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.

Abstract: Microwave photonics has recently come to the forefront as a valuable approach to generating, processing, and measuring signals in high-performance domains such as communication, radar, and timing systems. Recent studies have introduced a range of photonics-based phase-noise analyzers (PNAs) that utilize a variety of architectures, including phase detection, frequency discrimination, and hybrid mechanisms that combine optical with electronic processing. This review delves into the microwave photonics methodologies developed with the specific purpose of measuring phase noise, by exploring their fundamental principles, system design frameworks, and performance indicators. Through the integration of insights garnered from recent publications, our objective is to deliver a comprehensive understanding of the strengths and limitations associated with PNAs and to pinpoint new and promising areas for advancing research in the field of oscillator metrology.

Abstract: In complex traffic environments, image degradation caused by haze, low illumination, and occlusion significantly undermines the reliability of vehicle and pedestrian detection. To address these challenges, this paper proposes an aerial vision framework that tightly couples multi-level image enhancement with a lightweight detection architecture. At the image preprocessing stage, a cascaded “dehazing + illumination” module is constructed. Specifically, a learning-based dehazing method, Learning Hazing to Dehazing, is employed to restore long-range details affected by scattering artifacts. Additionally, HVI-CIDNet is introduced to decouple luminance and chrominance in the Horizontal/Vertical Intensity (HVI) color space, thereby simultaneously enhancing structural fidelity in low-light regions and achieving global brightness consistency. On the detection side, a lightweight yet robust detection architecture, termed GDEIM-SF, is designed. It adopts GoldYOLO as the lightweight backbone and integrates D-FINE as an anchor-free decoder. Furthermore, two key modules, CAPR and ASF, are incorporated to enhance high-frequency edge modeling and multi-scale semantic alignment, respectively. Evaluated on the VisDrone dataset, the proposed method achieves improvements of approximately 2.5–2.7 percentage points in core metrics such as mAP@50–90 compared to similar lightweight models (e.g., the DEIM baseline and YOLOv12s), while maintaining low parameter count and computational overhead. This ensures a balanced trade-off among detection accuracy, inference efficiency, and deployment adaptability, providing a practical and efficient solution for UAV-based visual perception tasks under challenging imaging conditions.

Abstract: Sulfate-reducing bacteria (SRB)-induced corrosion presents a considerable challenge to the integrity of shale gas pipelines. Conventional reliance on chemical biocides is limited by the potential for microbial resistance and environmental impact. As an alternative, the bio-competitive exclusion approach, utilizing microbes such as denitrifying bacteria (DNB), offers a promising strategy. This study investigates an integrated control method, combining the biocide glutaraldehyde with DNB to synergistically inhibit SRB activity and corrosion. The efficacy and mechanisms were systematically evaluated through electrochemical measurements, weight-loss analysis, surface characterization, and microbial community profiling. Following synergistic treatment with glutaraldehyde and DNB, the average corrosion rate was reduced by 44.2% and the maximum corrosion depth decreased by 84.3% compared to the SRB-inoculated system. Microbial community analysis revealed a substantial decline in SRB abundance from 62.7% on day 1 to 11.9% by day 14 under the synergistic treatment. The combined approach proves economically and environmentally viable, offering the advantages of reduced chemical dosage and the avoidance of additional corrosion typically associated with DNB. These results provide a novel strategy for developing microbial-influenced corrosion control measures in shale gas infrastructure.

Abstract: Water supply and drainage networks are essential components of urban infrastructure, directly influencing both residents' quality of life and the efficiency of city operations through their safety and stability. Over time, these networks often develop non-structural turbid water conditions, which present challenges for traditional maintenance methods. Leveraging the advantages of spatial visualization, three-dimensional environmental reconstruction technology has emerged as a promising solution to address these issues, while also advancing the use of intelligent maintenance technologies within water supply and drainage systems. This paper focuses on the causes of non-structural turbid water in these networks, and evaluates the optimization, effectiveness, and limitations of turbid water imaging, image feature recognition, and 3D environmental reconstruction technologies. Additionally, it re-views the current technical challenges and outlines potential future research directions, aiming to support the development and application of 3D reconstruction technologies for pipeline networks under non-structural turbid water conditions.

Abstract: The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising costly-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation slope and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation. In this work, a set of analytical approximations for the frequency-dependent attenuation and Raman gain coefficient is presented, providing an effective balance between computational efficiency and physical fidelity. Through extensive simulations covering C, C+L, and ultra-wideband U-to-E transmission scenarios, the accuracy in reproducing the behavior of the power evolution and NLI profiles of fully numerical SRS models with the proposed approximations is demonstrated.