Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation

The purpose of this research was to derive promising technologies for the transport of hydrogen fuel cells, thereby supporting the development of research and development policy and presenting directions for investment. We also provide researchers with information about technology that will lead the technology field in the future. Hydrogen energy, as the core of carbon neutral and green energy, is a major issue in changing the future industrial structure and national competitive advantage. In this study, we derived promising technology at the core of future hydrogen fuel cell transportation using the published US patent and paper databases (DB). We first performed text mining and data preprocessing and then discovered promising technologies through generative topographic mapping analysis. We analyzed both the patent DB and treatise DB in parallel and compared the results. As a result, two promising technologies were derived from the patent DB analysis, and five were derived from the paper DB analysis.

HIV-1 drug resistance profiling using amino acid sequence space cartography

Motivation: Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance demands treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. Yet, the currently existing computational methods for drug resistance prediction are either not suitable for complex mutational patterns in emerging HIV strains or lack interpretability of prediction results which is of paramount importance in clinical practice. Hence, to overcome these limitations, new approaches for the HIV drug resistance prediction combining high accuracy and interpretability are required. Results: In this work, a new methodology for the analysis of protein sequence data based on the application of generative topographic mapping was developed and applied for HIV drug resistance profiling. It allowed achieving high accuracy of resistance predictions and intuitive interpretation of prediction results. The developed approach was successfully applied for the prediction of HIV re-sistance towards protease, reverse-transcriptase and integrase inhibitors and in-depth analysis of HIV resistance-inducing mutation patterns. Hence, it can serve as an efficient and interpretable tool to suggest optimal treatment regimens. Availability: https://github.com/karinapikalyova/ISIDASeq

Seismic characterization of a Triassic-Jurassic deep geothermal sandstone reservoir, onshore Denmark, using unsupervised machine learning techniques

The Triassic-Jurassic deep sandstone reservoirs in onshore Denmark are known geothermal targets that can be exploited for sustainable and green energy for the next several decades. The economic development of such resources requires accurate characterization of the sandstone reservoir properties, namely, volume of clay, porosity, and permeability. The classic approach to achieving such objectives has been to integrate prestack seismic data and well logs with geologic information to obtain facies and reservoir property predictions in a Bayesian framework. Using this prestack inversion approach, we can obtain superior spatial and temporal variations within the target formation. We then examine whether unsupervised facies classification in the target units can provide additional information. We evaluated several machine learning techniques and find that generative topographic mapping further subdivided intervals mapped by the Bayesian framework into additional subunits.

Recreation of the periodic table with an unsupervised machine learning algorithm

In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional grid system for a tabular display. In this study, we seek to answer the question of whether machine learning can reproduce or recreate the periodic table by using observed physicochemical properties of the elements. To achieve this goal, we developed a periodic table generator (PTG). The PTG is an unsupervised machine learning algorithm based on the generative topographic mapping, which can automate the translation of high-dimensional data into a tabular form with varying layouts on-demand. The PTG autonomously produced various arrangements of chemical symbols, which organized a two-dimensional array such as Mendeleev’s periodic table or three-dimensional spiral table according to the underlying periodicity in the given data. We further showed what the PTG learned from the element data and how the element features, such as melting point and electronegativity, are compressed to the lower-dimensional latent spaces.

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, 13C cross-polarization (CP)–magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO2. Using these methods, the 1H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design.

Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems

In this study, we identify promising, currently vacant technologies for a Truck on Flatcar or Truck on Freight Train (TFTFT) system by analyzing the relevant patent information. We then apply network analysis from macro- and microperspectives to establish technology development strategies. We first researched the patent database from the United States Patent and Trademark Office (USPTO) by extracting relevant keywords for the TFTFT system. We then preprocessed the patent data to develop a patent-International Patent Classification (IPC) matrix and a patent-keyword matrix. Next, we developed a generative topographic mapping (GTM)-based patent map using the patent-IPC matrix and detected any patent vacuums. Then, in order to confirm the promising patent vacuums, we technically examined criticality and trend analyses. Finally, we designed an IPC-based network and a keyword network with promising patent vacuums to derive a technology development strategy from a macro- and microperspective for the TFTFT system. As a result, we confirmed two promising patent vacuums. The patent vacuums found were defined as the technical field of rail vehicles suitable for TFTFT systems and the technical field of equipment and systems for freight transfer to rail vehicles. The proposed procedure and analysis method provide useful insights for developing a research and development (R&D) strategy and technology development strategy for a TFTFT system.

Development of Morphology Analysis-Based Technology Roadmap Considering Layer Expansion Paths: Application of TRIZ and Text Mining

Morphology analysis (MA)-based roadmapping has been considered an effective means to support the process of technology innovation in a business environment. However, previous research on MA-based roadmaps has commonly focused on the process of developing existing technology roadmaps (TRMs), while the paths of layer expansion for seeking new opportunities is rarely a focus. Thus, the aim of this research is to develop MA-based TRMs by utilizing MA to describe the characteristics of the technology and product layers in the TRMs and apply the improved theory of inventive problem solving (TRIZ) inventive principles to establish innovation paths for new opportunities with the aid of text mining tools. This study suggests using a morphological matrix to construct existing TRMs by calculating the correlations among different technology and product nodes and two sparse generative topographic mapping (SGTM)-based maps to discover new technology and product opportunities by identifying technology and product development trends and innovation elements in sparse areas, which is the objective of simplifying TRIZ application. To illustrate the performance of the proposed approach, a case study is conducted using patents and product manuals for underwater vehicles, which are becoming popular high-tech and secure tools to explore sub-sea resources. This approach contributes by suggesting a semi-autonomous and systematic procedure to extend the existing MA-based TRM and simplifying TRIZ application according to the occurrence frequency of the keywords.