The LAIR at East Texas A&M
Not a member yet
7179 research outputs found
Sort by
Introduction to Nuclear Reactions
No full textUntil the publication of the first edition of Introduction to Nuclear Reactions in 2004, an introductory reference on nuclear reactions had been unavailable. Now, fully updated throughout, this second edition continues to provide an authoritative overview of nuclear reactions. It discusses the main formalisms, ranging from basic laws to the final formulae used in academic research to calculate measurable quantities.
Well known in their fields, the authors begin with a basic introduction to elements of scattering theory followed by a study of its applications to specific nuclear reactions. Early chapters give a framework of compound nucleus formation and its decay, fusion, fission, and direct reactions, that can be easily understood by the novice. These chapters also serve as prototypes for applications of the underlying physical ideas presented in previous chapters. The largest section of the book comprises the physical models that have been developed to account for the various aspects of nuclear reaction phenomena, including reactions in stellar environments, cosmic rays, and during the big bang. The final chapters survey applications of the eikonal wavefunction and of nuclear transport equations to nuclear reactions at high energies.
By combining a thorough theoretical approach with applications to recent experimental data, Introduction to Nuclear Reactions helps you understand the results of experimental measurements rather than describe how they are made. A clear treatment of the topics and coherent organization make this information understandable to students and professionals with a solid foundation in physics as well as to those with a more general science and technology background
A Narrative Inquiry to Explore the Experiences of African American Male Graduates from an Alternative Learning Program
No full textDecades after the historic ruling in Brown v. Board of Education, AAM students are not only performing poorly academically but are also dropping out of traditional high schools at an alarming rate and enrolling into Alternative Education (AE) or Alternative Learning Programs (ALP). Public education officials and policymakers were mandated to establish a system to help reduce the nation’s dropout crisis. The nation responded with three alternatives to help reduce the nation’s dropout crisis: the No Child Left Behind (NCLB) Act of 2001, ALPs, and Disciplinary Alternative Education Programs (DAEPs). Despite these instituted efforts, however, AAM students have become the nation’s new civil rights issue of the early 2000s. AAMs compared to all other segments of student groups are being referred to Alternative Education Programs (AEPs) and on to the school-to-prison pipeline. The inclusion of the voices of AAM students in the education reform process is long overdue to help discover a personal perspective of how to institute constructive change
El Paradigma de Cenicienta en The Edible Woman y Como Agua para Chocolate
No full textEste estudio analiza dos intertextos que dialogan con “Cenicienta”, el cuento de hadas más leído de la historia de la humanidad. Esta tesis involucra dos paradigmas, uno de pasividad y otro de resiliencia. El diálogo incluye temas inquietantes sobre la mujer, intersubjetividad, identidad, libertad, subversión y ruptura de normas tradicionales. Los textos seleccionados traen en su tejido textual paradigmas de subversión que dejan en evidencia los vacíos dejados por versiones clásicas a lo largo de la historia del cuento. Aspectos de la intertextualidad paródica de Hutcheon permiten desconstruir escenas retorcidas para encontrar el sentido dentro del sinsentido en escenas significativas de las narrativas. La teoría permite encontrar similitudes y diferencias entre los paradigmas. La metodología se da a través de un análisis comparativo que involucra el paradigma de cenicientas clásicas del tipo Perrault y Grimm, caracterizadas por la pasividad, y dos versiones subversivas con Cenicientas resilientes; lo que permite ampliar los espacios de estudio de Cenicienta en el idioma español y comenzar la conversación hacia Latinoamérica. El estudio permite evidenciar efectos de los cuentos de hadas , como fortalecedores de la pasividad y el silencio en la mujer y el protagonismo, la valentía y el heroísmo por parte del hombre; también, permite establecer espacios de reflexión sobre sistemas controladores obsoletos
As (S)He Thinks, so (S)He Is: Mindset as Moderator of Stereotype Threat and Impostor Phenomenon in Black Women Doctoral Students
No full textBlack women have a unique experience that no other race or gender has or fully understands because they encounter the resultant racism of being Black and the sexism of being a woman. If a Black woman doctoral student believes the stereotypes held for either her race or gender, she can be subject to stereotype threat in testing situations. She may also fall victim to impostor phenomenon, defined as the internal feeling that you are an intellectual fraud, as she navigates the challenging waters of academia where very few look like her. However, if she has a mindset that although she may not currently have all of the knowledge to succeed at the doctoral level, she can gain the necessary knowledge and thus be successful no matter the challenges she faces. Stereotype threat and impostor phenomenon would no longer be stumbling blocks for her.The purpose of this study was to relate stereotype threat to impostor phenomenon in Black women doctoral students and determine if having a growth mindset moderated the relationship. Participants were randomized into two conditions – stereotype threat or control. Both groups completed a mindset scale, verbal reasoning test, Clance Impostor Phenomenon Scale, and a demographic questionnaire. The stereotype threat group was told that the verbal reasoning test was a measure of their ability; whereas, the control group was told they were testing the validity of the questions. Two-way analysis of variance determined that having a growth mindset does not moderate the relationship between impostor phenomenon nor verbal reasoning test scores. However, those with a fixed mindset did have higher impostor phenomenon scores than those with a growth mindset, no matter the condition
