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The number of compounds forming from coke ash components during heating in a stream of gases of highly differential reductiveness reflects the complexity of the physicochemical phenomena.
Although the test has confirmed that coke undergoes grain degradation and volatile matter content reduction as it comes down the blast furnace, the scatter of these effects in particular samples in Table 1 suggests that the analysis of samples taken from the tuyères may not allow different coke batches to be compared.
The number of compounds forming from the constituents of coke ash when heated in a stream of gases with a highly diverse reducing capacity reflects the complexity of the physicochemical phenomena.

The technology of obtaining such materials on the basis of disperse systems is characterized by a number of general and typical processes associated with interaction and mutual distribution of dispersion phases [3‒5].
A change in the number of stationary states with a simultaneous change in the type of stability occurs at bifurcation values of the control parameter, which correspond to kinks of the S-shaped curve of stationary states; it is the multiplicity of stationary states that determines the hysteresis effects [8].
The lower the viscosity, the greater the number of oscillations (discontinuities).
It is proposed to describe and analyze the curves of temperature deformations of dry and wet fine-grained concrete which has N-shaped kinks using a model «fold».
Along with the above, there are [16] a number of other ideas regarding water which is removed during recession, and the mechanism of its influence on the structure.

This also underlines the growing number of varied solutions that have appeared on the market in the meantime (figure 1, for a rough classification and selected technologies).
Fig. 4: Several points of measurement to ensure accuracy of measurements with a nuclear gauge On the construction site, an asphalt concrete with grain size 16 mm (AC 16 N) according to the Swiss standard SN 640 431-1aNA has been laid (see figure 5).
Fig. 5: Grading Curve – AC 16 N according to Swiss Standard SN 640-431-1a The compaction behavior of the two types of asphalt mixture has been analyzed as a function of the number of roller passes.
The relationship can be expressed as y=a ×lnx+b (1) where y is the current compaction, a is the rate coefficient of the compaction increase, x the number of roller passes and b can be seen as the theoretical initial compaction before the first roller pass.
Fig. 8: Warm mix asphalt was laid at around 100°C To ensure pavement quality, a number of drilling cores has been taken of both types of asphalt.

Accordance with greenhouse gas calculation in 2006 IPCC, the ecological footprint of greenhouse gas of agricultural nitrogen is calculated as follows: In the formula: EFF or efF -Ecological footprint of livestock breeding manure or ecological footprint of per capita livestock breeding manure, hm2 or hm2/person; N2Odir-N2O direct emission, t; N2Oind-N2O indirect emissions, t; GWPN2O -N2O global warming potential factor, is 298;P-Regional population; FSN-N2O emissions of inorganic nitrogen fertilizer, t; FAW-N2O emissions of organic fertilizer, t; NFERT-Amount of inorganic nitrogen fertilizer, t; FracGASF -The integrated coefficient of the N fertilizer lost to the air, the default value is 0.1; T-The types of livestock (pigs, cattle, sheep, chicken); NT-The number of livestock and poultry T, head / only (N1 is the number of pigs, N2 is the number of cattle, N3 is the number of sheep, N4 is the number of chickens); NexT -The emission factor of per head of livestock T，kg/head
Beijing suburbs grain of soil nutrients and fertilizer.

Over denotes the number of one node’s neighbors which is needed to estimates its position.
(13) where U is the node set of nodes whose positions have been estimated in this method, n is the numbers of the nodes in U, r is the sensing range.
Due to in one iteration the number of nodes whose positions are estimated by utilizing Collaborative multilateration is more than by utilizing atomic multilateration, so less iteration is required and higher accuracy is obtained.
The results of each scheme. a) Scheme 1 b) Scheme 2 c) Scheme 3 Table 1 Statistics of the simulation results The information for positioning locate mode The numbers of unresolved nodes Location error Scheme 1 ranging measurements atomic multilateration 28 0.6938 Scheme 2 Hybrid information atomic multilateration 18 0.4982 Scheme 3 Hybrid information collaborative multilateration 10 0.3684 Conclusions In this paper we have presented a new localization method for wireless sensor networks.
(In Chinese) [12] Savvides A, Han C-C, Srivastava MB: Dynamic fine-grained localization in ad-hoc networks of sensors (Proc. of the 7th Annual Int’l Conf. on Mobile Computing and Networking.

As sand gravel spaces filling to clay and powder grain, resulting in aquifer runoff not is not smooth, permeability decrease, superadd the thickness of aquifer is thin, maldistribution, The aquifer water storage space is limited, the above various factors determining storage of groundwater conditions and distribution characteristics.
On the basis of the hydrogeological model and boundary conditions, the mathematical model for groundwater resources: （7） Where: H,B-The water level of aquifer and the floor elevation(m); X,Y-Node coordinates(m); K, μ-Aquifer penetrates mysteries (m/d) and specific yield; δ(X-XW,Y-YW)-δ – function(l/m2); np-Design of mining well number; FP-Present situation of groundwater mining rate(m3/m2·d) RI-Atmospheric precipitation vertical infiltration recharge intensity(m3/m2·d); Ω-Calculation of area; T1,T2-part for calculated area a kind of boundary and two categories of boundary； H0,H1-part for initial water level and a kind of boundary water level(m)； Q-the two categories of boundary of two single wide recharge(m3/m·d)； QW-Design of single well production(m3/d) The model of the partial differential equation is nonlinear, solve very difficult, and must be linearized.
Given the initial water level and time of Δt within the volume of unit changes cases, Inverse solution of the aquifer hydrogeological parameters, but if the parameters are known number, can be used to predict the future different design exploitation schemes of groundwater dynamic.
Subdivision node number 241, in which the computing node number 216, a kind of boundary points number 25, the calculation of subdivision graph is figure 2.

