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Evaluation of IL-6 and TNF-α in tears and serum in age-Related macular degeneration
No full textObjective: The purpose of this study was to assess the levels of interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) in the tears and serum of patients with age-related macular degeneration, as well as to identify the factors associated with IL-6 and TNF-α levels in tears.
Methods: This was a comparative, cross-sectional study involving age-related macular degeneration patients and a control group. Tear samples were collected using Schirmer paper strips, while 3mL of blood was obtained from each patient. IL-6 and TNF-α levels in tears and serum were measured using a commercial human enzyme-linked immunosorbent assay (ELISA) kit. The study analysed the effects of duration of age-related macular degeneration, disease stage, and smoking status on IL-6 and TNF-α levels in tears, aiming to determine their associations.
Results: A total of 142 patients were recruited for this study, including 56 patients with early age-related macular degeneration, 56 patients with late neovascular age-related macular degeneration, and 30 patients in the control group. Age-related macular degeneration patients exhibited significantly higher mean levels of IL-6 in both tears and serum, as well as TNF-α in serum, compared to the control group, both before and after adjusting for covariates (21.97±10.95 vs. 16.06±10.00pg/mL, p=0.008 and p=0.014; 12.00±6.04 vs. 8.53±4.13pg/mL, p=0.004 and p=0.004; 18.58±7.90 vs. 13.61±4.86pg/mL, p=0.001 and p=0.004, respectively). Within the age-related macular degeneration group, the mean IL-6 level in serum was significantly higher in pa�tients with late neovascular age-related macular degeneration compared to those with early age-related macular degeneration (13.89±6.08 vs. 10.11±5.41pg/mL, p=0.001). The levels of IL-6 and TNF-α in tears were not associated with the duration of age-related macular degeneration, the stages of age-related macular degeneration, or smoking status.
Conclusion: There are significantly higher levels of IL-6 in both tears and serum, whereas tears and serum TNF-α serve as non�specific biomarkers for age-related macular degeneration. This study could serve as a basis for future research
A competitive co-evolutionary approach for the nurse scheduling problem
No full textThe Nurse Scheduling Problem (NSP) is a constrained combinatorial optimisation problem that plays a critical role in healthcare scheduling and constraint optimisation. Traditional evolutionary approaches often rely on static fitness evaluation, which struggles to balance feasibility and solution quality under complex real-world constraints. This study proposes a competitive co-evolutionary algorithm for the NSP that introduces adaptive adversarial evaluation, where candidate schedules are assessed under dynamic competitive pressure to expose structural weaknesses and guide evolution more effectively. The proposed competitive NSP is evaluated on a 20-nurse, oneweek scheduling instance and compared against a classical Genetic Algorithm (GA) under identical conditions for 30 independent runs. Experimental results show that the competitive NSP achieves a mean best penalty of 447.28, compared to 651.30 for the classical GA, corresponding to an average improvement of approximately 31%. The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems
Rule-based to Intelligent Chatbots: understanding librarian insights in library reference services
No full textDeployment of an artificial intelligence chatbot in libraries has primarily supported reference-oriented and short conversation tasks. The current phase of technological convergence and automation demands a reimagined chatbot capable of sustaining natural, context-aware conversations without human intervention. This study aimed to explore librarians’ insights on readiness, opportunities, and challenges toward implementing artificial intelligence chatbots in reference services. Qualitative research design was employed in this study by using semi-structured interview to gain an in-depth insight from the librarians in the academic libraries. The responses have been analyzed using Nvivo 14 software to organize data into structural findings. The findings showed a favorable indicator of the implementation of artificial intelligence chatbots based on sufficient budget, staff competency, and adequate training. Issues of privacy, relevancy, excessive cost, and human dependency were paramount challenges in the development of artificial intelligence chatbots. Collaborative development between libraries, chatbot service providers, and users will determine the extent of this technology's ability while maintaining the best practice of reference services in the future
A hybrid overlay architecture for social feature integration in browser-based cloud gaming
