Abstract:

This review delves into the production of sustainable aviation fuels derived from biomass and residual wastes through pyrolysis. The article addresses the challenges associated with the pyrolysis of wastes and provides an overview of both conventional and emerging pyrolysis technologies. The diverse forms of biomass and its significant economic benefits on a global scale. The underlying reason for it is the establishment of widely acknowledged renewable and sustainable energy sources. Approximately half of the global population relies on biomass as their primary energy source. Generating energy, heat, and electricity is a highly important source. The minimal levels of environmental pollution have facilitated the utilization of biomass as a sustainable energy source in recent technological advancements. Three types of biomass energy are biogas, bio-liquid, and bio-solid. In the domains of transportation and energy, it can serve as a substitute for fossil fuels. The primary focus of this study is to examine the data, explore the potential of biomass, and analyze the mechanisms of pyrolysis carried out using various processes, technologies (such as pyrolysis speed and temperature), and different types of reactors to produce bio-oil. This text also examines the current state and forthcoming obstacles of the pyrolysis process. In addition to the diverse array of pyrolysis byproducts. Based on this research, it can be inferred that the characteristics of pyrolysis products are influenced by the diversity of the materials utilized. Furthermore, pyrolysis products, such as bio-oil, have the potential to make a lucrative contribution to the expanding economy. To overcome future problems, further exploration is ultimately necessary. The primary factors of significance in pyrolysis technology are government subsidies and scientific advancements. The discussion emphasizes the significant barriers posed by the energy efficiency and capital costs involved in converting biomass and residual wastes into aviation fuels, hindering widespread adoption. To meet the aviation industry's greenhouse gas reduction targets by 2050, there is a pressing need for further advancements in technology development, highlighting the critical role of advanced technologies in overcoming these barriers.

Abstract: Precipitation within specific return periods plays a crucial role in the design of hydraulic infrastructure for water management. Traditional analytical approaches involve collecting annual maximum precipitation data from a station followed by the application of statistical probability distributions, and selecting the best-fit distribution based on goodness-of-fit tests (e.g., Kolmogorov-Smirnov). However, this methodology relies on current data, raising concerns about its suitability for outdated data. This study aims to compare Probability Density Functions (PDFs) with the Random Forest (RF) machine learning algorithm for estimating precipitation at different return periods. Using data from five stations located in various parts of the Arequipa province in Peru, it was evaluated the performance of both methods using the Root Mean Square Error (RMSE) metric. The results show that RF outperforms PDFs in most cases, yielding lower RMSE values for precipitation estimates at return periods of 2, 5, 10, 20, 50, and 100 years for the studied stations.

Abstract: Water Infrastructure (WI) incorporating water supply, wastewater, and stormwater systems is vulnerable to Climate Change (CC) impacts that can disrupt their functionality; thus, WI needs to be adapted to CC. In 2021 the European Commission (EC) released the technical guidelines on “Climate-proofing Infrastructure” that include mitigation and adaptation strategies; these guidelines and the relevant guides that followed, focus mainly on CC aspects without examining sufficiently the engineering features of WI that are described mainly in the relevant hydro-environment research; this research is vast and includes various terminologies and methods for all aspects of CC adaptation. The adaptation procedure of WI to CC can be significantly improved when this research is known to guidelines’ developers. To facilitate this knowledge transfer, we performed a review on the hydro-environmental research that we present in this paper as follows: firstly, we introduce and typologize the climate hazards for WI systems and identify the most important of them in the Mediterranean Region that we classify into seven groups; then, we classify the hydro-environmental research into five categories that is based on the EC guidelines, present the main aspects for each of these categories, discuss the future research, and finally we summarize the conclusions.

Abstract: Long-term impacts of sea-level changes and trends in storm magnitude and frequency along the Mediterranean coasts are key aspects of effective coastal adaptation strategies. In enclosed basins as a gulf, this requires a step beyond global and regional analysis toward high resolution modelling of hazards and vulnerabilities at different time scales. In this paper we present the compound future projection of static (sea level) and dynamic (wind-wave) impacts to the geomorphological evolution of a vulnerable sandy coastal plan located in the south Sardinia (west Mediterranean Sea). Based on local temporal trends in Hs (8 mm yr-1) and sea level (SLR of 5.4 mm yr-1), a 2-year return time flood scenario at 2100 shows the flattening of the submerged morphologies triggering the process of marine embayment. The research proposes adaptation strategies to be adopted to design the projected new coastal area under vulnerabilities at local and territorial scales.

Abstract: The last decades have offered new challenges to researchers worldwide through the problems our planet is facing both in the environment protection field and the need to replace fossil fuels with new environmentally friendly alternatives. Bioenergy as a form of renewable energy is an acceptable option from all points of view and biofuels due to their biological origin have the ability to satisfy the new needs of humanity. By releasing some non-polluting combustion products into the atmosphere, biofuels have already been adopted as additives in traditional liquid fuels, being intended mainly for internal combustion engines of automobiles. The current work proposes an extension of biofuels application in combustion processes specific to industrial furnaces. This technical concern is not found in the literature, except for achievements of the research team involved in this work, which has performed previous investigations. A 51.5 kW-burner was designed to operate with glycerine originating from triglycerides of plants and animals, mixed with ethanol, an alcohol produced by the chemical industry recently used as an additive in gasoline for automobile engines. Industrial oxygen was chosen as the oxidizing agent necessary for the liquid mixture combustion, allowing to obtain much higher flame temperatures compared to the usual combustion processes using air. Mixing glycerine with ethanol in 8.8 ratio allowed growing flame stability, accentuated also by creating swirl currents in the flame through the speed regime of fluids at the exit from the burner body. Results were excellent both through the flame stability and low level of polluting emissions.

