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Found 6289 publications
The strategic and regulatory framework for autonomous mobility in the European Union
Publication Name: Future of Legal Europe Will We Trust in It Liber Amicorum in Honour of Wolfgang Heusel
Publication Date: 2021-05-12
Volume: Unknown
Issue: Unknown
Page Range: 223-246
Description:
A significant majority of engineering professionals think that the technical and technological achievements of the fourth industrial revolution can provide an appropriate framework for the realisation of road transport based on autonomous mobility, assuming that a suitable legal, social and infrastructural background is ensured. Artificial intelligence (AI), the technological prerequisite of autonomous mobility, along with robotics and the Internet of Things (IoT) will result in a paradigm shift in many fields of our life and of the economy (Ződi 2018), and will reshape our current idea on road transport. Autonomous mobility based on artificial intelligence will have many benefits. For example, it will promote mobility of elderly or ill people, or people who are unable or have a limited ability to drive. Environmental pollution will be reduced, cities will be more liveable, the expected economic growth and other resultant economic advantages will be significant, and the efficiency of transport will be improved. Among the benefits, the considerable improvement of road safety will be of special importance. The European Union aspires to play a globally-leading role in the introduction of connected and automated mobility, in order to realise the ambitious goal set in the White Paper of 2011 (the Roadmap to a Single European Transport Area), in particular to halve the number of road fatalities in the European Union by 2030 and to reach zero fatalities by 2050. The elimination of human errors such as driving without due care and attention, driving under the influence of alcohol or other drugs etc., which account for 93-94% of road accidents require the realisation of autonomous mobility powered by artificial intelligence.
Open Access: Yes
Exhaustive Generation of the Complete Multidimensional Pareto Front for Multi-Objective Process Network Synthesis
Publication Name: Chemical Engineering Transactions
Publication Date: 2025-01-01
Volume: 121
Issue: Unknown
Page Range: 157-162
Description:
In sustainable systems design, optimizing complex process networks often involves multiple conflicting objectives, such as minimizing cost, reducing environmental impact, and maximizing performance. Traditional single-objective optimization methods frequently fail to address this complexity, resulting in suboptimal and inflexible solutions. This study focuses on a comprehensive approach to multi-objective optimization for a single fixed process structure, where all integer decisions are predetermined through a prior process synthesis phase, such as the Solution Structure Generator algorithm from the P-graph framework. The remaining task involves optimizing continuous parameters—specifically, the operational volumes within the network—to generate the complete Pareto front, representing all non-dominated solutions. Each objective function is assumed to be a linear function of operational volumes, allowing for a scalable mathematical formulation. An algorithmic framework is developed to address the challenges associated with generating infinite-point Pareto fronts in high-dimensional spaces, incorporating genetic algorithms, machine learning models, and the P-graph methodology. This hybrid approach supports dynamic adaptation to changing data and improves computational efficiency. The methodology is demonstrated through a case study. The example highlights how balancing cost and environmental criteria using Pareto optimal solutions leads to more sustainable system designs. Ultimately, this work underscores the practical importance of generating and evaluating the complete set of Pareto optimal solutions in sustainable system design. Moving beyond a single optimal configuration, the proposed methodology offers robust decision support across diverse industrial applications, bridging the gap between theoretical optimality and real-world implementation.
Open Access: Yes
DOI: 10.3303/CET25121027
Molecular investigation of endoparasites of marine mammals (Cetacea: Mysticeti, Odontoceti) in the Western Mediterranean
Publication Name: Frontiers in Veterinary Science
Publication Date: 2024-01-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
Introduction: Whales, dolphins, and porpoises are susceptible to infections by protozoan and metazoan parasites. Methods: In this study, tissue samples, as well as flatworms and roundworms, were collected from a common bottlenose dolphin (Tursiops truncatus), three short-beaked common dolphins (Delphinus delphis), two striped dolphins (Stenella coeruleoalba), a harbor porpoise (Phocoena phocoena), a long-finned pilot whale (Globicephala melas), and a fin whale (Balaenoptera physalus). These samples were molecularly analyzed. Results: In one D. delphis, Toxoplasma gondii was detected in multiple organs, including the cerebellum. The cysts of the tapeworms Clistobothrium delphini and Clistobothrium grimaldii were identified in G. melas. Flukes collected from D. delphis belong to Brachycladium atlanticum, while those removed from S. coeruleoalba probably represent a new species. Four species of lungworms were also identified: Halocercus delphini in S. coeruleoalba, Halocercus sp. in T. truncatus, Stenurus globicephalae in G. melas, and a potentially new Pharurus sp. in P. phocoena. Conclusion: These findings show, to the best of our knowledge, for the first time, the presence of T. gondii DNA in D. delphis. The cerebellum of the animal was Toxoplasma-infected, which might be relevant to inadvertent stranding. In this study, new genetic markers were sequenced for several helminth parasites of marine mammals, possibly including undescribed species.
