Optimization technique based on learning automata

Optimization techniques are finding increasingly numerous applications in process design, in parallel to the increase of computer sophistication. The process synthesis problem can be stated as a largescale constrained optimization problem involving numerous local optima and presenting a nonlinear and nonconvex character. To solve this kind of problem, the classical optimization methods can lead to analytical and numerical difficulties. This paper describes the feasibility of an optimization technique based on learning systems which can take into consideration all the prior information concerning the process to be optimized and improve their behavior with time. This information generally occurs in a very complex analytical, empirical, or know-how form. Computer simulations related to chemical engineering problems (benzene chlorination, distillation sequence) and numerical examples are presented. The results illustrate both the performance and the implementation simplicity of this method.Nomenclature c _i penalty probability - cp precision parameter on constraints - D variation domain of the variablex - f(·) objective function - g(·) constraints - i,j indexes - k iteration number - N number of actions - P probability distribution vector - p _i ith component of the vectorP as iterationk - r number of reactors in the flowsheet - u(k) discrete value or action chosen by the algorithm at iterationk - u _i discrete value of the optimization variable in [u _min,u _max] - u _min lowest value of the optimization variable - u _max largest value of the optimization variable - Z random number - x variable for the criterion function - xp precision parameter on criterion function - W(k) performance index unit output at iterationk - ₀, ₁ reinforcement scheme parameters - _p sum of the probability distribution vector components     

Operator stable laws: Series representations and domains of normal attraction

LetX,X ₁,X ₂,... be i.i.d. random vectors in ^d. The limit laws that can arise by suitable affine normalizations of the partial sums,S _n=X ₁+...+X _n, are calledoperator-stable laws. These laws are a natural extension to ^d of the stable laws on. Thegeneralized domain of attraction of [GDOA()] is comprised of all random vectorsX whose partial sums can be affinely normalized to converge to . If the linear part of the affine transformation is restricted to take the formn ^–B for some exponent operatorB naturally associated to thenX is in thegeneralized domain of normal attraction of [GDONA()]. This paper extends the theory of operator-stable laws and their domains of attraction and normal attraction.     

On consistency of bounding operations in deterministic global optimization

This technical comment refers to the discussion of strong consistency of several bounding procedures in Lemma 2.1 and Proposition 2.1 of Ref. 1. A necessary clarification is given of the notion of convergence _q in Lemma 2.1, and a derivation of Proposition 2.1 is presented that includes a new and simple consistency proof of the classical bounding by convex envelopes used in many branch-and-bound procedures.     

Slow quenching for a one-dimensional kinetic Ising model: Residual energy and domain growth

A one-dimensional kinetic Ising model with Glauber dynamics subjected to a slow continuous quench to zero temperature is studied. For a rather general class of cooling schemes, described by a time-dependent temperatureT(t), the mean domain sizeL(t) is calculated along with the residual energye _res (r) as a function of the cooling rater. If the attempt frequency =₀ exp(–/kT), entering into the transition rates, is temperature dependent (i.e., the barrier is non-zero), the asymptotic growth ofL(t) is given byL()–L(t)~exp[–/kT(t)]. For this case the residual energy exhibits a power-law behaviore _res(r) ~r ^{/2(1 + )} forr small, where =4J/ andJ is the nearest neighbor coupling constant. For =0 and for certain cooling schemes the residual energy is zero andL(t)~t1/2, independent ofr.     

离聚体的结构表征——Ⅴ.含氟离聚体中离子微区稳定性的研究

本文用DSC方法研究了羟酸型含氟离聚体中不同金属离子种类、可离子化基团含量、配位结构单元构型及离子微区大小对离聚体中离子微区稳定性的影响。实验表明:离聚体中金属离子配位能力愈强,羟酸含量愈高,离子微区尺寸愈大,则相应离聚体中离子微区稳定性愈大。铅高聚体>锌离聚体>钙离聚体>钠离聚体。     

Dielectric relaxation studies of 1-hexanol and 1,2-hexanediol in heptane

Solutions of 1-hexanol and 1,2-hexanediol in heptane have been investigated tigated by means of dielectric time domain spectroscopy (TDS). The permittivity spectrum of 1-hexanol in heptane is characterized by a model function containing a sum of three elementary Debye dispersions, while 1,2-hexanediol in heptane is best described by a Cole-Davidson model function. It is shown that dilute solutions of 1-hexanol in heptane have a completely different behavior to that of 1,2-hexanediol. For the diol, the relaxation time levels off at a high value indicating an existence of higher hydrogen bonded complexes. It is possible to quantify the relative amount of monomeric 1-alcohol molecules from the dielectric spectrum. The monomerization rate for 1-hexanol upon dilution with heptane is initially low, but increases rapidly for mole fractions of heptane exceeding 0.4.     