No full textCurrent cloud gaming platforms force a trade-off between streaming performance and integrated social features, typically requiring resource-intensive dedicated clients. This paper presents an architecture that eliminates this compromise through a Hybrid Overlay engine. Built with vanilla TypeScript/HTML5 and decoupled from the WebRTC video pipeline, the engine renders social overlays (chat, friend lists) directly onto the game canvas, avoiding DOM overhead. A Rust/Actix-web backend ensures low-latency streaming. The system was validated through comprehensive testing. Performance and security tests confirmed: automatic streamer binary compilation, successful WebRTC stream initiation, automated SSL generation, and strict HTTPS enforcement. Functional tests demonstrated robust authentication (registration, session persistence), real-time message synchronization (<200ms), and correct social workflows. Crucially, the input sandbox isolated chat keystrokes from the game stream, and Firebase RBAC rules blocked all unauthorized data writes. By unifying high-fidelity streaming with lightweight, native social integration, this work provides a performant, zero-install platform that makes social cloud gaming accessible on low-end devices, establishing a new model for architecting these service
Identifying key parameters for school bus monitoring system using a triangulation method: a study in Zanzibar
No full textThe design and development of school bus monitoring systems require a clear understanding of the key parameters that ensure student safety, reliable communication and parental satisfaction. While various studies have proposed technological solutions to improve school bus transportation safety, they often do not clearly identify the key parameters required for school bus monitoring systems. This study aimed to identify the critical parameters for such systems by first conducting the literature review which revealed three frequently referenced parameters which are student identification, bus location tracking, and SMS notification alerts. To validate the importance of these parameters in real world context, a triangulation method was applied, involving interviews with 2 school authorities, focus group discussion with 4 school bus drivers and questionnaire survey completed by 60 parents. Data was collected from the stakeholders in Zanzibar Urban West Region. The findings across all three methods confirmed that student identification, bus location tracking and SMS notification alerts are essential parameters for school bus monitoring systems. These validated parameters provide a strong foundation for designing and developing technological solutions for school bus monitoring
A retrieval-augmented generation model for multimodal medical question-answering system
No full textThis paper addresses the limitations of existing medical question-answering systems, which are often unimodal and lack retrieval-augmented capabilities or expert-guided learning. To overcome these challenges, a Retrieval-Augmented Generation (RAG) framework was developed to handle multimodal medical data by integrating GPT-2 for text generation and BLIP for visual understanding. The system was fine-tuned using the MedQuAD and VQA-RAD datasets, and a FAISS-based retriever was used to supply relevant external context. Additionally, reinforcement learning from human feedback (RLHF) was applied to align responses with expert knowledge. Experimental results showed that the GPT-2 model achieved a BERTScore F1 of 0.8204, while the multimodal RAG-enhanced GPT-2 model improved to 0.8411, demonstrating the slight effectiveness of combining retrieval and multimodal learning in enhancing medical answer quality. For RAG-enhanced BLIP, the model shows 0.8627 BERTScore with sample question and image
Artificial intelligence and machine learning in postharvest fruit quality assessment: current challenges, recent advances, and future prospects
No full textThe postharvest phase is critical to maintaining the quality, safety, and marketability of horticultural produce. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools in this domain, offering rapid, non-destructive, and highly accurate methods for assessing fruit quality. This review provides a comprehensive and critical analysis of the current state of AI and ML applications in postharvest quality assessment, with an emphasis on recent advancements in deep learning, computer vision, and predictive modelling. Despite significant progress, notable challenges persist including limited model generalizability across fruit types and environments, the high cost of implementation, data scarcity, and a lack of standardized protocols. These issues are particularly acute for smallholder farmers and low-resource settings. This review identifies critical research gaps such as the need for scalable, interpretable, and low-cost AI solutions, robust models capable of operating under dynamic environmental conditions, and interdisciplinary collaboration for practical deployment. It highlights novel approaches, including lightweight AI for edge computing, multi-modal sensor integration, and the use of open-source platforms to enhance accessibility. By synthesizing existing knowledge and mapping out future research directions, this review offers a roadmap for the development of inclusive, efficient, and sustainable AI-driven postharvest systems
