Hydrogen Infrastructure Project Risks in The Netherlands

This study aims to assess the potential risks of setting up a hydrogen infrastructure in the Netherlands. An integrated risk assessment framework, capable of analyzing projects, identifying risks and comparing projects, is used to identify and analyze the main risks in the upcoming Dutch hydrogen infrastructure project. A time multiplier is added to the framework to develop parameters. The impact of the different risk categories provided by the integrated framework is calculated using the discounted cash flow (DCF) model. Despite resource risks having the highest impact, scope risks are shown to be the most prominent in the hydrogen infrastructure project. To present the DCF model results, a risk assessment matrix is constructed. Compared to the conventional Risk Assessment Matrix (RAM) used to present project risks, this matrix presents additional information in terms of the internal rate of return and risk specifics.

Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features

X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew’s correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

Valuation of small to medium sized companies using spatial information: An empirical example from the fruit subsector

The Discounted Cash Flow (DCF) model, similar to other firm valuation models, uses temporal information for a firm to forecast future results. However, the lack of temporal information for many companies hinders the application of the DCF model. To overcome this limitation, we proposed an approach based on the spatial information of the analysed companies. In particular, to get firms’ valuation our approach combined both data from companies that are geographically proximal to the analysed company and data from the analysed company. Based on this approach, we provided an empirical example to demonstrate that the economic value computed with our proposal, the Spatial-Firm Economic Value, was consistent with the traditional economic value after application of the DCF model. In particular, we found a minimal difference in terms of absolute deviations between our proposal and the firm’s valuation applying traditional valuation techniques. Thus, this study demonstrated the relevance of considering the spatial dimension as an additional source of information to determine firms’ value in the Fruit subsector when there is not available temporal information to apply traditional valuation methods.

Performance Evaluation of Stocks’ Valuation Models at MSE

Subject and purpose of work: The main task of this paper is to examine the proximity of valuations generated by different valuation models to stock prices in order to investigate their reliability at Macedonian Stock Exchange (MSE) and to present alternative “scenario” methodology for discounted free cash flow to firm valuation. Materials and methods: By using publicly available data from MSE we are calculating stock prices with three stock valuation models: Discounted Free Cash Flow, Dividend Discount and Relative Valuation. Results: The evaluation of performance of three stock valuation models at the MSE identified that model of Price Multiplies (P/E and other profitability ratios) offer reliable stock values determination and lower level of price errors compared with the average stocks market prices. Conclusions: The Discounted Free Cash Flow (DCF) model provides values close to average market prices, while Dividend Discount (DDM) valuation model generally mispriced stocks at MSE. We suggest the use of DCF model combined with relative valuation models for accurate stocks’ values calculation at MSE.

Synergies between an Observed Port and a Logistic Company: Application of the Discounted Cash–Flow Model and the Monte Carlo Simulation

The paper addresses an analysis of potential synergies in collaboration between an observed Port in the Mediterranean Sea and Central-European logistic railway-services based company. Both companies have established a strategic partnership. The main motive was cooperation in rail transport, with a particular emphasis on potential synergies that would a rail traffic have brought to a port’s business. For the purpose of synergies valuation under uncertain conditions, a Monte Carlo simulation-based framework with integrated discounted cash flow (DCF) model is applied. The possible values of future synergies are calculated via the DCF model by simultaneously changing values of different uncertain financial parameters at each repetition of a Monte Carlo scenario-playing mechanism. In this process, predicted forecasts of future synergetic throughputs are also used for various types of observed cargo. As it turned out, the generated synergies’ values follow the approximate normal distribution. Based on statistical inference and analysis of probability intervals it was discovered that there might indeed exist certain important synergies in the collaboration between both companies. This fact has convinced us into a belief in the correctness of companies′ decision to enter into such kind of strategic cooperation.