Abstract: Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the Urban Heat Island effect and enhancing stormwater man-agement, alongside benefits for public health and biodiversity. Effective GI imple-mentation remains challenging, particularly in dense, rapidly urbanized Mid Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project. The project aims to bridge the theo-ry-practice gap, enabling pilot implementations in multiple Italian Mid Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, traf-ficked urban area projected to face "extremely strong heat stress" by 2050. Design in-terventions included spatial optimization, strategic species replacement, creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area, a measurable reduction in the UTCI index, a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces.

Abstract: Hardware-in-the-Loop (HIL) simulation has become an indispensable tool for rapid and cost-effective development and validation of power electronic systems. This paper presents a detailed experimental characterization and validation of a PLECS-based HIL model for a Dual Active Bridge (DAB) DC-DC converter controlled using Single Phase Shift (SPS) modulation. An extensive experimental investigation is conducted to characterize the converter's performance across a wide range of operating conditions. The primary objective of this work is to validate and fine-tune the PLECS-based HIL model of a single DAB converter, laying the foundation for building more complex models, such as configurations with multiple converters connected in series or parallel. A DAB prototype has been characterized by varying the PWM phase shift angle between the input-output full-bridges over a range of equivalent input-output voltage levels. The power flow and efficiency were also analyzed at different voltage gains (M = 0.6, 1, and 1.4). In addition, the influence of key parameters like switching frequency and leakage inductance on the converter’s power flow and efficiency was experimentally evaluated. The experimental efficiency trends and power characteristics across the operating points provide valuable insight into the optimal modulation range and loss mechanisms of the DAB converter under SPS control. The HIL model is thoroughly tested against the experimental hardware prototype by comparing key metrics, including transferred power and system efficiency. The results demonstrate a high degree of accuracy between the HIL model and the physical system across all tested operating conditions. This work provides a validated, high-fidelity HIL model and a comprehensive dataset that confirms the effectiveness of the PLECS platform for the development and optimization of DAB converters, thereby reducing design time and mitigating risks in subsequent prototyping stages.

Abstract: Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO2, and O2, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow.

Abstract: The rapid development of the low-altitude economy is driving significant societal and industrial transformation. Unmanned aerial vehicles (UAVs), as key enablers of this emerging domain, offer substantial benefits in many applications. However, their unauthorized or malicious use poses serious security, safety, and privacy risks, underscoring the critical need for reliable UAV detection technologies. Among existing approaches, such as radar, acoustic, and vision-based methods, radio frequency (RF)-based UAV detection has gained prominence due to its long detection range, robustness to lighting and weather conditions, and capability to identify RF-emitting UAVs even when visually obscured. Nevertheless, conventional RF-based approaches often suffer from limited feature representation and poor generalization. In the past few years, convolutional neural networks (CNNs) have become the mainstream solution for RF signal recognition. However, most real-valued CNNs (RV-CNNs) process only the magnitude component of RF signals, discarding the phase information that carries valuable discriminative characteristics, which may degrade recognition performance. To address this limitation, this paper proposes a complex-valued CNN (CV-CNN) for UAV RF signal recognition, which exploits the full complex-domain structure of RF signals to enhance recognition accuracy and robustness. The proposed CV-CNN accounts for both the magnitude and phase components of RF signals from UAVs, thereby enabling true complex-valued convolutional operations without loss of phase information. The effectiveness of this approach is validated on the DroneRFa dataset, which encompasses RF signals from 25 distinct UAV categories. The impact of model hyperparameters, including network depth, convolutional kernel size, and dropout strategy on recognition performance is investigated through a series of ablation experiments. Comparisons are also conducted between the performance of CV-CNN with identical parameters and RV-CNN, both in noise-free and noisy conditions. The experimental results demonstrate that the CV-CNN exhibits superior robustness and interference resistance in comparison to its real-valued counterpart, maintaining high recognition accuracy even under low signal-to-noise ratio (SNR) conditions.