Expression and Characterization of Catalytic Domain of T Cell Protein Tyrosine Phosphatase（△TC-PTP） —— Immunohistochemical Study of △TC-PTP Expression in Non-small Cell Lung Carcinomas

This study objective was to express and characterize the catalytic domain of the human T cell protein tyrosine phosphatase（△TC-PTP） and to study immunohistochemically the expression of △TC-PTP in human non-small cell lung cancers. △TC-PTP gene was PCR amplified with the cDNA of human TC-PTP as template, and cloned into the pT7 expression vector. The recombinant pT7-△TC-PTP was expressed in E. coli Rosetta （ DE3 ） host cells and puri- fied. The enzymatic characteristics of △TC-PTP including enzyme activity and kinetics assay were measured. The antiserum was prepared by immunizing rabbit with the purified recombinant △TC-PTP. Rabbit polyclonal antibody against △TC-PTP was purified by PVDF immobilized antigen affinity chromatography. Immunohistochemical staining of lung cancer tissues was performed with antibody against △TC-PTP protein. △TC-PTP gene was correctly cloned, expressed, and purified. The recombinant △TC-PTP had a highly catalytic activity of PTPase. Squamous cell lung carcinoma showed a significantly higher expression rate of △TC-PTP （76. 92%, 10/13 ） than adenocarcinoma （57.14%, 4/7） and normal lung tissue（20%, 1/5 ）. This study represents the first demonstration that △TC-PTP is highly expressed in human squamous cell lung carcinomas. In addition, this study provides an important basis for further studying the biological function of TC-PTP and its relationship with lung carcinomas and other diseases.     

Synthesis and characterization of segmented liquid crystal poly(azoxy polyester-co-polyoxypropylene)

In this study a series of a segmented copolyester, poly(4,4′-dioxy-2,2′-dimethyl-azoxybenzene dodecanedioyl) (PMABD)-co-polyoxypropylene 400 (POP), was prepared. The chain length of PMABD studied (n) was varied from 7.8-18.2, and that of POP was unchanged. The intrinsic viscosity of the segmented copolyesters was 1.04-1.30, and the number average molecular weight obtained was 2.53 × 10⁴?3.49 × 10⁴ g/mol. The mesophase texture and thermal properties of the segmented copolyesters were measured as functions of n. It was found that the insert of flexible POP between those liquid crystalline domains of PMABD did affect thermotropic properties of PMABD. As the n value was 9.0 and 7.8 (or 7.4 and 8.6% by weight POP) the texture appeared as cholesteric-like oily streaks. The effect could not be attained by simply copolymerizing a mesogenic moiety with a pair of spacers of different lengths. The fluidity and domain structure of the flexible dodecanedioyl-POP-dodcanedioyl segments are taken into account for the obtained results. © 1995 John Wiley & Sons, Inc.     

Fast folding and comparison of RNA secondary structures

Summary Computer codes for computation and comparison of RNA secondary structures, the Vienna RNA package, are presented, that are based on dynamic programming algorithms and aim at predictions of structures with minimum free energies as well as at computations of the equilibrium partition functions and base pairing probabilities.An efficient heuristic for the inverse folding problem of RNA is introduced. In addition we present compact and efficient programs for the comparison of RNA secondary structures based on tree editing and alignment.All computer codes are written in ANSI C. They include implementations of modified algorithms on parallel computers with distributed memory. Performance analysis carried out on an Intel Hypercube shows that parallel computing becomes gradually more and more efficient the longer the sequences are.

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Schnelle Faltung und Vergleich von Sekundärstrukturen von RNA

Zusammenfassung Die im Vienna RNA package enthaltenen Computer Programme für die Berechnung und den Vergleich von RNA Sekundärstrukturen werden präsentiert. Ihren Kern bilden Algorithmen zur Vorhersage von Strukturen minimaler Energie sowie zur Berechnung von Zustandssumme und Basenpaarungswahrscheinlichkeiten mittels dynamischer Programmierung.Ein effizienter heuristischer Algorithmus für das inverse Faltungsproblem wird vorgestellt. Darüberhinaus präsentieren wir kompakte und effiziente Programme zum Vergleich von RNA Sekundärstrukturen durch Baum-Editierung und Alignierung.Alle Programme sind in ANSI C geschrieben, darunter auch eine Implementation des Faltungs-algorithmus für Parallelrechner mit verteiltem Speicher. Wie Tests auf einem Intel Hypercube zeigen, wird das Parallelrechnen umso effizienter je länger die Sequenzen sind.

     

Genomic variation and point mutations analysis of Indian COVID-19 patient samples submitted in GISAID database

Corona virus disease 2019 (COVID-19) endemic has havoc on the world; the causative virus of the pandemic is SARS CoV-2. Pharmaceutical companies and academic institutes are in continuous efforts to identify anti-viral therapy or vaccines, but the most significant challenge faced is the highly evolving genome of SARS CoV-2, which is imparting evolutionary selective benefits to the virus. To understand the viral mutations, we have retrieved nine hundred and thirty-four samples from different states of India via the GISAID database and analyzed the frequency of all types of point mutation in all structural, non-structural proteins, and accessory factors of SARS CoV-2. Spike glycol protein, nsp3, nsp6, nsp12, N and NS3 were the most evolving proteins. High frequency point mutations were Q496P (nsp2), A380V (nsp4), A994D (nsp3), L37F (nsp6), P323L & A97V (nsp12), Q57H (ns3), D614G (S), P13L (N), R203K (N), G204R (N) and S194L (N).