Cloud computing is a critical infrastructure to the modern digital services, which provides the ability to store data on a scale, distributed computing, and the ability to deploy services flexibly. Moreover, the high rate of cloud environment development has also contributed to the risk of malicious intrusions like the spread of malware, unauthorized access, insider threats, and suspicious network activity. Such threats are hard to detect because of the very high dimensionality of cloud activity datasets and redundant or irrelevant attributes. This research suggests a Dynamic Attribute Filtration framework to identify malicious activities in cloud environments with high accuracy to report this issue. The proposed system dynamically determines the importance of attributes based on statistical measures of importance (information gain and correlation analysis), and selects the useful features based on an adaptive threshold mechanism. The filtered feature set is then used by a machine learning classifier to differentiate between normal and malicious cloud activities. It was tested with Python and traditional cloud security datasets with thousands of networks and system activity records. According to the Investigational results, the proposed method considerably extends detection performance in opposition to the traditional feature selection methods. The explicit model has an accuracy of 98.2%, precision of 97.8%, recall of 98.5%, and a F1-score of 98.1% with a false positive rate of 1.6%. The comparative analysis, with no filtering and all feature models, had an accuracy of 94.1%, and the static feature selection methods led to an accuracy of about 95.6. The proposed framework saved the time of computational processing approximately 20-25%, which is more efficient when it comes to large-scale data analysis of clouds. The findings indicate the effectiveness of dynamic attribute filtering in developing malicious activity recognition in cloud settings. The proposed framework increases the detection accuracy, minimizes false alarms, and provides an efficient method to protect modern cloud infrastructures.
The speed of the increasing digital content requires the creation of successful Automatic Text Summarization (ATS) systems. Although major improvements have been made in the summarization of high-resource languages, the summarization of Arabic texts has not been effectively studied, especially in terms of comparative studies of preprocessing methods of documents and word-embedding algorithms. This paper explores the effects of some of the most important variables on the work of graph-based extractive summarization of Arabic news articles, namely, preprocessing methods, word embeddings, ranking methods, and compression ratios. There were experiments using the Essex Arabic Summary Corpus (EASC) with four preprocessing methods (Khoja, Farasa, Qalsadi, and Stanza), two word embedding models (GloVe and AraBERT), two ranking algorithms (PageRank and HITS), and two compression ratios (30% and 40%). The quality of summarizing was measured by the ROUGE-1 F- score. The findings indicated a significant difference (p < 0.001) in all factors, and GloVe performs better than AraBERT (average ROUGE-1 F-score of 0.389 vs. 0.36), and a higher compression ratio (40% more) achieved better performance. To be more precise, such preprocessing techniques as Khoja and Farasa yielded the same ROUGE-1 F-scores of 0.381 and 0.379, respectively, and Stanza gave much lower ones (0.364). It was statistically significant that there have been interactions between preprocessing model and word embedding model, ranking algorithm and compression ratio. Future research will offer more extensive guidelines on how to choose the best preprocessing and representation strategies to use with Arabic ATS systems by including larger and more varied datasets, as well as human evaluation methods to offer a wider range of evaluation. More studies will also be done on the fusion of the supervised summarization technique and deep learning-based systems and multilingual summarization systems.
This study investigates how the reputation of social media influencers affects online prosocial behaviour, focusing on the sequential mediating roles of parasocial interaction and perceived homophily. Grounded in Source Credibility Theory, Parasocial Interaction Theory, the Similarity-Attraction Paradigm, and Social Cognitive Theory, the research addresses a critical gap in understanding the psychological pathways linking influencer traits to socially beneficial outcomes. The study specifically explores male followers of fashion and grooming influencers on platforms such as Instagram Reels and YouTube Shorts. A cross-sectional design was employed using purposive sampling. Data were collected from 459 male social media users who follow at least one male fashion/grooming influencer. Validated scales were used to measure influencer reputation, parasocial interaction, perceived homophily, and online prosocial behaviour. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test the hypothesized sequential mediation model. Findings reveal that influencer reputation positively predicts both parasocial interaction and perceived homophily, which, in turn, significantly influence online prosocial behavior. The strongest mediator was perceived homophily, followed by parasocial interaction. Notably, the sequential mediation path from influencer reputation to prosocial behavior via parasocial interaction and perceived homophily was statistically significant, underscoring the dual affective and cognitive mechanisms at play. The statistical results indicated that perceived homophily is positively influenced by the influencer's reputation (beta = 0.998, t = 58.393, p < .001) and prosocial behavior (beta = 0.574, t = 2.381, p = .017). This study contributes to influencer marketing literature by shifting the focus from commercial outcomes to prosocial digital behaviors. Practical implications suggest that brands should collaborate with reputable influencers who evoke emotional bonds and perceived similarity with their followers to foster socially responsible engagement online.
