The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.
Phosphate beneficiation process tailings are fine particles that greatly impact the efficiency of metallurgical recoveries, water resource management, and tailings storage facility performance. Current conventional approaches to sustainability in the mining sector often involve qualitative commitments rather than quantitative operational performance indicators. This research has proposed an integrated Key Performance Indicator (KPI) framework for phosphate beneficiation process tailings, which connects metallurgical recoveries, water resource management, and tailings storage facility capacity optimization. The proposed integrated Key Performance Indicator framework is applicable to three operational performance domains. In the metallurgical recovery’s domain, KPI-1 is proposed for measuring the improvement in metallurgical recoveries through the reduction of tailings grade. The reduction of the grade of phosphate tailings from the flotation process from 6.5% to 4.5% P2O5 is estimated to improve point flotation recoveries by 3.64%, which is equivalent to extending mine life by 3-5%. In the water resource domain, KPI-2 is proposed for measuring the improvement in thickener hydraulic performance. Using the proposed thickener hydraulic performance equation, it is shown that increasing tailings underflow density from 1.40 t/m3 to 1.46 t/m3 is equivalent to increasing water recoveries by 18 m3/h, which is equivalent to approximately 0.158 million m3/year. In the tailings storage facility domain, the proposed integrated Key Performance Indicator framework connects the proposed operational performance indicators through tailings storage facility capacity optimization. In the proposed integrated Key Performance Indicator framework, a governance-ready Key Performance Indicator dashboard is proposed for translating engineering performance outcomes into quantitative sustainability performance indicators for ESG reporting.
Abstract The paper provides an assessment of the impact of the uranium industry on the state of the environment and the population based on the development and implementation of engineering solutions and measures to reduce gamma radiation and radon concentration in the premises, the implementation of environmental and social rehabilitation of the contaminated territory, residential buildings, objects social sphere and the population living in the zone of influence of uranium facilities. The specified measures are aimed at improving the environmental safety of the environment and the population in order to minimize the negative impact of radiation and other factors. Used radiometric methods (measurement of the exposure dose and intensity of γ-radiation, measurement of the radioactivity of γ- and β-radiation, measurement of the power of the exposure dose of γ-radiation, determination of the power of the absorbed dose of γ-radiation in air, analysis of natural radionuclides (PRN); statistical and mathematical research methods using an integrated system approach Radioecological studies included: measurement of the exposure dose rate (PED) of γ-radiation by the network 100 x 10 m (walking survey) in the area of 40 km2, soil sampling for radium and uranium content, determination of radon concentration in residential premises specialists of various bodies of state power and local self-government in solving the above-mentioned tasks, for the regions located in the zone impact of mining and beneficiation plants for mining and primary processing of uranium ores. It was established that exceeding the normative level of the equivalent equilibrium concentration of radon of 50 Bq/m3 in individual rooms is caused by its release from the underground space and input channels of external heat and water supply networks, internal channels of distribution of heat networks. It has been proven that filling the heat network channel from the outside of the building with a layer of clay to a depth of 1 m and sealing it, sealing the entry of heat and water networks through the foundation of the building and concreting the pit reduce the volumetric activity of radon in the room by 5-6 times. The so far rare experience of increasing radiation and social protection of the population of the city of Zhovti Vody, Ukraine, which has been living in the zone of influence of uranium industry facilities for more than 70 years, is presented. It is proposed to continue research and provide funds (at the expense of enterprises, local and central state authorities) for conducting scientific substantiation and developing preventive measures to minimize the negative effects on human health from the action of heavy metals and radiation factors, taking into account the peculiarities of their combined impact on the population and workers of the uranium mining and mining and metallurgical industry.
Karmina Miteva, Slavcho Aleksovski, Jelena Stanojevic
et al.
