Exploring Brain Age Calculation Models Available for Alzheimer's Disease

The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.

Distributed generator-based distribution system service restoration strategy and model-free control methods

The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

In-Situ Annealed Ti_(3)C_(2)T_(x) MXene Based All-Solid-State Flexible Zn-Ion Hybrid Micro Supercapacitor Array with Enhanced Stability

Zn-ion hybrid supercapacitors(SCs)are considered as promising energy storage owing to their high energy density compared to traditional SCs.How to realize the miniaturization,patterning,and flexibility of the Zn-ion SCs without affecting the electrochemical performances has special meanings for expanding their applications in wearable integrated electronics.Ti_(3)C_(2)T_(x) cathode with outstanding conductivity,unique lamellar structure and good mechanical flexibility has been demonstrated tremen-dous potential in the design of Zn-ion SCs,but achieving long cycling stability and high rate stability is still big challenges.Here,we proposed a facile laser writing approach to fabricate patterned Ti_(3)C_(2)T_(x)-based Zn-ion micro-supercapacitors(MSCs),followed by the in-situ anneal treatment of the assembled MSCs to improve the long-term stability,which exhibits 80%of the capacitance retention even after 50,000 charge/discharge cycles and superior rate stability.The influence of the cathode thickness on the electrochemical performance of the MSCs is also studied.When the thickness reaches 0.851μm the maximum areal capacitance of 72.02 mF cm^(−2)at scan rate of 10 mV s^(−1),which is 1.77 times higher than that with a thickness of 0.329μm(35.6 mF cm^(−2)).Moreover,the fab-ricated Ti_(3)C_(2)T_(x) based Zn-ion MSCs have excellent flexibility,a digital timer can be driven by the single device even under bending state,a flexible LED displayer of“TiC”logo also can be easily lighted by the MSC arrays under twisting,crimping,and winding conditions,demonstrating the scalable fabrication and application of the fabricated MSCs in portable electronics.

Coupling Co-Ni phosphides for energy-saving alkaline seawater splitting

The coupling of energy-saving small molecule conversion reactions and hydrogen evolution reaction(HER)in seawater electrolytes can reduce the energy consumption of seawater electrolysis and mitigate chlorine corrosion issues.However,the fabrication of efficient multifunctional catalysts for this promising technology is of great challenge.Herein,a heterostructured catalyst comprising CoP and Ni_(2)P on nickel foam(CoP/Ni_(2)P@NF)is reported for hydrazine oxidation(HzOR)-assisted alkaline seawater splitting.The coupling of CoP and Ni_(2)P optimizes the electronic structure of the active sites and endows excellent electrocatalytic performance for HzOR and HER.Impressively,the two-electrode HzOR-assisted alkaline seawater splitting(OHzS)cell based on the CoP/Ni_(2)P@NF required only 0.108 V to deliver 100 mA·cm^(−2),much lower than 1.695 V for alkaline seawater electrolysis cells.Moreover,the OHzS cell exhibits satisfactory stability over 48 h at a high current density of 500 mA·cm^(−2).Furthermore,the CoP/Ni_(2)P@NF heterostructured catalyst also efficiently catalyzed glucose oxidation,methanol oxidation,and urea oxidation in alkaline seawater electrolytes.This work paves a path for high-performance heterostructured catalyst preparation for energy-saving seawater electrolysis for H_(2) production.

基于流固耦合的激波/边界层干扰作用下壁板颤振特性

为了解激波/边界层干扰作用下壁板气动弹性及其对流动分离的影响,采用自主开发的双向流固耦合求解器,对不同激波冲击位置下壁板的振动响应和流动特性进行了数值模拟研究。壁板几何非线性运动方程采用有限差分法求解,基于有限体积法求解Navier-Stokes方程组,对流通量采用MUSCL和AUSMPW+格式离散,双向流固耦合采用交错迭代算法。研究结果表明:激波/边界层干扰作用下壁板振动位移先增大后减小,经若干振荡周期后达到稳定颤振状态,呈现二阶振动模态,壁板变形相对于激波冲击位置呈现非对称性,壁板前部分的振幅始终小于壁板后部分;激波冲击位置可显著改变壁板的颤振振幅、频率及分离区长度,当激波冲击位置靠近壁板两端时,壁板振动最终收敛达到静稳定状态;壁板振动响应与流场特征不随激波冲击位置的改变而单调变化,对于激波冲击位置x/a=0.35工况,壁板颤振可有效抑制激波/边界层干扰流动分离。

Power-to-gas technology in energy systems:current status and prospects of potential operation strategies

Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas(PtG) technology has attracted much research attention in recent years.PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.

Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices

In recent years, the increasing penetration level of renewable generation and combined heat and power(CHP) technology in power systems is leading to significant changes in energy production and consumption patterns. As a result, the integrated planning and optimal operation of a multi-carrier energy(MCE) system have aroused widespread concern for reasonable utilization of multiple energy resources and efficient accommodation of renewable energy sources. In this context, an integrated demand response(IDR) scheme is designed to coordinate the operation of power to gas(P2 G) devices, heat pumps,diversified storage devices and flexible loads within an extended modeling framework of energy hubs. Subsequently, the optimal dispatch of interconnected electricity,natural gas and heat systems is implemented considering the interactions among multiple energy carriers by utilizing the bi-level optimization method. Finally, the proposed method is demonstrated with a 4-bus multi-energy systemand a larger test case comprised of a revised IEEE 118-bus power system and a 20-bus Belgian natural gas system.

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

In recent years,much attention has been devoted to the development and applications of smart grid technologies,with special emphasis on flexible resources such as distributed generations(DGs),energy storages,active loads,and electric vehicles(EVs).Demand response(DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations.Despite their potential contributions to power system secure and economic operation,uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned.In addition,the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments.In this context,this paper aims to propose a DR based congestion management strategy for smart distribution systems.The general framework and procedures for distribution congestion management is first presented.A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established.Subsequently,the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices.The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.

Data-driven Operation Risk Assessment of Wind-integrated Power Systems via Mixture Models and Importance Sampling

The increasing penetration of highly intermittent wind generation could seriously jeopardize the operation reliability of power systems and increase the risk of electricity outages. To this end, this paper proposes a novel data-driven method for operation risk assessment of wind-integrated power systems. Firstly, a new approach is presented to model the uncertainty of wind power in lead time. The proposed approach employs k-means clustering and mixture models(MMs) to construct time-dependent probability distributions of wind power.The proposed approach can also capture the complicated statistical features of wind power such as multimodality. Then, a nonsequential Monte Carlo simulation(NSMCS) technique is adopted to evaluate the operation risk indices. To improve the computation performance of NSMCS, a cross-entropy based importance sampling(CE-IS) technique is applied. The CE-IS technique is modified to include the proposed model of wind power.The method is validated on a modified IEEE 24-bus reliability test system(RTS) and a modified IEEE 3-area RTS while employing the historical data of wind generation. The simulation results verify the importance of accurate modeling of shortterm uncertainty of wind power for operation risk assessment.Further case studies have been performed to analyze the impact of transmission systems on operation risk indices. The computational performance of the framework is also examined.