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Hydrogen has the potential to revolutionize the energy industry due to its clean-burning and versatile properties. It is the most abundant element in the universe and can be produced through a variety of methods, including electrolysis. The widespread adoption of hydrogen faces various challenges, including the high cost of production; thus, it is important to optimise the production processes. This research focuses on development of models for hydrogen production optimisation based on various external factors and parameters. Models based on electricity prices are developed and compared between different market situations. To run hydrogen production more effectively, it is required to use renewable energy sources for the production process. Adding the solar power component to the economic evaluation model outcome is more positive. The Monte Carlo tree search (MCTS) algorithm is adapted to effectively control the electrolysis process. MCTS schedule optimization was performed for a 24 h time horizon applying two time-resolution settings—1 h and 15 min. The results demonstrate the potential of the MCTS algorithm for finding good schedules for water electrolyser devices by taking into account variable environmental factors. Whereas the MCTS with a 15 min resolution ensures mathematically better results, it requires more computational power to solve the decision tree.

Information and communication technologies are part of almost any branch of human lives. During the last decade also beekeeping joined the direction of application of IT tools and solutions and precision beekeeping was defined. Still in the beekeeping many operations and observations are completed manually, and there is a potential to switch to the digital realisation. Information technologies can be used in the beekeeping to partly support the beekeepers by implementation of automatic or semi-automatic solutions for bee colony remote monitoring, apiary record making and other actions. The aim of this paper is to make a summary of the usage of information technology tools by the beekeepers in Latvia, summarizing precision beekeeping development status and conclude about its future development potential. To achieve this aim, in cooperation with Latvian Beekeepers Association, a beekeepers survey was conducted. More than 200 beekeepers shared thoughts and opinions about their application of information and communication technologies for monitoring the bee colonies and apiary management. The summary of the survey conducted is described in this study.

Since honey bees are one of the most important actors in the whole world, it is important to follow the life of these insects in order to preserve them from danger, via a range of risk factors such as Colony Collapse Disorder, pesticides, pests etc. Therefore it is important to identify any abnormalities inside the honey bee colony at an early stage, which may be possible using modern technologies e.g. monitoring systems, data processing, and analysis. This research proposes a solution for honey bee colony state identification using temperature data and fuzzy logic. The detection process proposes a Fuzzy Inference System that receives five input parameters and provides an output (defined as “assessment of the colony”) pointing to (for now) three defined possible states – normal, death, and extreme. The rule base for the inference system was defined taking into account the knowledge of field experts, literature research, previous observations and was based only on temperature data and temperature changes inside the hive during different seasons. The proposed system proved to be quite robust, showing an accuracy value of ~98%, 100% precision and specificity, ~97% recall and ~98% F1 score when tested with validation set.

Precision beekeeping, or precision apiculture, focuses on individual beehive remote monitoring using different measurement systems and sensors. Sometimes, there are debates about the necessity for such systems and the real-life benefits of the substitution of bee colony manual inspection by automatic systems. Remote systems offer many advantages, but also have their disadvantages and costs. We evaluated the economic benefits of the remote detection of the bee colonies’ reproductive state of swarming. We propose two economic models for predicting differences in the benefits of catching a swarm depending on its travel distance. Models are tested by comparing the situation in four different countries (Austria, Ethiopia, Indonesia, and Latvia). The economic model is based on financial losses caused by bee colony swarming and considers the effort needed to catch the swarm following a remote swarm detection event. The economic benefit of catching a swarm after a remote precision beekeeping notification is shown to be a function of the distance/time to reach the apiary. The possible technical range is tempting, but we demonstrated that remote sensing is economically limited by the ability to physically reach the apiary and interact in time, or alternatively, inform a person living close by. An advanced economic model additionally includes the swarm catching probability, which decreases based on travel distance/time. Based on exemplary values from the four countries, the economic potential of detecting and informing beekeepers about swarming events is calculated.