Boosting psychological capital by reflecting on the Qur’an
No full textThis study tested the relationship between reflecting on the Qur’an and developing psychological capital. Two ways of reflecting on the Qur’an was used. One way was to get students to keep a diary about their reflections on surah al-Fatihah. The second approach consisted of getting groups of students to discuss ayat from surah al-Baqarah. At the beginning of the semester, the psychological capital was established. Using a 12-items questionnaire, the average score was 52.46. After reflecting on the Qur’an in groups throughout the semester, the average score increased to 56.32 by the end of the semester. The overall PsyCap score increase (M = 3.895, p < .001) shows that the intervention was effective. A control group established that the result was not due to chance. The implications of the studies are discusse
Comparative evaluation of lightweight CNN and YOLOv8 models for brain tumor detection in resource-constrained settings
No full textBrain tumor detection is essential for timely diagnosis, early intervention, and effective treatment planning. With advancements in artificial intelligence (AI), deep learning methods have emerged as powerful tools in medical imaging, offering automated, consistent, and efficient detection of brain abnormalities. However, achieving clinically reliable performance requires models that can accurately differentiate between tumor and non-tumor cases. This paper investigates and compares the performance of two deep learning models which are a lightweight Convolutional Neural Network (CNN) and the You Only Look Once (YOLO) YOLOv8 model for brain tumor classification in resource-constrained setting. Both models were trained and evaluated using the BR35H dataset, which comprises 3,000 MRI scans categorized into tumor and non-tumor classes. The performance of the models are evaluated using accuracy, precision, recall, F1-score as well as inference time supplemented by confusion matrix, ROC analysis and Grad-CAM visualizations to assess class-wise prediction performance. The experimental results indicate that YOLOv8 demonstrated high predictions across both tumor and non-tumor categories. YOLOv8 outperformed the CNN, achieving an accuracy of 0.998, precision of 0.997, recall of 1.00, and an F1-score of 0.998. However, only a minimal difference was observed in the inference time per image between YOLOv8 and the CNN, with YOLOv8 being slower by just 10.6 ms. Finally, the results demonstrate YOLOv8’s robustness and reliability for early tumor detection, a critical factor in preventing diagnostic delays. The findings further highlight YOLOv8’s suitability for integration into clinical decision-support systems, particularly in resource-constrained environments where accurate and fast automated diagnosis can significantly enhance patient car
The dynamics of exchange rate pass-through to consumer prices in Tanzania: pre- and post-liberalisation analysis
No full textThis study examines the relationship between exchange rate pass-through and inflation in Tanzania from 1988 to 2023, highlighting the effects of exchange rate fluctuations on consumer prices within an open economy. The Vector Error Correction Model indicates a significant long-term relationship between exchange rates and inflation. Granger causality tests demonstrate a bidirectional causal relationship between exchange rates and inflation, alongside a unidirectional influence from exchange rates to money supply, highlighting the significant impact of exchange rate dynamics on inflationary trends. Impulse response functions show that while the immediate effect of exchange rate shocks on inflation is modest, it intensifies over time and stabilises thereafter. Variance decomposition further identifies money supply as a key driver of inflation, particularly in the long run. Prior to liberalisation, ERPT was weak and statistically insignificant; post-liberalisation, however, it became negative and persistent, suggesting that stronger monetary policy frameworks and enhanced central bank independence have moderated inflationary pressures. The findings highlight the significance of exchange rate stability in maintaining price control and affirm the effectiveness of liberalisation in strengthening Tanzania’s monetary transmission mechanis