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Graph Neural Networks are gaining attention in Fifth-Generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classification in multiclass settings. Digital twins of 5G networks increasingly employ graph classification as the main method for identifying failure types. However, the skewed distribution of failure occurrences is a major class imbalance issue that prevents effective graph data mining. Previous studies have not sufficiently tackled this complex problem. In this paper, we propose Class-Fourier Graph Neural Network (CF-GNN) introduces a class-oriented spectral filtering mechanism that ensures precise classification by estimating a unique spectral filter for each class. We employ eigenvalue and eigenvector spectral filtering to capture and adapt to variations in the minority classes, ensuring accurate class-specific feature discrimination, and adept at graph representation learning for complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed CF-GNN could help with both the creation of new techniques for enhancing classifiers and the investigation of the characteristics of the multi-class imbalanced data in a network digital twin system., Comment: arXiv admin note: substantial text overlap with arXiv:2406.06595

Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, healthcare professionals in Africa often rely on manual and time-consuming processes to retrieve drug information, exacerbated by limited access to pharmacists due to brain drain and healthcare disparities. This paper presents "Drug Insights," an open-source Retrieval-Augmented Generation (RAG) chatbot designed to streamline medication lookup for healthcare workers in Africa. By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, including Pinecone databases and GPT models, the system delivers accurate, context-specific responses with minimal hallucination. The chatbot integrates prompt engineering and S-BERT evaluation to optimize retrieval and response generation. Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access while highlighting areas for enhancement, such as UI/UX refinement and extended corpus integration., Comment: 4 pages, 2 tables and 3 figures

In this paper, conformal fractional order discretization [20, 24, 25] is used to analyze bifurcation analysis and stability of a predator-prey system. A continuous model has been discretized into a discrete one while preserving the fractional-order dynamics. This allows us to look more closely at the stability properties of the system and bifurcation phenomena, including period-doubling and Neimark-Sacker bifurcation. Through numerical and theoretical methods, this research investigated how the modification in system parameters affects the overall dynamics, which may have implications for ecological management and conservation strategies.

The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.

The rapid progress in machine learning models has significantly boosted the potential for real-world applications such as autonomous vehicles, disease diagnoses, and recognition of emergencies. The performance of many machine learning models depends on the nature and size of the training data sets. These models often face challenges due to the scarcity, noise, and imbalance in real-world data, limiting their performance. Nonetheless, high-quality, diverse, relevant and representative training data is essential to build accurate and reliable machine learning models that adapt well to real-world scenarios. It is hypothesised that well-designed synthetic data can improve the performance of a machine learning algorithm. This work aims to create a synthetic dataset and evaluate its effectiveness to improve the prediction accuracy of object detection systems. This work considers autonomous vehicle scenarios as an illustrative example to show the efficacy of synthetic data. The effectiveness of these synthetic datasets in improving the performance of state-of-the-art object detection models is explored. The findings demonstrate that incorporating synthetic data improves model performance across all performance matrices. Two deep learning systems, System-1 (trained on real-world data) and System-2 (trained on a combination of real and synthetic data), are evaluated using the state-of-the-art YOLO model across multiple metrics, including accuracy, precision, recall, and mean average precision. Experimental results revealed that System-2 outperformed System-1, showing a 3% improvement in accuracy, along with superior performance in all other metrics., Comment: 7 Pages, 7 figures, 1 table

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks.

The complexity of software is increasing day by day the requirement and need for a verity of softwareproducts increases, this necessitates the provision of a strong tool that will make a balance betweenproduction and quality. The practice of applying software metrics to the development process and to asoftware product is a critical task and crucial enough that requires study and discipline and whichbrings knowledge of the status of the process and/or product of software in regards to the goals toachieve, this discipline is known as quality assurance which is the key factor behind the success ofevery software engineering project, the quality assurance activities are what result in the qualitativeproduct as well as the process in both conventional software development methodology and agilemethodology. However, agile methodology is now becoming one of the dominant method adopted bymost of the software industries because it allows developing of software with very limited requirementand supports rapid changes in the requirement, the method may produce the product very fast but wemight not guarantee the quality of the product unless we apply the SQA activities to the process. Thisresearch paper aimed to study the quality assurance activities practice in agile software developmentmethodology, investigate the common problems and key drivers of quality in agile, and propose asolution to improve the practice of SQA in agile methodology by analyzing the parameters that assurequality in agile software.

