Determination of Hydraulic Characteristics of Porous Pipe Irrigation Laterals and Water Distribution Pattern in Sandy Soil

Irrigation systems are well known for their low efficiencies. Microirrigation system is becoming popular even in humid areas because of the many advantages it offers. Microirrigation is really the first irrigation method that can potentially maximize productivity while conserving soil, water and fertilizer resources and simultaneously protecting the environment. Since a micro irrigation system can achieve very high application efficiency, it should be further explored even for supplemental irrigation in a high-rainfall tropical country like Malaysia, with annual rainfall exceeding 2500mm. Porous pipe is useful both for surface and subsurface micro irrigation systems and it can be used in a variety of ways to meet any irrigation need. However very little information is available about the discharge uniformity, operating characteristics and the moisture distribution pattern of porous pipe irrigation laterals. This research work on the hydraulics of two types of porous pipe was carried out to determine such performance criteria as the pressure-discharge relationship, pressure headloss, friction factor Reynolds number relationship and water dispersion in the soil. The water distribution pattern was observed in a soil box. Several lengths of imported porous pipes were subjected to various upstream pressure inputs to determine the average discharge along the lateral and the associated pressure losses

What limits performance of weakly supervised deep learning for chest CT classification?

Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about the limitations of such weakly supervised learning and the effect of these constraints on disease classification performance. In this paper, we test the effects of such weak supervision by examining model tolerance for three conditions. First, we examined model tolerance for noisy data by incrementally increasing error in the labels within the training data. Second, we assessed the impact of dataset size by varying the amount of training data. Third, we compared performance differences between binary and multi-label classification. Results demonstrated that the model could endure up to 10% added label error before experiencing a decline in disease classification performance. Disease classification performance steadily rose as the amount of training data was increased for all disease classes, before experiencing a plateau in performance at 75% of training data. Last, the binary model outperformed the multilabel model in every disease category. However, such interpretations may be misleading, as the binary model was heavily influenced by co-occurring diseases and may not have learned the specific features of the disease in the image. In conclusion, this study may help the medical imaging community understand the benefits and risks of weak supervision with noisy labels. Such studies demonstrate the need to build diverse, large-scale datasets and to develop explainable and responsible AI.Comment: 16 pages , 8 figures. arXiv admin note: text overlap with arXiv:2202.1170

Virulence of entomopathogenic fungus, metarhizium anisopliae to sweetpotato whitefly, bemisia tabaci (hemiptera: aleyrodidae) under osmotic stress

The aim of the present study was to investigate the virulence of the entomopathogenic fungus Metarhizium anisopliae (isolates PR1 and GT3) under osmotic stress condition. The virulence study of the fungus was conducted by three ways—growth (germination, vegetative growth and sporulation); enzymatic activities (chitinase, protease and lipase) of M. anisopliae and percentage mortality of Bemisia tabaci to M. anisopliae. Conidia of M. anisopliae were produced under different osmotic stress conditions as SDA medium as control, SDA medium with 0.5 M NaCl, SDA medium with 0.5 M KCl, SDA medium with 1 M NaCl and SDA medium with 1 M KCl. The germination percentage, vegetative growth, sporulation, chitinase and protease activities were highest for control of PR1 isolate, reaching up to 97 %, 4.1 cm and 6.6 × 106 conidia/ml, 2.6 mU/ml and 1.7 µg/ml/min, respectively. These values decreased up to 86.7 %, 3.6 cm and 4.1 × 106 conidia/ml, 1.6 mU/ml and 1.0 µg/ml/min, respectively under osmotic stress. The lipase activity was highest for 0.5 M NaCl of PR1 isolate, reaching up to 18.2 µmol/ml/min. The mortality percentage of B. tabaci was highest for control of PR1 and GT3 isolates, reaching up to 83.9 and 83.8 %, respectively. These values decreased up to 77.4 and 77.5 %, respectively under osmotic stress. This paper concludes that both the isolate PR1 and GT3 are virulent to B. tabaci under osmotic stress condition