Andrii Herasymenko, Leonid Shyrin, Rostyslav Yegorchenko
et al.
This article presents a comprehensive study on the management and optimization of frame-anchor support systems for seam preparatory excavations designed for the transportation of large-tonnage cargo via suspended monorail transport. With the intensification of underground mining operations and the increasing use of heavy mechanized transport, ensuring the stability and safety of mine workings under dynamic load conditions has become a critical challenge for engineering. The research proposes an innovative support technology based on the combined fastening of monorail systems to the crowns of metal arches and directly to the roof using deep-embedded anchors. This approach aims to reduce dynamic impacts on the excavation roof and improve the overall reliability of the support system. To evaluate the effectiveness of the proposed support design, a numerical modelling method was employed to simulate the interaction of components within the dynamic system “suspended monorail – support – rock mass.” The stress-strain behaviour of the frame-anchor structure under real load scenarios was analyzed using SolidWorks Simulation software. During the simulation, various parameters were systematically varied, including the spacing of support frames, the length and anchorage depth of the rock bolts, and the mechanical properties of the surrounding rock mass. The results of the analysis enabled the identification of rational design parameters that minimize deformation and enhance load-bearing capacity. In particular, optimal combinations of frame spacing and anchor configurations were found to significantly reduce stress concentrations and improve the stability of preparatory workings under dynamic loading from moving monorail trains. The study demonstrates that effective management of support system parameters can lead to improved safety, reduced material consumption, and faster development of mining panels. The findings have practical significance for the design of underground transport routes and can be incorporated into normative documents governing support systems in dynamically loaded mine environments.
The influence of sulfide (S<sup>2−</sup>) concentration on the corrosion resistance of Cr<sub>3</sub>C<sub>2</sub>-25(Ni20Cr) cermet coating on Al7075 (EN, AW-7075) substrate (Cr<sub>3</sub>C<sub>2</sub>-25(Ni20Cr)/Al7075) was investigated. The coating was produced by the cold-sprayed (CS) method. The Cr<sub>3</sub>C<sub>2</sub>-25(Ni20Cr)/Al7075 coatings were modified chemically in solutions containing thioacetic acid amide (TAA). The surface and microstructure of the specimens were both observed by a scanning electron microscope (SEM). The mechanical properties of the Cr<sub>3</sub>C<sub>2</sub>-25(Ni20Cr) coatings were characterized using microhardness (HV) measurements. The corrosion tests of the materials were carried out using the electrochemical method in a acidic chloride solution. The adsorbed (Me<sub>m</sub>S<sub>n</sub>)<sub>ads</sub> layer effectively separates the Cr<sub>3</sub>C<sub>2</sub>-25(Ni20Cr)/Al7075 coating surface from contact with the aggressive corrosive environment. More than a twice lower value of corrosion rate (CW) was obtained for the Cr<sub>3</sub>C<sub>2</sub>-25(Ni20Cr)/Al7075 coating after exposure to the environment with 0.15 M TAA.
Mechanical behaviors of deformation, rupture, and ejection failure of coal with impact inclination under uniaxial compression were investigated. Uniaxial compression tests were conducted on coal with impact inclination at different loading rates. The classification characteristics of post-peak stress drop in coal samples have been identified. Based on the 3D reconstruction of the primary fracture of coal samples through CT scanning, the distinct mechanisms of instability failure of coal samples with various stress drop types are analyzed. Based on the non-contact full-field strain measurement results, the pre-peak and post-peak fracture evolution and the fracture-ejection rule of coal samples are analyzed. The stress change and transformation characteristics of the coal sample ejection region are statistically calculated. The ejection mode of the coal body is disclosed, and the classification characteristics of the fracture-ejection and the stress drop type of the coal sample ejection mode are obtained. Additionally, the correlation between the existing impact tendency index of coal sample and the impact kinetic energy of post-peak ejection is analyzed, and Wsdr is established to characterize the intensity of post-peak ejection of coal sample. The results show that: Post-peak stress drop of coal samples can be divided into three types: continuous drop, stepped drop and instantaneous drop, and the post-peak stress drop type of coal samples is closely related to the development of primary fractures. Surface crack propagation before the impact inclined coal ejection has the nonlinear characteristics of slow and accelerated crack propagation, and the incubation time of class Ⅰ, class Ⅱ and class Ⅲ impact ejection decreases successively. Coal with impact inclination can be classified into local ejection, regional through-out ejection and global instability ejection. Coal samples of Class Ⅰ, Ⅱ and Ⅲ are dominated by local ejection, regional through-out ejection and global instability ejection respectively. The coal sample has two types of ejection modes: parent power source and ejection block power source. The same stress drop type coal sample can be caused by parent power source or ejection block power source. Proposed index Wsdr has a linear positive correlation with the ejection kinetic energy.
