The purpose of this article is to establish the social and/or economic impact of the implemented Cohesion Fund (CF) projects on development of regions of Latvia. Three tasks are addressed to achieve this goal - to study CF projects by regions; analyse distribution of funding by sectors of economy and their compliance with CF objectives; to draft recommendations for improving the absorption of funds and achieving the goals of the European Union (EU) regional policy. The article consists of three chapters, an introduction and conclusions. New scientifically grounded recommendations are developed for improving socio-economic impact of the CF in Latvia. The key recommendation relates to the EU involvement in dealing with problems of increase in costs, namely - by proving that the increase in costs affects several of the new EU member states or Community regions, the EC will have to take part in addressing the problem. Further assessment of the socio-economic impact of the CF on development of regions of Latvia requires additional research.

Agriculture is a unique sector, because it is associated with negative outcomes stemming from imperfectly predictable biological and climatic variables. These variables include natural adversities (for example, pests and diseases), climatic factors are not within the control of agricultural producers. So in agriculture it is very important to identify and evaluate risk, only then decisions made in farm will bring profit and other positive results. Scientific literature distinguishes a lot of methods for risk evaluation, but it is not clear what methods could be adapted in agriculture sector. This article presents main risk types in agriculture and their features as well as introduces most popular risk evaluation methods and their possible use in assessing risks in agriculture.

International audience | The existing Andean dairy production patterns are scarcely known and yet they play a major role in the household food security of many families. This study examines the consistency of forage management through the monitoring of 8 dairy farmers of a community living in the harsh conditions of the dry Peruvian Andes and the analysis of their production practices. The Sinto farmers are able to produce milk throughout the year despite the very dry climatic conditions by adjusting cattle groups, grazing rotationaly on irrigated alfalfa fields, farming large rangelands zones and exchanging standing forage with other farmers. Although the milk production per cow is low, the milk yield per hectare of alfalfa field is relatively high. This can be interpreted both as an optimisation of the system with regards to the restrictive factor which is the irrigated land and as a risk management, two components that may be considered to evaluate the sustainability of these livestock farming systems and to propose development options.

Agriculture more than any other branch of production is influenced by various natural, anthropogenic and economic risk factors on which the profitability of production and preservation of the environment in rural areas depend. The prerequisite of prevention or alleviation of risks is their all-round knowledge and consideration. In order to understand the nature of risks, they will be dealt with in the present study on the example of the cultivation of the most common cereal in Estonia - barley. The difference in barley yields, resulting from the pedoclimatic coditions of Estonia, may be 2-4-fold in different regions and in different years. At the same time, differences in the profitability of barley cultivation, depending on economic factors and on the specificity of production, may be up to 6-fold.

Social enterprises, as an interdisciplinary form of business, often face problems that traditional enterprises and civil society organizations have already overcome: lack of legal framework, complex business models, business identity problems, lack of public awareness, lack of visibility and difficulty in accessing investment markets. The name ‘social entrepreneurship’ makes it clear that it is not easy to have a business oriented towards a social goal, because ‘social’ implies that the business is directed to the goal of reducing social problems and enhancing the quality of life, which, besides being expensive, is also a complex process. The purpose of the article is to reflect the challenges faced by representatives of social entrepreneurship in Latvia: before April 1, 2018, when this type of business was non-profit, and after, when the opportunity was given to establish a profit-making social enterprise. In order to achieve the goal, following tasks are set — to provide a theoretical explanation of social entrepreneurship, as well as, with the help of empirically obtained information, to reveal the challenges faced by representatives of social entrepreneurship in Latvia. According to interviewed informants, the main challenges for social entrepreneurs in Latvia are the lack of qualified employees, institutional cooperation and funding, which would allow organizations to effectively carry out their business; in addition, difficulties are caused by situations when financing is too complex or difficult to access, and social entrepreneurship lacks public support. Rather often, social entrepreneurs lack knowledge about business risks and how to plan their business in the long term.

Cyberattacks have become a regular part of network activity. To mitigate the risks from possible threats, organisations have implemented firewalls and intrusion detection systems, which can help stop network attacks. The problem is that often the accuracy of these systems is not effective enough. Another part of network security is security information and management platforms. These systems are more advanced versions of Threat Intelligence Platforms, because it is possible to make in-depth analyses of real-time events in a network. This research paper proposes improving intrusion detection system functionality using Open-Source Intelligence. Anomaly-based intrusion detection systems often generate alerts, but these alerts require deeper analysis to understand whether it is a real attack or just a false alarm. By making Open-Source Intelligence requests and evaluating extra information, it is possible to make more precise rules to stop attacks against network infrastructure. Open-Source Intelligence requests are generated directly from the intrusion detection system or with Python scripts based on the organisation’s infrastructure profile. The proposed architecture was experimentally tested by automating Open-Source Intelligence requests and intrusion detection rule generation by Python scripts.