Deep coal resources with abundant reserves and considerable thermal potential are receiving increased attention in mining engineering, given the accelerating transformation of the global energy structure and the growing demand for clean energy. To address extraction challenges and environmental pressures while ensuring economic feasibility and sustainable development, efforts are made to enable carbon reduction and green transformation under high-efficiency utilization of deep coal resources. A systematic review of “deep coal resource fluidized mining”, “coal chemical mining”, and “coal-based power” informs the introduction of a detonation-generation mining approach and its technical framework. The approach places coal-powder detonation combustion technology at its core and integrates advanced detonation combustion-mechanical/magnetohydrodynamic power generation, forming a detonation-turbine/MHD hybrid power system that supports efficient conversion and clean utilization of coal resources. Four fundamental theories are presented, including the Coal-powder Detonation Energy Release mechanism, the Coupled Coal-powder Detonation-generation Power Scheme, a Full Life Cycle Detonation-power Generation Dynamic Management Mechanism, and the Blasting-electric Power Deep coal mining theory and method. Discussion centers on four key technologies: Stable coal/gas two-phase detonation, detonation model construction and dynamic process optimization, detonation-based power generation efficiency assessment, and comprehensive design for detonation-based coal mining, demonstrating their role in upgrading deep coal mining practices. On this foundation, a systematic engineering strategy is proposed to clarify the synergy between mining processes and the detonation-based power generation mode, highlight safety management and process optimization priorities at each critical stage, and refine the overall detonation-generation pathway for deep coal resource development. This pathway offers valuable insights for establishing a coal-based power system and promoting the clean and efficient utilization of deep coal resources in China.
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo
et al.
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.
Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre
et al.
The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.
Microseisms in mining engineering may not only damage the structure of mines, but also interfere with their production activities and induce other geological disasters. Therefore, accurately distinguishing micro-seismicity in mining engineering is of great significance for ensuring mine safety and preventing geological disasters. To distinguish microseisms in mining engineering, a signal denoising method grounded on variational mode decomposition (VMD) algorithm and permutation entropy was studied and designed, and sparrow search algorithm was introduced to optimise the parameters of VMD algorithm. High-quality input data foundation for subsequent micro-seismic resolution models was provided through this denoising method. Subsequently, a micro-seismic resolution model combining transformer and convolutional neural network was developed, which utilises transformer to focus on important information and obtains feature information through depthwise separable convolution. The findings denoted that the designed denoising method achieved maximum signal-to-noise ratios of 33.142 dB, 34.021 dB and 33.743 dB on simulated signals from Blocks, Doppler and Heavyisine, respectively, all of which were higher than the comparison method. The average root mean square error of this method in practical applications was 3.088 × 10 −6 . The accuracy and maximum root mean square error of the micro-seismic resolution model were 97.54% and 2.95 × 10 −6 , respectively. The average time consumption and F1 score were 7.12 ms and 0.9456, which were better than the comparison model. On the training set, the model correctly identified 491, 497, 493, 508 and 498 micro-seismic information, rubber hammer vibration information, iron hammer vibration information, excavation vibration information and blasting vibration information, respectively, which were closer to the true values. The designed noise reduction method and resolution model have good effects and can provide accurate signal processing and analysis tools for micro-seismic resolution in mining engineering. The novelty of the research lies in the combination of micro-seismic signal denoising and micro-seismic identification resolution, which avoids the uncertainty caused by manually setting parameters and comprehensively improves the resolution accuracy of micro-seismic research in mining engineering, surpassing previous research efforts in micro-seismic monitoring accuracy in mining engineering.
Laura Nistor, Cătălin Lisa, Tsuyoshi Michinobu
et al.
Background: 2-[4-(Dimethylamino)phenyl]-3-([4-(dimethylamino)phenyl]ethynyl)buta-1,3-diene-1,1,4,4-tetracarbonitrile (DDMEBT) is a thermally robust organic material of interest for applications requiring controlled volatility. Understanding its thermal stability, decomposition mechanism, and sublimation behavior is critical for optimizing deposition conditions in industrial processes. Methods: A comprehensive set of techniques was employed, including thermogravimetric analysis coupled with mass spectrometry and FTIR spectroscopy (TG/MS/FTIR), differential scanning calorimetry (DSC), ATR-FTIR spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), dynamic vapor sorption (DVS) analysis, polarized light microscopy (POM), and molecular modeling. Sublimation kinetics were investigated under isothermal conditions (130–150 °C) using anthracene as reference. Significant findings: DDMEBT exhibits a sequential three-step degradation mechanism, independent of heating rate, with high thermal stability (final residue ∼77 %) attributed to its nonplanar architecture and intermolecular π–π/dipole–dipole interactions. Thermal analysis revealed melting at ∼190 °C, structural rearrangements (196–230 °C), and an amorphous-to-crystalline transition at 270 °C. Sublimation proceeds via zero-order kinetics with low volatility (0.178 μg/min at 130 °C) and an activation energy of 66.5 kJ/mol. The determined vapor pressure (1998–4000 Pa) and transport coefficients confirm a thermally activated, hydrodynamically stable process. These findings establish a reliable basis for sublimation modeling and provide guidelines for optimizing material processing in high-temperature, low-volatility applications.
