Abstract:
This paper talk about the complexity of computation by Turing Machine. I take attention to the relation of symmetry and order structure of the data, and I think about the limitation of computation time. First, I make general problem named "testing problem". And I get some condition of the P complete and NP complete by using testing problem. Second, I make two problem "orderly problem" and "chaotic problem". Orderly problem have some order structure. And DTM can limit some possible symbol effectly by using symmetry of each symbol. But chaotic problem must treat some symbol as a set of symbol, so DTM cannot limit some possible symbol. Orderly problem is P complete, and chaotic problem is NP complete. Finally, I clear the computation time of orderly problem and chaotic problem. And P != NP.

Abstract:
This paper talk about the influence of Connection and Dispersion on Computational Complexity. And talk about the HornCNF's connection and CNF's dispersion, and show the difference between CNFSAT and HornSAT. First, I talk the relation between MUC decision problem and classifying the truth value assignment. Second, I define the two inner products ("inner product" and "inner harmony") and talk about the influence of orthogonal and correlation to MUC. And we can not reduce MUC to Orthogonalization MUC by using HornMUC in polynomial size because HornMUC have high orthogonal of inner harmony and MUC do not. So DP is not P, and NP is not P.

Abstract:
This paper talk about that NP is not AL and P, P is not NC, NC is not NL, and NL is not L. The point about this paper is the depend relation of the problem that need other problem's result to compute it. I show the structure of depend relation that could divide each complexity classes.

Abstract:
This paper divide some complexity class by using fixpoint and fixpointless area of Decidable Universal Turing Machine (UTM). Decidable Deterministic Turing Machine (DTM) have fixpointless combinator that add no extra resources (like Negation), but UTM makes some fixpoint in the combinator. This means that we can jump out of the fixpointless combinator system by making more complex problem from diagonalisation argument of UTM. As a concrete example, we proof L is not P . We can make Polynomial time UTM that emulate all Logarithm space DTM (LDTM). LDTM set close under Negation, therefore UTM does not close under LDTM set. (We can proof this theorem like halting problem and time/space hierarchy theorem, and also we can extend this proof to divide time/space limited DTM set.) In the same way, we proof P is not NP. These are new hierarchy that use UTM and Negation.

Abstract:
This paper talks about difference between P and NP by using topological space that mean resolution principle. I pay attention to restrictions of antecedent and consequent in resolution, and show what kind of influence the restrictions have for difference of structure between P and NP regarding relations of relation. First, I show the restrictions of antecedent and consequent in resolution principle. Antecedents connect each other, and consequent become a linkage between these antecedents. And we can make consequent as antecedents product by using some resolutions which have same joint variable. We can determine these consequents reducible and irreducible. Second, I introduce RCNF that mean topology of resolution principle in CNF. RCNF is HornCNF and that variable values are presence of restrictions of CNF formula clauses. RCNF is P-Complete. Last, I introduce TCNF that have 3CNF's character which relate 2 variables relations with 1 variable. I show CNF complexity by using CCNF that combine some TCNF. TCNF is NP-Complete and product irreducible. I introduce CCNF that connect TCNF like Moore graph. We cannot reduce CCNF to RCNF with polynomial size. Therefore, TCNF is not in P.

Abstract:
This article provide new approach to solve P vs NP problem by using cardinality of bases function. About NP-Complete problems, we can divide to infinite disjunction of P-Complete problems. These P-Complete problems are independent of each other in disjunction. That is, NP-Complete problem is in infinite dimension function space that bases are P-Complete. The other hand, any P-Complete problem have at most a finite number of P-Complete basis. The reason is that each P problems have at most finite number of Least fixed point operator. Therefore, we cannot describe NP-Complete problems in P. We can also prove this result from incompleteness of P.

Abstract:
This article describes the solvability of HornSAT and CNFSAT. Unsatisfiable HornCNF have partially ordered set that is made by causation of each clauses. In this partially ordered set, Truth value assignment that is false in each clauses become simply connected space. Therefore, if we reduce CNFSAT to HornSAT, we must make such partially ordered set in HornSAT. But CNFSAT have correlations of each clauses, the partially ordered set is not in polynomial size. Therefore, we cannot reduce CNFSAT to HornSAT in polynomial size.

Abstract:
This paper talks about that monotone circuit is P-Complete. Decision problem that include P-Complete is mapping that classify input with a similar property. Therefore equivalence relation of input value is important for computation. But monotone circuit cannot compute the equivalence relation of the value because monotone circuit can compute only monotone function. Therefore, I make the value constraint explicitly in the input and monotone circuit can compute equivalence relation. As a result, we can compute P-Complete problem with monotone circuit. We can reduce implicit value constraint to explicit with logarithm space. Therefore, monotone circuit is P-Complete.

Abstract:
This article describes about the difference of resolution structure and size between HornSAT and CNFSAT. We can compute HornSAT by using clauses causality. Therefore we can compute proof diagram by using Log space reduction. But we must compute CNFSAT by using clauses correlation. Therefore we cannot compute proof diagram by using Log space reduction, and reduction of CNFSAT is not P-Complete.

Abstract:
We extend
our previous analysis and consider the interacting holographic Ricci dark
energy (IRDE) model in non-flat universe. We study astrophysical constraints on
this model using the recent observations including the type Ia supernovae
(SNIa), the baryon acoustic oscillation (BAO), the cosmic microwave background
(CMB) anisotropy, and the Hubble parameter. It is shown that the allowed
parameter range for the fractional energy density of the curvature is ？in the presence of the interactions between
dark energy and matter. Without the interaction, the flat universe is
observationally disfavored in this model.