José Peixoto, Alexis Gonzalez, Janki Bhimani
et al.
Programmable caching engines like CacheLib are widely used in production systems to support diverse workloads in multi-tenant environments. CacheLib's design focuses on performance, portability, and configurability, allowing applications to inherit caching improvements with minimal implementation effort. However, its behavior under dynamic and evolving workloads remains largely unexplored. This paper presents an empirical study of CacheLib with multi-tenant settings under dynamic and volatile environments. Our evaluation across multiple CacheLib configurations reveals several limitations that hinder its effectiveness under such environments, including rigid configurations, limited runtime adaptability, lack of quality-of-service support and coordination, which lead to suboptimal performance, inefficient memory usage, and tenant starvation. Based on these findings, we outline future research directions to improve the adaptability, fairness, and programmability of future caching engines.
The value of proof-of-work cryptocurrencies critically depends on miners having incentives to follow the protocol. However, the Bitcoin mining protocol proposed by Nakamoto (2008) and implemented in practice is well known not to constitute an equilibrium: Eyal and Sirer (2018) construct a profitable deviation called ``selfish mining'' which relies on strategically delaying disclosure of newly mined blocks rather than publishing them immediately. We propose inertial mining, a novel mining protocol. When miners follow inertial mining, they produce the outcome intended by Nakamoto, i.e., a single longest chain. But unlike the Bitcoin mining protocol, inertial mining constitutes an equilibrium (assuming no miner controls more than half of the mining power). Indeed, neither selfish mining nor any other deviation is profitable. Furthermore, inertial mining only changes miners' behavior in the event of off-path forks, and can be implemented in Bitcoin without any changes to its consensus mechanism or blockchain architecture.
Zoltán Lukács, Péter Benjamin Vidosits, Szabina Tomasek
et al.
The conventional determination of electrochemical parameters of corrosion systems, first of all the corrosion rate, is based on the Butler-Volmer equation. A simplified method, based on the Stern-Geary equation, is typically used in industrial corrosion monitoring systems (Linear Polarization Resistance, LPR). Both methods have multiple sources of uncertainties that make the assessment of the corrosion rate erroneous. These error sources are even more expressed in the case of the measurement of stainless steel corrosion. Recently two linearized transformations of the Butler-Volmer equation were proposed in order to provide more accurate alternatives for the assessment of the corrosion rate. In this work these models are tested for the assessment of the corrosion rate of stainless steel 316L in neutral and acidic chloride solutions. The new models provide results consistent with each other, with conventional linearized semilogarithmic extrapolation and with literature data, indicating that the proposed new methods are applicable for the assessment of corrosion rate of stainless steels.
The notorious corrosion resistance of carbon steel significantly limits its scope of application. In this study, a strategy involving electrodeposited nickel followed by annealing was proposed to improve the corrosion resistance of materials. The effects of annealing on the microstructure of nickel-plated steel were investigated by scanning electron microscopy (SEM) and energy-dispersive spectrometry (EDS). The results reveal annealing induces Fe-Ni alloy layer formation at the plating–substrate interface, with thicknesses ranging from 2.2 μm to 4.8 μm as the temperature increases from 650 °C to 800 °C and the duration extends from 1 min to 5 min. Additionally, a diffusion kinetics model based on Fick’s second law was established to describe the diffusion process of nickel atoms in the iron matrix. The diffusion coefficients of nickel atoms were determined to be in the range of 4.4 × 10−16 m<sup>2</sup>·s<sup>−1</sup> to 1.26 × 10−15 m<sup>2</sup>·s<sup>−1</sup> under the tested conditions. This model provides a theoretical framework for optimizing annealing treatments to enhance the performance of nickel-coated carbon steel components.
