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Motivate-OER
Full text linkThis session will overview the many ways in which OER can be a motivator at any institution. Exploring the different roles on campus we will see the stimulators for each. Attendees will leave as a Motivate-OER, with ideas and resources to inspire others and build enthusiasm for OER
Minimum Feeding Time Required for Haemaphysalis longicornis To Transmit Severe Fever with Thrombocytopenia Syndrome Virus
No full textHidden behind the world’s best understood pathogens lie the often overlooked, high-risk tick-borne viruses. Among them, few were recognized by the Word Health Organization as a high priority arbovirus with notable public health risk and recognized person-to-person transmission. One of these viruses is Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). SFTSV causes the disease Severe Fever with Thrombocytopenia Syndrome (SFTS). SFTSV is a highly pathogenic vector-borne pathogen with its major vector being the Asian Longhorn tick, Haemaphysalis longicornis. The first step in this study was to generate and compare different SFTSV nymph infection methods. The most appropriate nymph infection method was used in tick-to-host transmission experiments. We fed these nymphs on mice for 2, 4, or 8 hours to determine the minimum feeding time required for an H. longicornis nymph to transmit SFTSV to a mouse host. The results of this study demonstrated that SFTSV RNA and SFTSV-specific antibodies were detected in the host after only 2 and 4 hours of H. longicornis feeding, respectively. These results support the hypothesis that SFTSV can be transmitted from H. longicornis to a naïve host within minutes or hours of that tick feeding
Turbulence Prediction Using Non-Linear Phase Space Analysis
Full text linkOur research presents a novel approach for turbulence prediction in computational fluid dynamics (CFD) simulations using a non-linear phase space analysis (NLPSA) and threshold algorithm. NLPSA has been utilized in medical applications to predict seizures, as well as in cybersecurity to detect malicious control and utilization of computing systems. NLPSA uses time-series data to learn the normal operating state of the system, then sets a threshold to predict when the system becomes abnormal. Turbulence prediction is similar, such that a fluid system changes from normal to abnormal. Turbulence prediction methods currently utilize machine learning tools, such as convolutional neural networks (CNN), which use images created from CFD simulation data. Typical CNN methods require datasets with high resolution to ensure accuracy in their predictions. However, these high-resolution images require increased computational cost to run the prediction models. To combat this, the proposed method uses an NLPSA and threshold algorithm that takes direct time-series data as input to predict when or if a system becomes abnormal. We will compare this approach with traditional CNN prediction models to test accuracy and feasibility. The main advantages to using NLPSA are the reduced computing cost and the ability to use data directly from the CFD simulation, rather than needing to interpolate and extrapolate images as with a CNN. We expect the proposed NLPSA method to extend the understanding and study of turbulence prediction by providing a new and novel approach.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1012/thumbnail.jp
Incorporating Recycled and Repurposed Plastic Into 100% Reclaimed Asphalt Pavement
Full text linkThe increasing awareness of sustainability in the asphalt industry has driven the use of recycled materials like reclaimed asphalt pavement (RAP) and waste plastic. This study examines the incorporation of recycled polypropylene (rPP), recycled high-density polyethylene (rHDPE), and recycled low-density polyethylene (rLDPE) at 0.3%, 0.6%, and 0.8% dosages into 100% RAP, along with the warm mix asphalt additive Evotherm® P25. Laboratory tests were conducted to evaluate the mechanical and durability performance of the modified asphalt mixtures while rutting and fatigue life were evaluated using mechanistic-empirical (ME) analysis in PerRoad software. Both plastic type and dosage had a statistically significant impact on the overall performance of the asphalt mixture. Results indicated that incorporating recycled plastics improves cracking resistance and abrasion loss: except for rPP and rHDPE at 0.8% dosage. However, rutting resistance decreased for modified mixtures except for rPP and rHDPE at 0.3% dosage. Additionally, plastic-modified asphalt mixtures exhibited lower moisture resistance and decreased fatigue and rutting life at higher plastic dosages. Overall, rHDPE showed superior performance in rutting, rPP in abrasion, and rLDPE in cracking resistance.
