A comparative insight into the status of the lobbying regulation debate in Estonia

This paper focuses on different approaches towards regulating lobbying. Special attention is paid to policy formulation regarding lobbying regulation in Estonia. The Estonian case study is juxtaposed with the regulatory solutions of lobbying in Scandinavian and post-communist countries. The reason for choosing such a comparative framework is justified by the fact that these two sets of countries have chosen rather dissimilar approaches in interpreting the role of lobbying in society. As Estonia's policy decisions are often determined by both of these two spaces of political culture, the article polemicizes over the question of which path Estonia could take in terms of regulatory framework for lobbying. The analysis concludes with policy recommendations to Estonia as well as with suggestions for further theoretical research directions for understanding better the policy impact of lobbying regulations designed and implemented by post-communist states. Copyright © 2014 John Wiley & Sons, Ltd.     

Corporate Governance and Corporate Social Responsibility in Profit- and in Non-Profit-Organizations

Corporate Governance governs corporations. To discuss the law and economics of corporations, their performance on the market and management control by legislation and government, is to discuss Corporate Governance. Now, Corporate Governance is being challenged by a new concept to conceive the role of corporations in the world and to control their managers: this is the concept of Corporate Social Responsibilty (CSR). This concept focuses on other than merely profit centered functions of corporations; rather, performance of corporations is measured by criteria of good corporate citizenship. In the Non-Profit sector, corporations also play a major role, although organizations in this sector typically differ from business corporations in several aspects. Yet, deficits of transparency and control of NPO are being discussed here as well. The tendency is to draw from the concept of corporate governance to develop appropriate remedies. Furthermore, the discussion of Corporate Social Responsibilty has also made its way to the NPO sector.     

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors*,†

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.     

Increased responsiveness of electric precipitation (electrosynerese) by means of a chemical method.

Electric precipitation is a method in serological diagnostics (determination of species-specificity). Since serum protein fractions (especially immunoglobulins) and extracts from blood stains migrate in agar-gel at pH 8.2--8.6 towards the cathode they cannot be used for the determination of the species specificity. By a chemical method using glutaraldehyde authors have produced an albumin-immunoglobulin complex, altering in this way the direction of electrophoretic mobility. Treatment of the blood samples with glutaraldehyde increases the sensitivity and realiability of the method.     

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 10¹⁰ magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.     

Hydrogen: Fueling EU-Morocco Energy Cooperation?

The war in Ukraine and the looming threat of climate change are driving the strategic need to diversify sources of energy, including renewables. Therefore, the European Union aims to develop energy relations with non-EU member states, and Morocco has become a key priority. Both Brussels and Rabat are pursuing ambitious green policies and cooperation initiatives, including on hydrogen. Drawing on theories of international institutions and political economy, this article analyzes hydrogen-related relationship patterns and explains demand and supply factors as drivers of institutionalized energy cooperation. We examine the EU's hydrogen approach and development, its relations with Morocco, and the political interests of both sides. Our analysis shows that the complex interdependencies in the evolving EU-Morocco hydrogen relations indicate that demand can be met by supply. However, challenges remain, including the need to deepen the partnership and prepare the market for commercial hydrogen production.