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Authors - 10204
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Publications - 6515
Advanced elasto-plastic topology optimization of steel beams under elevated temperatures
Publication Name: Advances in Engineering Software
Publication Date: 2024-04-01
Volume: 190
Issue: Unknown
Page Range: Unknown
Description:
A topology optimization algorithm of steel beams under the influence of elevated temperature, considering the geometrically nonlinear analysis of imperfect structures, is proposed in this work. The proposed methodology is developed for addressing topology optimization problems in the presence of initial geometric imperfections and thermoelastic-plastic analysis by developing the bi-directional evolutionary structural optimization (BESO) method. Two comprehensive examples of lipped channel beams and steel I-section beams are provided to demonstrate the effectiveness of the proposed approach. The considered examples explore the impact of elevated temperature on the topology optimization of imperfect steel beams, considering the interplay between thermal effects, structural imperfections, and nonlinear behavior. The results highlight the significance of integrating temperature effects in achieving optimal and robust steel beam designs. Furthermore, the openings generated by the proposed algorithm can efficiently disrupt the continuous heat flow within the material, leading to regions with reduced thermal conductivity compared to solid regions.
Open Access: Yes
Effects of Lip Length and Inside Radius-to-Thickness Ratio on Buckling Behavior of Cold-Formed Steel C-Sections
Publication Name: Buildings
Publication Date: 2024-03-01
Volume: 14
Issue: 3
Page Range: Unknown
Description:
Cold-formed steel (CFS) sections constructed with high-strength steel have gained prominence in construction owing to their advantages, including a high strength-to-weight ratio, shape flexibility, availability in long spans, portability, cost-effectiveness, and design versatility. However, the thin thickness of CFS members makes them susceptible to various forms of buckling. This study focuses on addressing and mitigating different types of buckling in columns and beams by manipulating the lip length (d) and the ratio of inside radius to thickness (Ri/t) in CFS C-sections. To achieve this objective, a comprehensive analysis involving 176 models was conducted through the Finite Element Method (FEM). The findings reveal that an increase in lip length leads to a corresponding increase in critical elastic buckling load and moment ((Formula presented.), (Formula presented.), (Formula presented.), (Formula presented.), (Formula presented.), and (Formula presented.)). It is recommended to utilize a lip length greater than or equal to 15 mm for both columns and beams to mitigate various buckling types effectively. Conversely, an increase in the ratio of inside radius to thickness (Ri/t) results in an increase in critical elastic local buckling load ((Formula presented.)) and moment ((Formula presented.)). Thus, lip length (d) significantly influences column and beam buckling, whereas Ri/t exhibits a relatively impactful effect. Subsequently, the experimental test results were used to verify finite element models. These insights contribute significant knowledge for optimizing the design and performance of CFS C-sections in structural applications.
Open Access: Yes
AI in medical diagnosis: AI prediction & human judgment
Publication Name: Artificial Intelligence in Medicine
Publication Date: 2024-03-01
Volume: 149
Issue: Unknown
Page Range: Unknown
Description:
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, ‘explainability’, and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
Open Access: Yes
Improving Flash Flood Hydrodynamic Simulations by Integrating Leaf Litter and Interception Processes in Steep-Sloped Natural Watersheds
Publication Name: Water Switzerland
Publication Date: 2024-03-01
Volume: 16
Issue: 5
Page Range: Unknown
Description:
More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model’s calibration demonstrated this approach’s effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies.
Open Access: Yes
DOI: 10.3390/w16050750
Comparative Analysis of Ascaris suum and Macracanthorhynchus hirudinaceus Infections in Free-Ranging and Captive Wild Boars (Sus scrofa) in Hungary
Publication Name: Animals
Publication Date: 2024-03-01
Volume: 14
Issue: 6
Page Range: Unknown
Description:
Ascaris suum and Macracanthorhynchus hirudinaceus cause a large loss of yield in farm animals as well as in free-living and captive wild boar herds, thereby causing economic damage. This study compared A. suum and M. hirudinaceus infections in free-ranging and captive wild boars (Sus scrofa) in Hungary. The authors measured the A. suum and M. hirudinaceus infections of a 248-hectare wild boar garden and an 11,893-hectare free-living wild boar herd in the sample area. In all cases, samples were collected from shot wild boars. In total, 216 wild boars were examined from June 2015 to June 2023 in Hungary. Of the 173 dissected wild boars from the wild, 57 (32.9%) were infected with A. suum, while 30 (69.8%) of the 43 individuals from the captive area were infected. The prevalence of M. hirudinaceus in the free-living area population was 9.25% (16 wild boars), while that of the captive population was 34.89% (15 wild boars). In the case of the examined helminths, the captive herd was 36.9% more infected than the herd living in the open area.
