Salt forms of the pharmaceutical amide dihydrocarbamazepine

Carbamazepine (CBZ) is well known as a model active pharmaceutical ingredient used in the study of polymorphism and the generation and comparison of cocrystal forms. The pharmaceutical amide dihydrocarbamazepine (DCBZ) is a less well known material and is largely of interest here as a structural congener of CBZ. Reaction of DCBZ with strong acids results in protonation of the amide functionality at the O atom and gives the salt forms dihydrocarbamazepine hydrochloride {systematic name: [(10,11‐dihydro‐5H‐dibenzo[b,f]azepin‐5‐yl)(hydroxy)methylidene]azanium chloride, C₁₅H₁₅N₂O⁺·Cl⁻}, dihydrocarbamazepine hydrochloride monohydrate {systematic name: [(10,11‐dihydro‐5H‐dibenzo[b,f]azepin‐5‐yl)(hydroxy)methylidene]azanium chloride monohydrate, C₁₅H₁₅N₂O⁺·Cl⁻·H₂O} and dihydrocarbamazepine hydrobromide monohydrate {systematic name: [(10,11‐dihydro‐5H‐dibenzo[b,f]azepin‐5‐yl)(hydroxy)methylidene]azanium bromide monohydrate, C₁₅H₁₅N₂O⁺·Br⁻·H₂O}. The anhydrous hydrochloride has a structure with two crystallographically independent ion pairs (Z′ = 2), wherein both cations adopt syn conformations, whilst the two hydrated species are mutually isostructural and have cations with anti conformations. Compared to neutral dihydrocarbamazepine structures, protonation of the amide group is shown to cause changes to both the molecular (C=O bond lengthening and C—N bond shortening) and the supramolecular structures. The amide‐to‐amide and dimeric hydrogen‐bonding motifs seen for neutral polymorphs and cocrystalline species are replaced here by one‐dimensional polymeric constructs with no direct amide‐to‐amide bonds. The structures are also compared with, and shown to be closely related to, those of the salt forms of the structurally similar pharmaceutical carbamazepine.     

Having Fun (and Commercial Success) with Josiphos and Related Chiral Ferrocene Based Ligands.

The Josiphos ligand family is arguably one of the most versatile classes of chiral ligands with a wide range of applications, including one of the two largest known enantioselective catalytic hydrogenations. In this personal account, we take a look back at the beginning and describe the role Antonio Togni has played to reach this point and the significance of his contributions for the commercial success, especially concerning today's ligand business of Solvias. This seems a fitting point in time in light of Antonio’s statement on his ETH home page that he ‘is working as a professor at this institution since 1992 (a few more months to go), not looking for a job’ and that the three authors have already retired for some time (H. U. B.) or are about to retire soon (B. P., F. S.).     

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusions: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.     

Message Passing-Based Inference for Time-Varying Autoregressive Models

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by a factor graph and solve the inference problem by automated message passing-based inference for states and parameters. We derive structured variational update rules for a composite “AR node” with probabilistic observations that can be used as a plug-in module in hierarchical models, for example, to model the time-varying behavior of the hyper-parameters of a time-varying AR model. Our method includes tracking of variational free energy (FE) as a Bayesian measure of TVAR model performance. The proposed methods are verified on a synthetic data set and validated on real-world data from temperature modeling and speech enhancement tasks.     

MIMO Self-Heterodyne OFDM Using Band Selection Technique

The 5G technology is a promising technology to cope with the increasing demand for higher data rate and quality of service. In this paper, two proposed techniques are implemented for multiple input multiple output (MIMO) self-heterodyne OFDM system to enhance data rate and minimize the bit error rate (BER). In both of the two proposed techniques, Band Selection (BS) approach is used, once with Space Time Block Coded (STBC) for the first proposed technique (BS- STBC), and once again with Frequency Space Time Block Coded (FSTBC) for the second proposed technique (BS-FSTBC). The use of the BS in the proposed techniques helps to choose the sub-band with better subchannels gains for sending the information and consequently, minimize the BER. Moreover, the use of the FSTBC instead of STBC helps to use the spectral efficiently and hence increase data rate. The simulation results show that the proposed techniques BS-STBC and BS-FSTBC, for the MIMO self-heterodyne OFDM system, provide a great enhancement in the BER performance when compared to the conventional techniques. Moreover, the simulation results show that the first proposed technique BS-FSTBC outperform the second propose technique BS-STBC in term of the BER performance.     

Causal Geometry

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore, we introduce a geometric version of “effective information”—a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that is well matched to those interventions. This is a consequence of “causal emergence,” wherein macroscopic causal relationships may carry more information than “fundamental” microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions—as we illustrate on toy examples.     

Modification of the CO2 surface tension calculation model under low-temperature and high-pressure condition

Surface tension is an important thermodynamic parameter. In researching the removal mechanism of CO₂ in natural gas in supersonic expansion process, the most basic and important task is to calculate the surface tension of CO₂ under low-temperature and high-pressure condition. In this paper, a comparative study on the relevant calculation methods is carried out and the computational method applied to simulate the phase transition of CO₂ supersonic condensation is selected. On this basis, the CO₂ surface tension calculation model is modified and the calculation method, by using the piecewise function, is proposed. The results show that the average deviation of the piecewise function is only 0.95%, which is lower than the deviation of any single correlation. It can achieve an accurate prediction of the surface tension of the liquid CO₂.     

A limit distribution of credit portfolio losses with low default probabilities

This paper employs a multivariate extreme value theory (EVT) approach to study the limit distribution of the loss of a general credit portfolio with low default probabilities. A latent variable model is employed to quantify the credit portfolio loss, where both heavy tails and tail dependence of the latent variables are realized via a multivariate regular variation (MRV) structure. An approximation formula to implement our main result numerically is obtained. Intensive simulation experiments are conducted, showing that this approximation formula is accurate for relatively small default probabilities, and that our approach is superior to a copula-based approach in reducing model risk.     

Multi-criteria optimization model integrated with AHP for evaluation and selection of COTS components

This paper deals with COTS evaluation and selection for developing a modular software system under single application development task. We consider both quantitative and qualitative criteria which fulfils the specific needs of a software system. We use analytical hierarchy process (AHP) technique for evaluating the fitness of COTS components based upon various criteria and sub-criteria thereby providing overall score of each COTS component. We develop optimization models integrating AHP and multi-criteria decision-making, which aim at: (i) maximize the total value of purchasing (TVP) subject to budget, compatibility and reliability constraints, and (ii) maximize TVP and minimize the total cost of purchase simultaneously subject to compatibility and reliability constraints. The efficiency of the models is illustrated by means of numerical illustrations.