Remembering Alex Fubini: a journey through urbanism, education, and friendship

Umberto Janin Rivolin remembers the memory of Alex Fubini on behalf of the Politecnico di Torino DIST friends

Microbial-modified coal-based solid waste backfill material: Mechanical improvement and its effect on the water environment

This study explored the application of microbially induced calcite precipitation (MICP) technique for enhancing backfill microbial-modified material strength and reducing cement use. Laboratory tests assessed the strength of microbial-modified materials and their environmental impact by characterizing harmful elements speciation in the material and examining pH and concentrations of harmful elements in different water environments after soaking. The results revealed that microbial-modified materials achieve higher strength than traditional ones composed of coal gangue, fly ash (FA), and cement without microbial modification, with optimal performance at 30% FA compared to 25% FA in traditional materials. Cement addition does not alter the interaction between Bacillus pasteurii and coal-based solid wastes, but increasing cement content from 3% to 5% further boosts strength through combined effects of cement hydration and microbial modification. Microbial-modified materials without cement achieve a strength of 471.1 KPa, similar to traditional materials with about 3.5% cement, and require 36% and 42.67% less cement for target strengths of 1000 KPa and 2000 KPa, respectively. Additionally, Microbial-modified materials improve water pH, ensuring all tested water types and harmful elements meet quality standards within the Class I-III range. This approach not only reduces cement use and enhances material strength but also improves environmental safety, making it a promising option for backfill applications

A Cutting-Edge Energy Management System for a Hybrid Electric Vehicle relying on Soft Actor–Critic Deep Reinforcement Learning

Thanks to its superior learning capabilities and its model-free nature, Reinforcement Learning (RL) is increasingly regarded as an effective solution for addressing complex optimization tasks such as energy management in Hybrid Electric Vehicles (HEVs). In this paper, we implement a Soft Actor-Critic (SAC) agent on a digital twin of a plug-in Hybrid Electric Vehicle (pHEV) operating in charge-sustaining mode. We employ multi-cycle training, which significantly improves the SAC model’s ability to generalize across diverse conditions. We fist evaluate the SAC agent capabilities on the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) by comparing its performance to the global optimum achieved by Dynamic Programming (DP), a local optimization strategy, i.e., Equivalent Consumption Minimization Strategy (ECMS), and a Double Deep Q-Learning (DDQL) algorithm. Furthermore, we test the agent across a broad range of driving cycles to assess its ability to generalize to scenarios beyond those used during training. Simulation results show that the SAC agent achieves results close to the optimal benchmark set by the DP, with CO emissions differing by only 3-4%

Impact of radiative cooling on the thermal behavior of multi-junction solar cells

Thermal radiation is a key aspect of solar cell thermal management. In this work we study, through detailed balance and multiphysics simulations, the thermal behavior of multi-junction solar cells and the impact of different radiative cooling designs on their achievable efficiency. We discuss the influence of the mid-infrared emissivity of the semiconductors constituting the cell and possible encapsulating materials, with the goal of evaluating the performance improvements achievable with an ideal thermal emitte

Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review

Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 and August 2024 that used machine learning (ML), deep learning (DL), or both of these two methods to detect neurological and mental health disorders automatically using EEG signals. The most common and most prevalent neurological and mental health disorder types were sourced from major databases, including Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore. Epilepsy, depression, and Alzheimer's disease are the most studied conditions that meet our evaluation criteria, 32, 12, and 10 studies were identified on these topics, respectively. Conversely, the number of studies meeting our criteria regarding stress, schizophrenia, Parkinson's disease, and autism spectrum disorders was relatively more average: 6, 4, 3, and 3, respectively. The diseases that least met our evaluation conditions were one study each of seizure, stroke, anxiety diseases, and one study examining Alzheimer's disease and epilepsy together. Support Vector Machines (SVM) were most widely used in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. DL methods generally outperformed traditional ML, as they yielded higher performance using huge EEG data. We observed that the complex decision process during feature extraction from EEG signals in ML-based models significantly impacted results, while DL-based models handled this more efficiently. AI-based EEG analysis shows promise for automated detection of neurological and mental health conditions. Future research should focus on multi-disease studies, standardizing datasets, improving model interpretability, and developing clinical decision support systems to assist in the diagnosis and treatment of these disorders

Multiscale Mechanical Study of Turritellidae Seashells and Design of A Bioinspired Tonotopic Metasensor

L'abstract è presente nell'allegato / the abstract is in the attachmen

CoDÆN: Benchmarks and Comparison of Evolutionary Community Detection Algorithms for Dynamic Networks

Web data are often modelled as complex networks in which entities interact and form communities. Nevertheless, web data evolves over time, and network communities change alongside it. This makes Community Detection (CD) in dynamic graphs a relevant problem, calling for evolutionary CD algorithms. The choice and evaluation of such algorithm performance is challenging because of the lack of a comprehensive set of benchmarks and specific metrics. To address these challenges, we propose CoDÆN – COmmunity Detection Algorithms in Evolving Networks – a benchmarking framework for evolutionary CD algorithms in dynamic networks, that we offer as open source to the community. CoDÆN allows us to generate synthetic community-structured graphs with known ground truth and design evolving scenarios combining nine basic graph transformations that modify edges, nodes, and communities. We propose three complementary metrics (i.e. Correctness, Delay, and Stability) to compare evolutionary CD algorithms. Armed with CoDÆN, we consider three evolutionary modularity-based CD approaches, dissecting their performance to gauge the trade-off between the stability of the communities and their correctness. Next, we compare the algorithms in real Web-oriented datasets, confirming such a trade-off. Our findings reveal that algorithms that introduce memory in the graph maximise stability but add delay when abrupt changes occur. Conversely, algorithms that introduce memory by initialising the CD algorithms with the previous solution fail to identify the split and birth of new communities. These observations underscore the value of CoDÆN in facilitating the study and comparison of alternative evolutionary community detection algorithms

Durable Bio‐Based Nanocomposite Coating on Urinary Catheters Prevents Early‐Stage CAUTI‐Associated Pathogenicity

Recurrent catheter-associated urinary tract infections (CAUTIs) in catheterized patients, increase their morbidity and hospital stay at substantial costs for healthcare systems. Hence, novel and efficient strategies for mitigating CAUTIs are needed. In this work, a bio-based nanocomposite coating is engineered with bactericidal, antibiofilm, and antioxidant properties on commercial silicone catheters using a combined ultrasound/nanoparticles (NPs) driven coating approach. This approach integrates citronellal-loaded lauryl gallate NPs (CLG_NPs), as both antimicrobial and structural elements, with chitosan (CS), in a substrate-independent sonochemical coating process. The hybrid CS/CLG_NPs coating shows pH-dependent citronellal release, strong antibacterial activity toward the common CAUTI pathogens Escherichia coli and Staphylococcus aureus, alongside strong antioxidant activity, and biocompatibility to fibroblast and keratinocytes. Moreover, the nano-enabled coating significantly mitigated bacterial biofilm formation after a week in a simulated human bladder environment, outperforming the commercially-available silicone catheters. These results underscore the potential of the novel biopolymer nanocomposites obtained by ultrasound coating technology, offering a straightforward antimicrobial/antibiofilm solution for indwelling medical devices