Search Results (3,782)

The high-temperature rutting performance of asphalt mixtures is strongly dependent on the aggregate skeleton and particle movement under loading. Such mechanisms were addressed in the present study by a combined experimental and simulation approach based on the triaxial strength test. A single type of asphalt with two different aggregate gradations (dense and gap) was incorporated to highlight the role of gradation in resisting shear dilation. The simulation was carried out by coupling the discrete and finite element methods considering the realistic three-dimensional aggregate shapes and gradations as well as the flexible boundary prescribed by latex membranes as routinely employed in triaxial testing. In order to represent contact failure-induced cracks within the virtual specimens, the linear parallel bond model was mixed with the Burgers or linear model through random distribution at contacts involving the mortar units. Model verification was achieved by comparing the resulting stress–strain data against those from the laboratory. The calibrated model provided a platform for systematic investigation from the perspectives of particle movement, crack development and distribution, and interparticle contacts. The results showed that the gap-graded mixture yielded lower triaxial strengths and yet softened at a lower rate and exhibited smaller volumetric expansion in the post-peak region. A faster loss of internal cohesion was inferred in the dense-graded mixture based on the higher accumulation rate of cracks that were concentrated at the middle height towards the perimeter of the virtual specimen. Contact analysis indicated that aggregate skeleton was more influential in the strength and stability of gap-graded mixtures. Full article

In an era of growing climate concerns and complex environmental policy challenges, novel approaches for accurate carbon emissions measurement are urgently needed. This article introduces an innovative approach for predicting carbon dioxide emissions by analyzing the interaction between energy consumption and production efficiency, measured through an entropy-to-marginal product ratio. Unlike conventional metrics such as Eurostat measurements or the Kaya identity, our framework establishes explicit connections to fundamental physical laws governing energy transformation while offering flexible elasticity parameters that capture non-linear relationships between efficiency improvements and emission reductions. The research combines theoretical modeling with empirical validation across ten European countries, demonstrating how the entropy-based methodology accounts for both production complexity and energy efficiency where traditional linear models fall short. Analysis reveals that energy-efficient countries demonstrate lower entropy maximization under stable conditions, indicating a direct relationship between operational efficiency and environmental impact. Although the model demonstrates strong predictive capabilities with an exceptional accuracy/information cost ratio, limitations exist in achieving accuracy in some country cases. This study concludes by evaluating these strengths and constraints, acknowledging the need for extended time series analysis and sector-specific applications, and providing clear directions for future research that bridge this promising theoretical contribution with practical environmental policy applications. Full article

The unprecedented rise of deepfake technologies, leveraging sophisticated AI like Generative Adversarial Networks (GANs) and diffusion-based models, presents both opportunities and challenges in terms of digital media authenticity. In response, this study introduces a novel deep neural network ensemble that utilizes a tree-based hierarchical architecture integrating a vision transformer, ResNet, EfficientNet, and DenseNet to address the pressing need for effective deepfake detection. Our model exhibits a high degree of adaptability across varied datasets and demonstrates state-of-the-art performance, achieving up to 97.25% accuracy and a weighted F1 score of 97.28%. By combining the strengths of various convolutional networks and the vision transformer, our approach underscores a scalable solution for mitigating the risks associated with manipulated media. Full article

Concrete segregation can lead to variations in hardened concrete’s properties, such as strength and Young’s modulus, or permeability, resulting in changing volume ratios between aggregates and paste within a concrete element. One approach to mitigate this potential risk is to conduct a performance test to assess vibrated concrete’s segregation sensitivity. This paper outlines various methods to evaluate the segregation sensitivity of vibrated concrete, aiming to support adequate concrete casting. The focus is on practical feasibility while maintaining test accuracy. For hydraulic engineering in Germany, test procedures to evaluate segregation sensitivity on fresh and hardened concrete based on aggregate distribution are described in the “BAW-Code of practice MESB”. However, this method is very complex and, therefore, difficult to implement in practice. Another procedure for hardened concrete is based on concrete density. In this paper, both methods are compared to investigate if evaluating fresh concrete using a simple density criterion leads to a comparably significant differentiation of vibrated concrete with different segregation sensitivities. The primary emphasis lies in accurately classifying examined concretes in terms of their segregation sensitivity, evaluating the scatter of results, and assessing the practical applicability of these methods. The investigations demonstrate that a density-based method can yield reliable and comparable results to those obtained through the wash-out test according to “BAW-Code of practice MESB”. Additionally, a simpler and faster procedure is achievable with the density approach. Hence, density evaluation offers a practical alternative to the wash-out test. Full article

