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Tumor-associated macrophages (TAMs) are major component of leukocytic infiltrate of tumors and play important roles in progression and regression of tumors. Tumor microenvironment determines the mutual conversion between M1 and M2 macrophages. In many kinds of tumors, M2 type macrophages are of the majority in TAMs and promote tumor progression and metastasis. The dynamic balance and interaction between TAMs and tumor cells have important effects on the occurrence and development of tumor. TAMs in malignant tumors are useful for clinical diagnosis and may provide a novel target for cancer treatment.

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Many websites have a hierarchical organization of content. This organization may be quite different from the organization expected by visitors to the website. In particular, it is often unclear where a specific document is located. In this paper, we propose an algorithm to automatically find pages in a website whose location is different from where visitors expect to find them. The key insight is that visitors will backtrack if they do not find the information where they expect it: the point from where they backtrack is the expected location for the page. We present an algorithm for discovering such expected locations that can handle page caching by the browser. Expected locations with a significant number of hits are then presented to the website administrator. We also present algorithms for selecting expected locations (for adding navigation links) to optimize the benefit to the website or the visitor. We ran our algorithm on the Wharton business school website and found that even on this small website, there were many pages with expected locations different from their actual location.

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O ne critical operational decision facing online advertisers when they engage in sponsored search advertising is concerned with the allocation of a limited advertising budget. In particular, dealing with multi-keyword search markets over multiple decision periods poses significant decision-making challenges. In this paper, we develop a novel budget allocation optimization model with multiple search advertising markets and a finite time horizon. One key element of our modeling work is developing a customized advertising response function when considering distinctive features of sponsored search, including the quality score and the dynamic advertising effort. We derive a feasible solution to our budget model and study its properties. Computational experiments are conducted on real-world data to evaluate our budget model and perform parameter sensitivity analysis. Experimental results indicate that our budget allocation strategy significantly outperforms several baseline strategies. In addition, the identified properties derived from the solution process illuminate critical managerial insights for advertisers in sponsored search.

SUMMARYIn this paper, without assuming the boundedness, monotonicity and di erentiability of the activation functions, we present new conditions ensuring existence, uniqueness, and global asymptotical stability of the equilibrium point of bidirectional associative memory neural networks with ÿxed time delays or distributed time delays. The results are applicable to both symmetric and non-symmetric interconnection matrices, and all continuous non-monotonic neuron activation functions.

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