ObjectiveWith continuously increasing coal mining depth, the risks of hazards such as roof collapse and personnel entrapment caused by high in situ stress and mining-induced pressure have significantly intensified. Drilling emerges as a key means of rapidly establishing rescue passageways. However, the drilling process tends to face multiple challenges, including complex geological structures, unstable collapsed bodies, and low drilling efficiency. Methods This study aims to reveal the rock-breaking mechanisms during the drilling of collapsed bodies, optimize drilling parameters to ensure reliable borehole formation, and achieve efficient drilling of collapsed bodies. To this end, this study developed a large-scale experimental platform that integrated impact-rotary horizontal drilling and real-time measurement while drilling. This platform allowed for real-time acquisition and control of nine drilling parameters, including drilling speed, propulsive force, propulsive pressure, rotational pressure, and percussive pressure, meeting the requirements of full-process dynamic monitoring. Based on this platform, model experiments for simulating typical fabrics and structures of collapsed bodies were designed using M20, C30, and C50 materials, followed by the comparison of the variation patterns of drilling parameters under varying strengths and states. Results and Conclusions The strength and occurrence states of the drilled rock masses can be effectively characterized using parameters including the curve morphology of the drilling stroke, the abrupt change response of drilling speed, the transition interval and periodic fluctuations of propulsive force, and the fluctuating amplitude of rotational pressure. In combination with borehole morphology and fracturing patterns, this study proposed that the strategy characterized by low impact pressure, low propulsive pressure, and high rotational speed, which assists in reducing disturbance and improving drilling stability in roadway penetration sections. The experiments revealed the rock-breaking mechanism of graded responses based on rock block size, cascaded transformation of fracturing modes, and progressive and cyclic drilling within the collapsed bodies. Specifically, small loose fragments were prone to be disturbed and discharged. Medium-sized rock blocks tended to form temporary support structures together with loose fragments and were then fractured and involved in the cutting discharge circulation. In contrast, large, isolated rocks either entered the discharge circulation after partial fracturing or remained in place after forming penetration pathways due to their high integrity. This reflects the interactions between the drill system and rocks, as well as the dynamic structural evolution, during drilling. The results of this study provide theoretical support for rock identification, parameter optimization, and safety control during the drilling of underground collapsed bodies, thus contributing to scientific decision-making and precise operations during emergency rescue.
Bioactive glasses 58S in the SiO2-CaO-P2O5 combination have been successfully employed as bone-filling materials in orthopedic and dental surgery, but their low mechanical strength limits their utilization in load-bearing positions. Various methods for the synthesis of bio-glass and its composites have been explored to present, including traditional melt quench, flame synthesis, sol-gel, and microwave irradiation. Various groups have looked at bio glass synthesis. These synthesis methods are relatively successful, however they have a high preparation cost. In order to lower the expense of preparation, Bio-active glass compositions will indeed be Nano sized, which will improve bonding ability with lowering production costs. In this present study, Nano-sized silicon dioxide powder was manufactured through ball milling in order to achieve the best possible combination between mechanical and biological attributes. The key benefit is that by machining, the density is also lowered. It provides extra assistance in mixing and connecting with other particles. The nano silicon dioxide is produced by ball milling at varied rotational speeds like 100 rpm, 200 rpm, and 300 rpm with two different durations as 2 and 4 h. The six nanopowder samples were obtained and densities were analyzed with TEM, XRD and SEM for each samples. This study proved, based on the aforementioned observations, that the proposed ball milling method for nanoparticle production is capable which would be significantly beneficial in the manufacturing of bioactive glasses.
Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distribution of the latent space. The proposed method aims to minimize the Fisher divergence between the true and the modeled data distribution in order to train an autoencoder that can capture the normal patterns of aero-engine behavior. The Fisher divergence is robust to model uncertainty, meaning it can handle noisy or incomplete data. The Fisher autoencoder also has well-defined latent space regions, which makes it more generalizable and regularized for various types of aero-engines as well as facilitates diagnostic purposes. The proposed approach improves the accuracy of anomaly detection and reduces false alarms. Simulations using the CMAPSS dataset demonstrate the model's efficacy in achieving timely anomaly detection, even in the case of an unbalanced dataset.