Design a Convolutional Neural Network for Melanocytic Skin Lesion Identification
No full textMalignant melanoma is a type of skin cancer which stands with the highest mortality rate out of all cancers. Back in the days, dermatologists would use their naked eye to diagnose a lesion. Further, the dermatologists have improved their ways of diagnosing the cancer more accurately by using dermoscops. With the aid of the Convolutional Neural Network throughout the past years, mathematicians and computer scientists are now able to detect skin cancer more easily. In this study, we designed a new Convolutional Neural Network (CNN) for binary classification of skin lesion images to benign and malignant. Further, for the same kind of classification we designed a hybrid deep learning Convolutional Neural Network (CNN) with Support Vector Machine (SVM). The first proposed networks consist of several layers including convolutional layers, max pooling and fully connected layers. ReLu activation function is applied on each convolutional layer. Finally, in the fully connected layer, we used sigmoid activation function for binary classification. To minimize the loss function we applied two machine learning methods from the Tensorflow-Keras library: SGD and ADAM. To analyze accuracy and model performance, we modified our CNN model by adding convolutional layers and max pooling. We applied l2 regularization and drop out layers in our modified CNN model to decrease overfitting. In order to enhance the classification accuracy of our new proposed Hybrid CNN model with SVM, we applied segmentation process by several new dermascopic techniques such as isodata threshold, clustering based method and watershed based methods. To validate our experiments we used ISIC 2020 skin lesion image dataset and we achieved the highest accuracy, sensitivity, specificity and precision is 90%, 93%, 94% and 93% respectively from our CNN and 88%, 94%, 75% and 86% respectively from our CNN-SVM model
A Narrative Inquiry on the Processes and Data Used by LPAC Administrators to Reclassify English Learners
No full textThe English learner (EL) population, specifically Spanish-speaking ELs, has more than doubled in the last decade in the United States. Bilingual education programs seek to prepare ELs to master grade-level content and improve their English proficiency. English learners who demonstrate English proficiency by meeting state criteria can be reclassified as English proficient (EP). Federal policy requires that a Language Proficiency and Assessment Committee (LPAC) be created to oversee this reclassification process. A campus administrator is given the task to chair the LPAC through all required policy implementation and procedures, including ELs’ classification and reclassification. However, some ELs never meet the reclassification criteria; meanwhile, their academic achievement gap continues to increase compared to their EP counterparts. The purpose of this study was to explore LPAC chairs’ experiences with the processes and data used when reclassifying EL students. Specifically, the researcher investigated how the LPAC administrator interprets the educational policies governing the reclassification process and implements these policies through the LPAC decision-making during a school year. A South Texas school district was selected for this study based on its demographic composition of ELs, who account for over 52% of the student population. Purposeful sampling was used to select the nine participants who met the following criteria: (a) has a minimum of 2 years of experience as a school administrator, and (b) has served a minimum of 1 school year as an LPAC chair. The participants’ narratives provided differences in perspectives and interpretations regarding ELs and even highlighted different procedures by the chairs for reclassifying EL students
A Qualitative Study of the Relationship Between Latina Daughters-in-Law and Non-Latina White Mothers-in-Law and Its Effect on Their Interracial Marriages
No full textThis study had eight Latina participants who shared their experiences of the relationship they had with their non-Latina White mothers-in-law and the effect it had on their interracial marriages. This qualitative transcendental phenomenological study provides counselors an understanding of the cultural and background factors that lead these couples to counseling. Significant themes that affected the relationship and marriage were communication, respect, trust, support, expectations, and boundaries. The researcher further reduced these themes and identified one overall theme of cultural relationship expectations as the significant theme affecting the mothers-in-law relationship provided by the interview responses. All the daughters-in-law shared a deficiency in more than four areas of significance creating barriers to the mothers-in-law relationship that affected their marriage. The researcher found that cultural relationship expectations affected the Latina daughters-in-law relationship with their non-Latina White mothers-in-law, and the relationship affected their interracial marriages. Three of the daughters-in-law divorced within three to eight years. Another participant and her husband divorced themselves from the mother-in-law/mother’s toxicity. These four participants were all in a toxic relationship with their mothers-in-law. The study’s divorce rate was 38%, which was lower than the overall interracial divorce rate of 41%. Keywords: Latina, Latinx, confianza, familismo, personalismo, respeto, simpatí
