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This paper investigates the influence of research and development (R&D) cooperation on the creation of spillover effects for sustainable firms in the chemical industry. We explore the evidence for the origin of knowledge spillovers derived from cooperation amongst firms and universities and R&D organizations as well as to test the influence of internal/external financial support on these effects. The results confirm that when firms acquire knowledge from internal sources, this leads to increased innovation and sustainable performance. We have proved that internal expenditure results in increased internal knowledge spillovers. These findings may be specific for Central and Eastern (CEE) transition countries, indicating their efforts to build path-dependent structures based on knowledge institutions and businesses as well as knowledge networks. However, this study also provides a more “global” contribution to the knowledge spillover effect theory. It shows that a firm’s cooperation both with universities and with other firms promotes different types of knowledge spillovers and can affect diverse modes of sustainable activities in innovation.

A new metric for the quantitative and qualitative evaluation of bone stiffness is introduced. It is based on the spectral decomposition of stiffness matrix computed with finite element method. The here proposed metric is defined as an amplitude rescaled eigenvalues of stiffness matrix. The metric contains unique information on the principal stiffness of bone and reflects both bone shape and material properties. The metric was compared with anthropometrical measures and was tested for sex sensitivity on pelvis bone. Further, the smallest stiffness of pelvis was computed under a certain loading condition and analyzed with respect to sex and direction. The metric complements anthropometrical measures and provides a unique information about the smallest bone stiffness independent from the loading configuration and can be easily computed by state-of-the-art subject specified finite element algorithms.

Models that predict corporate financial risk are important early-warning systems for corporate stakeholders. Most models to date have been developed using financial indicators. However, in financial decision-making, increasing attention is being paid to the role of textual information, which may provide additional insight into managerial opinions and intentions and which has recently been used to more effectively predict corporate financial performance. Previous approaches in this regard have predominantly focused on sentiment analysis of managerial communication. However, the role of context-related sentiment remains poorly understood in the financial risk domain. Here, we investigate how risk-related sentiment in verbal managerial communication might predict corporate financial performance, including indebtedness, profitability, market value and bankruptcy risk. To ensure deductive content validity, we propose specific word lists for each type of corporate financial risk and assign each word with positive / negative labels. Our findings provide evidence for a major role of risk-related sentiment as an indicator of corporate performance in terms of financial risks. Notably, using novel risk-related word lists in regression models, we show that a proactive and opportunity-seeking risk management has a significantly positive impact on financial performance, implying that stakeholders should carefully consider the risk-related managerial communication in corporate annual reports. First published online 19 November 2020

Diffeomorphic shape registration allows for the seamless geometric alignment of shapes. In this study, we demonstrated the use of a registration algorithm to automatically seed anthropological landmarks on the CT images of the pelvis. We found a high correlation between manually and automatically seeded landmarks. The registration algorithm makes it possible to achieve a high degree of automation with the potential to reduce operator errors in the seeding of anthropological landmarks. The results of this study represent a promising step forward in effectively defining the anthropological measures of the human skeleton.

We sought to analyse plasma levels of peripheral blood microRNAs (miRs) as biomarkers of ST-segment-elevation myocardial infarction (STEMI) due to type-1 myocardial infarction as a model situation of vulnerable plaque (VP) rupture. Samples of 20 patients with STEMI were compared both with a group of patients without angina pectoris in whom coronary angiogram did not reveal coronary atherosclerotic disease (no coronary atherosclerosis-NCA) and a group of patients with stable angina pectoris and at least one significant coronary artery stenosis (stable coronary artery disease-SCAD). This study design allowed us to identify miRs deregulated in the setting of acute coronary artery occlusion due to VP rupture. Based on an initial large scale miR assay screening, we selected a total of 12 miRs (three study miRs and nine controls) that were tested in the study. Two of the study miRs (miR-331 and miR-151-3p) significantly distinguished STEMI patients from the control groups, while ROC analysis confirmed their suitability as biomarkers. Importantly, this was observed in patients presenting early with STEMI, even before the markers of myocardial necrosis (cardiac troponin I, miR-208 and miR-499) were elevated, which suggests that the origin of miR-331 and miR-151-3p might be in the VP. In conclusion, the study provides two novel biomarkers observed in STEMI, which may be associated with plaque rupture.

