Polymer Mimetics for Soil Modeling and Detection of Biomarkers

The population of the world is increasing day by day and is expected to reach 9.8 billion by the year 2050. The ever-increasing demand for agricultural products is putting an unprecedented strain on the world\u27s soils as the human population continues to expand. Soil degradation caused by over-farming and the agrochemicals (fertilizers, pesticides, etc.) used in agriculture is a growing problem, although its causes remain murky. In addition, little is understood about the molecular-level interactions of substances that are subsequently introduced to soils, such as agricultural chemicals (ACs). Therefore, it is expected that these constraints may be circumvented by the synthesis of natural mimics of soil, known as Engineered Soil Surrogates (ESSs), to associate their bulk attributes with structure compositions. A series of polymeric ESSs were synthesized and the sorption behavior of Norflurazon as the ACs was observed and compared to the sorption behavior of a natural soil, Pahokee peat. On the other hand, the world is currently dealing with the drug overdose epidemic mainly due to the illicit use of synthetic opioids, primarily fentanyl. More than 70000 people died due to fentanyl overdose in 2021, which is often mixed with other drugs such as heroin, cocaine, etc. with or without the knowledge of the end-users. Therefore, the detection of fentanyl by law enforcement agencies and end-users is of utmost importance. Molecularly Imprinted Polymer (MIP) based sensors can be a solution to this problem. A MIP was made by using methacrylic acid (MAA), and ethylene glycol dimethacrylate (EGDMA) as the functional monomer and cross-linking monomer respectively, and benzyl fentanyl as the target template. Binding sites that are complementary to the analyte in size and shape are revealed after the template has been removed. Selectivity studies comprising various drugs such as heroin, cocaine, and methamphetamine showed that the synthesized MIP can selectively detect benzyl fentanyl. Finally, a molecularly imprinted hydrogel employing diffraction grating techniques was developed to detect microRNA. The hydrogels imprinted with the miR-21 DNA target were sensitive to the target sequence\u27s reintroduction and selective among comparable nucleotide sequences

Hot Zone Identification: Analyzing Effects of Data Sampling on SPAM Clustering

Email is the most common and comparatively the most efficient means of exchanging information in today\u27s world. However, given the widespread use of emails in all sectors, they have been the target of spammers since the beginning. Filtering spam emails has now led to critical actions such as forensic activities based on mining spam email. The data mine for spam emails at the University of Alabama at Birmingham is considered to be one of the most prominent resources for mining and identifying spam sources. It is a widely researched repository used by researchers from different global organizations. The usual process of mining the spam data involves going through every email in the data mine and clustering them based on their different attributes. However, given the size of the data mine, it takes an exceptionally long time to execute the clustering mechanism each time. In this paper, we have illustrated sampling as an efficient tool for data reduction, while preserving the information within the clusters, which would thus allow the spam forensic experts to quickly and effectively identify the ‘hot zone’ from the spam campaigns. We have provided detailed comparative analysis of the quality of the clusters after sampling, the overall distribution of clusters on the spam data, and timing measurements for our sampling approach. Additionally, we present different strategies which allowed us to optimize the sampling process using data-preprocessing and using the database engine\u27s computational resources, and thus improving the performance of the clustering process. Keywords: Clustering, Data mining, Monte-Carlo Sampler, Sampling, Spam, Step Sequence Sampler, Stepping Random Sampler, Hot Zon

Hot Zone Identification: Analyzing Effects of Data Sampling On Spam Clustering

Email is the most common and comparatively the most efficient means of exchanging information in today\u27s world. However, given the widespread use of emails in all sectors, they have been the target of spammers since the beginning. Filtering spam emails has now led to critical actions such as forensic activities based on mining spam email. The data mine for spam emails at the University of Alabama at Birmingham is considered to be one of the most prominent resources for mining and identifying spam sources. It is a widely researched repository used by researchers from different global organizations. The usual process of mining the spam data involves going through every email in the data mine and clustering them based on their different attributes. However, given the size of the data mine, it takes an exceptionally long time to execute the clustering mechanism each time. In this paper, we have illustrated sampling as an efficient tool for data reduction, while preserving the information within the clusters, which would thus allow the spam forensic experts to quickly and effectively identify the ‘hot zone’ from the spam campaigns. We have provided detailed comparative analysis of the quality of the clusters after sampling, the overall distribution of clusters on the spam data, and timing measurements for our sampling approach. Additionally, we present different strategies which allowed us to optimize the sampling process using data-preprocessing and using the database engine\u27s computational resources, and thus improving the performance of the clustering process