Search Results (616)

This paper investigated the tunnel slags generated from a specific tunnel project to systematically assess their environmental risk through phase composition, chemical composition, acidification potential, and heavy metal speciation. Leaching experiments were conducted under various influencing factors, including particle size, time, liquid-to-solid ratio, pH, temperature. The release concentration of heavy metals from the tunnel slag particles follows the following order: Zn > Cu > Cr. This is primarily attributed to the preferential release of Zn under acidic conditions due to its high acid-soluble state, while Cr, which is predominantly present in the residual state, exhibits very low mobility. Furthermore, decreased particle sizes, increased liquid-to-solid ratios, elevated leaching temperatures, extended leaching times, and lower pH values can effectively promote the dissolution of heavy metals from the tunnel slag. The cumulative leaching curves of Cr, Cu, and Zn from the three types of tunnel slags conform to the Elovich equation (R² > 0.88), indicating that the release process of heavy metals is primarily controlled by diffusion mechanisms. The S- and Fe/Mg-rich characteristics of D3 confers a high acidification risk, accompanied by a rapid and persistent heavy metal release rate. In contrast, D2, which is influenced by the neutralizing effect of carbonate dissolution, releases heavy metals at a steady rate, while D1, which is dominated by inert minerals like quartz and muscovite, exhibits the slowest release rate. It is recommended that waste management engineering prioritize controlling S- and Fe/Mg-rich tunnel slags (D3) and mitigating risks of elements like Zn and Cu under acidic conditions. This study provides a scientific basis and technical support for the environmentally safe disposal and resource utilization of tunnel slag. Full article

Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering. Full article

This systematic review examined the health risk assessment methods of studies of whole-body vibration exposure from occupational vehicles or machines utilizing the International Standard ISO 2631-1 (1997) and/or the European Machine Directive 2002/44. This review found inconsistent reporting of measurement parameters in studies on whole-body vibration (WBV) exposure. Although many authors treat the ISO 2631-1 HGCZ as a medical health standard with defined threshold levels, the epidemiological evidence for these limits is unclear. Similarly, the EU Directive offers more comprehensive risk management guidance, but the numeric limits are equal without supporting scientific evidence. Both guidelines likely represent the prevailing societal and interdisciplinary consensus at the time. Authors note discrepancies between international and national standards and adverse WBV exposure outcomes are reported below given boundaries. Future publications should report all relevant parameters from ISO 2631-1 and clearly state study limitations, exercising caution when applying ISO 2631-1 HGCZ in health and safety assessments and considering different susceptibility of diverse populations. We advise reducing WBV exposure to the lowest technically feasible limits wherever possible and applying the precautionary principle with attention to individual differences, instead of depending solely on numeric limits. Full article

Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical resilience, decarbonization, and social/community engagement. The framework conceptualizes metropolitan and building scales as analytically distinct but operationally linked, allowing strategies to reflect the different systems, stakeholders, and capacities at each level. It also outlines a three-phase progression—Initial (assessment and goal setting), Readiness (planning and implementation), and Steady-State (monitoring and iterative adjustment)—to support staged, adaptive deployment. Each phase includes sample metrics and SMART goals that can be tailored to local context and tracked over time. By integrating theoretical insights with practical implementation tools, the framework offers a flexible yet rigorous approach for advancing urban sustainability. It emphasizes the importance of aligning technical interventions with institutional capacity and community participation to enhance effectiveness and equity. This model contributes to both planning theory and applied sustainability efforts by providing a structured pathway for cities to enhance climate readiness across systems and scales. Full article

Forecasting outcomes in high-stakes competitive spectacles like the Olympic Games, World Cups, and professional league championships has grown increasingly vital, directly impacting strategic planning, resource allocation, and performance optimization across a multitude of fields. However, accurate forecasting remains challenging due to complex, nonlinear interactions inherent in high-dimensional time-series data, further complicated by socioeconomic indicators, historical influences, and host-country advantages. In this study, we propose a comprehensive forecasting framework integrating structured time-series modeling with ensemble learning. We extract key structural features via two novel indices: the Advantage Index (measuring a competitor’s dominance in specific areas) and the Herfindahl Index (quantifying performance outcome concentration). We also evaluate host-country advantage using a Difference-in-Differences (DiD) approach. Leveraging these insights, we develop a dual-branch predictive model combining an Attention-augmented Long Short-Term Memory (Attention-LSTM) network and a Random Forest classifier. Attention-LSTM captures long-term dependencies and dynamic patterns in structured temporal data, while Random Forest handles predictions for unrecognized contenders, addressing zero-inflation issues. Extensive stability and comparative analyses demonstrate that our model outperforms traditional and state-of-the-art methods, exhibiting strong resilience to input perturbations, consistent performance across multiple runs, and appropriate sensitivity to key features. Our key contributions include the development of a novel integrated forecasting framework, the introduction of two innovative structural indices for competitive dynamics analysis, and the demonstration of robust predictive performance that bridges technical innovation with practical strategic application. Finally, we transform our modeling insights into actionable strategic insights. This translation is powered by interpretable feature importance rankings and stability analysis that rigorously validate the robustness of key predictors. These insights apply across multiple dimensions—encompassing advantage assessment, resource distribution, strategic simulation, and breakthrough potential identification—providing comprehensive decision support for strategic planners and policymakers navigating competitive environments. Full article

