Sebastian Levin, Jendrik-Alexander Tröger, Hagen Lukas-Kock
et al.
While laser powder bed fusion enables rapid and resource-efficient production, challenges such as microstructural defects, porosity, and unfavourable residual stresses compromise the durability of components under dynamic loading. Thus, we investigated methods to enhance fatigue life of AlSi10Mg produced by laser powder bed fusion. To do so, we explore the effects of manual polishing, heat treatment, and deep rolling on the mechanical properties and fatigue performance of AlSi10Mg.Specimens were fabricated and divided into five groups: as-built, as-built with manual polishing, heat-treated and manually polished, as-built with deep rolling, and heat-treated with deep rolling. These groups underwent surface roughness measurements, residual stress analysis, hardness testing, and microscopy. The primary evaluation of fatigue performance was conducted using a rotating bending test rig under a load ratio of R = −1, following the high-cycle fatigue string-of-pearl method.The fatigue tests revealed significant differences among the treatment groups. The as-built specimens exhibited the lowest fatigue life, with cracks initiating from surface defects. While polishing and heat treatment provided moderate improvements, specimens treated with deep rolling exhibited the highest bearable stress amplitudes and the flattest S–N curves, indicating a significant improvement in fatigue resistance. The slope of the S–N curve in this condition is 7.8 times flatter compared to the untreated as-built condition. At a defined number of load cycles of 1E+06, the bearable stress in the “as-built + deep rolling” condition reaches 251 MPa, which is ∼8.5 times the stress amplitude tolerated in the untreated as-built condition. Interestingly, combining heat treatment with deep rolling resulted in a decrease in performance compared to deep rolling alone.Our results indicate that surface treatment is critical for improving the fatigue life of additively manufactured AlSi10Mg components. It has turned out that deep rolling is an effective and economical method, as it reduces surface roughness and induces beneficial compressive residual stresses that counteract crack initiation. Furthermore, deep rolling eliminates the need for subsequent heat treatment, which may even be counter-productive, thus saving both time and energy costs. Our results help to exploit the potential of laser powder bed fusion of AlSi10Mg by combining near-net-shape production with effective surface enhancement.
Stope stability is a critical factor in underground mining, directly influencing safety, productivity, and overall mining efficiency. Traditional stope design methods often employ uniform stope lengths, disregarding geotechnical variability and thereby increasing the risk of instability or suboptimal dimensions. This study introduces an automated stability analysis approach that iteratively evaluates multiple stope dimension scenarios based on the Modified Stability Number (N’) to identify the optimum stable configuration. By conducting detailed stability assessments for each stope wall, the method provides a more accurate representation of geotechnical conditions compared to the conventional methods with uniform stope length. The case study demonstrates that this approach effectively reduces the total number of stopes while maintaining geotechnical stability, in contrast to conventional methods where 14%–40% of stopes exhibit instability. Furthermore, the optimization method achieves a balanced outcome between dilution control and operational efficiency resulted in lower stope production cost. The optimized configurations generated by the proposed method deliver the lowest total production cost, with estimated savings of approximately USD 1.6–2.4 million compared to conventional designs. These findings confirm that the optimization framework not only enhances geotechnical stability but also provides a demonstrable economic advantage, underscoring the importance of integrating geotechnical variability into stope design.
Aurio Erdi, Syahreza S. Angkasa, Eko Suwarno
et al.
The Early–Middle Miocene Ombilin Formation in the intermontane Ombilin Basin, Central Sumatra, records a critical interval of paleoenvironmental change shaped by regional tectonics and global sea-level fluctuations. Despite its significance, the formation’s sedimentological architecture, provenance, and sea-level history have been poorly constrained. This study integrates detailed outcrop-based sedimentary logging and petrographic analysis to address these gaps. Ten lithofacies are identified and grouped into three facies associations: open marine, tidal-influenced channels, and intertidal mixed tidal flats deposits. Their vertical distributions show normal and inverse transitions. The normal transition indicates a shift from sublittoral to tidally dominated environments, interpreted as a result of seaward progradation driven by increased sediment supply from the uplifting Barisan Mountains and short-term global sea-level falls (e.g. SEA34–SEA46). The inverse transition, however, indicates a transgression occurred in the Ombilin Basin. Petrographic analysis in the Ombilin Formation show arkosic sandstones sourced primarily from a magmatic arc, consistent with contemporaneous volcanism west of the basin, with minor reworking of marine units and older clastic materials. Stratigraphic evidence suggests an initial regression, likely driven by the uplifting of Barisan Mountain and global sea-level fall, which occurred in the Ombilin Basin during Early Miocene. This event was followed by deposition aligned with global transgressive trends. These findings provide new insight into the sedimentary evolution, provenance, and tectono-eustatic interplay controlling Ombilin Basin development during the Early–Middle Miocene.
