Magnesia-stabilised zirconia solid electrolyte assisted electrochemical investigation of iron ions in the SiO2-CaO-MgO-Al2O3 molten slag at 1723 K

Production of metallic iron through molten oxide electrolysis using inert electrodes is an alternative route for fast ironmaking without CO2 emissions. The fact that many inorganic oxides melt at ultrahigh temperatures (>1500 K) challenges conventional electro-analytical techniques used in aqueous, organic and molten salt electrolytes. However, in order to design a feasible and effective electrolytic process, it is necessary to best understand the electrochemical properties of iron ions in molten oxide electrolytes. In this work, a magnesia-stabilised zirconia (MSZ) tube with a closed end was used to construct an integrated three-electrode cell with the “MSZ | Pt | O2 (air)” assembly functioning as the solid electrolyte, the reference electrode and also the counter electrode. Electrochemical reduction of iron ions was systematically investigated on an iridium (Ir) wire working electrode in the SiO2-CaO-MgO-Al2O3 molten slag at 1723 K by cyclic voltammetry (CV), square wave voltammetry (SWV), chronopotentiometry (CP) and potentiostatic electrolysis (PE). The results show that the electro-reduction of the Fe2+ ion to Fe on the Ir electrode in the molten slag follows a single two-electron transfer step, and the rate of the process is diffusion controlled. The peak current on the obtained CVs is proportional to the concentration of the Fe2+ ion in the molten slag and the square root of scan rate. The diffusion coefficient of Fe2+ ions in the molten slag containing 5 wt% FeO at 1723 K was derived to be (3.43 ± 0.06)×10-6 cm2 s-1 from CP analysis. However, a couple of following processes, i.e. alloy formation on the Ir electrode surface and interdiffusion were found to affect the kinetics of iron deposition. An ECC mechanism is proposed to account for the CV observations. The findings from this work confirm that zirconia-based solid electrolytes can play an important role in electrochemical fundamental research in high temperature molten slag electrolytes

Comparison of cold hardiness in the leaves of various grape cultivars based on photochemical reflectance index

We compared cold hardiness in the leaves of 12 grape cultivars, including two wild species, two Vitis vinifera × V. labrusca, three HPDs (interspecific crosses) and five V. vinifera L. cultivars using PRI-T curves and analysis of variance (ANOVA). The photochemical reflectance index (PRI) of most grape leaves decreased linearly when frozen progressively in darkness, but these patterns varied. The PRI of diploid cultivars and blanc cultivars at the standard temperature (PRI 4°C) remained relatively steady during exposure to successively lower temperatures (0, -2, -4 and -6°C) compared with polyploid and noir cultivars, representing a boundary dividing grape cultivars into frost tolerant and vulnerable. According to this principle, which was tested by ANOVA, the cold hardiness of the four species was ranked (from high to low) as V. vinifera L. > HPD = wild species > V. vinifera × V. labrusca. The cold hardiness of the 12 cultivars was divided into three classes: Resistant: PRI increased markedly compared with the control, RPRI((PRI-PRIcontrol)/PRIcontrol) > 0 (p < 0.05), indicating high freezing tolerance and wide temperature adaption; Tolerant: PRI changed little compared with the control (p < 0.05), RPRI trended to 0, indicating relative stability when exposed to a short period of freezing temperatures; Vulnerable: PRI decreased dramatically compared with the control, RPRI < 0 (p < 0.05), indicating that photosynthesis was inhibited or damaged due to freezing. We also observed seasonal differences in the cold hardiness of the cultivars; grape leaves were more vulnerable to cold in fall than in spring. This study provides a practical method for estimating cold hardiness in grape

Solving the Batch Stochastic Bin Packing Problem in Cloud: A Chance-constrained Optimization Approach

This paper investigates a critical resource allocation problem in the first party cloud: scheduling containers to machines. There are tens of services and each service runs a set of homogeneous containers with dynamic resource usage; containers of a service are scheduled daily in a batch fashion. This problem can be naturally formulated as Stochastic Bin Packing Problem (SBPP). However, traditional SBPP research often focuses on cases of empty machines, whose objective, i.e., to minimize the number of used machines, is not well-defined for the more common reality with nonempty machines. This paper aims to close this gap. First, we define a new objective metric, Used Capacity at Confidence (UCaC), which measures the maximum used resources at a probability and is proved to be consistent for both empty and nonempty machines, and reformulate the SBPP under chance constraints. Second, by modeling the container resource usage distribution in a generative approach, we reveal that UCaC can be approximated with Gaussian, which is verified by trace data of real-world applications. Third, we propose an exact solver by solving the equivalent cutting stock variant as well as two heuristics-based solvers -- UCaC best fit, bi-level heuristics. We experimentally evaluate these solvers on both synthetic datasets and real application traces, demonstrating our methodology's advantage over traditional SBPP optimal solver minimizing the number of used machines, with a low rate of resource violations.Comment: To appear in SIGKDD 2022 as Research Track pape

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation

Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as ``thoughts''. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.Comment: 17 pages, 5 figure

Xpert: Empowering Incident Management with Query Recommendations via Large Language Models

Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend on crafting domain-specific language (DSL) queries to analyze telemetry data. However, writing these queries can be challenging and time-consuming. This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft. The findings obtained underscore the importance and viability of KQL queries recommendation to enhance incident management. Building upon these valuable insights, we introduce Xpert, an end-to-end machine learning framework that automates KQL recommendation process. By leveraging historical incident data and large language models, Xpert generates customized KQL queries tailored to new incidents. Furthermore, Xpert incorporates a novel performance metric called Xcore, enabling a thorough evaluation of query quality from three comprehensive perspectives. We conduct extensive evaluations of Xpert, demonstrating its effectiveness in offline settings. Notably, we deploy Xpert in the real production environment of a large-scale incident management system in Microsoft, validating its efficiency in supporting incident management. To the best of our knowledge, this paper represents the first empirical study of its kind, and Xpert stands as a pioneering DSL query recommendation framework designed for incident management.Comment: Accepted as a reseach paper at ICSE 202

Yttria-stabilized zirconia aided electrochemical investigation on ferric ions in mixed molten calcium and sodium chlorides

Electrolytic reduction of dissolved iron oxide to metal iron in molten salts with an inert anode is an alternative short route for steelmaking without CO2 emissions. A novel and simple integrated yttria-stabilized zirconia (YSZ) cell was constructed from a YSZ tube with a closed end. The YSZ tube played multiple functions, including the container for the molten salts, the solid electrolyte membrane in the O2−|YSZ|Pt|O2 (air) reference electrode (RE), and the solid electrolyte membrane between the working and counter electrodes (WE and CE). Electrochemical behavior of ferric ions (Fe3+) that were formed by dissolution of 0.5 wt pct Fe2O3 in the molten CaCl2-NaCl eutectic mixture was investigated on a Pt WE at 1273 K by various electrochemical techniques including cyclic voltammetry, linear scan voltammetry, square wave voltammetry, chronopotentiometry, chronoamperometry, and potentiostatic electrolysis. Analysis of the mechanism of electrode reactions was further assisted by scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction. Some electrochemical parameters were obtained, including the number of exchanged electrons and the diffusion coefficient of ferric ions in the mixed molten salts. The results from various electrochemical techniques are in good agreement with each other, and show that the electrochemical reduction of Fe3+ to Fe in the molten salt mixture could be a single three-electron transfer step and diffusion-controlled reaction that was also possibly reversible. This work may form the foundation for extraction of iron and alloys from molten salts and also provide a stable O2−|YSZ|Pt|O2 (air) RE with wide applicability for investigation on electrochemical properties of other electroactive metal oxides in molten salts

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints

COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy

We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk. Traditional supervised prediction or forecasting models are ineffective in learning adaptive policies whereas standard online optimization or reinforcement learning is difficult to deploy on real systems. Offline methods such as imitation learning (IL) are ideal since we can directly leverage historical resource usage telemetry. But, the underlying aleatoric uncertainty in such telemetry is a critical bottleneck. We solve this with our proposed novel chance-constrained imitation learning framework, which ensures implicit safety against uncertainty in a principled manner via a combination of stochastic (chance) constraints on resource congestion risk and ensemble value functions. This leads to substantial ($\approx 3-4\times$) improvement in resource efficiency and safety in many oversubscription scenarios, including resource management in cloud services.Comment: 9 pages, 4 figure

Risk-aware Adaptive Virtual CPU Oversubscription in Microsoft Cloud via Prototypical Human-in-the-loop Imitation Learning

Oversubscription is a prevalent practice in cloud services where the system offers more virtual resources, such as virtual cores in virtual machines, to users or applications than its available physical capacity for reducing revenue loss due to unused/redundant capacity. While oversubscription can potentially lead to significant enhancement in efficient resource utilization, the caveat is that it comes with the risks of overloading and introducing jitter at the level of physical nodes if all the co-located virtual machines have high utilization. Thus suitable oversubscription policies which maximize utilization while mitigating risks are paramount for cost-effective seamless cloud experiences. Most cloud platforms presently rely on static heuristics-driven decisions about oversubscription activation and limits, which either leads to overloading or stranded resources. Designing an intelligent oversubscription policy that can adapt to resource utilization patterns and jointly optimizes benefits and risks is, largely, an unsolved problem. We address this challenge with our proposed novel HuMan-in-the-loop Protoypical Imitation Learning (ProtoHAIL) framework that exploits approximate symmetries in utilization patterns to learn suitable policies. Also, our human-in-the-loop (knowledge-infused) training allows for learning safer policies that are robust to noise and sparsity. Our empirical investigations on real data show orders of magnitude reduction in risk and significant increase in benefits (saving stranded cores) in Microsoft cloud platform for 1st party (internal services).Comment: 9 pages, 3 figure

A robust methodology to subclassify pseudokinases based on their nucleotide-binding properties

Protein kinase-like domains that lack conserved residues known to catalyse phosphoryl transfer, termed pseudokinases, have emerged as important signalling domains across all kingdoms of life. Although predicted to function principally as catalysis-independent protein-interaction modules, several pseudokinase domains have been attributed unexpected catalytic functions, often amid controversy. We established a thermal-shift assay as a benchmark technique to define the nucleotide-binding properties of kinase-like domains. Unlike in vitro kinase assays, this assay is insensitive to the presence of minor quantities of contaminating kinases that may otherwise lead to incorrect attribution of catalytic functions to pseudokinases. We demonstrated the utility of this method by classifying 31 diverse pseudokinase domains into four groups: devoid of detectable nucleotide or cation binding; cation-independent nucleotide binding; cation binding; and nucleotide binding enhanced by cations. Whereas nine pseudokinases bound ATP in a divalent cation-dependent manner, over half of those examined did not detectably bind nucleotides, illustrating that pseudokinase domains predominantly function as non-catalytic protein-interaction modules within signalling networks and that only a small subset is potentially catalytically active. We propose that henceforth the thermal-shift assay be adopted as the standard technique for establishing the nucleotide-binding and catalytic potential of kinase-like domains