Markov chains in Banach spaces on cycles

The problem of defining denumerable Markov chains by a countable infinity of weighted directed cycles is solved by using suitable Banach spacesl _p on cycles and edges. Furthermore, it is showed that the transition probabilities of such chains may be described by Fourier series on orthonormal collections of homologic ingredients.     

Orthogonal cycle transforms of stochastic matrices

In this paper we investigate new Fourier series with respect to orthonormal families of directed cycles , which occur in the graph of a recurrent stochastic matrixP. Specifically, it is proved thatP may be approximated in a suitable Hilbert space by the Fourier series . This approach provides a proof in terms of Hilbert space of the cycle decomposition formula for finite stochastic matricesP.     

Alkyl Formate Ester Synthesis by a Fungal Baeyer–Villiger Monooxygenase

We investigated Baeyer–Villiger monooxygenase (BVMO)‐mediated synthesis of alkyl formate esters, which are important flavor and fragrance products. A recombinant fungal BVMO from Aspergillus flavus was found to transform a selection of aliphatic aldehydes into alkyl formates with high regioselectivity. Near complete conversion of 10 mm octanal was achieved within 8 h with a regiomeric excess of ～80 %. Substrate concentration was found to affect specific activity and regioselectivity of the BVMO, as well as the rate of product autohydrolysis to the primary alcohol. More than 80 % conversion of 50 mm octanal was reached after 72 h (TTN nearly 20 000). Biotransformation on a 200 mL scale under unoptimized conditions gave a space‐time yield (STY) of 4.2 g L^?1 d^?1 (3.4 g L^?1 d^?1 extracted product).     

DISCOVERING ROBUST EMBEDDINGS IN (DIS)SIMILARITY SPACE FOR HIGH‐DIMENSIONAL LINGUISTIC FEATURES

Recent research has shown the effectiveness of rich feature representation for tasks in natural language processing (NLP). However, exceedingly large number of features do not always improve classification performance. They may contain redundant information, lead to noisy feature presentations, and also render the learning algorithms intractable. In this paper, we propose a supervised embedding framework that modifies the relative positions between instances to increase the compatibility between the input features and the output labels and meanwhile preserves the local distribution of the original data in the embedded space. The proposed framework attempts to support flexible balance between the preservation of intrinsic geometry and the enhancement of class separability for both interclass and intraclass instances. It takes into account characteristics of linguistic features by using an inner product‐based optimization template. (Dis)similarity features, also known as empirical kernel mapping, is employed to enable computationally tractable processing of extremely high‐dimensional input, and also to handle nonlinearities in embedding generation when necessary. Evaluated on two NLP tasks with six data sets, the proposed framework provides better classification performance than the support vector machine without using any dimensionality reduction technique. It also generates embeddings with better class discriminability as compared to many existing embedding algorithms.     

Product-service system (PSS) complexity metrics within mass customization and Industry 4.0 environment

The design and evaluation of product-service systems (PSS) constitutes a challenging problem due to its multidimensionality. This challenge becomes bigger when the PSS customization is required within the new manufacturing paradigm of Industry 4.0. Nevertheless, limited literature work is observed regarding the customization of PSS and the PSS investigation within the Industry 4.0. Towards bridging these gaps, the present research work proposes a methodology for the quantification of PSS customization complexity, considering Industry 4.0 aspects. The proposed metrics are applied in a real industrial case study from a large laser machining industry, aiming to evaluate the different PSS alternatives in terms of complexity. It is demonstrated that the proposed approach can support the strategic level decision-making of a company, by quantifying the complexity and producing additional meaningful information towards the selection of the product and services that could be designed and offered to the customers.     

Smartphone-based colorimetric determination of sulfadiazine and sulfasalazine in pharmaceutical and veterinary formulations

A novel, simple, accurate, and low-cost colorimetric device based on an Android smartphone was developed for sulfonamide determination. A software program was developed to be used for analyzing the samples. Various parameters for digital colorimetric detection were investigated and optimized, such as the volume of the sample drop, the type of sample holder, the distance from the mobile phone camera to the sample holder, and the effect of ambient light. From the optimized conditions, a calibration curve was created by the intensity of blue channel for sulfonamides for the concentration range of 0.5–2.5?µg?mL^?1 with good linearity and a regression coefficient (R²) of 0.996. The results obtained by the smartphone method were compared with a spectrophotometric procedure at the 95% confidence level (n?=?3). Both methods correlated well with a regression coefficient (R²) of 0.997. The limit of detection of both methods was equal to 0.11?µg?mL^?1. The developed smartphone system was successfully used for the determination of sulfonamides in pharmaceutical and veterinary formulations with recoveries of 102 and 98.7%, respectively. The developed method provides good accuracy (relative error <5%) and precision (relative standard deviation <7%) and offers simple, convenient, rapid, and inexpensive determination of sulfonamides.