Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity.This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. (edited by author)
The increasing penetration of intermittent renewable energy sources and the retirement of thermal units have widened the power system flexibility gap. Industrial demand response (DR) driven by real-time pricing is widely regarded as a viable solution. In this paper, we propose a framework to quantify the DR potential of a zero-carbon hydrogen metallurgy system (ZCHMS) considering shaft furnace's flexibility. First, we model the shaft furnace as a constrained flexible load and validate the model via simulation, achieving a root mean square error of 4.48\% of the rated load. Second, we formulate a DR potential evaluation method that determines baseline and DR-based production scheduling schemes by minimizing operating cost subject to production orders. Finally, the numerical results show that compared with the baseline, DR-based ZCHMS reduces operating cost by 6.6\%, incentivizing demand-side management in ironmaking and strengthening power-ironmaking synergies.
Magnetostrictive Fe–Ga alloy sheets have significant application prospects in fields such as drives and sensors due to their ability to greatly reduce eddy current losses during high-frequency applications and enhance magneto-mechanical conversion efficiency. However, enhancing the magnetostriction of Fe–Ga alloy sheets still poses challenges. In this work, the (Fe81Ga19)99.8Tb0.1(NbC)0.1 alloy sheets were prepared by conventional rolling processes. The magnetostriction strain (λ//) as large as 320 ppm along rolling direction in stress-free condition was achieved. The evolution of texture during rolling deformation and continuous annealing processes was systematically studied, and the effects of Tb-rich precipitates and NbC on texture evolution and secondary recrystallization were analyzed. The pinning effect of precipitates on the primary recrystallized grains induced the abnormal growth of Goss grains and promoted the secondary recrystallization. The high-temperature annealing at 1200 °C followed by salt-water quenching promoted the solid solution of Tb atoms in the A2 matrix. The 3D-APT results shown that the solid solution of Tb atoms as high as 0.102 at. % was achieved in the A2 matrix vicinity of the Tb-rich precipitates, which increased the L60 nanoheterogeneities in the Goss textured sheets. The Goss texture and more nano-heterogeneous structures lead to the improvement in magnetostriction. The Goss textured sheets also exhibited excellent soft magnetic properties with Ms ∼200.5 emu/g and Hc ∼0.42 kA/m. This study demonstrates that a well-designed alloy composition and preparation processes could achieve the desired texture and Tb supersaturation in the Fe–Ga rolled sheets, which leads to the improved magnetostrictive and magnetic properties.
This study systematically investigates the annealing temperature-dependent microstructural evolution and strengthening mechanisms in a cold-rolled (50 % reduction) high-manganese TWIP steel (0.4C-23.8Mn-0.2Si-3.7Cr-0.5Cu). EBSD and TEM analyses reveal that annealing at 600 °C triggers preferential recrystallization nucleation along deformation twin interfaces, generating a bimodal lamellar heterostructure (1.32 ± 1.98 μm) characterized by fine recrystallized grains and recovered coarse grains. This bimodal distribution arises from divergent growth kinetics, where recrystallized grains nucleate while recovered grains coarsen, evidenced by a sharp decline in Σ3 boundary fraction (51.5 % → 38.6 %) with minimal change in grain orientation spread (7.08° → 5.98°). The heterostructure enables synergistic strengthening via hetero-deformation-induced (HDI) hardening at hetero-zone boundaries, which alleviates stress concentration and delays strain localization. Consequently, the 600 °C annealed sample achieves an optimal strength-ductility balance (yield strength: 1051 MPa, total elongation: 22 %), superior to samples annealed at other temperatures (400–800 °C). Nanoindentation analysis further quantifies substructure contributions, confirming dislocation-dominated hardening in nucleation-stage microstructures and validating the inadequacy of conventional Hall-Petch models for heterogeneous systems. The work establishes annealing at 600°C-700 °C as critical for activating bimodal lamellar heterostructures, providing a mechanistic framework to overcome strength-ductility trade-offs in TWIP steels.
