Optimization of catalyst preparation conditions for hydrogen generation in the presence of Co–B using taguchi method

Efficient hydrogen generation is a significant prerequisite of future hydrogen economy. Therefore, the development of efficient non-noble metal catalysts for hydrolysis reaction of sodium borohydride (NaBH₄) under mild conditions has received extensive interest. Since the transition metal boride based materials are inexpensive and easy to prepare, it is feasible to use these catalysts in the construction of practical hydrogen generators. In this work, temperature, pH, reducing agent concentration, and reduction rate were selected as independent process parameters and their effects on dependent parameter, such as hydrogen generation rate, were investigated using response surface methodology (RSM). According to the obtained results of the RSM prediction, maximum hydrogen generation rate (53.69 L. min^?1g_cat^-1) was obtained at temperature of 281.18 K, pH of 5.97, reducing agent concentration of 31.47 NaBH₄/water and reduction rate of 7.16 ml min^?1. Consequently, after validation studies it was observed that the RSM together with Taguchi methods are efficient experimental designs for parameter optimization.     

Web accessibility investigation of Slovenian municipalities’ websites before and after the adoption of European Standard EN 301 549

Universal Access in the Information Society - Digitalization and technological innovations provide many benefits for citizens worldwide, increasing the quality of life in several ways, including by...     

Alternative to Poly(2-isopropyl-2-oxazoline) with a Reduced Ability to Crystallize and Physiological LCST

In this work, we sought to examine whether the presence of alkyl substituents randomly distributed within the main chain of a 2-isopropyl-2-oxazoline-based copolymer will decrease its ability to crystallize when compared to its homopolymer. At the same time, we aimed to ensure an appropriate hydrophilic/lipophilic balance in the copolymer and maintain the phase transition in the vicinity of the human body temperature. For this reason, copolymers of 2-ethyl-4-methyl-2-oxazoline and 2-isopropyl-2-oxazoline were synthesized. The thermoresponsive behavior of the copolymers in water, the influence of salt on the cloud point, the presence of hysteresis of the phase transition and the crystallization ability in a water solution under long-term heating conditions were studied by turbidimetry. The ability of the copolymers to crystallize in the solid state, and their thermal properties, were analyzed by differential scanning calorimetry and X-ray diffractometry. A cytotoxicity assay was used to estimate the viability of human fibroblasts in the presence of the obtained polymers. The results allowed us to demonstrate a nontoxic alternative to poly(2-isopropyl-2-oxazoline) (PiPrOx) with a physiological phase transition temperature (LCST) and a greatly reduced tendency to crystallize. The synthesis of 2-oxazoline polymers with such well-defined properties is important for future biomedical applications.     

The effect of cryo-grinding and size separation on bioactive profile of buckwheat hulls

Buckwheat hulls (BWH) are a lignocellulosic material with valuable nutrient contents, especially rutin and dietary fibre, which are worth exploring from a food industry perspective. BWH are also a voluminous material that is difficult to grind to very fine particle sizes, which can be achieved under cryogenic conditions. Therefore, we investigated the effect of fractionation by cryo-grinding and size separation (<56, 56–180 and >180 µm) on the dietary fibre content and bioactive profile of BWH. While we hypothesised that micronisation would increase fibre solubility and release of bioactive compounds, we found that BWH must be fractionated to a size <56 µm to show a significant increase in soluble fibre content (106%), rutin (66%), total free phenolic compounds (94%) and antioxidant capacity (45–63%). Moreover, we have shown that near-infrared spectroscopy combined with partial least squares regression can be easily used for rapid scanning of BWH to select the most suitable pre-processing conditions for maximum nutritional value of this food industry by-product.     

Non-seismic soft-sediment deformation structures from Late Pleistocene lacustrine deposits of Lake Van (Eastern Turkey): Storm and overloading effect

Soft-sediment deformation structures of different types and sizes are frequently observed in the lacustrine deposits of Lake Van. According to sedimentary features and regional factors, these structures are categorized as non-seismic originated and seismically-induced, soft-sediment deformation structures. Well-preserved non-seismic originated, soft-sediment deformation structures were observed in fine-grained sandy and silty deposits at three locations (Çatakdibi, Yumrutepe, and Yukar????kl?), and occur at different stratigraphic horizons, exhibiting morphological variability as they consist of load, flame, and slump structures. The formation mechanisms of these structures are determined by the characteristics of their sedimentary facies and environmental conditions. Overloading, caused by rapid coarse sediment deposition or underwater landslides, and storm waves are identified as triggering mechanisms, while rapid sediment accumulation and underwater mass movements caused by volcanogenic shakes are the conditions responsible for the formation of non-seismic soft-sediment deformation structures, in terms of regional geodynamics.     

Semi-supervised classification trees

In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and unlabeled data, while preserving all of the appealing characteristics of standard supervised decision trees: being non-parametric, efficient, having good predictive performance and producing readily interpretable models. Moreover, we further improve their predictive performance by using them as base predictive models in random forests. We performed an extensive empirical evaluation on 12 binary and 12 multi-class classification datasets. The results showed that the proposed methods improve the predictive performance of their supervised counterparts. Moreover, we show that, in cases with limited availability of labeled data, the semi-supervised decision trees often yield models that are smaller and easier to interpret than supervised decision trees.