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SYNTHESISING KOJIC PALMITATE NONIONIC SURFACTANTS AS AN ALTERNATIVE APPROACH TO REMOVE ENVIRONMENTALLY POLLUTING COOKING OIL
Get PDFNon-ionic compared with charged surfactants have many advantages, such as hard water tolerant, and are therefore, widely used in many applications such as detergents.
However, most synthetic non-ionic surfactants are non-biodegradable and harmful to aquatic organisms. Discarded cooking oil waste on the other hand, has contributed to
environmental pollution and is also harmful to aquatic organisms due to the inability of oxygen to penetrate through the oil layer. In this study, biodegradable non-ionic surfactant was synthesised via esterification between palmitoyl chloride and kojic acid; the synthesis
was later applied to remove cooking oil via cloud point extraction. The FTIR spectrum shows stretching bands, such as v(C=O) ester and v(C-O) ester at 1,697.77 cm-1 and
1,187.06 cm-1 respectively suggesting the successful formation of the compound and further confirmed with 13C NMR spectrum of the ester carbon (=C-O) signal observed at
168.59 ppm. The physicochemical properties of the synthesised surfactant showed typical critical micelle concentration (CMC) of 0.3% for non-ionic surfactants, emulsion stability (72.32%), and cloud point (60°C). The optimum removal of cooking oil (68.55%) was at surfactant concentration 0.4% (w/v) and mixing speed 2,500 rpm at 65°C. The findings of this study showed a promising environmentally friendly non-ionic surfactant which is a
good alternative approach in eliminating waste cooking oil
Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
Get PDFIn recent years, there has been increasing interest in intelligent optimization algorithms, such as the Whale Optimization Algorithm (WOA). Initially proposed for continuous domains, WOA mimics the hunting behavior of humpback whales and has been adapted for discrete domains through modifications. This paper presents a novel discrete Whale Optimization Algorithm approach, integrating the strengths of population-based and local-search algorithms for addressing the examination timetabling problem, a significant challenge many educational institutions face. This problem remains an active area of research and, to the authors’ knowledge, has not been adequately addressed by the WOA algorithm. The method was evaluated using real-world data from the first semester of 2023/2024 for faculties at the Universiti of Sarawak, Malaysia. The problem incorporates standard and faculty-specified constraints commonly encountered in real-world scenarios, accommodating online and physical assessments. These constraints include resource utilization, exam spread, splitting exams for shared and non-shared rooms, and period preferences, effectively addressing the diverse requirements of faculties. The proposed method begins by generating an initial solution using a constructive heuristic. Then, several search methods were employed for comparison during the improvement phase, including three Variable Neighborhood Descent (VND) variations and two modified WOA algorithms employing five distinct neighborhoods. These methods have been rigorously tested and compared against proprietary heuristic-based software and manual methods. Among all approaches, the WOA integrated with the iterative threshold-based VND approach outperforms the others. Furthermore, a comparative analysis of the current decentralized approach, decentralized with re-optimization, and centralized approaches underscores the advantages of centralized scheduling in enhancing performance and adaptability
Optimal Curing Temperature for Determination of Compressive Strength of Geopolymer Concrete via Artificial Neural Network (ANN)
Get PDFGeopolymer concrete offers a promising alternative to traditional Portland cement concrete, exhibiting comparable mechanical and durability performance while reducing
environmental impacts. However, achieving desirable properties in geopolymer concretes through heat curing remains challenging. This study proposes the use of an
unsupervised Artificial Neural Network (ANN): Self-Organizing Map (SOM) to determine the optimal curing temperature of geopolymer concrete based on experimental
datasets. The novelty of this study lies in utilizing SOM for clustering and pattern recognition to establish the relationship between curing temperatures and compressive
strength, providing a novel data-driven methodology for enhancing material performance. Data on compressive strength at different curing temperatures were collected and used to train and validate SOM models. Fly ash based geopolymer
concretes of size 100mm3 cubes were prepared in two sets of activators; sodium hydroxide (NaOH) and a combination of sodium silicate (Na2SiO¬3) with NaOH. These samples underwent curing under three conditions: ambient, 60° and 80° for 28 days. Clustering analysis generated by the SOM model provides valuable insights into the relationship between curing conditions, activator dosages, and compressive strength. Consequently, a cluster of mix proportion was developed, enabling the selection of
specific curing conditions that result in targeted compressive strength. The results show that curing temperatures of 80°C offers optimal compressive strength ranging from 27MPa to 34MPa. This method introduces a novel "cluster mix proportion" for selecting curing parameters and demonstrates the potential of machine learning in advancing
sustainable construction materials. The approach provides a distinct advantage by reducing reliance on trial-and-error methods, saving time and resources, and establishing a foundation for further exploration of data-driven techniques in cement and concrete research
Normotensive Primary Aldosteronism – Does it Exist?