This study explores the application of Lean principles in the service sector, addressing the complexities of translating manufacturing-focused methodologies to intangible service activities. Lean Services, a relatively recent concept, lacks a standardised definition, leading to varied interpretations ranging from customer-centric approaches to waste reduction strategies. Through a comprehensive literature review and a case study of a European scooter and motorcycle manufacturer, this research identifies a consolidated list of service-specific wastes, bridging a critical gap in Lean Services research. Additionally, the study compares two prominent methodologies—DMAIC (Define–Measure–Analyse–Improve–Control) from Six Sigma and the Cost Deployment pillar from World Class Manufacturing (WCM)—in the context of Lean Services. The analysis highlights DMAIC’s strength in advanced statistical tools and targeted problem-solving, contrasting with WCM’s systemic approach, emphasising economic feasibility and broader resource integration. By examining their individual and combined applicability, this research provides actionable insights for selecting methodologies based on specific objectives, time constraints, and resources. This work contributes to the evolving understanding of Lean Services, offering a framework for practitioners to enhance efficiency and drive continuous improvement in service-based processes. Full article

The accurate prediction of the residual strength of defective pipelines is a critical prerequisite for ensuring the safe operation of oil and gas pipelines, and it holds significant implications for the pipeline’s remaining service life and preventive maintenance. Traditional machine learning algorithms often fail to comprehensively account for the correlative factors influencing the residual strength of defective pipelines, exhibit limited capability in extracting nonlinear features from data, and suffer from insufficient predictive accuracy. Furthermore, the predictive models typically lack interpretability. To address these issues, this study proposes a hybrid prediction model for the residual strength of defective pipelines based on Bayesian optimization (BO) and eXtreme Gradient Boosting (XGBoost). This approach resolves the issues of excessive iterations and high computational costs associated with conventional hyperparameter optimization methods, significantly enhancing the model’s predictive performance. The model’s prediction performance is evaluated using mainstream metrics such as the Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²), Root Mean Square Error (RMSE), robustness analysis, overfitting analysis, and grey relational analysis. To enhance the interpretability of the model’s predictions, reveal the significance of features, and confirm prior domain knowledge, Shapley additive explanations (SHAP) are employed to conduct the relevant research. The results indicate that, compared with Random Forest, LightGBM, Support Vector Machine, gradient boosting regression tree, and Multi-Layer Perceptron, the BO-XGBoost model exhibits the best prediction performance, with MAPE, R², and RMSE values of 5.5%, 0.971, and 1.263, respectively. Meanwhile, the proposed model demonstrates the highest robustness, the least tendency for overfitting, and the most significant grey relation degree value. SHAP analysis reveals that the factors influencing the residual strength of defective pipelines, ranked in descending order of importance, are defect depth (d), wall thickness (t), yield strength (σ_y), external diameter (D), defect length (L), tensile strength (σ_u), and defect width (w). The development of this model contributes to improving the integrity management of oil and gas pipelines and provides decision support for the intelligent management of defective pipelines in oil and gas fields. Full article

Financial distress poses a significant risk to companies worldwide, irrespective of their nature or size. It refers to a situation where a company is unable to meet its financial obligations on time, potentially leading to bankruptcy and liquidation. Predicting distress has become a crucial application in business classification, employing both Statistical approaches and Artificial Intelligence techniques. Researchers often compare the prediction performance of different techniques on specific datasets, but no consistent results exist to establish one model as superior to others. Each technique has its own advantages and drawbacks, depending on the dataset. Recent studies suggest that combining multiple classifiers can significantly enhance prediction performance. However, such ensemble methods inherit both the strengths and weaknesses of the constituent classifiers. This study focuses on analyzing and comparing the financial status of Indian automobile manufacturing companies. Data from a sample of 100 automobile companies between 2013 and 2019 were used. A novel Firm-Feature-Wise three-step missing value imputation algorithm was implemented to handle missing financial data effectively. This study evaluates the performance of 11 individual baseline classifiers and all the 11 baseline algorithm’s combinations by using ensemble method. A manual ranking-based approach was used to evaluate the performance of 2047 models. The results of each combination are inputted to hard majority voting mechanism algorithm for predicting a company’s financial distress. Eleven baseline models are trained and assessed, with Gradient Boosting exhibiting the highest accuracy. Hyperparameter tuning is then applied to enhance individual baseline classifier performance. The majority voting mechanism with hyperparameter-tuned baseline classifiers achieve high accuracy. The robustness of the model is tested through k-fold Cross-Validation, demonstrating its generalizability. After fine-tuning the hyperparameters, the experimental investigation yielded an accuracy of 99.52%, surpassing the performance of previous studies. Furthermore, it results in the absence of Type-I errors. Full article