Finding a proper location for a bee apiary is a crucial task for beekeepers and especially for travelling beekeepers. Normally beekeepers choose an appropriate apiary location based on their previous experience and sometimes the location may not be optimal for the bee colonies. This can be explained by different flowering periods, variation of resources at the known fields, as well as other factors. In addition it is very challenging to evaluate how many bee colonies should be placed in one geographical location for an optimal nectar foraging process. This research presents a model for finding the number of honey bee colonies needed for the optimal foraging process in the specific location, taking into account several assumptions. Authors propose to take into account potential field productivity, possible chemical contamination, surroundings of the apiary. To run the model, several steps have to be completed, starting from the selection of area of interest, conversion to polygons for further calculations, defining the roads in the selected area. The outcome of the model number of colonies that should be placed is presented to the user. The Python language was used for the model development. The model can be extended to use additional factors and values to increase the precision of the evaluation. In addition, input from users (farmers, agricultural specialists, etc.) about external factors that can affect the number of bee colonies in the apiary can be taken into account. This work is conducted within the Horizon 2020 FET project HIVEOPOLIS (Nr.824069).

Honey bees are the main pollinators of the most agricultural plants on our planet. Foraging behaviour is one of the distinctive behaviours of honey bees and is like a link between the honey bee colony and the ambient environment. It is well known that environmental and meteorological factors can have either positive or negative impact on the foraging activity and the amount of gathered nectar and honey production. This research summarises the effect of the main agrometeorological factors, like temperature, humidity, wind and intensity of precipitation on the foraging activity. Within this research, a model for potential honey bee colony foraging activity based on the agrometeorological factors is presented and described. For the calculations weather forecast is used, either by the manual input or by the automatic uploading using the JSON format. The outcome of the model allows the beekeepers to predict and evaluate the potential amount of nectar to be foraged during the active season or specific period of time.

In a time marked by ecological decay and by the perspective of a severe backlash of this ecosystem decay and climate devastation onto human society, bold moves that employ novel technology to counteract this decline are required. We present a novel concept of employing Artificial Life technology, in the form of cybernetically enhanced bio-hybrid superorganisms as a countermeasure and as a contingency plan. We describe our general conceptual paradigm, consisting of three interacting action plans, namely: (1) Organismic Augmentation; (2) Bio-Hybrid Socialization and (3) Ecosystem Hacking, which together compose a method to create a novel agent for ecosystem stabilization. We demonstrate, through early results from the research project HIVEOPOLIS, a specific way how classic Artificial Life technologies can create such a living, ecologically active and technologically-augmented superorganism that operates outside in the field. These technologies range from cellular automata and biomimetic robots to novel and sustainable biocompatible materials. Aiming at having a real-world impact on the society that relies on our biosphere is an important aspect in Artificial Life research and is fundamental to our methodology to create a physically embodied and useful form of Artificial Life.

Doctoral thesis had been developed in the period from September 2009 until September 2012 in Department of Computer Control of Faculty of Information Technologies, Latvia University of Agriculture. The PhD thesis consists of 152 pages, which include 13 tables, 108 figures and 7 annexes. For the development of this paper 150 literature sources were used including 141 in foreign languages. The aim of the thesis is to improve the implementation of Precision Agriculture approach using the integration of various mathematical models in computer control of a multiobject biosystem. In order to achieve the aim of the PhD thesis several tasks were completed 1. Analyze the implementation of sensor networks and computer control in biosystem control and Precision Agriculture. 2. Analyze automatized control system development principles for biosystem control. 3. Develop a model based automatized computer control system architecture for biosystem control. 4. Develop an individual biological object monitoring and control system for Precision Agriculture realization for the honey bee wintering process. 5. Evaluate usability of the developed system for the bee colony state evaluation and control during the wintering process. The content of the PhD thesis: it is structured according to the aim and the tasks of PhD thesis consisting of 4 chapters. First chapter includes analysis of the application of computer control in Precision Agriculture and in biosystem control. The methodology for the application of the sensor network for biological system monitoring is provided. This chapter also provides introduction to practical work problematic. Second chapter includes description of the author developed model based control system architecture for the biological process monitoring and control. It emphasizes the focus on application of various multi-dimensional models for the decision support system realization. Third chapter includes detailed and sequent description of the bee wintering process control and monitoring task completion. In fourth chapter experimental data of the bee colony behaviour during the wintering process is summarized and analyzed. The precision of the bee colony temperature measurement system is evaluated. Calculations for commercial deployment of the system are provided. The main results, conclusions and future development prospects are described in the conclusion of the PhD thesis.