Open Access: Yes
Performance of Low-Cost Air Temperature Sensors and Applied Calibration Techniques—A Systematic Review
Publication Name: Atmosphere
Publication Date: 2025-07-01
Volume: 16
Issue: 7
Page Range: Unknown
Description:
Low-cost air temperature sensors are an emerging theme in environmental monitoring. These sensors offer the advantage of making microclimate monitoring feasible due to their affordability. However, they are limited by the quality of the data they provide; in many cases, they have been reported to have presented errors in the sensor readings. These errors have been shown to improve after calibration was applied. The lack of a comprehensive understanding of the available calibration techniques, models, and sensor types has led to studies presenting heterogeneity in models and techniques alongside different performance metrics. To address this gap, this study conducted a systematic review following the PRISMA guidelines, reviewing studies from 2015 to 2024 across the databases Web of Science and Scopus, alongside the search engine Google Scholar. The aim was to identify the calibration techniques and models, the commercially available low-cost air temperature sensors used, the performance metrics utilised, and the calibration settings. The findings presented three main categories of calibration models utilised in the collected studies: linear, polynomial, and machine learning. Twenty-two commercially available low-cost sensors were identified, with the DHT22 sensor being the most utilised. Indoor settings were identified as the most preferred for conducting calibrations. Key challenges included limitations in reported results for calibration by the studies, the use of different performance metrics across studies, insufficient studies conducting calibration, and the diversity in sensor types utilised.
Open Access: Yes
Investigation of the Effect of a New Type of Copper–Sucrose Complex Compound on the Yield and Quality Parameters of Winter Wheat (Triticum aestivum L.)
Publication Name: Agronomy
Publication Date: 2025-07-01
Volume: 15
Issue: 7
Page Range: Unknown
Description:
We conducted experiments on winter wheat grown in copper-deficient soil, where soil tests revealed a more pronounced deficiency in the deeper layers. As climate change reduces precipitation, plants increasingly rely on nutrients from these deeper layers. A copper–sucrose complex—previously unused in agriculture—was applied as a foliar spray during the tillering and flowering stages. Across the three-year average, significant increases were observed starting from the 1 kg ha−1 copper dose in yield, from 0.3 kg ha−1 in crude protein content, and from 0.5 kg ha−1 in wet gluten content compared to the untreated control. For all three parameters, the highest values were achieved with the 2 kg ha−1 dose. Yield increased by 1.03 t ha−1, crude protein by 0.9%, and wet gluten by 2.3% relative to the control. In 2019, high humidity and favorable temperatures during flowering led to fungal infections in control plots, with DON toxin concentrations exceeding the regulatory safety threshold. Following copper–sucrose complex application, DON levels dropped below this threshold, demonstrating a measurable protective effect.
Open Access: Yes
Investigating the effect of two-state approaches on students’ understanding of quantum measurement: A quasiexperimental field study
Publication Name: Physical Review Physics Education Research
Publication Date: 2025-07-01
Volume: 21
Issue: 2
Page Range: Unknown
Description:
With the rise of quantum computing, interest has grown in using two-state quantum systems (qubits) at the secondary level to foster students’ conceptual understanding. Quantum measurement, in particular, is central to quantum theory and its accurate conceptualization by students is crucial for grasping fundamental quantum principles. However, instructional methods typically make use of different contexts (i.e., different two-state systems), significantly affecting students’ conceptual development in quantum physics. In this paper, we report findings from a cluster-randomized field trial involving 181 students taught through three inquiry-based, two-state approaches: the which-path-encoded single-photon, the polarization, and the double-well potential approach. All three approaches supported students’ conceptual development, yet students taught using photon polarization and the double-well potential significantly outperformed those participating in a course following the which-path-encoded single-photon approach. Our findings indicate that students participating in the which-path-encoded single-photon approach often retain mixed-thinking frameworks, whereas those taught with photon polarization or the double-well potential approaches were more likely to develop toward quantum thinking. Thus, our findings underpin how influential the choice of (experimental) context is on students’ conceptual development (also) in quantum physics.
