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Extended high-frequency hearing and suprathreshold neural synchrony in the auditory brainstem
No full textElevated hearing thresholds in the extended high frequencies (EHFs) (\u3e8 kHz) are often associated with poorer speech-in-noise recognition despite a clinically normal audiogram. However, whether EHF hearing loss is associated with disruptions in neural processing within the auditory brainstem remains uncertain. The objective of the present study was to investigate whether elevated EHF thresholds influence neural processing at lower frequencies in individuals with normal audiograms. Auditory brainstem responses (ABRs) were recorded at a suprathreshold level (80 dB normal hearing level) from 45 participants with clinically normal hearing. The recording protocol was optimized to obtain robust wave I of the ABR. Results revealed no significant relationship between the pure tone average for EHFs and any ABR metrics at either rate, while adjusting for the effects of age, sex, and hearing thresholds at standard frequencies (0.25–8 kHz). Rate-dependent significant sex effects for wave I and V amplitude, I-V amplitude ratio, and III and V latency were observed. Elevated EHF hearing thresholds do not significantly affect the brainstem processing in the lower frequencies (kHz)
[Port Isabel] Photograph of Tinkler Family
No full textTinkler family fishing results.https://scholarworks.utrgv.edu/hidalgohist_aa/1169/thumbnail.jp
[San Benito] Photograph of Baggage and U.S. Mail Railway Car
No full textSan Benito and Rio Grande Valley Railroad baggage and U.S. Mail railroad car.https://scholarworks.utrgv.edu/hidalgohist_aa/1164/thumbnail.jp
Development of a Wearable Biosensor for Cortisol Detection in Human Sweat Utilizing PANI/PEO Polymer Blend and Nitrogen-Doped Graphene Quantum Dots
No full textSince the development of the first biosensor in 1962, the evolution of commercially available wearable biosensors has been driven by an increasing consumer interest in real-time health tracking. However, challenges related to sensor sensitivity and cost have hindered progress. Deploying a novel approach, the biosensor integrates a blend of Polyaniline (PANi) and Polyethylene Oxide (PEO) along with Nitrogen-doped Graphene Quantum Dots (N-GQDs) to enhance sensitivity and electroactivity, crucial for cortisol detection. To optimize performance, three distinct synthesis methods for N-GQDs; hydrothermal, microwave, and hot plate synthesis were compared employing a bottom-up approach utilizing citric acid (CA) as the primary reactant for graphene production. The efficiency of each synthesis method were evaluated based on sensitivity to cortisol detection. Characterization techniques employed included UV-Vis spectroscopy for n-π* confirmation, Photoluminescent spectroscopy for emission spectrum analysis, and atomic force microscopy (AFM) for size and quantity determination of N-GQDs . The N-GQDs were uniformly dispersed throughout the PEO/PANi film, facilitating even distribution, and enhancing the attachment of antigens and enzymes for cortisol detection. Electrochemical analysis was conducted utilizing cyclic voltammetry (CV), while characterization techniques such as, Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and atomic force microscopy (AFM) were used to assess the structural and chemical properties of the biosensor
Bilateral Leading Edges With Tubercle Modifications: An Experimental Study
No full textThe increasing demand for better performance and maneuverability from airfoils to sustain superior performance over a wide range of platforms, including vehicles, propellers, and wing designs, continues to grow. Researchers have drawn inspiration from nature, looking at birds of prey, dragonflies, and humpback whales for aerodynamic improvements. Among these, tubercle airfoils, inspired by the humpback whale’s flipper, have gained increasing interest. This experimental study presents the results of bilateral tubercle leading-edge airfoils based on a modified NACA 0018 design with a chord length of 1.82 inches, a span width of 7.5 inches, and an amplitude of 6.3c%. Five configurations were tested: 8T, 14T, 16T, 20T, and 24T in a 12 x 12 x 36-inch test section at a velocity of 13 m/s and angles of attack ranging from 0° to 20°. Performance analysis showed the 20T airfoil provided the best overall lift-to-drag efficiency, followed by the 24T and 16T, while the 14T offered modest improvements over the baseline design. These results demonstrate the aerodynamic advantages of properly configured tubercle designs in enhancing airfoil efficiency and improving stall performance, providing valuable insights for applications that require increased lift and enhanced maneuverability
Transformation with Genes to Improve Lipid Production and Barcoding to Identify Unknown Algal Species
No full textWith the rising demand for alternative fuels, algal-based biofuels are a promising, sustainable source that meets commercial interest while producing lower greenhouse gas emissions compared to fossil fuels. This thesis explored two studies, evaluating microalgae for their future potential in lipid yield for biofuel production.