Composition design of high-entropy carbides is a topic of great scientific interest for the hot-end parts in the aerospace field. A novel theoretical method through an inverse composition design route, i.e. initially ensuring the oxide scale with excellent anti-ablation stability, is proposed to improve the ablation resistance of the high-entropy carbide coatings. In this work, the (Hf0.36Zr0.24Ti0.1Sc0.1Y0.1La0.1)C1-δ (HEC) coatings were prepared by the inverse design concept and verified by the ablation resistance experiment. The linear ablation rate of the HEC coatings is −1.45 μm/s, only 4.78 % of the pristine HfC coatings after the oxyacetylene ablation at 4.18 MW/m2. The HEC possesses higher toughness with a higher Pugh's ratio of 1.55 in comparison with HfC (1.30). The in-situ formed dense (Hf0.36Zr0.24Ti0.1Sc0.1Y0.1La0.1)O2-δ oxide scale during ablation benefits to improve the anti-ablation performance attributed to its high structural adaptability with a lattice constant change not exceeding 0.19 % at 2000–2300 °C. The current investigation demonstrates the effectiveness of the inverse theoretical design, providing a novel optimization approach for ablation protection of high-entropy carbide coatings.
The rupture of super-thick key strata is a crucial factor in triggering mine dynamic disasters. Investigating the impact of their non-uniform thickness on mining-induced stress environments and rupture dynamic loads, and further revealing the mechanism of dynamic and static loading on rock burst pressure, is the theoretical foundation for predicting rock burst risks and disaster prevention and control. This paper investigates the non-uniformly thick “bow-shaped” super-thick key strata in the Binchang mining area of Shaanxi. Through comprehensive theoretical analysis and numerical simulation, it analyzes the mechanical principle of abnormal stress concentration in the coal rock mass beneath the “bow-shaped” super-thick key strata, clarifies the influence of the “bow-shaped” morphology on the rupture characteristics of the super-thick key strata and reveals the mechanism of dynamic and static load superposition in the area beneath the “bow-shaped” formation, leading to rock bursts. Based on the study above, a method for predicting the disaster risks caused by the rupture of “bow-shaped” super-thick key strata is proposed. The results show that in the convex area under the “bow-shaped” formation of the super-thick key strata, the high stress is exceptionally concentrated, increasing the coal rock body stress by an additional 22.1 MPa, with an increase rate of up to 56%, which is the fundamental reason for the high static load formation in the underlying coal-rock body. At the same time, the principal stress in the concave area of the “bow-shaped” formation concentrates and undergoes significant deformation, increasing the risk of strong dynamic loads due to rupture. Under the combined action of dynamic and static loads in the non-uniformly thick “bow-shaped” super-thick key strata, the rock bursts are likely to occur in the pillar and roadway areas. The proposed method for predicting the disaster risks due to the rupture of “bow-shaped” super-thick key strata effectively guides the disaster prevention and control in high-risk mining areas prone to rock bursts. Additionally, the distributed fiber optic field measurement results validate the intrinsic connection between the rupture of super-thick key strata and the generation of dynamic loads.
Vincenzo Lunetto, Dario Basile, Valentino Razza
et al.
This study investigates active filling friction stir repair (AF-FSR) and passive filling friction stir repair (PF-FSR) for repairing AISI 304 stainless steel sheets, focusing on addressing the challenges posed by high melting point metals. The research involved repairing overlapping 2 mm thick sheets with pre-drilled holes of 2, 4, and 6 mm diameters, simulating broken components. Various process parameters, including rotational speed, dwell time, and the use of metal fillers, were tested to evaluate their impact on repair quality. The results demonstrated that PF-FSR provided superior mechanical strength to AF-FSR, particularly for larger pre-hole diameters. PF-FSR achieved higher shear tension strength due to better defect filling and reduced void formation, with shear tension strengths exceeding 25 kN for larger pre-holes and lower variability in strength measurements. AF-FSR was less effective for larger pre-holes, resulting in significant voids and reduced strength. Microstructural analysis revealed that PF-FSR facilitated more efficient material mixing and filling, minimizing unrepaired regions. However, excessive rotational speeds and dwell times in PF-FSR led to deformation and flash formation, highlighting the need for optimal parameter selection. Although further studies are needed, this study confirms the feasibility of FSR techniques for repairing small defects in AISI 304 steels, offering valuable insights for sustainable manufacturing practices in industries such as automotive and aerospace, where efficient and reliable repair methods are critical.
