Estimation of Extreme Quantiles for Functions of Dependent Random Variables

We propose a new method for estimating the extreme quantiles for a function of several dependent random variables. In contrast to the conventional approach based on extreme value theory, we do not impose the condition that the tail of the underlying distribution admits an approximate parametric form, and, furthermore, our estimation makes use of the full observed data. The proposed method is semiparametric as no parametric forms are assumed on all the marginal distributions. But we select appropriate bivariate copulas to model the joint dependence structure by taking the advantage of the recent development in constructing large dimensional vine copulas. Consequently a sample quantile resulted from a large bootstrap sample drawn from the fitted joint distribution is taken as the estimator for the extreme quantile. This estimator is proved to be consistent. The reliable and robust performance of the proposed method is further illustrated by simulation.Comment: 18 pages, 2 figure

Engineering Klebsiella sp. 601 multicopper oxidase enhances the catalytic efficiency towards phenolic substrates

<p>Abstract</p> <p>Background</p> <p>Structural comparison between bacterial CueO and fungal laccases has suggested that a charged residue Glu (E106) in CueO replaces the corresponding residue Phe in fungal laccases at the gate of the tunnel connecting type II copper to the protein surface and an extra α-helix (L351-G378) near the type I copper site covers the substrate binding pocket and might compromise the electron transfer from substrate to type I copper. To test this hypothesis, several mutants were made in <it>Klebsiella sp</it>. 601 multicopper oxidase, which is highly homologous to <it>E. coli </it>CueO with a similarity of 90% and an identity of 78%.</p> <p>Results</p> <p>The E106F mutant gave smaller <it>K</it>_{<it>m </it>}(2.4-7fold) and <it>k</it>_{<it>cat </it>}(1-4.4 fold) values for all three substrates DMP, ABTS and SGZ as compared with those for the wild-type enzyme. Its slightly larger <it>k</it>_<it>cat</it><it>/K</it>_{<it>m </it>}values for three substrates mainly come from the decreased <it>K</it>_<it>m</it>. Deleting α-helix (L351-G378) resulted in the formation of inactive inclusion body when the mutant ^Δα351-378 was expressed in <it>E. coli</it>. Another mutant α351-380M was then made <it>via </it>substitution of seven amino acid residues in the α-helix (L351-G378) region. The α351-380M mutant was active, and displayed a far-UV CD spectrum markedly different from that for wild-type enzyme. Kinetic studies showed the α351-380M mutant gave very low <it>K</it>_{<it>m </it>}values for DMP, ABTS and SGZ, 4.5-, 1.9- and 7-fold less than those for the wild type. In addition, <it>k</it>_<it>cat</it><it>/K</it>_{<it>m </it>}values were increased, 9.4-fold for DMP, similar for ABTS and 3-fold for SGZ.</p> <p>Conclusion</p> <p>The Glu residue at position 106 appears not to be the only factor affecting the copper binding, and it may also play a role in maintaining enzyme conformation. The α-helix (L351-G378) may not only block access to the type I copper site but also play a role in substrate specificities of bacterial MCOs. The α351-380M mutant catalyzing oxidation of the phenolic substrate DMP effectively would be very useful in green chemistry.</p

Adapting LLM Agents Through Communication

Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Through iterative exploration and PPO training, LTC empowers the agent to assimilate short-term experiences into long-term memory. To optimize agent interactions for task-specific learning, we introduce three structured communication patterns: Monologue, Dialogue, and Analogue-tailored for common tasks such as decision-making, knowledge-intensive reasoning, and numerical reasoning. We evaluated LTC on three datasets: ALFWorld (decision-making), HotpotQA (knowledge-intensive reasoning), and GSM8k (numerical reasoning). On ALFWorld, it exceeds the instruction tuning baseline by 12% in success rate. On HotpotQA, LTC surpasses the instruction-tuned LLaMA-7B agent by 5.1% in EM score, and it outperforms the instruction-tuned 9x larger PaLM-62B agent by 0.6%. On GSM8k, LTC outperforms the CoT-Tuning baseline by 3.6% in accuracy. The results showcase the versatility and efficiency of the LTC approach across diverse domains. We will open-source our code to promote further development of the community.Comment: Preprin