Pipelines are extensively used in offshore equipment. Accurate and non-destructive measurement of hoop stress conditions within pipes is critical for ensuring the integrity of offshore structures. However, the existing technology to measure the hoop stress of the pipeline needs to planarize the surface of the pipeline, which greatly limits the detection efficiency. This study proposes a method for pipeline hoop stress measurement using a planar longitudinal critically refracted (LCR) probe, based on correcting LCR wave-propagation paths, which solves the problem of pipeline planarization in pipeline hoop stress measurement. First, a linear relationship between stress variations and ultrasonic time-of-flight changes in the material was established based on the acoustoelastic effect. Finite element analysis was then used to construct an acoustic simulation model for the hoop direction of the pipeline. Simulation results showed that LCR waves propagated within a wedge as quasi-plane waves and, upon oblique incidence into the pipeline, traveled along the chordal direction. Furthermore, using ray tracing methods, a mapping relationship between the pipeline geometry and the ultrasonic propagation path was established. Based on this, the LCR pipeline hoop stress measurement (LCR-HS) method was proposed. Finally, a C-shaped ring was employed to verify the measurement accuracy of the LCR-HS method. Experimental results indicated that the measurement error decreased with increasing pipe diameter and fell below 8% when the diameter exceeded 400 mm. This method enables precise measurement of hoop stress on curved surfaces by revealing the hoop propagation behavior of LCR waves in pipelines. The findings provide a technical reference for evaluating pipeline stress states, which is of significant importance for assessment of pipeline integrity. Full article

Pulse wave velocity (PWV) is a key marker of aortic stiffness and cardiovascular risk, yet current methods typically offer only global or regional estimates and lack the possibility to measure local variations along the thoracic aorta. This study aimed to develop and evaluate a pipeline for assessing local aortic PWV using the flow–area (QA) method (PWV_QA) by combining high-resolution 4D MRI techniques. A 3D cine balanced steady-state free precession (bSSFP) sequence was used to capture dynamic changes in aortic geometry, while 4D flow MRI measured time-resolved blood flow. The QA method was applied during the reflection-free early systolic phase. Scan–rescan reproducibility was assessed in six healthy volunteers, and feasibility was additionally explored in Marfan syndrome patients. The mean ± SD values of the Pearson correlation coefficients for per-slice maximum area, velocity, flow, and PWV_QA were 0.99 ± 0.00, 0.82 ± 0.11, 0.96 ± 0.01, and 0.20 ± 0.35, respectively. The median (Q1–Q3) average PWV_QA was 6.6 (5.4–9.4) m/s for scan 1 and 9.1 (6.7–11.3) m/s for scan 2 (p = 0.16) in healthy volunteers and 7.1 (6.9–8.0) m/s in Marfan patients. Combining 4D bSSFP and 4D flow MRI is technically feasible, but the derived PWV_QA maps show high variability, particularly in the aortic root and descending aorta, requiring further optimization. Full article

The increasing demand for natural and health-promoting food ingredients has spotlighted seaweed-derived pigments as promising alternatives to synthetic colorants. This review explores the potential of chlorophylls, carotenoids, and phycobiliproteins extracted from various seaweed species for use in the food industry. These pigments offer not only a wide range of colors but also exhibit bioactivities such as antioxidant, anti-inflammatory, and anticancer effects. The paper discusses recent advancements in sustainable aquaculture practices, extraction, purification, and stabilization techniques, including green and microencapsulation methods, to enhance pigment yield and shelf life. Furthermore, it highlights the regulatory landscape in the European Union and the United States, identifying key differences and challenges regarding pigment approval and commercialization. Despite their potential, large-scale industrial adoption remains constrained by technical, economic, and regulatory hurdles. Bridging these gaps through optimized bioprocesses and safety assessments is essential to fully leverage seaweed pigments in food systems. Full article

Software development projects are highly susceptible to risks during the design phase, which plays a crucial role in shaping the architecture, functionality, and quality of the final product. Decisions made during the design stage significantly affect the outcomes of the subsequent phases, including coding, testing, deployment, and maintenance. However, the complexities and uncertainties inherent in the design phase are often inadequately addressed by traditional risk management tools as they rely on deterministic models that oversimplify interdependent risks. This research introduces a fuzzy logic-based risk assessment model tailored specifically for the design phase of software development projects. The proposed fuzzy model, unlike the existing state-of-the-art models, regards the iterative nature of the design phase, the interaction between diverse stakeholders, and the potential inconsistencies that may arise between the initial and final version of the software design. More specifically, it develops a customized fuzzy model that incorporates design-specific risk factors such as evolving architectural requirements, technical feasibility concerns, and stakeholder misalignment. Finally, it integrates expert-driven rule definitions to enhance model accuracy and real-world applicability, ensuring that risk assessments reflect actual challenges faced by software design teams. Simulations conducted across diverse real-world scenarios demonstrate the model’s robustness in predicting risk levels and supporting mitigation strategies. The simulation results confirm that the proposed fuzzy logic model outperforms conventional approaches by offering greater flexibility and adaptability in managing design-phase risks, assisting project managers in prioritizing mitigation efforts more effectively to improve project outcomes. Full article

