The analysis of electrocardiogram (ECG) signals is profoundly affected by the presence of electromyographic (EMG) noise, which can lead to substantial misinterpretations in healthcare applications. To address this challenge, we present ECGDnet, an innovative architecture based on Transformer technology, specifically engineered to denoise multi-channel ECG signals. By leveraging multi-head self-attention mechanisms, positional embeddings, and an advanced sequence-to-sequence processing architecture, ECGDnet effectively captures both local and global temporal dependencies inherent in cardiac signals. Experimental validation on real-world datasets demonstrates ECGDnet’s remarkable efficacy in noise suppression, achieving a Signal-to-Noise Ratio (SNR) of 19.83, a Normalized Mean Squared Error (NMSE) of 0.9842, a Reconstruction Error (RE) of 0.0158, and a Pearson Correlation Coefficient (PCC) of 0.9924. These results represent significant improvements from traditional deep learning approaches while maintaining complex signal morphology and effectively mitigating noise interference.

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration.

The analysis of electrocardiogram (ECG) signals is profoundly affected by the presence of electromyographic (EMG) noise, which can lead to substantial misinterpretations in healthcare applications. To address this challenge, we present ECGDnet, an innovative architecture based on Transformer technology, specifically engineered to denoise multi-channel ECG signals. By leveraging multi-head self-attention mechanisms, positional embeddings, and an advanced sequence-to-sequence processing architecture, ECGDnet effectively captures both local and global temporal dependencies inherent in cardiac signals. Experimental validation on real-world datasets demonstrates ECGDnet's remarkable efficacy in noise suppression, achieving a Signal-to-Noise Ratio (SNR) of 19.83, a Normalized Mean Squared Error (NMSE) of 0.9842, a Reconstruction Error (RE) of 0.0158, and a Pearson Correlation Coefficient (PCC) of 0.9924. These results represent significant improvements over traditional deep learning approaches, while maintaining complex signal morphology and effectively mitigating noise interference.

As the urban population and the car ownership rate increase, traffic is becoming a serious urban problem. It remains difficult to reduce the large number of vehicles circling in search for free and cheaper lots in urban area. The driver's choice for on-street lots, which are generally cheaper than off-street lots but also rare, causes congestion, impact negatively the parking revenues and increases the total cost of drivers' trips. However, we propose to intervene on price as effective tool to influence driver's behavior, balance parking demand and enhance parking turnover in urban environment. This article highlights the interactions between parking price, real-time parking demand and parking time. We analyze the parking time under near-real-life conditions using the Discrete Batch Markovian Arrival Process (D-BMAP). Subsequently, we identify the optimal arrival rate that will seek to make the best use of parking resources. Finally, we propose to adopt a dynamic pricing policy that changes prices proportionally to the arrival rates on each parking and therefore reduces congestion (cruising time) and eliminates the driver's preference for some parking.

Predictive maintenance (PdM) has emerged as a critical enabler for improving reliability, operational efficiency, and regulatory compliance in pharmaceutical manufacturing. Despite its proven effectiveness in several industrial sectors, PdM adoption within the pharmaceutical industry remains comparatively limited due to sector-specific technical, regulatory, and organizational constraints. This paper presents a structured technical analysis of recent academic and industrial works addressing PdM implementation in pharmaceutical manufacturing systems. The analysis examines applied AI and machine learning techniques, sensor and data acquisition strategies, implementation maturity, and regulatory considerations relevant to highly regulated environments. The findings indicate that, while PdM solutions can significantly improve equipment availability and reduce maintenance-related costs, major barriers persist, including limited failure data, validation and re-validation requirements, and organizational resistance to data-driven maintenance practices. Based on this analysis, the paper argues that hybrid approaches combining physics-based models (PBMs) with data-driven methods and explainable artificial intelligence (XAI), supported by digital twins and robust data governance frameworks, represent a practical and regulation-aware pathway for the broader adoption of predictive maintenance in pharmaceutical manufacturing