The meat sector is one of the main sources of organic waste in the food processing industry. Various materials such as bones, fats, teeth, hair, skins, bacon fringes, and tallow are examples of the waste that remains. This type of waste has the potential to contaminate the environment and endanger human health. The ability to convert waste into energy could lead to new alternative fuels and specific adsorbent materials, suggesting enhanced resource management and sustainability. Several thermal treatments for destroying these wastes through combustion, incineration, gasification, or pyrolysis could serve as interesting alternatives. This study employed an innovative waste recovery strategy to explore the potential of using diverse meat waste components, specifically fat and bone, to produce sustainable energy fuels for the metallurgy and cement industries etc. Specifically, this research focused on employing pyrolysis as a thermal process to elevate the energy value of meat waste. Processes of pyrolysis are the thermal decomposition of waste, typically at 400–800 °C, in the absence of oxygen, that produces a carbon-rich solid residue (biochar), liquid (bio-oil), and gaseous products (syngas, which are not condensable gases) with high energy value. Furthermore, the quality and distribution of the pyrolysis products mostly depend on a few experimental parameters: reaction temperature, heating rate, residence time, reactor type, and raw material used. The fundamental objectives of this study were to establish the optimal pretreatment approach and pyrolysis conditions and then investigate their effect on yields, qualities of pyrolysis products and its utilisation
Fluorocarbon surfactants could change coal structure and improve wettability characteristics, thus, it plays a significant role in coal dust prevention and control. Composite dust suppressant based on fluorocarbon surfactant (FS-31) enhancement was developed, and the surface tension and contact angle characteristics were analyzed. Based on the infrared spectrum (FTIR) test and peak fitting, the changes of hydrophobic and hydrophilic functional groups of coal were quantitatively analyzed. Meanwhile, particle agglomeration behavior and characteristic particle size were studied by combining Malvin particle size experiment and scanning electron microscope test. Results show that the surface tension of binary surfactant compound solution is approximately 30 mN/m. The settling time of some solutions could be as long as 12 hours. Under the enhancement effect of fluorocarbon surfactant, the contact angle of coal becomes smaller and the wettability becomes better. The surface tension of 0.1% FS-31 solution and 0.05% APG, 0.05% sophoricoside solution is as low as 18.04 mN/m, and the settling time of pulverized coal is as low as 74 seconds. With the addition of fluorocarbon surfactant, the hydrophilic functional groups increase. The hydrophilic functional groups of coal treated by 0.3% MES, 0.3% APG and 0.1% FS-31 combined solution is 2.69% and 1.37% more than those of coal treated by MES and APG combined solution and raw coal, respectively. In the same crushing conditions, there are more large coal particles and larger characteristic particle size. The proportion of respirable dust decreases by 53.74%. Therefore, the addition of fluorocarbon surfactant is realistically meaningful to improve prevention and control effect of coal dust.
Retail analytics has become a transformative force, leveraging data-driven insights to optimize operations, personalize customer experiences, forecast demand, and enhance supply chain efficiency. This study provides a comprehensive bibliometric analysis of 563 documents indexed in Scopus, profiling the evolution of retail analytics over the past ten years. Key findings include 131 emerging topics clustered into 13 core trends. The analysis highlights the growing application of artificial intelligence, machine learning, and big data to drive decision-making, improve profitability, and enhance competitiveness in the retail industry. This paper addresses critical questions of "what," "where," "when," and "who" in retail analytics research, identifying areas of innovation and future growth, especially in predictive analytics, customer insights, and business operations optimization.
Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.
Andrea Maldonado, Christian M. M. Frey, Sai Anirudh Aryasomayajula
et al.
Process mining aims to extract and analyze insights from event logs, yet algorithm metric results vary widely depending on structural event log characteristics. Existing work often evaluates algorithms on a fixed set of real-world event logs but lacks a systematic analysis of how event log characteristics impact algorithms individually. Moreover, since event logs are generated from processes, where characteristics co-occur, we focus on associational rather than causal effects to assess how strong the overlapping individual characteristic affects evaluation metrics without assuming isolated causal effects, a factor often neglected by prior work. We introduce SHAining, the first approach to quantify the marginal contribution of varying event log characteristics to process mining algorithms' metrics. Using process discovery as a downstream task, we analyze over 22,000 event logs covering a wide span of characteristics to uncover which affect algorithms across metrics (e.g., fitness, precision, complexity) the most. Furthermore, we offer novel insights about how the value of event log characteristics correlates with their contributed impact, assessing the algorithm's robustness.
Hashini Gunatilake, John Grundy, Rashina Hoda
et al.
Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings provide insights into the expression of empathy in SE, the drivers behind empathetic practices, SE activities where empathy is perceived as useful or not, and the other factors that influence empathy. In addition, we offer practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.
Nathalia Sartori da Silva, Aila Cossovan Alves, Jaine Aparecida da Silva Pereira
et al.
In the present work, the corrosion properties and the surface chemistry of a graphene oxide-coated AZ91D alloy were investigated. The coatings were deposited on the substrate specimens by immersion in solutions with GO concentrations of 0.05% and 0.1% (<i>m</i>/<i>v</i>). An intermediate silane layer was firstly obtained to improve adhesion between the GO films and the AZ91D substrate. The electrochemical behavior of the coated specimens was assessed using electrochemical impedance spectroscopy and potentiodynamic polarization curves in 3.5 wt.% NaCl solution. The surface chemistry was assessed using X-ray photoelectron spectroscopy (XPS). The GO films consisted of a mixture of carbon-based bonds (C-C, C-OH, C=O, and O-C=O). The surface morphology of the coated specimens was examined using scanning electron microscopy. The results revealed that the compactness of the GO films was dependent on the deposition conditions. The corrosion resistance was affected by the surface morphology.