Owing to the differences in crystallographic orientations among individual grains, dislocation structures in polycrystals are inherently inhomogeneous from grain to grain. Since intergranular incompatibility is inevitable during plastic deformation, it may consequently lead to unpredictable plastic strain localization, which in turn facilitates the initiation of fatigue crack. Therefore, to elucidate the mechanisms underlying inhomogeneous deformation in polycrystals, this study systematically examines the fatigue-induced dislocation structures in polycrystalline SUS316L stainless steel. We then directly compare them with those in copper single crystals to clarify the dependence of the dislocation structures on crystallographic orientation. SEM characterization demonstrates that high plastic strain near grain boundaries promotes the formation of secondary cell bands (CBs) overlapping the primary CBs, which is attributable to the simultaneous activation of multiple-slip systems under high plastic strain amplitudes. In addition to strain localization, competition among candidate secondary slip systems strongly governs the dislocation structures. Notably, a new type of deformation band (DB) on the (010) plane is identified in a non-coplanar double-slip-oriented grain, a feature not observed in single crystals, indicating that polycrystals accommodate plastic strain through distinct mechanisms. Detailed dislocation structure analysis provides theoretical guidance for mitigating fatigue crack initiation through the manipulation of dislocations.
The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.
Jose Manuel Torralba, Alberto Meza, S. Venkatesh Kumaran
et al.
The development of high-entropy alloys (HEAs) has marked a paradigm shift in alloy design, moving away from traditional methods that prioritize a dominant base metal enhanced by minor elements. HEAs instead incorporate multiple alloying elements with no single dominant component, broadening the scope of alloy design. This shift has led to the creation of diverse alloys with high entropy (AHEs) families, including high-entropy steels, superalloys, and intermetallics, each highlighting the need to consider additional factors such as stacking fault energy (SFE), lattice misfit, and anti-phase boundary energy (APBE) due to their significant influence on microstructure and performance. Leveraging multiple elements in alloying opens up promising possibilities for developing new alloys from multi-component scrap and electronic waste, reducing reliance on critical metals and emphasizing the need for advanced data generation techniques. With the vast possibilities offered by these multi-component feedstocks, modelling and Artificial Intelligence based tools are essential to efficiently explore and optimize new alloys, supporting sustainable progress in metallurgy. These advancements call for a reimagined alloy design framework, emphasizing robust data acquisition, alternative design parameters, and advanced computational tools over traditional composition-focused methodologies.
Sharon Guardado, Risha Parveen, Zheying Zhang
et al.
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.
Gas turbines require high power density for applications like aircraft and future mobility. Increasing operating temperature, particularly turbine inlet temperature, boosts performance. However, conventional cooling methods limit this increase. Selective laser melting (SLM) offers a promising avenue for realizing advanced cooling configurations beyond conventional fabrication techniques. However, unexpected defects in the final product can occur, making pre-fabrication quality estimation difficult. This necessitates thermal analysis during SLM and design guidelines for suitable fabrication. This study investigates the thermal design guidelines for AlSi7Mg alloy using SLM for high-heat-flux applications. Simulations explored melt pool size dependence on operating parameters like laser power and scan speed. Comparisons with experimental results revealed diverse heat transfer modes under different operating conditions. Finally, a normalized enthalpy factor, integrating operating parameters like scan speed and laser power, was proposed for thermal design in SLM processes for high-heat-flux applications.
Oleksandr Diachenko, Maksym Delembovskyi, Kateryna Levchuk
et al.
The production of concrete mixes, along with their use in the production of building materials and structures, is one of the key processes in the construction industry during the construction, restoration and repair of buildings and structures. Because of this, the need to create modern concrete mixing plants that will meet the requirements of minimum energy consumption and maximum productivity of concrete mixture production is an urgent task. Not only the main operations, which include the dosing of the components of the mixture and their mixing, but also the maintenance operations, namely operations that ensure the timely movement of the components of the concrete mixture from warehouses to the main technological equipment, affect the set rhythm of the concrete mixture production. Conveyors of various types and designs are used to move bulk materials, such as crushed stone and sand.
