Search Results (953)

This paper conducts a comprehensive experimental comparison of two widely used additive manufacturing (AM) processes, Fused Deposition Modeling (FDM) and Stereolithography (SLA), under standardized conditions using the same test geometries and protocols. FDM parts were printed with both Polylactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS) filaments, while SLA used a general-purpose photopolymer resin. Quantitative evaluations included surface roughness, dimensional accuracy, tensile properties, production cost, and energy consumption. Additionally, environmental considerations and process reliability were assessed by examining waste streams, recyclability, and failure rates. The results indicate that SLA achieves superior surface quality ($R_a\approx 2\mathsf{\mu }\mathrm{m}$ vs. 12–$13\mathsf{\mu }\mathrm{m}$) and dimensional tolerances ($\pm 0.05\mathrm{mm}$ vs. $\pm 0.15$–$0.20\mathrm{mm}$), along with higher tensile strength (up to $70\mathrm{MPa}$). However, FDM provides notable advantages in cost (approximately 60% lower on a per-part basis), production speed, and energy efficiency. Moreover, from an environmental perspective, FDM is more favorable when using biodegradable PLA or recyclable ABS, whereas SLA resin waste is hazardous. Overall, the study highlights that no single process is universally superior. FDM offers a rapid, cost-effective solution for prototyping, while SLA excels in precision and surface finish. By presenting a detailed, data-driven comparison, this work guides engineers, product designers, and researchers in choosing the most suitable AM technology for their specific needs. Full article

New quality productivity (NQP) is an important engine that promotes China’s economy in order to achieve high-quality development in the new era. The study of the measurement of the development level of NQP is conducive to accelerating its formation and development. Based on the panel data of 30 provinces in China from 2010 to 2022 and the connotation of NQP, this paper constructs an evaluation index system for the development level of NQP in Chinese provinces in four dimensions: new industry, new kinetic energy, new model, and new factor. This paper uses the entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) for quantitative measurement. It also uses the Dagum Gini coefficient decomposition and kernel density estimation methods to analyze the regional differences and dynamic evolution trend of the development of NQP in China and makes a scientific diagnosis of the obstacles affecting the development of NQP. The results show that there are significant regional differences in the development of NQP in China, which are mainly driven by regional differences, resulting in a huge gap between the eastern and non-eastern regions. This study shows that the overall gap in the development level of NQP in China is gradually increasing, and there is a “Matthew effect” in which the quality of laborers is the key factor restricting the rapid development of NQP in China. This study expands the research perspective of NQP evaluation, provides valuable suggestions and guidance for government decision-making and policy-making, and is conducive to promoting the rapid development of NQP in China. Full article

This article proposes a novel tube foundation intended for use under transmission line poles. The glass fibre reinforcement polymer (GFRP) piles were driven into sand. A steel tube pole, approximately 6 m high, was mounted on the foundation. The analysed foundations were designed as a monopile to be implemented in the construction of low- and medium-voltage overhead transmission lines. Experimental field tests of innovative piles made of the composite material were carried out on a 1:1 scale. The aim of this work was to develop an isotropic material model treating the GFRP composite as homogeneous. This approach does not fully reproduce the anisotropic behaviour of the composite, but it allows for the engineering design of structures made of the composite material. Laboratory tests in the form of a static tensile test on the samples and a tensile test on the rings cut from a hollow section were performed. The results of the experimental tests and FEM models of the GFRP rings and monopile embedded in sand were compared. The ultimate limit state (ULS) and serviceability limit state (SLS) of the analysed pile were assessed as 14.4 and 9.6 kNm, respectively. The developed numerical model, based on FEM, allows for the load-bearing capacity of the monopile made of GFRP to be reliably determined. From an engineering point of view, the developed numerical model of the GFRP material can be used to calculate the pile load-bearing capacity using engineering software that has limited capabilities in defining material models. Full article

As emission regulations become increasingly stringent, diesel/natural gas dual-fuel engines are regarded as a promising solution and have attracted extensive research attention. However, their complex combustion processes pose significant challenges to traditional combustion modeling approaches. Data-driven modeling methods offer an effective way to capture the complexity of combustion processes, but their performance is critically constrained by the quantity and quality of the test data. To address these limitations, this study proposes a combustion prediction model framework for dual-fuel engines based on neural networks and data augmentation, aiming to achieve high-quality and fast predictions of the heat release rate curve. First, a hybrid regression data augmentation architecture based on an improved Generative Adversarial Network (GAN) is introduced to enable high-quality dataset augmentation. Subsequently, a Bayesian Neural Network (BNN) is employed to construct a Wiebe parameter prediction model for dual-fuel engines with an accelerated and optimized training model. Meanwhile, an adaptive weight allocation method is proposed based on the model’s precision performance, achieving balanced accuracy distribution across multiple output dimensions and further enhancing the model’s generalization ability. Overall, the proposed modeling approach introduces tradeoff optimizations in both data and model dimensions, enhancing the training and learning efficiency, which offers a valuable direction for data-driven prediction models with practical significance. Full article

