Saleha Muzammil, Mughees Ur Rehman, Zoe Kotti
et al.
Software source code often harbours "hotspots": small portions of the code that change far more often than the rest of the project and thus concentrate maintenance activity. We mine the complete version histories of 91 evolving, actively developed GitHub repositories and identify 15 recurring line-level hotspot patterns that explain why these hotspots emerge. The three most prevalent patterns are Pinned Version Bump (26%), revealing brittle release practices; Long Line Change (17%), signalling deficient layout; and Formatting Ping-Pong (9%), indicating missing or inconsistent style automation. Surprisingly, automated accounts generate 74% of all hotspot edits, suggesting that bot activity is a dominant but largely avoidable source of noise in change histories. By mapping each pattern to concrete refactoring guidelines and continuous integration checks, our taxonomy equips practitioners with actionable steps to curb hotspots and systematically improve software quality in terms of configurability, stability, and changeability.
The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.
Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae
et al.
Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.
Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao
et al.
[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.
Detection and identification of faults are crucial in the process of coal exploration and mining, and the traditional manual method of fault interpretation can no longer meet the needs of actual production, and the deep learning-based seismic fault interpretation method performs better in the field of fault segmentation. Conventional convolutional neural network (CNN) has limited sensory field and cannot make good use of the global information, which will lead to some predicted faults with insufficient continuity and missing faults, etc. Transformer has the advantage of extracting global information, and introduces the TransUNet network which is a fusion of CNN and Transformer to construct a CBAM- based seismic fault identification method. TransUNet seismic fault identification method to identify 2D seismic fault images. Firstly, the CBAM-Block attention module is integrated into the TransUNet network, and the module is added into the CNN tomography encoder part and the 3-layer jump connection part connecting the tomography encoder and the tomography decoder, respectively, to enhance the recognition ability of the seismic tomography image from two dimensions, namely, the channel and the space; secondly, the loss function optimised jointly by the Dice loss function and the cross-entropy loss function is selected to make the segmentation of the tomography image more accurate. function and cross-entropy loss function to make the fault image segmentation more accurate, and the DICE and IOU values obtained by the CBAM-TransUNet fault identification network on the synthetic seismic dataset are increased to 0.84 and 0.75, respectively, and the experimental results show that the continuity of the fault identification is stronger, which is obviously superior to other classical segmentation methods; finally, the constructed model is used to interpret the faults on the real seismic dataset of the F3 block of the North Sea, off the coast of the Netherlands. Finally, the constructed model was used to interpret the faults in the real seismic data set of Block F3 in the North Sea off the Netherlands. The experimental results show that the seismic fault identification method based on CBAM-TransUNet can effectively identify the faults while removing the redundant fault information, and performs well in terms of fault identification accuracy and fault identification continuity, and the identified faults are richer in details, which improves the accuracy of fault identification, and can be effectively applied to identify the faults in the real seismic data.
In the present work, the novel Al0.15CoCrFeNiW0.15 High-Entropy Alloy (HEA) has been designed by CALPHAD (CALculation of PHAse Diagrams) computations with the in-house built Genova High-Entropy Alloys (GHEA) database, aiming to a mostly monophasic face-centered cubic (FCC) alloy strengthened by the precipitation of secondary μ phase. To explore different preparation routes, alloy samples have been synthesized by both arc melting (AM) and spark plasma sintering (SPS). Samples were characterized by low optical microscopy (LOM), scanning and transmission electron microscopy (SEM and TEM), X-ray diffraction (XRD), and microhardness measurements. Long-term annealing at 1100 °C has been performed, followed by quenching or furnace cooling. AM as-cast sample showed a monophasic FCC microstructure, characterized by large grains. Precipitation of μ phase was observed in the equilibrated and quenched sample, in good agreement with the thermodynamic calculations. On the other hand, SPS samples resulted in a finer microstructure, characterized by the presence of small particles of Al2O3 and μ phase, already present before annealing. Contrary to the thermodynamic predictions, after equilibration and quenching, the dissolution of the μ phase was observed due to the Gibbs-Thomson effect, which enhanced W solubility in the FCC solid solution. Annealing of the SPSed alloy followed by furnace cooling, however, allowed the precipitation of μ, thanks to the slower cooling rate. Overall, this study highlighted CALPHAD's utility for composition selection in complex multicomponent systems and demonstrated how AM and SPS lead to significantly different microstructures and properties, with grain size playing a key role in determining the alloy performances.
