Anomalous Stoichiometry and Antiferromagnetic Ordering for the Extended Hydroxymanganese(II) Cubes/Hexacyanometalate-Based 3D-Structured [MnII4(OH)4][MnII(CN)6](OH2)6⋅H2O

The reaction of Mn^II(O₂CMe)₂ and NaCN or LiCN in water forms a light green insoluble material. Structural solution and Rietveld refinement of high-resolution synchrotron powder diffraction data for this unprecedented, complicated compound of previously unknown composition revealed a new alkali-free ordered structural motif with [Mn^II₄(μ₃-OH)₄]⁴⁺ cubes and octahedral [Mn^II(CN)₆]⁴⁻ ions interconnected in 3D by Mn^II-N≡C-Mn^II linkages. The composition is {[Mn^II(OH₂)₃][Mn^II(OH₂)]₃}(μ₃-OH)₄][Mn^II(μ-CN)₂(CN)₄] ⋅ H₂O=[Mn^II₄(μ₃-OH)₄(OH₂)₆][Mn^II(μ-CN)₂(CN)₄] ⋅ H₂O, which is further simplified to [Mn₄(OH)₄][Mn(CN)₆](OH₂)₇ ( 1 ). 1 has four high-spin (S=5/2) Mn^II sites that are antiferromagnetically coupled within the cube and are antiferromagnetically coupled to six low-spin (S=1/2) octahedral [Mn^II(CN)₆]⁴⁻ ions. Above 40 K the magnetic susceptibility, χ(T), can be fitted to the Curie–Weiss expression, χ ∝(T−θ)⁻¹, with θ=−13.4 K, indicative of significant antiferromagnetic coupling and 1 orders as an antiferromagnet at T_c=7.8 K.     

A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning

The most common machine-learning methods solve supervised and unsupervised problems based on datasets where the problem’s features belong to a numerical space. However, many problems often include data where numerical and categorical data coexist, which represents a challenge to manage them. To transform categorical data into a numeric form, preprocessing tasks are compulsory. Methods such as one-hot and feature-hashing have been the most widely used encoding approaches at the expense of a significant increase in the dimensionality of the dataset. This effect introduces unexpected challenges to deal with the overabundance of variables and/or noisy data. In this regard, in this paper we propose a novel encoding approach that maps mixed-type data into an information space using Shannon’s Theory to model the amount of information contained in the original data. We evaluated our proposal with ten mixed-type datasets from the UCI repository and two datasets representing real-world problems obtaining promising results. For demonstrating the performance of our proposal, this was applied for preparing these datasets for classification, regression, and clustering tasks. We demonstrate that our encoding proposal is remarkably superior to one-hot and feature-hashing encoding in terms of memory efficiency. Our proposal can preserve the information conveyed by the original data.     

Development and Application of Different Non-thermal Plasma Reactors for the Removal of Perfluorosurfactants in Water: A Comparative Study

Plasma Chemistry and Plasma Processing - The degradation of perfluorosurfactants (PFS), particularly of PFOS, has been studied in dielectric barrier discharge (DBD) and nano-pulse corona discharge...     

Multiplicity distributions from quarks and diquarks in deep inelastic lepton-nucleon scattering

We present an analytic prediction for the full hadronic multiplicity distribution in deep-inelastic lepton-nucleon scattering. We obtain a distinctive relationship between the average multiplicity at any W and that in e⁺e^? annihilation.     

An example of intermittent behavior withn-dependence,from multiplicity distributions over the impact-parameter plane

We exhibit intermittent behavior in multiparticle production from explicit calculations within an idealized model in configuration space. The origin of intermittency with dependence uponn lies in the existence of a multitude of independent multiplicity distributions, of different form, over the impact-parameter plane. We also generalize these considerations to the longitudinal direction, along the collision line.