Previously, it was suggested that the “persona-driven” approach can contribute to producing sufficiently diverse synthetic training data for Large Language Models (LLMs) that are currently about to run out of real natural language texts. In our paper, we explore whether personas evoked from LLMs through HCI-style descriptions could indeed imitate human-like differences in authorship. For this end, we ran an associative experiment with 50 human participants and four artificial personas evoked from the popular LLM-based services: GPT-4(o) and YandexGPT Pro. For each of the five stimuli words selected from university websites’ homepages, we asked both groups of subjects to come up with 10 short associations (in Russian). We then used cosine similarity and Mahalanobis distance to measure the distance between the association lists produced by different humans and personas. While the difference in the similarity was significant for different human associators and different gender and age groups, neither was the case for the different personas evoked from ChatGPT or YandexGPT. Our findings suggest that the LLM-based services so far fall short at imitating the associative thesauri of different authors. The outcome of our study might be of interest to computer linguists, as well as AI/ML scientists and prompt engineers. Full article

The escalating prevalence of cyber threats across industries underscores the urgent need for robust analytical frameworks to understand their clustering, prevalence, and distribution. This study addresses the challenge of quantifying and analyzing relationships between 95 distinct cyberattack types and 29 industry sectors, leveraging a dataset of 9261 entries filtered from over 1 million news articles. Existing approaches often fail to capture nuanced patterns across such complex datasets, justifying the need for innovative methodologies. We present a rigorous mathematical framework integrating chi-square tests, Bayesian inference, Gaussian Mixture Models (GMMs), and Spectral Clustering. This framework identifies key patterns, such as 1150 Zero-Day Exploits clustered in the IT and Telecommunications sector, 732 Advanced Persistent Threats (APTs) in Government and Public Administration, and Malware with a posterior probability of 0.287 dominating the Healthcare sector. Temporal analyses reveal periodic spikes, such as in Zero-Day Exploits, and a persistent presence of Social Engineering Attacks, with 1397 occurrences across industries. These findings are quantified using significance scores (mean: 3.25 ± 0.7) and posterior probabilities, providing evidence for industry-specific vulnerabilities. This research offers actionable insights for policymakers, cybersecurity professionals, and organizational decision makers by equipping them with a data-driven understanding of sector-specific risks. The mathematical formulations are replicable and scalable, enabling organizations to allocate resources effectively and develop proactive defenses against emerging threats. By bridging mathematical theory to real-world cybersecurity challenges, this study delivers impactful contributions toward safeguarding critical infrastructure and digital assets. Full article

Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels; whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field; theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism; and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future research directions. It is hoped that this review can provide useful references for theoretical research and practical engineering applications involving hydrodynamic noise, and point the way toward further exploration in related fields. Full article

Desertification presents major environmental challenges in Central Asia, driven by climatic and anthropogenic factors. The present study quantifies desertification risk through an integrated approach using Bayesian networks and the ESAS model, offering a holistic perspective on desertification dynamics. Four key variables—vegetation cover, precipitation, land-use intensity, and soil quality—were incorporated into a Bayesian model to evaluate their influence on desertification. A probabilistic model was developed to gauge desertification intensity, with simulations conducted at 200 geospatial points. Hazard maps for 2030–2050 were produced under climate scenarios SSP245 and SSP585, incorporating projected land-use changes. All procedures for desertification risk assessment, land-use mapping, and climate downscaling were performed using the Google Earth Engine platform. The findings suggest a 4% increase in desertification risk under SSP245 and an 11% increase under SSP585 by 2050, with the greatest threats observed in western regions such as Kazakhstan, Uzbekistan, and Turkmenistan. Sensitivity analysis indicated that vegetation quality exerts the strongest influence on desertification, reflected by a Vegetation Quality Index (VQI) ranging from 1.582 (low in Turkmenistan) to 1.692 (very low in Kazakhstan). A comparison of the Bayesian and ESAS models revealed robust alignment, evidenced by an R² value of 0.82, a Pearson correlation coefficient of 0.76, and an RMSE of 0.18. These results highlight the utility of Bayesian networks as an effective tool for desertification assessment and scenario analysis, underscoring the urgency of targeted land management and proactive climate adaptation. Although reclaimed land presents opportunities for afforestation and sustainable agriculture, carefully considering potential trade-offs with biodiversity and ecosystem services remains essential. Full article

Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning outcomes. However, few empirical studies have directly compared the quality of LLM feedback with feedback from novices and experts. This study investigates (1) the types of prompts needed to ensure high-quality LLM feedback in teacher education and (2) how feedback from novices, experts, and LLMs compares in terms of quality. Methods: To address these questions, we developed a theory-driven manual to evaluate prompt quality and designed three prompts of varying quality. Feedback generated by ChatGPT-4 was assessed alongside feedback from novices and experts, who were provided with the highest-quality prompt. Results: Our findings reveal that only the best prompt consistently produced high-quality feedback. Additionally, LLM feedback outperformed novice feedback and, in the categories explanation, questions, and specificity, even surpassed expert feedback in quality while being generated more quickly. Conclusions: These results suggest that LLMs, when guided by well-crafted prompts, can serve as high-quality and efficient alternatives to expert feedback. The findings underscore the importance of prompt quality and emphasize the need for prompt design guidelines to maximize the potential of LLMs in teacher education. Full article

The operational effectiveness of Architectural, Engineering, and Construction (AEC) consultants, whose services have a substantial impact on project execution and results, depends on effective risk management. Using 336 survey responses from professionals in the construction industry, such as consultants, contractors, and employers working on a range of infrastructure and building projects, this study validates a hybrid Partial Least Squares Structural Equation Modeling–Artificial Neural Network (–ANN) approach. In order to ensure both causal analysis and predictive insights for AEC consultant performance assessment, this study combines PLS–SEM and ANN to develop an integrated performance evaluation framework. While ANN ordered their relative relevance in a non-linear predictive model, the PLS–SEM analysis found that the two most important predictors of consultant performance were communication and relationship management (G03) and document and record management (G06). The hybrid approach is a more efficient and data-driven tool for evaluating AEC consultants than traditional regression models since it accurately captures both causal links and predictive performance. These results contribute to a robust and sustainable framework for performance evaluation in the AEC sector by offering practical insights into risk reduction and operational improvement. Full article

The automated classification of texts into predefined categories has become increasingly prominent, driven by the exponential growth of digital documents and the demand for efficient organization. This paper serves as an in-depth survey of text classification and machine learning, consolidating diverse aspects of the field into a single, comprehensive resource—a rarity in the current body of literature. Few studies have achieved such breadth, and this work aims to provide a unified perspective, offering a significant contribution to researchers and the academic community. The survey examines the evolution of machine learning in text categorization (TC), highlighting its transformative advantages over manual classification, such as enhanced accuracy, reduced labor, and adaptability across domains. It delves into various TC tasks and contrasts machine learning methodologies with knowledge engineering approaches, demonstrating the strengths and flexibility of data-driven techniques. Key applications of TC are explored, alongside an analysis of critical machine learning methods, including document representation techniques and dimensionality reduction strategies. Moreover, this study evaluates a range of text categorization models, identifies persistent challenges like class imbalance and overfitting, and investigates emerging trends shaping the future of the field. It discusses essential components such as document representation, classifier construction, and performance evaluation, offering a well-rounded understanding of the current state of TC. Importantly, this paper also provides clear research directions, emphasizing areas requiring further innovation, such as hybrid methodologies, explainable AI (XAI), and scalable approaches for low-resource languages. By bridging gaps in existing knowledge and suggesting actionable paths forward, this work positions itself as a vital resource for academics and industry practitioners, fostering deeper exploration and development in text classification. Full article

The wastewater conveyance systems in the United States are facing severe structural challenges, with the nation’s overall wastewater infrastructure receiving a critically low grade of D- from the American Society of Civil Engineers (ASCE). Innovative trenchless technologies, such as Cured-in-Place Pipe Renewal Technology (CIPPRT), offer a cost-efficient substitute for traditional open-cut construction methods (OCCM). However, the possibility of a comprehensive life-cycle cost analysis (LCCA) comparing these methods remains unexplored. LCCA examines the comprehensive financial impact, encompassing installation, operation, maintenance, rehabilitation, and replacement expenses, using net present value (NPV) over a set duration. The objective of this study is to systematically review the existing literature to explore advancements in calculating the LCCA for CIPPRT and compare the latter approach to OCCM. A rigorous PRISMA-guided methodology applied to academic databases identified 845 publications (1995–2024), with 83 documents being selected after stringent screening. The findings reveal limited use of artificial intelligence (AI) or machine learning (ML) in predicting CIPPRT costs. A bibliometric analysis using VOSviewer visualizes the results. The study underscores the potential of intelligent, data-driven approaches, such as spreadsheet models and AI, to enhance decision-making in selecting rehabilitation methods tailored to project conditions. These advancements promise more sustainable and cost-effective management of sanitary sewer systems, offering vital insights for decision-makers in addressing critical infrastructure challenges. Full article

