7 research outputs found
Application of Nonlinear Site Response Analysis in Coastal Plain South Carolina
No full textThe 1933 Long Beach, 1957 San Francisco, 1967 Caracas, 1985 Mexico City, 1989 Loma Prieta, and 1994 Northridge earthquake events left evidences of how the local site condition can affect the characteristics of propagating earthquake wave from the bedrock through the top soil. The ground motion amplitude, frequency content or the duration can be affected by the local site condition and thus can cause significant amplification or de-amplification to the original bedrock motion which can seriously affect the structures. Quantification of such site effect on ground motions is a challenging task. This dissertation is dedicated to improve the existing ground response quantification techniques and the related knowledge base. The first major attempt towards ground response quantification was the development of the 1994 NEHRP (BSSC, 1995) seismic site factor provision. Since the development of the NEHRP provisions, several studies have found these factors to produce inadequate predictions for the state of South Carolina. In an attempt to generate seismic site factors based on conditions specific to South Carolina Coastal Plain (SCCP), a series of nonlinear one-dimensional ground response analyses are performed by this author as part of a research team considering appropriate soil profiles and location specific ground excitations. After the generation of this new site factor model, a systematic repercussions study is performed by applying earthquake loads, considering both NEHRP and the new site factors, on typical highway bridge structures
Application of Deep Learning Framework for Early Prediction of Diabetic Retinopathy
No full textDiabetic retinopathy (DR) is a severe microvascular complication of diabetes that affects the eyes, leading to progressive damage to the retina and potential vision loss. Timely intervention and detection are crucial for preventing irreversible damage. With the advancement of technology, deep learning (DL) has emerged as a powerful tool in medical diagnostics, offering a promising solution for the early prediction of DR. This study compares four convolutional neural network architectures, DenseNet201, ResNet50, VGG19, and MobileNetV2, for predicting DR. The evaluation is based on both accuracy and training time data. MobileNetV2 outperforms other models, with a validation accuracy of 78.22%, and ResNet50 has the shortest training time (15.37 s). These findings emphasize the trade-off between model accuracy and computational efficiency, stressing MobileNetV2’s potential applicability for DR prediction due to its balance of high accuracy and a reasonable training time. Performing a 5-fold cross-validation with 100 repetitions, the ensemble of MobileNetV2 and a Graph Convolution Network exhibits a validation accuracy of 82.5%, significantly outperforming MobileNetV2 alone, which shows a 5-fold validation accuracy of 77.4%. This superior performance is further validated by the area under the receiver operating characteristic curve (ROC) metric, demonstrating the enhanced capability of the ensemble method in accurately detecting diabetic retinopathy. This suggests its competence in effectively classifying data and highlights its robustness across multiple validation scenarios. Moreover, the proposed clustering approach can find damaged locations in the retina using the developed Isolate Regions of Interest method, which achieves almost a 90% accuracy. These findings are useful for researchers and healthcare practitioners looking to investigate efficient and effective powerful models for predictive analytics to diagnose diabetic retinopathy
Soil Thermal Properties: Influence of No-Till Cover Crops
No full textSoil thermal properties, which determines heat transport, can influence soil health parameters and crop productivity. The objective of this study was to evaluate the 2-year effects of no-till cover crops (CCs) and no-till no cover crop (NC) on soil thermal properties (thermal conductivity [λ], volumetric heat capacity [CV], and thermal diffusivity [D]). Two levels of CCs were used for this study: CC vs NC. The CCs included crimson clover (Trifolium incarnatum, L.), hairy vetch (Vicia villosa Roth.), winter peas (Lathyrus hirsutus L.), oats (Avena sativa), winter wheat (Triticum aestivum L.), triticale (Triticale hexaploide Lart.), flax (Linum usitassimum L.), and barley (Hordeum vulgare L.). Soil samples were collected at 0-10, 10-20, and 20-30 cm depths and their λ, CV, and D were measured in the laboratory. Additionally, soil organic carbon (SOC), bulk density (BD), and