Tuberculosis, primarily caused by Mycobacterium tuberculosis, is an airborne lung disease and continues to pose a significant global health threat, resulting in millions of deaths annually. The current treatment for tuberculosis involves a prolonged regimen of antibiotics, which leads to complications such as recurrence, drug resistance, reinfection, and a range of side effects. This scenario underscores the urgent need for novel therapeutic strategies to combat this lethal pathogen. Over the last two decades, microbiome therapeutics have emerged as promising next-generation drug candidates, offering advantages over traditional medications. In 2022, the Food and Drug Administration approved the first microbiome therapeutic for recurrent Clostridium infections, and extensive research is underway on microbiome treatments for various challenging diseases, including metabolic disorders and cancer. Research on microbiomes concerning tuberculosis commenced roughly a decade ago, and the scope of this research has broadened considerably over the last five years, with microbiome therapeutics now viewed as viable options for managing drug-resistant tuberculosis. Nevertheless, the understanding of their mechanisms is still in its infancy. Although autophagy has been extensively studied in other diseases, research into its role in tuberculosis is just beginning, with preliminary developments in progress. Against this backdrop, this comprehensive review begins by succinctly outlining tuberculosis’ characteristics and assessing existing treatments’ strengths and weaknesses, followed by a detailed examination of microbiome-based therapeutic approaches for drug-resistant tuberculosis. Additionally, this review focuses on establishing a basic understanding of microbiome treatments for tuberculosis, mainly through the lens of autophagy as a mechanism of action. Ultimately, this review aims to contribute to the foundational comprehension of microbiome-based therapies for tuberculosis, thereby setting the stage for the further advancement of microbiome therapeutics for drug-resistant tuberculosis. Full article

The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination (R²) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability. Full article

Enhancing the thermal resistance of carbon fiber-reinforced polymers (CFRPs) with flame retardants or coatings often leads to increased weight and reduced mechanical integrity. To address these challenges, this study introduces an innovative approach for developing nanocomposites using carbon-based nanoparticles, while preserving the structural lightweight properties. For this, carbon black particles (CBPs) up to 10% and carbon nanotubes (CNTs) up to 1.5% were incorporated into the RTM6/G939 composite material. The obtained samples were then analyzed for their properties and heat resistance under one-sided thermal loading at a heat flux of 50 kW/m². Results demonstrate that integrating these particles improves heat conduction without compromising the material’s inherent advantages. As a result, thermo-induced damage and the resulting loss of mechanical strength are delayed by 17% with CBPs and 7% with CNTs compared to the unmodified material. Thereby, the thermal behavior can be accurately modeled by a straightforward approach, using calibrated, effective measurements of the nanoparticles in the polymer matrix rather than relying on theoretical assumptions. This approach thus provides a promising methode to characterize and improve thermal resistance without significant trade-offs. Full article