Abstract: This paper introduces a novel Magnetorheological (MR) damper integrating a ball-screw mechanism (SMRB damper), designed to unify translational and rotational motion for enhanced automotive suspension performance. While shear-mode rotary MR dampers offer excellent responsiveness and stability, prior designs face persistent issues such as high off-state torque, structural complexity, or limited damping force. The proposed damper aims to overcome these limitations. Its design and operating principle are presented, followed by the development of a mathematical model based on the Bingham-plastic formulation and finite element analysis. To maximize damping capability, the key structural parameters are optimized using an Adaptive Particle Swarm Optimization (APSO) algorithm. Finally, a prototype is fabricated based on the optimized results, and experimental tests validate its performance against simulation predictions, demonstrating its improved potential for vibration control applications.

Abstract: Transitioning to more efficient and digital industrial processes requires plant design methodologies that go beyond traditional approaches and respond to the operational challenges of Industry 4.0. The objective of this study was to integrate Artificial Intelligence (AI) and Augmented Reality (AR) into SLP methodology for the design of a cosmetic emulsion production plant. A case study was developed based on the layout of a previously reported cosmetic plant by creating a preliminary layout using SLP and evaluating it using AI based on technical prompts. Subsequently, the refined model was represented in three dimensions and validated in a real environment using AR. The results show that AI identified opportunities for improvement in operational flows, relationships between critical areas, and space proportions, allowing for precise adjustments without altering the original design logic. Likewise, AI verification and immersive validation using AR confirmed the spatial compatibility of the layout with the selected site, facilitating the early assessment of circulation, access, and volumetric behavior. Thus, the sequential integration of SLP + AI + AR demonstrated its potential to reduce uncertainty in the early stages and move toward modernizing plant design in line with Industry 4.0 principles.

Abstract: Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors.

Abstract: Connected and autonomous vehicle (CAV) platoons face the dual challenge of maintaining longitudinal formation stability while ensuring lateral safety in dynamic traffic environments, yet existing control approaches often address these objectives in isolation. This paper proposes a hierarchical cooperative control framework that integrates a differential game-based longitudinal controller with a risk potential field-driven model predictive controller (MPC) for lateral motion. At the coordination control layer, a differential game formulation models inter-vehicle interactions, with analytical solutions derived for both open-loop Nash equilibrium under predecessor-following (PF) topology and an estimated Nash equilibrium under two-predecessor-following (TPF) topology. The motion control layer employs a risk potential field model that quantifies collision threats from surrounding obstacles and road boundaries, guiding the MPC to perform real-time trajectory optimization. A comprehensive co-simulation platform integrating MATLAB/Simulink, Prescan, and CarSim validates the proposed framework across three representative scenarios: ramp merging with aggressive cut-in maneuvers, emergency braking by a preceding obstacle vehicle, and multi-lane cooperative obstacle avoidance involving multiple dynamic obstacles. Across all scenarios, the CAV platoon achieves safe obstacle avoidance through autonomous decision-making, with spacing errors converging to zero and smooth velocity adjustments that ensure both formation stability and ride comfort. The results demonstrate that the proposed framework effectively adapts to diverse and complex traffic conditions.

Abstract: Bolt connections are widely used in engineering structures but are prone to loosening during service, which may lead to serious safety issues. Therefore, reliable bolt-loosening detection is of great importance. Traditional detection methods often suffer from drawbacks such as low efficiency, limited accuracy, and the need for contact sensors. To overcome these limitations, this study proposes a novel non-contact approach for bolt preload monitoring based on Digital Image Correlation (DIC). In this method, an industrial camera captures speckle images of the bolt head before and after deformation, enabling measurement of the surface strain. The DIC technique is employed to calculate the strain field on the bolt head surface, which exhibits a linear relationship with the bolt preload. By tracking changes in this strain field, the proposed method allows effective and accurate monitoring of bolt preload. Experimental results demonstrate that the method provides a precise, efficient, and user-friendly solution for bolt preload monitoring, showing great potential for applications in structural health monitoring.

Abstract: This research aims to assess and quantify the significance of incorporating the seismic performance of global and local engineering demand parameters (EDPs) within the probabilistic frameworks, when structural pounding of adjacent buildings occurs. For this purpose, the seismic performance of 6-story and 12-story reinforced concrete (RC) RC frames subjected to floor-floor pounding is assessed. The pounding is caused by an adjacent shorter and stiffer structure with the top contact point at the middle of the tall building’s total height. Displacement-based and ductility-based EDPs are evaluated at different performance levels (PLs) and at different separation distances (dg). The seismic performance of the RC frames without considering pounding is also evaluated. Incremental dynamic analyses (IDAs) are performed, and probabilistic seismic demand models (PSDMs) are developed to establish fragility curves of the examined RC frames. The probability of earthquake-induced pounding between adjacent structures is properly involved with the median value of Sa,T1 that corresponds to an acceptable capacity level (acceptable PL) of an EDP. The results of this study indicate that excluding structural pounding consequences from the probabilistic frameworks related to the seismic risk of colliding buildings leads to unsafe seismic assessment or design provisions.

