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Quantum Mechanics I
A Problem Text
The Far Horizons of Time
Time and Mind in the Universe
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Switching from petro-plastics to microbial polyhydroxyalkanoates (PHA): the biotechnological escape route of choice out of the plastic predicament?

Abstract

The benefit of biodegradable “green plastics” over established synthetic plastics from petro-chemistry, namely their complete degradation and safe disposal, makes them attractive for use in various fields, including agriculture, food packaging, and the biomedical and pharmaceutical sector. In this context, microbial polyhydroxyalkanoates (PHA) are auspicious biodegradable plastic-like polyesters that are considered to exert less environmental burden if compared to polymers derived from fossil resources.

The question of environmental and economic superiority of bio-plastics has inspired innumerable scientists during the last decades. As a matter of fact, bio-plastics like PHA have inherent economic drawbacks compared to plastics from fossil resources; they typically have higher raw material costs, and the processes are of lower productivity and are often still in the infancy of their technical development. This explains that it is no trivial task to get down the advantage of fossil-based competitors on the plastic market. Therefore, the market success of biopolymers like PHA requires R&D progress at all stages of the production chain in order to compensate for this disadvantage, especially as long as fossil resources are still available at an ecologically unjustifiable price as it does today.

Ecological performance is, although a logical argument for biopolymers in general, not sufficient to make industry and the society switch from established plastics to bio-alternatives. On the one hand, the review highlights that there’s indeed an urgent necessity to switch to such alternatives; on the other hand, it demonstrates the individual stages of the production chain, which need to be addressed to make PHA competitive in economic, environmental, ethical, and performance-related terms. In addition, it is demonstrated how new, smart PHA-based materials can be designed, which meet the customer’s expectations when applied, e.g., in the biomedical or food packaging sector.

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Dimensionless characterization of the non-linear soil consolidation problem of Davis and Raymond. Extended models and universal curves

[ {\frac{{{{{\rm{\sigma '}}}_{\rm{f}}} - {\rm{\;}}{{{\rm{\sigma '}}}_{\rm{o}}}}}{{\sigma_{\rm{m}}^{\rm{'}}}}} \right] \end{array}$$ (26) where Ψ is an arbitrary and unknown function of its argument. Adopting for σ ′ m the value, for example, σ ′ o (it can also be the value σ ′ f or the average between them), the above equation is simplified to τ o , σ ′ = H o 2 c v Ψ σ ′ f σ ′ o $$\begin{array}{} \displaystyle \tau_{{\rm{o}},{\rm{\sigma '}}} = \frac{{{\rm{H}}_{\rm{o}}^2}}{{{{\rm{c}}_{\rm{v}}}}} \Psi \left[ {\frac{{{{{\rm{\sigma '}}}_{\rm

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Boltzmann and the Statistical Multifractals

1 Introduction The multifractal formalism introduced by Halsey et al [ 1 ] can be understood in a simple way by applying a similar reasoning that was used by Boltzmann for obtaining the thermodynamics of an ideal gas using statistical arguments instead of the microscopic description of a system conformed by 10 23 particles. The central idea was to introduce the relation between the entropy and the probability associated with a macrostate [ 2 , 3 ]. In the second section recalls briefly the Boltzmann fundamental ideas for obtaining the statistical

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An Allee Threshold Model for a Glioblastoma(GB)-Immune System(IS) Interaction with Fuzzy Initial Values

Ö. A Tumor-Macrophage Interaction Model with Fuzzy Initial Values IAIP Proceedings 1648 2015 Article ID 370007 [9] B.G.Birkhuadetal, A mathematical model of the development of drug resistance to cancer chemotherapy , Europ. J. of Cancer and Clinical Oncology, 23 (1987), 14211427. Birkhuadetal B.G. A mathematical model of the development of drug resistance to cancer chemotherapy Europ. J. of Cancer and Clinical Oncology 23 1987 14211427 [10] F. Bozkurt, Modeling a Tumor Growth with Piecewise Constant Arguments , Discrete Dynamics in Nature and Society, 841764

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Analysis of fractional factor system for data transmission in SDN

,                                     $$\left( a+\Delta -2 \right)\left| S \right|+{{d}_{R-S}}\left( T \right)-\left( b-\Delta \right)\left| T \right|\le -1,$$ and (9) T = { x : x ∈ V ( R ) − S , d R − S ( x ) ≤ b − Δ − 1 } .                                 $$T=\left\{ x:x\in V\left( R \right)-S,{{d}_{R-S}}\left( x \right)\le b-\Delta -1 \right\}.$$ It follows from (8) that T ≠ / 0. In the following, we define h = min {d R−S ( x ) : x ∈ T} . In terms of (9), we obtain 0 ≤ h ≤ b − Δ − 1. For 1 ≤ h ≤ b − Δ − 1, we apply the same argument as in Theorem 1 . In the following

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Predicting the separation of time scales in a heteroclinic network

instants before and after application of the map g i . Upon the evolution in time, the role of the variables r i , i ∈ {1, . . .,9} changes with respect to their directions ( es, el, cs, cl ). What was a cs ( i )-direction before the map, becomes the direction i at the subsequent saddle. Thus we apply cs ( i ) to the indices at time T 2 (terms in the middle of eqs. (12) to (14)) , using cs ( es ( i )) = i , cs ( cs ( i )) = es ( i ), etc. This way we trace back the origin of the new directions. The arguments of the double primed terms show how the

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Multidimensional BSDE with Poisson jumps of Osgood type

. $$\begin{array}{} \begin{split}{} \displaystyle {\bf E}\left[\sup_{t\le s \le T}|\widehat{Y}_s^{n, m}|^2\right] &+ {\bf E}\left[\int_t^T|\widehat{Z}_s^{n, m}|^2ds\right] + {\bf E}\left[\int_t^T\int_E|\widehat{U}_s^{n, m} (e)|^2\lambda(de)ds\right] \\ &\leq c \int_t^T\gamma(s) H \left({\bf E}\left[\sup_{s\le r \le T}|\widehat{Y}_r^{n, m}|^2\right]\right) d s + c \times \int_{0}^{T} \gamma^{2}(s) ds. \end{split} \end{array}$$ Then using the same arguments as in [ 2 ], we deduce that the sequence (Θ n ) = ( Y n , Z n , U n ) is a Cauchy sequence in the space ℬ 2 ( R k

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Can aphids be controlled by fungus? A mathematical model

{array}{} \displaystyle R_0^2 \end{array}$ may be written as R 0 2 = γ β N 0 q ( γ + p + c N 0 ) . $$\begin{array}{} \displaystyle R_0^2 = \frac{\gamma \beta N_0}{q(\gamma + p + cN_0)}. \end{array}$$ This may be interpreted as follows, with arguments that can be made mathematically rigorous. An infected aphid introduced into the system at ( N 0 , 0, 0), the primary, leaves the infected aphid ( E ) class at rate γ + p + cN 0 and so remains in the E class for a time 1/( γ + p + cN 0 ), on average. While it is in the class it produces fungus F at rate γ , so it

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