Empowering Youth through Play : Promoting Awareness of Sexual Grooming among Schoolchildren through Game-based Learning

Sexual grooming is engaging with a child to build a relationship with intentions of sexual exploitation. In some cases, it precedes child sexual abuse. The need for child sex education can be further illustrated by the severity of the trauma that results from the abuse. Hence, it is ideal to recognise the signs of grooming to reduce the risk of child sexual grooming. This project aims to develop an age-appropriate game to assess game-based learning to educate children on preventing sexual grooming. The methodology implemented in this project is the outcome-based methodology. First, a survey was distributed to guide the design of the game and to identify the relevant learning outcomes relating to sex education the respondents wish their children to learn. Based on the results, there are four learning outcomes to be achieved. Second, the genre of the game was determined to be a visual novel. Third, the premise of the game was written. Fourth, assets to enhance the playing experience were either created or sourced online. Fifth, the game mechanics were developed using Godot Engine. Sixth, the game mechanics were play tested iteratively, before and after integration with one another. Seventh, all the mechanics and non-mechanic elements were integrated to complete development. Eighth, the game was play tested online. Pre- and post-test results from the playtest were recorded and evaluated. Using paired samples t-test with a 95% confidence interval, the calculated t-value 12.011 was more significant than the critical t-value of 2.042, and the p-value 0.00001 was lesser than the significance level of 0.05. The result suggests that using the game to create awareness of physical and online grooming towards children was effective

Effects of Alginate-Encapsulation on Bacterial Cell Viability

Alginate-encapsulation of bacterial cells is a type of cell immobilisation technique used in various biotechnology fields due to its numerous benefits. One of the cited benefits is the increased preservation of bacterial cell viability post-encapsulation. However, the process of encapsulation itself may inherently negatively affect cell viability. This thesis aims to study changes in cell viability after subjecting Escherichia coli BL21 DE3 cells to alginate-encapsulation. Cell viability of free cells were compared with cells that have undergone alginate-encapsulation by analysis using resazurin microtitre assay (REMA) and viability-PCR (vPCR). To reliably assess the viability of the encapsulated cells, a decapsulation step was designed and it was found that the optimal decapsulation conditions determined involved incubating encapsulated cells at 37 °C for 2 hours in PBS containing 55 mM sodium citrate and 500 ug/mL alginate lyase. To ensure REMA and vPCR were performing optimally, heat-killed cells were prepared by heating at 75 °C to preserve DNA integrity, and DNA extracts were treated with 10 ug/uL RNAse A to eliminate interference by RNA during DNA quantification. Overall, it was found that cell viability decreased following cell encapsulation with both REMA and vPCR showing a mean reduction of 14.43% and 31.54% respectively. These results could have important implications in applications whereby cell viability is of utmost importance. Our study is also the first, to our current knowledge, to employ vPCR in encapsulated cells and our workflow may serve as a reference for other relevant ongoing studies. Further research should be conducted to elucidate the relationship between encapsulated cell viability with more practical endpoints

BOX-PCR and ERIC-PCR evaluation for genotyping Shiga toxin-producing Escherichia coli and Salmonella enterica serovar Typhimurium in raw milk

Over the past decade, the occurrence of milk-borne infections caused by Shiga toxin-producing Escherichia coli (STEC) and Salmonella enterica serovar Typhimurium (S. Typhimurium) has adversely affected consumer health and the milk industry. We aimed to detect and genotype the strains of E. coli and S. Typhimurium isolated from cow and goat milks using two genotyping tools, BOX-PCR and ERIC-PCR. A total of 200 cow and goat milk samples were collected from the dairy farms in Southern Sarawak, Malaysia. First, E. coli and Salmonella spp. detected in the samples were characterized using PCRs to identify pathogenic strains, STEC and S. Typhimurium. Next, the bacterial strains were genotyped using ERIC-PCR and BOX-PCR to determine their genetic relatedness. Out of 200 raw milk samples, 46.5% tested positive for non-STEC, 39.5% showed the presence of S. Typhimurium, and 11% were positive for STEC. The two genotyping tools showed different discrimination indexes, with BOX-PCR exhibiting a higher index mean (0.991) compared to ERIC-PCR (0.937). This suggested that BOX-PCR had better discriminatory power for genotyping the bacteria. Our study provides information on the safety of milk sourced from dairy farms, underscoring the importance of regular inspections and surveillance at the farm level to minimize the risk of E. coli and Salmonella outbreaks from milk consumption

Effects of anti-rainbow trout germ-cell monoclonal antibody on germ cells and gonadal tissues in rainbow trout (Oncorhynchus mykiss)

The development of sterilization technology is important to improve the production efficacy of aquaculture and prevent genetically modified fish from escaping. Immunosterilization is a novel sterilization method that has already been used in mammals. In this study, anti-rainbow trout germ-cell monoclonal antibodies (anti-GC mAbs) were injected into immature rainbow trout Oncorhynchus mykiss, and their effects were analyzed. First, anti-GC mAbs labeled with Alexa Fluor 488 were injected into the body cavity of larvae 20 days (group 1) and 102 days (group 2) after fertilization. Second, anti-GC mAbs were injected into the body cavity of yearling trout (group 3), and their impacts on gonadal tissue were analyzed in terms of histology and gene expression levels. As a result, anti-GC mAbs were observed to bind to some primordial GCs in group 1, whereas there were no obvious positive cells in male testes of group 2. In contrast, green fluorescent signals were observed in an arrangement parallel to the ovarian lamina in female ovaries of group 2, which was similar to the arrangement of positive signals on ovarian tissue sections immunostained with anti-GC mAbs. GCs in group 1 were observed again after 76 days, and double-positive cells remained. In group 3, there were changes in the expression levels of GC-specific genes, but the changes were not seen as biologically meaningful. Moreover, there was no histological abnormality in gonadal tissues after immunization. In conclusion, these results suggested that anti-GC mAbs could only reach GCs in early developmental stages or in the ovary, and injection of anti-GCs mAbs did not have harmful effects on GCs after binding

