First Technical University

Abstract In this study, a hybrid injection–volumetric additive manufacturing (HIVAM) approach is introduced to overcome key material limitations of volumetric additive manufacturing (VAM) and enable the ultrafast fabrication of composite structures and multi material parts. VAM has recently emerged as a rapid vat photopolymerization technique that addresses several key limitations of conventional additive manufacturing methods, including low printing speed, poor surface quality, the need for support structures, and anisotropic mechanical properties. Despite these advantages, VAM is currently restricted to highly transparent resins, and the use of opaque formulations or resin systems containing light-scattering fillers, as well as low-viscosity materials that are essential for composite and multi-material fabrication, remains extremely challenging. The proposed HIVAM approach integrates directional volumetric curing with synchronized in situ injection of a secondary material, enabling the fabrication of hollow, multi-material, and composite structures. Different injection algorithms and processing conditions are systematically investigated to identify stable fabrication windows and ensure dimensional integrity. The versatility of the method is demonstrated by combining materials with distinct physical states and matrices, including opaque resins, graphene oxide-reinforced resin, and injected shear-thickening fluids (STF). As proof of concept, composite parts containing up to 1.5 wt% graphene oxide and liquid-filled architectures exhibiting strain-rate-dependent mechanical behavior are successfully fabricated. The results show that HIVAM preserves the ultrafast nature of volumetric additive manufacturing while significantly expanding its material palette and architectural complexity. This work establishes a general platform for rapid multi-material and composite fabrication beyond the transparency and viscosity constraints of conventional volumetric processes.

Background: The urgency of alternative, effective and less toxic treatment options has driven research towards natural products of plant sources. Irvingia gabonensis possesses promising anticancer properties due to its rich phytochemical content and antioxidant potential. Objective: This study investigated the antioxidant properties and anticancer effect of I. gabonensis leaf extract and its fractions in a rat model of prostate cancer. Method: Prostate cancer was induced in Wistar rats using N-methyl-nitrosourea (NMU) and testosterone. Prostate specific antigen (PSA) levels, liver function enzymes, and antioxidant markers (catalase, GSH, SOD) were evaluated, and histological analyses of prostate and liver tissues were performed by Hematoxylin and Eosin staining. Phytochemical characterisation of the crude extract was conducted using Gas Chromatography-Mass Spectroscopy. Data analysis was performed by ANOVA at 95% confidence level. Result: Treatment with the I. gabonensis extract and fractions led to consistent weight gain and decreased PSA levels that were significant (p<0.05) to the cancer control group. The dichloromethane fraction significantly (p<0.05) increased catalase activity, and the methanol fraction significantly (p<0.05) increased GSH levels. Histological examination revealed preserved prostate tissue integrity in the treated groups compared to the untreated cancer group, although the treated groups exhibited some changes in liver function markers and histology. Phytol (17.07%), hexadecanoic acid, methyl ester (16.28%), 9-hexadecenoic acid, methyl ester (5.36%), 7-octadecenoic acid, methyl ester (7.11%), n-hexadecanoic acid (7.45%), and oleic acid (5.84%) were identified in abundance. Conclusion: I. gabonensis leaf extract and fractions exhibited anti-prostate cancer effects by modulating antioxidant defence mechanisms, but the observed effects on liver function require further investigation.

Digital storytelling (DST) has emerged as an innovative approach to technology-enhanced teaching and learning in culturally diverse environments, supporting intercultural competence and multiliteracies development. By integrating multimedia elements, DST provides a multimodal space where learners craft personal narratives and communicate across cultural differences. This study introduces the Digital Storytelling Web-Based Learning Tool (DST-WBLT), designed to facilitate DST practices associated with intercultural learning. Grounded in intercultural and multimodal theories and pedagogical frameworks, the DST-WBLT comprises three learning modules that guide learners in: (a) understanding DST concepts, elements, and processes; (b) analysing and reflecting on intercultural digital story cases; and (c) producing their own digital stories as transformative learning outcomes. Key features of the DST-WBLT include a multimodal learning environment, authentic and interactive activities, and the integration of digital and generative AI tools to enhance engagement and creativity. This work offers a novel lens for examining how multimodal designs can foster meaningful intercultural learning.