Armin Ariamajd, Raquel López-Ríos de Castro, Andrea Volkamer
The increasing importance of Computational Science and Engineering has highlighted the need for high-quality scientific software. However, research software development is often hindered by limited funding, time, staffing, and technical resources. To address these challenges, we introduce PyPackIT, a cloud-based automation tool designed to streamline research software engineering in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) and Open Science principles. PyPackIT is a user-friendly, ready-to-use software that enables scientists to focus on the scientific aspects of their projects while automating repetitive tasks and enforcing best practices throughout the software development life cycle. Using modern Continuous software engineering and DevOps methodologies, PyPackIT offers a robust project infrastructure including a build-ready Python package skeleton, a fully operational documentation and test suite, and a control center for dynamic project management and customization. PyPackIT integrates seamlessly with GitHub's version control system, issue tracker, and pull-based model to establish a fully-automated software development workflow. Exploiting GitHub Actions, PyPackIT provides a cloud-native Agile development environment using containerization, Configuration-as-Code, and Continuous Integration, Deployment, Testing, Refactoring, and Maintenance pipelines. PyPackIT is an open-source software suite that seamlessly integrates with both new and existing projects via a public GitHub repository template at https://github.com/repodynamics/pypackit.
Leonhard Applis, Yuntong Zhang, Shanchao Liang
et al.
The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities including maintenance and evolution of a project. In this context, the concept of LLM agents has gained traction, which utilize LLMs as reasoning engines to invoke external tools autonomously. But is an LLM agent the same as an AI software engineer? In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent. Unlike existing work which builds specialized agents for specific software tasks such as testing, debugging, and repair, our goal is to build a unified agent which can orchestrate and handle multiple capabilities. This gives the agent the promise of handling complex scenarios in software development such as fixing an incomplete patch, adding new features, or taking over code written by others. We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans. To evaluate the efficacy of USEagent, we build a Unified Software Engineering bench (USEbench) comprising of myriad tasks such as coding, testing, and patching. USEbench is a judicious mixture of tasks from existing benchmarks such as SWE-bench, SWT-bench, and REPOCOD. In an evaluation on USEbench consisting of 1,271 repository-level software engineering tasks, USEagent shows improved efficacy compared to existing general agents such as OpenHands CodeActAgent. There exist gaps in the capabilities of USEagent for certain coding tasks, which provides hints on further developing the AI Software Engineer of the future.
The inspiratory rise time in mechanical ventilation refers to the rate at which airway pressure reaches the set target during inspiration. When appropriately adjusted, it enhances patient-ventilator synchrony and improves comfort by increasing the tolerance of ventilator support. However, an excessively rapid rise time may result in elevated airway pressures and abrupt gas delivery, potentially contributing to lung injury or increased patient effort. An inverse correlation exists between rise time and the mechanical work of breathing, such that a shorter rise time is associated with a disproportionately increased in respiratory workload. As the intensity of respiratory effort and duration of mechanical ventilation increase, so does the risk of ventilator-associated lung injury (VALI). It is therefore imperative that ventilator manufacturers incorporate adjustable rise time parameters and corresponding time intervals into their devices to allow precise, individualized ventilator settings that minimize the risk of iatrogenic lung injury.
Neeraphat Kunbuala, Kasama Srirussamee, Chinnawich Phamornnak
et al.
Titanium (Ti) and its alloys are widely used for biomedical applications due to their excellent mechanical properties and biocompatibility. However, the selection of an appropriate manufacturing process is critical to ensuring the optimal performance of Ti-based implants. This study investigates the effects of two fabrication methods –vacuum arc remelting (VAR) and spark plasma sintering (SPS) – on the microstructure and corrosion behavior of commercially pure titanium (Cp-Ti). VAR-Ti ingots were fabricated using arc-melting with multiple remelting cycles, whereas SPS-Ti specimens were sintered from Ti powders under pressure and pulsed current in a high-vacuum environment. Both specimens were subsequently heat-treated at 800 °C and furnace cooled. Microstructural characterization revealed coarser grains and porosity in VAR-Ti, while SPS-Ti showed refined, uniform α-phase structures. Electrochemical tests, including OCP, polarization, EIS, and ICP-MS, indicated slightly enhanced corrosion resistance in SPS-Ti, attributed to its defect-free microstructure. XPS analysis confirmed TiO2 surface formation on both samples. Additionally, both materials exhibited high ductility and excellent biocompatibility, with cell viability exceeding ISO 10993-5 thresholds. These findings highlight the advantage of SPS in producing defect-minimized Cp-Ti with improved corrosion behavior for biomedical applications.
