Accelerated Electrophotocatalytic C(sp3)−H Heteroarylation Enabled by an Efficient Continuous-Flow Reactor**

Electrophotocatalytic transformations are garnering attention in organic synthesis, particularly for accessing reactive intermediates under mild conditions. Moving these methodologies to continuous-flow systems, or flow ElectroPhotoCatalysis (f-EPC), showcases potential for scalable processes due to enhanced irradiation, increased electrode surface, and improved mixing of the reaction mixture. Traditional methods sequentially link photochemical and electrochemical reactions, using flow reactors connected in series, yet struggle to accommodate reactive transient species. In this study, we introduce a new flow reactor concept for electrophotocatalysis (EPC) that simultaneously utilizes photons and electrons. The reactor is designed with a transparent electrode and employs cost-effective materials. We used this technology to develop an efficient process for electrophotocatalytic heteroarylation of C(sp³)−H bonds. Importantly, the same setup can also facilitate purely electrochemical and photochemical transformations. This reactor represents a significant advancement in electrophotocatalysis, providing a framework for its application in flow for complex synthetic transformations.     

A new multi-objective self-organizing optimization algorithm (MOSOA) for spatial optimization problems

Spatial planning is an important and complex activity. It includes land use planning and resource allocation as basic components. An abundance of papers can be found in the literature related to each one of these two aspects separately. On the contrary, a much smaller number of research reports deal with both aspects simultaneously. This paper presents an innovative evolutionary algorithm for treating combined land use planning and resource allocation problems. The new algorithm performs optimization on a cellular automaton domain, applying suitable transition rules on the individual neighbourhoods. The optimization process is multi-objective, based on non-domination criteria and self-organizing. It produces a Pareto front thus offering an advantage to the decision maker, in comparison to methods based on weighted-sum objective functions. Moreover, the present multi-objective self-organizing algorithm (MOSOA) can handle both local and global spatial constraints. A combined land use and water allocation problem is treated, in order to illustrate the cellular automaton optimization approach. Water is allocated after pumping from an aquifer, thus contributing a nonlinearity to the objective function. The problem is bi-objective aiming at (a) the minimization of soil and groundwater pollution and (b) the maximization of economic profit. An ecological and a socioeconomic constraint are imposed: (a) Groundwater levels at selected places are kept above prescribed thresholds. (b) Land use quota is predefined. MOSOA is compared to a standard multi-objective genetic algorithm and is shown to yield better results both with respect to the Pareto front and to the degree of compactness. The latter is a highly desirable feature of a land use pattern. In the land use literature, compactness is part of the objective function or of the constraints. In contrast, the present approach renders compactness as an emergent result.     

Higher-order accurate polyspectral estimation with flat-top lag-windows

Improved performance in higher-order spectral density estimation is achieved using a general class of infinite-order kernels. These estimates are asymptotically less biased but with the same order of variance as compared to the classical estimators with second-order kernels. A simple, data-dependent algorithm for selecting the bandwidth is introduced and is shown to be consistent with estimating the optimal bandwidth. The combination of the specialized family of kernels with the new bandwidth selection algorithm yields a considerably improved polyspectral estimator surpassing the performances of existing estimators using second-order kernels. Bispectral simulations with several standard models are used to demonstrate the enhanced performance with the proposed methodology.     

Robust Autocorrelation Estimation

In this article, we introduce a new class of robust autocorrelation estimators based on interpreting the sample autocorrelation function as a linear regression. We investigate the efficiency and robustness properties of the estimators that result from employing three common robust regression techniques. We discuss the construction of robust autocovariance and positive definite autocorrelation estimates, and their application to AR model fitting. We perform simulation studies with various outlier configurations to compare the different estimators.     

On Testing for the Number of Components in a Mixed Poisson Model

Poisson mixtures are usually used to describe overdispersed data. Finite Poisson mixtures are used in many practical situations where often it is of interest to determine the number of components in the mixture. Identifying how many components comprise a mixture remains a difficult problem. The likelihood ratio test (LRT) is a general statistical procedure to use. Unfortunately, a number of specific problems arise and the classical theory fails to hold. In this paper a new procedure is proposed that is based on testing whether a new component can be added to a finite Poisson mixture which eventually leads to the number of components in the mixture. It is a sequential testing procedure based on the well known LRT that utilises a resampling technique to construct the distribution of the test statistic. The application of the procedure to real data reveals some interesting features of the distribution of the test statistic.     

A Sharp Threshold for k‐Colorability

Let k be a fixed integer and f_k(n, p) denote the probability that the random graph G(n, p) is k‐colorable. We show that for k≥3, there exists d_k(n) such that for any ϵ>0, (1) As a result we conclude that for sufficiently large n the chromatic number of G(n, d/n) is concentrated in one value for all but a small fraction of d>1. ©1999 John Wiley & Sons, Inc. Random Struct. Alg., 14, 63–70, 1999     

Study of the 'teaching system' according to systems theory

The traditional triadic 'Teaching System' (the teacher, the student and mathematics) and the new quaternary version (the teacher, the student, mathematics and new technologies), are studied according to Systems Theory. Systems Theory is described and the model of the traditional 'Teaching System' and that of the new system are analysed with the aid of this theory; that is the structure of each system (elements, interactions) and its function are described. Finally, comments are made about the application of the new system on didactic, on the one hand, and the philosophical background of the incorporation of the fourth element (new technologies) in the traditional system, on the other.     

Characteristic parameter sets and limits of circulant Hermitian polygon transformations

Polygon transformations based on taking the apices of similar triangles constructed on the sides of an initial polygon are analyzed as well as the limit polygons obtained by iteratively applying such transformations. In contrast to other approaches, this is done with respect to two construction parameters representing a base angle and an apex perpendicular subdivision ratio. Furthermore, a combined transformation leading to circulant Hermitian matrices is proposed, which eliminates the rotational effect of the basic transformation. A finite set of characteristic parameter subdomains is derived for which the sequence converges to specific eigenpolygons. Otherwise, limit polygons turn out to be linear combinations of up to three eigenpolygons. This leads to a full classification of circulant Hermitian similar triangles based polygon transformations and their limit polygons. As a byproduct classical results as Napoleon’s theorem and the Petr-Douglas-Neumann theorem can be easily deduced.