Get PDFHeightened aldosterone levels are associated with increased risk of renal sequelae, cardiovascular morbidity and mortality. Historically, primary aldosteronism is linked to hypertension. However, growing evidence reveals its presence even in normotensive individuals. This review consolidates data from diverse sources, delves into clinical studies of this underexplored condition, discusses the potential mechanisms, and provides a comprehensive and an up-to-date overview of the current state of knowledge. It highlights the evidence and understanding of normotensive primary aldosteronism, summarizes findings, and identifies opportunities for future research in this area. By addressing the clinical evidence, risk of hypertension development and possible mechanisms involved, this review aims to advance the understanding of this distinct form of primary aldosteronism and inspire further research in this emerging field
Antibacterial Activities of Boesenbergia stenophylla (Jerangau Merah) against Selected Waterborne Bacteria
Get PDFBoesenbergia stenophylla is a critically endangered ginger native to the highlands of Borneo and has been traditionally used as herbal medicine. Nevertheless, there is a scarcity of information regarding the specific components of ginger and the techniques used to extract and preserve the bioactive compounds of B. stenophylla rhizome and leaf parts. Thus, the objectives of this study were to identify the chemical constituents of ethyl acetate, ethanolic and methanolic leaf and rhizome extracts via Gas Chromatography-Flame Ionization Detector (GC-FID) and Gas Chromatography-Mass Spectrometry (GC-MS), while evaluating their antibacterial activities. In this study, soxhlet extraction with ethyl acetate, ethanol and methanol were utilized to produce crude extract from rhizomes and leaves of B. stenophylla. Then, the identities of phytochemicals in the crude extracts were then identified using GC-FID and GC-MS. The crude extracts were diluted and tested for antibacterial activities against selected waterborne bacteria, namely Bacillus sp., Staphylococcus sp., Citrobacter sp., Enterobacter sp. and Klebsiella sp through disc-diffusion and colorimetric broth microdilution assays. From the yield extraction, the ethanolic rhizome extract has the most yield (5.817 ± 0.613 mm), followed by methanolic rhizome extract (5.329 ± 0.536 mm) and ethanolic leaf crude extract (3.387 ± 0.774 mm). Based on the GC-FID and GC-MS analysis, 72 out of 110 phytochemicals were identified for various biological effects including potential antibacterial effects in the crude extracts. From the disc-diffusion assay, ethanolic leaf crude extracts showed the largest inhibition zone against Enterobacter sp. and Citrobacter sp. with average diameters of 21.6 ± 0.3 mm and 20.0 ± 0.6 mm, respectively. This is followed by methanolic rhizome crude extract against Bacillus sp. (16.3 ± 0.8 mm). These crude extracts were tested in the colorimetric broth microdilution assay where the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of ethyl acetate rhizome extract against Bacillus sp. were identified as low as 0.781 μg/μL. The MIC/MBC ratio of ethanolic and methanolic B. stenophylla extracts tested were found to be less than 4, which indicates the extracts can be utilized as bactericidal agents. However, the ethyl acetate crude extracts, except for rhizome extracts against Bacillus sp. and Klebsiella sp. as well as leaves extract against Klebsiella sp. showed MIC/MBC ratio same or greater than 4, suggesting the potential to be utilized as bacteriostatic agents. From this study, the crude extracts of B. stenophylla, especially ethanolic and methanolic rhizome and leaves displayed the highest extraction to treat the selected waterborne bacteria. Thus, these mentioned crude extracts should be intensively studied by purification and identification of the fractions or molecules that had potential to be used as the antibacterial agents to treat the waterborne bacteria
Bearing fault diagnosis in high noise environment using multi-scale processing, channel-attention and feature-enhanced convolutional neural network model
Get PDFThis paper presents a model using deep learning techniques which includes Multi-scale processing, Channel attention, Feature enhancement, and anomaly Classification layers, referred to as MCFCNN, for bearing fault diagnosis in noisy industrial environments. The MCFCNN network combines multi-channel parallel convolution, effectively capturing spatial information, and introduces channel attention mechanisms to adaptively recalibrate channel-level feature responses. Secondary neurons are introduced to enhance the model’s ability to capture complex nonlinear patterns related to bearing faults. The model was tested and compared to other models using a publicly available data set. In a simulated high-noise environment, the proposed model outperforms existing models in fault diagnosis, with accuracy greater than 80% even at high signal-to-noise (SNR) ratio. At SNR = -6, the MCFCNN records higher accuracy (83%), precision (89%), and recall rates (84.5%) as compared to prior models. The proposed model can be integrated into the maintenance management system to enhance bearing health assessment and prediction, improving machine prognostics
Psychometric properties of the Chalder Fatigue Scale 14 in women with postpartum depression: A cross-sectional study
Get PDFThis study aims to assess the reliability and validity of the Chalder Fatigue Scale 14
(CFS-14) in women with postpartum depression (PPD). This cross-sectional work
employed purposive sampling to recruit 247 participants from three hospitals in China.
