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Found 5157 publications
Key Performance Indicators for Evaluating Electric Buses in Public Transport Operations
Publication Name: Vehicles
Publication Date: 2025-06-01
Volume: 7
Issue: 2
Page Range: Unknown
Description:
The evaluation of electric buses used in public transportation operations encompasses several critical factors that directly influence the operational efficiency, as well as the economic viability, environmental advantages, and user experience. Energy consumption is a critical metric for assessing the energy efficiency of electric buses. It facilitates a better understanding of vehicle performance across varying road conditions and advances the implementation of energy-saving solutions. The passenger demand model is a tool used to assess the quality and experience of electric buses, with the assessment being based on real usage. The operational mileage is defined as the driving distance of electric buses on a single charge. This parameter has a significant impact on both urban coverage and route optimization. The article under consideration identifies evaluation indicators for electric buses. These indicators are derived from a set of 100 questionnaire responses, which were collected in Győr, Hungary. The classification of the indicators into three segments—mechanical, operational and bus transportation system—is proposed, with the underlying rationale and significance of each indicator’s selection being elucidated. The findings indicate that this component is essential for developing a comprehensive evaluation system for electric buses and serves as a solid foundation for more intricate future studies.
Open Access: Yes
A structured framework for HBIM standardization: Integrating scan-to-BIM methodologies and heritage conservation standards
Publication Name: Digital Applications in Archaeology and Cultural Heritage
Publication Date: 2025-06-01
Volume: 37
Issue: Unknown
Page Range: Unknown
Description:
Heritage conservation demands innovative approaches that integrate advanced technologies with traditional principles to protect monuments and historic buildings. This research investigates the potential of Building Information Modeling (BIM) in heritage conservation, with a focus on developing and adapting workflows tailored to Heritage Building Information Modeling (HBIM). Through a systematic analysis of literature, the research highlights the adaptation of scan-to-BIM methodologies for HBIM creation and their significant role in enhancing preservation efforts. Key technologies, including laser scanning, photogrammetry, and machine learning, are discussed for their contributions to generate accurate and information-rich digital models of heritage structures. Furthermore, this work discovers critical specifications and proposes a structured framework for balancing these specifications within HBIM workflows. This framework addresses challenges such as standardization, scalability, and adaptability, which are essential for accurately capturing the complexity of heritage buildings. By examining these issues, the study identifies opportunities to improve HBIM's capability to monitor, document, and manage culturally significant assets. The findings provide a comprehensive understanding of HBIM processes and their potential to support the effective conservation of heritage.
Open Access: Yes
Improving Material Tracking for Sustainable Construction: A Standard Operating Procedure (SOP) Framework for Resource Efficiency
Publication Name: Buildings
Publication Date: 2025-06-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
Inefficient material tracking continues to be a major challenge in sustainable construction, often leading to unnecessary waste, budget overruns, and project delays. While many digital tools have been introduced in recent years, there is still a lack of practical, field-tested frameworks that combine these technologies with clear, structured procedures, especially in resource-constrained environments. This study introduces a Standard Operating Procedure (SOP) framework designed to improve materials tracking systems (MTSs) by integrating QR codes, GPS tracking, and cloud-based dashboards. Together, these tools support more accurate planning, smoother coordination, and real-time monitoring from the early design stages to on-site implementation. A mixed-methods approach was used, combining surveys with construction professionals and focus group discussions with engineers, IT specialists, and logistics staff. The findings highlight procurement and implementation as the phases most prone to inefficiencies, particularly around material receiving, quality checks, and on-site placement. The validated SOP framework shows strong potential to improve tracking accuracy, reduce material waste, and streamline construction workflows. It offers a flexible, easy-to-use system for integrating sustainability into everyday project practices. Looking ahead, this study also points to future opportunities for applying AI-based tools—such as predictive tracking and automated quality checks—to further improve decision-making and resource efficiency in construction projects.
