Miki Kajihara, Hiroto Suzuki, Tatsuya Amamiya
et al.
Molybdenum disulfide (MoS2) is widely used as a solid lubricant, owing to its layered crystalline structure. However, for tribological performance, the stable bonding of solid lubricants to metallic substrates remains a critical issue. In conventional methods, solid lubricants dispersed in grease are sprayed and deposited onto the substrate surface. However, the lubricant layer may delaminate and disappear during frictional contact. Here, we employ laser-induced particle impact testing (LIPIT), which ejects micro-sized particles at high velocities via laser ablation. Particle impact with high velocity induces severe plastic deformation, forming deep micro-dimples that can retain lubricants and improve tribological performance. We propose a two-step LIPIT: the first step deposits and bonds MoS2 particles and the second step generates micro-dimple structures through the impact of hard particles. The treated surface is characterized using energy-dispersive X-ray spectroscopy, focused ion-beam analysis, and transmission electron microscopy, revealing that the MoS2 is strongly bonded at the crater center (lowest depth). Frictional tests with ball-on-disk contact sliding indicate that micro-dimples with adhered MoS2 created by LIPIT function as lubricant reservoirs and significantly improve tribological performance. The results show that our proposed LIPIT-treated surfaces exhibit both a reduced friction coefficient and enhanced resistance to seizure.
This study investigated the microstructural evolution of the γ′′, γ′, and δ phases during creep for alloy 718. Creep tests on two-step aged samples were conducted under different stresses and temperatures ranging from 600 to 700 °C. Analysis of precipitates was performed on two-step aged and crept samples using scanning electron microscopy and transmission electron microscopy. The creep rupture lives were decreased with increasing stresses and temperatures and showed a linear correlation between experimental data and Larson-Miller parameter curve predictions. The γ′′ phase was formed as a disc shape in the grain interior and had an orientation relationship of (001)γ′′//{001}γ and [100]γ′′//<100>γ with γ matrix. The length of the γ′′ phases increased with increasing temperatures and creep exposure time. The activation energy for γ′′ lengthening was 323 kJ/mol, similar to that for lattice diffusion of Nb in Ni. It was found that the formation mechanisms of δ phase were different from temperatures. The δ phase was formed in grain interiors and grain boundaries and had a blocky shape during a sub-δ solvus annealing process. On the other hand, the δ phase was formed as a plate shape and had an orientation relationship of (010)δ//(111¯)γ and [102]δ//[011]γ with γ matrix at creep temperatures. The growth rates of plate δ phase were faster than blocky δ phase at all creep temperatures.
Non-metallic minerals such as crystalline graphite, fluorite, high-purity quartz and boron are widely used in strategic emerging industries such as high-end equipment manufacturing, biomedicine, new materials, new energy and new generation information technology. This paper comprehensively summarizes the characteristics of strategic non-metallic mineral resources in Qinghai, and completes the analysis and evaluation of their availability by mineral types in combination with the beneficiation and purification test results of graphite, quartz, talc and garnet, and the processing technical performance of fluorite, boron ore and barite. According to the demand and development trend of lithium battery, new energy, new materials and other industries in the province for various mineral raw materials, it is considered that the five minerals of crystalline graphite, fluorite, high-purity quartz, boron ore and barite have the most development and utilization prospects. The research results will provide a basis for the development of mineral resources and the layout of related industries in Qinghai.
From its first adoption in the late 80s, qualitative research has slowly but steadily made a name for itself in what was, and perhaps still is, the predominantly quantitative software engineering (SE) research landscape. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we reflect on the state of qualitative SE research with a focus group of experts. Among other things, we discuss why qualitative SE research is important, how it evolved over time, common impediments faced while practicing it today, and what the future of qualitative SE research might look like. Joining the conversation are Rashina Hoda (Monash University, Australia), Carolyn Seaman (University of Maryland, United States), and Klaas Stol (University College Cork, Ireland). The content of this paper is a faithful account of our conversation from October 25, 2025, which we moderated and edited for our column.
Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.
Rock mass may be exposed to high temperature and cooled by water in fire, geothermal resource exploitation and deep underground engineering, which affects the safety of these engineering. In this study, cracked straight through Brazilian disc (CSTBD) specimen was used to study the fracture properties of granite treated with different water-cooling time from 25 °C to 800 °C. A camera was used to record the fracture process of the specimen, the evolution of strain at the specimen surface was obtained by using digital image correlation technology, and the morphological characteristics of fracture surface were observed by using scanning electron microscopy. The results show that water-cooling time has some effects on fracture properties of granite, but its effect is less than the temperature. With the increase of water-cooling time, the mode I fracture toughness decreases first, then increases and finally decreases at the same temperature, and the fracture toughness ranges from 0.419 MPa m1/2 to 0.567 MPa m1/2 at 400 °C. The crack tip opening displacement (CTOD), crack propagation path and fracture surface are related to the energy released during the specimen failure. When the temperature is 25 °C or 200 °C, the energy released is large and the fracture surface is relatively flat with irregular sharp edges, the main cracks are straight lines. At 600 °C, the main crack is zigzag, the CTOD and the maximum principal strain in order can reach 0.143 mm and 0.731 %. This study can help to understand the effect of water-cooling time on engineering rock mass.