Abstract: Accurate load identification serves as a fundamental requirement for achieving lightweight and efficient structural designs. This paper presents a dynamic load identification method for structures with parameter uncertainties, integrating the ellipsoidal model with shape functions. The approach explicitly accounts for correlations among uncertain structural parameters, leading to improved identification accuracy and more compact load bounds. The method first establishes an ellipsoidal model for the uncertain parameters, representing their feasible domain as a compact ellipsoidal set. This model is then incorporated into the dynamic load identification framework. Convex optimization theory and the Lagrangian multiplier method are employed to derive analytical expressions for the load bounds. Shape functions are utilized to describe the temporal variation of the load, reducing the ill-posedness of the inverse problem, while central finite difference approximations are applied to compute load sensitivities with respect to the uncertain parameters. The efficacy of the proposed method is validated through a numerical example involving a 21-bar truss structure, demonstrating its advantages in both identification accuracy and boundary compactness compared to conventional interval methods.

Abstract: Packaging plays a crucial role in product preservation and distribution but also constitutes a major source of environmental burden. In the beverage sector, where unit value is low, secondary and tertiary packaging significantly influence the environmental profile of the final product. This study applies a standardized Life Cycle Assessment (LCA) approach to evaluate the environmental impacts of five packaging configurations for aluminum beverage cans. The analysis compares three current market scenarios with two alternative solutions based on reusable plastic crates (RPCs). The system boundaries include production, distribution, end-of-life, and, where applicable, reverse logistics. A functional unit of one fully packaged 0.33 L aluminum can is adopted. Results reveal that while single-use cardboard solutions achieve favorable performance under certain impact categories, reusable systems outperform them when a sufficient number of reuse cycles is achieved and reverse logistics are efficiently managed. Sensitivity analyses highlight the critical influence of transport distances and reuse frequency on overall impacts, with performance deteriorating for reusable systems beyond 200 km or below 50 reuse cycles. These findings underscore the importance of logistics optimization and reuse planning to maximize environmental benefits. The study contributes to current research by providing a comprehensive comparison of entire packaging systems, addressing a gap in LCA literature, and offering practical guidance for the transition toward circular packaging strategies in line with recent EU regulatory targets.

Abstract: Cost-sensitive advanced driver-assistance systems (ADAS) increasingly rely on embedded platforms without discrete GPUs, where power-intensive deep neural networks are often impractical to deploy and difficult to certify for safety-critical functions. At the same time, classical geometry-based lane detection pipelines still struggle under strong backlighting, low-contrast night scenes, and heavy rain. This work revisits geometry-driven lane detection from a sensor-layer perspective and proposes a Binary Line Segment Filter (BLSF) that exploits the structural regularities of lane markings in bird’s-eye-view (BEV) images. The filter is integrated into a three-stage pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and simplified Hough-based sliding-window fitting with RANSAC. On a self-collected dataset of 297 challenging frames (strong backlighting, low-contrast night, heavy rain, and high curvature), the full pipeline improves lane detection robustness over the same implementation without BLSF while maintaining real-time performance on a 2 GHz ARM CPU-only platform. To assess generality, we further evaluate BLSF on the Dazzling Light and Night subsets of the large-scale CULane and LLAMAS benchmarks, where it achieves a consistent 6–7% improvement in F1-score over a line-segment baseline under a fixed pre-processing configuration, along with corresponding gains in IoU. These results demonstrate that explainable, geometry-driven lane feature extraction can deliver competitive robustness under adverse illumination on low-cost, CPU-only embedded hardware, and can serve as a complementary design point to lightweight deep-learning models in cost- and safety-constrained ADAS deployments.