This research introduces a new model to predict the roll force during hot rolling of steel, based on a statistical analysis of approximately 38,980 sets of measurements in a commercial mill with five finishing stands. The study includes ten different steel grades and features models of both single grades and the entire dataset. Three models are developed and compared: a temperature-dependent strain rate model (M1), a strain rate model (M2), and a simplified strain rate model (M3). The decrease in temperature with roll stand has a strong cross-correlation with compensating decreases in strain and contact length by roll stand, such that both the temperature and strain terms are statistically insignificant. The final model (M3)—<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mrow><mo>[</mo><mi mathvariant="normal">N</mi><mo>]</mo></mrow><mo>=</mo><mn>113.1</mn><mo>·</mo><mover accent="true"><mi>ϵ</mi><mo>˙</mo></mover><msup><mrow><mo>[</mo><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>]</mo></mrow><mrow><mn>0.3141</mn></mrow></msup><mo>·</mo><mi>w</mi><mrow><mo>[</mo><mi>mm</mi><mo>]</mo></mrow><mo>·</mo><mo>ℓ</mo><mrow><mo>[</mo><mi>mm</mi><mo>]</mo></mrow></mrow></semantics></math></inline-formula>—relates force (<i>F</i>) to strain rate (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>ϵ</mi><mo>˙</mo></mover></semantics></math></inline-formula>), width (<i>w</i>), and contact length (<i>ℓ</i>) and achieves an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> fit of 0.946 over all 10 steel grades. Although the single-grade models show slightly higher accuracy, the final model retains robust predictive capability with only two fitting parameters. This model enables fast and easy estimation of roll force for commercial hot rolling of low-carbon, medium-carbon, and high-strength–low-alloy steels.
Fernando Andrés Muñoz-Peña, Jason Steve Pulido-Reina
Soft skills and employee productivity are key factors in sustainability and corporate performance in competitive and dynamic environments. The purpose of this article is to identify current findings related to soft skills, employee productivity and their relationship with organizational performance, and to propose a structural model that allows establishing this relationship. The results of some research conducted individually reveal positive and significant relationships in the proposed fields. Some skills found in the literature are communication, problem solving and decision making. To carry out this research, multiple research papers were collected between 2005 and 2024. A structural equation model was used as a way to propose a relationship between the aforementioned factors. This research theoretically demonstrated that soft skills and employee productivity contribute positively and significantly to organizational performance. Limitations may arise depending on the particularities of the industrial sector and the economic context. Since there is little research in the analyzed fields, this study contributes significantly to the identification of key variables.
Coal fly ash, a by-product of coal combustion in thermal power plants, offers valuable opportunities for reuse in construction and material engineering. This study explores the thermal behaviour of fly ash samples collected from different sections of the REK Bitola Power Plant. Thermal characteristics of the fly ashes obtained from hot stage microscopy revealed distinct transformation stages, including sintering, softening, and melting intervals. X-ray fluorescence analysis was employed for determination of the chemical composition, while sieve analysis was used for investigation of granulometry of the materials. The physical and chemical characteristics of the ashes, particularly their grain size and content of silica, alumina, calcium oxide, and iron oxide play a crucial role in determining their response to heat. These findings help guide more effective use of fly ash in environmentally friendly applications, supporting waste reduction and promoting sustainable practices.
Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 representative studies, we demonstrate that a goal-oriented prompt formulation, which guides LLMs to follow established human logical thinking, significantly improves the performance of LLMs. Furthermore, We introduce a novel taxonomy that categorizes goal-oriented prompting methods into five interconnected stages and we demonstrate the broad applicability of our framework. With four future directions proposed, we hope to further emphasize the power and potential of goal-oriented prompt engineering in all fields.