An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA

Network intrusion detection is an essential component of contemporary cybersecurity strategies, and the development of efficient techniques to accurately identify malicious activities has become a priority. This study investigates the performance of various conventional machine learning algorithms, including decision trees, naive Bayes, naive Bayes trees, random forest, random trees, MLP, and SVM, in detecting network intrusions using binary and multi-classification approaches. Furthermore, the study proposes a deep learning method, CNN-LSTM-SA, which consistently outperforms conventional machine learning techniques in terms of precision, recall, F1 score, and overall accuracy for network intrusion detection. Specifically, the proposed method combines CNN and LSTM with SA in machine learning theory to extract more optimized, strongly correlated features. The proposed method is evaluated using the benchmark NSL-KDD database. The results indicate that the CNN-LSTM-SA method holds great potential in enhancing the efficacy of network intrusion detection systems

Treatment of Borneo midstream river water affected by palm oil plantation run-off with sustainable batch electrocoagulation system

Water degradation from palm oil plantation runoff in Borneo rural areas has compelled the local communities to rely on untreated river water which does not meet the Malaysia National Water Quality Standards. As such, this study aims to design a batch electrocoagulation system to treat midstream river water affected by palm oil plantation run-off water from rural Borneo by utilizing aluminium electrodes. Correspondingly, this study investigates the effect of electric current and residence time on contaminants removal by conducting both water quality and energy operating cost analyses. Subsequent, this study found the optimal reduction in turbidity, colour, and total suspended solids through electrocoagulation treatment is achieved with a high electric current of 5 A and a residence time of 50 min. Besides, the treatment system effectively reduced 99% of turbidity, 99% of colour, and 99% of TSS from Batang Kayan river, while achieving reductions of 97% of turbidity, 98% of colour, and 99% of TSS from the Batang Sadong river. This study has also noticed that the treated levels of turbidity, colour, and TSS in both midstream rivers water meet the Malaysia Class I standard in National Water Quality Standard. From the energy operating cost analysis conducted, the treatment system cost only Ringgit Malaysia (RM) 3.35 or United States Dollars (USD) 0.74 per meter cubic of treated midstream river water. Overall, it is deduced that batch electrocoagulation treatment is deemed effective to treat Borneo midstream river water affected by palm oil plantation run-off by producing water that suitable for domestic consumption at a reasonable cost

HEADS : hybrid ensemble anomaly detection system for Internet-of-Things networks.

The rapid expansion of Internet-of-Things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due to the presence of sensitive nature of data exchanged within IoT networks. To address these concerns, network-based anomaly detection systems play a crucial role in ensuring the security of IoT networks through continuous network traffic monitoring. However, despite significant efforts from researchers, these detection systems still suffer from lower accuracy in detecting new anomalies and often generate high false alarms. To this end, this study proposes an efficient Hybrid Ensemble learning-based Anomaly Detection System (HEADS) to secure an IoT network from all types of anomalies. The proposed solution is based on a novel hybrid approach to improve the voting strategy for ensemble learning. The ensemble prediction is assisted by a Random Forest-based model obtained through the best F1 score for each label through dataset subset selection. The efficiency of HEADS is evaluated using the publicly available CICIoT2023 dataset. The evaluation results demonstrate an F1 score of 99.75% and a false alarm rate of 0.038%. These observations signify an average 4% improvement in the F1 score while a reduction of 0.7% in the false alarm rate comparing other anomaly detection-based strategies

Scanning Electron Microscopy (SEM) Leaf Anatomy and Micromorphology for New Zingiberaceae Species in Borneo

This study aims to explore and document the micromorphological characteristics of four newly identified species of the Zingiberaceae family in Singai Bau, Sarawak: Sulettaria meekiongii, Alpinia songet, Zingiber singaiensis, and Amomum jackliamii, using Scanning Electron Microscopy (SEM). Conducted in the biodiverse region of Mount Sijanjang Singai, the research focuses on the anatomical features of leaves, including trichomes, stomata, epidermal cells, subsidiary cells, epicuticular wax, and glands. Collaborating with botanists and utilizing herbarium records and MyBIS for classification, the study meticulously prepared leaf samples for high-resolution SEM imaging. Findings revealed distinct anatomical features: Sulettaria meekiongii exhibited long non-glandular trichomes and rectangular wax patterns; Alpinia songet displayed diverse trichome distributions and protective wax layers; Zingiber singaiensis showed smooth resinous layers and stunted trichomes; and Amomum jackliamii featured dense stomatal arrangements and fissured wax. Additionally, the study assessed these species' traditional uses and ecological significance within the Bidayuh community, highlighting the integration of traditional knowledge and scientific techniques. This research provides a foundational understanding of micromorphological diversity within the Zingiberaceae family, demonstrating the value of combining traditional knowledge with modern scientific approaches for holistic biodiversity conservation. Key Words: Zingiberaceae, Micromorphology, Scanning Electron Microscopy (SEM), Traditional Knowledge and Biodiversity Conservatio