The optimization of mechanical properties in acrylonitrile butadiene styrene-like (ABS-like) photopolymer utilizing neural network techniques presents a promising methodology for enhancing the performance and strength of components fabricated through stereolithography (SLA) 3D printing. This approach uses machine learning algorithms to analyze and predict the relationships between various printing parameters and the resulting mechanical properties, thereby allowing the engineering of better materials specifically designed for targeted applications. Artificial neural networks (ANNs) can model complex, nonlinear relationships between process parameters and material properties better than traditional methods. This research constructed four ANN models to predict critical mechanical properties, such as tensile strength, yield strength, shore D hardness, and surface roughness, based on SLA 3D printer parameters. The parameters used were orientation, lifting speed, lifting distance, and exposure time. The constructed models showed good predictive capabilities, with correlation coefficients of 0.98798 for tensile strength, 0.9879 for yield strength, 0.9823 for Shore D hardness, and 0.98689 for surface roughness. These high correlation values revealed the effectiveness of ANNs in capturing the intricate dependencies within the SLA process. Also, multi-objective optimization was conducted using these models to find the SLA printer’s optimum parameter combination to achieve optimal mechanical properties. The optimization results showed that the best combination is Edge orientation, lifting speed of 90.6962 mm/min, lifting distance of 4.8483 mm, and exposure time of 4.8152 s, resulting in a tensile strength of 40.4479 MPa, yield strength of 32.2998 MPa, Shore D hardness of 66.4146, and Ra roughness of 0.8994. This study highlights the scientific novelty of applying ANN to SLA 3D printing, offering a robust framework for enhancing mechanical strength and dimensional accuracy, thus marking a significant benefit of using ANN tools rather than traditional methods. Full article

Health equity is shaped by multiple factors intersecting with service delivery in community-based organizations (CBOs). Providers in under-resourced areas are often the first point of contact for families seeking child development, mental health, and behavioral support. However, system-level barriers hinder service delivery and access. This study explores provider perspectives to identify barriers and inform system-level changes that promote equity in child and family health. Using a narrative qualitative design, in-depth interviews were conducted with 21 health and mental health professionals from child- and family-serving CBOs. Guided by ecological and strengths-based frameworks, interviews examined provider insights on challenges, strengths, and supports affecting service delivery. Key themes emerged across macro (rights-based policies, racism/oppression), community (environmental impacts, social cohesion), organizational (secondary stress, system fragmentation, provider supports), and family levels (basic needs, parenting support, service access). Findings highlight the need for a multilevel approach that prioritizes rights-based policies, strengthens community cohesion, and improves system integration. Enhancing CBO capacity to address these determinants could advance equity-oriented service delivery and mitigate structural barriers that perpetuate health disparities. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article

Background/Objectives: Childhood trauma has a documented impact on development, and may also affect functioning and well-being in transition-age youth (TAY). There is a need to explore approaches, such as trauma-informed care (TIC), to enhance the services provided during the transition to adulthood. The objective of this scoping review was to explore the extent of the literature on the potential of TIC for supporting TAY. Methods: We focused on initiatives grounded in TIC to support TAY between the ages of 14 and 25 who have histories of trauma. The search strategy involved nine databases and the gray literature. The titles, abstracts, and full text were screened in duplicate by reviewers, and then data were extracted. Results: A total of 19 references were included and classified into three categories: (1) importance of TIC to support TAY (k = 5); (2) description of TIC initiatives (k = 6); and (3) evaluation of TIC initiatives supporting TAY (k = 2). Seven references were classified into more than one category. The references documented 10 TIC models or initiatives, half of which were evaluated and showed promising results. Important components of TIC initiatives supporting TAY included staff training and support; collaborative and multidisciplinary work; systemic changes; addressing trauma and its impacts; and a strength-based and youth-focused approach. Conclusions: The review emphasizes the importance of acknowledging and responding to trauma and its impact in TAY and advances the core components of TIC in the context of the TA, including its systemic nature. Although we cannot conclude that TIC is effective in supporting the TA at the moment—given that the literature is still in its early stages—the review shows that it is at least promising. Limitations, as well as future lines of work are discussed. Full article

With the development of prefabricated buildings, complex-shaped cement products, represented by heating-type elevated floors, have appeared on the market. These cement products can only be produced by the pouring method, with low efficiency and poor precision. Among the existing processing methods for preparing cement products, compression dewatering offers the greatest ability to produce cement products with complex shapes. However, the pressed mixing material comprises a plastic fresh mortar, which inherently lacks fluidity, making it difficult to completely fill the cavity of the shaped mold. Few studies have been conducted on the experimental method and design ratios of mortar for the compression dewatering process in the industry, with no effective solution. To achieve the efficient production of complex-shaped cement products, this study explored the experimental method of testing the strength and flowability of mortar formed through compression dewatering as the forming process. Mortar ratios suitable for producing complex-shaped cement products using the compression dewatering process were determined, the relationship between material rheology and product forming performance was analyzed, and the influence of the compression process on the strength and micro-properties was studied. Finally, a cement-based heating-type elevated floor forming technology was developed, offering a novel approach for the efficient forming of complex-shaped cement products. Full article