In this research paper, the researcher looks into how diversity in advertising has impacted the purchasing behavior of Generation Z consumers. With inclusiveness emerging as a major consideration among this group of people, it is important to learn how their perceptions of the brand and their intention to purchase the product are influenced by diversity in its representation. The study is a mixed-method study, which includes both quantitative and qualitative studies. The quantitative techniques involve Descriptive statistics, regression analysis, and Structural Equation Modeling (SEM), whereas qualitative data will be analyzed by using thematic Analysis and sentiment analysis. The most important statistical results show that the diversity in advertisement has a significant impact on purchase intention (0.45, p = 0.001), brand trust (0.32, p = 0.002), and cultural relevance (0.40, p = 0.001). Regression and SEM analyses also substantiate the existence of positive results of perceived diversity in increasing consumer trust and engagement that subsequently increases purchase decisions. Thematic analysis qualitative results show that authenticity of brands and cultural relevance are the crucial predictors of consumer attention, and that the respondents stressed the significance of authentic representations in advertisements. Sentiment analysis demonstrates that 60 % of respondents had a positive feeling about various advertisements, and 10 % were doubtful. These findings imply that Generation Z would appreciate authenticity and cultural depiction and that a brand with an inclusive approach to advertising can potentially build a deeper emotional engagement and brand loyalty. This paper concludes that to be relevant to the Generation Z market, a brand should focus on authentic, culturally appropriate advertising that appeals to the socially conscious generation, which will eventually result in increased consumer interest and increased purchasing patterns.
Based on an analysis of the mediating impact of digital literacy, platform accessibility, and digital ecosystem trust, this paper examines the impact of quick commerce (q-commerce) on financial inclusion in urban India. Reliability analysis, correlation analysis, multiple regression analysis, and parallel mediation analysis were used using survey data of 180 active users of q-commerce. The findings show that the total effect of the q-commerce use on financial inclusion is significant and positive (c = 0.910, p < .001). The direct effect was also found to be significant (c' = 0.231, p =.013) in situations where the mediators were included, and this means that there is partial mediation. The most important pathway between the mediators was the availability of platforms (indirect = 0.352, 95% CI 0.111 0.557), although the digital literacy and trust had no significant indirect effects. The impact of all the indirect effects was high (0.680, 95% C.I. 0. 430 -0.906), indicating the combined effect of the mediating variables. These results imply that usability and convenience in the interaction with the platform, and not necessarily digital capability or trust, are the drivers of the benefits of financial inclusion of q-commerce. The paper highlights the importance of platform design with ease of use and intuitiveness to provide equal opportunity to digital access and to reduce the digital divide, which is emerging in the quickly expanding e-commerce sector.
Neurodivergent language learners, including those with differences in attention regulation, sensory processing, and other measures, typically experience higher levels of intrinsic and extraneous cognitive load during vocabulary acquisition, leading to poorer vocabulary retention and slower semantic integration. The proposed study applies the Cognitive Load Optimization (CLO) framework, leveraging Augmented Reality (AR), to improve vocabulary learning efficiency and reduce cognitive overload. The results are a combination of adaptive multimedia presentation, dual-channel input balancing, and real-time monitoring of cognitive load that uses performance-based proxies, as well as a subjective rating scale. A controlled experimental study was conducted involving 120 neurodivergent learners, who were divided into a traditional digital learning group and an AR-assisted CLO group for 6 weeks. A modified NASA-TLX scale was used to measure cognitive load, and immediate and delayed post-tests were used to assess vocabulary retention. The findings show that the AR-CLO group showed 27.8% improvement in immediate recall and 34.5% improvement in delayed retention compared with the control group (p < 0.01). Extraneous cognitive load was reported to have decreased by 22.3%, with a corresponding increase in germane load of 18.7%, suggesting that schema-building efficiency may be enhanced. Processing fluency was also indicated by a 16.4% reduction in learning time per vocabulary set. Regression analysis revealed that the decrease in cognitive load explained 41% of the variance in retention performance (R2 = 0.41). The findings are consistent with the hypothesis that adaptive AR environments can systematically configure cognitive load distribution, thereby improving vocabulary acquisition among neurodivergent learners. The proposed design offers a scalable framework for inclusive language teaching focused on cognitive personalization and interface design with sensory consideration.