Keywords: waste plastic, reclaimed asphalt pavement, modification, performanc
Application of Graph Neural Networks with Phase Space Graphs
Full text linkNon-linear phase-space analysis models data represented as a graph transitioning between states in the time domain. By studying data transitions, we can predict the time a particular behavior occurs and classify the events (states) in a system. For example, we could classify neurological sensor data to determine if a person is asleep (state), or predict the direction in which a stock will move (transitions) based on micro trade patterns.
Previous research has demonstrated success in phase-space graphs in classifying malware, detecting network intrusions, and predicting seizures. However, the solutions either require calculating global graph features as inputs to a classifier, which results in information loss, or converting the graph into an image as inputs to convolutional neural networks (CNNs). The CNN solutions, however, require fixed-sized images, which limits the size of a graph.
This study proposed graph neural networks (GNNs) to analyze phase-space graphs without the limitations above. GNNs do not limit the graph complexity or size and do not require the upfront calculation of either global or local features, which is time prohibitive. Preliminary results of this research to power measurements from computer operating systems for rootkit detection, indicate that GNNs can obtain a high accuracy (99.6%) with substantially less training time than other methods. Future work includes examining the use of different GNNarchitectures and the effectiveness of the approach for similar problems such as epilepsy prediction and network intrusion detection.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1025/thumbnail.jp
Analysis of Forensic Techniques for Additive Manufacturing Devices
Full text linkAdditive Manufacturing (AM) is a set of newer computer-dependent production technologies that is seeing rapid adoption across a wide variety of industries, including defense, aerospace, automotive, and healthcare. With increased adoption comes an increased opportunity for misuse and abuse of such systems, which will lead to an increased need for Digital Forensic investigations into these platforms. This research forensically analyzes a number of AM devices to explore the options available for data acquisition as well as the impacts of hardware and software design choices on the analysis and investigation results. Hardware is investigated using Open-Source Intelligence (OSINT) sources to determine access routes. Acquisitions are then taken from the devices at predefined points in the production process and compared sequentially by cryptographic hashes of files or data blocks to determine the changes caused by operation. The aggregate data from the case studies will then be compared to form the basis of a taxonomy to discuss forensic investigations of AM devices.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1007/thumbnail.jp
A Framework for Design Recovery
No full textDue to the increase in diverse chip production over the past decade, reverse engineering has become a difficult and daunting task. This research will create a guideline for methods to achieve design recovery of microchip logic. To accomplish this, we plan on using hardware tools such as laser delayering and microscopy imaging. We will be focusing on the DA14580 Dialog semiconductor, commonly implemented in tile trackers, to extract information for design recovery. This implementation technique is novel due to tools that have not been performed with this type of microchip. The purpose for this research is to create a framework for future machine learning implementations to increase efficiency in the design recovery. To ultimately expand knowledge on counterfeit chip detection.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1008/thumbnail.jp
Dual-Band Metamaterial Unit Cell for 5G and 6G Applications
No full textThis poster presents a novel metamaterial unit cell engineered to resonate at two distinct frequencies: 28 GHz and between 92~100 GHz (W band allocated by ITU-R for future Sub-THz communication). The dual-band operation is achieved by integrating complementary split-ring resonator (CSRR) structures, which allow independent tuning of the resonant frequencies. The larger split-ring is optimized for 28 GHz operation, while the smaller one resonates between 92~100 GHz range, enabling a multi-functional response within a single unit cell. This innovative approach allows for compact, high-performance electromagnetic components that operate across widely separated frequency bands. At 28 GHz, it can be utilized in 5G millimeter-wave (mmWave) networks to enhance antenna performance, beamforming, and signal filtering. The 92~100 GHz resonance aligns with the emerging demand for terahertz (THz) communication systems, enabling advancements in 6G networks, high-speed data transmission and super low latency. Additionally, the