Ivica Kladarić, Stjepan Golubić, Danko Ćorić
et al.
The article examines the effect of different types of two-layer nanostructured coatings (cVIc and nACVIc) deposited on three types of steel substrates, 45S20, C45E, and 42CrMo4, to determine the resistance to adhesive wear of the substrate/coating system. The samples underwent different heat treatments, including normalising, quenching, and quenching and tempering, followed by PVD (physical vapour deposition) treatment at temperatures of 450 °C (cVIc) and 460 °C (nACVIc). The thickness of the cVIc layers for all three steels ranged from 0.9 to 3.4 μm, while the thickness of the nACVIc layers on all steels was slightly greater, ranging from 1.9 to 3.1 μm. Tribological tests were conducted using the pin-on-disc method, and the results were statistically analysed. Results indicate that steel grade, heat treatment, and PVD coating significantly affect adhesive wear resistance, with the type of PVD coating showing the strongest influence. For all three steels, quenched and uncoated samples exhibited the lowest adhesion wear index values. Normalised and quenched with or without tempering steels coated with cVIc layer exhibit higher resistance to adhesive wear due to better adhesion of the layer compared to the nACVIc coating.
Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.
The metallurgical industry raw material industrial sector, providing metal materials for all sectors of the national economy, is also the material basis of economic development, but its dependence on natural resources, consumption intensity, and has a greater impact on the quality of the environment, so the development of metallurgical industry ecology has become an important topic. The study examines Sinosteels practices in areas such as low-carbon mining, energy-efficient engineering, employee environmental training, and international cooperation, highlighting its efforts to promote industrial ecology. Key initiatives include the adoption of advanced technologies for waste heat recovery, hydrogen-rich carbon cycle processes, and digital solutions to optimize resource utilization and minimize emissions. Furthermore, this paper discusses the broader development direction of Chinas metallurgical industry, emphasizing low-carbon innovation, digital transformation, talent cultivation, and enhanced pollution management. By addressing systemic inefficiencies and fostering green practices, Sinosteel exemplifies the potential for metallurgical enterprises to achieve sustainable growth. The findings underscore the importance of integrating technological innovation, policy reforms, and corporate responsibility to ensure the industrys green transformation. This research offers valuable insights for global metallurgical enterprises aiming to balance economic growth with environmental preservation.
Practical mining aspects should be considered when conducting pillar designs for bord-and-pillar layouts. The current methodology for pillar design will result in increasing pillar sizes with depth. This affects the extraction ratio and will result in onerous ventilation requirements when cutting large pillars. A holistic approach, including all mining engineering requirements, is required to ensure that the rock engineering designs are optimized to ensure efficient mining operations and sustainable production. Bord widths should not only be a function of the rock mass ratings, but should also be selected to fit the specifications of the mechanized equipment. The use of a 'squat pillar' formula for hard rock is discussed in the paper and the formula based on the exponents of the Hedley and Grant pillar formula, is explored. The effect of abutments and geological losses on average pillar stress is also explored. These factors must be considered when designing layouts at increasing depths.
عملیات آتشباری یکی از مهمترین و پرهزینهترین عملیات در فرایند معدنکاری است. آتشباری درواقع، ابتدای سیستم خردایش سنگ در معدن است. همچنین آتشباری تولیدکننده سنگ موردنیاز مراحل بعدی خردایش است. ابعاد محصول تولیدی آتشباری، نقش بسزایی در قابلیت خردایش سنگشکنی و آسیاکنی خواهد داشت، به این دلیل، جهت تولید محصول با ابعاد مناسب ابتدا لازم است شناخت کافی از شرایط فیزیکی، مکانیکی و شیمیایی بلوکهای معدنی حاصل شود. در این پژوهش با مطالعه 26 بلوک سنگآهن معدن چغارت بعد از عملیات آتشباری، نسبت به برداشت ویژگیهای فیزیکی، مکانیکی و شیمیایی بلوکهای معدن چغارت اقدام شد. در ادامه، بعد از عملیات آتشباری با تصویربرداری از سطح بلوک منفجرشده، اقدام به آنالیز تصویری ابعاد محصول آتشباری گردید. در مرحله بعد، از سطح بلوک آتشباری شده نمونههای سنگیای جهت انجام آزمایشات ژئومکانیکی و شیمیایی اخذ شد. بعد از انجام آزمایشات، ارتباط بین ویژگیهای فیزیکی، شیمیایی و مکانیکی سنگهای مورد آزمایش با ابعاد محصول آتشباری (D50) موردبررسی قرار گرفت. ویژگیهای موردبررسی در این مطالعه شامل: مقاومت فشاری تکمحوره، مدول الاستیسیته، ضریب پواسون، مقاومت کششی غیرمستقیم، چسبندگی، زاویه اصطکاک داخلی، درصد سیلیس، آهن و اکسید آهن موجود در بلوک آتشباری میباشند. در پایان، با استفاده از روش تحلیل آماری رگرسیون خطی تک متغیره و چند متغیره، معادلاتی برای تخمین ابعاد محصول تولیدی (D50) حاصل از انفجار با استفاده از ویژگیهای فیزیکی، مکانیکی و شیمیایی توده سنگهای محصول آتشباری با ضریب تعیین (48/92) درصد برای D50 پیشبینی گردید.
According to the engineering geological characteristics and production technology level of Qianyingzi Coal Mine, the stability control technology of surrounding rock of the cutting roof and pressure relief roadway is proposed. For the advanced section, lagging section and stable section of the mining roadway, technical measures of advanced reinforcement support, side wall protection and lagging temporary support were proposed. Through the real-time monitoring system, the stress of anchor cable and surrounding rock deformation of goaf roadway are monitored, the deformation characteristics of roadway and stope surrounding rock are studied, and the law of mine pressure development in self-formed roadway without coal pillar is revealed by the comparison and analysis with the existing goaf roadway. The results show that the deformation rate of surrounding rock reaches the maximum at the position of 170 m away from the working face, and the deformation of surrounding rock is the most intense at this stage, and then tends to be gentle, and the deformation of surrounding rock reaches the stable stage when the working face lags 220 m. The application effect of the pre-cracking blasting cutting roof for gob-side entry retaining in the W3233 working face of Qianyingzi Coal Mine has reached the expected goal.
The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito
et al.
Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.
A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.
Riccardo Graziosi, Massimiliano Ronzani, Andrei Buliga
et al.
In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of the selected processes. However, current DL generative models are limited in their ability to adapt the learned distributions to generate data samples based on specific conditions or attributes. This limitation is particularly significant because the ability to control the type of generated data can be beneficial in various contexts, enabling a focus on specific behaviours, exploration of infrequent patterns, or simulation of alternative 'what-if' scenarios. In this work, we address this challenge by introducing a conditional model for process data generation based on a conditional variational autoencoder (CVAE). Conditional models offer control over the generation process by tuning input conditional variables, enabling more targeted and controlled data generation. Unlike other domains, CVAE for process mining faces specific challenges due to the multiperspective nature of the data and the need to adhere to control-flow rules while ensuring data variability. Specifically, we focus on generating process executions conditioned on control flow and temporal features of the trace, allowing us to produce traces for specific, identified sub-processes. The generated traces are then evaluated using common metrics for generative model assessment, along with additional metrics to evaluate the quality of the conditional generation