This study used reliability testing, exploratory factor analysis (EFA) and confirmatory
factor analysis (CFA) with the CFS-14 and the Multidimensional Fatigue Inventory
(MFI-20) as the research instruments. The findings showed: (i) Reliability: The CFS-14
demonstrated good internal consistency with a Cronbach's alpha of 0.88 and a split-half
reliability of 0.86. (ii) Validity: EFA identified four factors with item loadings over 0.5
except for item 14. CFA indicated an excellent model fit. Composite reliability (CR)
ranged from 0.839 to 0.887 and the square root of the average variance extracted
(√AVE) ranged from 0.754 to 0.893 demonstrating good construct validity. The CFS-
14 exhibited moderate to strong correlations with the MFI-20 (r = 0.456 to 0.742)
supporting acceptable criterion validity. In conclusion, the instrument exhibited
satisfactory reliability and validity with the CFS-14 demonstrating a solid four-factor
structure instead of the original two-factor model in women with PPD after removing
item 14. This enables health workers to assess fatigue and implement interventions
more effectively
Decoding Innocence in the Israeli-Palestinian Conflict: Semiotics of Palestinian Children's Cartoons on Twitter
Get PDFFollowing the October 7, 2023 attacks in Israeli-Palestinian conflict zones, social media, especially Twitter, saw a spike in cartoon sharing. This study examines the visual representation of Palestinian children in cartoons on Twitter to decode the perspectives of child characters within the broader contexts of conflict and innocence. The study employed social identity theory (SIT) to frame the children’s innocence shown in the cartoons. Visual content for this qualitative study on Palestinian children's cartoons was collected from Twitter. Data were counted for frequency, categorised into themes, and analysed using Barthes’ semiotic framework. Findings reveal 6 themes: Victimhood, Emotional Appeal and Social
Responses, Cultural Context: Displaced Histories, Political Commentary, Resilience and Resistance, and Unaligned
Representation. The predominant theme of Victimhood in the Palestinian children's cartoons underscores their
vulnerabilities within the Israeli-Palestinian conflict. The discovery of Unaligned Representation in this study indicates a
recontextualisation of diverse perspectives that challenge singular narratives
Integrated Marketing Communication Practices as the Predictors on University Reputation of Malaysian Private Higher Education Institutions
Get PDFIntegrated marketing communication (IMC) has piqued the fascination of both management and marketing specialists
since its inception. Thus, it is worthwhile to investigate it in the context of private higher education institutions (PHEIs) as PHEIs operate like business entities and need students (customers) to support their survivability. The goal of this research is to examine the impact of IMC practices on the reputation of PHEIs. The research method used for this study is quantitative and guided by a stimulus-response (S-R) model. 331 valid data was collected and the findings revealed that word-of-mouth, online marketing, advertising, and public relations were the predictors of the university's reputation, however, the university website, university brand logo, sales promotion, and direct marketing were not the predictors. The study contributed to the management of PHEIs to structure and utilize their IMC strategies to disseminate persuasive
messages to influence the customers (students) and gain a competitive advantage. The conclusion, implications, and
future research avenues were also discussed.
Keywords: integrated marketing communication practices, university reputation, stimulus-response model, private
higher education institutions, educational governanc
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