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At the moment of t=1, 30 new ants from A to B were found to have traces of intensity 15 on the path to H, which was left by 15 ants who took that path from point B; On the path to C, there are traces of an intensity of 30, which is obtained by the sum of the traces left by the 15 ants who took the road from point B and the 15 ants who passed from D to C to B (Fig. 2(c)).

In this way, the probability of choosing a particular path is skewed, so that the number of ants heading towards point C is expected to be twice as high as going to point H, i.e., 20:10, respectively. This is also true for the 30 new ants that come from point E to point D.

These processes continue until all ants gradually choose the shortest path.

Here's the idea: if an ant has to choose one of the different paths at a given moment, the paths that were massively chosen by the previous ant (i.e., have a high level of traces) have a higher chance of being chosen. Further, high trace levels with short paths are consent words.

The structure of this article is as follows: Section 2 contains a description of the ant system as it is currently being implemented, as well as a definition of the application problem: since the algorithm structure partially reflects the problem structure, we will cover it together.

Section III describes three slightly different approaches to applying the proposed algorithm. Sections 4 and 5 are reports of the experiments. In Section 6 we compare the ant algorithm with other search algorithms.

In Section 7, we demonstrate the versatility and robustness of the Ant algorithm by showing how the Ant algorithm solves other optimization problems. In Section 8 we discuss informally how the paradigm of the ant system works and how it works. It is summarized in Section IX.

In t = 1 30 new ants, from finding a path of B with an intensity of 15 to hydrogen, lay the trail in a way that goes to 15 ants from B, a string of strength 30 on the path of C, triggering ants like 15 and 15ants to B, reaching B from D through C (Figure 2(C). The likelihood of choosing a career path is biased, so the expected number of ants towards c will be double those towards h: 20 and 10.

The same applies to the new 30 ants in d from e.

This process will continue until all the ants will eventually choose the shortest path.

The idea is that if an ant has chosen a different path for a given given period, the ants that were re-elected before (i.e., have a high trail level) are more likely to be selected. And the high trail level is synonymous with the short path.

The article is organized as follows. Section2 contains the same as the one described, as it is currently implemented by definition and application of the problem: the structure partially reflects the structure of the problem, and we brought them together.

Section 3 describes the application of the algorithm in three slightly different ways. Sections4 and 5 reported experiments. In Section 6 we compare with other heuristic algorithms, and in Section 7 we demonstrate versatility and robustness to show that it can be applied to other optimization problems.

In Section 8 we discuss informally why and how for the function of the paradigm. In conclusion 9.

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The univariate model suggests that our specialization index indicates:

1) The strongest reaction is in small landscape gradients.

width (200 m); (2) a negative response, threshold urbanization, significantly better than linear; (3) There is no evidence.

Landscape changes with a time lag effect.

Interestingly, our model (i.e., linear battles.

The degree of influence of the threshold) at its peak.

Effect to be tested. Multivariate model linkages.

Specialization index, as well as reservation, authorization.

The species richness index shows a gradient of landscapes.

Year-to-year changes (i.e. composition, process parameters.

estimated, interpreted), there is no change.

Depends on the degree of specialization of the organism.

Our findings show that the violation of the common.

Assumptions (e.g. choosing the appropriate degree, lack of time lag effect, etc.), can affect:

As a result, this will lead to a contradiction in the DLMS inter-study.

asporin(aspn)

Asporin, a small leucine-rich protein that encodes an extracellular matrix protein, maintains.

Structural composition, including extracellular matrix.

Collagen and TGF.

Rats lack small.

Leucine-rich proteins, such as fibromodulin, or in Biglycan

and fibromodulin, as well as severe cases will be slightly premature, respectively38 39). Asporin mRNA is also expressed indeed OA cartilage (37). Epidemiology study in one day (40 people).

A specific aspartate repeat polymorphism is shown.

Inhibition of chondrogenesis in ASPN is associated with and increases the effect on OA (Table 3). In this.

In the study, the D14 allele (referring to a unique repetitive region in the n-terminal region) was significantly more frequent.

Knee office patients were significantly less common than controls, while the D13 allele was common.

Patient-to-patient ratios in controls, suggesting a protective effect.

asporin（aspn）

Asporin, a small leucine-rich repeating protein, encodes an extracellular matrix protein that binds to others.

Extracellular matrix, including structural components.

Collagen and TGF.

Small-deficient mice.

Leucine-rich proteins, such as fiber-regulated, or in disaccharides and fiber-regulated, develop a mild yet severe.

Premature work, respectively (38, 39). Asporin mRNA also has a significant effect on OA cartilage (37). Epidemiology.

There is a population study (40) in Japan.

The results suggest that specific aspartate repeat polymorphisms.

In ASPN is with inhibition of cartilage.

Increased susceptibility to osteoarthritis (see Table 3). In this.

In the study, D14 performed on the allele (referring to the unique repeat region in the N-terminal region) significantly more frequently.

Compared to the control group, it is common to have a significant reduction in the frequency of the D13 allele compared to the control group.

patients than the control group, which means a protective effect.