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Viability selection in Drosophila pseudoobscura in response to heat stress
No full textMeiotic recombination rates vary in response to intrinsic and extrinsic factors. Recently, heat stress has been shown to reveal plasticity in recombination rates in Drosophila pseudoobscura. Here, a combination of molecular genotyping and X-linked recessive phenotypic markers were used to investigate differences in recombination rates due to heat stress. In addition, haplotypes from the genetic crosses were compared to test if they deviated from equal proportions, which would indicate viability selection. To avoid this potential bias, SNP genotyping markers overlapping the regions assayed with mutant markers were used to further investigate recombination rate. Interestingly, skews in haplotype frequency were consistent with the fixation of alleles in the wild type stocks used that are unfit at high temperature. Evidence of viability selection due to heat stress in the wild type haplotypes was most apparent on days 7-9 when more mutant non-crossover haplotypes were recovered in comparison to wild type (p<0.0001). Recombination analysis using SNP markers showed days 9-10 as significantly different due to heat stress in two pairs of consecutive SNP markers (p=0.018; p=0.015), suggesting that this time period is when recombination rate is most sensitive to heat stress. This peak timing for recombination plasticity is consistent with D. melanogaster based on comparison of similarly timed key meiotic events, enabling future mechanistic work of temperature stress on recombination rate
Assessing Private Well Water Quality in Mobile and Baldwin Counties, Alabama
No full textApproximately 43 million people in the United States rely on private well water. Lack of regulation is important due sources of contamination from geogenic and anthropogenic sources. One source of anthropogenic contamination that may impact well water quality is land use, specifically agricultural land use. Contaminants associated with agricultural land use include pesticides, herbicides and nitrate. An objective of this study was to understand if agricultural land use impacts nitrate concentration in private well water. We found a statistically significant relationship between agricultural land use and nitrate. Nitrate was also found to have an inverse relationship with pH, which was supported by interpolations, principal components analysis and a multilinear regression. Areas with lower pH can have more corrosive water which can leach toxic elements that may impact human health. Low levels of saltwater intrusion were also found in this study. In general, water quality was deemed as excellent using a Water Quality Index (WQI), which can be used to qualitatively understand water quality. Considering the vast amount of parameters affecting water quality, we determined that a more holistic approach should be taken when researching water quality.
Lack of federal regulation means that well owners are solely responsible for ensuring their water is safe. The most effective way for well owners to understand their water quality is to get it tested. Well owners can test their water by sending it to a laboratory or purchasing an at-home test kit. At-home test kits can serve as a quick and cost-effective way for well owners to understand their water quality. However, there are several different test kits and principles that citizens may use to test their water. We aimed to determine which kits and principles were the most accurate and applicable for well owners. Combination test kits were determined to be the most accurate test kits. We also found that some tests, like bacterial test have limits of detection above the Environmental Protection Agency’s Maximum Contaminant Level (EPA MCL), which may give well owners a false sense of security in their water quality. Concentration ranges and increments on comparator charts may also impact result interpretation. Though there are some limitations to using at-home test kits, at-home test kits can provide a cheap and quick way to measure water quality
Spinning the Web to Stay: Teacher Retention Factors and Job Embeddedness in Alabama's Rural Public Schools
No full textRetaining quality teachers is an assurance of successful schools. Moreover, research shows that high teacher turnover has academic and social impacts that are detrimental for students. Unfortunately, teacher turnover is often more pernicious in rural schools, and many teachers do not make it past the first five years. "The Theory of Staying" (Watson & Olson-Buchanan, 2016, p. 5), also known as job embeddedness, is a construct hypothesized as critical to organizational success (Shibiti, 2019).
This study will utilize a sample of teachers drawn from 496 rural public schools in Alabama to analyze teacher retention factors through the lens of job embeddedness. Through a survey of rural public-school teachers in Alabama that have remained in the school for three consecutive years or more, this research will examine the relationship between job embeddedness and factors that support teacher retention. Participants will complete an online survey intended to measure their perceptions of retention factors and a job embeddedness scale. This study utilized Pearson’s correlation between retention factors and job embeddedness. This will be followed by a multiple regression analysis to determine which retention factors are significantly associated with job embeddedness.