Purpose: The growth in platform-based jobs has created a need to have competencies that go beyond traditional digital capabilities. The conceptualization of Gig Literacy 2.0 in this work is an adaptation of the DigComp 2.0 framework to the changing, platform-specific skills that freelancers and gig workers need to have in order to function in the algorithm-mediated labor markets. Methodology: The study's identification of Gig Literacy practices was based on a comprehensive literature review regarding digital competence, platform work, and freelancer skills. This informed the contextual adaptation of DigComp competencies for gig work. According to these results, a more specific Gig Literacy tool was created and distributed to freelancers and gig workers across various digital platforms (N = 257). Confirmatory Factor Analysis (CFA), construct validity, and reliability were assessed. Findings: The measurement model established high standards of fit (CMIN/DF = 1.43; GFI = 0.947; CFI = 0.985; TLI = 0.980; RMSEA = 0.041). Construct convergent validity was verified by Composite Reliability, which ranged between 0.87 and 0.91, and Average Variance Extracted (AVE), when all constructs showed an average value of over 0.69. The structural results indicated that demographic variables played a significant role in Gig Literacy practices (p < .05), especially in terms of the type of work and level of the intensity of skills. Conclusion: The results confirm that platform work necessitates multidimensional competencies beyond conventional digital literacy. The professionalized and competitive conditions of the freelancer environment demand highly developed strategic and evaluative platform skills that would help a freelancer to increase the rating of clients, win projects, and ensure a stable income. The research goes further to elaborate on digital competence theory by developing a proven Gig Literacy framework to fit the platform economy.
The successive Ionic Layer Adsorption and Reaction (SILAR) process was used to form Zinc Oxide (ZnO) thin films on glass substrates, and deposition cycles of 5, 15, 20, and 25 were used to investigate the effect of the number of cycles on the structural and optical characteristics of the film. The X-ray diffraction (XRD) was determined to be a polycrystalline structure of ZnO, having the hexagonal wurtzite structure with a preferential orientation of the (100) plane. The size of the grain and crystallinity increased with the number of deposition cycles, and the density of dislocations reduced, which implies better quality of the film. The UV- Vis spectroscopy was used to determine the optical properties of the material, such as the optical band gap and absorbance. The findings indicated that the optical band gap was different in the range of 3.67 eV to 3.81 eV, with the largest band gap found in the 20-cycle sample. The absorbance reduced with an increase in the cycle number, indicating improved scattering and grain boundary effects with increasing number of cycles. The movies proved to be more transparent as the cycles were increased, and thus can be applied in the field of optoelectronics, where solar cells and light-emitting devices can be used. SILAR has emerged as an economical approach to prepare ZnO thin films and has provided an easy and effective methodology to regulate the film thickness and quality. The research also gives important information on the depositional cycle as it affects the characteristics of ZnO films, which is important in the optimization of ZnO thin films for different technological uses. Further research to improve the performance of the film, such as the refinement of the deposition parameters and the possibility of incorporating the films into the real world, such as solar cells, sensors, and transparent conductive layers, will be undertaken in the future.
