Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

PEMODELAN PENGERINGAN KUNYIT (CURCUMA DOMESTICA VAL.) BERBASIS MACHINE VISION DENGAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

ABSTRAK Pengeringan pada kunyit (Curcuma Domestica Val.) bertujuan untuk memperpanjang umur simpan serta mengurangi kadar air hingga batas perkembangan mikroorganisme dan kegiatan enzim yang menyebabkan pembusukan menjadi terhambat. Saat ini, pengeringan kunyit menggunakan sinar matahari dan alat pengering mekanis dengan kontrol waktu dan suhu. Banyaknya kendala pada proses pengeringan meyebabkan dibutuhkannya suatu teknologi yang dapat memonitoring kadar air dari kunyit secara pasti dan akurat, yaitu dengan mesin pengering berbasis machine vision dan artificial neural network (ANN). Tujuan penelitian untuk mengetahui waktu terbaik untuk pengeringan kunyit berbasis machine vision dengan menggunakan ANN, mengetahui perbedaan grafik ANN untuk gambar yang memenuhi syarat kadar air standar pengeringan kunyit, mengetahui ANN terbaik dalam proses pengeringan kunyit. Penelitian ini menggunakan metode deskriptif yang terdiri dari lama waktu pengeringan yaitu 5 jam dengan 5 kali pengulangan dan menggunakan bahan kunyit. Metode aplikasi mesin pengering yang dilengkapi dengan machine vision sebagai pengambil data gambar pada bahan, kemudian di ekstrak warnanya untuk mengetahui nilai (red, green, dan blue). Proses pembangunan model ANN digunakan learning rate sebesar 0.1, 0.2, 0.3, 0.4, dan 0.5 pada momentum rate sebesar 0.5, 0.6, 0.7, 0.8, dan 0.9. Hasil learning process terbaik adalah learning rate 0.3 dan momentum rate 0.9. Model ANN dengan nilai error terendah yaitu untuk training 0.005 MSE, dan 24.59% ARE (Average Error), untuk validasi 0.005 MSE dan 25.35% ARE   ABSTRACT To maintain turmeric (Curcuma domestica Val.) to be durable is by drying. The purpose of drying to reduce the moisture content up to limit the development of microorganisms and enzyme activities that cause spoilage. Nowadays, turmeric drying is using sunlight and mechanical drier with time and temperature control. However, drying process often arise various problems, therefore require a technology to monitor the moisture content of turmeric definitively and accurately, that is using drying machine-based machine vision and ANN (Artificial Neural Network). The purpose of this study to determine the best time for drying turmeric-based machine vision by using ANN, to know the difference of ANN’s graph for image that qualify the standard of moisture content in drying turmeric, to know the best ANN in the turmeric drying process. This research use descriptive method that consisted of duration of drying time, 5 hours with five repetitions. The application of drying machine equipped with a machine vision is to take data image on the materials, then color was extracted to know the value of (red, green, and blue). In the development process of ANN model, use learning rate of 0.1, 0.2, 0.3, 0.4, and 0.5 on the momentum rate of 0.5, 0.6, 0.7, 0.8, and 0.9. Best results is showed on the learning process of learning rate 0.3 and momentum rate 0.9. ANN models with the lowest error value is for training 0005 MSE and 24.59% ARE, for validation MSE 0005 and 25.35% AR

The Effect of Storage Temperature for the Detection of Silver Nanoparticles via Engineered Biomolecules

Temperature plays an important role in biology as a way to regulate reaction. In this study, we report the effect of storage temperature (4, 25, and 37oC) for the detection of silver nanoparticles via engineered biomolecules by monitoring the fluorescence intensity. We genetically engineered a biomolecule consisting of silver binding peptide that fused with cellulose binding domain and green fluorescent protein (GFP). This modular protein was a genetically designed peptide, possesses unique and specific interaction with cellulose as a matrix immobilized surface and can be able to capture silver nanoparticle from wastewater solution. Samples were instrumentally analysed everyday. We aim to assess the long-term stability of our genetically modular protein. This strategy was demonstrated a rapid and green environmentally monitoring

Classification of non-heat generating outdoor objects in thermal scenes for autonomous robots

We have designed and implemented a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. In the context of this research, non-heat generating objects are defined as objects that are not a source for their own emission of thermal energy, and so exclude people, animals, vehicles, etc. The resulting classification model complements an autonomous bot\u27s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment. Since GPS depends on the availability of satellites and onboard terrain maps which are often unable to include enough detail for smaller structures found in an operational environment, bots will require the ability to make decisions such as go through the hedges or go around the brick wall. A thermal infrared imaging modality mounted on a small mobile bot is a favorable choice for receiving enough detailed information to automatically interpret objects at close ranges while unobtrusively traveling alongside pedestrians. The classification of indoor objects and heat generating objects in thermal scenes is a solved problem. A missing and essential piece in the literature has been research involving the automatic characterization of non-heat generating objects in outdoor environments using a thermal infrared imaging modality for mobile bots. Seeking to classify non-heat generating objects in outdoor environments using a thermal infrared imaging system is a complex problem due to the variation of radiance emitted from the objects as a result of the diurnal cycle of solar energy. The model that we present will allow bots to see beyond vision to autonomously assess the physical nature of the surrounding structures for making decisions without the need for an interpretation by humans.;Our approach is an application of Bayesian statistical pattern classification where learning involves labeled classes of data (supervised classification), assumes no formal structure regarding the density of the data in the classes (nonparametric density estimation), and makes direct use of prior knowledge regarding an object class\u27s existence in a bot\u27s immediate area of operation when making decisions regarding class assignments for unknown objects. We have used a mobile bot to systematically capture thermal infrared imagery for two categories of non-heat generating objects (extended and compact) in several different geographic locations. The extended objects consist of objects that extend beyond the thermal camera\u27s field of view, such as brick walls, hedges, picket fences, and wood walls. The compact objects consist of objects that are within the thermal camera\u27s field of view, such as steel poles and trees. We used these large representative data sets to explore the behavior of thermal-physical features generated from the signals emitted by the classes of objects and design our Adaptive Bayesian Classification Model. We demonstrate that our novel classification model not only displays exceptional performance in characterizing non-heat generating outdoor objects in thermal scenes but it also outperforms the traditional KNN and Parzen classifiers