Nickel-based single-crystal superalloys are designed for extreme conditions due to their superior corrosion and creep resistance properties. However, these pose challenges in the subsequent recycling after reaching their end-of-life. Molten magnesium (Mg) can rapidly corrode the stable spent nickel-based superalloys and selectively dissolve nickel (Ni). This waste-free process represents an effective method for recycling spent superalloys and accomplishing metal regeneration. This study investigates the mechanism of selectively dissolving Ni from DD5, a nickel-based single-crystal superalloy, by optimizing process temperature, time, and Mg content in an inert atmosphere. Vacuum distillation was employed to separate the resulting Mg, residual superalloy (i.e., the material left post-extraction), and Ni-rich alloy (i.e., the metal product selectively extracted). The findings revealed that the residual superalloy after selective Ni dissolution is characterized by a porous skeleton structure with pore sizes predominantly ranging from 2 to 30 nm and a low compressive strength which is 1/10 of the original DD5 superalloy.
Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyze the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.
Due to the rapid sintering and densification, spark plasma sintering (SPS) technology can significantly inhibit grain coarsening, and obtain alloy with high density and uniform microstructure. Tantalum-tungsten (Ta-W) alloy had been fabricated by powder metallurgy and consolidated by SPS at temperature of 1600 °C for 5 min at the pressure of 35 MPa. Specimens of pure Ta and four tantalum-based alloys with different concentrations of tungsten ranging from 2.5 to 10 were used to investigate the behavior of developed alloys. X-ray diffraction analyses were applied for all compositions of Ta-W alloys. The morphology of fracture sections was analyzed by scanning electron microscopy (SEM). Morphologies of initial Ta and W powders, microstructures of sintering Ta-W alloy and tensile fractographs of the specimens with different components were observed. When the concentrations of tungsten were distributed with 2.5 wt%, 5 wt%, 7.5 wt% and 10 wt%, the measured densities were 16.151 g/cm<sup>3</sup>, 15.756 g/cm<sup>3</sup>, 15.711 g/cm<sup>3</sup>, 15.665 g/cm<sup>3</sup> and 15.670 g/cm<sup>3</sup> respectively. As the content of tungsten increased, the density of the alloy decreased and the grain was refined, meanwhile the micro-hardness of the samples increased gradually. Furthermore, the addition of tungsten could greatly enhance the strength of the alloys, but decrease the plasticity of the alloys. Ta-2.5 wt%W shows the maximum bending strength with a value of 832.29 MPa, while the percentage of transgranular fracture increased with the increase of tungsten content.
Large non-metallic inclusions affect the mechanical properties and service life of nickel-based superalloys. This study investigates the effects of magnesium elements on non-metallic inclusions in K4169 nickel-based superalloys, exploring the formation mechanisms and aggregation relationships of the inclusions. Four gradients of the magnesium content were used to simulate the erosion of MgO refractories on inclusions in the vacuum induction melting of nickel-based superalloys. The observed inclusions mainly comprised MgO, TiN, MgAl2O4, and MgAl2O4–TiN composites. To study the formation mechanism of inclusions in Ni-based alloys with different Mg contents, the predominance diagram of Mg–Al–Ti–O inclusions in nickel-based superalloy systems was calculated using Factsage thermodynamics software and classical thermodynamics separately. The aggregation behavior of MgAl2O4 and MgO inclusions on the surface of molten nickel-based superalloys was observed in situ under a high-temperature confocal laser scan microscope. The capillary force acting on inclusions in nickel-based superalloys was calculated using the Kralchevsky–Paunov model (K–P model), the calculation was verified with experimental results, and the influencing factors were analyzed. The capillary force acting on inclusions on the surface of molten nickel-based superalloys weakened in the order of MgAl2O4 > MgO > Al2O3, which is the opposite of the order for the force acting on inclusions in molten steel. Minimizing the magnesium content in the alloy is crucial to preventing the formation of large inclusions in nickel-based superalloys, and the use of magnesium-containing materials in the smelting process should thus be avoided.