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We study whether differences in shareholder protection by countries are important in explaining lower corporate social performance (CSP) of family firms vis-à-vis non-family firms. We create a dataset covering 46 countries for the period 2002–2016. Using a novel approach, we show that the difference in CSP of family firms relative to similar non-family firms is smaller when family firms experience stronger minority shareholder protection to reduce conflicts of interest than non-family firms do. Moreover, we find that the difference in CSP of family firms relative to similar non-family firms is larger when family firms experience stronger shareholders’ rights in corporate governance than non-family firms do. Our results support the view that differences in country-level shareholder protection play an important role in explaining the differences in CSP between family and non-family firms.

We investigate whether the quality of financial markets matters for the relationship between net external financing, voluntary carbon disclosure and firms' cost of capital. External financing needs may create incentives for firms to engage in environmental strategies, such as disclosing their carbon emissions in response to demands from stakeholders. In countries with low financial market quality, firms may build a reputation when complying with stakeholders' demands. In these countries, firms disclosing carbon emissions and with high external financing needs may be rewarded with a lower cost of capital. Using an international sample of 24,253 firm-year observations from 35 countries, we show that the higher firms' net external financing in the previous year, the more likely they are to disclose their carbon emissions in the current year. Moreover, the positive association between the likelihood of carbon disclosure and net external financing needs is stronger in countries with low financial market quality. We further show that disclosing firms with high external financing needs have a lower cost of capital as compared to disclosing firms with low external financing needs. This evidence also differs between countries with high versus low financial market quality.

Aim: multidrug-resistant (MDR) tuberculosis (TB) and drug-sensitive-TB have long treatment periods which affect patient compliance and treatment outcomes. We planned to determine the factors that will affect the management of this disease in the elderly population. Methods: in a period of two years, a total of 82 elderly patients with bacteriologically proven TB enrolled into our retrospective study between 2011-2018. The patients' demographic features, laboratory findings, and hospital records were analyzed. Results: we enrolled 67 (87.7%) patients with drug-sensitive TB and 15 (18.2%) MDR-TB in our study. In the study population, 73.2% (n=60) were male and the mean age was 75 +/- 9 years. When we compare treatment complications of MDR-TB and drug-sensitive-TB, we found out the rate of electrolyte imbalance, nephrotoxicity, and ocular toxicity more common among the MDR-TB group (p=0.008, p=0.008, and p=0.032, respectively). When we compare the mortality rate, cure, and treatment success between MDR-TB and drug sensitive TB, there were no statistically significant results (p=0.898, p=0.549, p=0.488; respectively). Conclusion: however, we think that this was due to the low sample size. Nevertheless, we should be careful in terms of complications management of the geriatric population., NA

This article examines the recent proposals and reports on the regulation of digital mergers in the European Union, United Kingdom, United States and other jurisdictions, with a particular focus on the proposals for presumptions against mergers. It argues that any intervention in digital mergers needs to calibrate a balance between preventing excessive levels of market concentration and promoting innovation and that any departure from existing laws should be justified. Against this background, this article concludes that the recent arguments for lowering the threshold for blocking digital mergers undermines the risk of chilling innovation and losing significant efficiencies, and does not rely on concrete evidence and sound economic theories.

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD.Significance⍰Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC).⍰Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation.⍰Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

In this study, we investigate the effect of board gender diversity on the decision to disclose carbon emissions voluntarily. Using an international sample consisting of 22,841 firm-year observations from 38 countries for the period 2010–2019, we determine the existence of a positive relationship between the percentage of female directors on the board and carbon disclosure. This evidence supports agency and resource dependency theories, as a gender diverse board indicates strong governance and better communication among stakeholders. Additionally, we examine the moderating effect of gender quotas across sample countries, where either soft or hard quotas have been implemented. We show that the number of firms disclosing their carbon emissions is, on average, higher in countries with either hard or soft quotas than in countries with no quota. Moreover, the positive effect of board gender diversity on voluntary carbon emission disclosure is similar across firms in countries with quotas and without quotas. The reported results demonstrate that there seems to be no need for country-level strict regulations regarding the firm-level percentage of female representation on the board to be effective, as gender board diversity in countries with no quotas has a similar effect in explaining voluntary carbon disclosure as in countries with quotas and those changing to quota regulation.

Background: Screening and accurate diagnosis of chronic thromboembolic pulmonary hypertension(CTEPH) are critical for managing the progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting CTEPH using electrocardiography (ECG). Methods: ECG signals were obtained from 54 regular and 23 CTEPH patients to test the technique. A dataset was created by converting the ECG results to digital. The 12-channel ECG signal received from 77 individuals is 924x1300 in size. The end-point was the diagnosis of CTEPH. By applying the suggested Nigerian motif pattern method to this data set, we obtained a feature matrix of 924x15010. FSCmRMR algorithm determined the most influential 947 characteristics among 15010 features and obtained a matrix of 924x947. We used a decision tree, SVM(Support Vector Machine), and KNN(K-Nearest Neighbour )algorithms to classify the selected most weighted features. Results: We achieved 98.05% success with the decision tree algorithm, 99.89% with the SVM algorithm, and 99.67% with the KNN algorithm. AI algorithm focused on each patient's S-wave, P-wave, and T-wave by QRS complex characteristics. Conclusion: The AI algorithm demonstrated high accuracy for CTEPH prediction using 12-lead and single-lead ECGs.

We investigate the relationship between research and development (R&D) and firm-level carbon emissions to determine whether firm type matters. Multinational corporations (MNCs) with high-level R&D expenditure have a greater ability than domestic companies to generate technologies that will contribute to controlling environmental pollution and climate change. However, we know less about whether MNCs contribute to reducing carbon emissions worldwide, because they also have the ability to overcome controls on pollution levels by shifting their production facilities from regions with more restrictions to those with fewer restrictions. The sample we use includes roughly 20,000 firm-year observations from 44 countries for the period 2003-2019. We find that MNCs decrease their carbon emissions by increasing their R&D spending more than domestic companies do. We further demonstrate that foreign direct investment (FDI) creates opportunities for MNCs to adjust their overall carbon emissions if they are located in developed countries. Key policy insights R&D investment in low-carbon technologies and practices decreases carbon emissions intensity and the impact on average is larger for MNCs than for domestic companies, showing that firm-type matters to emission reduction outcomes. MNCs manage their geographically diversified production so as to avoid reducing overall net global carbon emissions. MNCs may seek to operate in places that are weaker in enforcement of emissions reduction, which in turn raises their net global carbon emissions. MNCs located in developed countries are comparatively more important as corporate actors to drive carbon emission reductions due to changing location of operations.