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AS-947-22 Resolution on Temporary Adoption of a 4-Year Catalog During Quarter-to-Semester Conversion
Get PDFApproves adopting a 4-year catalog designated the 2022-2026 Catalog; and furthermore resolves that the 4-year catalog use the same arguments and policy outlined in AS18 930-22 and its attachments, appropriately scaled to the one-year extension. It also resolves that the curricular review timeline remain adaptive to changes in the quarter-to-semester conversion timeline, and that the 2022-2026 Catalog go into effect for Fall 2022 through Summer 2026. This resolution will expire in Fall 2026, returning Cal Poly to a one year cycle of Catalog review
Potassium and Sodium Sensing ISFET Device and Array
Get PDFThe use of Ion-Sensitive Field Effect Transistors (ISFETs) as a means of testing a person’s potassium concentration in real-time has broad applications in the consumer space. An avid runner could use such a device to keep track of their hydration and salt levels. A hospital could use it for patients who require around-the-clock remote monitoring, and a variation of ISFETs are currently being used as continuous glucose monitors for diabetes patients. While ISFETs are not a new development in the field of microelectronics, their use as wearable devices has recently become relevant. The goal of this project is primarily to develop a working ISFET with a selectivity bias of potassium and sodium ions with a high level of sensitivity to allow for implementation of the device into a type of “Smart Wristband” someone can wear. In this particular application, the ISFET device will be fed ionic biomolecules via a reverse iontophoresis process, where it can then act as a sensor used to determine the potassium and sodium concentrations of the wearer. A device of this specific nature could be incredibly useful in the medical field as a more convenient means of patient monitoring, specifically for patients with chronic kidney disease or diabetic ketoacidosis [1]. In order to make a device effective enough in this sort of application, the sensitivity of the ISFETs must be very high, and the cost must be low. The following will be a detailed description and analysis of a proposed device design and the associated fabrication methodology used in realizing the proposed design. The following will contain a description and analysis of the designed ISFET device as well as the designed fabrication process and testing methodologies that will be used
The Site Logistics Design of the SHABANG SLO 2022 VIP Stage
Get PDFThe objective of this project is to take some of the burden off the SHABANG Music Festival organizers and create a detailed plan on how the VIP section of the festival is going to be organized. The SHABANG music festival is an annual, local weekend event for the community of San Luis Obispo. The goal of this project is to create a plan that will be effectively utilized at this year\u27s SHABANG festival. In working toward this goal, every aspect of the Construction Management curriculum that has been taught in our careers thus far will be utilized. For the Construction Management Department, the advantage is the building of a relationship with the festival organizers. Each year this festival continues to grow and add new additions to the site plan. If Construction Management students start to partner with SHABANG year after year more students will get the chance to be involved in a truly valuable Senior Project. SHABANG SLO’s coordinators are passionate about the alternative 3D design software, SketchUp, which illustrates a visual of the structure in mind. This project hones in on the use of this software to elevate the logistics and aesthetic representation aimed for
Evaluation of Cost-Effective Alternative Designs for Rural Expressway Intersections
Get PDFDespite numerous studies demonstrating the effectiveness of Restricted Crossing U-Turn (RCUT) intersection design, its implementation remains uneven and close to zero in some large states such as California. This research provides a comprehensive framework to estimate the operational and safety performance of future RCUT designs in California. The framework is demonstrated for five intersections located on high-speed rural expressways in California using VISSIM microsimulation models to measure operational performance for each intersection including the base condition with the existing Two-Way Stop-Controlled (TWSC) intersection and two RCUT designs. To evaluate future safety performance, the microsimulation models were further utilized to compile vehicle trajectory data to use with the Surrogate Safety Assessment Model (SSAM) to develop a surrogate measure-based approach to estimating future safety performance. Detailed Intersection Control Evaluation (ICE) studies found that the RCUT was cost-effective and the preferred alternative. This framework may be applied to the analysis of locations where a RCUT intersection may be appropriate. The framework demonstrated here may be used by agencies to estimate the future benefits of the first-time application of treatments that have been successful elsewhere. Based on simulation results, the proposed RCUT designs reduced or eliminated the more severe crossing conflicts
Construction Industry Hesitation in Accepting Wearable Sensing Devices to Enhance Worker
