Simple procedure for generating sequences of daily radiation values using a library of Markov transition matrices

This article describes how sequences of daily global radiation can be generated for any location, using as input only the average monthly radiation for that location (or the average monthly number of sunshine hours—insolation). The generated sequences are statistically indistinguishable from real ones and the method derived here is, therefore, a way of obtaining radiation sequences for locations were such sequences have not been measured, and for which many types of long-term performance calculations could not be made until now. The method is based on the observation that (1) there is a significant correlation only between radiation values for consecutive days and (2) that the probability of occurrence of radiation values is the same for months with the same K_t (clearness index). The method employs a library of Markov transition matrices, each corresponding to a specific interval in K_t. This article explains the derivation of the matrices, how they are to be used to generate radiation sequences, and compares synthetized and measured sequences.     

Palladium on Calcium Carbonate Combined to 2‐Hydroxypropyl‐α/β‐cyclodextrins: A Selective Catalytic System for Aqueous Heck Coupling and Hydroarylation

An efficient, selective and recoverable catalytic system for ligand‐free aqueous Heck reactions using hydroxypropylated cyclodextrins (HPCDs) and palladium on calcium carbonate (Pd/CaCO₃) is highlighted. Remarkably, stereo‐ and chemoselectivities could be tuned by the cavity size of cyclodextrins, exploiting the relevance of host‐guest interactions. UV‐Vis experiments have led to strong evidence concerning an interplay between Pd(II) and α‐HPCD, possibly ascribed to a reduction/stabilization effect of CDs. Unexpectedly, hydroarylation was the favored pathway with acrylonitrile which provided access to 3‐phenylpropionitrile derivatives without usual hydride donors. Finally, determination of soluble Pd(0/II) via AAS enabled the definition of a predominant homogeneous mechanism in which TONs over 5000 were observed.     

Nanocomposites of ultrahigh molar mass polyethylene and modified carbon nanotubes

In this work, the use of a laboratory twin-screw extruder was evaluated to process ultrahigh molar mass polyethylene and composites with carbon nanotubes (CNTs). Commercial polymer samples with lubricant (1%) and different percentages (0.01%, 0.05%, and 0.1%) of pure, oxidized, and chemically surface treated multi-walled carbon nanotubes (MWCNTs) were evaluated. The results showed that polymer melting and crystallization temperatures were not affected by CNTs, although an increase in the degree of crystallinity in all nanocomposites was observed along with a decrease in crystal size. Therefore, CNTs behaved as nucleating agents. All ultrahigh molar mass polyethylene (UHMWPE)/CNT samples showed increased initial degradation temperature, although this was not very great when introducing acetylated and stearic acid modified CNTs. Both oxidized CNTs and stearic acid CNTs did not markedly improve the composites' mechanical properties. Therefore, the nanocomposites containing pure CNTs and most of those with acetylated CNTs resulted in higher reinforcement for UHMWPE. The addition of the lubricant allowed the polymer matrix to be processed in the extruder, whereas the increase in CNT content in UHMWPE improved the stiffness of the material. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47459     

Prediction and comparison of textural properties of magnetic copolymer supports for enzyme immobilization

Magnetic polymers supports have proven to be valuable materials for enzyme immobilization, as they allow recovering the catalyst by magnetic separation, precluding the need for costly and time-consuming separation steps. In this study, magnetic copolymer supports were synthesized using styrene (STY) and different crosslinking agents (divinylbenzene, ethylene glycol dimethacrylate, or triethylene glycol dimethacrylate) and initiators (azobisisobutyronitrile or benzoyl peroxide) and used to immobilize Candida antarctica lipase B (CALB). The aim was to obtain biocatalysts with high enzymatic activity and satisfactory morphological properties for use in biotransformation reactions. Two morphological properties known to influence the immobilization yield were taken into consideration, specific surface area, and swelling index. Experimental data were compared to the predictions of a model based on molar balance, method of moments, numerical fractionation, and elementary gel structures. The high correlation (R² = 0.9974) between experimental and predicted values demonstrated the suitability of the model for estimating the textural properties of enzyme supports. CALB was successfully immobilized, showing high hydrolytic activity (500–700 U g⁻¹) and good thermal stability at 50°C. CALB/STY-EGDMA-M was 14 times more stable than free CALB. The results confirm the efficiency of the immobilization method and the suitability of the copolymers for enzyme immobilization.     

Synthesis of Isopropyl Palmitate by Lipase Immobilized on a Magnetized Polymer Matrix

Penicillium camemberti lipase immobilized on a magnetized poly(styrene-co-divinylbenzene) was used as a biocatalyst for isopropyl palmitate synthesis. The reaction conditions were determined by 2² factorial central composite design. A mathematical model based on a simplified kinetic approach was developed to describe the system and validated with the experimental data. An assay carried out in a stirred-tank reactor confirmed the proposed model. The ester was purified and the properties such as density and water content were similar to those found in commercially available isopropyl palmitate.     

Textile industry and environment: can the use of bacterial cellulose in the manufacture of biotextiles contribute to the sector?

Clean Technologies and Environmental Policy - The textile industry is one of the most complex sectors in relation to environmental degradation, both with regard to the materials used from petroleum...     

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system-centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user-centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user-centric comparison of the following SASs based on Hadoop and Spark: Hadoop-GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real-world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.     

Multi-view Performance Capture of Surface Details

This paper presents a novel approach to recover true fine surface detail of deforming meshes reconstructed from multi-view video. Template-based methods for performance capture usually produce a coarse-to-medium scale detail 4D surface reconstruction which does not contain the real high-frequency geometric detail present in the original video footage. Fine scale deformation is often incorporated in a second pass by using stereo constraints, features, or shading-based refinement. In this paper, we propose an alternative solution to this second stage by formulating dense dynamic surface reconstruction as a global optimization problem of the densely deforming surface. Our main contribution is an implicit representation of a deformable mesh that uses a set of Gaussian functions on the surface to represent the initial coarse mesh, and a set of Gaussians for the images to represent the original captured multi-view images. We effectively find the fine scale deformations for all mesh vertices, which maximize photo-temporal-consistency, by densely optimizing our model-to-image consistency energy on all vertex positions. Our formulation yields a smooth closed form energy with implicit occlusion handling and analytic derivatives. Furthermore, it does not require error-prone correspondence finding or discrete sampling of surface displacement values. We demonstrate our approach on a variety of datasets of human subjects wearing loose clothing and performing different motions. We qualitatively and quantitatively demonstrate that our technique successfully reproduces finer detail than the input baseline geometry.