Irrigation-water quality plays an important role in agriculture productivity. In this study, water quality of the old and new Phuleli canal located in Hyderabad, Pakistan, was characterized for irrigation purpose. Water samples were collected from four different locations of each old and new Phuleli canal. Twelve different irrigation-water quality standards were assessed under the study: pH, Electricity Conductivity, Biochemical Oxygen Demand (BOD), Total Dissolved Solids, Chemical Oxygen Demand (COD), Dissolved Oxygen, Calcium, Magnesium, Sodium, Potassium, Sodium adsorption ratio, Fecal Coliforms. The results showed that the Electricity Conductivity ranges from 910–3,090 MS/cm, Potassium 13–112 mg/l, BOD 61–285 mg/l, COD 97–361 mg/l and Fecal Coliforms 400–2,350 MPN/100 ml concentrations in water of both canals were higher than the National Environmental Quality Standards permissible limits. It was found that the water quality parameters were higher than the permissible pollution level of canal water for the use of irrigation in agriculture.
Alzheimer’s disease (AD) is a global health issue that predominantly affects older people. It affects one’s daily activities by modifying neural networks in the brain. AD is categorized by the death of neurons, the creation of amyloid plaques, and the development of neurofibrillary tangles. In clinical settings, an early diagnosis of AD is critical to limit the problems associated with it and can be accomplished using neuroimaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Deep learning (DL) techniques are widely used in computer vision and related disciplines for various tasks such as classification, segmentation, detection, etc. CNN is a sort of DL architecture, which is normally useful to categorize and extract data in the spatial and frequency domains for image-based applications. Batch normalization and dropout are commonly deployed elements of modern CNN architectures. Due to the internal covariance shift between batch normalization and dropout, the models perform sub-optimally under diverse scenarios. This study looks at the influence of disharmony between batch normalization and dropout techniques on the early diagnosis of AD. We looked at three different scenarios: (1) no dropout but batch normalization, (2) a single dropout layer in the network right before the softmax layer, and (3) a convolutional layer between a dropout layer and a batch normalization layer. We investigated three binaries: mild cognitive impairment (MCI) vs. normal control (NC), AD vs. NC, AD vs. MCI, one multiclass AD vs. NC vs. MCI classification problem using PET modality, as well as one binary AD vs. NC classification problem using MRI modality. In comparison to using a large value of dropout, our findings suggest that using little or none at all leads to better-performing designs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.