The implications of this study will include distinctive findings linked to the dynamics surrounding rural public schools in Alabama while offering insights for school administrators, policymakers, and institutions of higher learning in program development to improve teacher retention. Furthermore, the results of this study will provide information for the development of Human Resource Management practices that increase the probability of sustained collaborative relationships, a climate of administrative support, and opportunities for professional growth; such are the elements that encourage teachers to become embedded in a school and the profession
Rippling Doubt: A Grounded Theory Study of Foster Placement Disruptions to Inform Counseling
No full textThis study utilized a GT methodology to better understand foster placement disruptions to inform counseling interventions. Despite the fact counselors are important stakeholders in the child welfare system, investigations into phenomena of foster families are largely absent from the counseling literature. The purpose of this study was to elicit from interview data of licensed foster parents an explanatory model of foster placement disruptions to assist counselors who provide services to foster children and their families. The key finding of this study was a generated theory of rippling doubt, explaining how foster parents increasingly doubt their abilities to continue providing care for a child due to overwhelming emotional/behavioral/cognitive challenges that impact key protective domains. A discussion of how the results of this study inform targeted counseling interventions within each of the impacted domains is included
When Alice meets the ``Hardware'' Bob --- Attacks and Solutions in the Digital World
Get PDFModern cryptography and the digital world (CMOS mainly) have been advancing side-by-side. Although cryptographic algorithms only need to be proven with theoretical soundness, cryptographic engineering on digital device requires more efforts to achieve a secure implementation than simply just design theories. Although properties like energy-efficiency, low-latency, and minimal area overhead are desired in hardware-based implementations for cryptographic modules, they could potentially weaken the theoretical justification of cryptosystems, where adversaries can break the secret key inside the hardware.
In this dissertation, we develop novel attack to uniquely determine the secret key for hardware implementation of GIFT-COFB, one of the ten finalists for National Institute of Standards and Technology (NIST) Lightweight Cryptography Standardization Process. The 2-round partial unrolled design of GIFT-COFB is shown to be the most energy-efficient among all other -round partial unrolling and fully unrolled settings. Our proposed chosen-plaintext attack can effectively break the master key on this 2-round partial unrolled GIFT-COFB. In addition, we present two chosen-plaintext attacks on multicycle AES implementations with fault-based attacks. Both attacks on GIFT-COFB and multicycle AES include explanations from algebraic cryptanalysis perspective.
In parallel, the adoption of horizontal business models in semiconductor manufacturing is negatively affected by the overproduction of integrated circuits (ICs) and the piracy of intellectual properties (IPs), which compromised the integrity of the digital world's semiconductor supply chain. Logic locking emerges as a primary design-for-security measure to counter these threats, where ICs become fully functional only when unlocked with a secret key. However, Boolean satisfiability-based attacks have rendered most locking schemes ineffective. This gives rise to numerous defenses and new locking methods to achieve SAT resiliency. Subsequent attacks have been proposed to target these newly proposed solutions. The reasons behind the effectiveness of SAT attack and the following SAT-based attack have yet to be explored. In this dissertation, we provide a unique perspective on SAT attack efficiency based on conjunctive normal form (CNF) stored in SAT solver. We demonstrate how this attack learns new relations between keys in every iteration using distinguishing input patterns and the corresponding oracle responses. Each input-output pair gives additional CNF clauses of unknown keys to be appended to SAT formulation, which leads to an exponential reduction in incorrect key values. Overall, SAT attack is shown to break most locking scheme within the linear iteration complexity of key size. Our analysis provides a new perspective on the capabilities of SAT attack against multiplier benchmark c6288 with possibly new directions to achieve SAT resiliency.
