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Publications - 6273

Evaluating 3D-Printed Polylactic Acid (PLA)-Reinforced Materials: Mechanical Performance and Chemical Stability in Concrete Mediums

Szabolcs Fischer Szabolcs Szalai Mykola Sysyn Dmytro Kurhan Hanna Csótár

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-02-01

Volume: 15

Issue: 4

Page Range: Unknown

Description:

The optimization and evaluation of 3D-printed polylactic acid (PLA) materials for reinforcing concrete elements present a promising avenue for advancing sustainable construction methods. This study addresses the challenges associated with PLA’s dual nature—biodegradable yet mechanically limited for long-term applications—while leveraging its potential to enhance concrete reinforcement. The research identifies gaps in understanding PLA’s mechanical and chemical behavior in alkaline environments, particularly its interactions with concrete matrices. To bridge this gap, four distinct PLA variants (high-impact PLA, engineering PLA, electrical ESD PLA, and gypsum PLA) and ABS (acrylonitrile butadiene styrene) were subjected to dissolution tests in NaOH solutions (pH 12 and 12.55) and mechanical evaluation under three-point bending using digital image correlation (DIC) technology. Test specimens were prepared using optimized 3D printing strategies to ensure structural consistency and were embedded in concrete beams to analyze their reinforcement potential. Force–displacement data and GOM ARAMIS measurements revealed significant differences in mechanical responses, with peak loads ranging from 0.812 kN (high-impact PLA) to 1.021 kN (electrical ESD PLA). Notably, electrical ESD PLA exhibited post-failure load-bearing capacity, highlighting its reinforcement capability. Chemical dissolution tests revealed material-specific degradation patterns, with high-impact and Gypsum PLA showing accelerated surface changes and precipitation phenomena. Observations indicated white crystalline precipitates, likely lime (calcium hydroxide—Ca(OH)2), residue from the dissolution tests (sodium hydroxide—NaOH), or material-derived residues formed on and near PLA elements, suggesting potential chemical interactions. These findings underline the critical role of material selection and optimization in achieving effective PLA–concrete integration. While PLA’s environmental sustainability aligns with industry goals, its structural reliability under long-term exposure remains a challenge. The study concludes that electrical ESD PLA demonstrates the highest potential for application in reinforced concrete, provided its chemical stability is managed, as its peak value (1.021 kN) showed 25.7% higher load-bearing capacity than high-impact PLA (0.812 kN) and did not lose any of its structural stability in the dissolution tests. This work advances the understanding of PLA as a sustainable alternative in construction, offering insights for future material innovations and applications.

Open Access: Yes

DOI: 10.3390/app15042165

Impact of technological advancement and greener energy on sustainable agriculture in Asia: Evidence from selected Asian countries

Gagan Deep Sharma Ritika Chopra Amar Rao Umer Shahzad Muhammad Ibrahim Shah

Publication Name: Sustainable Development

Publication Date: 2025-02-01

Volume: 33

Issue: 1

Page Range: 221-237

Description:

Regardless of major advancements in food production, Asia continues to confront severe food security challenges. Sustainable agriculture presents entirely new prospects by prioritizing the productive worth of human, social, and natural capital—all of which are abundant in Asian nations or can be replenished at a relatively low financial expense. This paper sets out to explore the role of technological innovation, renewable energy use, financial development, globalization, and institutional quality on the environmental sustainability of agriculture, measured by the greenhouse gas emissions from the agricultural sector for top 10 agricultural economies of Asia from 1990 to 2019. To attain the above objective, we employ a variety of econometric models capable of accounting for cross-sectional dependence, including the CS-ARDL model and the Dumitrescu-Hurlin Panel Granger Causality tests. The result indicates that technological innovation as well as the use of renewable energy can reduce the greenhouse gas emissions from the agricultural sector and thus contribute towards enhancing environmental performance of this sector in short and long run. Although globalization result is revealed to be positive, it turns out to be insignificant in both short and long run. Financial development exerts positive and significant effects on agricultural emissions while the institutional quality is found to be increasing the agricultural environmental performance. Finally, we provide policy recommendations based on the results of the study.

