The reliance of organisations on computer networks is enabled by network programmability, which is typically achieved through Service Function Chaining. These chains virtualise network functions, link them, and programmatically embed them on networking infrastructure. Optimal embedding of Service Function Chains is an NP-hard problem, with three sub-problems, chain composition, virtual network function embedding, and link embedding, that have to be optimised simultaneously, rather than sequentially, for optimal results. Genetic Algorithms have been employed for this, but existing approaches either do not optimise all three sub-problems or do not optimise all three sub-problems simultaneously. We propose a Genetic Algorithm-based approach called GENESIS, which evolves three sine-function-activated Neural Networks, and funnels their output to a Gaussian distribution and an A* algorithm to optimise all three sub-problems simultaneously. We evaluate GENESIS on an emulator across 48 different data centre scenarios and compare its performance to two state-of-the-art Genetic Algorithms and one greedy algorithm. GENESIS produces an optimal solution for 100% of the scenarios, whereas the second-best method optimises only 71% of the scenarios. Moreover, GENESIS is the fastest among all Genetic Algorithms, averaging 15.84 minutes, compared to an average of 38.62 minutes for the second-best Genetic Algorithm.

Accurate annual average daily traffic (AADT) data are vital for transport planning and infrastructure management. However, automatic traffic detectors across national road networks often provide incomplete coverage, leading to underrepresentation of minor roads. While recent machine learning advances have improved AADT estimation at unmeasured locations, most models produce only point predictions and overlook estimation uncertainty. This study addresses that gap by introducing an interval prediction approach that explicitly quantifies predictive uncertainty. We integrate a Quantile Random Forest model with Principal Component Analysis to generate AADT prediction intervals, providing plausible traffic ranges bounded by estimated minima and maxima. Using data from over 2,000 minor roads in England and Wales, and evaluated with specialized interval metrics, the proposed method achieves an interval coverage probability of 88.22%, a normalized average width of 0.23, and a Winkler Score of 7,468.47. By combining machine learning with spatial and high-dimensional analysis, this framework enhances both the accuracy and interpretability of AADT estimation, supporting more robust and informed transport planning.

In this study, we present a machine learning (ML) framework to predict the axial load-bearing capacity, (kN), of cold-formed steel structural members. The methodology emphasizes robust model selection and interpretability, addressing the limitations of traditional analytical approaches in capturing the nonlinearities and geometrical complexities inherent to buckling behavior. The dataset, comprising key geometric and mechanical parameters of steel columns, was curated with appropriate pre-processing steps including removal of non-informative identifiers and imputation of missing values. A comprehensive suite of regression algorithms, ranging from linear models to kernel-based regressors and ensemble tree methods was evaluated. Among these, Gradient Boosting Regression exhibited superior predictive performance across multiple metrics, including the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE), and was consequently selected as the final model. Model interpretability was addressed using SHapley Additive exPlanations (SHAP), enabling insight into the relative importance and interaction of input features influencing the predicted axial capacity. To facilitate practical deployment, the model was integrated into an interactive, Python-based web interface via Streamlit. This tool allows end-users-such as structural engineers and designers, to input design parameters manually or through CSV upload, and to obtain real-time predictions of axial load capacity without the need for programming expertise. Applied to the context of steel storage rack columns, the framework demonstrates how data-driven tools can enhance design safety, streamline validation workflows, and inform decision-making in structural applications where buckling is a critical failure mode

This paper presents an improved forward-backward splitting algorithm with two inertial parameters. It aims to find a point in the real Hilbert space at which the sum of a co-coercive operator and a maximal monotone operator vanishes. Under standard assumptions, our proposed algorithm demonstrates weak convergence. We present numerous experimental results to demonstrate the behavior of the developed algorithm by comparing it with existing algorithms in the literature for regression and data classification problems. Furthermore, these implementations suggest our proposed algorithm yields superior outcomes when benchmarked against other relevant algorithms in existing literature.