Manual comparative methods have long been the main source for reconstructing Proto-Indo-European (PIE) dialects, with their weaknesses including fragmentary corpora, interpretive bias, and a lack of direct textual evidence. This paper introduces a probabilistic semantic reconstruction model that combines computational comparative linguistics and deep neural archiving to learn and reconstruct the dialectal variations that have been lost in PIE. A multilingual dataset of 12 Indo-European language branches and 18,742 cognate sets, with phonological, morphological, and semantic feature embeddings, was compiled and entered. An inverse phylogenetic inference model based on Bayesian inference and a transformer-based deep neural network trained on 4.6 million aligned lexical tokens was used to predict proto-forms and semantic shifts. When tested against known scholarly reconstructions, the proposed model achieved 86.3% accuracy in phonological reconstruction and 0.81 semantic consistency (cosine similarity metric). Cross-validation indicated a 14.7% decrease in reconstruction variance compared to traditional rule-based methods. Probabilistic confidence intervals (95% CI) also showed consistent predictions for high-frequency lexical roots, with posterior probabilities greater than 0.90 for the reconstructed forms (63%). Moreover, statistically significant divergence patterns (p < 0.01) were observed in the dialectal clustering analysis and were consistent with established Indo-European subgroup stratifications. The results show that probabilistic modelling with deep neural semantic archiving can significantly improve the reliability and interpretability of reconstruction. This framework offers a computational approach to historical linguistics that can be scaled and replicated. Also, it provides a new quantitative understanding of the evolution of proto-languages and dialect differentiation within the Indo-European family.
Village rooms (K & Ouml;Y ODALARI) in the village of SUSUZOSMANIYE/AFYONKARAHISAR are a significant feature of the rural heritage of Anatolia, combining architectural features and sociocultural values like hospitality, solidarity, collective memory, and local governance. Though the village rooms have been analyzed from an architectural and typological point of view, the analysis of the rooms as a hybrid heritage space (both tangible and intangible) has been relatively little discussed so far. The concept of living heritage is used to interpret various aspects of village rooms, their spatial organization, architectural continuity, and sociocultural functions in a larger context of heritage. The study area consists of two village rooms in SUSUZOSMANIYE Village, AFYONKARAHISAR, T & uuml;rkiye (Ahmet Sad & imath;k Ta & scedil;p & imath;nar and Emin & Ccedil;avu & scedil; Village Rooms), which are currently in use. The research approach involves architectural documentation, measured drawings, spatial analysis, field observation, and the oral history interviews of local users and owners. The results showed that the village room is a space that has multiple functions as a place for welcoming guests to the village, as well as a space for multi-functional village governance, social interaction, cultural transmission, and collective decision-making. Although both buildings have undergone physical change and the rural life has changed, both buildings remain in use and retain their social and cultural importance. The study suggests that the village rooms are living heritage spaces that are closely linked to both tangible architecture and intangible social activities, which can be integrated into community-based approaches to rural heritage conservation and can be used in contemporary discussions on sustainable rural heritage conservation.
This research introduces a sophisticated deep learning framework for automatically identifying diseases in rice leaves by combining the Inception V3 architecture with spatial attention mechanisms. Rice is one of the most important foods in the world, in terms of food security and agricultural economics, so the establishment of efficient disease surveillance systems is now necessary to support sustainable farming activities. This is particularly important in achieving SDG 2 (Zero Hunger), which aims to ensure access to sufficient food, and SDG 12 (Responsible Consumption and Production), which promotes sustainable farming practices. Convolutional neural networks have been successfully used to classify plant diseases in the past, but traditional convolutional neural network models are sometimes not capable of ranking the most diagnostically relevant features. The attention mechanisms used in this methodology defeat this challenge by integrating the Inception V3 framework. In particular, the spatial attention aspect guides the model to areas that are of disease-specific features. The study used a dataset of 18,160 images of the rice leaf, which included nine separate disease types and controls that were selected through the Kaggle and Plant Village datasets. Results showed that the attention-enhanced hybrid model reached 92.98% accuracy in classification tasks, surpassing the standard Inception V3 baseline in fewer than 12 training epochs. Significant improvements were also noticed in the cases of distinguishing diseases having similar visual appearance, especially in early and late blight conditions. The model showed strong performance across all ten rice disease categories that were tested, reaching its best validation accuracy of 92.98% during the 12th training epoch. These findings indicate that there is indeed a benefit of incorporating attention processes into the InceptionV3 architecture for the task of disease detection in agricultural crops.