Mining fairness in blockchain refers to equality between the computational resources invested in mining and the block rewards received. There exists a dilemma wherein increasing the transaction processing capacity of a blockchain compromises mining fairness, thereby undermining its decentralization. This dilemma remains unresolved despite methods such as the greedy heaviest observed subtree (GHOST) protocol, indicating that mining fairness is an inherent bottleneck in the transaction processing capacity of the blockchain system. However, despite its significance, existing analyses neglect the impact of blockchain forks, resulting in imprecise evaluations and limited insights. To address this issue, we propose a method for calculating mining fairness that explicitly captures the influence of forks. First, we approximate a complex blockchain network using a simple mathematical model, assuming that no more than two blocks are generated per round. Within this model, we quantitatively determine local mining fairness and derive several measures of global mining fairness based on local mining fairness. Subsequently, we validated by blockchain network simulations that our calculation method computes mining fairness in networks much more accurately than existing methods. The proposed method facilitates a rigorous evaluation of trade-offs between scalability and decentralization by offering a clear, quantitative framework for measuring and comparing reward distribution among miners. Consequently, it is expected to provide valuable insights for future mining fairness research and the design of next-generation blockchain systems.
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.
With the widespread application of efficient pattern mining algorithms, sequential patterns that allow gap constraints have become a valuable tool to discover knowledge from biological data such as DNA and protein sequences. Among all kinds of gap-constrained mining, non-overlapping sequence mining can mine interesting patterns and satisfy the anti-monotonic property (the Apriori property). However, existing algorithms do not search for targeted sequential patterns, resulting in unnecessary and redundant pattern generation. Targeted pattern mining can not only mine patterns that are more interesting to users but also reduce the unnecessary redundant sequence generated, which can greatly avoid irrelevant computation. In this paper, we define and formalize the problem of targeted non-overlapping sequential pattern mining and propose an algorithm named TALENT (TArgeted mining of sequentiaL pattErN with consTraints). Two search methods including breadth-first and depth-first searching are designed to troubleshoot the generation of patterns. Furthermore, several pruning strategies to reduce the reading of sequences and items in the data and terminate redundant pattern extensions are presented. Finally, we select a series of datasets with different characteristics and conduct extensive experiments to compare the TALENT algorithm with the existing algorithms for mining non-overlapping sequential patterns. The experimental results demonstrate that the proposed targeted mining algorithm, TALENT, has excellent mining efficiency and can deal efficiently with many different query settings.
As the most important auxiliary transportation equipment in coal mines, mining electric locomotives are mostly operated manually at present. However, due to the complex and ever-changing coal mine environment, electric locomotive safety accidents occur frequently these years. A mining electric locomotive control method that can adapt to different complex mining environments is needed. Reinforcement Learning (RL) is concerned with how artificial agents ought to take actions in an environment so as to maximize reward, which can help achieve automatic control of mining electric locomotive. In this paper, we present how to apply RL to the autonomous control of mining electric locomotives. To achieve more precise control, we further propose an improved epsilon-greedy (IEG) algorithm which can better balance the exploration and exploitation. To verify the effectiveness of this method, a co-simulation platform for autonomous control of mining electric locomotives is built which can complete closed-loop simulation of the vehicles. The simulation results show that this method ensures the locomotives following the front vehicle safely and responding promptly in the event of sudden obstacles on the road when the vehicle in complex and uncertain coal mine environments.
Zhiling Zheng, Oufan Zhang, Christian Borgs
et al.
We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines.
High-strength press-hardened steels (PHSs) are characterized by a martensite structure of high strength and adequate ductility. Strengthening PHSs with high C contents is usually accompanied by a loss of ductility and toughness. To overcome this inherent strength–ductility trade-off dilemma, we propose a novel strategy to achieve outstanding mechanical performance by introducing stable high-density Cr-rich cementite, which refines the martensite structure via Zenner pinning effect in a novel 2000 MPa grade PHS. Specifically, a high tensile strength of 2085 MPa with an appreciable total elongation of 10.1% is achieved in the novel PHS, which is far superior to commercial 22MnB5 steel (1519 MPa and 10%). The strength increase is predominantly induced by a high density of dislocations and cementite in the novel PHS, while the good ductility is attributed to the refined martensite structure coordinating plastic deformation and the enhanced work-hardening ability and dislocation storage capability mediated by massive cementite. The work can lay foundations for designing high-strength PHSs with good ductility.
Ramin Salamat Mamakani, Amin Azhari, Lohrasb Faramarzi
et al.
