Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

A numerical mechanical model integrating actin treadmilling and receptor recycling to explain selective disengagement of immune cells.

Abstract: T-killer cells of the immune system eliminate virus-infected cells by releasing toxic granules through a direct cell to cell surface interface (synapse). The release of toxic granules only through the cell surface interface assures the specificity of the immune response. The toxic granule releasing apparatus, however, may not be aligned toward the synapse at the moment of the synapse formation. Therefore, mechanisms for reorienting the killing apparatus inside the T-killer cell to the interface with the target is required. Numerous research works were reported to suggest the mechanisms with direct and indirect evidence, but the most adversary situation, that is when the cell's initial orientation is the complete opposite of the desired direction, either remained answered or brought skepticism toward the suggested mechanisms. To address this issue, a computational mechanical model of T-killer cell synapse formation is constructed to test previously suggested models in a more realistic setting and at the same time to test previously neglected component in the model, namely the actin network formation on the synapse. In this model, by capturing the mechanical interaction between T-killer cell surface receptor dynamics and mechanical properties of synapse formation, it is shown that T-killer cells can selectively engage or disengage from the target cell depending on the killing apparatus alignment with the target. The mechanism works as a safeguard measure ensuring target-cell killing and specificity, so it will be effective when T-killer cells are stranded in situations where the alignment of the killing apparatus is challenging.

Unsupervised Learning on the Health and Retirement Study using Geometric Data Analysis

Abstract: A geometric data analysis that builds a lower dimensional representation of both individuals and measured variables is used to detect and represent underlying structures in the US Health and Retirement Study, a longitudinal survey of a representative sample of Americans over age 50 that captures information on how changing health interacts with social, economic, and psychological factors and retirement decisions. Multiple correspondence analysis is performed on a subset of the survey responses, creating a lower dimensional representation of the respondents and their response patterns, and a hierarchical clustering method is applied to test and validate specific structures in this population study.

Ameliorating the Accuracy & Dimensional Reduction of Multi-modal Biometrics by Deep Learning

Abstract: Enhancement of an individual's security and protection is the major concern in the current era. Biometric security becomes a positive solution, adopted for many identification problems in various sectors, including Aviation. As the avionics business keeps growing worldwide, air terminals face the difficulties of increasingly more passenger volumes; with that, travelers' desire for a secure and consistent pass through the airport terminals is just expanding. COVID-19 has managed a hit in all parts of our lives. Maintaining social distance and wearing masks are very important in maintaining a person's health in this pandemic situation. In this scenario, facial recognition for passenger identification is difficult and tends to false acceptance of passengers with identical faces. To address this issue, contactless biometric traits like iris and fingerprints are considered for security growth. The paper deals with the fusion of iris and fingerprint to identify passengers without removing the masks and promotes safe travel. The iris and fingerprint features are extracted using Gray Level Co-occurrence Matrix(GLCM) and cross number technique. Maintaining these templates requires more space for storage. To make this simple, Sparse autoencoder, an unsupervised deep learning technique is incorporated on the fused template for attaining the dimensionality reduction. A minimum cost matcher(MCM) is employed to increase the multimodal biometric system's accuracy and performance. Thus, the proposed system honors passenger security builds confidence in air travel, and also strives for economic growth.