Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure analysis.However,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency.Therefore,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression models.We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods.For different application scenarios,we also provide selection suggestions based on predictive ability and variable selection accuracy.Finally,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our method.Our method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.

A stable actor-critic algorithm for solving robotic tasks with multiple constraints

1 Introduction.Deep reinforcement learning has achieved great success especially in game[1]and control areas.Unfortunately,for real world environments that involve more than one objectives.For example,an autonomous car should consider constraints such as the driving speed,energy efficiency,comfort and safety of the passengers[2,3].To solve the problems,Constrained Markov Decision Process(CMDP)was proposed to model tasks with constraints[4,5].

Perspectives and challenges for lead-free energy-storage multilayer ceramic capacitors

The growing demand for high-power-density electric and electronic systems has encouraged the development of energy-storage capacitors with attributes such as high energy density,high capacitance density,high voltage and frequency,low weight,high-temperature operability,and environmental friendliness.Compared with their electrolytic and film counterparts,energy-storage multilayer ceramic capacitors(MLCCs)stand out for their extremely low equivalent series resistance and equivalent series inductance,high current handling capability,and high-temperature stability.These characteristics are important for applications including fast-switching third-generation wide-bandgap semiconductors in electric vehicles,5G base stations,clean energy generation,and smart grids.There have been numerous reports on state-of-the-art MLCC energy-storage solutions.However,lead-free capacitors generally have a low-energy density,and high-energy density capacitors frequently contain lead,which is a key issue that hinders their broad application.In this review,we present perspectives and challenges for lead-free energy-storage MLCCs.Initially,the energy-storage mechanism and device characterization are introduced;then,dielectric ceramics for energy-storage applications with aspects of composition and structural optimization are summarized.Progress on state-of-the-art energy-storage MLCCs is discussed after elaboration of the fabrication process and structural design of the electrode.Emerging applications of energy-storage MLCCs are then discussed in terms of advanced pulsed power sources and high-density power converters from a theoretical and technological point of view.Finally,the challenges and future prospects for industrialization of lab-scale lead-free energy-storage MLCCs are discussed.

Optimizing the grain size and grain boundary morphology of (K,Na) NbO_(3)-based ceramics: Paving the way for ultrahigh energy storage capacitors

Relaxor dielectric ceramic capacitors are very attractive for high-power energy storage.However,the low breakdown strength severely restricts improvements to the energy storage density and practical application.Here,a strategy of designing small grain sizes and abundant amorphous grain boundaries is proposed to improve the energy storage properties under the guidance of phase field theory.0.925(K_(0.5)Na_(0.5))NbO_(3)-e0.075Bi(Zn_(2/3)(Ta_(0.5)Nb_(0.5))1/3)O_(3)(KNNe-BZTN)relaxor ferroelectric ceramic is taken as an example to verify our strategy.The grain sizes and grain boundaries of the KNNeBZTN ceramics are carefully controlled by the high-energy ball milling method and twoestep sintering strategy.Impedance analysis and diffusion reflectance spectra demonstrate that KNNeBZTN ceramics with a small grain size and abundant amorphous grain boundary exhibit a lower charge carrier concentration and higher band gap.As a consequence,the breakdown electric field of KNNeBZTN ceramics increases from 222 kV/cm to 317 kV/cm when the grain size is decreased from 410 nm to 200 nm,accompanied by a slightly degraded maximum polarization.KNNeBZTN ceramics with an average grain size of~250 nm and abundant amorphous grain boundaries exhibit optimum energy storage properties with a high recoverable energy density of 4.02 J/cm^(3) and a high energy efficiency of 87.4%.This successful local structural design opens up a new paradigm to improve the energy storage performance of other dielectric ceramic capacitors for electrical energy storage.

Energy storage properties of 0.87BaTiO3-0.i3Bi(Zn2/3(Nb0.85Ta0.15)1/3)O3 multilayer ceramic capacitors with thin dielectric layers

Multilayer ceramic capacitors(MLCCs)for energy storage applications require a large discharge energy density and high discharge/charge efficiency under high electric fields.Here,0.87BaTiO3--0.13Bi(Zn23(Nbog8sTao.1s)u3)O3(BTBZNT)MLCCs with double active dielectric layers were fabricated,and the effects of inner electrode and sintering method on the energy storage properties of BTBZNT MLCCs were investigated.By using the pure Pt as inner electrode instead of Ago.6Pdo4 aloys,an alternating current(AC)breakdown strength(BDS)enhancement from 1047 to 1500 kV/cm was achieved.By investigating the leakage current behavior of BTBZNT MLCCs,the Pt inner electrode and two-step sintering method(TSS)were confirmed to enhance the Schottky barrier and minimize the leakage current density.With relatively high permitivity,dielectric sublinearity,and ultra-high BDS,the Pt TSS BTBZNT MLCCs exhibited a surprisingly discharge energy density(Udis)of 14.08 J/cm2.Moreover,under an operating electric field of 400 kV/cm,the MLCCs also exhibited thermal stability with Udis variation<±8%over a wide temperature (t) range from-50 to 175℃ and cycling reliability with Uais reduction<0.3%after 3000 charge-discharge cycles.These remarkable performances make Pt TSS BTBZNT MLCCs promising for energy storage applications.

Improving deep reinforcement learning by safety guarding model via hazardous experience planning

1Introduction and main contributions Deep reinforcement learning that considers the advantages of both deep learning and reinforcement learning has achieved success in many fields[1],However,during the learning process,a possibility still exists that the agent fails in the task because of falling into hazardous states due to taking improper actions.