unit cell\u27s compact and scalable design allows for integration into reconfigurable metasurfaces, frequency-selective surfaces (FSS), and electromagnetic shielding applications. To validate the design, simulations are performed in HFSS (High Frequency Structure Simulator) software using full-wave analysis, demonstrating strong resonance characteristics at both target frequencies. The results indicate that this metamaterial unit cell offers an efficient solution for multi-band applications, reducing component size and complexity while maintaining high performance. Overall, the introduction of this dual-resonant metamaterial unit cell paves the way for innovative solutions in next-generation high-frequency technologies, driving advancements across multiple industries.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1024/thumbnail.jp
Low-Cost Memory Design for Data Privacy
No full textIn memory design, silicon area and power savings have always been a major concern as it directly contributes to performance improvement and cost reduction. The chip area is defined by the area occupied by each memory cell, while the cell area greatly depends on the size of the transistors. In custom design of memory cells, the transistor size is determined based on static noise margin (SNM) which defines the maximum tolerable noise for a cell without any changes in its operational state. This research introduces a hardware based custom 4-transitor (4T) static random-access memory (SRAM) design that supports differential privacy in Internet of Things (IoT) devices as well as reduces 61.92% silicon area and 38% power compared to the traditional 6-transistor (6T) SRAM. Combining 6T and 4T cells together and applying voltage level variations inside the array introduces a high-quality differential privacy mechanism. In addition to that, the 4T cells show a great potential to minimize silicon area compared to 6T cells as these cells do not include the pull-up devices. Moreover, a mirror-based highly compact layout design of the 4T SRAM is presented where the adjacent cells in the array can share some specific elements both vertically and horizontally. This mirrored cell structure greatly helps to reduce the area occupied by each 4T cell as well as the overall silicon area. For physical design, implementation, verification and analysis, a 130nm CMOS technology from SkyWater and EDA tools provided by Efabless (such as NGSPICE, Magic, and XSchem) are used.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1029/thumbnail.jp
Toxicity and Biodegradability of Novel Boronium vs Conventional Ammonium-based Antimicrobial Compounds in Wastewater Treatment Systems
Full text linkQuaternary ammonium compounds (QACs) are highly effective as disinfectants, herbicides, and pesticides; thus, overuse causes elevated levels of residual toxicity in domestic and industrial wastewater. QACs can be toxic to essential bacteria breaking down pollutants in wastewater treatment plants (WWTPs) and can remain untreated in effluent, harming the environment, and contributing to antibiotic resistance, posing risks to human health. Novel boronium-based antimicrobial compounds have demonstrated efficacy in eliminating bacteria, fungi, and viruses. If the boronium compounds exhibit lower residual toxicity, they could offer a promising alternative to QACs. Because these compounds are still in development, their potential toxicity to the biological WWTP process is yet to be fully evaluated. Therefore, the objective of this study is to conduct a comparative toxicity analysis between the QAC (alkyl (ethylbenzyl) dimethylammonium chloride) and three novel boronium compounds. The analyses performed follow guidelines set by the Organization for Economic Cooperation and Development (OECD) and utilize a standard freeze-dried aerobic bacterial culture that closely mimics WWTPs’ mixed bacteria communities. Toxicity is assessed by measuring and comparing the dissolved oxygen consumption rate (DOCR) of the bacterial culture, which was fed an ideal substrate, in the presence of the test compounds at varying concentrations. Experiments were conducted by comparing QAC to boronium compounds at concentrations ranging from 0.1 to 22 mg/L, based on expected residual levels in wastewater. Initial results show that the QAC is less toxic than the boronium C16 compound and that the QAC is less toxic than the pyridine boronium C16 compound. However, the bacteria used in this study exhibited signs of adaptation to the boronium C16 compound with concentrations of 22 mg/L, 5.5 mg/L and 0.344 mg/L and to the pyridine boronium C16 compound with concentrations of 11 mg/L, 5.5 mg/L, 2.75 mg/L and 1.375 mg/L. Additionally, initial results show that the Gemini salt is less toxic than the QAC. The duration of future experiments for the QAC and the three boronium compounds will extend beyond 7 days to better capture bacterial adaptation and to determine if boronium compounds exhibit lower residual toxicity than QACs