Md Ershadul Alam, Takuya Yamamoto, George Robert Odette
Primary and low-strain creep represents a very important integrity challenge to large, complex structures, like fusion reactors. Here, we develop a predictive empirical primary creep model for 9Cr tempered martensitic steels (TMS), relating the applied stress (σ) to strain (ε), time (t) and temperature (T). The most accurate model is based on the applied σ normalized by the steel’s T-dependent ultimate tensile stress (σ<sub>o</sub>), σ/σ<sub>o</sub>(T). The model, fit to 17 heats of 9Cr TMS, yielded a σ root mean square error (RMSE) of ≈±11 MPa. Notably, the model also provides robust predictions for all the other TMS, when calibrated only by the fusion candidate Eurofer97 database. The model was extended to explore two possible effects of neutron irradiation, which produces both displacements per atom (dpa) and helium (He in atomic parts per million, appm) damage. These effects, which have not been previously considered, include: (a) softening, as a function of dpa, at T > ≈400–450 °C, in low-He fission environments (<1 He/dpa); and (b) subsequent re-hardening in high-He (≥10 He/dpa) fusion first-wall environments. The irradiation effect models predict (a) accelerated primary creep due to irradiation softening; and (b) fully arrested creep due to high-He re-hardening.
This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open-ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands-on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self-defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem-solving skills that can be applied in real-world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real-world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project-based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.
Empirical research on code review processes is increasingly central to understanding software quality and collaboration. However, collecting and analyzing review data remains a time-consuming and technically intensive task. Most researchers follow similar workflows - writing ad hoc scripts to extract, filter, and analyze review data from platforms like GitHub and GitLab. This paper introduces RevMine, a conceptual tool that streamlines the entire code review mining pipeline using large language models (LLMs). RevMine guides users through authentication, endpoint discovery, and natural language-driven data collection, significantly reducing the need for manual scripting. After retrieving review data, it supports both quantitative and qualitative analysis based on user-defined filters or LLM-inferred patterns. This poster outlines the tool's architecture, use cases, and research potential. By lowering the barrier to entry, RevMine aims to democratize code review mining and enable a broader range of empirical software engineering studies.
The mining industry consumes about 1.7% of the energy generated worldwide, which is expected to increase in the coming decades. Milling is the most energy-intensive process of a typical mining operation. Many variables (e.g., rock characteristics, mineral matrix, and equipment properties) affect energy consumption. This paper proposes that Random Forests and the Generalised Linear Model (GLM) be used to predict the energy consumption of the SAG mill, which significantly contributes to the energy consumption of mining operations. To show the performance of the proposed approach, a case study was applied to a copper mine dataset from South America. The proposed approaches were applied to forecast the SAG mill energy consumption. The outcomes demonstrated that these methods could be used to predict energy consumption. Random Forest can have a high prediction accuracy of 95% but lacks explanatory ability, as shown in R2 at 50%. GLM provided additional insights by showing the feature importances and their relationships with SAG mill energy consumption, along with considering the potential uncertainties and generating posterior probability distributions for the model outcomes. Both models identified key variables as significant predictors identically, with the GLM offering a more comprehensive view of best-case and worst-case energy consumption scenarios.
