The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives

Diabetes is a complex metabolic disorder affecting the glucose status of the human body. Chronic hyperglycaemia related to diabetes is associated with end organ failure. The clinical relationship between diabetes and atherosclerotic cardiovascular disease is well established. This makes therapeutic approaches that simultaneously target diabetes and atherosclerotic disease an attractive area for research. The majority of people with diabetes fall into two broad pathogenetic categories, type 1 or type 2 diabetes. The role of obesity, adipose tissue, gut microbiota and pancreatic beta cell function in diabetes are under intensive scrutiny with several clinical trials to have been completed while more are in development. The emerging role of inflammation in both type 1 and type 2 diabetes (T1D and T1D) pathophysiology and associated metabolic disorders, has generated increasing interest in targeting inflammation to improve prevention and control of the disease. After an extensive review of the possible mechanisms that drive the metabolic pattern in T1D and T2D and the inflammatory pathways that are involved, it becomes ever clearer that future research should focus on a model of combined suppression for various inflammatory response pathways

A Prototype 5G/IoT Implementation for Transforming a Legacy Facility to a Smart Factory

Part 1: 6th Workshop on “5G – Putting Intelligence to the Network Edge” (5G-PINE 2021)International audienceA typical factory consists of several low functionality sensors. This work presents an architecture that can enhance the day-to-day operation of the site by combining modern IoT and legacy equipment and transforming the site for the 5G era. Operation and security of the site will benefit from the low latency the proposed Mobile Edge Computing architecture offers. Constant and uninterrupted monitoring makes preventive maintenance decisions even more accurate. Multitier architecture allows monitoring of each site to be performed locally without delays, while overall monitoring and control of remote sites is feasible as well

European Union Innovation Efficiency Assessment Based on Data Envelopment Analysis

Though much attention is dedicated to the development of its research and innovation policy, the European Union constantly struggles to match the level of the strongest innovators in the world. Therefore, there is a necessity to analyze the individual efforts and conditions of the 27 member states that might determine their final innovative performance. The results of a scientific literature review showed that there is a growing interest in the usage of artificial intelligence when seeking to improve decision-making processes. Data envelopment analysis, as a branch of computational intelligence methods, has proved to be a reliable tool for innovation efficiency evaluation. Therefore, this paper aimed to apply DEA for the assessment of the European Union’s innovation efficiency from 2000 to 2020, when innovation was measured by patent, trademark, and design applications. The findings showed that the general EU innovation efficiency situation has improved over time, meaning that each programming period was more successful than the previous one. On the other hand, visible disparities were found across the member states, showing that Luxembourg is an absolute innovation efficiency leader, while Greece and Portugal achieved the lowest average efficiency scores. Both the application of the DEA method and the gathered results may act as viable guidelines on how to improve R&I policies and select future investment directions

A Novel Dynamic Approach for Determining Real-Time Interior Visual Comfort Exploiting Machine Learning Techniques

The accurate assessment of visual comfort in indoor spaces is crucial for creating environments that enhance occupant well-being, productivity, and overall satisfaction. This paper presents a groundbreaking contribution to the field of visual comfort assessment in occupied buildings, addressing the existing research gap in methods for evaluating visual comfort once a building is in use while ensuring compliance with design specifications. The primary aim of this study was to introduce a pioneering approach for estimating visual comfort in indoor environments that is non-intrusive, practical, and can deliver accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method was evaluated and compared using real-life scenario. The experimental results demonstrated that the suggested model surpassed the mathematical model with an impressive performance improvement of 99%, requiring fewer computational resources and exhibiting a remarkable 95% faster processing time

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection

A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization

The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments

Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns