Industry 4.0, Vol. 3 (2018), Issue 2, pg(s) 77-79

    Fish like many living organisms have specific tolerant range of various environmental parameters, thus fish farming of specific types of fish species requires certain conditions that have to be reached. Moreover, the people that work in the fish farming ponds have to be engaged in all day activities to maintain the living fish habitat. Therefore, monitoring and taking actions to maintain the habitat’s sustainable environment for certain fish species inside of fishing ponds over distributed machine to machine communication, which will shorten the time needed for some basic actions, is the main motivation for this paper. In this paper we present an upgrade on a functional Internet of Things (IoT) system for monitoring fish farming ponds. The IoT system consists of various sensors that measure important factors of the water quality like temperature, light intensity or water level, as well as small board computer that processes the data and sends sound and visual notifications to the fish farming manager. The current system lacks the ability to process the data to the end-user via web or mobile platform. Due to remote distance of the fish farming ponds and their location dependence of clean fresh water, one solution of this problem is using expansion module like Wivity modem to enable the end users in real time to monitor and control certain aspects of the fish farming pond IoT system. Wivity modem allows user to communicate to the IoT system via WiFi connection, cellular, LoRaWAN or satellite communication; all in one product. Later on, this module can be integrated with IoT platforms including Japser, Microsoft Azure or Amazon Web Services. For future work, we plan to expand not only the applicable services on different platforms, but also add more control modules and sensors to the existing IoT system for specific fish species.



    Machines. Technologies. Materials., Vol. 11 (2017), Issue 10, pg(s) 494-496

    Horseradish peroxidase represents one of the most exploited enzymes in the process of enzymatic phenol removal from aqueous solutions. It has a catalytic ability over a broad pH range, temperature and contaminant concentration. In this study we have investigated the influence of pH and temperature on process of phenol removal by crude horseradish peroxidase from aqueous solution. Reaction was performed in the presence of low molecular polyethylene glycol (PEG 300) at different temperatures (4, 12, 17, 20, 25, 30, 35, 40 and 45 °C) and pH values (3, 4, 5, 6, 7, 8 and 9). Reaction was monitored by measuring of absorbance changes of the samples taken at certain time intervals from reaction mixture. Obtained results shown that phenol removal from aqueous solution increases by temperature increase up to 35 °C, after which this effect no longer exists. Also, phenol removal increases in the pH range of 3 – 7, while a further increase of pH value leads to the opposite effect. Based on this it can be concluded that phenol removal from aqueous solutions greatly depends of peroxidase activity, because this temperature and pH values represents the optimum values of peroxidase enzymatic activity.


    Mechanization in agriculture & Conserving of the resources, Vol. 61 (2015), Issue 10, pg(s) 3-5

    Conventional soil sampling usually implemented in Croatia considers sample weight of 2 kg per 4-5 ha area, which means that representative sample in relation to soil mass up to 30 cm depth is presented through the ratio 1:10000000. New sampling method changes the ratio to 1:625000, thus increasing amount of sampled soil 16 times with assumption that such sample better describes investigated area. Moreover, new soil sampling probe can be used for precision farming purposes where the central point of the probe ring is positioned with precision of ±1 cm and represented with 4, 8 or 16 samples taken in 50 cm radius from the center. Soil probe prototype was tested on agricultural land of 4 ha area with total number of 200 samples. To justify application of new constructed probe, this study gives results of geostatistical analysis of spatial variability in soil pH values up to 30 cm depth.