The first study involved developing a cost-efficient electroporation protocol that allowed for the genetic transformation of Chlamydomonas reinhardtii using the pChlamy_4 vector, which supports future cloning of genes to enhance lipid production. Parameters affecting DNA uptake and integration into algal genomes were studied: the effect of cell density, the settings of the electroporation device, and the gap size of the electroporation cuvettes were tested. A set of optimal parameters was identified that resulted in the maximum number of algae cells transformed by the vector DNA.
The second study involved identifying five-field isolates of algae that were collected in South Texas via barcoding for potential use in biofuel production. Ribosomal DNA primers 23S (chloroplast genome) and ITS2 (EUK) (nuclear genome) were used to amplify target regions that are within the ribosomal RNA genes. Genomic DNA was amplified using these primers, and the resulting PCR products were sequenced. The DNA sequences obtained were then compared to those of known algal species in databases to phylogenetically characterize the unknown algal species
Sequential Data Modeling of Influenza A via Traditional, DWT-GPR Hybrid, and Deep Learning Architectures
No full textInfluenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all the deep learning models, especially the state-of-the-art Transformer with respective average testing MSE and MAE of 0.0433±0.0020 and 0.1126±0.0016 for capturing the temporal complexities associated with Influenza A data, outperforming the well-known traditional ARIMA, ETS and DWT-GPR models. The findings of this study provide evidence that state-of-the-art deep learning architectures can enhance predictive modelling for infectious diseases and indicate a more general trend toward using deep learning methods to strengthen public health forecasting and intervention planning strategies. By shifting to more complex forecasting techniques, public health strategies could be significantly impacted, ultimately leading to timely intervention resulting in a decrease in Influenza A morbidity and mortality. Future work should focus on how these models can be incorporated into real-time forecasting and preparedness systems at an epidemic level and integrated into existing surveillance systems
Comparative Analysis of Sequential and Non-Sequential Modeling Techniques for DDoS Attack Detection with Explainable AI
No full textCybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber-attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to discover. Because of that, there have been several approaches, as well as solutions that have been devised to detect it as accurately and efficiently as possible. In this study, four sequential data modeling techniques: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRUs), Transformers and two (2) non-sequential data modeling techniques: Random Forest (RF) and Deep Neural Network (DNN) have been used to build the intrusion detection system. The CIC-DDoS-2019 dataset was utilized in this work for training and testing the performance of the models. In this work, we concentrated on addressing data imbalance issues, which arise from the high volume of attack data compared to benign data. The problem of data imbalance was addressed using five distinct data balancing techniques: four oversampling and one undersampling, after which the performance of both sequential and nonsequential models was evaluated. The performance of these models was measured using precision, recall, F1 score, balanced accuracy, and AUC score, across each of the data balancing strategies implemented
Photograph of Fiesta Edinburg Music Festival
No full textPhotograph of Fiesta Edinburg Music Festival.https://scholarworks.utrgv.edu/spanglishrgv/1009/thumbnail.jp
Photograph of Fares Arabic Cuisine We Are Hiring! Ad
No full textPhotograph of Fares Arabic Cuisine We Are Hiring! Ad (Experto en taco de trompo)https://scholarworks.utrgv.edu/spanglishrgv/1005/thumbnail.jp