For the rational selection of such equipment in accordance with the characteristics of the cargo to be transported, knowledge of the types of conveyors, their structures and parameters, understanding of operation issues and methods of parameter calculation are required. In addition, it is worth paying attention to the following parameters: maximum cargo transportation productivity, low energy consumption per unit of moved products, low metal content of the structure.
The work reviewed the most common designs of conveyors used to move bulk materials in concrete mixing plants, analyzed the disadvantages and advantages of conveyors, as well as technical parameters. As a result, the predominant directions for the use of belt and plate conveyors at construction enterprises were determined. The advantages of belt conveyors, which contribute to their widespread distribution, are high productivity, simplicity of design, reliability, quiet operation, low specific power consumption.
When choosing a conveyor, it is recommended to choose the equipment with the highest productivity and the lowest power of the drive motors, however, the performance should be clearly related to other technological equipment.
Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy
et al.
In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.
Frost accumulated on the surface of the evaporator leads to the increase of heat transfer resistance between the refrigerant and environmental medium, which reduces the system performance of cold storage and air source heat pump. To solve this problem, the surface-modified evaporator can achieve retardation of the frost formation, which greatly improves the heat exchange effect between the refrigerant and the external environment. In this paper, a novel coating sprayed on the evaporator was developed using the sol-gel method and the hydrophobicity, durability, and thermal conductivity were investigated. Moreover, a modified method of frost weight measurement under different temperatures and humidity was first proposed. The results showed that the coating has a favorable hydrophobic effect and thermal conductivity, and long service life. Under the standard experimental condition, the frost weight on the coating surface can be reduced up to 26.1 %. Additionally, the effect of coating composition ratio on its performance was further investigated. To sum up, this new coating has the potential for industrialized application.
We are delighted to welcome you to the new issue of the journal on the history of science and technology! This issue is unique as it explores diverse aspects of the development of science and technology in various countries and historical periods.
We invite you on an exciting journey through the pages of this issue, where you will find works by distinguished scientists such as Maryna Gutnyk, Florian Nürnberger, Tetiana Karmadonova, Natalya Pasichnyk, Renat Rizhniak, Нanna Deforzh, Liudmyla Zhuravlova, and many others. Their research covers various facets of history and technology.
The collaborative work by Maryna Gutnyk and Florian Nürnberger presents a comprehensive exploration of the evolution of the Fe-C diagram, tracing its historical development through the lenses of various scientific contributions over time. Their analysis underscores the rich history behind this diagram, highlighting the foundational studies dating back to the early 19th century, marking crucial milestones in understanding the carbon content in steel and its implications for industrial applications. The authors' meticulous use of comparative analysis, synthesis, and chronological examination sheds light on the gradual refinement and evolution of the Fe-C diagram. From the initial recognition of graphite as pure carbon to the establishment of phase diagrams through collaborative efforts at international congresses, the Fe-C diagram's progression intertwines with the advancements of the industrial revolution.
Tetiana Karmadonova's work on the migration trends of Ukrainian researchers from 1991 to 2023 provides a comprehensive analysis of the multifaceted factors driving the migration of scientists from Ukraine to various destination countries, particularly against the backdrop of recent events in the country. The study delves into the intricate landscape of migration among Ukrainian researchers across different historical periods.