The monitoring and predictions of various multi-level states of honeybee colonies are performed using emerging Internet of Things technologies and data processing methods. It is become common to use multiple sensors and devices providing multi-modal data to monitor a single activity. Modern data analysis and data processing procedures include a step of data fusion in order to provide more accurate input data. This, however, requires implementation of machine learning and large data sets, whereas gathering large data sets of real time and observation data is a common problem for small to medium size apiaries. This why there are no real implementation of data fusion method in precision beekeeping field. The aim of this paper was to introduce the concept of data layering, which aims to solve the global precision beekeeping problems without implementation of machine learning. The concept was demonstrated within the scope of foraging optimization problem using three data sets: flowering calendar data, rainfall precipitation data and bee activity data.

Real time, continuous and remote monitoring of the honeybee colonies with application of information and communication technologies (ICT) is becoming increasingly frequent in industry and in a scientific research. Combination of ICT and beekeeping led to the development of the Precision Beekeeping approach. Successful implementation of the Precision Beekeeping system includes development of the bee colony monitoring hardware solution and computer software for data collection and further analysis. This paper describes developed and implemented bee colony monitoring unit for weight and temperature monitoring. Bee colony weight is one of the key metrics of the strength of a colony. Changes in weight can reflect the productivity rate of the colony, as well as its health and state. Developed monitoring system is based on Raspberry Pi Zero W single board computer with several connected sensors for bee colony temperature and environmental parameter monitoring. Weight is measured using single point load cell with possibility to measure weight up to 200kg, which is enough for the beehive measurements. Data transfer from the remote bee colony is provided by the external 3G router. For data storage and analysis cloud-based data warehouse is developed. Collected data is accessible in the web system with user friendly interface for data visualisation and reporting. Within this research scale calibration process is described and accuracy of the weighting is evaluated and possible challenges are discussed. Described monitoring system is developed within the Horizon 2020 project SAMS, which is funded by the European Union within the H2020-ICT-39-2016-2017 call. To find out more visit the project website https://sams-project.eu/.

Successful implementation of the Precision Beekeeping system includes development of the bee colony monitoring hardware and software for data collection and analysis. This paper focuses on development and installation of such system in two developing countries: Ethiopia and Indonesia. Challenges and peculiarities of the mentioned countries are compared and described in regards of the system installation process, beekeeper’s apiary management practices and economic aspects. Bee colony monitoring hardware is based on Raspberry Pi Zero W single board computer as a main controller of the system and connected sensors for temperature, humidity, weight and acoustics. Software part includes cloud-based data warehouse solution with additional models for data analysis and web interface for the remote data monitoring and visualisation. Authors’ approach is presented for linking together hardware components with a cloud software into single solution oriented on user needs in target countries. Main purpose of Precision Beekeeping systems is detecting the actual status of the colony and informing the beekeeper in case if unwanted colony behaviour occurs. Another significant aspect of the research is evaluation of the possibility to transfer knowledge and expertise to the experts in target countries. This work is carried out within the SAMS - Smart Apiculture Management Services project, which is funded by the European Union within the H2020-ICT-39-2016-2017 call. To find out more visit the project website https://sams-project.eu/.

Remote and automatic monitoring of two Apis Cerana bee colonies was conducted in Indonesia to demonstrate precision beekeeping approach in that region. Successful implementation of the precision beekeeping system includes development of the bee colony monitoring hardware and software for data collection, analysis and visualisation. This paper focuses on development and installation of such systems at the private apiary in Indonesia. For bee colony monitoring at the apiary a developed monitoring unit was used, which is based on ESP microchip, and for the data storage SAMS data warehouse was used. The monitoring results showed that the choice of the location of the temperature sensor is important, as the temperature at the hive sides changes synchronously with the outside temperature. Also, feedback from the beekeeper is collected to further improve the system and monitoring process. This research is conducted within the SAMS – Smart Apiculture Management Services project, which is funded by the European Union within the H2020-ICT-39-2016-2017 call and with close collaboration with the local private beekeeper. To find out more, visit the project website https://sams-project.eu/.