Research of Driving Circuit in Coaxial Induction Coilgun

Power supply is crucial equipment in coaxial induction coil launcher. Configuration of the driving circuit directly influences the efficiency of the coil launcher.This paper gives a detailed analysis of the properties of the driving circuit construction based on the capacitor source. Three topologies of the driving circuit are compared including oscillation, crowbar and half-wave circuits. It is proved that which circuit has the better efficiency depends on the detailed parameters of the experiment, especially the crowbar resistance. Crowbar resistor regulates not only efficiency of the system, but also temperature rise of the coil. Electromagnetic force (EMF) applied on the armature will be another problem which influences service condition of the driving circuits. Oscillation and crowbar circuits should be applied to both of the synchronous and asynchronous induction coil launchers, respectively. Half-wave circuit is seldom used in the experiment. Although efficiency of the half-wave circuit is very high, the speed of the armature is low. A simple independent half-wave circuit is proposed in this paper. In general, the comprehensive property of crowbar circuit is the most practical in the three typical circuits. Conclusions of the paper could provide guidelines for practice

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application

Effects of Ink Formulation on Construction of Catalyst Layers for High-Performance Polymer Electrolyte Membrane Fuel Cells

Rational design of catalyst layers in a membrane electrode assembly (MEA) is crucial for achieving high-performance polymer electrolyte membrane fuel cells. Establishing a clear understanding of the property (catalyst ink)-structure (catalyst layer)-performance (MEA) relationship lays the foundation for this rational design. In this work, a synergistic approach was taken to correlate the ink formulation, the microstructure of catalyst layers, and the resulting MEA performance to establish such a property-structure-performance relationship. The solvent composition (n-PA/H2O mixtures) demonstrated a strong influence on the performance of the MEA fabricated with an 830-EW (Aquivion) ionomer, especially polarization losses of cell activation and mass transport. The performance differences were studied in terms of how the solvent composition affects the catalyst/ionomer interface, ionomer network, and pore structure of the resulting catalyst layers. The ionomer aggregates mainly covered the surface of catalyst aggregates acting as oxygen reduction reaction active sites, and the aggregate sizes of the ionomer and catalyst (revealed by ultrasmall angle X-ray scattering and cryo-transmission electron microscopy) were dictated by tuning the solvent composition, which in turn determined the catalyst/ionomer interface (available active sites). In n-PA/H2O mixtures with 50∼90 wt % H2O, the catalyst agglomerates could be effectively broken up into small aggregates, leading to enhanced kinetic activities. The boiling point of the mixed solvents determined the pore structure of ultimate catalyst layers, as evidenced by mercury porosimetry and scanning electron microscopy. For mixed solvents with a higher boiling point, the catalyst-ionomer aggregates in the ink tend to agglomerate during the solvent evaporation process and finally form larger catalyst-ionomer aggregates in the ultimate catalyst layer, resulting in more secondary pores and thus lower mass transport resistance. Both the enlarged catalyst/ionomer interface and appropriate pore structure were achieved with the catalyst layer fabricated from an n-PA/H2O mixture with 90 wt % H2O, leading to the best MEA performance

A Design for a Novel Open, Intelligent and Integrated CNC System Based on ISO 10303-238 and PMAC

The combination of the high-level data model called ISO 10303-238 with the open programmable multi-axis controller (PMAC) presents a vision for the open, intelligent and integrated computer numerical control (CNC) systems whose demands have been growing with the rapid development of modern manufacturing. Evolved from design philosophy, this paper proposes a novel open, intelligent and integrated CNC system based on ISO 10303-238 and PMAC. In the system, ISO 10303-238 is chosen as the numerical control (NC) data in order to make the CNC system interoperable. And the open master-slave hardware structure on the basis of industrial process computer (IPC) + PMAC with double central processing units (CPUs) is designed in order to make the CNC system flexible. Also, the open and modular software structure is designed in order to make the CNC system intelligent. In addition, the development of the prototype system is given. At the end, it has been verified by case study that the proposed CNC system is feasible and effective