As global efforts intensify to transition toward cleaner and more sustainable energy sources, the blending of hydrogen with natural gas emerges as a promising strategy to reduce carbon emissions and enhance energy security. This study employs a systematic approach to assess the economic viability of hydrogen blending, considering factors such as gas costs and heat values. Various hydrogen blending scenarios are analyzed to determine the optimal blend ratios, taking into account both technical feasibility and economic considerations. The study discusses potential economic benefits, challenges, and regulatory implications associated with the widespread adoption of hydrogen–natural gas mixtures. Furthermore, the study explores the impact of this integration on existing natural gas infrastructure, exploring the potential for enhanced energy storage and delivery. The findings of this research contribute valuable insights to policymakers, industry stakeholders, and researchers engaged in the ongoing energy transition, by providing a nuanced understanding of the economic dimensions of hydrogen blending within the natural gas sector.

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

Apple orchards are perennially planted where pesticides are applied to control numerous pests and diseases. The extensive long-term use of fungicides can lead to overall environmental load and resistance risk. This study aims to assess which fungicide-active substances have been used more intensively in the last decade in Latvia, evaluating the overall environmental load using the Pesticide Load Indicator (PLI). It was essential to see whether the amount of active substance usage rises, how it correlates with the total changes of the PLI and which substances are with the highest scores. The other issue was to test the sensitivity of Venturia inaequalis populations to systemic fungicides. Six full-bearing apple orchards that reflected local plant protection practices were selected from the different growing regions of Latvia to analyze fungicide use from 2012 to 2021 and test V. inaequalis populations’ sensitivity to systemic substances difenoconazole and cyprodinil. The PLI demonstrated that the protective fungicides were the most crucial group overall, with the highest potential impact on the environment and human health. Systemic fungicides had a relatively lower environmental impact, but after long-term use, the pathogen population’s sensitivity to difenoconazole and cyprodinil was reduced. Introducing new fungicide classes and biological control agents could help growers improve plant protection strategies against V. inaequalis, reducing the risk of resistance and environmental load.

Nowadays control and monitoring of bioprocesses is an important problem and challenge for researchers and information technology specialists. Specific task range of bioprocess control is defined, which needs realization of intensive calculations and participation of operator in decision making. In a situation, when bioprocess or production process does not need human attendance and the process environment is not suitable for computing machinery it is necessary to use shared control techniques. Shared control means that computational equipment and operators are not located at the production site. That can be the case in various fields: agriculture, forestry, medicine. Common examples of applications are: greenhouse monitoring and control systems, control of drying processes, livestock management, agricultural field processing systems, fire emergency monitoring, etc. Honey bee indoor wintering process is one of such bioprocesses which can be monitored and analyzed using shared and adaptive control methods. During the winter time honey bees can be placed in a special wintering building with controlled microclimate environment in order to save physiological resources of the bees and preserve honey. This can be advantageous compared with outdoor wintered bees. During this period the main risk factor for beekeeper is that bees, while kept at temperature of 6°-8°C, can conclude that spring has started when in the reality it is still winter. As a result, bees can start an intensive process of brood rearing in winter and this can lead to the death of colonies. In order to control this process it is necessary to monitor the activity of bees by measuring temperature above the bee nest in the hive. The temperature and air humidity of bee wintering building is not suitable for computational equipment. Additional challenge is that the wintering building has to be kept dark to inhibit flight reflexes of the bees. Thus shared control techniques should be used. Wired temperature sensor network is developed to measure and control activity of bee colonies. For measurements one wire network is used, where digital thermal sensors are parallel connected with the interface device, which in turn is used to transfer data from the sensors to the end PC. According to the temperature changes it is possible to understand the brood rearing state of a particular colony. If an intensive brood rearing begins the beekeeper should receive a suggestion from the computer to slow down the process. The process can be slowed down by decreasing the temperature in the wintering building. On the other hand, the acceleration of the process can be achieved through increasing the temperature. The problem of compromise decision making has to be solved because the temperature changes will affect all the colonies. Decision making process is supported by mathematical modelling of bee colony dynamics to simulate possible outcomes of various control strategies. Additionally WEB interface is developed to ease the access to temperature data. An e-mail sending application is developed for notifications about error situations. The developed beehive temperature monitoring system is a part of the whole honey bee wintering building temperature control system.