We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular \textit{the ability to utilize information at arbitrary input locations}, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training~(e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the \textit{quantity} and \textit{quality} of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize \textit{domain balance} and \textit{length upsampling}. Concretely, we find that naively upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance, and that a balanced domain mixture is important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.
How to simultaneously improve the ionic conductivity and mechanical properties is a key problem facing currently used anion-exchange membranes (AEMs). Here, biomass-based bacterial cellulose (BC) was used as a porous template to make TiO2 localized mineralization around the surface of BC nanofibers, and constructed a TiO2-coated BC porous substrate (TiO2@BC) with hierarchical structure. Then, the coated TiO2 nanoparticles was densely grafted by quaternary ammonium groups to obtain high ionic conduction ability. After filling with a polymeric ionic liquid (PIL) with high ion exchange capacity through in situ polymerization and crosslinking, the obtained novel PIL-filled AEM possessed ultrahigh ionic conductivity of 100.5 mS cm−1 at 80 °C, which was 72.1% higher than that of the PIL-filled pure BC membrane (only 58.4 mS cm−1). Moreover, by the aid of the synergistic reinforcement effect of TiO2@BC, the membrane exhibited extremely high dry strength of 95.3 MPa and satisfactory wet strength and flexibility. When at fully hydrate state, the membrane with the size of 1 × 4 cm (width × length) can hang a bottle containing 1000 g of water. The single cell equipped with this membrane output the peak power density of 40.2 mW cm−2, showing its great potential as a high-performance biomass-based AEM.
High automation, high speed and high efficiency are the advantages of laser welding metallurgical products. It is a great significance to study the quality of laser welding. In the paper, the input current and pulse width of laser are used as variables to improve the tensile strength of welding samples. Firstly, the effect of current and pulse width on tensile strength is obtained through experiments. Then the fitting formula and cureve of experimental data are obtained by orthogonal regression method. Finally, the prediction and optimization of tensile strength are carried out, and the error of the results is less than 12,5 %, indicating that they have a certain guiding role.
Géssica da Silva Nicolau, Ricardo Pondé Weber, Sergio Neves Monteiro
et al.
The recycling of polymeric materials is a sustainable alternative to reduce the use of virgin raw materials and avoid unnecessary disposal. A good example is its reprocessing through the solution blow-spinning (SBS) processing, which might produce nanofibers for coatings. This reprocessing technique has several advantages, such as lower production costs, a relatively simple system, and excellent yield. In this short communication, for the first time, three different solution concentrations, 5, 10 and 15 g/L of recycled polycarbonate (RPC) were solubilized with tetrahydrofuran solvent to produce nanofibers using the SBS technique. The nanofiber morphology was investigated by scanning electron microscopy (SEM) analysis. SEM micrographs showed that at a concentration of 5 g/L RPC it was not possible to produce nanofibers. Although nanofibers were formed at 10 g/L RPC, the appearance of undesirable droplets was inevitable. The 15 g/L was found as the ideal lower concentration of RPC for production of nanofiber without droplets.
Gioelkis Espinosa-Batista, Dayanis Alcántara-Borges, Dailén González-Martín
et al.
Se estableció el comportamiento microestructural en barra de acero estructural del tipo ASTM A615 grado 60 G. La unión de las barras se realizó a tope con bisel a 60o con electrodos E 6010 y E 7018, se determinó el comportamiento microestructural, el por ciento de fases y la dureza en las zonas de la unión soldada. Con el E 6010 se obtiene estructuras del tipo ferrita acicular y de perlita, con un 38,3 % y 21,3 % respectivamente, con el E 7018 la ferrita acicular es de 31,5 % y de 10,33 % de martensita, con ninguno de los aportes se determinó la presencia de grietas. En el barrido de dureza realizado en las diferentes zonas, para el primero desde 230 HV incrementa hasta 383 HV en la ZAC, con un descenso de 285 HV en la ZF, para el segundo en la ZAC es de 224 HV y de 190 HV en la ZF.
Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.