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Guest Editorial Special Issue on the Next-Generation Deep Learning Approaches to Emerging Real-World Applications
Get PDFDevelopment and Implementation of Safe, Human-like Autonomous Vehicle Control on Horizontal Curves
No full textPublisher Copyright: © 2025 IEEE.Recent advancements in deep learning and hard-ware technologies have enabled the application of novel control techniques for autonomous vehicles to handle challenging road conditions such as horizontal curves. This study presents a novel approach that blends human driver data with the Pure Pursuit algorithm, utilizing the law of propagation of uncertainty. Trained on the combined data, two deep learning architectures - Long Short-Term Memory (LSTM) networks and Fully Connected Deep Neural Networks (FCDNN) - are adopted: one predicts future speed profiles for calculating braking deceleration. In contrast, the other predicts steering rates for tracking a reference path, considering lateral acceleration and jerk constraints to ensure safety and comfort following the American Association of State Highway and Transportation Officials (AASHTO) and International Organization for Standardization (ISO) 15622 standards while emulating an expert human driving behavior. The proposed work has been implemented and tested using a Chevy Bolt Electric Utility Vehicle (EUV) during a U-turn, with test results demonstrating successful constraint satisfaction and acceptable cross-track error in reference path tracking.Peer reviewe
Standards and codes for hydrogen in pipeline infrastructure: testing, qualification, and integrity assessment
Get PDFPublisher Copyright: © 2025This paper presents a comprehensive review of standards and codes for testing, qualifying, and assessing metallic materials and pipeline infrastructure for hydrogen service, with a view to shed light on the gaps and limitations of current procedures for repurposing EU natural gas infrastructure to hydrogen service. Current standards provide a foundational framework for the qualification of materials in hydrogen-rich environments; however, significant gaps persist in addressing the unique challenges posed by hydrogen-natural gas mixtures. This review explores standards for material testing, qualification methods in codes and standards, and design criteria for hydrogen service, with a critical emphasis on structural integrity assessment. Through a detailed analysis of the different documents, the study has identified limitations in existing testing methods and protocols, including insufficient harmonization, vague testing specifications, and an absence of guidelines for evaluating materials in blended environments. Findings highlight an urgent need for harmonization of procedures to facilitate consistent methodologies across different countries in the EU and beyond, as well as for providing the necessary knowledge in terms of design, testing and assessment methods for the hydrogen-natural gas infrastructure. To address these gaps, this review proposes targeted research areas that can support future updates of standards and codes, contributing to safer, more efficient, and globally aligned approaches to hydrogen blending.Peer reviewe
Exploring pseudohalide substitution in α-cobalt-based layered hydroxides
No full textPublisher Copyright: © 2025 The Royal Society of Chemistry.While halide substitution has significantly influenced the electrical and magnetic properties of α-layered hydroxide frameworks (α-LH), the incorporation of pseudohalides remains limited. In this study, we present a detailed investigation of two-dimensional cobalt-layered hydroxides modified with tricyanomethanide (C4N3−) and thiocyanate (SCN−) pseudohalides, synthesized via a simple epoxide route at room temperature. Pseudohalide incorporation induces subtle structural modifications relative to pristine cobalt-chloride layered hydroxide (α-Co-Cl), including changes in interlayer spacing and the confirmation of a distinct bridging coordination in thiocyanate-modified samples. Magnetic measurements reveal broadly similar behavior across all samples, with the thiocyanate compound reflecting a structural difference that affects its magnetic response. These findings underscore the influence of pseudohalides on the structure and the effect of pseudohalide substitution on the magnetic response of α-cobalt-based layered hydroxides, demonstrating the chemical and structural versatility of Simonkolleite-like hydroxides as tunable materials for designing novel hybrids with dynamic structures.Peer reviewe
Exopolysaccharide pullulan production from enzymatic hydrolysate of quinoa stalks via citric acid–assisted hydrothermal pretreatment
No full textPublisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.Quinoa waste is often overlooked due to the limited scientific understanding of its potential applications. However, the carbohydrates it contains can be hydrolyzed and utilized in biotechnological processes to produce valuable products, such as pullulan, an exopolysaccharide used in the food and pharmaceutical industries. This approach can also help mitigate pollution from waste incineration. To effectively extract sugars from quinoa stalks, pretreatment is essential to modify their structural properties. In this context, citric acid–assisted hydrothermal pretreatment (CA-HTP) was evaluated to enhance the enzymatic digestibility of the carbohydrates, allowing for the subsequent production of pullulan from the extracted sugars. A central composite design (CCD) was employed to assess the impact of pretreatment time (20 to 60 min) and citric acid loading (3 to 12% w/w) at 180 °C. Under the optimized condition of CA-HTP (37.12 min, 9.43% w/w CA loading), 59 g of glucose per 100 g of pretreated biomass (94% hydrolysis yield) was achieved after 24 h of enzymatic process. Low CA loading during HTP produced 6.8 g of xylose per 100 g of biomass and 10 mg/g of XOS (xylobiose + xylotriose). Finally, using the enzymatic hydrolysate, 8.9 g/L of pullulan (yield 0.51 g/g) was achieved. CA-HTP enhances biomass susceptibility to enzymatic hydrolysis, releasing a high concentration of fermentable sugars and prebiotics. This process could be applied in the valorization of other organic residues, contributing to the development of a circular bioeconomy.Peer reviewe
Optimized SARS-CoV-2 spike protein detection via coupling coefficient-driven fast fourier transform analysis in a peptide-functionalized fiber optic biosensor
