7424 research outputs found
Sort by
Development and Validation of LC-MS Methods for the Detection of TMAO and Multi-Class Drug Residues in Food Products
Get PDFMass spectrometry is an analytical technique to identify and characterize compounds based on their molecular mass. It works by ionizing chemical compounds to generate charged molecules or fragments and then separate them according to their mass-to-charge ratio. Measuring these masses provides information about the analyzed substances\u27 chemical structure, composition, and quantity. This technique can be used to formulate the composition of a sample and measure the concentration of a specific compound within the sample matrix. A set of analytical methods has been developed by utilizing the following technique. The process for extraction and detection of Trimethylamine oxide (TMAO) in tissues of marine organisms was created to determine its concentration in rainbow trout. TMAO is an essential molecule for farm-raised fish and, therefore, can be commonly found as a food additive to their diet. The goal was to ensure the accumulation of the supplement in the intestinal and muscular tissues of dissected fish. Another analytical method has been instituted for the multi-drug residues (MDR) analysis in food products, such as dry milk and beef and porcine tissues. The list of drugs includes nine antibiotic families found in food products.
Residues can either be administered drug or a metabolite of a drug. Consumption of these residues can pose a threat due to allergic reactions, carcinogenicity, and antimicrobial resistance. Regulatory bodies, such as the EU, US FDA, and Codex, formulated a list of maximum residue limits (MRL) in animal-derived products. Thus, the method for simultaneous analysis of these drugs and their metabolites is crucial to uphold the guidelines set on the market. Both detection methods, TMAO and MDR, were put on the Liquid chromatography-tandem mass spectrometry (LC-MS/MS) systems. This analytical technique is commonly used for detecting residual amounts of contaminants due to its high sensitivity. Through the analysis, traces of TMAO and drug residues were found in fish tissues, dry milk, bovine and porcine tissues
Investigation of an Energy-Saving Strategy in a Rotating Paper Dryer Through Fundamental Research of Multiphase Flow Behavior
Get PDFIn the paper industry, the process of transforming pulp into finished paper involves several crucial stages that ensure paper quality. Among these stages, the paper drying stage is one of the most important stages. In this stage, multiple rotating cylinders are used in which superheated steam is ejected to heat wet paper web rolling over the cylinders. Steam relieves latent heat and condenses into water; this condensate accumulates and is extracted through stationary or rotary siphons. Due to rotation, the pool of condensate would go through different types of flow behaviors depending on the rotation speed, mainly appearing as puddling, cascading, rimming, and transitions between them, which predominantly affect heat transfer rates and rotating power consumptions. During rimming, the condensate would rotate with the inner cylinder forming a thin liquid layer, which behaves as an extra thermal resistance, resulting in reduction of effective transfer of heat. For that reason, the objective of this research is to conduct fundamental thermal-flow and heat transfer research in multiphase behavior in a Rotating Paper Dryer with a goal to achieve significant energy savings. This project has successfully completed the following tasks: (a) performing analytical study to establish a more accurate correlation for predicting rimming speed, (b) establishing a simplified 2D computational model by incorporating a source and sink sub model to simulate the steam-injection and condensate extraction behavior in 3D operation to improve understanding of condensate rimming behavior and heat transfer and power consumptions, (c) implementing steam condensation and condensate flashing sub-models in a comprehensive simulation of a complete 3-D rotating dryer, (d) developing an experimental test rig to study the flow movement of condensate, (e) exploring the innovative idea of applying hydrophobic coatings to the dryer inner surface to prevent the occurrence of condensate rimming via both computational and experimental approaches
Enhancing Password Security and Memorability Using Machine Learning and Linguistic Patterns
Get PDFIn the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using transformer architecture, we automate the process of generating password phrases based on these features, ensuring a balance between complexity and ease of recall. These generations are evaluated in a user study, given the subjective nature of language memorability. Our system evaluates password strength using LSTM-layered recurrent neural network models that assess the likelihood of successful password cracking attempts, and we provide users with memorability aids, such as narrative cues or mnemonic devices, using large language models to enhance long-term retention. The machine learning models for password security are evaluated using validation and test splits, as well as cross-validation, and compared analytically. These tools are integrated into a user-friendly interface designed to educate users on best practices while simplifying the process of creating and managing passwords. This approach to using lyric-based features for generating passwords is both generalizable to other pieces of literature and novel in its application using machine learning. Similar work has generated sequences from lyrics and analyzed security of passwords, however generation has not been done using machine learning and the combination of these applications has not been realized.
Drawing from cognitive science, the research demonstrates that familiar linguistic structures can enhance password recall without compromising security. We compare traditional password generation methods to our machine learning-based approach through user studies, revealing improved usability and security. This study offers a forward-thinking method that redefines how passwords can be both secure and user-friendly, enhancing overall cybersecurity while addressing the common issues of password fatigue and memory overload
2024 Quality of Life Survey - Orleans and Jefferson Parishes
No full textThe University of New Orleans Survey Research Center (SRC) conducted a live interviewer telephone survey of active registered voters in Orleans Parish and Jefferson Parish. Four hundred and ninety-one randomly selected respondents from Orleans Parish and 487 randomly selected respondents from Jefferson Parish were interviewed from September 9th through October 1st, 2024. Each survey yields a margin of error of +/- 4.4% at a 95% confidence level