A nonlinear model for marble sulphation including surface rugosity and mechanical damage

Here we propose and analyze a mathematical model that aims to describe the marble sulphation process occurring in a given material. The model accounts for rugosity as well as for damaging effects. This model is characterized by some technical difficulties that seem hard to overcome from a theoretical viewpoint. Therefore, we introduce some physically reasonable modifications in order to establish the existence of a suitable notion of solution on a given time interval. Numerical simulations are presented and discussed, also in view of further research.     

Parametric error bounds for convex approximations of two-stage mixed-integer recourse models with a random second-stage cost vector

We consider two-stage recourse models with integer restrictions in the second stage. These models are typically non-convex and hence, hard to solve. There exist convex approximations of these models with accompanying error bounds. However, it is unclear how these error bounds depend on the distributions of the second-stage cost vector q. In this paper, we derive parametric error bounds whose dependence on the distribution of q is explicit: they scale linearly in the expected value of the ${?}_1$-norm of q.     

Analysis of a new multispecies tumor growth model coupling 3D phase-fields with a 1D vascular network

In this work, we present and analyze a mathematical model for tumor growth incorporating ECM erosion, interstitial flow, and the effect of vascular flow and nutrient transport. The model is of phase-field or diffused-interface type in which multiple phases of cell species and other constituents are separated by smooth evolving interfaces. The model involves a mesoscale version of Darcy’s law to capture the flow mechanism in the tissue matrix. Modeling flow and transport processes in the vasculature supplying the healthy and cancerous tissue, one-dimensional (1D) equations are considered. Since the models governing the transport and flow processes are defined together with cell species models on a three-dimensional (3D) domain, we obtain a 3D–1D coupled model.     

Ill-posedness of the Camassa–Holm and related equations in the critical space

We prove norm inflation and hence ill-posedness for a class of shallow water wave equations, such as the Camassa–Holm equation, Degasperis–Procesi equation and Novikov equation etc., in the critical Sobolev space $H^{3/2}$ and even in the Besov space $B_{p,r}^{1+1/p}$ for $p\in [1,\infty ],r\in (1,\infty ]$. Our results cover both real-line and torus cases (only real-line case for Novikov), solving an open problem left in the previous works ([5], [14], [16]).     

Shedding vortex simulation method based on viscous compensation technology research

Owing to the influence of the viscosity of the flow field, the strength of the shedding vortex decreases gradually in the process of backward propagation. Large-scale vortexes constantly break up, forming smaller vortexes. In engineering, when numerical simulation of vortex evolution process is carried out, a large grid is needed to be arranged in the area of outflow field far from the boundary layer in order to ensure the calculation efficiency. As a result, small scale vortexes at the far end of the flow field cannot be captured by the sparse grid in this region, resulting in the dissipation or even disappearance of vortexes. In this paper, the effect of grid scale is quantified and compared with the viscous effect through theoretical derivation. The theoretical relationship between the mesh viscosity and the original viscosity of the flow field is established, and the viscosity term in the turbulence model is modified. This method proves to be able to effectively improve the intensity of small-scale shedding vortexes at the far end of the flow field under the condition of sparse grid. The error between the simulation results and the results obtained by using fine mesh is greatly reduced, the calculation time is shortened, and the high-precision and efficient simulation of the flow field is realized.     

Modeling Thioredoxin Reductase-Like Activity with Cyclic Selenenyl Sulfides: Participation of an NH⋅⋅⋅Se Hydrogen Bond through Stabilization of the Mixed Se−S Intermediate

At the redox-active center of thioredoxin reductase (TrxR), a selenenyl sulfide (Se−S) bond is formed between Cys497 and Sec498, which is activated into the thiolselenolate state ([SH,Se⁻]) by reacting with a nearby dithiol motif ([SH^Cys59,SH^Cys64]) present in the other subunit. This process is achieved through two reversible steps: an attack of a cysteinyl thiol of Cys59 at the Se atom of the Se−S bond and a subsequent attack of a remaining thiol at the S atom of the generated mixed Se−S intermediate. However, it is not clear how the kinetically unfavorable second step progresses smoothly in the catalytic cycle. A model study that used synthetic selenenyl sulfides, which mimic the active site structure of human TrxR comprising Cys497, Sec498, and His472, suggested that His472 can play a key role by forming a hydrogen bond with the Se atom of the mixed Se−S intermediate to facilitate the second step. In addition, the selenenyl sulfides exhibited a defensive ability against H₂O₂-induced oxidative stress in cultured cells, which suggests the possibility for medicinal applications to control the redox balance in cells.     