Open Access: Yes

DOI: 10.1002/sd.3106

A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils

Ammar N. Alnmr Mounzer Omran Alzawi Richard P. Ray

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

Contribution to the taxonomy of the Rotundabaloghia (Circobaloghia) mites (Acari: Uropodina: Rotundabaloghiidae)

Jenő Kontschán

Publication Name: Acta Phytopathologica Et Entomologica Hungarica

Publication Date: 2025-01-21

Volume: 59

Issue: 2

Page Range: 176-186

Description:

Four new Rotundabaloghia (Circobaloghia) species are described from South America and South-East Asia based on the collection of the Natural History Museum, London, UK. Rotundabaloghia (Circobaloghia) salebrosa sp. nov. was collected in Malaysia, Rotundabaloghia (Circobaloghia) bakerae sp. nov. was found in Sarawak (Malaysia), Rotundabaloghia (Circobaloghia) microseta sp. nov. is described from Guyana and Rotundabaloghia (Circobaloghia) peritremata sp. nov. was reported from Borneo (Indonesia).

Open Access: Yes

DOI: 10.1556/038.2024.00228

Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles

R. Toth Ernö Horváth Gergo Igneczi Krisztian Nyilas

Publication Name: Automotive Innovation

Publication Date: 2024-02-01

Volume: 7

Issue: 1

Page Range: 59-70

Description:

Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.

Open Access: Yes

DOI: 10.1007/s42154-023-00259-8

Potential of Green Hydrogen Synthesis from Sewage Sludge: Assessing Emission Factors

Éva V.P. Rácz Gergely Zoltán Macher Dóra Sipos Judit Pécsinger Dalma Bódizs

Publication Name: Chemical Engineering Transactions

Publication Date: 2025-01-01

Volume: 119

Issue: Unknown

Page Range: 559-564

Description:

The study aims to explore the feasibility of synthesizing hydrogen from sewage sludge in EU countries, with a particular focus on assessing associated emission factors. This research is driven by the increasing interest in utilizing wastewater as a valuable resource for clean energy production in the context of the global energy transition. Despite the need for extensive treatment of the resulting sludge, research efforts are focused on the production of clean energy, particularly hydrogen. The research methodology uses statistical approaches to derive potential values from baseline data through emission factors and categorizes study areas based on this information. Europe produces approximately 10 million tons of sewage sludge annually, with Hungary contributing nearly a quarter of a million tons, hence this significant byproduct should be treated as a valuable resource in accordance with the principles of a circular economy. The paper delves into hydrogen generation from sewage sludge, specifically through anaerobic digestion, and thoroughly reviews and contrasts existing systems, examining key factors and prospects influencing hydrogen production efficacy within the EU context. Overall, this study addresses a topic of growing significance, offering insights beneficial for both policy formulation and practice in the spheres of energy management and environmental conservation, with a special emphasis on its application and implications for European Union countries.

Open Access: Yes

DOI: 10.3303/CET25119094

The Role of BIM in Managing Risks in Sustainability of Bridge Projects: A Systematic Review with Meta-Analysis

Zsolt Bencze Rana Ahmad Maya Dema Munef Ahmad László Gáspár

Publication Name: Sustainability Switzerland

Publication Date: 2024-02-01

Volume: 16

Issue: 3

Page Range: Unknown

Description:

With the significant and rapid growth observed in bridge projects worldwide, the associated environmental, economic, and social concerns are on the rise. A systematic review of bridge sustainability with meta-analysis according to the PRISMA guidelines was performed, aiming to improve understanding of the importance of using building information modeling (BIM) in bridge projects by investigating the role of proper implementation of this technology to avoid and mitigate risks and improve sustainability. The relevant international literature was collected and scrutinized. The findings demonstrated that the accurate implementation of BIM significantly enhances the efficient management of risks in bridge projects. Consequently, this has a positive effect on improving the three essential (environmental, economic, and social) aspects of sustainability. The impact mentioned is especially apparent in enhancing the management of information throughout the entire lifespan of a bridge. This, in turn, facilitates precise decision-making during the design phase, aligns with assessments of environmental impact, enables real-time monitoring during execution, effectively manages the maintenance of the structure, facilitates efficient allocation and utilization of resources, and improves design practices by providing designers with accurate information. Delving into the nuances of this review has shed light on the transformative potential of BIM in shaping sustainable bridge projects, laying the groundwork for future advancements in this critical field.

