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Modelling supply chain visibility: a framework with considerations for manufacturing and business
Get PDFSupply chain visibility plays a pivotal role in ensuring stakeholders have access to and share mutually beneficial information - information that is critical to processes, operations, and informed decision-making. This study leverages a framework to explore the influence of four key factors on supply chain visibility: supply chain linkages, supply chain relationships, green absorptive capacity, and information sharing. This investigation adopted a survey-based research methodology to collect data. A sampling strategy was employed to recruit participants from various industry sectors, with a primary focus on manufacturing and business. A total of 204 useable questionnaires were obtained. Exploratory factor analysis was conducted to identify underlying factors within the data. Confirmatory factor analysis (CFA) was then used to assess the validity and reliability of the identified factors. Finally, structural equation modelling was employed to test the hypothesised relationships between the constructs studied. This study's findings, particularly the significant positive correlations observed between information sharing, supply chain relationship, internal linkage, and green absorptive capacity, provide evidence that these factors are key drivers of supply chain visibility. Additionally, the analysis revealed that external linkages with supply chain partners further enhance information sharing within the chain. This study offers a unique contribution by exploring the interplay between green absorptive capacity, information sharing, internal and external supply chain linkages, and their combined influence on supply chain visibility. Extending prior research that focused primarily on information sharing and traditional supply chain relationships, this study integrates green absorptive capacity and linkages within a novel framework. Our findings suggest that green absorptive capacity enhances information sharing within the supply chain network, ultimately leading to improved visibility. Furthermore, the study distinguishes the influence of internal vs external linkages on visibility
Harnessing generative AI for self-directed learning: Perspectives from top management
Get PDFPurposeThe purpose of this paper is to explore the potential of generative AI-driven self-directed learning from the perspective of top management in the Sri Lankan software industry. By applying open innovation theory, the study aims to understand how business leaders perceive the integration of generative AI tools in organizational learning processes. The insights gained are intended to inform and encourage top management to promote generative AI-driven self-directed learning within their organizations. Design/methodology/approach The research utilized a qualitative approach, conducting semi-structured interviews with eight senior managers from IT companies in Colombo, Sri Lanka. Data was synthesized and analyzed thematically to identify patterns and insights regarding generative AI-driven self-directed learning and its organizational impact. Findings The study reveals that top management in Sri Lanka's software industry perceives generative AI-driven self-directed learning positively. This perception is rooted in the alignment of such learning with open innovation principles, emphasizing knowledge sharing, collaboration and the integration of external expertise to drive innovation. Generative AI tools empower employees to access diverse knowledge sources, fostering continuous learning and adaptability. Leaders recognize these tools' potential to enhance organizational innovation ecosystems and competitive advantage. The findings suggest that active support from top management, customized training programs and a culture that embraces continuous learning and innovation are crucial for successful implementation. Originality/value This paper uniquely explores generative AI-driven self-directed learning through the lens of top management in Sri Lanka's software industry, integrating open innovation theory to highlight its potential in enhancing organizational knowledge, collaboration and competitive advantage
Promoting active learning with ChatGPT: A constructivist approach in Sri Lankan higher education
Get PDFThis study investigates strategies for transforming traditional didactic learning environments into active ones using ChatGPT in a resource-constrained higher education setting in Sri Lanka. It identifies 16 strategies categorized under five constructivist learning themes: active learning, social interaction, contextual learning, scaffolding, and reflective thinking. These practices leverage ChatGPT to personalize learning, foster critical thinking, encourage collaboration, enhance teaching strategies, and offer immediate feedback, addressing educational challenges in developing countries. The findings extend constructivist learning theory by demonstrating AI’s role in facilitating interactive, learner-centered experiences. The study also highlights educators’ evolving role as collaborators with AI, providing personalized support and interactive learning experiences. Practical implications include the immediate applicability of these practices in resource-constrained settings without additional resources, offering a cost-effective solution for enhancing educational quality. However, limitations such as the study’s generalizability to other contexts and the need to investigate potential negative impacts of AI are acknowledged. Future research should explore the integration of multiple AI tools and conduct similar studies in large classrooms and different contexts to enhance generalizability. Expanding this research can help leverage technology to improve educational outcomes and create sustainable, resource-efficient learning environments in the developing world
Plume, Black Rain, and Snow in the Tropics
Get PDFAs part of 42nd Symposium at the Museum of Contemporary Art in Baie-Saint-Paul, the installation Fumée, pluie noire et neige sous les tropiques (Plume, Black Rain, and Snow in the Tropics) was developed over the period of 4 weeks during which the public had access to the working-in-progress of the installation. I have attached an English and French notes that were displayed at the entrance of the room where my work was exhibited.The installation followed my 448.11Kilometer running from Hiroshima to Nagasaki in Spring 2024 to collect radiation data along the way. The data was translated to make kinetic sculptures to move the installation as a way of making invisible ironizing radiation "visible". Some items in the installation visually brought up discussions about memories of Black Rain in 1945 as one example. I, as an artist, discussed with public how environmental data may be used in art, and think about uranium mining, civilian nuclear energy and disposal, as well as the current Fukushima situation.<br/