Get PDFThe construction industry is one of the most unsafe industries for workers in the United States. Advancements in wearable technology have been proven to create a safer construction environment. Despite the availability of these devices, use within the construction industry remains low. The objective of this research is to identify and analyze the causes behind the reluctance of the construction industry to implement two specific wearable safety devices, a biometric sensor, and a location tracking system. Device acceptance was analyzed from the perspective of the user (construction field labor) and company decision makers (construction managers). A modified unified theory of acceptance and use of technology (UTAUT) model was developed specific to barriers commonly found within technology adoption in the construction industry including: perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, data security, and facilitating conditions. A structured questionnaire was designed to test for association between the mentioned constructs and either behavioral intention or actual use. The questionnaire went through an expert review process, and a pilot study was conducted prior to being distributed to industry. Once all data was received Pearson chi-squared analysis was used to test for association between the constructs. A minority (46%) of labor respondents would not agree to voluntarily use the biometric wearable sensing device. Constructs associated with this finding included perceived performance expectancy, perceived effort expectancy, and social influence. A majority (59%) of labor respondents would not agree to voluntarily use the location tracking wearable sensing device. Constructs associated with this finding included perceived performance expectancy, social influence, and data security. A majority (56%) of management respondents would not implement the biometric wearable sensing device. Constructs found to be associated with this finding included perceived performance expectancy, openness to data utilization, and social influence of the client. A supermajority (68%) of management respondents would not implement the location tracking wearable sensing device. Constructs found to be associated with this finding include perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, and data security. This study will aid in the successful implementation of wearable sensing devices within the construction industry. Findings from this study can be used to aid those hoping to implement wearable sensing devices by identifying causes of wearable sensing device rejection. The results of this study can be used by both project managers and health and safety professionals to aid in device acceptance by field labor, and by those whose goal is to increase device use among construction firms
A Network Analysis of COVID-19 in the United States
Get PDFThrough methods in network theory and time-series analysis, we will analyze the spread of COVID-19 in the United States by determining trends in state-by-state daily cases through a network construction. Previous researchers have found frameworks for approximating the spread of the COVID-19 pandemic and identifying potential rises in cases by a network construction based on correlation of cases between regions [1]. Applying this network construction we determine how this network and its structure act as a predictor for overall COVID-19 cases in the United States by preforming a trend analysis on a variety of network statistics and US COVID-19 cases
Specialized Named Entity Recognition for Breast Cancer Subtyping
Get PDFThe amount of data and analysis being published and archived in the biomedical research community is more than can feasibly be sifted through manually, which limits the information an individual or small group can synthesize and integrate into their own research. This presents an opportunity for using automated methods, including Natural Language Processing (NLP), to extract important information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There are several NLP tools that are designed for entity recognition, but rely on large established corpus for training data. Biomedical research has the potential to guide diagnostic and therapeutic decisions, yet the overwhelming density of publications acts as a barrier to getting these results into a clinical setting. An exceptional example of this is the field of breast cancer biology where over 2 million people are diagnosed worldwide every year and billions of dollars are spent on research. Breast cancer biology literature and research relies on a highly specific domain with unique language and vocabulary, and therefore requires specialized NLP tools which can generate biologically meaningful results. This thesis presents a novel annotation tool, that is optimized for quickly creating training data for spaCy pipelines as well as exploring the viability of said data for analyzing papers with automated processing. Custom pipelines trained on these annotations are shown to be able to recognize custom entities at levels comparable to large corpus based recognition
Neural Network Based Diagnosis of Breast Cancer Using the Breakhis Dataset
Get PDFBreast cancer is the most common type of cancer in the world, and it is the second deadliest cancer for females. In the fight against breast cancer, early detection plays a large role in saving people’s lives. In this work, an image classifier is designed to diagnose breast tumors as benign or malignant. The classifier is designed with a neural network and trained on the BreakHis dataset. After creating the initial design, a variety of methods are used to try to improve the performance of the classifier. These methods include preprocessing, increasing the number of training epochs, changing network architecture, and data augmentation. Preprocessing includes changing image resolution and trying grayscale images rather than RGB. The tested network architectures include VGG16, ResNet50, and a custom structure. The final algorithm creates 50 classifier models and keeps the best one. Classifier designs are primarily judged on the classification accuracies of their best model and their median model. Designs are also judged on how consistently they produce their highest performing models. The final classifier design has a median accuracy of 93.62% and best accuracy of 96.35%. Of the 50 models generated, 46 of them performed with over 85% accuracy. The final classifier design is compared to the works of two groups of researchers who created similar classifiers for the same dataset. This will show that the classifier performs at the same level or better than the classifiers designed by other researchers. The classifier achieves similar performance to the classifier made by the first group of researchers and performs better than the classifier from the second. Finally, the learned lessons and future steps are discussed