Predicting Human Lifespan Limits

Recent discoveries show steady improvements in life expectancy during modern decades. Does this support that humans continue to live longer in future? We recently put forward the maximum survival tendency, as found in survival curves of industrialized countries, which is described by extended Weibull model with age-dependent stretched exponent. The maximum survival tendency suggests that human survival dynamics may possess its intrinsic limit, beyond which survival is inevitably forbidden. Based on such tendency, we develop the model and explore the patterns in the maximum lifespan limits from industrialized countries during recent three decades. This analysis strategy is simple and useful to interpret the complicated human survival dynamics.Comment: 11 pages, 3 figures, 2 tables; Natural Science (in press

Asset management and maintenance: a smart grid perspective

This paper presents the importance, issues and challenges related to Smart Grid. It also evaluates various approaches for Smart Grid planning and operation. It discusses tools for asset management and their applicability to the next generation grid. Aging assets, uncertainty in load demand profile and renewable energy resources, and demand management create a challenge for the optimal operation and maintenance of electrical grid. This paper addresses the challenges and opportunities to improve transmission and distribution systems asset maintenance. This paper also presents the asset replacement alternatives. This paper also presents the cost-benefit analysis of asset management using the information/real time data from the utility company. This paper will serve a guide for doing the asset management to the electrification  process, investment and  recovery to sustain reliable and efficient power delivery

Power System Control with an Embedded Neural Network in Hybrid System Modeling

The output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to non-smooth nonlinearities from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures can been used. A feedforward neural network (FFNN) (with a structure of multilayer perceptron neural network) is applied to identify the dynamics of an objective function formed by the states, and thereafter to compute the gradients required in the nonlinear parameter optimization. Moreover, its derivative information is used to replace that obtained from the trajectory sensitivities based on the hybrid system model with the differential-algebraic-impulsive-switched (DAIS) structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in both a single machine infinite bus system (SMIB) and a multi-machine power system (MMPS)

Parameter Optimization of PSS Based on Estimated Hessian Matrix from Trajectory Sensitivities

This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates the second-order derivatives of an objective function J, which is used during iterations of optimization process. The performance of the optimal output limits tuned by the proposed method is evaluated by applying a large disturbance to a power system

Power System Control with an Embedded Neural Network in Hybrid System Modeling

Output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to nonsmooth nonlinearities arising from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures can been used. A feedforward neural network (with a structure of multilayer perceptron neural network) is applied to identify the dynamics of an objective function formed by the states and, thereafter, to compute the gradients required in the nonlinear parameter optimization. Moreover, its derivative information is used to replace that obtained from the trajectory sensitivities based on the hybrid system model with the differential-algebraic-impulsive-switched structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in both a single-machine infinite bus system and a multimachine power system

Thrombospondin-1 protects against Aβ-induced mitochondrial fragmentation and dysfunction in hippocampal cells.

Alzheimer's disease (AD) is often characterized by the impairment of mitochondrial function caused by excessive mitochondrial fragmentation. Thrombospondin-1 (TSP-1), which is primarily secreted from astrocytes in the central nervous system (CNS), has been suggested to play a role in synaptogenesis, spine morphology, and synaptic density of neurons. In this study, we investigate the protective role of TSP-1 in the recovery of mitochondrial morphology and function in amyloid β (Aβ)-treated mouse hippocampal neuroblastoma cells (HT22). We observe that TSP-1 inhibits Aβ-induced mitochondrial fission by maintaining phosphorylated-Drp1 (p-Drp1) levels, which results in reduced Drp1 translocation to the mitochondria. By using gabapentin, a drug that antagonizes the interaction between TSP-1 and its neuronal receptor α2δ1, we observe that α2δ1 acts as one of the target receptors for TSP-1, and blocks the reduction of the p-Drp1 to Drp1 ratio, in the presence of Aβ. Taken together, TSP-1 appears to contribute to maintaining the balance in mitochondrial dynamics and mitochondrial functions, which is crucial for neuronal cell viability. These data suggest that TSP-1 may be a potential therapeutic target for AD

Universal convex covering problems under translation and discrete rotations

We consider the smallest-area universal covering of planar objects of perimeter 2 (or equivalently closed curves of length 2) allowing translation and discrete rotations. In particular, we show that the solution is an equilateral triangle of height 1 when translation and discrete rotation of $\pi$ are allowed. Our proof is purely geometric and elementary. We also give convex coverings of closed curves of length 2 under translation and discrete rotations of multiples of $\pi/2$ and $2\pi/3$. We show a minimality of the covering for discrete rotation of multiples of $\pi/2$, which is an equilateral triangle of height smaller than 1, and conjecture that the covering is the smallest-area convex covering. Finally, we give the smallest-area convex coverings of all unit segments under translation and discrete rotations $2\pi/k$ for all integers $k\ge 3$