volumetric water content (ϴ) at saturation, -33kPa, and -100 kPa soil water pressures were measured. Results showed that BD was 18% and 14% higher under CC compared with NC management during 2021 and 2022, respectively. Furthermore, ϴ at all measured soil water pressures was slightly higher under CC compared with NC management during both years. As a result, λ and D were significantly higher under NC compared with CC management, while CV was significantly higher under CC compared with NC management, during both years and at all measured soil water pressures. Generally, soil thermal properties were directly proportional to ϴ, suggesting that ϴ may be the most important factor influencing soil thermal properties.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author
Conservation Agriculture for Rice-Based Intensive Cropping by Smallholders in the Eastern Gangetic Plain
No full textWe review the recent development of Conservation Agriculture (CA) for rice-based smallholder farms in the Eastern Gangetic Plain (EGP) and the underpinning research on agronomy, weed control, soil properties and greenhouse gas emissions being tested to accelerate its adoption in Bangladesh. The studies are based mostly on minimum soil disturbance planting in strip planting (SP) mode, using the Versatile Multi-crop Planter (VMP), powered by a two-wheel tractor (2WT). One-pass SP with the VMP decreased fuel costs for crop establishment by up to 85% and labour requirements by up to 50%. We developed strip-based non-puddled rice (Oryza sativa) transplanting (NPT) in minimally-disturbed soil and found that rice grain yield increased (by up to 12%) in longer-term practice of CA. On farms, 75% of NPT crops increased gross margin. For non-rice crops, relative yield increases ranged from 28% for lentil (Lens culinaris) to 6% for wheat (Triticum aestivum) on farms that adopted CA planting. Equivalent profit increases were from 47% for lentil to 560% for mustard (Brassica juncea). Moreover, VMP and CA adopting farms saved 34% of labour costs and lowered total cost by up to 10% for production of lentil, mustard, maize (Zea mays) and wheat. Effective weed control was obtained from the use of a range of pre-emergent and post-emergence herbicides and retention of increased crop residue. In summary, a substantial body of research has demonstrated the benefits of CA and mechanized planting for cost savings, yield increases in many cases, increased profit in most cases and substantial labour saving. Improvement in soil quality has been demonstrated in long-term experiments together with reduced greenhouse gas emissions
Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing
No full textSome recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein–protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk. © 2023, The Author(s)
Conservation agriculture for rice-based intensive cropping by smallholders in the Eastern Gangetic Plain
No full textWe review the recent development of Conservation Agriculture (CA) for rice-based smallholder farms in the Eastern Gangetic Plain (EGP) and the underpinning research on agronomy, weed control, soil properties and greenhouse gas emissions being tested to accelerate its adoption in Bangladesh. The studies are based mostly on minimum soil disturbance planting in strip planting (SP) mode, using the Versatile Multi-crop Planter (VMP), powered by a two-wheel tractor (2WT). One-pass SP with the VMP decreased fuel costs for crop establishment by up to 85% and labour requirements by up to 50%. We developed strip-based non-puddled rice (Oryza sativa) transplanting (NPT) in minimally-disturbed soil and found that rice grain yield increased (by up to 12%) in longer-term practice of CA. On farms, 75% of NPT crops increased gross margin. For non-rice crops, relative yield increases ranged from 28% for lentil (Lens culinaris) to 6% for wheat (Triticum aestivum) on farms that adopted CA planting. Equivalent profit increases were from 47% for lentil to 560% for mustard (Brassica juncea). Moreover, VMP and CA adopting farms saved 34% of labour costs and lowered total cost by up to 10% for production of lentil, mustard, maize (Zea mays) and wheat. Effective weed control was obtained from the use of a range of pre-emergent and post-emergence herbicides and retention of increased crop residue. In summary, a substantial body of research has demonstrated the benefits of CA and mechanized planting for cost savings, yield increases in many cases, increased profit in most cases and substantial labour saving. Improvement in soil quality has been demonstrated in long-term experiments together with reduced greenhouse gas emissions
