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Protection of the Edwards Aquifer during Emergency Response
No full textThis video is the recording of a presentation by Texas A&M University-San Antonio as part of the training sessions for first responders. The karstic Edwards Aquifer in south-central Texas, classified as a sole source aquifer by the Environmental Protection Agency, provides potable water for more than two million residents and businesses in the fast-growing San Antonio area. Because of its karstic nature, the aquifer is vulnerable to runoff contamination, including those generated during emergency responses in the sensitive aquifer recharge zone. The U.S. Department of Agriculture funded Texas A&M University-San Antonio to develop Best Management Practices and provide training to first responders, health and safety officials, agricultural producers, and other stakeholders
Water Resources Science and Technology Fall Seminar Series: Mr. Shaun Donovan (San Antonio River Authority)
No full textMonitoring and Managing Environmental Health of the San Antonio River Basin. Mr. Donovan is currently the manager of the Environmental Science at San Antonio River Authority. He is a professionally certified Aquatic Scientist and Project Manager
Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques
Get PDFAir writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16
Water Resources Science and Technology Fall Seminar Series: Jill Shackelford (Entrepreneur) and Molly Cagle (Legal Counsel)
No full textMs. Jill Shackelford shared her experience in starting a mining company, how she encountered challenges by the environmental groups and local residents, the lessons learned, and how she eventually worked with her legal counsel, Molly Cagle, to gain clarity on water rights and the trust of her oppositions by investing in the dry suppression technology from Europe. She shared her view on the co-existence of industrial development and environmental compliance
The Gift of Mourning
Get PDFIs mourning possible? Or impossible? And if impossible, in what sense impossible? What does this mean, in turn, for what we do as human beings in the face of the normal, natural experience of mourning the death of the other? How can we mourn? How should we mourn? For some, these questions arise on account of the death of a beloved pet, a friend, a child, a spouse, and/or a parent. Perhaps they arise even on account of the death of their own faith in God, others, humanity, and/or the universe. Yet since 2020, these questions have become especially emphatic with Covid-19 spreading across the globe disrupting, transforming, and ruining many people’s lives. With little risk for hyperbole, I suspect that not a single person’s life was left untouched by the effects of Covid. Moreover, I suspect that how Covid touched each person’s life in some degree or another centered around each person experiencing the inflexible law of life: that one of two people will experience the other die.2 This world-event of a pandemic gave rise to worldwide deaths each of which touched someone somewhere, each of us, personally thereby leaving virtually everyone wondering what is happening to me, to us, to the world, etc. For some, this event led to a mourning that overcame them leading them to be added to the number of deaths during Covid though not from the virus but by their own hand. For others who survived not just the deaths of the others around them but, perhaps, also their own appeal to end their own life, the mourning left to be done and left to be undergone left them in a place teeming with possibility. This place teeming with possibility in the aftermath of the death of the other or in the throws of mourning is the site that I explore in this paper with Jacques Derrida, and a few others, as my guide
Enhancing Neural Text Detector Robustness with μAttacking and RR-Training
Get PDFWith advanced neural network techniques, language models can generate content that looks genuinely created by humans. Such advanced progress benefits society in numerous ways. However, it may also bring us threats that we have not seen before. A neural text detector is a classification model that separates machine-generated text from human-written ones. Unfortunately, a pretrained neural text detector may be vulnerable to adversarial attack, aiming to fool the detector into making wrong classification decisions. Through this work, we propose µAttacking, a mutation-based general framework that can be used to evaluate the robustness of neural text detectors systematically. Our experiments demonstrate that µAttacking identifies the detector’s flaws effectively. Inspired by the insightful information revealed by µAttacking, we also propose an RR-training strategy, a straightforward but effective method to improve the robustness of neural text detectors through finetuning. Compared with the normal finetuning method, our experiments demonstrated that RR-training effectively increased the model robustness by up to 11.33% without increasing much effort when finetuning a neural text detector. We believe the µAttacking and RR-training are useful tools for developing and evaluating neural language models
Resource Allocation Methods in Vanets: A Systemic Literature Review
Get PDFAutonomous vehicles take on a more prominent part of our everyday life activities. As the number of vehicles grows, so does the need for resources to ensure safe and consistent operations of these vehicles. Today’s networks and power grid are already used heavily just supplying resources for our day-to-day lives and leisure. The continuous increase in demand for such resources drives the need for more advanced resource management tools and techniques and more precise resource allocation schemes. This paper is a systematic literature review on the current methods and research trends for resource allocation in vehicular networks. The purpose of this review is to understand current research trends and motivations. Our hope is to provide a comparative analysis of resource allocation solutions, research trends and challenges in vehicular networks
GIRLS DON’T GO TO SCHOOL: UNCOVERING THE SCHOOLING EXPERIENCES OF A TEJANA GROWING UP ON THE WESTSIDE OF SAN ANTONIO
No full textAN AUTOETHNOGRAPHY JOURNEY: SOCIAL AND EMOTIONAL LEARNING IN A POST-COVID CLASSROOM
Get PDFIn this autoethnography journey, we explore the question, What resources are available for teachers to use to guide SEL instruction, and how effective are those resources post-pandemic? Post-COVID-19 pandemic, we noticed that our students needed more social interaction time in the classroom. Student behaviors increased alongside teacher frustration. Districts immediately began to see this struggle in the school systems and the growing concern from parents and teachers. Our children were not taught how to respectfully express their social and emotional needs due to the lack of opportunity to practice with others. During this study, we found that teachers are reaching around for any access to resources to help guide this instruction. The question of the resource\u27s validity and reliability. This particular study does utilize district-given resources for the lessons. After the research, we found that implementing SEL with fidelity and purpose allowed our students to find their voice in a safe and comfortable culture alongside their teachers in the classroom. The lack of support and resources could make our students suffer later
A Longitudinal Study of Behavioral Consultation in Inclusive Preschool Classrooms
No full textTeachers in early childhood classrooms face a diverse child population, including children with developmental delays and differences (DD), requiring teachers to exhibit professional skills to address a broad spectrum of developmental needs. At times, early learners with and without DD exhibit challenging behaviors (e.g., aggression, tantrums). Nevertheless, teachers find themselves ill-prepared to manage such behavior and teach under these circumstances due to limited training in classroom behavior management. Applied Behavior Analysis (ABA) is an effective treatment for children with various developmental differences and has proven successful in classroom settings when applied. This report describes a behavior consultation model used in a four-year project in 44 early childhood classrooms with 196 teachers and 97 children identified as having developmental delays and challenging behavior. Recommendations for choosing a behavior consultant for similar settings and a discussion of recurring behavior strategies recommended are presented