The practical deployment of alkaline water electrolysis remains constrained by the sluggish kinetics of the oxygen evolution reaction (OER). Although the nickel oxide electrocatalyst exhibits strong intrinsic activity, its poor electronic conductivity and structural instability under high anodic bias limit sustained performance. Graphene is frequently employed as a conductive additive, and achieving its optimal integration without blocking active sites or pores remains a key unresolved challenge. We make an attempt to overcome these limitations by integrating picosecond laser-ablated NiO with electrophoretically deposited graphene to form a binder-free, hierarchically porous, and coupled heterointerface. Picosecond laser processing generates oxygen-deficient, high-surface-area NiO with exposed Ni redox centers, while controlled graphene electrophoretic deposition (EPD) establishes a continuous conductive network without obstructing catalytic accessibility. Two NiO base samples fabricated at low and high laser powers were further modified by EPD of graphene for 30 min at 5 and 2.5 V, and their OER performance was evaluated in 1 M KOH. A comprehensive mathematical model rationalizes the existence of an optimal graphene coverage window at lower EPD voltage and short-to-medium deposition times (∼30 min), where a continuous conductive network forms without blocking the porosity. The optimized NiO/graphene electrode (10 W laser power, 2.5 V EPD) delivers an overpotential of 347 mV at 10 mA cm–2, Tafel slope of 53 mV dec–1, and mass activity of ∼4000 A g–1, outperforming pristine NiO and many reported Ni-based TMOs. Chronoamperometry at 100 mA cm–2 shows exceptional stability over 70 h, attributed to graphene-assisted suppression of surface reconstruction and dissolution. The large-scale electrocatalyst synthesis and scalable electrode fabrication always remain as a significant challenge. This hybrid laser-EPD approach can make cost-efficient roll-to-roll manufacturing plausible, taking green hydrogen production toward widespread deployment.

This chapter provides a bibliometric review of research on Generative Artificial Intelligence (GAI) in Computer Science (CS) education over the past decade. A total of 582 documents been catalogued in Scopus for the period of 2014-2024 was used. The publications were analyzed using Biblioshiny for Bibliometrix and the results were used to identify gaps in the literature and guide future research directions on GAI's impact in education. The findings revealed the surge in publications and citations post 2020 can be attributed to large language models and AI coding assistants. Thematic and keyword analyses show that GAI has been applied primarily in programming education, intelligent tutoring, personalized learning, and assessment. These applications yield outcomes such as enhanced efficiency, student engagement, and scalability, yet also raise concerns about academic integrity, over-reliance, and algorithmic bias. GAI research is spearheaded by developed nations which poses fairness concerns, and simultaneously fosters advancement and moral quandaries in computer science pedagogy.

Permeability is a critical parameter in subsurface fluid flow analysis, reservoir management, hydrocarbon recovery, and carbon dioxide sequestration. Traditional permeability measurement methods involve costly and time-consuming laboratory tests or well-related data. Machine learning (ML), specifically convolutional neural networks (CNN), is proposed as a cost-effective and rapid permeability prediction solution, harnessing interrelationships of input-output variables. In this study, empirical permeability correlation was developed using CNN. Forty nuclear magnetic resonance (NMR) T2 spectrums and 89 logarithmic mean NMR T2 distributions (T2lm) were preprocessed, screened and key spectra were identified using the principal component analysis (PCA). To develop the correlations, a custom-designed CNN architecture was employed to leverage the spatial patterns and intricate relationships embedded in the NMR data. The model was trained and validated rigorously using k-fold cross validation scheme to ensure robustness and generalization. Performance metrics like R-squared (R2), root mean squared error (RMSE), mean absolute error (MAE), standard deviation (SD), absolute deviation (AD), average absolute deviation (AAD), average absolute percentage relative error (AAPRE), and maximum error (Emax) were deployed to evaluate the model’s accuracy and ability to predict permeability values accurately. Among the folds considered, the fold 1 emerged as the best-performing model with the highest R2 value of 0.9544. This CNN-based correlation outperformed conventional and other AI-based models in terms of R2, Emax, AD, AAD, AAPRE, among other metrics. Overall, the study demonstrates the effectiveness of CNN in predicting permeability, offering a superior alternative to costly and limited traditional methods, with fold 1 showing the most promising results.

