The Hoek-Brown empirical failure envelope is widely used for determining the strength of brittle intact rock and rock masses. One of the two independent parameters in the formula is the HoekBrown's material constant (m i ), which has several uncertainties, since it relies on a set of triaxial tests. The present paper introduces a new approach to determine the material constant by using the uniaxial compressive strength, rather than the triaxial strength values. This newly calculated Hoek-Brown parameter was cross-checked by using published data and new experimental data obtained from granitic samples of Bátaapáti (Hungary). Based on these new equations new material constants are suggested to be used in calculations.

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Determination of the mechanical behaviour of intact rock is one of the most important parts of any engineering projects in the field of rock mechanics. The most important mechanical parameters required to understand the quality of intact rock are Young's modulus (E), Poisson's ratio (m), the strength of rock (r c) and the ratio of Young's modulus to the strength of rock known as modulus ratio (M R), which can be used for calculations. The particular interest of this paper is to investigate the relationship between these parameters for Hungarian granitic rock samples. To fulfil this aim, Modulus of elasticity (E), Modulus of rigidity (G), Bulk modulus (K) and the modulus ratio (M R = E/r c) of 50 granitic rock samples collected from Bátaapáti radioactive waste repository were examined. Fifty high-precision uniaxial compressive tests were conducted on strong (r c-[ 100 MPa) rock samples, exhibiting the wide range of elastic modulus (E = 57.425-88.937 GPa), uniaxial compressive strength (r c-= 133.34-213.04 MPa) and Poisson's ratio (m = 0.18-0.32). The observed value (M R = 326-597) and mean value of M R = 439.4 are compared with the results of similar previous researches. Moreover, the statistical analysis for all studied rocks was performed and the relationship between M R and other mechanical parameters such as maximum axial strain (e a, max) for studied rock samples was discussed. Finally, the validity of the proposed mathematical model by Palchik (Geomech Geophys Geo-energy Geo-resour 6:1-12, 2019) for stress-strain behaviour of granitic rock samples was investigated. Keywords Uniaxial compressive test Á Modulus of elasticity (E) Á Modulus of rigidity (G) Á Bulk modulus (K) Á Modulus ratio (M R) Á Maximum axial strain (e a, max) Á Mórágy granite formation Á Mathematical model 1 Introduction Rock engineering properties are considered to be the most important parameters in the design of ground works. Two important mechanical parameters, uniaxial compressive strength (r c) and elastic modulus of rock (E) should be estimated correctly. There are different empirical relations between r c and

Pricing an insurance product covering motor third-party liability is a major challenge for actuaries. Comprehensive statistical modelling and modern computational power are necessary to solve this problem. The generalised linear and additive modelling approaches have been widely used by insurance companies for a long time. Modelling with modern machine learning methods has recently started, but applying them properly with relevant features is a great issue for pricing experts. This study analyses the claim-causing probability by fitting generalised linear modelling, generalised additive modelling, random forest, and neural network models. Several evaluation measures are used to compare these techniques. The best model is a mixture of the base methods. The authors’ hypothesis about the existence of significant interactions between feature variables is proved by the models. A simplified classification and visualisation is performed on the final model, which can support tariff applications later.

Aminergic G-protein coupled receptors (GPRCs) represent well-known targets of central nervous-system related diseases. In this study a structure-based consensus virtual screening scheme was developed for designing targeted fragment libraries against class A aminergic GPCRs. Nine representative aminergic GPCR structures were selected by first clustering available X-ray structures and then choosing the one in each cluster that performs best in self-docking calculations. A consensus scoring protocol was developed using known promiscuous aminergic ligands and decoys as a training set. The consensus score (FrACS-fragment aminergic consensus score) calculated for the optimized protein ensemble showed improved enrichments in most cases as compared to stand-alone structures. Retrospective validation was carried out on public screening data for aminergic targets (5-HT1 serotonin receptor, TA1 trace-amine receptor) showing 8-17-fold enrichments using an ensemble of aminergic receptor structures. The performance of the structure based FrACS in combination with our ligand-based prefilter (FrAGS) was investigated both in a retrospective validation on the ChEMBL database and in a prospective validation on an in-house fragment library. In prospective validation virtual fragment hits were tested on 5-HT6 serotonin receptors not involved in the development of FrACS. Six out of the 36 experimentally tested fragments exhibited remarkable antagonist efficacies, and 4 showed IC50 values in the low micromolar or submicromolar range in a cell-based assay. Both retrospective and prospective validations revealed that the methodology is suitable for designing focused class A GPCR fragment libraries from large screening decks, commercial compound collections, or virtual databases.

Molecular design is of utmost importance in lead optimization programs ultimately determining the fate of the project and the speed to reach preclinical stage. Newly designed lead analogues or new chemotypes must successfully address the challenges in the multidimensional optimization process throughout several optimization cycles. The speed, quality, and creativity of the designs can have a major impact on the cycle time, the number of required cycles, and the number of compounds needed to be synthesized and evaluated that in combination affect the overall timeline and cost of the lead optimization phase. Recently, a new concept, generative design with deep learning, has become popular for de novo design of project relevant analogue sets. We have developed a de novo design technology called "derivatization design" that applies artificial-intelligence-assisted forward in silico synthesis for the generation of near neighbor lead analogues as well as scaffold variations. The several attractive features of the methodology include synthetic feasibility, reagent availability and cost data associated with each new molecule; thus, detailed synthetic assessment is automatically generated during the design. As a result, these practically important data types can become an early part of the ranking and selection process for cycle time reduction. The power of derivatization design is demonstrated in a simple design study of DDR1 inhibitors and comparison of the produced molecules to a recently published data set obtained with deep generative design.

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Standfirst:The Catholic Church is challenged by scientific and technological innovation but can help to integrate multiple voices in the ongoing dialogue regarding AI-and machine ethics. In this context, a multi-disciplinary working group brought together by the Church reflected on roboethics, explored the themes of embodiment, agency and intelligence.