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In recent years, the reduction work of the nine-dimension one-compartment complex model of CA1 pyramid neuron developed by David[2] is done by Yueping Peng, and et al.
Based on the electrophysiological experimental data under AD’s part pathology condition, we build the neuron model under AD’s part pathology condition, and discuss comparatively the neuron model’s dynamic variation characteristics and bioinformatics’ change before and after the effect of AD.
Based on the above electrophysiological experiment data and results, we can modify suitably the parameters’ values of the one-compartment model of CA1 pyramidal neuron in references [2], and get the CA1 pyramidal neuron model under the AD pathology condition, which is showed in formula (1)
Based on the above electrophysiological experiment data, parameters’ values related to the delay rectification K+ current in the neuron dynamic model under the AD condition are as follows: ; ; ; In addition, the state variable (V, h, n, b, z, r, y, q, [Ca2+]i) is the same as the normal neuron model, and is (-65, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.05).
Based on the electrophysiological experimental data under AD’s part pathology condition, we build the neuron model under AD’s part pathology condition, and analyze and discuss comparatively the neuron model’s dynamic variation characteristics before and after the effect of AD.

Obtained results for the individual samples group are depicted in Fig. 1a as a solid line and fits experimental data very well.
It corresponds to the extracted data form the noise measurement as presented before.
Experimental data (see Fig. 2a) pointed out 1/f noise development with the bias current according to the Eq. 1 as is expected.
Instead of this, we observe invariant behavior with the marginal reduction of the measurement uncertainty.
Nevertheless, the experimental data must be corrected because of the high samples resistance, self-capacitance and the finite amplifier input resistance.

Through testing an actual project which uses solar energy-water source heat pump hot water system, analysis of the operation of the system, combined with the measured data, studied respectively of the factors of solar heating system and water source heat pump heating system.
Analysis the differences of collection time and power consumption under different temperature rise Test and analysis, select the temperature rise of 10 ℃ for a period, divided into four stages, respectively is 40 ℃ ~ 50 ℃, 50 ℃ ~ 60 ℃, 60 ℃ ~ 70 ℃, 70 ℃ ~ 80 ℃, screening the test data ,research under different temperature rise condition, the differences of pump running time and power consumption.
According to the engineering test data, filter out a few days that the irradiation is similar, compare system in different drain water temperatures, the change trend of daily heating efficiency and daily useful heat capacity, refer to Fig.5, Fig.6.
Pump with frequency adjustment, quantity of water intake is reduced with the reduction of the load, so the pump power consumption reduced. 2.2.
Summary By testing the operation of solar energy-water source heat pump hot water system in a practical engineering, combined with test data to analyze and research, given some suggestions for engineering optimization.

Table 2 Factors and levels in the test Factor Level A B C Heating temperature T (℃) Pushing speed v (mm/s) Friction coefficient f 1 650 3 0.14 2 700 3.5 0.16 3 750 4 0.18 4 800 4.5 0.20 Simulation design scheme and data analysis Simulation design scheme and data are shown in table 3.
Table 3 Simulation design scheme and data Number Simulation scheme Simulation result Results analysis Means Variance 1 A1B1C1 6.1592 0.0873 Wall thickness means analysis (R=Kmax-Kmin) 2 A1B2C2 6.3388 0.0390 K1 K2 K3 K4 R 3 A1B3C3 6.3664 0.0099 A 6.321 6.276 6.244 6.522 0.278 4 A1B4C4 6.4196 0.0245 5 A2B1C2 6.1742 0.0769 B 6.240 6.299 6.378 6.444 0.204 6 A2B2C1 6.0780 0.0904 7 A2B3C4 6.3678 0.0409 C 6.257 6.343 6.380 6.382 0.125 8 A2B4C3 6.4846 0.0756 9 A3B1C3 6.1468 0.2064 Wall thickness variance analysis（R=Kmax－Kmin） 10 A3B2C4 6.2590 0.1485 K1 K2 K3 K4 R 11 A3B3C1 6.2432 0.0656 A 0.0402 0.0709 0.1194 0.0564 0.0792 12 A3B4C2 6.3257 0.0572 13 A4B1C4 6.4810 0.0623 B 0.1082 0.0855 0.0364 0.0569 0.0718 14 A4B2C3 6.5238 0.0639 15 A4B3C2 6.5364 0.0290 C 0.0784 0.0505 0.0889 0.0691 0.0384 16 A4B4C1 6.5468 0.0705 The wall thickness mean value of No. 6 scheme in table 3 (A2B2C1) is 6.078 mm which is the nearest to the initial wall thickness 6 mm.
In this paper, the simulation results and measured data both refer to the second elbow pipe, as shown in figure 5.
As can be seen from figure 6, the wall thickness of the elbow pipe on the convex side is decreasing slightly, but distributed evenly and the thickness value is around 5.8 mm (the wall thickness reduction ratio is about 3.3%).