Geothermal energy has emerged as a cornerstone in renewable energy, delivering reliable, low-emission baseload electricity and heating solutions. This review bridges the current knowledge gap by addressing challenges and opportunities for engineers and scientists, especially those transitioning from other professions. It examines deep and shallow geothermal systems and explores the advanced technologies and skills required across various climates and environments. Transferable expertise in drilling, completion, subsurface evaluation, and hydrological assessment is required for geothermal development but must be adapted to meet the demands of high-temperature, high-pressure environments; abrasive rocks; and complex downhole conditions. Emerging technologies like Enhanced Geothermal Systems (EGSs) and closed-loop systems enable sustainable energy extraction from impermeable and dry formations. Shallow systems utilize near-surface thermal gradients, hydrology, and soil conditions for efficient heat pump operations. Sustainable practices, including reinjection, machine learning-driven fracture modeling, and the use of corrosion-resistant alloys, enhance well integrity and long-term performance. Case studies like Utah FORGE and the Geysers in California, US, demonstrate hydraulic stimulation, machine learning, and reservoir management, while Cornell University has advanced integrated hybrid geothermal systems. Government incentives, such as tax credits under the Inflation Reduction Act, and academic initiatives, such as adopting geothermal energy at Cornell and Colorado Mesa Universities, are accelerating geothermal integration. These advancements, combined with transferable expertise, position geothermal energy as a major contributor to the global transition to renewable energy. Full article

This study evaluates “I Learn with Prompt Engineering”, a self-paced, self-regulated elective course designed to equip university students with skills in prompt engineering to effectively utilize large language models (LLMs), foster self-directed learning, and enhance academic English proficiency through generative AI applications. By integrating prompt engineering concepts with generative AI tools, the course supports autonomous learning and addresses critical skill gaps in language proficiency and market-ready capabilities. The study also examines EnSmart, an AI-driven tool powered by GPT-4 and integrated into Canvas LMS, which automates academic test content generation and grading and delivers real-time, human-like feedback. Performance evaluation, structured questionnaires, and surveys were used to evaluate the course’s impact on prompting skills, academic English proficiency, and overall learning experiences. Results demonstrated significant improvements in prompt engineering skills, with accessible patterns like “Persona” proving highly effective, while advanced patterns such as “Flipped Interaction” posed challenges. Gains in academic English were most notable among students with lower initial proficiency, though engagement and practice time varied. Students valued EnSmart’s intuitive integration and grading accuracy but identified limitations in question diversity and adaptability. The high final success rate demonstrated that proper course design (taking into consideration Panadero’s four dimensions of self-regulated learning) can facilitate successful autonomous learning. The findings highlight generative AI’s potential to enhance autonomous learning and task automation, emphasizing the necessity of human oversight for ethical and effective implementation in education. Full article

The increasing global demand for renewable energy has resulted in a high interest in wind power, with offshore wind farms offering better performance than onshore installations. Coastal nations are thus, actively developing offshore wind turbines, where monopiles are the predominant foundation type. Despite their widespread use, the effects of monopile installation methods on the overall foundation behaviour are not sufficiently yet understood. This study investigates how different pile installation procedures—jacked and impact-driven—affect the lateral capacity of monopile foundations under both monotonic and dynamic lateral loads, by comparing them with wished-in-place monopiles, the usual assumption in design, for which no soil disturbance due to installation is considered. Three finite element 3D models were employed to simulate these cases, i.e., wished-in-place monopile, jacked, and impact-driven pile, incorporating soil zoning in the latter cases to replicate the effects of the installation methods. Comparisons between all these models, when subject to lateral monotonic and cyclic loads, are presented and discussed in terms of displacements in the soil and horizontal normal stresses. Results reveal that these installation methods significantly influence soil reactions, impacting the lateral performance of monopiles under both monotonic and dynamic conditions. The impact-driven pile demonstrated the most significant influence on the monopile behaviour. These findings highlight the need for engineers to account for installation effects in the design of monopile foundations to enhance performance and reliability, as well as the optimisation of their design. Full article