In this study, the synergistic behavior of Ni and Al in two low-density steels (Fe-26Mn-10.2Al-0.98C-0.15V (wt. %) and Fe-29Mn-5Al-1C-12Ni (wt. %)) and their influence on microstructures and mechanical properties were investigated. The chemical composition of κ-carbides and B2 precipitated particles as a function of annealing and aging temperature and the matrix within which they formed were elucidated. The microstructures and deformation mechanisms of both steels were studied based on their strengthening contribution. The Fe-26Mn-10.2Al-0.98C-0.15V steel mainly realized precipitation strengthening through κ-carbides and grain boundary strengthening due to full recrystallization. The strengthening caused by Fe-29Mn-5Al-1C-12Ni steel was mainly due to the presence of the B2 phase in the matrix, which was non-coherent with FCC. This led to the Orowan bypass mechanism, which made precipitation strengthening the main strengthening contribution. The synergistic effect led to the shear or bypass mechanism of both steels when plane dislocation slip occurred. In addition, it also had an influence on the work-hardening capability during plastic deformation. This study provides a promising way to further enhance the yield strength of low-density austenitic steels through the synergistic effect of Ni and Al.
Leonardo Vieira de Sousa, Rodrigo Rafael da Silva, Maria Vanessa Pires de Souza
et al.
Sweet sorghum (Sorghum bicolor [L.] Moench) is a plant that can be an alternative for the production of bioethanol in semi-arid regions. The objective of this work was to evaluate sweet sorghum 'BRS 506' under salt and water stress. The experimental design was in randomized blocks, in a factorial scheme (4x4), with the first factor referring to the electrical conductivities of the irrigation water (1.5; 3.0; 4.5; and 6.0 dS m-1) and the second refers to irrigation depths (53, 67, 85 and 95% of crop evapotranspiration). Gas exchange, leaf water status, leaf sugars and plant growth were evaluated. Salt and water stress cause negative effects on the growth of sweet sorghum 'BRS 506'. Salt stress causes disturbances in gas exchange and sugar levels. Sweet sorghum 'BRS 506' is tolerant to combined salt and water stress.
This is an article in the field of mineral processing engineering. The flake graphite flotation concentrate in Madagascar was purified by hydrofluoric acid method. The grade of raw material flake graphite was 94.96%. Based on the single-factor condition tests, the purification process was established that the mixture of hydrochloric acid and hydrofluoric acid was 3.5 mL/g, HF volume fraction was 40%, and the graphite grade could be purified to 99.98% by water bath reaction at 60 ℃ for 8 h. In view of the poor purification effect of fine graphite, the graphite was screened and classification before purification. The fine graphite class was subjected to ultrasonic shock pretreatment test, and acid leaching was carried out for purification after 60 min of ultrasonic shock. After purification, the fixed carbon content of fine graphite class was ≥99.92%.
Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development -- from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction Engineering -- a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research.
The grouting technology of bed separations has been proved to be a new method which can meet the requirements of non-destructive mining and solid waste reduction. To effectively control the subsidence of ground structures caused by mining, the whole process of bed separation grouting mining is analyzed in a steady state based on the key stratum theory. For the first time, a “four zones” control model for surface subsidence under grouting bed separation was proposed, which includes natural zone, transition zone, warning zone, and protection zone, and the calculation formula for the “four zones” range was derived. Based on the engineering back-ground of controlling the subsidence of the ground coking plant at the 3501 panel, the proposed “four zones” model was validated by combining physical modelling and field measurement of subsidence. The results show that the surface subsidence curve of bed separation grouting in physical modelling shows an irregular “V” shape, and the surface subsidence of the panel first increases rapidly. After reaching the maximum subsidence, the surface subsidence first decreases rapidly, and then the reduction rate gradually slows down. The subsidence curve shows a clear “four zones” distribution, with a maximum subsidence of 1589 mm, appearing at the contact boundary between the natural zone and the transition zone. The subsidence of the contact boundary between the transition zone and the warning zone is 497.94 mm, and there is basically no subsidence within the protection zone. The predicted surface subsidence, horizontal deformation, slope, and curvature caused by mining under grouting conditions based on probability integral method are consistent with the field measured results, but significantly smaller than the predicted values under non-grouting conditions. It is determined that the surface deformation under grouting conditions meets the requirements of Grade I damage level for structures. Based on the practical engineering geology and observed data of the mine, the natural area is 261.19 m, the transition area is 246.09 m, the warning area is 655.25 m, and the protection area is 199.53 m. The proposed “four zones” control model provides a fundamental theoretical basis for studying the subsidence of bed separation grouting mining.