Precision Livestock Farming (PLF) approach requires immense amount of datacollection, aggregation and processing, using various hardware and softwaresolutions, and is applied in many farms trying to achieve the most efficient andeffective way of production. Hardware and software compatible systems capable ofachieving this goal are called Farm Management Information Systems (FMIS), andare a necessity for a complete and successful implementation for PrecisionAgriculture (PA) branch approaches. However, most of commercially availableFMIS do not only focus on crop management, but also have limited availability tosmall and average sized farms, in terms of price, supported language and specificfeatures. Simpler FMIS, on the other hand, do not have necessary capabilities tofully support PLF. There are currently very small amount of high grade pig farmoriented FMIS, especially for farms with less than a hundred of sows. Therefore,there exists the need for solutions for managing farms with limited number ofsows. To help address this need, authors proposed and developed architecture forunified data warehouse (DW), which was scalable and extendable cloud based datastorage and processing system with support of individual data analysis. DW hascapabilities to data interexchange and/or be integrated in existing FMIS throughoutvariety of data-in/data-out interfaces, like UIs, unmanned data supplier orconsumer systems. The core of the DW is designed to provide data processingflexibility and versatility, whereas data flow within the core is organized betweendata vaults in a controllable and reliable way.

Measurements of the wind energy at the sites of planned location of wind turbines (WTs) require significant expenditures for installation and maintenance of a measuring complex with sensors positioned at the height of WT location. To reduce these expenditures, physical measurements of wind parameters using the sensors placed at lower heights could be complemented by modelling the wind shear up to the height of WT location. Investigations of the wind shear up to the height of 200 (m) on the Latvian shore of the Baltic Sea have been carried out in the time from 01.02.2014 to 01.01.2015 using a Pentalum SpiDAR laser measuring complex. The wind shear modelling is performed based on the power law approximation. Calculations of the operational efficiency of a 2300 (kW) WT (type E82) have been performed for heights of 100, 140 and 180 (m) based on the analysis of average wind speeds and standard deviations measured for three levels: 30, 40, 50 (m), and using five modelling approaches: Rayleigh's model, three Weibull models with k, c coefficients, and the cubic wind speed model. The results of WT operational efficiency calculations using the mentioned models are compared with the data obtained by analysing the wind shear values. It is shown that the modelling curve deviations from the experimental results for the heights of 100, 140 and 180 (m) are affected by the shape (k) and scale (c) coefficients of Weibull function. The results are presented in the form of tables and curves.

Precision Beekeeping (or Precision Apiculture) aims to help beekeepers monitor bee colonies remotely and identify different colony states including deviant behaviour. One monitoring target is the remote identification of bee colony swarming since this is one of the factors that can significantly reduce profitability. To identify temperature dynamics and its patterns for swarming detection, ten colonies were constantly monitored for four months from 1 May to 31 August 2015. Nine swarms were observed during experiments. During the warm-up stage, in the last 10–20 min before take-off, a temperature rise by 1.5–3.4 °C from typical range 34–35 °C to range 37–38 °C was registered by a temperature sensor placed above the polyethylene foil covering the upper hive body under the pillow. For all swarming events it was common that a bee colony needs a relatively small amount of time (from 8 to 20 min) to warm up before take-off. It was concluded that a single temperature sensor above the bee nest combined with a proposed decision support algorithm can be used for automatic remote detection of swarming at take-off stage.

The paper presents a review of wind parameter measurement complexes and investigation methods used for potential wind energy evaluation. Based on results of long-term investigations of wind shear distribution regularities are shown up to 160 m height on the Baltic Sea shore. Distribution of potential wind energy in Latvia is shown as a map and table of average and average cubic wind speed values. Database of wind parameter measurements is available at a public website.