Open Access: Yes
Global trade of medicinal and aromatic plants. A review
Publication Name: Journal of Agriculture and Food Research
Publication Date: 2025-06-01
Volume: 21
Issue: Unknown
Page Range: Unknown
Description:
Medicinal and aromatic plants (MAPs) are essential natural resources with applications in pharmaceuticals, food, cosmetics, and pesticides. With growing consumer preferences for natural products, the global trade of MAPs (HS code: 1211) has grown significantly. This study analyzed global MAP market trends using export and import data from the International Trade Center (ITC) from 2010 to 2023. During this period, global export and import values surged by 97.8 % and 98.1 %, reaching $4.18 billion and $4.25 billion, respectively, in 2023. China and India emerged as key exporters, with India achieving a 240 % growth in export value, while the United States, Germany, and Japan were leading importers due to high domestic demand and advanced processing infrastructure. HS 121190, comprising plants for perfumery, pharmacy, and pest control, accounted for over 90 % of total trade value, ranking as the 976th most traded product globally in 2022. MAPs prices vary by origin, with vanilla ($115–255.39/kg) as the most expensive and arugula ($0.12/kg) the cheapest. Certifications like WHO-GACP and GMP are critical for quality assurance, traceability, and market competitiveness. Challenges include overharvesting, habitat destruction, trade barriers, and inconsistent quality control, necessitating sustainable cultivation, advanced processing technologies, and harmonized regulations. While Asia-Pacific, led by China and India, dominates production due to biodiversity and supportive policies, Europe and North America focus on value-added re-export. This study underscores the pivotal role of MAPs in global trade. It also provides actionable insights for stakeholders to optimize strategies, embrace sustainability, and capitalize on the expanding demand for these versatile plants.
Open Access: Yes
Performance comparison of polymer and fiber modified asphalt mixtures
Publication Name: Discover Applied Sciences
Publication Date: 2025-06-01
Volume: 7
Issue: 6
Page Range: Unknown
Description:
The performance of SBS-modified asphalt mixtures can be enhanced by incorporating various types of fibers offering a cost-effective alternative to increasing the SBS content. In this study, three different fibers, Basalt, Polyester, and Lignin fibers were added to a 3% SBS-modified bitumen binder, and their performance was compared to a 7% SBS mixture without fibers. Laboratory tests, including indirect tensile strength and dynamic shear rheometer tests, were used to evaluate the mixtures. The indirect tensile strength of all samples was assessed at loading rates ranging from 10 to 70 MPa/s, while stiffness moduli were tested at frequencies of 5 Hz, 3.5 Hz, 1.9 Hz, and 1.2 Hz. Finite element simulations using the Burger’s Logit model have been performed and microstrain analysis has been carried out to assess rutting and fatigue damage, complementing the experimental results. The findings demonstrated that fiber-modified mixtures exhibited superior performance, with increased tensile strength and complex shear modulus. Among the fiber types, Basalt fiber showed the best results, outperforming the others, while Polyester and Lignin fibers displayed nearly identical performance. The Basalt fiber mixture outperformed the SBS-7% mixture by 25% in rutting resistance and 28% in fatigue damage.
Open Access: Yes
A Novel Method for Simulation Model Generation of Production Systems Using PLC Sensor and Actuator State Monitoring
Publication Name: Journal of Sensor and Actuator Networks
Publication Date: 2025-06-01
Volume: 14
Issue: 3
Page Range: Unknown
Description:
This article proposes and validates a novel methodology for automated simulation model generation of production systems based on monitoring sensors and actuator states controlled by Programmable Logic Controllers during regular operations. Although conventional Discrete Event Simulation is essential for material flow analysis and digital experimentation in Industry 4.0, it remains a resource-intensive and time-consuming endeavor—especially for small and medium-sized enterprises. The approach introduced in this research eliminates the need for prior system knowledge, physical inspection, or modification of existing control logic, thereby reducing human involvement and streamlining the model development process. The results confirm that essential structural and operational parameters—such as process routing, operation durations, and resource allocation logic—can be accurately inferred from runtime data. The proposed approach addresses the challenge of simulation model obsolescence caused by evolving automation and shifting production requirements. It offers a practical and scalable solution for maintaining up-to-date digital representations of manufacturing systems and provides a foundation for further extensions into Digital Shadow and Digital Twin applications.