Generative Poisoning Attacks on Neural Network Models in Autonomous Driving
No full textPoisoning attacks have been described as a grave danger to neural networks, they tend to change the complete meaning of the model and also define how the model interprets the data provided to it. Certain inputs are retrained after which poisoned data can be induced into the model. As the popularity of self-driving cars are growing exponentially, we need to look into every major flaw in the system and have security measures in place for the safety of the passengers. There have been limited studies on progressive poisoning attacks especially when it comes to autonomous driving vehicles. In this work, we first generate the poisoned data and also propose a generative method that will increase the generation of the poisoned data. This method of poisoning helps us determine the extent of inaccuracy and unexpected side effects when the poisoned data is fed to the model
A Method of Autonomous Vehicle Path Planning Through Constrained Artificial Bee Colony Optimization
No full textFor vehicle routing, the ability to optimize path planning and travel time, allows an or-ganization to reduce time and money to deliver cargo. This is particularly important for vehicles such as ambulances, where the fastest path to the hospital can save lives. The progress of artificial intelligence has shown remarkable results to the application of vehicle lane change optimization, as well as showing applicability for autonomous vehicles. Currently, systems for autonomous driving are mainly focused on staying in one lane, and following a set rate of speed to reduce the amount of uncertainty of the systems against human driver behaviors. However, upon a change of information, for example, debris upon a roadway, a vehicle will have to modify its planned path to best fit this change of data, while also maintaining a buffer of safety for the surrounding vehicles. Due to the time constraints for decision-making, this paper explores a method of heuristically searching for a safer path through the Artificial Bee Colony Algorithm, while also applying Constraint Satisfaction techniques to optimize its search space relative to the vehicles current position. Keywords: Artificial Bee Colony Algorithm, Path Planning, Constraint Satisfaction, Constraint Programming
Gaze Based Mind Wandering Detection Using Deep Learning
No full textMind wandering (MW) is a phenomenon where a person shifts their attention from task-related to task-unrelated information. Mind Wandering is an omnipresent phenomenon for human beings. The consequence of mind wandering can impact a person’s performance negatively. Reorienting the mind’s attention using technology shows great promise to improve the performance and productivity of people in learning or other performative tasks. In this research, we investigate 62 eye gaze features by dividing them into four sets of global features: eye movement descriptive features, pupil diameter descriptive features, blink features, and miscellaneous features to detect mind wandering during reading from a computer interface. Our dataset, which was collected from a previous study, contains a mind wandering report where 135 participants were recorded “mind wandering” or “not mind wandering” using self-reporting during a computerized reading task. During this process, a remotely placed eye tracker tool recorded eye gaze data. Models were created using six supervised conventional machine learning (ML) algorithms: logistic regression, k-nearest neighbors (k-NN), support vector machine (SVM), decision tree, random forest and naive Bayes. Machine learning models were trained on eye gaze dataset and evaluated using 5-fold cross validation. We measured the performance using area under the receiver operating characteristics (AUC-ROC) score, AUC-ROC curve, and confusion-matrix. To further improve the AUC-ROC score and other evaluation metrics, we trained standard neural networks and deep learning models using the data. Four sets of deep learning architectures were trained and evaluated. We found that dense neural network with one dimensional convolutional layer (DNN+Conv1D) outperformed the performance of conventional machine learning models. Naïve Bayes achieved mean test AUC-ROC score of 0.6595 and mean test accuracy of 0.6416. DNN+Conv1D beat the AUC-ROC score and achieved a score of 0.8024 and mean test accuracy of 0.7278. Our implementation used missing data values rather than discarding them which in fact improved our results. Our findings also showed that an automated mind wandering detection using deep learning models generalize well for new participants. This finding may help laboratory studies of mind wandering and for building systems to detect attention of inattentive drivers, students, or people in other work contexts that need focus to improve performance on a task