Achieving competitive advantage is becoming increasingly difficult in today's rapidly changing environment, and it is increasingly related to differentiation among competing companies. This concerns not only quality of products and economic results but also company’s visibility as such. One way to present a responsible approach to entrepreneurship is via corporate social responsibility (CSR) – presenting this not only to company owners but also to other stakeholders, especially external ones. Potential investors can also be approached in this way. The total of 1380 listed US companies have been assessed as part of this research in 2014, both in terms of selected financial indicators and information they have published concerning their CSR activities. The aim was to find out which areas of CSR are presented by companies in their annual reports and whether a greater incidence of CSR information correlates with the selected financial indicators, which include company’s market value and bankruptcy risk. For this purpose, a CSR dictionary was used. Four areas were evaluated: the environment, social community, human rights, and employee welfare. We demonstrate that companies with favorable financial performance show an emphasis on these areas. This finding has important implications for all stakeholders.

Automated textual analysis of firm-related documents has become an important decision support tool for stock market investors. Previous studies tended to adopt either dictionary-based or machine learning approach. Nevertheless, little is known about their concurrent use. Here we use the combination of financial indicators, readability, sentiment categories, and bag-of-words (BoW) to increase prediction accuracy. This paper aims to extract both sentiment and BoW information from the annual reports of US firms. The sentiment analysis is based on two commonly used dictionaries, namely a general dictionary Diction 7.0 and a finance-specific dictionary proposed by Loughran and McDonald (J Finance 66:35–65, 2011 . doi: 10.1111/j.1540-6261.2010.01625.x ). The BoW are selected according to their tf–idf. We combine these features with financial indicators to predict abnormal stock returns using a multilayer perceptron neural network with dropout regularization and rectified linear units. We show that this method performs similarly as naïve Bayes and outperforms other machine learning algorithms (support vector machine, C4.5 decision tree, and k -nearest neighbour classifier) in predicting positive/negative abnormal stock returns in terms of ROC. We also show that the quality of the prediction significantly increased when using the correlation-based feature selection of BoW. This prediction performance is robust to industry categorization and event window.

Indicators of financial performance, especially financial ratio analysis, have become important financial decision-support information used by firm management and other stakeholders to assess financial stability and growth potential. However, additional information may be hidden in management communication. The article deals with the analysis of the annual reports of U.S. firms from both points of view, a financial one based on a set of financial ratios, and a linguistic one based on the analysis of other information presented by firms in their annual reports. Spearman correlation coefficient is used to compare the values of financial and linguistic indicators. For the purpose of the comprehensive assessment, novel word lists are proposed, specifically designed for each category of financial analysis. The aim is to assess the information ability of annual reports and whether successful firms present their results precisely or not. The results show that the proposed topic dictionaries can be beneficial, especially for the assessment of cash flow and leverage ratios.

In this paper we explore the following question: how weak can a logic be for Rosser’s essential undecidability result to be provable for a weak arithmetical theory? It is well known that Robinson’s Q is essentially undecidable in intuitionistic logic, and P. Hájek proved it in the fuzzy logic BL for Grzegorczyk’s variant of Q which interprets the arithmetic operations as nontotal nonfunctional relations. We present a proof of essential undecidability in a much weaker substructural logic and for a much weaker arithmetic theory, a version of Robinson’s R (with arithmetic operations also interpreted as mere relations). Our result is based on a structural version of the undecidability argument introduced by Kleene and we show that it goes well beyond the scope of the Boolean, intuitionistic, or fuzzy logic.

IntroductionUse of the mechanically expandable transcatheter aortic valve (MEV) has been recently linked to increased risks of valve dysfunction and cardiovascular mortality. The risk of developing conduction disturbance with the MEV valve is well known, and the negative prognostic impact of permanent pacemaker implantation (PPI) after transcatheter aortic valve implantation is another consideration.AimThis study aimed to compare the mid-term survival of patients with MEV and self-expandable valves (SEV), and to examine survival of both groups according to the presence or absence of PPI.MethodsThis single-centre, retrospective, observational study examined data from MEV and SEV groups comprising 92 and 373 patients, respectively. The mean clinical follow-up was 2.5±1.7 years. Mortality information was obtained from the National Institutes of Health Information and Statistics.ResultsBaseline characteristics were comparable between the groups. The log-rank test showed higher cardiovascular mortality in the MEV group (p=0.042; the relative risk (RR) 1.594 (95% CI 1.013 to 2.508)). The Cox proportional hazards model identified MEV implantation as an independent predictor of cardiovascular mortality. The rate of PPI was twice as high in the MEV vs SEV group (33.7% vs 16.1%; p<0.001). We compared the survival of both groups according to the presence or absence of PPI and found higher mortality in the MEV group without PPI versus the SEV group without PPI (p=0.007; RR 2.156 (95% CI 1.213 to 3.831)). Survival did not differ in the groups with PPI.ConclusionsA higher mid-term cardiovascular mortality rate was observed with MEV versus SEV implants. Comparing both groups according to the presence or absence of PPI, we observed a higher mortality risk in patients with MEV without PPI than in SEV without PPI. In contrast, mortality did not differ between the groups when PPI was implanted.