Natalya Pasichnyk, Renat Rizhniak, and Нanna Deforzh's meticulous study on the publications in the "Bulletin of Experimental Physics and Elementary Mathematics" from 1886 to 1917 offers invaluable insights into the organization, proceedings, and outcomes of domestic and international congresses of mathematicians and natural scientists during that period. Their research, focused on a comprehensive and quantitative analysis of these journal publications, sheds light on the pivotal role of these gatherings in the scientific and pedagogical realms
Liudmyla Zhuravlova's research on the evolution of techno-nationalism and the pivotal role of space in this phenomenon from the 1980s to the 2020s offers a compelling exploration into the intricate dynamics of technological advancements and their influence on international relations and national strategies. The article delves deeply into the theoretical comprehension of techno-nationalism, particularly examining its relationship with space policy and its relevance within the context of US-China relations. Employing an interdisciplinary approach, drawing from historical, economic, political sciences, and international relations theory, the research unravels the dichotomous evolution of techno-nationalism juxtaposed against techno-globalism. Zhuravlova's work accentuates the ongoing power struggle between the US and China within the space industry, amplifying the techno-nationalist dimensions within innovation systems.
Artemii Bernatskyi and Mykola Sokolovskyi's research presents a comprehensive review of the evolution of additive manufacturing (AM) processes within the realm of metallurgy, spanning from the foundational theories of layer-by-layer manufacturing to the contemporary landscape of AM technologies. This work illuminates the rapid advancements within the AM sector, capturing the profound interest of the scientific community. It underscores the dual significance of AM technologies - not only as an alternative manufacturing method for existing structures but also as a gateway to crafting new, intricately complex structures unattainable through traditional methodologies. Through meticulous analysis and classification of prior studies focusing on technological advancements and implementations, the research establishes a structured approach towards comprehensively mapping the development of additive manufacturing technologies in various trajectories. As a result, the research proposes a systematic approach to formulate a comprehensive scheme for AM technology development, thereby offering a framework that navigates the intricate landscape of technological advancements in various directions.
Mykhailo Klymenko's meticulous study offers a comprehensive evaluation of Professor Tomasz Nikodem Ścibor-Rylski's pioneering contributions to the development of agricultural machinery testing during the latter half of the 19th century. This research sheds new light on Rylski's scientific endeavors and their significant impact on the evolution of agricultural equipment testing. Employing principles of historicism, scientific rigor, and objectivity, Klymenko utilizes historical-scientific methodologies, archival analysis, and generalization to present a nuanced understanding of Rylski's work. For the first time, archival documents are introduced, unveiling insights into the scientist's activities in advancing the field of agricultural machinery testing.
Mohamad Khairul Anuar Mohd Rosli, Ahmad Kamal Ariffin Mohd Rus, and Suffian Mansor's insightful study delves into the overlooked yet pivotal role of electricity, specifically facilitated by the Perak River Hydro-Electric Power Company (PRHEPC), in the tin-mining industry within Kinta Valley during the period of 1927 to 1940. The research illuminates the historical emergence of electricity as a dominant power source in the tin-mining industry of Colonial Malaya, a topic that has received minimal attention in Malaysian historiography.
Sana Simou, Khadija Baba, and Abderrahman Nounah's research represents a profound call to action amidst the urgent need to safeguard Morocco's cultural heritage, notably exemplified by the Marinid Madrasa within the Chellah archaeological site in Rabat. This research intricately weaves advanced technologies with a profound appreciation for the historical, social, and cultural significance of these sites. It charts a course that not only conserves architectural brilliance but also honors the profound stories encapsulated across epochs. Ultimately, it emerges as a blueprint for harmonizing the past with the present, ensuring the preservation of cultural heritage while embracing the imperatives of progress.
In his article, Oleh Strelko shows that the history of bridge construction is an important part of historical knowledge. Developments in bridge construction technology reflect not only engineering advances, but also social, economic and cultural aspects of society. Engineers and scientists faced unique challenges when designing and building bridges depending on the technological level of the era, available materials and the needs of society. This process may reflect technological progress, changes in transportation needs, and cultural and social changes. The purpose of this article is to briefly review key moments and stages in the history of metal bridge construction using welding technology in the 20th century.
We invite you on this exciting journey with our authors exploring the history of science, technology, and cultural heritage. May this issue broaden your knowledge and inspire new research endeavors!
History (General) and history of Europe, Science (General)
A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.