Abstract: The aim of this study was to assess the suitability of polymeric tubular ultrafiltration mem-branes for use in a closed-loop water system within a rubber manufacturing plant. The research focused on determining the transport and separation properties of polymeric tubular mem-branes during the ultrafiltration of wastewater generated from washing vulcanised rubber hoses. The tests were conducted using the installation of the UF-1 membrane supplied by APEKO Sp. z o.o. The study evaluated the performance of modified PES membranes with a molecular weight cut-off (MWCO) of 4 kDa and PVDF membranes with an MWCO of 100 kDa in the wastewater treatment process, as well as the effectiveness of membrane regeneration. Given the characteristics of wastewater, the key parameters for evaluating ultrafiltration performance included the determination of contaminant separation coefficients (R, %) for non-ionic surfactants (NIS) and chemical oxygen demand (COD), as well as turbidity reduc-tion. The results demonstrated that the tested membranes substantially improved the visual quality of the wastewater by reducing turbidity by more than 95%, and exhibited high sepa-ration efficiency for the analysed contaminants, with initial values of RNIS = 95% and RCOD = 85% at the beginning of the ultrafiltration cycle, decreasing to RNIS <  10% and RCOD <  10% after several hours of operation. During closed-loop filtration, when a twentyfold concentration of contaminants in the retentate was reached, membrane fouling occurred, sig-nificantly reducing filtration performance. Chemical cleaning enabled the recovery of ap-proximately 70% of the initial performance for modified PES membranes and full recovery (100%) for PVDF membranes.

Abstract: This paper describes the design and implementation of a laboratory system comprising customized hardware setups for each experiment and a dedicated software environment for interactive learning in the field of electronic devices. The hardware infrastructure is based on programmable equipment, including programmable power supplies and signal generators for input stimulus generation, programmable devices and circuits for signal routing, and data acquisition units for capturing output responses. The software framework is built using the LabVIEW graphical programming environment, enabling control of input signal generation, output signal acquisition, data processing, and result visualization in a user-friendly and comprehensible manner. All control functionalities are accessible via the virtual instrument’s front panel, allowing seamless remote operation through network-based control applications. By enabling remote control of physical equipment, the system provides access to laboratory resources without requiring the user’s physical presence at the laboratory site. The approach is demonstrated through experiments in analog electronics—such as plotting the static characteristics of diodes and transistors—as well as in digital electronics.

Abstract: Schools are increasingly recognised as critical public infrastructure for climate adaptation, particularly in heat-vulnerable and park-poor neighbourhoods. This study examines climate-resilient schoolyards as urban cooling systems, social spaces and educational landscapes. We conduct a comparative review of nine international programmes for schoolyard transformation (Paris, Barcelona, Madrid, Milan, Rotterdam, Los Angeles, New York, Melbourne and Santiago de Chile), drawing on municipal plans, reports and implementation guidelines. We examine programmes’ design strategies, governance configurations and monitoring approaches, and synthesise them through a CAME (Correct, Adapt, Maintain, Explore) framework. Building on this analysis, we develop a Multicriteria Analysis (MCA) structure to prioritise interventions according to four fam-ilies of criteria: environmental and climatic performance, social and educational equity, urban integration and accessibility, and feasibility and co-benefits. Results highlight a recurrent toolkit of measures—depaving, tree planting, cool and permeable surfaces, nature-based drainage systems, BIPV shade canopies and sensor-based monitoring—that can reduce surface temperatures by around 10–12 °C while improving thermal comfort, biodiversity and community use beyond school hours. We argue that climate-resilient schoolyards should be planned as networks of essential public infrastructure and that the combined CAME–MCA framework offers a robust, transferable decision-support tool for local governments and school authorities.

Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multi-dimensional framework for evaluating vulnerability in historic buildings where fragility cannot be explained by structural indicators alone. Existing models prioritise load-bearing behaviour but overlook typological discontinuity, spatial fragmentation and erosion of cultural or architectural value. STVSM addresses this through three weighted sub-indices—structural vulnerability (SV), typological degradation (TV) and heritage value (HV)—each calibrated using expert-derived micro–macro coefficients. Field-based deterioration scores (0–1) are multiplied by these final weights to produce SV, TV and HV values, then merged into a Conservation Priority Index (CPI).The model is applied to twenty-five buildings in three heritage contexts: Cumalıkızık traditional houses, vernacular dwellings in Balıkesir–Karesi and nineteenth-century Greek Orthodox churches in Bursa. The churches yield the highest CPI values due to roof loss, wall deformation and spatial discontinuity, reinforced by cultural significance. Vernacular houses show moderate structural deterioration but marked typological distortion linked to later additions and façade alterations. Cumalıkızık houses present heterogeneous conditions, combining preserved structures with material decay.By quantifying structural behaviour, typological integrity and heritage value within a single analytical system, STVSM offers a transparent and repeatable basis for conservation prioritisation across diverse historic building stocks.