Pakistan was hit by the world's deadliest flood in June 2022, causing agriculture and infrastructure damage across the country. Remote sensing technology offers a cost-effective and efficient method for flood impact assessment. This study is aimed to assess the impact of flooding on crops and built-up areas. Landsat 9 imagery, European Space Agency-Land Use/Land Cover (ESA-LULC) and Soil Moisture Active Passive (SMAP) data are used to identify and quantify the extent of flood-affected areas, crop damage, and built-up area destruction. The findings indicate that Sindh, a province in Pakistan, suffered the most. This impact destroyed most Kharif season crops, typically cultivated from March to November. Using the SMAP satellite data, it is assessed that the high amount of soil moisture after flood also caused a significant delay in the cultivation of Rabi crops. The findings of this study provide valuable information for decision-makers and stakeholders involved in flood risk management and disaster response., Comment: AAAI Symposium 2024

Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.

Today, children across Africa are at a growing risk from the Internet. Dangers include harmful content, violence, exploitation, abuse, and neglect. All these have increased due to increased mobile and Internet technology use, which not only places their safety at risk but also affects their ability to learn essential skills for their future. This paper provides an overview of the unique challenges faced by third-world African countries in ensuring the online safety of children while also supporting their developmental needs. It highlights effective practices and policies adopted by other nations to safeguard children from online threats and enhance their digital literacy. We are focusing on sharing the best practices and policies other countries have used to protect children from abuse and help them succeed in the digital world. The study emphasizes the online safety strategies, legal frameworks, and recommendations specific to the United Republic of Tanzania, along with the significance of international collaborations with organizations like UNICEF and the UN. The goal is to provide African policymakers, educators, and cybersecurity professionals with practical guidance and recommendations to strengthen child online safety initiatives both within and beyond the continent.

The communities that we live in affect our health in ways that are complex and hard to define. Moreover, our understanding of the place-based processes affecting health and inequalities is limited. This undermines the development of robust policy interventions to improve local health and well-being. News media provides social and community information that may be useful in health studies. Here we propose a methodology for characterising neighbourhoods by using local news articles. More specifically, we show how we can use Natural Language Processing (NLP) to unlock further information about neighbourhoods by analysing, geoparsing and clustering news articles. Our work is novel because we combine street-level geoparsing tailored to the locality with clustering of full news articles, enabling a more detailed examination of neighbourhood characteristics. We evaluate our outputs and show via a confluence of evidence, both from a qualitative and a quantitative perspective, that the themes we extract from news articles are sensible and reflect many characteristics of the real world. This is significant because it allows us to better understand the effects of neighbourhoods on health. Our findings on neighbourhood characterisation using news data will support a new generation of place-based research which examines a wider set of spatial processes and how they affect health, enabling new epidemiological research., Comment: Preprint - paper under submission

Crown-like structures (CLS) in breast adipose tissue are formed as a result of macrophages clustering around necrotic adipocytes in specific patterns. As a histologic marker of local inflammation, CLS could have potential diagnostic utility as a biomarker for breast cancer risk. However, given the scale of whole slide images and the rarity of CLS (a few cells in an entire tissue sample), microscope-based manual identification is a challenge for the pathologist. In this report, we describe an artificial intelligence pipeline to solve this needle-in-a-haystack problem. We developed a zero-cost, zero-footprint web platform to enable remote operation on digital whole slide imaging data directly in the web browser, supporting collaborative annotation of the data by multiple experts. The annotated images then allow for incremental training and fine tuning of deep neural networks via active learning. The platform is reusable and requires no backend or installations, thus ensuring the data remains secure and private under the governance of the end user. Using this platform, we iteratively trained a CLS identification model, evaluating the performance after each round and adding examples to the training data to overcome failure cases. The resulting model, with an AUC of 0.90, shows promise as a first-pass screening tool to detect CLS in breast adipose tissue, considerably reducing the workload of the pathologist. Platform available at: https://episphere.github.io/path

Identity Management Systems (IdMs) have complemented how users are identified, authenticated, and authorised on e-services. Among the methods used for this purpose are traditional IdMs (isolated, centralised and federated) that mostly rely on identity providers (IdPs) to broker trust between a user and service-providers (SPs). An IdP also identifies and authenticates a user on-behalf of the SP, who then determines the authorisation of the user. In these processes, both SP and IdP collect, process or store private users' data, which can be prone to breach. One approach to address the data breach is to relieve the IdP, and return control and storage of personal data to the owner. Self-sovereign identity (SSI) was introduced as an IdM model to reduce the possibility of data breaches by offering control of personal data to the owner. SSI is a decentralised IdM, where the data owner has sovereign control of personal data stored in their digital wallet. Since SSI is an emerging technology, its components and methods require careful evaluation. This paper provides an evolution to IdMs and reviews the state-of-the-art SSI frameworks. We explored articles in the literature that reviewed blockchain solutions for General Data Protection Regulation (GDPR). We systematically searched recent SSI and blockchain proposals, evaluated the compliance of the retrieved documents with the GDPR privacy principles, and discussed their potentials, constraints, and limitations. This work identifies potential research gaps and opportunities.