Fatigue indicator parameters (FIPs) can serve as a measure of fatigue crack initiation (FCI) in metals and alloys. FIPs are volume averaged over grains, bands, and sub bands in crystal plasticity (CP) simulation to investigate the influence of texture on FCI under low cycle fatigue. The equiaxed microstructure of Ti–6Al–4V was generated with three different textures: basal, basal/transverse and transverse. FIPs analysis shows that basal texture has the highest FCI resistance, basal/transverse texture has intermediate resistance, and transverse texture has the lowest resistance when tested along plate direction. All textures exhibit a lower FIP when deformed along rolling direction (RD) than that along transverse direction (TD). The interior of the alloys has a larger FCI resistance than the free surface. Further analysis shows a strong relationship between FIP distribution features and damage nucleation characteristics, with basal texture exhibiting the lowest and wider FIP distributions as a result of high resistance to damage and fatigue cracking; in contrast, the transverse texture exhibits intense and narrow FIP and damage nucleation along the grain boundaries (GBs), basal/transverse texture exhibits FIP and damage nucleation with mixed characteristics. The results can be used as a theatrical reference for the fatigue performance design of Ti alloy.
Abstract The post-peak characteristics of coal serve as a direct reflection of its failure process and are essential parameters for evaluating brittleness and bursting liability. Understanding the significant factors that influence post-peak characteristics can offer valuable insights for the prevention of coal bursts. In this study, the Synthetic Rock Mass method is employed to establish a numerical model, and the factors affecting coal post-peak characteristics are analyzed from four perspectives: coal matrix mechanical parameters, structural weak surface properties, height-to-width ratio, and loading rate. The research identifies four significant influencing factors: deformation modulus, density of discrete fracture networks, height-to-width ratio, and loading rate. The response and sensitivity of post-peak characteristics to single-factor and multi-factor interactions are assessed. The result suggested that feasible prevention and control measures for coal bursts can be formulated through four approaches: weakening the mechanical properties of coal pillars, increasing the number of structural weak surfaces in coal pillars, reducing the width of coal pillars, and optimizing mining and excavation speed. The efficacy of measures aimed at weakening the mechanical properties of coal is successfully demonstrated through a case study on coal burst prevention using large-diameter borehole drilling.
The study on the evolution characteristics of coal permeability is of great significance for rationally determining gas extraction parameters and increasing gas extraction efficiency. In order to study the effects of different coal stresses and gas pressures on coal permeability, the experiment on the deformation of stress-loaded coal and gas adsorption-diffusion was conducted, the segmented dynamic model of coal permeability was established, and the rationality of the established model was verified by the experimental results. The results shown that the gas adsorption amount and coal deformation both shown a Langmuir-type with the increasing gas pressure, and the dynamic diffusion coefficient of gas decreased exponentially with time. As the gas pressure decreased, the expansion deformation of the stress-loaded coal decreased, and the permeability increased gradually. The permeability and expansion deformation of stress-loaded coal gradually decreased with the increasing stress. The coal permeability shown a “V” shape with continuous stress loading, and it reached the smallest at the stress peak. The coupling between matrix and fracture deformation caused by gas adsorption, the dynamic diffusion of gas in matrix, and the mass exchange between matrix and fracture were all considered in the established permeability model of coal. The rationality of established segmented model of coal permeability was verified by the experimental results. The permeability model of coal based on elastic deformation can reflect the permeability evolution at the stage of elastic deformation. Within the experimental range, the absolute error between the experimental test and numerical simulation results of coal permeability was −0.135×10−15～0.296×10−15 m2, and the absolute error of volumetric strain of the coal due to gas seepage was −0.327×10−5～2.026×10−5. The permeability model considering plastic deformation can also reflect the permeability after stress peak. The error between experimental and numerical results was −0.435×10−15～0.997×10−15 m2.
Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco
et al.
Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.
In this practice paper, we propose a framework for integrating AI into disciplinary engineering courses and curricula. The use of AI within engineering is an emerging but growing area and the knowledge, skills, and abilities (KSAs) associated with it are novel and dynamic. This makes it challenging for faculty who are looking to incorporate AI within their courses to create a mental map of how to tackle this challenge. In this paper, we advance a role-based conception of competencies to assist disciplinary faculty with identifying and implementing AI competencies within engineering curricula. We draw on prior work related to AI literacy and competencies and on emerging research on the use of AI in engineering. To illustrate the use of the framework, we provide two exemplary cases. We discuss the challenges in implementing the framework and emphasize the need for an embedded approach where AI concerns are integrated across multiple courses throughout the degree program, especially for teaching responsible and ethical AI development and use.
Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber
et al.
Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.