Partial resistance was assessed through the infection type, final rust severity (FRS), area under rust progress curve (AURPC), infection rate (IR) and co-efficient of infection (CI

Studies were conducted to characterize the chestnut starch for physico-chemical properties. Chemical composition of chestnut starch showed low levels of protein and ash indicating purity of starch. The results revealed low water and oil absorption capacity of chestnut starch. Starch showed high swelling power and low solubility index. Swelling power and solubility index of chestnut starch increased with increase in temperature (50-90 °C). The results revealed high initial, peak, setback, breakdown, and fi nal viscosity but low paste development temperature. Transmittance (%) of the starch gel was low and decreased with increasing storage period. The chestnut starch gel showed increase in % water release (syneresis) with increase in time of storage but was less susceptible to repeated cycles of freezing and thawing. Starch was also characterized for granule morphology. Starch granules were of round and oval shapes, some granules showed irregular shape.

The biggest cause of mortality globally is cardiac illness. If the initial diagnosis was more accurate, cardiac problems may be avoided. ECG testing is typically used as a diagnostic technique to screen for heart disorders. The electric cardiac signal is recorded by an ECG to look for various heart conditions. Utilizing a variety of datasets, a number of algorithms and methodologies have been developed to detect different heart illnesses. However, this study proposes to examine whether convolutional neural network (CNN) model and ResNet 50 can be used to identify cardiac illnesses such arrhythmia or abnormal heartbeat (AHB), myocardial infarction (MI), and previous history of MI (PMI) from electrocardiogram (ECG) trace images. A 1937 ECG image dataset from Kaggle is examined... The ECG pictures in the dataset are separated into four categories: normal, MI, AHB, and PMI. The suggested approach examined categorization of normal and various heart disorders with 99.12% accuracy (MI, AHB, and PMI)

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