Volume-8, Issue-4, April 2022

Abstract

Water pollution from various types of pollutants is not only a serious environmental issue but also an economic and human health problem. This study investigated Amakera water springs located in Musanze District which is consumed by local people and tourists due to its taste. These springs take their source from underground aquifers. However, its quality is uncertain, therefore, its investigations come into prominence for its usability. Analysis of Physico-chemical and Bacteriological parameters to check its potable perspective in comparison with the international standard of drinking water was the main purpose. Samples were taken at three different sources in the dry season of 2020. In general, the results showed that the water is potable. Nevertheless, some parameters are present in high content especially dissolved salts which affect the taste of water andiron which affect the color of the river bed. The conductivity was found to vary from 8120µS/cm to 11,010 µS/cm while total hardness was found to be 637.50 mg/l as CaCO , 3,875.00mg/l as CaCO and 1,852.50mg/l as CaCO and TDS values were 3 3 3 in the same range (3,800-3070mg/l), iron content were 8.90, 3.10, and 2.45 mg/l. The analysis indicated that all the three points are practically the same and can be consumed fresh. However, their protection is highly recommended to avoid the possible pollution.

Keywords: Bacteriological, Physico-chemical parameters, pollution, Water quality

References

References not available

Abstract

Weeds are defined as any growing plant infield, where it is not wanted and weeds are also used as feed for the animals. Weeds are creating a big problem in agriculture by reducing the growth and development of crops and minimizing the yield of the crops. Weeds are the major problem in agriculture therefore management practices require increasing the yield of the crops. Sustainable agriculture is defined as a farming system that meets foods for the present population by reducing the use of chemicals. Integrated weed management (IWM) is defined as a process that synchronizes the use of major and minor information on the environment, ecology, and biology of weeds, and ecologically controlling the weeds from fields. Yield losses in soybean may range from 25 to 70 %, 40-80 % in onion, 40-70% in maize, 40-50% in rice, and 25-50% in wheat depending upon the intensity and infestation of weeds. Rice residues as mulching at 6 and 7 t/ha and adding post-emergence herbicides like clodinafop 60 g/ha, sulfosulfuron 25 g/ha, and mesosulfuron+iodosulfuron 14.4 g/ha were found more effective to control weeds like P. minor and also board leaf weeds from the wheat field. Zero tillage is generally done in wheat crops and also in maize crops to minimize of cost of cultivation. The incorporation of daincha and azolla in afield generally increases the yield of the crops during the early stages.

Keywords: Integrated weed management (IWM), Losses, Components, and Herbicides

References

References not available

Abstract

Sugarcane development on peatlands is constrained by drought conditions when entering the dry season, especially when climate anomalies occur, the dry season period becomes longer, as a result the number of tillers decreases and growth is not optimal. Planting drought stress-tolerant sugarcane clones through growth indicators is one solution to obtain clones that have the potential to be cultivated on peatlands. The use of drought tolerant clones is more profitable in the long term. The results of this study showed that the availability of media water and sugarcane clones had a significant effect on sugarcane plant height at early growth, but did not affect to the number of leaves and number of tillers. Sugarcane stem diameter at initial growth was influenced by a combination of media water availability and five sugarcane clones. PS881 is a clone that can adapt to drought stress conditions in peat media based on growth indicators of plant height, stem diameter and number of leaves.

Keywords: peat, clone, drought, growth, sugarcane

References

References not available

Abstract

An artificial neural network (ANN) approach was successfully deployed to model and predict the thermal conductivity of milk and concentrated milk systems during mechanized khoa production. Reliable data points (n=203) spanning wide operational boundaries of temperature (275.15–353.10 K), moisture content (48.82–92.00%), and fat content (0.00–11.17%) were compiled from established experimental studies to formulate and validate the model. A multi-layer feed-forward network optimized via the quasi-Newton algorithm using a 3:3:1 topology (three inputs, three hidden neurons with hyperbolic tangent activation functions, and a single linear output layer) demonstrated optimal predictive behaviour. The architecture yielded outstanding precision on independent testing subsets, demonstrating a strong correlation coefficient (R = 0.986), a minimal root mean squared error (RMSE = 0.0084 W/m•K), and a normalized squared error of 0.029 (normalized to the variance of the target data). Input sensitivity computations verified that product temperature (31.4% contribution) and moisture content (30.2% contribution) exert the highest thermodynamic control on thermal conductivity shifts, whereas fat content (4.4% contribution) exhibits a weaker but consistently inverse linear relationship. The resolved predictive equations were effectively embedded within a highly practical Microsoft Excel-based graphical user interface (GUI) to assist dairy process designers in real-time calculation, simulation, and industrial scaling of continuous scraped surface heat exchangers for indigenous milk confectionery manufacturing.

Keywords: Artificial Neural Network, Thermal Conductivity, Milk Desiccation, Khoa, Process Optimization, Scraped Surface Heat Exchanger

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