2-A Cosmological Model with Varying G and ∧ in General Relativity

Spatially homogeneous and anisotropic Cosmological models play a significant role in the description of the early stages of evolution of the universe. The problem of the cosmological constant is still unsettled. The authors recently considered time dependent G and L with Bianchi type–I Cosmological model .We considered in this paper homogeneous Bianchi type -I space-time with variable G and L containing matter in the form of a perfect fluid assuming the cosmological term proportional to R-2 (where R is scale factor). Initially the model has a point type singularity, gravitational constant G (t) is decreasing and cosmological constant L is infinite at this time. When time increases L decreases. Unlike in some earlier works we have neither assumed equation of state nor particular form of G. The model does not approach isotropy, if ‘t’ is small .The model is quasi-isotropic for large value of ‘t’.

1-A Cosmological Model with Varying G and ∧ in General Relativity

In this paper homogeneous Bianchi type -I space-time with variable G and L containing matter in the form of a perfect fluid assuming the cosmological term proportional to H2 (where H is Hubble Parameter). Initially the model has a point type singularity, gravitational constant G (t) is decreasing and cosmological constant L is infinite at this time. When time increases,L decrease. The model does not approach isotropy, if it is small. The model is quasi-isotropic for large value of it.

STABILITY OF TRANSONIC SHOCKS TO THE EULER-POISSON SYSTEM WITH VARYING BACKGROUND CHARGES

This paper is devoted to studying the stability of transonic shock solutions to the Euler-Poisson system in a one-dimensional nozzle of finite length.The background charge in the Poisson equation is a piecewise constant function.The structural stability of the steady transonic shock solution is obtained by the monotonicity argument.Furthermore,this transonic shock is proved to be dynamically and exponentially stable with respect to small perturbations of the initial data.One of the crucial ingredients of the analysis is to establish the global well-posedness of a free boundary problem for a quasilinear second order equation with nonlinear boundary conditions.

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

Time-variant fragility analysis of the bridge system considering time-varying dependence among typical component seismic demands

This paper presents a copula technique to develop time-variant seismic fragility curves for corroded bridges at the system level and considers the realistic time-varying dependence among component seismic demands. Based on material deterioration mechanisms and incremental dynamic analysis, the time-evolving seismic demands of components were obtained in the form of marginal probability distributions. The time-varying dependences among bridge components were then captured with the best fitting copula function, which was selected from the commonly used copula classes by the empirical distribution based analysis method. The system time-variant fragility curves at different damage states were developed and the effects of time-varying dependences among components on the bridge system fragility were investigated. The results indicate the time-varying dependence among components significantly affects the time-variant fragility of the bridge system. The copula technique captures the nonlinear dependence among component seismic demands accurately and easily by separating the marginal distributions and the dependence among them.

GENERAL DECAY FOR A VISCOELASTIC EQUATION OF VARIABLE COEFFICIENTS WITH A TIME-VARYING DELAY IN THE BOUNDARY FEEDBACK AND ACOUSTIC BOUNDARY CONDITIONS

A variable coefficient viscoelastic equation with a time-varying delay in the boundary feedback and acoustic boundary conditions and nonlinear source term is considered.Under suitable assumptions, general decay results of the energy are established via suitable Lyapunov functionals and some properties of the convex functions. Our result is obtained without imposing any restrictive growth assumption on the damping term and the elements of the matrix A and the kernel function g.

Variance Estimation for High-Dimensional Varying Index Coefficient Models

This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficients of the parameter part of the Varying Index Coefficient Model (VICM), while the unknown function part uses the B-spline to expand. Moreover, we combine the above two estimation methods under the assumption of high-dimensional data. The results of data simulation and empirical analysis show that for the varying index coefficient model, the re-adjusted cross-validation method is better in terms of accuracy and stability than traditional methods based on ordinary least squares.

Adaptive Variable Structure Control of MIMO Nonlinear Systems with Time-varying Delays and Unknown Dead-zones

In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.

Imputed Empirical Likelihood for Varying Coefficient Models with Missing Covariates

The empirical likelihood-based inference for varying coefficient models with missing covariates is investigated. An imputed empirical likelihood ratio function for the coefficient functions is proposed, and it is shown that iis limiting distribution is standard chi-squared. Then the corresponding confidence intervals for the regression coefficients are constructed. Some simulations show that the proposed procedure can attenuate the effect of the missing data, and performs well for the finite sample.