The concept of the smart city has been fashionable in the political arena in recent years. Cities are trying to be modern and provide various ICT based services for their citizens. An efficient public transportation service is critical for the citizens, but traffic congestions are still a problem in cities and are one of the main reasons for public transport delays. Therefore, it is important for citizens to know where the needed public transport vehicle is located at the moment, to know if the transport has already passed the stop or not. Authors of this research propose a real-time public transport tracking system using a global positioning system (GPS) technology module to receive the location of the vehicle in a real-time. System is based on the Raspberry Pi 3, which is used to transfer positioning data received from GPS module to the remote database. Based on received data, the location of the bus is visualised in the developed Web system.

The paper is devoted to the investigation into the wind energy potential based on long-term observations of the wind speed and energy density fluctuations at heights from 10 to 160 m on the Baltic Sea coast of Latvia. During the observations (2004 - 2013), the wind speed and direction values were measured, and the statistical database was accumulated using a LOGGER 9200 Symphonie measuring systems mounted on 60 m masts - one on the western coast and another on the north-east of Latvia. From June 2011 to May 2012, these measurements were complemented with the data for the heights from 40 to 160 m obtained by means of a ZephIR lidar and with the metrological data provided by “Latvian Environment, Geology and Meteorology Centre” for the same period. The graphs of seasonal fluctuations in the wind speed were obtained for the heights up to 160 m by measurements over the period of 2007 - 2013. The results of the research on the wind speed distribution up to 200 m are promising for evaluation of the wind energy potential of Latvia and will be helpful in assessment of prospective sites for construction of WPPs.

Precision Beekeeping (PB), a sub-branch of Precision Agriculture, is an apiary management strategy based on the monitoring of individual bee colonies to minimise resource consumption and maximise the productivity of bees. Although there are sufficient technical means and industrial products for the practical execution of PB, the process is slow due to the differing states of development of three implementation phases: data collection, data analysis and application. The technical execution, adequacy of earlier and current solutions and development perspectives are analysed to prioritise different challenges for PB. The development of decision support systems, which is usually a stumbling block in Precision Agriculture, is suggested to be a mid-term task. In the long term, specific DSS-controlled electronic devices should be developed to enable new functionalities for PB. Specific classes of actors are proposed to operate within future PB systems for the automatic execution of suggestions made by decision support systems, as well as for diagnostic purposes. PB systems should be optimised by searching for appropriate combinations of different sensors, and corresponding decision support systems must provide convenient, reliable and cost efficient solutions. The development and specification of PB systems should consider business interests, distance to the apiary, expected risks and other peculiarities.

Beehive monitoring can provide useful information for people associated with beekeeping to help to manage their honey bee ( Apis mellifera ) colonies. The information obtained from the monitoring process can contain data about beehive's temperature, humidity, weight etc. Such a monitoring system is a practical tool in Precision Beekeeping. Honey bee colonies can be monitored using various system architectures that are different in methods and approaches. Since there are several monitoring system architectures, beekeeper himself should choose the one that suits his needs. To facilitate the beekeeper's choice of the suitable architecture, a selection algorithm was developed. This paper focuses on different automatic monitoring system architectures for real-time beehive temperature monitoring, distinguishing their advantages and disadvantages.

Development of smart city concept is challenging for every city. Smart city approach consists of several components and one of them is smart traffic. Parking is one of the smart traffic components. Due to the rapid growth of vehicles on the streets, finding an available parking space is becoming a big obstacle in modern life. For cities with large number of residents or places where sports or artistic events take place, search for a free parking lot is a major problem and can be a frustrating experience. Parking management system affects and improves people's daily life experience and is an important component of a modern traffic system. Smart parking system allows drivers to obtain real-time parking data, by using Internet of things technologies to gather and process information from parking lots. Thus, intelligent, innovative and efficient ways for parking are needed to coordinate the parking demands efficiently. Authors of this paper share the approach, which is implemented in Jelgava city in Latvia, where two smart parking systems are developed. One system is based on inductive sensors, which can recognize vehicle on each parking lot. Second approach is based on vehicle identification on video stream, which is used to register cars on parking entrance and exit, counting total number of free parking lots, but without indicating which particular lots are free or occupied.