Open Access: Yes
DOI: 10.3390/jsan14030055
Exploring entrepreneurial phases with machine learning models: Evidence from Hungary
Publication Name: Entrepreneurial Business and Economics Review
Publication Date: 2025-06-01
Volume: 13
Issue: 2
Page Range: 101-122
Description:
Objective: The article aims to explore the potential differences between the two phases of entrepreneurship, i.e., total early-stage entrepreneurial activity and established business, as defined by the Global Entrepreneurship Monitor (GEM). The study aimed to classify entrepreneurs using various machine learning models and to evaluate their classification performance comparatively. Research Design & Methods: Using the Hungarian GEM datasets from 2021 to 2023, we analysed a subsample of 964 entrepreneurs. Due to inconsistent results from traditional analyses (e.g., correlations, regressions, principal component analyses), we employed machine learning approaches (supervised learning classification methods) to uncover latent relationships between variables. Findings: The study utilized seven machine learning classification methods to examine the feasibility of grouping companies within the sample using Hungarian GEM data. Findings indicate that machine learning techniques are particularly effective for classifying businesses, although the performance of each method varies significantly. Implications & Recommendations: These results provide valuable insights for researchers in selecting methodologies to identify various business phases. Moreover, they offer practical benefits for market research professionals, suggesting that machine learning techniques can enhance the classification and understanding of entrepreneurial phases. Contribution & Value Added: The study adds to the existing body of knowledge by demonstrating the effectiveness of machine learning methods in classifying business phases. It highlights the variability in performance across different machine learning techniques, thereby guiding future research and practical applications in market research and entrepreneurship studies.
Open Access: Yes
Dem-driven investigation and AutoML-Enhanced prediction of Macroscopic behavior in cementitious composites with Variable frictional parameters
Publication Name: Materials and Design
Publication Date: 2025-06-01
Volume: 254
Issue: Unknown
Page Range: Unknown
Description:
This study presents a numerical investigation and predictive modeling framework to evaluate the influence of microscale frictional parameters on the mechanical behavior and failure mechanisms of cementitious composites. In the first phase, discrete element modeling (DEM) was employed to analyze the effects of bonded friction angle and non-bonded friction coefficient on the stress–strain response, failure evolution, and macro-scale properties. The results revealed a distinct transition from tensile to shear-dominated failure modes beyond a critical friction angle, accompanied by notable changes in compressive strength and deformation characteristics. Additionally, the role of non-bonded friction coefficient in post-failure behavior was identified, emphasizing its influence on load-redistribution. In the second phase, an AutoML-driven artificial neural network (ANN) was optimized via grid search, selecting an optimal four-layer model to predict macroparameters from microscale DEM inputs. The proposed ANN demonstrated high predictive accuracy, effectively capturing nonlinear dependencies while significantly reducing the need for additional numerical simulations. This integration of DEM and AI-based predictive modeling provides a computationally efficient, scalable solution for material characterization, enabling faster, data-driven insights into cementitious composite behavior without reliance on extensive simulation campaigns.
Open Access: Yes
Montelukast Improves Urinary Bladder Function After Complete Spinal Cord Injury in Rats
Publication Name: International Journal of Molecular Sciences
Publication Date: 2025-06-01
Volume: 26
Issue: 12
Page Range: Unknown
Description:
Bladder dysfunction is among the most drastic and quality-of-life-reducing conditions after spinal cord injury (SCI). Neuroinflammation in the lower urinary tract (LUT) after SCI could be a key driver of neurogenic bladder dysfunction and tissue fibrosis. Leukotrienes, a group of highly active lipid mediators, are potent inflammatory mediators. Here, we explored the potential of early montelukast (MLK) therapy, a cysteinyl leukotriene receptor 1 antagonist, on LUT function and structure four weeks after severe SCI in rats. Rats (strain Lewis, female, n = 50) received a permanent bladder catheter, followed by a complete T9 spinal cord transection. MLK was given daily, starting on day one post-injury. Bladder and locomotor function were regularly assessed. Bladder tissue was histologically and immunhistochemically analyzed. Post-SCI, MLK concentrations in plasma and cerebrospinal fluid were clinically relevant. MLK improved bladder functionality. MLK had no impact on smooth muscle alignment and uroepithelial integrity at this early SCI time point. This pilot study gave first insights into early, continuous oral MLK treatment with the first promising results of preserved LUT function and possible subsequent improved tissue integrity.
Open Access: Yes
DOI: 10.3390/ijms26125606