Hybrid improved brain storm optimization with support vector machine for cardiovascular diseases classification
Get PDFMost existing classification algorithms for cardiovascular disease are limited to specific diseases and cannot categorize the severity of the diseases. These algorithms still need to be improved in terms of accuracy and generalizability. Therefore, a hybrid Improved Brain Storm Optimization with Support Vector Machine (IBSO-SVM) for cardiovascular disease classification is proposed. In this study, a knowledge-driven intelligent initialization method is proposed to enhance the optimization capability of IBSO and the accuracy of IBSOSVM. Experimental evaluations are conducted on multiple real-world datasets, and the results demonstrate the superior performance of IBSO-SVM in cardiac disease datasets. The accuracy of BSO-SVM reached 100% on the Heart Failure and Heart Disease datasets, and the accuracy of IBSO-SVM reached 99% on the Stroke dataset and 88% on the Cardiovascular disease dataset
Challenges and barriers for first-year home and international students in Higher Education in the UK and Ireland: A scoping review
Get PDFThe challenges and barriers that occur when transitioning to university are widely acknowledged within the Higher Education (HE) sector (Thompson et al., 2021). Previous literature has focused extensively on the importance of breaking down barriers and cultivating a sense of belonging in order to generate student success (Daniels &amp; McNeela, 2021; Thompson et al., 2021). There is also considerable research and literature surrounding the challenges and barriers that international students face (Gbadamosi, 2018). However, the direct comparisons between the challenges and barriers faced by home students and international students are less prominently researched. This scoping review aims to fill this gap by gathering literature on this topic and highlighting the similarities and differences between the challenges and barriers home and international students encounter
Enhancing brain tumor detection through custom convolutional neural networks and interpretability-driven analysis
Get PDFBrain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology
Agathisflavone modulates reactive gliosis after trauma and increases the neuroblast population at the subventricular zone
Get PDFBackground: Reactive astrogliosis and microgliosis are coordinated responses to CNS insults and are pathological hallmarks of traumatic brain injury (TBI). In these conditions, persistent reactive gliosis can impede tissue repopulation and limit neurogenesis. Thus, modulating this phenomenon has been increasingly recognized as potential therapeutic approach. Methods: In this study, we investigated the potential of the flavonoid agathisflavone to modulate astroglial and microglial injury responses and promote neurogenesis in the subventricular zone (SVZ) neurogenic niche. Agathisflavone, or the vehicle in controls, was administered directly into the lateral ventricles in postnatal day (P)8-10 mice by twice daily intracerebroventricular (ICV) injections for 3 days, and brains were examined at P11. Results: In the controls, ICV injection caused glial reactivity along the needle track, characterised immunohistochemically by increased astrocyte expression of glial fibrillary protein (GFAP) and the number of Iba-1+ microglia at the lesion site. Treatment with agathisflavone decreased GFAP expression, reduced both astrocyte reactivity and the number of Iba-1+ microglia at the core of the lesion site and the penumbra, and induced a 2-fold increase on the ratio of anti-inflammatory CD206+ to pro-inflammatory CD16/32+ microglia. Notably, agathisflavone increased the population of neuroblasts (GFAP+ type B cells) in all SVZ microdomains by up to double, without significantly increasing the number of neuronal progenitors (DCX+). Conclusions: Although future studies should investigate the underlying molecular mechanisms driving agathisflavone effects on microglial polarization and neurogenesis at different timepoints, these data indicate that agathisflavone could be a potential adjuvant treatment for TBI or central nervous system disorders that have reactive gliosis as a common feature
Enhancing classical cryptographic systems with modern encryption: a case study on integrating RSA into the enigma machine
No full textThe Enigma machine, a mechanical encryption device most known for its involvement in World War II, used a complicated system of rotors and a plugboard to protect communications. However, its security relied heavily on the secrecy of its configuration settings, which may be jeopardized if misdirected or intercepted. This vulnerability provides a fascinating parallel to modern security concerns and is a useful narrative tool for instructional purposes. This paper suggests improving the security of the Enigma machine by adding the RSA asymmetric public key cryptosystem to protect the transmission of these essential parameters. The work shows that by encrypting the Enigma machine's settings—such as plugboard configurations and rotor positions—with RSA and appending them to the Enigma-encrypted message, these settings may stay safe even if intercepted, due to RSA's strong cryptographic features.To investigate this connection, a digital emulation of the Enigma machine was constructed, together with RSA cryptosystem capability. The programming included methods for evaluating the combined system's cryptographic performance. The results show that adding RSA significantly improves the security of the original Enigma machine, bringing it up to contemporary standards and allowing for safe communications across digital networks. The findings emphasize substantial security advantages, such as enhanced resistance to interception and decryption efforts, while also addressing the practical issues involved with this integrated strategy. This work presents a theoretically sound encryption model that preserves the Enigma machine's historical context while considerably improving its security and usefulness in today's digital ecosystem.This work creates a unique instructional tool that bridges the gap between the past and the present by combining contemporary digital cryptography techniques with historical approaches such as the mechanical Enigma machine and the RSA public key scheme. This integration not only provides valuable insights into the design and implementation of cybersecurity engineering practices, but it also serves as an effective learning experience, demonstrating core cryptographic principles and improving understanding of both classical and contemporary cryptographic strategies. By concentrating on these educational advantages, the research complements the conference subjects and offers a dynamic approach to cybersecurity teaching and learning
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