Introduction Organophosphorus (OP) pesticides have been widely used for the control of insects in a wide range of fruit, vegetables, and grain all over the world.
Due to the increase of the voltage of single pulses, the energy is input to the reactor to augment and then the number of high-energy electrons and other active particles generated by discharge increased, which plays a more important role in the degradation of rogor.
It shows that the number of the active particles in the reactor nearly approach to saturation, resulting in lower utilization of active particles.
The number of rogor molecules increased along with increasing initial concentration.
Construction of the adsorption and chemical reaction model In this experiment，the reynolds number Re<100, so the adsorption and chemical reaction model of one-dimensional packed bed(fig.9) can be applied and reduced, which is as follow

Similarly, the graphite samples are of the size of 2.5x2.5x0.5 mm3 on which the grain size is a few micrometer.
We assume the Landau level quantum number n=1.
These states are degenerate, i.e., they have the equal energy for different quantum numbers.
However, the number of levels is very large, ~101.
Shrivastava, Rational numbers of the fractionally quantized Hall effect, Phys.

Repeated cyclic effect of temperature and load contributes to the occurrence of deformations and fractures of fatigue character after a certain number of cycles.
On grain boundaries of prior austenite and on subgrain boundaries, the M23C6 carbides were precipitated.
During fatigue tests, the changes in the basic parameters of hysteresis loop in the function of the number of stress cycles were observed.
Within these stages, a change in the shape of hysteresis loop in the function of the number of stress cycles was noticed.
On the basis of analysis of the graphs included in Fig. 5a and 5b it can be concluded that the extent of changes in the hysteresis loop parameters in the function of the number of stress cycles for the cast steel after ageing process is greater than in the case of material as delivered.

The following compositions were tested: · Composition 1 (LSHD): Basic adhesive diffusion mortar with no other finishing · Composition 2 (LSHD + PAS): Basic adhesive diffusion mortar + acrylic-silicon penetration · Composition 3 (LSHD + PAS + AZO3): Basic adhesive diffusion mortar + acrylic-silicon penetration + acrylic plaster with maximal grain 3 mm Other material compositions can be found in previous papers, for example “Diffusion Parameters of Basic Diffusion Adhesive Mortars with Silicate or Acrylic Plaster” [6].
Tab. 1 Composition 1 - Dimensions Sample mark Sample number Sample weight [g] Ø [mm] Average [mm] Sample thickness [mm] Average [mm] LSHD A 30.7 117,3 118.0 117.7 1.8 2.0 1.9 B 30.9 117.7 117.7 117.7 1,9 1,9 1.9 C 34.2 117.2 117.6 117.4 2.4 2.3 2.4 Tab. 2 Composition 2 - Dimensions Sample mark Sample number Sample weight [g] Ø [mm] Average [mm] Sample thickness [mm Average [mm] LSHD+PAS A 28.3 116.5 116.9 116.7 2.5 2.4 2.5 B 37.0 117.3 117.3 117.3 2.5 2.3 2.4 C 36.2 117.2 117.3 117.2 2.4 2.3 2.4 Tab. 3 Composition 3 - Dimensions Sample mark Sample number Sample weight [g] Ø [mm] Average [mm] Sample thickness [mm] Average [mm] LSHD+ PAS +AZO3 A 75.1 117.0 116.9 117.0 5.0 5.0 5.0 B 67.8 116.4 116.5 116.5 4.1 4.2 4.2 C 75.3 117.2 117.1 117.2 4.5 4.6 4.6 Diffusion Parameters Three samples from each composition were weighted on scales in accordance with ČSN EN ISO 7783 [1, 2] standard to determine water vapour permeability.
Tab. 4 Composition 1 - Water vapour permeability and water vapour resistance factor Sample mark Sample number Water vapour rate [*10-8 kg/s] Water vapour permeability [kg/(m2.s.Pa)] Water vapour resistance factor [-] LSHD A 3.44 1.24E-08 8.57 B 3.52 1.33E-08 7.96 C 3.49 1.29E-08 6.59 Average 3.48 1.29E-08 7.7 Tab. 5 Composition 2 - Water vapour permeability and water vapour resistance factor Sample mark Sample number Water vapour rate [*10-8 kg/s] Water vapour permeability [kg/(m2.s.Pa)] Water vapour resistance factor [-] LSHD+PAS A 1.59 2.96E-09 27.1 B 1.67 3.19E-09 26.7 C 1.78 3.55E-09 23.3 PRŮMĚR 1.68 3.23E-09 25.7 Tab. 6 Composition 3 - Water vapour permeability and water vapour resistance factor Sample mark Sample number Water vapour rate [*10-9 kg/s] Water vapour permeability [kg/(m2.s.Pa)] Water vapour resistance factor [-] LSHD+PAS +AZO A 3.83 4.51E-10 91.1 B 3.71 4.36E-10 110.3 C 3.13 3.64E-10 122.8 Average 3.56 4.17E-10 108.1 Detailed results showing average values (