Home Monitoring of Estrone-3-Glucuronide (E1-3G) Levels in Two Different Ovarian Stimulation Protocols: A Pilot Study

Background: Studies have shown a strong correlation between the growth of E2 in serum and estrone-3-glucuronide (E1-3G) in urine during ovarian stimulation. Thus, we developed theoretical models for using urinary E1-3G in ovarian stimulation and focused on their experimental verification and analysis. Methods: A prospective, observational pilot study was conducted involving 54 patients who underwent 54 cycles of ovarian stimulation. The goal was to establish the growth rate of urinary E1-3G during the course of stimulation and to determine the daily upper and lower limits of growth rates at which stimulation is appropriate and safe. Controlled ovarian stimulation was performed using two different stimulation protocols—an antagonist protocol in 25 cases and a progestin-primed ovarian stimulation protocol (PPOS) in 29 cases, with fixed doses of gonadotropins. From the second day of stimulation, patients self-measured their daily urine E1-3G levels at home using a portable analyzer. In parallel, a standard ultrasound follow-up protocol accompanied by a determination of E2, LH, and P levels was applied to optimally control stimulation. Results: The average daily growth rates in both groups were about 50%. The daily increase in E1-3G for the antagonist protocol ranged from 14% to 79%, while they were 28% to 79% for the PPOS protocol. Conclusion: This is the first study to analyze the dynamics of E1-3G in two different protocols and to estimate the limits of its increase during the entire course of the stimulation. The results confirm our theoretical model for the viability of using urinary E1-3G for monitoring ovarian stimulation.

Olfactory receptors in neural regeneration in the central nervous system

Olfactory receptors are crucial for detecting odors and play a vital role in our sense of smell,influencing behaviors from food choices to emotional memories.These receptors also contribute to our perception of flavor and have potential applications in medical diagnostics and environmental monitoring.The ability of the olfactory system to regenerate its sensory neurons provides a unique model to study neural regeneration,a phenomenon largely absent in the central nervous system.Insights gained from how olfactory neurons continuously replace themselves and reestablish functional connections can provide strategies to promote similar regenerative processes in the central nervous system,where damage often results in permanent deficits.Understanding the molecular and cellular mechanisms underpinning olfactory neuron regeneration could pave the way for developing therapeutic approaches to treat spinal co rd injuries and neurodegenerative diseases like Alzheimer's disease.Olfa ctory receptors are found in almost any cell of eve ry orga n/tissue of the mammalian body.This ectopic expression provides insights into the chemical structures that can activate olfactory receptors.In addition to odors,olfactory receptors in ectopic expression may respond to endogenous compounds and molecules produced by mucosal colonizing microbiota.The analysis of the function of olfactory receptors in ectopic expression provides valuable information on the signaling pathway engaged upon receptor activation and the receptor's role in proliferation and cell differentiation mechanisms.This review explo res the ectopic expression of olfa ctory receptors and the role they may play in neural regeneration within the central nervous system,with particular attention to compounds that can activate these receptors to initiate regenerative processes.Evidence suggests that olfactory receptors could serve as potential therapeutic targets for enhancing neural repair and recovery following central nervous system injuries.

A Simulation Study on Comparing General Class of Semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.

Invariant operator theory for the single-photon energy in time-varying media

After the birth of quantum mechanics, the notion in physics that the frequency of light is the only factor that determines the energy of a single photon has played a fundamental role. However, under the assumption that the theory of Lewis-Riesenfeld invariants is applicable in quantum optics, it is shown in the present work that this widely accepted notion is valid only for light described by a time-independent Hamiltonian, i.e., for light in media satisfying the conditions, ε（t） = ε（0）, u（t） = u（0）, and σ（t） = 0 simultaneously. The use of the Lewis Riesenfeld invariant operator method in quantum optics leads to a marvelous result： the energy of a single photon propagating through time-varying linear media exhibits nontrivial time dependence without a change of frequency.

Sufficient conditions of the various stabilities of the linear time-varying delayed differential equations

Due to the appearance and the study of the ornithopter and flexible-wing micro air vehicles, etc., the time-varying systems become more and more important and ubiquitous in the study of the mechanics. In this letter, the sufficient conditions of the uniform asymptotic stability are first presented for the delayed time-varying linear differential equations with any time delay by employing the Dini derivative, Lozinskii measure and the generalized scalar Halanay delayed differential inequality. They are especially based on the estimation of the arbitrary solutions but not the fundamental solution matrix since their solutions＇ space is infinite-dimensional. Then some sufficient conditions of the stability, asymptotic stability and uniform asymptotic stability of the delayed time-varying linear system with a sufficiently small time delay are reported by employing Taylor expansion and Dini derivative. It implies that these stabilities can be guaranteed by the Lozinskii measure of the matrix composing of the time delay and the coefficient matrices of the system.

