In-vitro validation and quantitative measurements of graded burn wounds on a porcine model using handheld laser speckle imaging

Burn wound severity can be difficult to assess and the diagnosis is usually subjective. Optical techniques have emerged as alternative methods for providing objective, non-contact assessment of burn wound severity. One such technique is Laser Speckle Imaging (LSI), which quantifies superficial blood flow using coherent laser light. We have previously demonstrated that LSI can be used to accurately assess burn wounds. However, LSI is conventionally used in static designs, such as cart-based or tripod mounted configurations, due to the susceptibility of LSI to motion artifact. This can limit the portability and usability of the device in a clinical. Handheld LSI can potentially overcome these limitations. However, accounting for motion artifact associated with user movement must be addressed to obtain accurate and reliable blood flow measurements. Please click Additional Files below to see the full abstract

Fostering Awareness and Personalization of Learning Artificial Intelligence

This paper illustrates the activities of the projects SMAILE and AILEAP, which are devoted to foster the growth of awareness and readyness to learn artificial intelligence in the general population. The first project was mainly oriented to children and young adults, while the second is more oriented to the personalization of the learning experience also in professionals

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

A new nanohybrid for electrocatalytic biodiesel production from waste Amalfi coast lemon seed oil

A PtIrRu nanocatalyst was prepared for the electrocatalytic production of biodiesel from waste Amalfi cost lemon seeds oil. The transesterification reaction occurred in a two-phase reaction mixture where an emulsion of oil in the alcohol phase is formed under strong stirring. Lemon seed oil conversion reached a value of 98.2 % after 2 h at 15 V, 0.3 wt. % of NaCl and 20 °C, methanol/oil ratio 10:1. Although a reduction of the FAME yield was observed in presence of 4 wt. % water content, a yield of 49.2 % was still measured. The biodiesel produces was analysed according with EN 14214, too. The results of this paper constitute a proof of concept of the valorisation in biodiesel of all lemon seed oils, also considering the large global scale citrus fruit consumption and processing, e.g. to produce fruit juices

Ag/graphene electrode for the electrochemical conversion of 5-hydroxymethylfurfural to 2,5-hexanedione at ambient pressure and temperature

Here we report the performance in 5-Hydroxymethylfurfural (HMF) reduction of Ag-graphene-based electrode. The prepared nanocomposite, synthesized according to a "wet chemistry" approach, was broadly characterized: SEM images and XRD spectrum indicate the formation of nanoparticles dispersed on fewlayers graphene. A multimodal pore distribution, indicating a network of larger and smaller pores enabling electrode wettability and exposing surface nanoparticles, was evaluated. The sample was tested for the oxidation of HMF in a sulfate buffer solution. A very high overpotential gap between hydrogen production and HMF conversion was demonstrated. High efficiency (FE = 65.1%) and selectivity (70.2%), with little amount of bis(hydroxymethyl)furan by-product (FE &lt; 6%), were demonstrated