A Self-Assembled Cage with Endohedral Acid Groups both Catalyzes Substitution Reactions and Controls Their Molecularity

A self-assembled Fe₄L₆ cage complex internally decorated with acid functions is capable of accelerating the thioetherification of activated alcohols, ethers and amines by up to 1000-fold. No product inhibition is seen, and effective supramolecular catalysis can occur with as little as 5 % cage. The substrates are bound in the host with up to micromolar affinities, whereas the products show binding that is an order of magnitude weaker. Most importantly, the cage host alters the molecularity of the reaction: whereas the reaction catalyzed by simple acids is a unimolecular, S_N1-type substitution process, the rate of the host-mediated process is dependent on the concentration of nucleophile. The molecularity of the cage-catalyzed reaction is substrate-dependent, and can be up to bimolecular. In addition, the catalysis can be prevented by a large excess of nucleophile, where substrate inhibition dominates, and the use of tritylated anilines as substrates causes a negative feedback loop, whereby the liberated product destroys the catalyst and stops the reaction.     

Kinetics of carbon steel dissolution in ammonium chloride solution containing sodium thiosulfate

The dissolution behavior of carbon steel in ammonium chloride (NH₄Cl) solution containing sodium thiosulfate (Na₂S₂O₃) of various concentrations (0.01 and 0.1 M) was investigated using electrochemical impedance spectroscopy (EIS) and other nonelectrochemical techniques. The weight loss and polarization measurements indicate a significant increase in the NH₄Cl corrosion rate of carbon steel on addition of Na₂S₂O_3. The EIS measurements exhibited two capacitive loops at multiple direct current (dc) potentials for both the concentrations. Electrical equivalent circuit (EEC) and reaction mechanism analysis (RMA) were employed to analyze the impedance data. A four-step mechanism with two intermediate adsorbate species of same charge was proposed to explain the dissolution behavior of carbon steel in the given system. The surface coverage values enumerated that the surface was entirely covered with adsorbed species unlike in the pure NH₄Cl system. Charge transfer resistance and polarization resistance values estimated from RMA parameters indicate the increase in a dissolution rate with dc potential. The surface morphology was inspected via field emission scanning electron microscopy, and the corrosion products including surface state of carbon steel electrode were analyzed using energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy.     

Order-Stability in Complex Biological,Social, and AI-Systems from Quantum Information Theory

This paper is our attempt, on the basis of physical theory, to bring more clarification on the question “What is life?” formulated in the well-known book of Schrödinger in 1944. According to Schrödinger, the main distinguishing feature of a biosystem’s functioning is the ability to preserve its order structure or, in mathematical terms, to prevent increasing of entropy. However, Schrödinger’s analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schrödinger also appealed to the ambiguous notion of negative entropy. We apply quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of classical entropy. We consider a complex biosystem S composed of many subsystems, say proteins, cells, or neural networks in the brain, that is, S=(Si). We study the following problem: whether the compound system S can maintain “global order” in the situation of an increase of local disorder and if S can preserve the low entropy while other Si increase their entropies (may be essentially). We show that the entropy of a system as a whole can be constant, while the entropies of its parts rising. For classical systems, this is impossible, because the entropy of S cannot be less than the entropy of its subsystem Si. And if a subsystems’s entropy increases, then a system’s entropy should also increase, by at least the same amount. However, within the quantum information theory, the answer is positive. The significant role is played by the entanglement of a subsystems’ states. In the absence of entanglement, the increasing of local disorder implies an increasing disorder in the compound system S (as in the classical regime). In this note, we proceed within a quantum-like approach to mathematical modeling of information processing by biosystems—respecting the quantum laws need not be based on genuine quantum physical processes in biosystems. Recently, such modeling found numerous applications in molecular biology, genetics, evolution theory, cognition, psychology and decision making. The quantum-like model of order stability can be applied not only in biology, but also in social science and artificial intelligence.