Traffic congestion, the main constituent of vehicular extrinsic flow resistance, has been an important factor in the design of routing algorithms. There are important intrinsic factors that can also aid these algorithms. This study proposes a new vehicular intrinsic flow resistance metric referred as sinuousness. To obtain this metric several scenarios are designed. Each scenario has a unique topology with varying amount of curves, curve-radii lengths, total path lengths, and other features. Simulated vehicles flow through these scenarios and velocity data is gathered. Simulations show that the velocity, and hence the sinuousness, depends on several street aspects, such as distance between curves, curve radii, and aggregated arc circumference. A sinuousness expression is derived. Results show that the proposed metric of sinuousness is proportional and in agreement with the inverse of the average velocity of the vehicles.

Implementation of precision apiculture methods into practice is highly depended on availability and cost-effectiveness of honey bee monitoring equipment. Expenses of monitoring system installation and maintenance activities also should be minimized. In order to successfully predict different bee colony states like: swarming, determine nectar collection activity and health of honey bees both in active and passive wintering period, minimum hive parameters that should be monitored are temperature and mass. This research presents conceptual design of Internet of Thing approach for the beekeeping. As well honey bee colony temperature monitoring system based on local wireless sensor network together with GSM/GPRS external interface for packet-based communication with remote server on the Internet is developed. Logical topology of the local network is star with one central module. The hive group consists of 10 units. Each hive in the group is equipped with integral temperature sensor chip (±0.4°C accuracy), low power microcontroller and radio frequency (RF) transceiver module. All components are powered by alkaline cell battery, which can provide energy for one year minimum of autonomous operation with 10-minute data logging interval. The central module is also placed on one of the hives and consists of temperature sensor, RF and GSM/GPRS modules, photovoltaic cell array, battery, charge controller and minimal user interface for operational status reporting. The system allows sensor data logging to local storage and periodic sending to a remote server for further detailed analysis in user web or mobile application.

Honey bee wintering in a wintering building (indoors) with controlled microclimate is used in some cold regions to minimize colony losses due to the hard weather conditions. The behavior and possible state of bee colonies in a dark room, isolated from natural environment during winter season, was studied by indirect temperature measurements to analyze the expression of their annual rhythm when it is not affected by ambient temperature, rain, snow, wind, and daylight. Thus, the observed behavior in the wintering building is initiated solely by bee colony internal processes. Experiments were carried out to determine the dynamics of temperature above the upper hive body and weight dynamics of indoors and outdoors wintered honey bee colonies and their brood-rearing performance in spring. We found significantly lower honey consumption-related weight loss of indoor wintered colonies compared with outdoor colonies, while no significant difference in the amount of open or sealed brood was found, suggesting that wintering building saves food and physiological resources without an impact on colony activity in spring. Indoor wintered colonies, with or without thermal insulation, did not have significant differences in food consumption and brood rearing in spring. The thermal behavior and weight dynamics of all experimental groups has changed in the middle of February possibly due to increased brood-rearing activity. Temperature measurement above the upper hive body is a convenient remote monitoring method of wintering process. Predictability of food consumption in a wintering building, with constant temperature, enables wintering without oversupply of wintering honey.

The principles of Precision Agriculture (PA) can be applied also to the beekeeping branch. Precision Beekeeping (PB) (Precision Apiculture) can be implemented as a three phase cycle including 1) data collection, 2) data analysis and 3) application. The first two phases are based information technologies in case of remote recognition. The third phase is realized manually according to the decisions made after data collection and analysis. This study is dedicated to the information processing approaches taking into account the peculiarities of the beekeeping branch. Classification of deviations at several levels is proposed: colony level (most colonies in the same location behave normally); apiary level (most apiaries at other locations behave normally); bee farm level (most apiaries of other bee farms behave normally) and regional level (most bee farms in the region do not behave normally). Two levels of information analysis are suggested: bee farm level (information about individual colonies in different apiaries) and regional level (summary of information collected at the level of bee farms). Decision support systems (DSS) are proposed to automate data analysis. Continuous operation and high processing capacities of electronics can significantly improve implementations of PB. DSS may be delegated to make some decisions automatically or request the analysis of proposed decision by a specialist in data processing or beekeeper.