Hand gesture recognition is a crucial aspect of how we interact with computers, allowing machines to understand and respond to our movements in a natural way. This paper explores an advanced method for recognising hand gestures using a 2D convolutional neural network (2D CNN), a type of deep learning technology. We built and tested our model on a specialised dataset, evaluating its performance through several metrics, including loss function, accuracy, precision, recall, root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE). We compared two different data splitting ratios, 80:20 and 70:30, to determine which one performed better. Our approach effectively captures the complexities of hand gestures, thanks to the spatiotemporal features in the input data. To enhance the model's robustness against varying conditions, such as lighting, backgrounds, and hand positions, we also employed data augmentation techniques. Our findings indicate that the model achieved an impressive accuracy of 80% and a precision of 85% on the training data, along with solid results on the testing data, including an accuracy of 79%. Ultimately, the 80:20 data splitting ratio showed the best overall performance, highlighting its effectiveness for our gesture recognition task.

This paper examines the lived realities of conducting a purposefully transdisciplinary, equity-focused international STEM research project during the COVID-19 pandemic. Using the Charter of Transdisciplinarity as an analytic lens, we explore how our team navigated visa delays, shifting political contexts, administrative turnover, and digital inequities while supporting marginalized undergraduate women in STEM. Through reflective prompts and critical event analysis, we show how belonging—understood as an active, ongoing practice—enabled us to move beyond disciplinary boundaries and confront entrenched forms of marginalization in STEM and academia. Technology simultaneously connected and divided us, requiring continual renegotiation of community membership. Dialogues around artificial intelligence served as key moments of transcultural exchange and vulnerability. We argue that transdisciplinarity is a human and relational endeavor that must be intentionally cultivated. Within STEM, it emerges not from the acronym itself but from practices that center humanity, resist othering, and foster collective flourishing. Keywords: transdisciplinarity, STEM education, belonging, transcultural collaboration, international research, equity in STEM, participatory action research, digital inequity, interdisciplinary collaboration

One of the most notable climatic phenomena associated with urbanization is the Urban Heat Island (UHI) effect. This research aim to detect and analyze Urban Heat Island of Akinyele Local Government Area, Oyo State, using remote sensing and GIS technology to comprehensively understand, monitor, and mitigate the Urban Heat effect in the study area. This research explores the dynamics of urban heat islands (UHIs) by analyzing l and use changes, temperature variations, and population growth in Akinyele LGA, Oyo State using Landsat 7 & 8 datasets and ArcGIS Software. Accuracy assessments were performed by comparing classified land cover maps with ground coordinates. Result shows that from 2004 to 2024, land use shifted significantly: Urban or built-up areas increased from 14.04% to 23.09%, forest areas increased from 29.78% to 37.82%, and light vegetation reduced from 56.18% to 39.09%. Concurrently, temperature data reveals a rising trend, the mean temperature increased from 29.574°C in 2004 to a 31.583°C in 2024. The population of Akinyele is projected to grow from 211,811 in 2006 to approximately 315,361 by 2024, driven by an annual growth rate of 2.39%. The future projections for urban land cover and land surface temperature (LST) indicate a significant rise in built-up areas and temperatures. The Built-up area is expected to reach 119.71 by 2034. Also, the LST is projected to rise to 36.031°C by 2034. This accelerated expansion of built-up areas intensifies the Urban Heat Island effect. These projections highlight the urgent need for effective urban planning strategy to mitigate the impacts of rapid urbanization and rising temperatures.