This paper explores the effect of different etching currents on the optical and electrical properties of Au/PS/p-Si/Al heterojunctions. PS layers were prepared in p-type silicon wafers by use of the electrochemical etching (ECE) technique, and then the metallization with gold (Au) was deposited through thermal evaporation. The etching currents were adjusted (0.2, 0.4, and 0.6 mA), and the obtained heterojunctions were analyzed in terms of current voltage (I-V), capacitance voltage (C-V), and reflectivity. The most important results included the fact that the etching current was increased, which essentially enhanced the detection efficiency to 93% at 0.6 mA, and the rectification ratio, which was indicative of improved diode-like behavior. Also, the capacitance was found to reduce with the increased etching current, which was also due to the enlargement of the depletion region in the PS layer. Optical findings revealed an increase in reflectivity with an increase in etching currents, with the growth in surface roughness and the enhancement of light scattering being the main reasons. This finding demonstrates the vital importance of etching current in the control of the electrical and optical properties of PS-based heterojunctions. The paper adds useful information to the optimization of porous silicon devices to be used in optoelectronic devices, showing how microstructural alterations can be used to influence performance. The future directions of the work should be further optimization of etching parameters, the effect of various objective electrolytes in their make-up, and the stability of such heterojunctions over time to be put into practical use as photodetectors, solar cells, and sensors, among others. Also, it will be important to scale up the fabrication process whilst ensuring high performance, as this will be essential in implementing the fabrication process in the real world.
The study explores the interaction between awareness and attitude in determining the cause related marketing (CRM) intentions of the Generation Z, localized in the Ernakulam District of Kerala. Although there are high literacy levels in the region, little has been done in determining the mechanisms used by CRM to mobilize youth on social and environmental issues. The study uses percentage analysis, ANOVA, and linear regression to analyze the cognitive basis of conscious consumerism using a primary sample of 100 participants. Statistical analysis indicates that there is a strong positive relationship, which is significant, between CRM awareness and consumer attitude (r = 0.549, p < 0.001). Further regression analysis indicates that awareness is a strong predictor of brand sentiment and indicates that it explains the entire variation between consumer attitude to 30.1 ((R-2 = 0.301). Demographic ANOVA analysis shows that the level of awareness in the groups is homogenous; whereas gender has a significant moderating effect on the intensity of attitudes (p = 0.028), female respondents are found to be more sensitive to morality. The findings indicate that in order to ensure CRM bridges the awareness-action gap, brands should go beyond the superficial disclosures and adopt transparent and localized approaches of communication. The study ends with a proposal for a future study on the importance of Artificial Intelligence (AI) transparency and longitudinal trust formation in maintaining the interest of Gen Z in social causes.
This paper examines the effectiveness of Generative AI (GenAI) as a socio-pragmatic aid that can improve Cross-Cultural Communicative Competence (CCCC) in advanced language learners (N = 80). Although such learners may be highly proficient in their language, A so-called pragmatic gap may also be faced in attempts to negotiate complex social interactions and in high-context cultural interactions. The study followed a quasi-experimental pre-test/ post-test control group study design where the influence of a four-week structured GenAI intervention was quantified. The experimental group went through several cycles of safe failure by using GenAI models that had to simulate different cultural personalities, whereas the control group used the traditional instructional materials. Analysis of the statistical results showed that the experimental group (n = 40, total sample N = 80) recorded a significant mean score of 20.85 points on the Written Discourse Completion Task (WDCT) as compared to a 2.65-point gain in the control group, which was not statistically significant relative to the intervention's impact. A one-way ANCOVA, controlling for initial proficiency, showed a significant main effect for the intervention (F(1, 77) = 295.70, p < .001), with a Partial Eta Squared (eta(2) = .794) indicating that the GenAI framework accounted for nearly 80% of the variance in post-test performance. Also, the effect size (Cohen d = 3.89) was found to be substantial, which implies that GenAI can be regarded as an effective artificial interlocutor to learn the nuances of cross-culture. The results have strong empirical support to include AI-informed socio-pragmatic support within the modern curricula in order to bridge the gap between grammatical accuracy and cultural fluency. The research paper ends by summarizing the pedagogical considerations of the concept of AI-mediated safe failure and outlining the existing limitations in the form of the lack of non-verbal communication in text-based AI models.