Abstract: Background: Germany's pioneering energy policy, the Energiewende, seeks to fundamentally transform its energy landscape by shifting from conventional fossil fuels to renewable sources.Aim: This current study examined the economic and social impact of Germany’s transition to renewable energy (Energiewende) since it officially started in 2000 when the Renewable Energy Sources Act (EEG – Erneuerbare-Energien-Gesetz) was passed.Method: Data was collected from nine renewable energy experts using semi-structured interviews. The views of the diverse experts were analyzed using a thematic approach.Results: The findings indicate that early policy instruments and subsidy schemes were critical in accelerating renewable energy deployment and improving cost competitiveness, especially for wind and solar technologies, though their relevance has evolved as the sector matured. Energy communities emerged as a central pillar of the transition, enhancing local participation, social acceptance, and socio-economic benefits through diverse ownership models. Economically, the Energiewende is widely perceived to have stimulated job creation, investment, and technological innovation, while also contributing to job losses in fossil fuel–dependent sectors, particularly coal. Social inequalities were identified as a significant challenge, driven by employment displacement and rising energy affordability concerns for low-income households. Public acceptance was found to vary by technology, with solar projects generally favored over wind due to noise-related concerns.Conclusion: This present research contributes to a more profound understanding of Germany's journey towards a green energy future and highlights the need for adaptive policy frameworks and socially inclusive strategies to support a just and sustainable energy transition.

Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains and many applications demand a high degree of automation, supported by reliable Conflict Detection and Resolution (CD&R) and Collision Avoidance (CA) systems. At the same time, public mistrust, safety and privacy concerns, the presence of uncooperative airspace users, and rising traffic density are driving a shift toward decentralized concepts such as free flight, in which each actor is responsible for its own safe trajectory. This survey reviews CD&R and CA methods with a particular focus on decentralized automation and encounters with noncooperative intruders. It analyzes classical rule-based approaches and their limitations, then examines Machine Learning (ML)–based techniques that aim to improve adaptability in complex environments. Building on recent regulatory discussions, it further considers how requirements for trust, transparency, explainability, and interpretability evolve with the degree of human oversight and autonomy, addressing gaps left by prior surveys.

Abstract: Building Information Modeling (BIM) represents a paradigmatic transformation in architecture and engineering, facilitating the transition from two-dimensional documentation to integrated multidimensional collaborative environments. This article synthesizes a systematic literature review (2014-2024) combining meta-analyses, case studies, and quantitative-qualitative research on the adoption of BIM in the AEC sector. The results document significant benefits: reductions of 25-30% in design errors, 20% in execution time and 15% in costs. However, adoption reveals geographic fragmentation (US 60%, UK 80%, Latin America <25%) and multidimensional barriers: lack of specialized training, cultural resistance, absence of specific legal frameworks in developing countries, and limited interoperability. The analysis identifies that successful integration requires deep organizational transformation, coordinated public policies, and educational curricular adaptation. Recommendations include micro-regional contextual strategies, contractual standardization (ISO 19650) and applied research in BIM-Facility Management integration and emerging technologies (XR, digital twins). BIM integrates geometric (3D), temporal (4D-schedule), economic (5D-costs) and operational (6D-facility management) information into collaborative parameterized models. Beyond visualization, the methodology calls for clarity on specific Development Levels (LODs) for each phase of the asset lifecycle, from LOD 100 (conceptual) to LOD 500 (as-built). Interoperability using IFC (ISO 16739) and ISO 19650 standards requires robust model validation and accurate definition of model views (MVDs), areas where 74% of projects in developing countries still have critical gaps. This article emphasizes that BIM is not only a software tool, but a comprehensive information management protocol that permeates processes from conceptual design to sustainable operation and demolition.