Inhibiting ceramide synthase 5 expression in microglia decreases neuroinflammation after spinal cord injury

Microglia,the resident monocyte of the central nervous system,play a crucial role in the response to spinal cord injury.However,the precise mechanism remains unclear.To investigate the molecular mechanisms by which microglia regulate the neuroinflammatory response to spinal cord injury,we performed single-cell RNA sequencing dataset analysis,focusing on changes in microglial subpopulations.We found that the MG1 subpopulation emerged in the acute/subacute phase of spinal cord injury and expressed genes related to cell pyroptosis,sphingomyelin metabolism,and neuroinflammation at high levels.Subsequently,we established a mouse model of contusive injury and performed intrathecal injection of siRNA and molecular inhibitors to validate the role of ceramide synthase 5 in the neuroinflammatory responses and pyroptosis after spinal cord injury.Finally,we established a PC12-BV2 cell co-culture system and found that ceramide synthase 5 and pyroptosis-associated proteins were highly expressed to induce the apoptosis of neuron cells.Inhibiting ceramide synthase 5 expression in a mouse model of spinal cord injury effectively reduced pyroptosis.Furthermore,ceramide synthase 5-induced pyroptosis was dependent on activation of the NLRP3 signaling pathway.Inhibiting ceramide synthase 5 expression in microglia in vivo reduced neuronal apoptosis and promoted recovery of neurological function.Pla2g7 formed a“bridge”between sphingolipid metabolism and ceramide synthase 5-mediated cell death by inhibiting the NLRP3 signaling pathway.Collectively,these findings suggest that inhibiting ceramide synthase 5 expression in microglia after spinal cord injury effectively suppressed microglial pyroptosis mediated by NLRP3,thereby exerting neuroprotective effects.

Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

Properties of Time-Varying Causality Tests in the Presence of Multivariate Stochastic Volatility

This paper compares the statistical properties of time-varying causality tests when errors of variables have multivariate stochastic volatility (SV). The time-varying causal-ity tests in this paper are based on a logistic smooth transition autoregressive model. The compared time-varying causality tests include asymptotic tests, heteroskedasticity-robust tests, and tests using wild bootstrap. Our simulation results show that asymptotic tests and heteroskedasticity-robust counterparts have size distortions under multivariate SV, whereas tests using wild bootstrap have better size properties regardless of type of error. In particular, the time-varying causality test with first-order Taylor approximation using wild bootstrap has better statistical properties.

g-C_(3)N_(4)负载双金属Rh@Ru催化氨硼烷析氢反应机理研究

本文采用密度泛函理论,研究了双金属Rh@Ru在g-C_(3)N_(4)上不同负载位点,确定了稳定的催化剂构型,并详细研究了该催化剂催化氨硼烷析氢的反应机理.通过比较氨硼烷析氢反应三条路径所需活化能,发现路径Ⅲ控制步骤活化能较低,而路径Ⅰ、Ⅱ所需活化能较高,反应路径Ⅲ更容易进行,Ⅰ为最优析氢路径.从微观角度揭示双贵金属Rh@Ru负载g-C_(3)N_(4)催化氨硼烷析氢的三条机理,希望为氨硼烷析氢催化剂的优化和设计提供理论信息.

基于4G技术的工程结构无线振动测试系统节点设计

结构振动测试是大型土木工程结构健康监测的有效保障,传统振动测试存在布线困难、低频率信号拾取困难、通信速率底、易受到测试现场条件限制等问题。以4G技术为研究对象,提出了一种基于4G技术的工程结构无线振动测试系统节点设计方案,通过节点工作原理介绍及各个模块的功能特性分析,研究了标准振动台性能对比测试和基于有线及无线的结构振动对比试验。结果表明:节点传感器的线性度误差为0.29%,且在短距离以及超远距离测试条件下,试验结构的各阶频率测试结果均与有线加速度传感器的测试结果一致,表明节点在测试精度和数据高速率传输等方面具有较大优势,解决了中远及超远距离中工程数据传输问题,具有一定的工程实用性,在土木工程健康监测和工程强震观测领域具有广泛的应用前景。

Equilibrium Control Theory and Direct Method of Feedback Linearization for Nonlinear Time Varying Systems

Aim To present a simple and effective method for the design of nonlinear and time varying control system. Methods A new concept of dynamic equilibrium of a system and its stability were presented first. It was pointed out that what is controlled directly by the input of a control system is the system's dynamic equilibrium rather than the states. Based on it, a new feedback linearization method for nonlinear system based on the Lyapunov direct method was given. Simulation studies were also carried out. Results The example and simulation show that by use of the method, the controller design becomes very simple and the control effect is quite satisfying. Conclusion The new method unifies the stabilizing problem(regulating problem) with the tracking problem. It is a very simple and effective method for the design of nonlinear and time varying control system.