Curvature-direction measures of self-similar sets

We obtain fractal Lipschitz-Killing curvature-direction measures for a large class of self-similar sets F in R^d. Such measures jointly describe the distribution of normal vectors and localize curvature by analogues of the higher order mean curvatures of differentiable submanifolds. They decouple as independent products of the unit Hausdorff measure on F and a self-similar fibre measure on the sphere, which can be computed by an integral formula. The corresponding local density approach uses an ergodic dynamical system formed by extending the code space shift by a subgroup of the orthogonal group. We then give a remarkably simple proof for the resulting measure version under minimal assumptions.Comment: 17 pages, 2 figures. Update for author's name chang

Comparison of two sowing systems for CTF using commercially available machinery

ArticleThe crop establishment belongs to crucial technology operations. The quality of sowing is the basis for obtaining efficiency of production. Controlled Traffic Farming (CTF) is a technology which prevents excessive soil compaction and minimizes compacted area to the smallest possible area of perman ent traffic lanes (PTL). There were two sowing systems compared, namely row and band sowing when growing winter barley. Sowing parameters as well as all other field operations were identical for both compared systems. Measurements were conducted at an expe rimental field on non - compacted and traffic lane areas where CTF system was introduced in 2009, with 64% of compacted and 36% of non - compacted soil. Six crop parameters were analysed. Generally, it can be concluded that the band sowing performed better in yield (by 9.3% in non - compacted area; by 3.8 % in traffic lane), ear number (by 5.2% in non - compacted area; by 10.1% in traffic lane) and grain number (by 6.3% in non - compacted area; by 8.1% in traffic lane) as well as crop height (by 6.6% in non - compacted area; and by 2.4% in traffic lane). The only parameter performing worse was TGW with decrease of 6.6% in non - compacted area and decrease 2.8% in traffic lane for band system. Differences in number of grain per ear were negligible

Effect of controlled traffic farming on weed occurrence

ArticleSoil compaction caused by field traffic is one of the most important yield limiting factors. Moreover, published results report that soil over-compaction inhibits the uptake of plant nutrients and decreases their ability to compete with weeds. Controlled Traffic Farming (CTF) is technology which prevents excessive soil compaction and minimizes compacted area to the least possible area of permanent traffic lines. A long-term experiment was established at University farm in Kolinany (Slovakia) in 2010 with 6 m OutTrack CTF system. Random Traffic Farming (RTF) is simulated by 1 annual machinery pass crossing the permanent traffic lines. Aim of presented study was to assess the effect of CTF on weed infection pressure. To achieve this, weed occurrence at different traffic treatments was determined. Emerged weeds per square meter were counted, identified and recorded at 14 monitoring points. Results showed that higher weed infection was found at the area with one machinery pass compared to the non-compacted area. Following weeds were identified: Bromus secalinus L., Stellaria media (L.) VILL., Veronica persica POIR. in LAMK., Poa annua L., Polygonum aviculare L., Convolvulus arvensis L. Occurrence of these weeds could be used as soil compaction indicator. Based on these results it can be concluded, that CTF technology has potential to decrease weed infestation in comparison to RTF system due to ration of non-compacted to compacted area. Moreover, with exact localization of weeds in traffic lines together with exact identification of weed species, it is possible to target the application of herbicides

Paving the pathways towards sustainable future? A critical review of STI policy roadmaps as policy instruments enabling sustainability transitions

Roadmaps and roadmapping techniques receive increasing attention in the Science, Technology and Innovation policy community, notably for the development of strategies and policies to address societal challenges and ambitious goals such as the SDGs. STI policy roadmaps are used to evoke future visions, align actor expectations and formulate, document, plan and implement public policies for long-term, ambitious sustainability goals. As a sophisticated strategic planning process, roadmapping seems appropriate for policy support aiming to foster sustainability transitions. Nevertheless, there is little research on the role and limitations of roadmaps as a policy instrument to support innovation for sustainability transitions. This paper critically assesses selected national and international policy and sectoral roadmaps that focus on technology areas and societal challenges relevant to sustainability and energy transitions. The assessment of the objectives, design features and embeddedness of roadmaps in policy processes shows that current policy roadmaps have several shortfalls. The paper outlines knowledge gaps and research priorities to understand how such limitations might be overcome and draws tentative lessons for future applications of roadmaps as policy instruments for sustainability transitions

Multiple conformational states in retrospective virtual screening : homology models vs. crystal structures : beta-2 adrenergic receptor case study

Background: Distinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined. In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered. Results: The docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination. Conclusions: For virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning method

Delays in IP routers, a Markov model

Delays in routers are an important component of end-to-end delay and therefore have a significant impact on quality of service. While the other component, the propagation time, is easy to predict as the distance divided by the speed of light inside the link, the queueing delays of packets inside routers depend on the current, usually dynamically changing congestion and on the stochastic features of the flows. We use a Markov model taking into account the distribution of the size of packets and self-similarity of incoming flows to investigate their impact on the queueing delays and their dynamics

The impact of spatial and verbal working memory load on semantic relatedness judgements

Studies using a relatedness judgement task have found differences between prime17 target word pairs that vary in the degree of semantic relatedness (Balota & Paul, 1996; Kuperberg et al., 2008). However, the influence of working memory load on semantic processing in this task and the role of the type of working memory task have not yet been investigated. The present study therefore investigated for the first time the effect of working memory load (low vs. high) and working memory type (verbal vs. spatial) on semantic relatedness judgements. Semantically strongly related (e.g. hip – KNEE), weakly related (e.g. muscle – KNEE) and unrelated (e.g. office – KNEE) Polish word pairs were presented in an experiment involving a dual working memory and semantic relatedness task. The data revealed that, relative to semantically unrelated word pairs, responses were faster for strongly related pairs but slower for weakly related pairs. Importantly, the verbal working memory task decreased facilitation for strongly related pairs and increased inhibition for weakly related pairs relative to the spatial working memory task. Furthermore, working memory load impacted only weakly related pairs in the verbal but not in the spatial working memory task. These results show that working memory type and load influence semantic relatedness judgements, but the direction and size of the impact depend on the strength of semantic relation

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP). This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivitiesÁ.A.K. and G.M.K. were supported by the National Brain Research Program (2017-1.2.1-NKP-2017-00002). K.R. is grateful for the ETIUDA scholarship of the National Science Center, Poland. J.B. and M.I.L. are grateful for the support from the Spanish Ministerio de Economía y Comptetitividad (SAF2017-85225-C3-1-R)S

Determining trafficked areas using soil electrical conductivity – a pilot study

ncrease in machinery size and its random traffic at fields cause soil compaction resulting in damage of soil structure and degradation of soil functions. Nowadays, rapid methods to detect soil compaction at fields are of high interest, especially proximal sensing methods such as electrical conductivity measurements. The aim of this work was to investigate whether electromagnetic induction (EMI) could be used to determine trafficked areas in silty clay soil. Results of randomized block experiment showed a high significant difference (p <0.01) in EMI data measured between compacted and non-compacted areas. EMI readings from compacted areas were, on average, 11% (shallow range) and 9% (deep range) higher than non-compacted areas, respectively. This difference was determined in both shallow and deep measuring ranges, indicating that the difference in soil compaction was detected in both topsoil and subsoil. Furthermore, the data was found to have a significant spatial variability, suggesting that, in order to detect the increase in EMI (which shows the increase in soil compaction), data within close surrounding area should be included in the analyses. Correlation coefficient of EMI and penetration resistance (average moisture content 32.5% and 30.8% for topsoil and subsoil) was found to be 0.66