThe aging population constitutes a pivotal demographic shift with substantial implications across social, economic, and healthcare spheres, notably in ethnically diverse territories such as Sarawak, Malaysia. This investigation delivers an exhaustive longitudinal examination of the distribution and demographic trends of the elderly among various ethnic communities in Sarawak from 1980 to 2020. Despite abundant scholarship on population aging, the confluence of ethnicity and aging has received scant attention, particularly within the Malaysian milieu. This research endeavors to bridge this gap by exploring the following question: How have the proportions of senior citizens within Sarawak's diverse ethnic groups evolved from 1980 to 2020? Employing secondary data sourced from the Department of Statistics Malaysia, this study utilises descriptive analysis, linear regression models, and Geographic Information System (GIS) techniques to delineate and expound these trends. The analysis uncovers pronounced disparities in aging trajectories among the ethnic groups. Notably, the Chinese community exhibits the most pronounced and consistent increase in the elderly population, while the Iban and other indigenous cohorts show a decline, possibly attributable to migration and diminished fertility rates. Conversely, the burgeoning proportion of elderly Malays indicates enhancements in health and socio-economic statuses, contrasting with the steady figures observed within the Chinese and Indian communities, which likely reflect enduring cultural traditions such as filial piety. These findings underscore the imperative for culturally attuned policies and interventions tailored to the distinct needs of each ethnic group as Sarawak's demographic landscape ages. This study underscores the importance of recognizing ethnic-specific aging patterns for efficacious public health and social service strategy formulation in a culturally heterogeneous region like Sarawak. By integrating insights into the spatial dynamics of aging, anchored by Tobler's First Law of Geography, this research contributes novel perspectives to the body of knowledge and supports the creation of more effective and inclusive policies
Pendekatan Sensitif Budaya dalam Pendidikan Kesihatan Seksual dan Reproduktif
Get PDFPendidikan Kesihatan Seksual dan Reproduktif (KSR) semakin menjadi isu penting dalam masyarakat moden, khususnya dalam menangani kes penderaan seksual dan fizikal terhadap kanak-kanak. Namun, dalam kalangan ibu bapa, perbincangan mengenai seksualiti masih dianggap sebagai topik tabu, menyebabkan kekurangan maklumat dan panduan bagi remaja dalam membuat keputusan berkaitan kesihatan seksual dan reproduktif. Ulasan ini membincangkan buku Berbual-bual dengan Anak tentang Seksualiti karya Wan Nur Syakira (2023) yang menawarkan pendekatan berbasis budaya dalam mendidik anak-anak mengenai seksualiti. Buku ini bertujuan meningkatkan kesedaran ibu bapa dan menyediakan panduan berstruktur untuk membina dialog terbuka yang selari dengan nilai dan norma masyarakat. Ulasan ini menekankan kepentingan penggunaan bahan rujukan yang sensitif budaya bagi memperkasa ibu bapa dalam mendidik anak-anak mereka tentang seksualiti secara holistik dan bertanggungjawab
Unveiling the benefts of Vitamin D3 with SGLT-2 inhibitors for hypertensive obese obstructive sleep apnea patients
Get PDFBackground Obstructive sleep apnea (OSA) is associated with poorer quality of life (QoL) and increased cardiovascular risks, which may be exacerbated by hypovitaminosis D. Sodium glucose transporter-2 inhibitor (SGLT2i) provides
cardiovascular benefts beyond glycemic control. As vitamin D3 and SGLT2i act through diferent pathways with similar mechanisms in improving cardio-metabolic health, this study aimed to investigate the synergistic efects of this
combination therapy in improving these parameters and QoL in hypertensive obese OSA patients.
Methods Patients who fulflled the study criteria were randomized to receive: (i) Dapaglifozin, (ii) vitamin D3, (iii) Dapaglifozin plus vitamin D3, or (iv) no treatment, for 16 weeks. The parameters evaluated included anthropometric measurements, uric acid, HbA1c, lipid profle, steatotic liver disease grade, plasma aldosterone concentration, plasma renin concentration, ultrasound fow-mediated dilatation of brachial artery, hsCRP, heart rate variability (HRV), Epworth Sleepiness Scale (ESS), and QoL scores.
Results A total of 163 patients were recruited and 153 completed the study. The combination of vitamin D3
and Dapaglifozin treatment led to signifcant improvements in metabolic parameters and nocturnal heart rates,
and prevented deterioration of HRV, with healthier HRV at the end of study visit compared to the control group. Only
the combination group exhibited improvements in both ESS and QoL scores.
Conclusions This is the frst study to demonstrate benefcial efects of combining vitamin D3 and SGLT2i in cardiometabolic outcomes and QoL in hypertensive obese OSA patients. These fndings highlight the potential of this combination therapy in addressing the cardio-metabolic challenges and QoL in this patient population.
