Proteomic Analysis of Bacillus thuringiensis Strain 4.0718 at Different Growth Phases

The growth process of Bacillus thuringiensis Bt4.0718 strain was studied using proteomic technologies. The proteins of Bt whole cells at three phases—middle vegetative, early sporulation, and late sporulation—were extracted with lysis buffer, followed with separation by 2-DE and identified by MALDI-TOF/TOF MS. Bioactive factors such as insecticidal crystal proteins (ICPs) including Cry1Ac(3), Cry2Aa, and BTRX28, immune inhibitor (InhA), and InhA precursor were identified. InhA started to express at the middle vegetative phase, suggesting its contribution to the survival of Bt in the host body. At the early sporulation phase, ICPs started their expression. CotJC, OppA, ORF1, and SpoIVA related to the formation of crystals and spores were identified, the expression characteristics of which ensured the stable formation of crystals and spores. This study provides an important foundation for further exploration of the stable expression of ICPs, the smooth formation of crystals, and the construction of recombinant strains

Universal Self-Consistency for Large Language Model Generation

Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled from large language models (LLMs). However, self-consistency relies on the answer extraction process to aggregate multiple solutions, which is not applicable to free-form answers. In this work, we propose Universal Self-Consistency (USC), which leverages LLMs themselves to select the most consistent answer among multiple candidates. We evaluate USC on a variety of benchmarks, including mathematical reasoning, code generation, long-context summarization, and open-ended question answering. On open-ended generation tasks where the original self-consistency method is not applicable, USC effectively utilizes multiple samples and improves the performance. For mathematical reasoning, USC matches the standard self-consistency performance without requiring the answer formats to be similar. Finally, without access to execution results, USC also matches the execution-based voting performance on code generation

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Mechanism and Properties of UO2–Graphene Composite Fuel Prepared by In Situ Synthesis

A nucleation method based on a composite of uranium dioxide (UO2) and graphene is presented by in situ synthesis, and the relevant mechanism and fuel properties are investigated. UO2–graphene composite fuel powders containing graphene volume (2%, 4%, 6%, and 8%) were prepared using a nucleation method through the reactive deposition of uranyl nitrate and aqueous ammonia on graphene by controlling the reaction parameters. The composite fuel pellets were prepared using spark plasma sintering (SPS). The results showed that the uniformity of UO2–graphene powder prepared by in situ synthesis reached up to 96.39%. An analysis on the relevant phase structure showed that only UO2 and graphene existed in the sintered pellets at 1723 K, graphene and UO2 were not destroyed during the reaction, and the pellet densities for the in-situ synthesis were 95.56%TD, 95.32%TD, 95.08%TD, and 94.76%TD for graphene contents of 2%, 4%, 6%, and 8%, respectively. The thermal conductivities of pellets at 293 K increased by 12.27%, 20.13%, 27.47%, and 34.13%, and by 18.36%, 35.00%, 47.07%, and 58.93% at 1273 K for 2%, 4%, 6%, and 8% graphene contents, respectively. The performance of graphene in the fuel was superior at high temperatures, which overcame shortcomings due to the low thermal conductivity of UO2 at high temperatures. SEM results showed that the grain sizes of the pellets prepared by synthesis in situ were 10–30 μm, and there was no obvious pore at the grain boundary because the grains were closely bound. The graphene was uniformly coated by UO2, and the thermal conductivity of the pellets improved upon the formation of a bridging heat conduction network

Mechanism and Properties of UO₂–Graphene Composite Fuel Prepared by In Situ Synthesis