This study examines how well e-commerce tactics work to encourage client retention in digital retail settings using the 4Ps Marketing Strategy (Product, Place, Price,and Promotion).The study addresses the gap of missing an integrated quantitative framework capable of systematically linking the conventional marketing mix dimensions with measurable customer retention results on e-commerce platforms. 356 active e-commerce customers from various Indian online buying platforms provided data for the current study, which used a method of quantitative investigation based on a cross-sectional survey design. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the gathered responses in order to look into the connections between various marketing mix constructions, customer satisfaction, and consumer retention behaviour. The measuring constructs' convergent validity and reliability were tested using Cronbach's alpha, Composite Reliability(CR), and Average Variance Extracted(AVE), which produced results above the suggested cut-off. The findings demonstrate that the retention and satisfaction of clientoutcomes are significantly impacted by all four marketing mix variables. The product had the greatest impact on customer satisfaction (beta = 0.41, p < 0.001), followed by Promotion (beta = 0.36, p < 0.001), Price (beta = 0.29, p < 0.01),and Place (beta = 0.22, p < 0.05). Additionally, having a path coefficient of 0.73, customer satisfaction significantly influenced customer retention. With an R2 of 0.68, the suggested model demonstrated a good explanatory power, meaning that the combined 4Ps framework accounted for 68% of the variance in retention of customers.The study concludes that the customer-focused execution of marketing mix strategies significantly impacts the retention performance of the e-commerce ecosystem and provides a statistically verified framework for measuring the effectiveness of digital marketing.
The rapid development of digital learning technologies has raised concerns about credential fraud, centralized data, restricted learner mobility, and limited epistemic agency. Traditional credentialing systems, generally organised by universities, accreditors, and centralised systems, restrict students' autonomy by lacking ownership, portability, and verifiable control over the personal records. This paper will analyse the effects of decentralised blockchain credentialing and how it transforms students' autonomy and epistemic agency in digital learning systems worldwide. A mixed-methods approach was utilized, and 1,248 learners were sampled in 18 countries that joined blockchain-based credential systems on infrastructures supporting Ethereum and Hyperledger. Perceived autonomy, credential portability, and epistemic agency were measured quantitatively using pre-and post-use platform data collected over a 12-month interval. Verifiable credentials and self-sovereign identity (n = 64) in learners' experiences may be examined through qualitative interviews. Findings reveal that perceived learner autonomy has increased by 37% (p < 0.01), credential verification time has decreased by 42%, and cross-border credential recognition has improved by 29%. Also, 68% of respondents reported increased control over the sharing of credentials, and disputes over credential verification declined by 23%. Structural equation modelling showed that decentralized ownership was a significant predictor of epistemic agency (beta = 0.54, p < 0.001). The results suggest blockchain-based credentialing enhances learners' autonomy through self-sovereign identity, verifiable transparency, and portability without borders. While it is necessary to point out considerable implementation issues, such as interoperability (between blockchain infrastructures) and the preponderance of low levels of digital literacy, the literature review concludes that peer-based credentialing is a decentralised system that can reorganise digital education governance by providing models that empower learners and democratize knowledge validation to a high degree.
Autism Spectrum Disorder (ASD) is a major neurodevelopmental disorder with a growing global incidence, and needs to be better understood in terms of its spatial patterns and risk factors to inform health policy. This study conducted a spatial-statistical analysis to detect spatial patterns and factors of ASD in children under 15 years in Iraq's Babylon Governorate. A descriptive-analytical approach was used, combining Geographic Information Systems (GIS) and statistical techniques, including Z-score normalization, Pearson's correlation and regression. The results reveal a high spatial variation in ASD incidence (7.45-45.78 per 10,000 children) and a higher incidence in urban administrative units. Genetics (34.5%) and heredity (31.5%) were the two most important factors; followed by environmental (13.9%), neurological (10.4%) and psychological (9.7%) factors. But there were no significant correlations between ASD incidence and other factors (r from -0.059 to 0.058), suggesting that ASD likely results from a combination of factors, rather than by any one factor. This study shows the usefulness of integration of spatial epidemiology and statistical methods to identify spatial inequalities and hotspots. This study provides valuable insights for policy makers, by showing the need for enhanced diagnostic services, health care services and targeted interventions in the under-serviced areas. Moreover, the paper highlights the necessity of implementing integrated spatial-health strategies in order to comprehend the regional differences in developmental disorders better and evidence-based decision-making to improve the strategies of healthcare planning and early intervention.