In the digital world, it is also crucial to reduce manufacturing defect escapes in today's safety-critical applications requires increased fault coverage. However, generating a test set using commercial automatic test pattern generation (ATPG) tools that lead to zero-defect escape is still an open problem. It is challenging to detect all stuck-at faults to reach 100\% fault coverage. It remains challenging to detect hard-to-detect and redundant faults for large VLSI circuits. More optimization needs to be done as undetected faults still exist under the state-of-the-art commercial ATPG tools. Rather than attacking logic locking with SAT solvers, in this dissertation, we propose a novel test pattern generation approach constructively using the powerful SAT attack on logic locking. A stuck-at fault is modeled as a locked gate with a secret key, where it can effectively deduce the satisfiable assignment with reduced backtracks under key initialization of the SAT attack. The input pattern that determines the key is a test for the stuck-at fault. We propose two different approaches for test pattern generation. First, a single stuck-at fault is targeted, and a corresponding locked circuit with one key bit is created. This approach generated one test pattern per fault. We also consider a group of faults and convert the circuit to its locked version with multiple key bits. The inputs obtained from the SAT attack tool are the test set for detecting this group of faults. Our approach finds test patterns for all hard-to-detect faults that were previously undetected in commercial ATPG tools. The proposed test pattern generation approach can efficiently detect redundant faults with ITC'99 benchmarks. The results show that we can detect all the hard-to-detect faults and identify redundant faults, and a 100\% stuck fault coverage is achieved.
Finally, we consider privacy-enhancing solutions to offer additional benefits for securing the digital world. In this dissertation, we develop an efficient, secure, and on-demand communication protocol using zero-knowledge proofs (ZKPs) that allow the prover to provide evidence of its secret without revealing that to the verifier. The edge device, acting as the prover, convinces the central server, the verifier, of the unique PUF response stored inside the device without requiring the actual storage of PUF responses on the server. The non-interactive characteristic of zk-SNARK, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge, offers better optimization to authentication frequency, communication bandwidth between device and server, and protection of device-specific secret, all of which contribute to constructing our proposed device authentication framework
Effect of weather variability in sweet corn production under subtropical environment of the Southeastern U.S.
Get PDFThe main goals of this study were to evaluate the performance of commercial sweet corn cultivars in southeastern U.S. and to identify the effect weather variability on cultivar development; to find best practices for nitrogen (N) management and to identify the effect of weather in N management; lastly, to use the CSM-CERES-Sweetcorn model to analyze sweet corn production under different N fertilizer rates and application timing in different weather scenarios. Field trials for this study were conducted in three locations of the State of Georgia, and two locations of the State of Alabama, in 2020, 2021, and 2022. Heavy rainfall events, unpredictable heat and drought stresses, and frequent high-temperature fluctuation create challenges during crop growing seasons. Results indicated that cultivar performance was rather impacted by season rather location, and yields were higher in the spring compared to fall. Affection, GSS1170, Passion, and SCI336 had best performance for most locations in both season and showed high potential against environmental stresses. Higher total soil N was found in treatments with high N rate; however, it was not translated to yield. Moreover, yield did not show a significant difference among treatments, which may be explained by the same amount of N uptake by the plant in all treatments. Nitrogen use efficiency (NUE) was higher in lower N fertilizer treatments, and it was positively correlated to yield. Therefore, there is no need to increase N fertilization to achieve higher yields, instead, it will increase N leaching and waste. The CSM-CERES-Sweetcorn model was able to simulate sweet corn growth and development under different N fertilizer rates across two years with different weather patterns. However, the model was not sensitive enough to detect differences in the N fertilizer rates applied, which requires further research to improve the model and allow better predictions among the different N fertilizer rates
Advancing Online Hate Speech Detection Using External Features and Large Language Models
No full textSocial media is a concept developed to link people and make the globe smaller. But it has recently developed into a center for hateful posts that target different people and communities. As a result, there are more events of hostile actions and harassing remarks present online. Since this issue can cause immense harm on a person, it needs to be addressed with immense priority.
There are many Natural Language Processing models that have been implemented for hate speech detection. In our study, we begin by using BERT combined with TFIDF representation to tackle the challenges of identifying irony and stereotype-spreading authors on Twitter. For the classification task, we employed a logistic regression classifier. Our findings indicated that the combination of BERT representation with TFIDF yielded very promising results.