Trials registration NCT06690723. Registered 10 October 2024—Retrospectively registered, https://register.clinicaltr
ials.gov/prs/beta/studies/S000EWGF00000085/recordSummar
Classification of Diabetic Retinopathy from Fundus Images Using Hybrid Deep Learning Feature Combination and Divide-andConquer Approach
Get PDFDiabetic Retinopathy (DR) is the leading cause of blindness in diabetic patients. Different ophthalmologists capture high-resolution fundus images of the eye with varying sizes and quality. The International Clinical Diabetic Retinopathy (ICDR) severity scales divide the progress of the DR into five stages. Multiclass classification of DR is a challenging task that relies on precisely identifying subtle anomalies. In this work, a multiclass classification system for DR from fundus images of the eye is proposed. The system considers the varying size and quality of the images and proposes a new pre-processing framework to deal with degraded fundus images. Two new frameworks are also proposed for multiclass DR classification. The processed images are used to extract high-quality features, which are passed on to different Machine Learning (ML) classifiers for training and testing. The first framework utilises the inherent feature extraction capabilities of Convolutional Neural Networks (CNN) to extract hybrid features that are used for classification. High-resolution fundus images can discern nuanced vessel textures and other critical features which can further improve multiclass DR classification but require high computational resources and memory requirements. The second framework introduces a memory efficient divide and conquer approach for extracting high-resolution fundus image features. The hybrid framework presented achieves a maximum accuracy of 92.69%, 88.02%, and 83.62% for the binary, three class, and five class DR classification respectively for the Diabetic Retinopathy Dataset (DDR) dataset. The divide and conquer approach further improve the accuracy of the system with values of 97.60%, 89.46%, and 85.64% for the binary, three class, and five class DR classification respectively for the DDR dataset. The results provide significant performance improvement compared to recent studies in the literature
Virtual Reality and Artificial Intelligence for Acrophobia Mitigation: An Analysis of EEG and Heart Rate Physiological Data
Get PDFThe advent and subsequent proliferation of virtual reality (VR) technology have seen its application extended beyond entertainment to the medical field. One such application is in the treatment of acrophobia, a common and costly phobia that often sees patients discouraged by long waiting lists and complex treatment processes. This research evaluated the efficacy of VR and artificial intelligence (AI) in treating acrophobia, focusing specifically on the relative merits of two biophysical measurements: heart rate (HR) and electroencephalogram (EEG). Utilizing graded exposure therapy, acrophobic individuals were gradually exposed to a VR environment commensurate with their acrophobia severity level. In lieu of a traditional therapist, the severity
level and corresponding exposure were determined by an AI agent using a Behavioural Avoidance Test (BAT). This AI agent was also programmed to provide tasks and encouragement to the user in the VR environment. This approach not only negates the safety risks typically associated with exposure therapy but also provides an immersive and realistic environment through high-resolution graphics across seven different height environments. Inclusion and exclusion criteria, as well as the severity of acrophobia, were determined using the Visual Height Intolerance Severity Scale (vHISS). Post-therapy fear levels were measured
using the Attitudes Towards Heights Questionnaires (ATHQ). Findings were analysed using the Mann-Whitney U test to check for significant differences between the two biophysical measurements. Results showed no significant difference between heart rate (HR) and electroencephalogram (EEG) measurements in gauging fear response during VR therapy. The utilization of VR and AI in treating acrophobia remains promising. The ATHQ score decrease highlights VR and AI's potential for effective acrophobia therapy, offering immersive experiences to improve outcomes. Future research should aim to replicate these findings in a sample size that exceeds the current one and encompasses greater demographic diversity, thus enhancing the generalizability of these results
Characterisation of Lowland Rice Landraces From the North-Western Region of Sarawak, Malaysia
Get PDFOryza sativa L. or rice is Malaysia’s primary staple food and food crop. In Malaysia, rice is cultivated either as lowland type or upland type. Lowland rice is mostly cultivated in Malaysia due to high yield potential. Sarawak is known for its richness in rice diversity making the rice valuable for breeding material. However breeding using Sarawak rice are challenging due to little documentation has been made on varietal characterisation. The objectives of the current work were to find distinct morphological traits, anatomical traits and Simple Sequence Repeat (SSR) markers to delineate lowland rice landraces collected across four divisions in North-Western of Sarawak. Utilisation of Maturase-K (matK) was to assist in varietal discrimination. A total of 22 rice accessions was chosen. A set of 25 qualitative (QL) and quantitative (QT) morphological traits together with anatomical observation were used for characterisation. Further observation was conducted on S1 generation derived from 10 accessions, by observing the 25 morphological traits and anatomy observation on leaf samples with triplicates. Accessions in first generation showed genetic variations in all QL traits except for ligule and auricle colour. The S1 generation exhibited variations in QL traits, except for blade colour. The QT traits were helpful in characterising the accessions in both generations due to great variations they provide. Present study showed QL yield-related traits such as compact panicle type with presence of secondary branching have positive impact on number of filled grains. Accessions in present study recorded the same fundamental cell structure in leaf, midrib, root and stele. No variation observed in anatomical cell arrangement. Stomata density in S1 generation was found lower compared to first generation present study. Out of the 39 SSR markers, seven were polymorphic and cluster analysis was performed using Unweighted Pair Group Method with Arithmetic Mean. The number of alleles recorded was 18 with an average of 2.57 and mean Polymorphic Information Content recorded 0.67, suggesting the markers used were highly informative. Mean number of effective alleles was 1.61. The recorded Nei’s gene diversity (0.36) and Shannon’s Information Index (0.53) indicate low diversity in present study. The cluster analysis using seven SSR markers did not show clear clustering based on morphological traits nor origins. Phylogenetic analysis based on matK grouped all accessions into one cluster together with other accessions of O. sativa and O. rufipogon accessions. The phylogenetic tree based on matK showed clustering based on the genome type. Information collected from morphological and anatomical characterisation along with molecular analysis will provide basic information on Sarawak lowland rice. The information obtained are important for conservation of Sarawak rice germplasm and can be used in criteria selection for breeding attempt using Sarawak rice to ensure food security of the country
Classification of Bearing Degradation Stage Based on Automatic Label Assignment and Multi-scale Channel-attention Network
Get PDFPredicting bearing degradation is crucial for precise maintenance. However, accurately predicting the degradation stages of bearings to achieve appropriate maintenance has always been challenging. To address this problem, we propose a network architecture based on automatic label assignment called FAEK and a multi-scale channel-attention classification (MCC) prediction model to predict the degradation stage of bearings at a given time. Our method achieved outstanding performance on the FEMTO dataset with an accuracy of 0.9665. This approach provides an efficient and reliable solution for the predictive maintenance of bearings