Beach ecosystems are important to biodiversity, tourism and coastal livelihoods yet are also facing the growing threat of plastic pollution and marine litter. Although the world has been working towards ensuring sustainability, young children especially the preschoolers are a group that is not fully addressed in terms of environmental education. The research focuses on examining how gamified mobile learning can be effective in teaching preschoolers about the sustainability of the beach at Langkawi, Malaysia, a UNESCO Global Geopark that is going through serious beach littering challenges. The goal of the study is (i) to determine the level of knowledge that preschoolers had about beach littering, (ii) improve it using a mobile game intervention, and (iii) measure the effect of the interventions. A total of 115 six-year-old were randomly divided into control and experimental groups, and pre- and post-intervention data were measured by the means of guided questions. The experimental condition involved interaction with an application called Clean My Beach, which is a mobile game that simulates responsible clean-up behaviour on the beach. The post intervention results indicated that there was much greater awareness of the experimental group (M = 4.67) than the control group (M = 1.58) with a significant difference (p < 0.001). The paper also shows how digital resource, such as mobile game, can encourage early digital literacy, which forms the basis of future technological skills. The results confirm that age-based gamified technologies can be used to effectively teach early sustainability education that can provide a scalable and economically viable ways to inculcate pro-environmental ideals at a young age. The paper is connected with SDGs 4 (Quality Education), 6 (Clean Water and Sanitation), 8 (Decent Work and Economic Growth) and 14 (Life Below Water) and shows the importance of digital innovations, parental involvement, and early intervention in creating environmentally responsible future generations.
Static Synchronous Compensators (STATCOMs) require precise controller tuning to maintain voltage stability in modern power grids. Fractional Order Proportional-Integral (FOPI) controllers offer enhanced flexibility over integer-order designs, yet existing tuning methods rely on weighted-sum formulations that conflate conflicting objectives through arbitrary weight selection, overlooking the inherent priority structure of power system stability requirements. This paper proposes a lexicographic bi-objective optimization framework that enforces strict priority ordering without weight selection, implemented through two formulations: a Lexicographic ITAE approach evaluated across five meta-heuristic algorithms with hard penalty constraints, and a Lexicographic PSO approach employing a dual-criteria ranking mechanism with a normalized sacrifice metric. Both were verified using an 11 kV cascaded H-bridge STATCOM in Simulink. Observations showed that there was a ''performance inversion'' effect where algorithms that provided the least cost gave oscillating or shifted voltage profiles, whereas those that were penalized resulted in improved physical performance. Obsessive settling time priority attained 0.25 s; however, it resulted in the steady-state shift by 140 V for an 82 V target voltage. Random sampling showed that only 9% of solutions gave a stable response. Between the two algorithms, Lex-ITAE with HHO performed better as it gave a smooth voltage profile close to the target 82 V despite having higher costs numerically.
The paper is aimed at enhancing the computational method of Geosynthetic-Encased Columns (GECs) that enhance the engineering structures' foundations on weak soils. This method has proven to be effective in many projects, and the current technique fails to consider the distribution of stresses in the soil-filler, especially in loose soils. This study aimed at improving the analytical methodology by incorporating the mechanics of granular media (MGM) in an attempt to increase the precision of the stress state calculations in Geosynthetic-Encased Columns. The methodology used incorporated the theories of the mechanics of the granular media with the previously used method of computation to calculate the stress distribution both vertically and horizontally in the soil-filler. The traditional method, as well as the improved method, was comparatively analyzed with the emphasis on the determination of horizontal stresses. Major findings showed that the two techniques were highly convergent, with the better technique giving marginally larger, more conservative values of horizontal stresses. Statistical analysis revealed that there was an average differentiation of 0.0015 kN/m2 in the values of vertical stress between the two methods, and the p-value was 0.02, which showed that there has been significant improvement in the new method. The incorporation of MGM in the calculations gave a closer description of the behavior of soil at the level of interaction of particles, and the design process has been made more reliable. To sum up, the introduction of the concept of granular media in the computational method of Geosynthetic-Encased Columns presents great advantages to the analysis of stress states and offers more valid and precise outcomes of the foundation system design on the weak soils. This improvement of the methodology increases the range of application of the method, especially in projects that contain loose and heterogeneous soils.