Open Access: Yes

DOI: 10.3390/su16031242

Optimizing Inter-Basin Water Transfer for Sustainable Energy Management and Multipurpose Water Utilization

Katalin Bene Máté Chappon Attila Kálmán Antal Bakony

Publication Name: Advances in Science and Technology

Publication Date: 2025-01-01

Volume: 165 AST

Issue: Unknown

Page Range: 297-309

Description:

Climate change has further exacerbated long-standing water use conflicts in the Lake Velence catchment area in Hungary. The lake is the ecological, social and economic central element of the area, with water scarcity as water levels having fallen to record lows in recent years due to severe summer droughts. As a result of infrastructure developments in the 20th century and the significant waves of immigrants in recent decades, the lake and its surroundings have been heavily modified, transformed into an artificial waterbody, while land and water use has significantly altered. Besides these negative effects on water resources and the lake’s water level, settlements in the catchment area have become the top solar energy producers per housing in Hungary in recent years. The aim of this research is to identify and develop a possible inter-basin water recharge solution that meets societal needs based on the suggested development ideas formulated in questionnaire responses. A sustainable alternative of these solutions is pumping from a nearby catchment, that was evaluated in detail. Based on ecological considerations, a multi-criteria analysis summarizing nearly 100 water quality and quantity parameters was developed to ensure that water supply meets qualitative requirements. To ensure economically sustainable operating costs, the nearby solar capacities were used for pumps operation and energy storage. For energy demand and carbon emissions reduction, the uphill pumping was complemented with a downhill turbine hydropower recovery system. Several scenarios of the pumped water recharge system were considered and hydrodynamically optimized in Matlab. The return on investment of the inter-basin pumped water replenishment systems were evaluated as well as the carbon emissions to assure additional economic benefits and low carbon-footprint. A bottom-up methodology with large scale stakeholder involvement that assesses social needs and applies well-balanced the three pillars of sustainable development, can achieve a Pareto effective displacement even during the development of a water replacement system at the catchment level and beyond, on an inter-basin level. With a comprehensive methodology developed for pumped water recharge from an external catchment using existing renewable energy sources, the deteriorating social atmosphere and ecological conditions caused by climate and land use changes may be improved. In the meantime, even economic benefits can be increased, all with a low energy demand and carbon footprint, in a sustainable way.

Open Access: Yes

DOI: 10.4028/p-jaWpD3

eSFILES: Intelligent process flowsheet synthesis using process knowledge, symbolic AI, and machine learning

Mauricio Sales-Cruz Vipul Mann Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Computers and Chemical Engineering

Publication Date: 2024-02-01

Volume: 181

Issue: Unknown

Page Range: Unknown

Description:

Process flowsheet synthesis, design, and simulation require integrated approaches that combine domain knowledge and data-driven methods for fast, efficient, and reliable solutions. However, due to the recent surge in data and machine learning capabilities, there has been a shift towards building purely data-driven systems for process flowsheet synthesis and related problems. Such approaches have certain drawbacks. Here, we present a hybrid method that combines data-driven approaches with domain knowledge to represent process flowsheets and solve problems related to process synthesis, design, and simulation. We present an extended SFILES (or eSFILES) representation, a multi-level hierarchical flowsheet representation with varying degrees of process knowledge. At level 0, flow diagrams are represented as purely text-based SFILES strings. At level 1, the SFILES grammar, along with inferencing algorithms, is used to construct a flowsheet hypergraph explicitly representing flow diagram connectivity. At level 2, specifications needed for material and energy balance calculations are introduced, and, after simulation, the results are also added using annotated flowsheet hypergraphs. Finally, at level 3, a process ontology is connected with the annotated flowsheet hypergraph to include design and operation parameters as well as the detailed simulation results. We discuss this hierarchical framework using several case studies.

Open Access: Yes

DOI: 10.1016/j.compchemeng.2023.108505