Open Access: Yes
DOI: 10.1103/15rm-rlnj
ELASTO-PLASTIC TOPOLOGY OPTIMIZATION UNDER CYCLIC LOADING FOR EFFICIENT STRUCTURAL DESIGN
Publication Name: Compdyn Proceedings
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: 3401-3412
Description:
This study introduces an elasto-plastic topology optimization approach tailored for structural systems subjected to cyclic loading. By incorporating material nonlinearity, the method provides a robust framework for designing structures that withstand repetitive loading efficiently, avoiding excessive material use. This optimization approach integrates the effects of cyclic loading to enhance structural resilience while minimizing mean compliance. The proposed method is particularly applicable for optimizing structural components in steel frameworks where material efficiency and load-bearing capacity are critical. By emphasizing the elasto-plastic behavior of materials, this study aims to offer a practical tool for engineers seeking resilient structural designs that balance strength and sustainability. Potential applications extend to structural elements in earthquake-resistant buildings, where managing material use is essential for both environmental and economic impact.
Open Access: Yes
Surrogate Model-based Optimization of Electrical Machines in Ārtap Framework
Publication Name: Proceedings 2021 IEEE 19th International Power Electronics and Motion Control Conference Pemc 2021
Publication Date: 2021-04-25
Volume: Unknown
Issue: Unknown
Page Range: 514-518
Description:
For accurate modeling of electrical machines the solution multiple physical fields simultaneously in 3D is necessary. Therefore, the optimization of these machines is an computationally expensive optimization problem. The novel artificial intelligence methods and surrogate modeling techniques based on hp-adaptive FEM techniques can significantly reduce the computational cost. In case of a cogging torque or a torque ripple calculation, many simulations should be performed to make an accurate estimation of a single quantity. The number of calculations can be reduced by using surrogate modeling techniques. However, the surrogate model-based model's extrema can differ from the original task's optima. This paper presents a surrogate-model based cogging torque minimization of an axial flux permanent magnet synchronous machine. The objective function of this optimization is the cogging torque, the full solution space is explored to examine and show the robustness of the different kind of solutions.
Open Access: Yes
Experimental Validation and Optimization of a Hydrogen–Gasoline Dual-Fuel Combustion Model in a Spark Ignition Engine with a Moderate Hydrogen Ratio
Publication Name: Energies
Publication Date: 2025-07-01
Volume: 18
Issue: 13
Page Range: Unknown
Description:
Hydrogen–gasoline dual-fuel spark ignition (SI) engines represent a promising transitional solution toward cleaner combustion and reduced carbon emissions. In a previous study, a predictive engine model was developed to simulate the performance and combustion characteristics of such systems; however, its accuracy was constrained by the use of estimated combustion parameters. This study presents an experimental validation based on high-resolution in-cylinder pressure measurements performed on a naturally aspirated SI engine operating with a 20% hydrogen energy share. The objectives are twofold: (1) to refine the combustion model using empirically derived combustion metrics, and (2) to evaluate the feasibility of moderate hydrogen enrichment in a stock engine configuration. To facilitate a more accurate understanding of how key combustion parameters evolve under different operating conditions, Vibe function was fitted to the ensemble-averaged heat release rate curves computed from 100 consecutive engine cycles at each static full-load operating point. This approach enabled the extraction of stable and representative metrics, including the mass fraction burned at 50% (MFB50) and combustion duration, which were then used to recalibrate the predictive combustion model. In addition, cycle-to-cycle variation and combustion duration were also investigated in the dual-fuel mode. The combustion duration exhibited a consistent and substantial reduction across all of the examined operating points when compared to pure gasoline operation. Furthermore, the cycle-to-cycle variation difference remained statistically insignificant, indicating that the introduction of 20% hydrogen did not adversely affect combustion stability. In addition to improving model accuracy, this work investigates the occurrence of abnormal combustion phenomena—including backfiring, auto-ignition, and knock—under enriched conditions. The results confirm that 20% hydrogen blends can be safely utilized in standard engine architectures, yielding faster combustion and reduced burn durations. The validated model offers a reliable foundation for further dual-fuel optimization and supports the broader integration of hydrogen into conventional internal combustion platforms.
Open Access: Yes
DOI: 10.3390/en18133501