Abstract: Lung ultrasound is an increasingly utilized non-invasive imaging modality for assessing the lung condition, but interpreting it can be challenging and depends on the operator experience. To address these challenges, this work proposes an approach that combines artificial intelligence (AI) with feature-based signal processing algorithms. We introduce a specialized deep learning model designed and trained to facilitate the analysis and interpretation of lung ultrasound images, by automating the detection and location of pulmonary features, including the pleura, A-lines, B-lines and consolidations. Employing Convolutional Neural Networks (CNNs) trained on a semi-automatically annotated dataset, the model delineates these pulmonary patterns, with the objective of enhancing diagnostic precision. Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second withl accuracies of 89% for consolidation, 92% for B-lines and 66% in case of A-lines compared with an expert opinion.

Abstract: In this study, a comprehensive examination, both theoretically and practically, is undertaken on Multi-Agent Reinforcement Learning algorithms (MARL). The investigation is situated within the context of real adaptive traffic signal control (ATSC) scenarios, with the primary objective being to validate the algorithms theoretical framework and evaluate their effectiveness, robustness, and applicability in real-world settings. The study uses two traffic networks in the city of Bologna, Italy, as examples. Key findings underscore the necessity of situating the algorithms within the context of a Partially Observable Markov Decision Process (POMDP), inherently characterizing them as non-Markovian. The equations are reformulated within this framework. Simulation results reveal that one of the studied algorithms, MA2C, consistently achieves significant traffic de-congestion in the considered scenarios. In general, its performance continually improves over time, resulting in a reduction of running vehicles by a factor of approximately 70 at the conclusion of the simulation. A training strategy independent of the specific vehicle flow has been implemented, rendering it adaptable for use with various traffic loads.

Abstract: Knowledge of experimentally obtained values of elastic deformations of rubber springs induced by applied compressive forces of known magnitudes is essential for the selection of rubber springs with optimal properties, which are used in vibration machines. This paper deals with the laboratory measurement of the characteristics of rubber springs using two types of sensors that sense the instantaneous value of the compressive force acting on the compressed spring. By sensory monitoring of the pressure forces acting on the springs supporting the trough of vibratory conveyors, it is possible to analyse, diagnose and automate the working operation of vibration machines in practice. The characteristics of eight types of rubber springs were measured in two ways on laboratory equipment, and the spring stiffnesses were calculated from the measured data. Experiments have shown that the actual stiffnesses of rubber springs are lower compared to the values stated by the manufacturer, in the least favourable case by 33.6%. It has been shown by measurements that at the beginning of the loading of the rubber spring, its compression is gradual, and the stiffness increases slowly, which is defined as the progressivity of the spring.

Abstract:

As urban areas face increasing challenges, integrating smart infrastructure, particularly IoT and AI technologies, has become vital for enhancing resilience. This study focuses on Baltimore as a case study to explore how scalable and adaptable smart infrastructure solutions can address diverse urban needs within a mid-sized U.S. city. Through a comprehensive review of Baltimore’s socioeconomic indicators and the development of a composite resilience score, this paper identifies key factors that facilitate or hinder the scalability and adaptability of smart infrastructure in economically and demographically varied urban contexts. The resilience score provides a quantitative measure of urban resilience, enabling the analysis of trends and dependencies among socioeconomic indicators over time. Findings reveal critical roles for both community engagement and policy support in adapting technologies to local needs, while economic and technical factors influence the scalability of IoT and AI projects. Based on these insights, the study proposes a framework that offers practical guidance for expanding Baltimore’s smart infrastructure in ways that are economically feasible, technically viable, and socially inclusive. This framework aims to assist Baltimore’s policymakers, urban planners, and technologists in advancing resilient, scalable solutions that align with the city's unique infrastructure needs and resource constraints.

Abstract: This study explores the use of a nonlinear vibration absorber to mitigate aeroelastic effects on a wing operating near the ground. An aeroelastic model, based on a typical airfoil section, equipped with a nonlinear tuned vibration absorber (NLTVA), is established to study the interactions between the airfoil’s dynamics, aerodynamics, and the nonlinear energy dissipation mechanisms. Geometric nonlinearity is incorporated into the airfoil's dynamics to account for possible large wing deflection and rotation. The flow is modeled based on the nonlinear unsteady discrete vortex method with the ground effect simulated using the mirror image method. Stability analyses are conducted to study the influence of NLTVA parameters on flutter instability and bifurcation behavior of the airfoil near the ground. The numerical results demonstrate that the NLTVA effectively delays the onset of flutter and promotes a supercritical bifurcation in the presence of ground effect. Optimally tuning the NLTVA’s linear parameters significantly increases flutter speed, while selecting the optimal nonlinear parameter is key to preventing subcritical behavior near the ground and reducing post-flutter limit cycle oscillations amplitude. Overall, this study highlights the potential of the NLTVA in enhancing the aeroelastic stability of flying vehicles with highly flexible wings, especially under the influence of ground effects during takeoff and landing.