Open Access: Yes
DOI: 10.3390/ani14060932
Effects of agro-climatic indices on wheat yield in arid, semi-arid, and sub-humid regions of Iran
Publication Name: Regional Environmental Change
Publication Date: 2024-03-01
Volume: 24
Issue: 1
Page Range: Unknown
Description:
This study aimed to analyze the impact of variations of drought-related agro-climatic indices including cumulative precipitation, cumulative potential evapotranspiration, cumulative actual evapotranspiration, cumulative crop evapotranspiration, cumulative water stress, and cumulative water deficit during nine consecutive phenological stages (emergence to physiological maturity) on wheat yield in arid, semi-arid, and sub-humid regions of Iran during 1999–2018. Principal component analysis was used to recognize the main components that largely explained the variations of agro-climatic indices during different stages of the crop growing period. Then, the relationships between the major components, retrieved from principal component analysis, and the crop yield were assessed. Wheat irrigation requirements were also calculated to investigate the regional water supply–demand patterns during the crop growing period. The findings highlighted increasing impacts of cumulative precipitation, cumulative potential evapotranspiration, cumulative crop evapotranspiration, and cumulative actual evapotranspiration and decreasing impacts of cumulative water stress and deficit on wheat yield, particularly in arid and semi-arid regions. The crop yield was more affected by variations of the agro-climatic indices during the reproductive phase than the vegetative phase. Accordingly, booting to flowering in the arid region, flowering in the sub-humid region, and stem elongation to booting in the semi-arid region were the most sensitive periods of wheat to agro-climatic indices variations. Wheat irrigation requirements in arid and semi-arid regions started earlier than in the sub-humid region. From the findings, it was concluded that adjusting the irrigation schedule based on wheat irrigation requirements during the wheat growing period could help farmers to achieve a favorable wheat yield.
Open Access: Yes
Exploring the impact of ChatGPT on education: A web mining and machine learning approach
Publication Name: International Journal of Management Education
Publication Date: 2024-03-01
Volume: 22
Issue: 1
Page Range: Unknown
Description:
ChatGPT, an artificial intelligence model, has garnered significant interest within education. This study examined public sentiment regarding ChatGPT's influence on education by utilizing web mining and natural language processing (NLP) techniques. By adopting an empirical approach and leveraging machine learning models to process 2003 web articles, the study extracts valuable insights. The results indicate that ChatGPT has emerged as a crucial educational tool, offering advantages for both students and educators. Notably, the study emphasized ChatGPT's role in enhancing students' writing abilities and fostering dynamic, interactive learning environments. ChatGPT's capacity to address a broad spectrum of questions demonstrates its versatility and adaptability, contributing to more inclusive and personalized educational experiences. However, the study also uncovered challenges tied to academic integrity, such as plagiarism and cheating, which stem from incorporating AI-driven tools like ChatGPT into education. This raises concerns regarding ethical aspects, including responsible AI usage and data privacy, and highlights the need for institutions to develop guidelines and policies for AI tool implementation in education. This study's findings hold theoretical and practical implications for integrating ChatGPT into educational settings. It is the first to employ web mining and NLP techniques to analyze public opinions on ChatGPT's impact on education comprehensively.
Open Access: Yes
Thermal, thermomechanical and structural properties of recycled polyethylene terephthalate (rPET)/waste marble dust composites
Publication Name: Heliyon
Publication Date: 2024-02-15
Volume: 10
Issue: 3
Page Range: Unknown
Description:
The main objective of this work is to review the capability of using waste marble dust (MD) particles as reinforcing materials in recycled polymeric composites to achieve environmentally friendly materials. In the present study, polymer composites were fabricated from recycled polyethylene terephthalate (rPET) and MD and then analyzed for their structural and thermal properties. Preparation of rPET-based composites containing 0–20 wt% MD was carried out through extrusion and injection molding. For their characterization Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and dynamic mechanical analysis (DMA) were applied. The DSC analysis revealed a nucleating effect of MD on rPET, which was manifested in a higher crystallization temperature (196.7 °C ⇒ 204.4 °C); however, the marble particles were also found to hamper chain mobility, thereby decreasing the crystallinity ratio (23.7 % ⇒ 19.2 %) of rPET and altering its crystalline structure. According to the TGA measurements, a slight increase occurred in the thermal stability of rPET, its major decomposition temperature increased from 446 °C to 451 °C when 20 wt% MD was incorporated into it. DMA showed an improved stiffness in the entire investigated temperature range for MD-filled composites versus neat rPET. Additionally, several factors were derived from the DMA data, including the effectiveness factor, degree of entanglement, and reinforcing efficiency factor which all suggested a decent interaction between the components indicating a proper reinforcing ability of marble powder. However, above 5 wt% MD content the reinforcing efficiency deteriorated due to the agglomeration of filler particles, which was also supported by scanning electron microscopic images.
Open Access: Yes
A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils
Publication Name: Applied Sciences Switzerland
Publication Date: 2024-02-01
Volume: 14
Issue: 4
Page Range: Unknown
Description:
Expansive soils pose significant challenges to structural integrity, primarily due to volumetric changes that can lead to detrimental consequences and substantial economic losses. This study delves into the intricate dynamics of expansive soils through loaded swelling pressure experiments conducted under diverse conditions, encompassing variations in the sand content, initial dry unit weight, and initial degree of saturation. The findings underscore the pronounced influence of these factors on soil swelling. To address these challenges, a novel method leveraging machine learning prediction models is introduced, offering an efficient and cost-effective framework to mitigate potential hazards associated with expansive soils. Employing advanced algorithms such as decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), extreme gradient boosting (XGBoost), support vector regression (SVR), and artificial neural networks (ANN) in the Python software 3.11 environment, this study aims to predict the optimal applied stress and dry unit weight required for soil swelling mitigation. Results reveal that XGBoost and ANN stand out for their precision and superior metrics. While both performed well, ANN demonstrated exceptional consistency across training and testing phases, making it the preferred choice. In the tested dataset, ANN achieved the highest R-squared values (0.9917 and 0.9954), lowest RMSE (7.92 and 0.086), and lowest MAE (5.872 and 0.0488) for predicting optimal applied stress and dry unit weight, respectively.
Open Access: Yes
DOI: 10.3390/app14041411