A control design for error reduction in the tracking control for a class of non-lower triangular nonlinear systems is presented by combining techniques of Variable Power Surface Error Function (VPSEF), backstepping, and dynamic surface control. At each step of design, a surface error is obtained, and based on its magnitude, the VPSEF technique decides the surface error to be used. Thus, the backstepping-based virtual and actual control law is designed to stabilize the corresponding subsystem. To address the issue of circular structure, a first-order low-pass filter is used to handle the virtual control signal at each intermediate stage of the recursive design. The stability analysis of the closed-loop system demonstrates that all signals indicate semi-global uniform ultimate boundedness. Moreover, by using the switching strategy of the control input using the VPSEF technique suitably, it is possible to ensure that the steady-state tracking error converges to a neighborhood of zero with an arbitrarily very small size. The effectiveness of the proposed concept has been verified using two different simulated demonstrations.

Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip, compromising navigation efficiency and increasing the risk of entrapment. To address this problem, we introduce SlipNet, a novel approach for predicting wheel slip in segmented regions of diverse terrain surfaces without relying on prior terrain classification. SlipNet employs dynamic terrain segmentation and slip assignment techniques on previously unseen data, enhancing rover navigation capabilities in uncertain environments. We developed a synthetic data generation framework using the high-fidelity Vortex Studio simulator to create realistic datasets that replicate a wide range of deformable terrain conditions for training and evaluation. Extensive simulation results demonstrate that our model, combining DeepLab v3+ with SlipNet, significantly outperforms the state-of-the-art TerrainNet method, achieving lower mean absolute error (MAE) across five distinct terrain samples. These findings highlight the effectiveness of SlipNet in improving rover navigation in challenging terrains.

Background: Tissue Microarrays (TMAs) significantly increase analytical efficiency in histopathology and large-scale epidemiologic studies by allowing multiple tissue cores to be scanned on a single slide. The individual cores can be digitally extracted and then linked to metadata for analysis in a process known as de-arraying. However, TMAs often contain core misalignments and artifacts due to assembly errors, which can adversely affect the reliability of the extracted cores during the de-arraying process. Moreover, conventional approaches for TMA de-arraying rely on desktop solutions.Therefore, a robust yet flexible de-arraying method is crucial to account for these inaccuracies and ensure effective downstream analyses. Results: We developed TMA-Grid, an in-browser, zero-footprint, interactive web application for TMA de-arraying. This web application integrates a convolutional neural network for precise tissue segmentation and a grid estimation algorithm to match each identified core to its expected location. The application emphasizes interactivity, allowing users to easily adjust segmentation and gridding results. Operating entirely in the web-browser, TMA-Grid eliminates the need for downloads or installations and ensures data privacy. Adhering to FAIR principles (Findable, Accessible, Interoperable, and Reusable), the application and its components are designed for seamless integration into TMA research workflows. Conclusions: TMA-Grid provides a robust, user-friendly solution for TMA dearraying on the web. As an open, freely accessible platform, it lays the foundation for collaborative analyses of TMAs and similar histopathology imaging data. Availability: Web application: https://episphere.github.io/tma-grid Code: https://github.com/episphere/tma-grid Tutorial: https://youtu.be/miajqyw4BVk, Comment: NA

Over time, agriculture is the most consistent activity, and it evolves every day. It contributes to a vast majority of the Gross Domestic Product (GDP) of Nigeria but as ironic as it may be, there is still hunger in significant parts of the country due to low productivity in the agricultural sector and comparison to the geometric population growth. During the first half of 2022, agriculture contributed about 23% of the country's GDP while the industry and services sector had a share of the remaining 77%. This showed that with the high rate of agricultural activities, Nigeria has not achieved food security for the teeming population. and more productivity levels can be attained. Technology can/will assist Nigeria in overcoming global poverty and hunger quicker in both rural and urban areas. Today, there are many types of agricultural technologies available for farmers all over the world to increase productivity. Major technological advancements include indoor vertical farming, automation, robotics, livestock technology, modern greenhouse practices, precision agriculture, artificial intelligence, and blockchain. Mobile phones have one of the highest adoption rates of technologies developed within the last century. Digitalization will bring consumers and farmers closer together to access the shortest supply chain possible and reduce rural poverty and hunger. The paper will review the different agricultural technologies and propose a mobile solution, code Sell Harvest, to make farming more sustainable and secure food. Keywords: Sell Harvest, Agriculture, Technology, Artificial Intelligence, and Digital Farming.