The unprecedented global COVID - 19 pandemic has prompted researchers to investigate both the biochemical changes associated with acute infection and the long - term effects of COVID - 19, with the goal of elucidating underlying mechanisms [ 1 4 ]. Among the diverse biochemical alterations observed in COVID - 19, change s in metabolomic and proteomic profiles have drawn particular attention due to their roles in fundamental biological processes, including protein expression and metabolic pathways [5, 6]. Early in the pandemic, several studies highlighted the significance of certain biomarkers for diagnosing COVID - 19 and assessing disease severity [7, 8]. These initial finding s reveal ed that specific biomarkers are involved in COVID - 19 pathogenesis and correlate with disease severity. S ubsequent research into post - acute sequelae of COVID - 19 (PASC, or long COVID) has further shown that variations in these biomarkers are associated with neurological and respiratory complications [9, 10]. Collectively, these studie s highlight the importance of identifying key biomarkers to support both acute COVID - 19 detection and the understanding of long COVID.

Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises significant privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two, one at five centers from LFQ E.coli experiments and one at three centers from TMT human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences no greater than $\text{$4 \times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-27. FedProt is available as a web tool with detailed documentation as a FeatureCloud App.

Selective labels occur when label observations are subject to a decision-making process; e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem called disparate censorship, where labeling biases vary across subgroups and unlabeled individuals are imputed as "negative" (i.e., no diagnostic test = no illness). Machine learning models naively trained on such labels could amplify labeling bias. Inspired by causal models of selective labels, we propose Disparate Censorship Expectation-Maximization (DCEM), an algorithm for learning in the presence of disparate censorship. We theoretically analyze how DCEM mitigates the effects of disparate censorship on model performance. We validate DCEM on synthetic data, showing that it improves bias mitigation (area between ROC curves) without sacrificing discriminative performance (AUC) compared to baselines. We achieve similar results in a sepsis classification task using clinical data.

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road safety by conducting a comprehensive review of recent advancements in applying machine learning (ML) techniques to traffic accident analysis and prediction. It examines 191 studies from the last five years, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data. To our knowledge, this study is the first to provide such a comprehensive review, covering the state-of-the-art across a wide range of domains related to accident analysis and prediction. The review highlights the effectiveness of integrating diverse data sources and advanced ML techniques to improve prediction accuracy and handle the complexities of traffic data. By mapping the current landscape and identifying gaps in the literature, this study aims to guide future research towards significantly reducing traffic-related deaths and injuries by 2030, aligning with the World Health Organization (WHO) targets.

Kurdish libraries have many historical publications that were printed back in the early days when printing devices were brought to Kurdistan. Having a good Optical Character Recognition (OCR) to help process these publications and contribute to the Kurdish languages resources which is crucial as Kurdish is considered a low-resource language. Current OCR systems are unable to extract text from historical documents as they have many issues, including being damaged, very fragile, having many marks left on them, and often written in non-standard fonts and more. This is a massive obstacle in processing these documents as currently processing them requires manual typing which is very time-consuming. In this study, we adopt an open-source OCR framework by Google, Tesseract version 5.0, that has been used to extract text for various languages. Currently, there is no public dataset, and we developed our own by collecting historical documents from Zheen Center for Documentation and Research, which were printed before 1950 and resulted in a dataset of 1233 images of lines with transcription of each. Then we used the Arabic model as our base model and trained the model using the dataset. We used different methods to evaluate our model, Tesseracts built-in evaluator lstmeval indicated a Character Error Rate (CER) of 0.755%. Additionally, Ocreval demonstrated an average character accuracy of 84.02%. Finally, we developed a web application to provide an easy- to-use interface for end-users, allowing them to interact with the model by inputting an image of a page and extracting the text. Having an extensive dataset is crucial to develop OCR systems with reasonable accuracy, as currently, no public datasets are available for historical Kurdish documents; this posed a significant challenge in our work. Additionally, the unaligned spaces between characters and words proved another challenge with our work.

Abstract-- Alzheimer's is a brain disease that gets worse over AD is a neurodegenerative disorder caused by abnormal protein deposits in the brain, causing nerve cells to degenerate The Transformer architecture, which dominates natural and eventually die. This leads to diminished cognitive language processing [8], has gained popularity in computer function, altered mood, and behavior [1], [2]. Alzheimer's vision due to its impressive results in tasks like image disease has no known cure, but treatments can manage classification, object detection, and semantic segmentation. Common symptoms include ViT, based on Transformers, has been applied to images with memory loss, difficulty with tasks, language difficulties, minimal modifications and has shown superior performance disorientation, poor judgment, abstract thought issues, object in many computer-vision tasks, making it a viable alternative misplacement, mood changes, and motivation loss. CNNs gradually collect Alzheimer's disease begins in the preclinical stage, where features from local to global using convolutional layers.