When fabricating magnetic components for micro-electro-mechanical systems, the intrinsic material properties as well as the magnetic anisotropy of the deposited material and the fabricated devices must be adjusted to the application. This work focuses on electrochemically deposited cobalt phosphorus layers from a sulfate-based electrolyte to be used as hard magnetic scales in position measurement systems. In preliminary tests round discs (5 mm diameter, 20 or 10 μm height) were fabricated, showing the influence of three process parameters (current density, pH-value and temperature) on the chemical composition and the magnetic behavior of the deposited cobalt phosphorus. Hereby deposition parameters to produce hard magnetic cobalt phosphorus are defined. Deposits with up to 6 wt.-% phosphorus show hard magnetic behavior, whereas deposits with more than 6 wt.-% show soft magnetic behavior. This is correlated with a transition from crystalline to amorphous structures. In further investigations arrays of micro scales (40 μm width, 10 μm height) were fabricated to show the influence of direct current and pulsed current on the properties of the deposits. Pulsed current increases coercive field strength by about 40 %, resulting in maximum values of 23 kA/m (in-plane) and 14 kA/m (out-of-plane). Remanence increases by about 30 %, resulting in maximum values of 0.40 T (in-plane) and 0.2 T (out-of-plane). Pulse plating changes preferred orientation from (110) to (100) and slightly increases grain size by about 20 %, resulting in an average grain size of 25 nm.
The materials charged into a converter comprise molten iron and scrap steel. Adjusting the ratio by increasing scrap steel and decreasing molten iron is a steelmaking raw material strategy designed specifically for China’s unique circumstances, with the goal of lowering carbon emissions. To maintain the converter tapping temperature, scrap must be preheated to provide additional heat. Current scrap preheating predominantly utilizes horizontal tunnel furnaces, resulting in high energy consumption and low efficiency. To address these issues, a three-stage shaft furnace for scrap preheating was designed, and Fluent software was used to compare and study the preheating efficiency of the new three-stage furnace against the traditional horizontal furnace under various operational conditions. Initially, a three-dimensional transient multi-field coupling model was developed for two scrap preheating scenarios, examining the effects of both furnaces on scrap surface and core temperatures across varying preheating durations and gas velocities. Simulation results indicate that, under identical gas heat consumption conditions, scrap achieves markedly higher final temperatures in the shaft furnace compared to the horizontal furnace, with scrap surface and core temperatures increasing notably with extended preheating times and higher gas velocities, albeit with a gradual decrease in heating rate as the scrap temperature rises. At a gas velocity of 9 m/s and a preheating time of 600 s, the shaft furnace achieves the highest waste heat utilization rate for scrap, with scrap averaging 325 °C higher than in the horizontal furnace, absorbing an additional 202 MJ of heat per ton. In the horizontal preheating furnace, scrap steel exhibits a heat absorption efficiency of 35%, whereas in the vertical furnace, this efficiency increases notably to 63%. In the vertical furnace, the waste heat recovery rate of scrap steel reaches 57%.
Conducting laboratory tests on natural rocks has significant value for the scientific and practical advancement of various types of rock engineering. The investigation of a rock-like materials that is similar in characteristics and structure to natural rock is key to the further advancement of laboratory rock experiments. The rapid development of sand powder 3D printing technology in recent years has provided a solution for fabricating rock-like materials. However, the low strength and elastic modulus of sand powder 3D-printed materials limit their application in simulating natural rocks. Therefore, this paper proposes a method to enhance the mechanical properties of sand powder 3D-printed materials utilizing glass fiber reinforcement by incorporating glass fiber into sand powder 3D-printed rock-like specimens. The mechanical properties and microstructural variations of the specimens with varying glass fiber contents are investigated utilizing mechanical testing, acoustic emission, scanning electron microscopy, etc. The results indicate that (1) it is feasible to utilize glass fiber to enhance the mechanical properties of sand powder 3D-printed materials; (2) The mechanical properties of sand powder 3D printing materials are significantly enhanced by the addition of glass fiber; and (3) the mechanisms controlling the reinforcement effect of glass fiber addition on sand powder 3D-printed specimens are determined by analyzing the microstructural characteristics of the specimens. This study improves the applicability of sand powder 3D-printed materials in simulating natural rock, thereby promoting the further application of 3D printing in the field of rock mechanics.
Mehrdad Agha Mohammad Ali Kermani, Hamid Reza Seddighi, Mehrdad Maghsoudi
In the rapidly evolving field of business process management, there is a growing need for analytical tools that can transform complex data into actionable insights. This research introduces a novel approach by integrating Large Language Models (LLMs), such as ChatGPT, into process mining tools, making process analytics more accessible to a wider audience. The study aims to investigate how ChatGPT enhances analytical capabilities, improves user experience, increases accessibility, and optimizes the architectural frameworks of process mining tools. The key innovation of this research lies in developing a tailored prompt engineering strategy for each process mining submodule, ensuring that the AI-generated outputs are accurate and relevant to the context. The integration architecture follows an Extract, Transform, Load (ETL) process, which includes various process mining engine modules and utilizes zero-shot and optimized prompt engineering techniques. ChatGPT is connected via APIs and receives structured outputs from the process mining modules, enabling conversational interactions. To validate the effectiveness of this approach, the researchers used data from 17 companies that employ BehfaLab's Process Mining Tool. The results showed significant improvements in user experience, with an expert panel rating 72% of the results as "Good". This research contributes to the advancement of business process analysis methodologies by combining process mining with artificial intelligence. Future research directions include further optimization of prompt engineering, exploration of integration with other AI technologies, and assessment of scalability across various business environments. This study paves the way for continuous innovation at the intersection of process mining and artificial intelligence, promising to revolutionize the way businesses analyze and optimize their processes.
Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi
et al.
Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.