The effect of mechanical properties of upstream tailings dams is investigated under seismic loads. For this, the finite-difference numerical method under the Finn-Byrne nonlinear elastoplastic constitutive model was implemented. Variations of elastic modulus and Poisson’s ratio in the typical range of tailings dam material were investigated in the phenomenon of liquefaction, horizontal displacement, and subsidence. The results showed that with increasing the elastic modulus of the dam body from 10 to 50 MPa, the maximum horizontal displacement, subsidence, and liquefaction coefficient in the dam body have increased 2.3, 3.5, and 2 times, respectively. Moreover, by increasing the Poisson’s ratio from 0.25 to 0.4, the maximum horizontal displacement, subsidence, and liquefaction coefficient in the dam body have raised 2.4, 2.3, and 1.75, respectively. The Poisson’s ratio of tailings had a significant effect on the liquefaction of the dam body. In which, increasing the Poisson’s ratio from 0.25 to 0.4, the maximum liquefaction coefficients were increased 1.75 times. Ultimately, it is concluded that despite the displacement which is not affected by the variation of tailings dam elastic modulus, the liquefaction coefficient is doubled by its variation, which may cause a serious threat to the stability of the dam.
This paper studies a fundamental problem regarding the security of blockchain PoW consensus on how the existence of multiple misbehaving miners influences the profitability of selfish mining. Each selfish miner (or attacker interchangeably) maintains a private chain and makes it public opportunistically for acquiring more rewards incommensurate to his Hash power. We first establish a general Markov chain model to characterize the state transition of public and private chains for Basic Selfish Mining (BSM), and derive the stationary profitable threshold of Hash power in closed-form. It reduces from 25% for a single attacker to below 21.48% for two symmetric attackers theoretically, and further reduces to around 10% with eight symmetric attackers experimentally. We next explore the profitable threshold when one of the attackers performs strategic mining based on Partially Observable Markov Decision Process (POMDP) that only half of the attributes pertinent to a mining state are observable to him. An online algorithm is presented to compute the nearly optimal policy efficiently despite the large state space and high dimensional belief space. The strategic attacker mines selfishly and more agilely than BSM attacker when his Hash power is relatively high, and mines honestly otherwise, thus leading to a much lower profitable threshold. Last, we formulate a simple model of absolute mining revenue that yields an interesting observation: selfish mining is never profitable at the first difficulty adjustment period, but replying on the reimbursement of stationary selfish mining gains in the future periods. The delay till being profitable of an attacker increases with the decrease of his Hash power, making blockchain miners more cautious on performing selfish mining.
The solidification characteristics of 70 steel at the stage of the superheat elimination and the liquid–solid phase transformation were analyzed at cooling rates from 10 to 150 °C/min based on a high-temperature confocal scanning laser microscope (HT-CSLM). Secondary dendrite arm spacing (SDAS) and fractal dimension (<i>D</i>) were used to quantitatively describe the local compactness and overall self-similar complexity of the solidification morphology. It was found that the cooling rate had a very important influence on the local and overall morphology characteristics of solidification structures. At the superheat elimination stage, the cooling rate affected the morphology of the microstructure through the dynamic structural fluctuation between the generation and disappearance of atomic clusters in the molten steel. At the liquid–solid phase transformation stage, the cooling rate affected the local morphology of the microstructure by affecting the solute diffusion rate between dendrite arms, while it affected the overall morphology by changing the concentration undercooling at the front of all solidified interfaces. The presented results show that adjusting the cooling system at the superheat elimination stage can also be an important way to control the solidified morphology of different alloys.
The fracture performance of axisymmetric notched samples taken from pearlitic steels with different levels of cold-drawing is studied. To this end, a real manufacture chain was stopped in the course of the process (on-site in the factory), and samples of all intermediate stages were extracted from the initial hot-rolled bar (not cold-drawn at all) to the final commercial product (prestressing steel wire). Thus, the drawing intensity or straining level (represented by the yield strength) is treated as the key variable to elucidate the consequences of manufacturing on the posterior fracture issues. On the basis of a materials science approach, the clearly anisotropic fracture behavior of heavily drawn steels (exhibiting deflection in the fracture surface) is rationalized on the basis of the markedly oriented pearlitic microstructure of the cold-drawn steel that influences the operative micromechanism of fracture. In addition, a finite element analysis of the stress distribution at the fracture instant allows the computation of the cleavage annular stress required to produce anisotropic fracture behavior and thus crack path deflection associated with mixed-mode cracking. Results show that such a stress is the variable governing initiation and propagation of anisotropic fracture by cleavage (a specially oriented and enlarged cleavage fracture) appearing along the wire axis direction in the case of sharply-notched samples of heavily drawn pearlitic steels.