No full textPublisher Copyright: © 2025 The AuthorsIn this work, we present a novel fiber optic-based biosensor designed for the detection of the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. The sensor leverages the precise control of light within a Fabry-Pérot cavity, where the external wall is chemically functionalized with two peptides derived from the ACE2 protein that exhibit a high affinity for the spike protein. Our approach is centered on monitoring variations in the coupling coefficient, which directly affects the power of the reflected light beam coupled back into the fiber core. These variations are detected by analyzing the interference patterns of the reflected light using Fourier Transform techniques. The sensitivity of the sensor is demonstrated through its ability to detect minute changes in the coupling coefficient as the concentration of the spike protein increases, with detection limits as low as 0.018 ng/mL when all peptides are used together. The sensor exhibits exceptional sensitivity, selectivity, and repeatability, with a strong linear correlation (R² = 0.99) between the FFT intensity and the protein concentration. This innovative biosensor design offers significant advantages, including real-time analysis, high sensitivity, and the potential for miniaturization, making it a promising tool for the rapid and accurate detection of SARS-CoV-2 and other viral pathogens containing the RBD region. Moreover, although our approach has been experimentally evaluated for the spike protein, the biosensor could be readily adapted for the detection of other pathogens by redesigning the functionalized peptides to target new biomarkers, thereby providing a versatile platform for addressing current and future challenges in viral detection.Peer reviewe
Roll-to-Roll (R2R) High-Throughput Manufacturing of Foil-Based Microfluidic Chips for Neurite Outgrowth Studies
Get PDFPublisher Copyright: © 2025 by the authors.Microfluidic devices have emerged as a pivotal in vitro technology for axon outgrowth studies, facilitating the separation of the cell body from the neurites by geometric constraints. However, traditional microfabrication techniques fall short in terms of scalability for large-scale production, hindering widespread application. This study presents the development of foil-based cell culture chips, made of polyethylene terephthalate and in-house formulated ultraviolet curable liquid resin by high-throughput roll-to-roll (R2R) manufacturing. Here, two microchannel designs were tested to optimize manufacturing quality and assess the neurite outgrowth behavior. The fabricated neuron-foil chips demonstrated biocompatibility and supported neurite outgrowth within microchannels under static cell culture conditions. Furthermore, fluidic flow, oriented either perpendicular or parallel to the microchannel direction, was applied to enhance the biological reproducibility within the neuron-foil chips. These findings suggest that R2R manufacturing offers a promising approach for the high-throughput production of biocompatible microfluidic devices, advancing their potential application in modeling neurological diseases within the biomedical industry.Peer reviewe
Multi-site comparison and source apportionment of equivalent Black Carbon mass concentrations (eBC) in the United States: Southern California Basin and Rochester, New York
No full textPublisher Copyright: © 2024 Turkish National Committee for Air Pollution Research and ControlSpatial and temporal variability of equivalent black carbon (eBC) mass concentrations were studied using Aethalometers (AE33 and AE21) at 10 sites, including 5 urban and 5 near-road across the south coast of California and New York-Rochester. Statistical methods were applied to perform intra-urban and multi-site comparisons. Given that nominal eBC values provided by the Aethalometer were significantly overestimated, eBC concentrations were corrected using an appropriate multiple-scattering enhancement correction factor (C0) to accurately calculate light absorption. Annual and seasonal variations highlighted the significant contributions of traffic emissions to eBC mass concentrations at all sites. Source apportionment using the Aethalometer approach demonstrated that fuel combustion—primarily from gasoline and diesel vehicles— accounted for up to 80% of eBC. Emission sources were found to be largely region specific. Our findings suggest that the implementation of restrictive regulations aimed at reducing gasoline and diesel vehicle emissions, such as California's Tier 3 (SULEV, 2015) and New York (2017), has led to a higher proportion of cleaner, emission-controlled vehicles in the South Coast Air Basin compared to Rochester. However, the effects of these measures may require more time to be fully observed in traffic-polluted areas across the US.Peer reviewe
GREENDAI: Towards an observability tool for sustainable green distributed artificial intelligence
No full textPublisher Copyright: © 2025 IEEE.The growth of intelligent systems based on distributed artificial intelligence has generated an urgent need to assess their environmental impact and sustainability. The expansion of AI technologies has driven an increase in demands for computing resources and has augmented the need to adapt to more efficient forms of computing. such as distributed AI or Edge AI. Additionally, there is growing concern about energy consumption and the environmental footprint of these technologies. In this paper, our main contribution is the definition of a Distributed Artificial Intelligence systems observability tool architecture for green and sustainability perspectives. This tool is designed to report adequate KPIs by measuring the energy consumption, data usage, computational efficiency and carbon footprint of decentralized AI based components. This innovative approach not only promotes the development of more sustainable technologies but also encourages transparency and responsibility in the sustainable use of distributed and large-scale AI systems. To validate the approach, its feasibility and integration have been analyzed in an experimental use case of a Distributed AI system. The use case is a Federated Machine Learning based system, in which the benefits of reporting energy efficiency and sustainability metrics are analyzed.Peer reviewe
FMCooler: Simulated annealing for feature model configuration selection
No full textPublisher Copyright: © 2025 Copyright held by the owner/author(s).FMCooler is a tool for the automatic optimization in the selection of a configuration from a feature model based on the Simulated Annealing metaheuristic. It is a python-based tool built on top of flamapy, qubovert and dwave-neal. The formulation abstractions and details are exposed, as well as examples of usage to understand the easy handling of the module. We discuss experimentation results to argue that is a valid candidate to be included in the toolset to address this problem given its competitive results and scalability. We also include a discussion on future extensions as the reuse of the abstractions for experimenting with quantum computation. Tool and video: https://github.com/jdanielescanez/fmcoolerPeer reviewe