A nucleation method based on a composite of uranium dioxide (UO2) and graphene is presented by in situ synthesis, and the relevant mechanism and fuel properties are investigated. UO2–graphene composite fuel powders containing graphene volume (2%, 4%, 6%, and 8%) were prepared using a nucleation method through the reactive deposition of uranyl nitrate and aqueous ammonia on graphene by controlling the reaction parameters. The composite fuel pellets were prepared using spark plasma sintering (SPS). The results showed that the uniformity of UO2–graphene powder prepared by in situ synthesis reached up to 96.39%. An analysis on the relevant phase structure showed that only UO2 and graphene existed in the sintered pellets at 1723 K, graphene and UO2 were not destroyed during the reaction, and the pellet densities for the in-situ synthesis were 95.56%TD, 95.32%TD, 95.08%TD, and 94.76%TD for graphene contents of 2%, 4%, 6%, and 8%, respectively. The thermal conductivities of pellets at 293 K increased by 12.27%, 20.13%, 27.47%, and 34.13%, and by 18.36%, 35.00%, 47.07%, and 58.93% at 1273 K for 2%, 4%, 6%, and 8% graphene contents, respectively. The performance of graphene in the fuel was superior at high temperatures, which overcame shortcomings due to the low thermal conductivity of UO2 at high temperatures. SEM results showed that the grain sizes of the pellets prepared by synthesis in situ were 10–30 μm, and there was no obvious pore at the grain boundary because the grains were closely bound. The graphene was uniformly coated by UO2, and the thermal conductivity of the pellets improved upon the formation of a bridging heat conduction network

Optimal Efficiency Analysis of MW-Level Direct-Drive Wind Generation System

Abstract Given the MW-level direct-drive wind generation system, the optimal efficiency of generators and converters is analyzed. The loss model of the generators including iron loss and the neutral point clamping three-level converters is established. Based on the loss model and the voltage, current limit, the generators and converters, optimal efficiency analytic expressions of direct current are obtained by analyzing the relationships of direct current and loss under given torque, and then optimal efficiency solution of the wind system is got. Finally, Simulation and experiment results on a 2MW "back to back" PWM converter prototype indicate: in the fixed torque and speed condition, such optimal efficiency control is more efficient than the traditional control strategy not only on the generator or converter efficiency but also on the wind generation system

Effect of the Fabrication Technique on Compressive Properties of Cu-PTFE Composites

In this paper, the effect of manufacturing methods on the compressive properties of Cu-PTFE (polytetrafluoroethylene) composites was investigated. Two types of specimens were prepared through different manufacturing methods (extrusion forming and hot-press sintering). The specimens were tested using an electrohydraulic press and split-Hopkinson pressure bars for quasi-static loading and dynamic impact, respectively. The specific fracture processes were recorded by using a high-speed camera, and the failure microstructures of the specimens were analysed by SEM. According to the results, hot-press sintered specimens have consistently higher strength and toughness under dynamic compression than the extruded specimens, while the mechanical properties of hot-press sintered specimens are inferior to those of extruded specimens under quasi-static compression. The failure of extruded specimens is primarily caused by the elastic mismatch between the PTFE matrix and Cu particles, as well as the polymerisation of plastic pores, which leads to particle pullout. However, the cracks in the hot-press sintered specimens were caused by the shear deformation and interface sliding of the PTFE matrix, which led to matrix tearing

Investigate on a Simplified Multi-Port Interline DC Power Flow Controller and Its Control Strategy

A DC power flow controller (DCPFC) can help to facilitate power flow routing in the multi-terminal high-voltage direct current (HVDC) transmission system. Realizing its multi-port output can effectively improve the device regulate range and capability. Based on analysis of the traditional multi-port interline DC power flow controller (MI-DCPFC), this paper presents a switches reduced topology of MI-DCPFC. In addition, for solving the problem of coupling of the port-output voltage of the traditional MI-DCPFC, a novel control strategy based on carrier phase shifting pulse width modulation (CPS-PWM) is proposed. It implements the decoupling of the port-output voltage of MI-DCPFC, which can ensure completely independent tracking of the power flow regulating commands for different controlled lines. Moreover, key relationships between the system state variables are also analyzed and detailed in this study. Finally, the performance of the proposed controller and control strategy are confirmed with the simulation and experiment studies under different conditions