The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prompt engineering and fine-tuning. Prompt engineering involves applying different strategies to query LLMs, like ChatGPT, while fine-tuning further adapts pre-trained models, such as CodeBERT, by training them on task-specific data. Despite the growth in the area, there remains a lack of comprehensive comparative analysis between the approaches for code models. In this paper, we evaluate GPT-4 using three prompt engineering strategies -- basic prompting, in-context learning, and task-specific prompting -- and compare it against 17 fine-tuned models across three code-related tasks: code summarization, generation, and translation. Our results indicate that GPT-4 with prompt engineering does not consistently outperform fine-tuned models. For instance, in code generation, GPT-4 is outperformed by fine-tuned models by 28.3% points on the MBPP dataset. It also shows mixed results for code translation tasks. Additionally, a user study was conducted involving 27 graduate students and 10 industry practitioners. The study revealed that GPT-4 with conversational prompts, incorporating human feedback during interaction, significantly improved performance compared to automated prompting. Participants often provided explicit instructions or added context during these interactions. These findings suggest that GPT-4 with conversational prompting holds significant promise for automated code-related tasks, whereas fully automated prompt engineering without human involvement still requires further investigation.
Michael Dorner, Maximilian Capraro, Oliver Treidler
et al.
The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this article, we discuss the unsolved problem of taxing collaborative software engineering across borders. We (1) introduce the reader to the basic principle of international taxation, (2) identify three main challenges for taxing collaborative software engineering making it a software engineering problem, and (3) estimate the industrial significance of cross-border collaboration in modern software engineering by measuring cross-border code reviews at a multinational software company.
High-voltage transmission lines are the main facilities for power transmission, and they are mainly composed of aluminum conductor steel-reinforced (ACSR). Over long-term outdoor use, overhead transmission lines will encounter lightning strikes, chemical pollutant corrosion, deicing, wind vibration, and other external forces. This often results in a series of potential failures, such as breakage, for the strands. In order to ensure the safe operation of the power grid and avoid fatal accidents, such as line breaks, it is necessary to identify and repair line faults. Among them, the main basis for the regular detection and replacement of high-voltage transmission lines is whether a broken strand defect appears. In this paper, a type of pulsed eddy current (PEC) sensor is developed to detect the broken strand defect in transmission lines. The simulation and experimental results showed that the designed PEC sensor could effectively and accurately identify the fault.
Social Network Mining (SNM) has become one of the main themes in big data agenda. As a resultant network, we can extract social network from different sources of information, but the information sources were growing dynamically require a flexible approach. To determine the appropriate approach needs the data engineering in order to get the behavior associated with the data. Each social network has the resources and the information source, but the relationship between resources and information sources requires explanation. This paper aimed to address the behavior of the resource as a part of social network analysis (SNA) in the growth of social networks by using the statistical calculations to explain the evolutionary mechanisms. To represent the analysis unit of the SNA, this paper only considers the degree of a vertex, where it is the core of all the analysis in the SNA and it is basic for defining the relation between resources and SNA in SNM. There is a strong effect on the growth of the resources of social networks. In total, the behavior of resources has positive effects. Thus, different information sources behave similarly and have relations with SNA.
Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number of sequences generated by SPM, but also improve the efficiency of users in performing pattern analysis. The current algorithms available on targeted sequence querying are based on specific scenarios and cannot be generalized to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes. Totally four pruning strategies are designed in TaSPM, and hence it can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conduct extensive experiments on different datasets to compare the existing SPM algorithms with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.