Ultrasound enhancing agents (UEAs, formerly called contrast agents) have enhanced echocardiographic diagnostics of myocardial disease and masses as well as myocardial perfusion abnormalities. This review provides up-to-date guidance on the procedures and interpretations according to current recommendations of imaging societies and considering the results of recent major studies. For the different indications, a standardized approach has been created including technical aspects, pre-assessment and primary scan planes, contrast-enhanced ultrasound (CEUS) procedure, interpretation and reporting. In a previous publication (part 1) the UEAs, imaging methods, preparation of the patients and assessment of global and regional LV function with UEAs were included. The two parts represent a comprehensive state-of-the-art compendium on how to perform CEUS examinations in clinical echocardiography and provide advice on education, qualification and quality control. Full article

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article

The increasing complexity and commercialization of the global space sector have elevated the strategic role of public–private partnerships (PPPs). However, the criteria for selecting suitable PPP models remain underexplored, particularly regarding the influence of national policy and market environments. This study proposes a decision-making framework that links six indicators—national strategic goals, government role preferences, regulatory structures, capital access, private-sector capabilities, and commercial demand—to four distinct PPP models in the space sector. Drawing on Eisenhardt’s multi-case theory-building methodology, this study analyzes PPP evolution in four countries representing mature, emerging, and nascent countries: the United States, Japan, India, and the United Arab Emirates. The cross-case analysis reveals that high-autonomy PPP models emerge only when institutional, financial, and market factors are systemically aligned. Divergence in PPP forms is driven not solely by technical capabilities but also by governance postures and regulatory designs. The findings contribute to addressing ongoing challenges related to policy reform and increasing private-sector involvement in the space sector by developing a practical decision-making tool for public and private-sector actors engaged in space governance. Specifically, the diagnostic framework enables stakeholders to assess national readiness and select appropriate PPP models. It also supports strategic planning by highlighting the reforms and capacity-building measures required for countries with nascent and emerging economies to transition from government-led missions to commercially integrated space ecosystems. Full article

There is an urgent need to implement sustainable solutions in the construction sector, particularly within the Peruvian context, where regulations on energy efficiency and building rehabilitation are still under development. This study addresses the energy and social rehabilitation of the Mercado Central in Lima, with the aim of identifying the most effective interventions from both energy and economic perspectives while promoting urban sustainability. A detailed assessment of the building’s original state—covering the thermal envelope and technical systems—was conducted, followed by fifty energy simulations using Ce3X© v.2.3. software. Based on the obtained energy rating, several envelopes and system improvements were proposed and evaluated in terms of energy savings, cost-effectiveness, and social benefits. The most advantageous option, Measure M9, combines interventions in roofs, openings, and installations. It achieved a global energy rating of 17.6 A, with a projected lifespan of 75 years and an investment of EUR 1,642,457.01, recoverable in just 1.4 years. The results highlight the potential of integrated retrofitting strategies to simultaneously improve energy performance and social impact. Measure M9 emerges as the most viable solution, providing a replicable model for sustainable urban rehabilitation in Peru and other regions facing similar challenges. Full article

This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish this, we trace the evolution of language models and describe core approaches, including parameter-efficient fine-tuning (PEFT). The methodology involves a thorough survey of real-world LLM applications across the scientific, engineering, healthcare, and creative sectors, coupled with a review of current benchmarks. Our findings indicate that high training and inference costs are shaping market structures, raising economic and labor concerns, while also underscoring a persistent need for human oversight in assessment. Key trends include the development of unified multimodal architectures capable of processing varied data inputs and the emergence of agentic systems that exhibit complex behaviors such as tool use and planning. We identify critical open problems, such as detectability, data contamination, generalization, and benchmark diversity. Ultimately, we conclude that overcoming these complex technical, economic, and social challenges necessitates collaborative advancements in adaptation, evaluation, infrastructure, and governance. Full article

In recent years, advancements in generative artificial intelligence have led to the development of sophisticated tools capable of mimicking diverse artistic styles, opening new possibilities for digital creativity and artistic expression. This paper presents a critical assessment of the style replication capabilities of contemporary generative models, evaluating their strengths and limitations across multiple dimensions. We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance in the generated images. The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past, holding potential for a wide range of applications: the “AI-Pastiche” dataset. This study is supported by extensive user surveys, collecting diverse opinions on the dataset and investigating both technical and aesthetic challenges, including the ability to generate outputs that are realistic and visually convincing, the versatility of models in handling a wide range of artistic styles, and the extent to which they adhere to the content and stylistic specifications outlined in prompts, preserving cohesion and integrity in generated images. This paper aims to provide a comprehensive overview of the current state of generative tools in style replication, offering insights into their technical and artistic limitations, potential advancements in model design and training methodologies, and emerging opportunities for enhancing digital artistry, human–AI collaboration, and the broader creative landscape. Full article