Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches.

Background/Objectives: Even with today’s advancements, cancer still represents a major cause of mortality worldwide. One important aspect of cancer progression that has a big impact on diagnosis, prognosis, and treatment plans is accurate lymph node metastasis evaluation. However, regardless of the imaging method used, this process is challenging and time-consuming. This research aimed to develop and validate an automatic detection and segmentation system for superficial lymph node evaluation based on multimodal ultrasound images, such as traditional B-mode, Doppler, and elastography, using deep learning techniques. Methods: The suggested approach incorporated a Mask R-CNN architecture designed specifically for the detection and segmentation of lymph nodes. The pipeline first involved noise reduction preprocessing, after which morphological and textural feature segmentation and analysis were performed. Vascularity and stiffness parameters were further examined in Doppler and elastography pictures. Metrics, including accuracy, mean average precision (mAP), and dice coefficient, were used to assess the system’s performance during training and validation on a carefully selected dataset of annotated ultrasound pictures. Results: During testing, the Mask R-CNN model showed an accuracy of 92.56%, a COCO AP score of 60.7 and a validation score of 64. Furter on, to improve diagnostic capabilities, Doppler and elastography data were added. This allowed for improved performance across several types of ultrasound images and provided thorough insights into the morphology, vascularity, and stiffness of lymph nodes. Conclusions: This paper offers a novel use of deep learning for automated lymph node assessment in ultrasound imaging. This system offers a dependable tool for doctors to evaluate lymph node metastases efficiently by fusing sophisticated segmentation techniques with multimodal image processing. It has the potential to greatly enhance patient outcomes and diagnostic accuracy.

Railway has been playing a significant role in passenger and cargo transportation. Electric railway systems are of growing importance with recent augments on energy saving and environmental friendly. The advantages of electrical railway compared to other transportation systems are cheaper sources of energy, lightweight locomotives, easier maintenance and a lower cost per passenger. Advances in power electronics allow trains to employ regenerative braking, resulting in a safer, more economical means of transport, with reduced maintenance and increased comfort. Estimating the regenerative braking process requires a complex model of the electrical and mechanical components, related to the mechanical behavior of the train, mechanical-electrical conversion in the traction motor operated as a generator, electrical equations of the power distribution system and of the power equipment in the traction substations.

Cyber-physical systems represent a new emerging field in automatic control. The fault system is a key component, because modern, large scale processes must meet high standards of performance, reliability and safety. Fault propagation in large scale chemical processes can lead to loss of production, energy, raw materials and even environmental hazard. The present paper develops a multi-agent fault-tolerant control architecture using robust fractional order controllers for a (¹³C) cryogenic separation column cascade. The JADE (Java Agent DEvelopment Framework) platform was used to implement the multi-agent fault tolerant control system while the operational model of the process was implemented in Matlab/SIMULINK environment. MACSimJX (Multiagent Control Using Simulink with Jade Extension) toolbox was used to link the control system and the process model. In order to verify the performance and to prove the feasibility of the proposed control architecture several fault simulation scenarios were performed.