The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.
The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.
A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.
The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends.
The first introduction of fuzzy theory in the nineteenth century created room for continuous research and application in various fields. Fuzzy set theory has been globally applied in geotechnical engineering, and research in this field continues to date. The fuzzy inference system is considered to be one of the most popular techniques adopted to resolve some of the geomechanical challenges faced in both surface and underground excavations. This paper unpacks fuzzy theory-based approaches in mine geomechanics with the aim of expanding the innovative application of the same approach specifically in tunnel geomechanics. This aim was achieved by conducting a review of recent successful and unsuccessful applications of fuzzy inference systems in underground excavations/tunnelling geomechanics. Indeed, this review has outlined some cardinal points associated with the ability of the technique to solve complex geomechanics problems. However, the success of the technique was accompanied by a few limitations associated with the methodology. Finally, a future outlook associated with the technique has been established.
Futuristic views of mine operations allude to the use of immersive virtual worlds that transport people into what is now called a Metaverse, a network of 3D virtual worlds that can be experienced by an unlimited number of people simultaneously. The concept and benefit of combining real and virtual worlds have been around for many decades and have evolved considerably in recent years. When these are presented as a digital twin with bidirectional data and collaboration capability, potential exists for a powerful communications tool for mining operations. Hence, it is time to discuss the Metaverse from a mining perspective and this paper asks, ‘What is the Metaverse and how does it relate to mining?’ This paper presents some case studies, concepts, a model and considers the benefits of a Mining Metaverse as a communications tool that enhances decision making via remote collaboration.
Belt conveyors, owing to their simple construction, high reliability and relatively low energy consumption, are the basic means of transporting loose and granular materials. Currently, thanks to continuous development, belt conveyors can reach a length of up to several kilometres, and their belt width can be more than two meters. Such possibilities are achieved thanks to increasingly better belts and drives. However, the most common are short belt conveyors with a length of up to 40 m and belt widths of up to 1 m, frequently referred to as belt feeders. Apart from the mining industry, they are widely used in power engineering, metallurgy and other industries (chemical plants, trans-shipment ports, storage yards, etc.). The design of machines, including belt feeders, is based on calculations. Modern design in technology is based on advanced computational methods and the possibilities of computer technology. Multi-variant simulation calculations are necessary, especially in the case of belt feeders, where none of the devices—despite the use of typical elements and subassemblies—are a repeatable solution. Only this procedure guarantees the selection of rational solutions already at the early stages of design. Therefore, in this article, an analytical model of a typical belt feeder was developed and its stability and forces in the supports were determined. This allowed the development of an application for testing the stability of the belt feeder at the design stage or when introducing structural changes.
The elastic hysteresis of bulk material is an important problem in many fields of science and technology, including mining and metallurgy, construction, energy, and engineering. This is due to the fact that loose materials consist of a large number of particles that can shift relative to each other under the influence of external loads. This process is accompanied by energy losses and changes in the shape of the material structure, which in turn leads to elastic hysteresis. Knowledge of material behavior under various loading conditions is essential for developing safe and effective engineering solutions. The purpose of the research is to determine the parameters of the deformation of loose material (for example, granular material) depending on its physical and mechanical properties in the Simcenter STAR-CCM+ software package. The elastic hysteresis loop reflects the behavior of the material under cyclic loading, when strain and stress changes occur. Loop area is a measure of material energy losses that occur during cyclic loading. As a result of numerical modeling of the process of deformation of loose material (on the example of granular material), the regression equations of the second order of the dependences of the area of the elastic hysteresis loop and the maximum force value at 25% relative deformation of the granular material from its physical and mechanical properties (density, Poisson's ratio and Young's modulus) were obtained. The presented results can be used in further modeling or in the creation of physico-mathematical models of the process of compression of grain material in granulators, extruders and expanders.
The island of Milos is an active mining site since the Antiquity. Exploitation began with the extraction of obsidian lumps and volcanic lavas and tuffs of specific type, that were used as millstones, sulphur and alum (alunite). Currently, the mining activity is focused on perlite and bentonite, even though some years ago, kaolin, silica, pozzolans and barite were also exploited. Milos bentonite is rather a specialty and not a commodity, as it has multifunctional properties, and therefore used in a wide range of industrial applications, such as foundry sand, drilling muds, lubricant oil, civil engineering structures (waterproofing and sealing, diaphragm wall construction, grouting, concrete workability additive, etc.), cat litter, iron ore pelletizing - Fe metallurgy in blast furnaces and also as “improver” for poor quality bentonites. The peculiar behaviour of Milos bentonites can be attributed to the combination of various geological and hydrogeological factors that occurred at the time of its formation from a glassy tuff precursor. Despite the continuous flourishing of tourism at the island, the mining activity does not face any threats to its existence, as both parties follow the specified regulations and environmental restrictions necessary to allow dual economic growth.