Bee colony remote monitoring based on IoT using ESP-NOW protocol.

Information and communication technologies, specifically the Internet of Things (IoT), have been widely used in many agricultural practices, including beekeeping, where the adoption of advanced technologies has an increasing trend. Implementation of precision apiculture methods into beekeeping practice depends on availability and cost-effectiveness of honey bee colony monitoring systems. This study presents a developed bee colony monitoring system based on the IoT concept and using ESP8266 and ESP32 microchips. The monitoring system uses the ESP-NOW protocol for data exchange within the apiary and a GSM (Global System for Mobile communication)/GPRS (General packet radio service) external interface for packet-based communication with a remote server on the Internet. The local sensor network was constructed in a star type logical topology with one central node. The use of ESP-NOW protocol as a communication technology added an advantage of longer communication distance between measurement nodes in comparison to a previously used Wi-Fi based approach and faster data exchange. Within the study, five monitoring devices were used for real-time bee colony monitoring in Latvia. The bee colony monitoring took place from 01.06.2022 till 31.08.2022. Within this study, the distance between ESP-NOW enabled devices and power consumption of the monitoring and main nodes were evaluated as well. As a result, it was concluded that the ESP-NOW protocol is well suited for the IoT solution development for honeybee colony monitoring. It reduces the time needed to transmit data between nodes (over a large enough distance), therefore ensuring that the measurement nodes operate in an even lower power consumption mode.

The Overall Environmental Load and Resistance Risk Caused by Long-Term Fungicide Use to Control Venturia inaequalis in Apple Orchards in Latvia.

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.

Composition of Polysaccharides in Hull-Less Barley Sourdough Bread and Their Impact on Physical Properties of Bread.

The complex of polysaccharides of the grain transforms during processing and modifies the physical and chemical characteristics of bread. The aim of the research was to characterize the changes of glucans, mannans and fructans in hull-less barley and wholegrain wheat breads fermented with spontaneous hull-less barley sourdough, germinated hull-less barley sourdough and yeast, as well as to analyze the impact of polysaccharides on the physical parameters of bread. By using the barley sourdoughs for wholegrain wheat bread dough fermentation, the specific volume and porosity was reduced; the hardness was not significantly increased, but the content of ß-glucans was doubled. Principal component analysis indicates a higher content of ß-glucans and a lower content of starch, total glucans, fructans and mannans for hull-less barley breads, but wholegrain wheat breads fermented with sourdoughs have a higher amount of starch, total glucans, fructans and mannans, and a lower content of ß-glucans. The composition of polysaccharides was affected by the type of flour and fermentation method used.

Model for finding the number of honey bee colonies needed for the optimal foraging process in a specific geographical location.

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).

Smart apiculture management services for developing countries-the case of SAMS project in Ethiopia and Indonesia.

The European Union funded project SAMS (Smart Apiculture Management Services) enhances international cooperation of ICT (Information and Communication Technologies) and sustainable agriculture between EU and developing countries in pursuit of the EU commitment to the UN Sustainable Development Goal "End hunger, achieve food security and improved nutrition and promote sustainable agriculture". The project consortium comprises four partners from Europe (two from Germany, Austria, and Latvia) and two partners each from Ethiopia and Indonesia. Beekeeping with small-scale operations provides suitable innovation labs for the demonstration and dissemination of cost-effective and easy-to-use open source ICT applications in developing countries. SAMS allows active monitoring and remote sensing of bee colonies and beekeeping by developing an ICT solution supporting the management of bee health and bee productivity as well as a role model for effective international cooperation. By following the user centered design (UCD) approach, SAMS addresses requirements of end-user communities on beekeeping in developing countries, and includes findings in its technological improvements and adaptation as well as in innovative services and business creation based on advanced ICT and remote sensing technologies. SAMS enhances the production of bee products, creates jobs (particularly youths/women), triggers investments, and establishes knowledge exchange through networks and initiated partnerships.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues.

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans' known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

Model-based metabolism design: constraints for kinetic and stoichiometric models.

The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.

Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model.

The application of biologically and biochemically relevant constraints during the optimization of kinetic models reduces the impact of suggested changes in processes not included in the scope of the model. This increases the probability that the design suggested by model optimization can be carried out by an organism after implementation of design in vivo. A case study was carried out to determine the impact of total enzyme activity and homeostatic constraints on the objective function values and the following ranking of adjustable parameter combinations. The application of constraints on the model of sugar cane metabolism revealed that a homeostatic constraint caused heavier limitations of the objective function than a total enzyme activity constraint. Both constraints changed the ranking of adjustable parameter combinations: no "universal" constraint-independent top-ranked combinations were found. Therefore, when searching for the best subset of adjustable parameters, a full scan of their combinations is suggested for a small number of adjustable parameters, and evolutionary search strategies are suggested for a large number. Simultaneous application of both constraints is suggested.