The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.

As food safety is undergoing through significant challenges due to recent food scandals, and the consumers demands for products of higher quality is increasing, the need for better knowledge of the food production processes and adoption of data sharing practices in the product and supply chain management are emerging. To address those issues, data sharing platforms have been introduced as essential tools for creating high value from data with secure and mutually beneficial multi-partner data sharing fascilitation. Blockchain technology, through its inhereted distributed nature can help to build trust mechanisms to enhance transparency and security dimension of food chains. In this work we propose a novel security mechanism for proper authentication and authorization when accessing resources through an agrifood data platform. Our proposed methodology aims to deliver sophisticated backbone service capabilities that will enable trusted, secure, automated, robust and controlled data transactions for food certification to all food sector businesses that demand easy, fast, and actionable access to variegating food safety data from multiple devices and in various settings.

Artificial Intelligence (AI) advances are pushing the boundaries across research domains with AI-driven solutions in healthcare claiming a significant share. A key objective of these studies concerns the timely prediction of various pathological conditions. Sepsis is a life-threatening syndrome and one of the main causes of death in intensive care unit (ICU) patients. As it becomes a major health problem worldwide, sepsis early prediction could assist healthcare professionals towards making informed clinical decisions, and thereby, significantly reducing the sepsis' morbidity and mortality. A notable body of literature involving the use of AI for sepsis prediction exists. However, to the best of our knowledge, only a handful of studies focus on performing a systematic review of the AI enabled solutions for sepsis prediction in ICUs. In this context, the present paper aims to identify knowledge gaps, stimulate interest and yield motivations for future research. Moreover, to discuss ethical and explainability aspects and associated challenges. The literature search was conducted between February 2023 and April 2023 and considered eligible articles published within the last five years.

This Paper presents an Internet of Things (IoT) based framework to monitor ECG for biometric recognition and acceleration for fall detection. To this end, an-IoT based Remote Elderly Monitoring System (REMS) platform is described. REMS consists of a Shimmer3TM device transmitting physiological signal wirelessly to a nearby gateway which routes the data to a remote IoT-platform, able to accommodate dynamically changing configurations. The Shimmer firmware has been modified to send data based on the compressive sensing theory in order to ameliorate energy consumption in addition of real data, and the analysis and processing are done locally on a heterogeneous multicore edge device in order to solve latency issues related to cloud reliance. Subsequently the framework has been designed to handle the different parameter settings and multiple scenarios in a user-friendly way. Furthermore, it allows the user to monitor physiological data and acquire some feedback related to their analysis. Depending on a scenario (energy save, secure communication) the system can be configured manually or automatically to monitor ECG or acceleration data and displays them, it can also identify the subject based on ECG recognition and detect fall if it occurs.