Offline path planning is the loading path planning process to be conducted until data sufficient enough to identify material properties are obtained.
Therefore, only prior information is used and the quantification of experiments is achieved by pseudo-experimental data derived via simulation (FEA).
Online path planning, on the other hand, updates loading path by taking sensor data or empirical information into account in addition to the prior information.
In order to investigate the effect of numerical issues first, the testing machine and the experimental data are created in a virtual environment.
Reifsnider, StiRness-reduction Mechanisms in Composite Laminates, SPT775 Damage in Composite Materials (1982), pp. 103-117

With technical progress and cost reduction, the lightweight wall greening technology represents the new development trend of wall greening.
This technology is convenient to change parameters or variables and even the model structure, input the command at any time through the keyboard or voice and output data, charts, renderings (Figure.3.) or even animations in the simulation process.
Under the limit of known conditions, we can scientifically compound matching data through variable selection, highlight the characteristics of local configuration, and predict the visual effect of the construction.
In addition, BIM also need to build the actual database to timely import and integrate the cost data in cost accounting, so that visibly collect or split the cost.
While BIM platform can establish the five dimensional relationship of time, space, process to the cost data.

Introduction Fibers optic sensors (FOS) are gaining an important role in structural monitoring thanks to their immunity to electrostatic discharges, fire safety compliance and capability to use the same fiber both for sensing and data transmission.
The control unit includes a 12-bit data acquisition (DAQ) card with a USB interface for connection to a PC that performs the elaboration of data and keeps historic record of displacements through a LabVIEW program.
Upon real-time application of the linear temperature compensation (dark grey triangles) to the raw data, the residual variations are lower than ±45µm (black squares).
To reduce such effect we introduced the packaging described in the previous section and we are still testing the new assembly repeatability, though the preliminary results suggest a two-fold reduction of the normalized signal scattering. 0 2 4 6 8 10 12 14 16 18 20 0.0 0.4 0.8 1.2 1.6 2.0 normalized signal sensor ±standard deviation band Fig. 5.

Specifications for Design According to the filtering requirement of project, the specifications are defined as follows: a lowpass filter whose cutoff frequency is 1MHz, and whose scale is 33taps(coefficients).The input signal comes from a 10-bits A/D convertor at the sampling rate of 100MHz, and 16-bits complement data are expected at the output.
The sign bit of product can be obtained by XOR (eXclusive OR) between the most significant bit of two input data, while the value part of product is the product of two positive number.
As a result of reduction on logic occupation, complement is conducive to high-speed FIR filter design.
Let’s assume the input signal is multi-frequency sampling data whose frequency components mainly include 500 KHz and 3 MHz The filtering appearance, as shown in Fig. 5, is enough to meet the design requirements.
By importing the output data of two FIR filters into MATLAB, the analysis indicates the average precision of the optimized filter design have already increased by 2.5%, compared to the traditional fixed point implementation.

The massive heat produced in the process will raise the temperature of the chip , leading to the reduction of photon that chip has ejaculated, the lower efficiency of acquiring light, the red shift of the ejaculatory spectrum and the decreasing of the color temperature quality.
The test focuses on the surface temperatures of LED lamp shell, heat pipe and three substrates. 2.4 Analysis of the testing data When the led light is under working, the heat sink temperature goes higher from the environment temperature, but will stay the same when the temperature reaches a balance after a while.
Figs. 6-8 are the data figures of the lamp shell, loop heat pipes, and substrates.
In addition, a contrastive analysis of the imitated result and testing data has also been made and the results are as follows: （1） The looped pulsating heat-pipe has the characteristics of low thermal resistance and good equalizing performance.
（4） The imitative results are almost in line with the testing data.

It is not used directly for the microcontroller but to be divided that the oscillation signal is generated by the oscillation circuit, the frequency can be various microcontroller associated clock signal, as shown in figure 2.Oscillation pulse is as the system clock signal by two divider, it generates ALE signal divided by three on the basis of the two divider, and get the machine cycle signal divided by six on the basis of two divider.The P0 port is used as the data sent to the D/A converter, the P0 port hasn’t pull-up supreme resistor internal, therefore connection with an external pull-up resistor RST1.
The system adopt to DAC0832 straight.They are grounded low-level access GND that the chip select signal CS, the data latch strobe input pin WR1,the data transfer control signal input pin XFER and DAC register strobe input pin WR2.The data latched allow eighth pin is reference voltage of Vref, the reference voltage is 2.5V if you adjust external sliding rheostat R15.Because of the chip DAC0832 being a form of current output, you have to add an op-amp that it convert the current to voltage directly.
The brightness of the lamp and 4.5V battery use time The 4.5v battery continuously supply for 86 hours.The similar products can only consecutively work for maximum 10 hours on the market, for there are always a significant reduction in electricity losses.