To delve deeper into the issue, we addressed sexism, another form of hate speech that predominantly targets women. For sexism detection, we introduced a fine-tuned RoBERTa model. This involved encoding the initial data representation using RoBERTa and implementing three distinct Multilayer Perceptrons (MLPs) for the three sub-tasks. The experimental results showcased the effectiveness of our proposed strategy.
Additionally, we explored the potential benefits of incorporating external features in the detection of sexism and hate speech. Specifically, we examined the impact of user gender information on online sexism detection in both binary and multi-class classification contexts. Given that most sexist comments are directed towards individuals of a particular gender, understanding the role of user gender information is crucial. Our experiments demonstrated that integrating user gender information with textual features enhanced classification performance in both binary and multi-class classifications.
Further advancing our research, we introduced OffensiveLang, a novel community-based implicit offensive language dataset generated by ChatGPT 3.5, covering 38 different target groups. Despite ethical constraints limiting the generation of offensive texts via ChatGPT, we devised a prompt-based approach to effectively generate implicit offensive language. To ensure data quality, we evaluated our dataset through human assessments. Moreover, we employed a prompt-based Zero-Shot method with ChatGPT and compared detection results between human annotations and ChatGPT annotations. We also utilized existing state-of-the-art models to evaluate their effectiveness in detecting such languages and investigated annotator biases in hate speech data annotation using large language models.
Lastly, we investigated gender, race, religion, and disability biases in LLMs used for hate speech detection and proposed mitigation strategies. We demonstrated the presence of these biases in LLMs such as GPT-4o and GPT-3.5 when annotating hate speech data. We then explored the underlying factors that contribute to these biases, providing a thorough analysis of the annotated data and emphasizing the role of subjective interpretations. Finally, we suggested potential solutions to mitigate these biases, highlighting the importance of tailored prompts and fine-tuning LLMs to enhance the fairness and accuracy of annotations
Application of Multispectral Imaging Combined with Data Analytics to Detect Carcass Condemnations during Poultry Processing
Get PDFA concern for consumers when buying food is the safety of their food and the risk of foodborne illnesses due to contaminated products. The poultry industry deals with known foodborne pathogens, Salmonella spp. and Campylobacter spp., that can be found in the gut biome of chicken that are being processed daily. To reduce likelihood contamination of pathogens spreading throughout processing plants, every chicken is visually inspected by USDA-FSIS inspectors or trained processing plant workers when processing whole chicken carcasses. Processing lines move at 140-175 birds per minute making it a difficult to inspect each bird properly for any carcass condemnations such as septicemia-toxemia (sep-tox) and fecal contamination, being a zero tolerance in all food processing plants enforced by USDA-FSIS ruling. With a real-time fluorescent spectral imaging system that can identify carcass condemnations, processing would be easier to target which carcasses will need to be removed for reprocessing or discarded if deemed unacceptable for consumption. With technology advancing daily, improving the inspection system on the processing line is beneficial for plant workers and the safety of consumers’ health. After configuring promising camera parameters with two exposure times for fluorescence of photosensitive cells with camera range to explore the advantages of a spectral imaging system, known as the CSI-D+ system, with the detection of sep-tox birds deemed by trained processing workers and fecal contamination on chicken carcasses. Images produced were used for image analysis for classification against carcasses that were condemned. Sep-tox birds were 100% classified correctly against normal carcasses when fluoresced with LED light. Further analysis revealed variations of sep-tox can be identified with an unsupervised image analysis system. Fecal contamination and digesta were identified from the ceca (46%), colon (39%), proventriculus (62%), and small intestine (59.50%) with small and large applications onto the breast of chicken carcasses with variation of small and large application dots. Additionally, deionized water rinsed fecal contamination carcasses were classified with a 97.80% accuracy against chicken carcasses samples inoculated with Salmonella Typhimurium. Rinsed inoculated samples were swabbed and tested with a Salmonella PCR assay for further analysis. The presence of Salmonella after rinsing was 97.14% for samples that were initially negative before inoculation. These results indicate that spectral imaging can reduce the spread of contamination by decreasing food safety risks at poultry processing plants through providing a resource imaging system for plant workers in comparison to visual inspection
Ammonium Thiosulfate and Potassium Thiosulfate as Potential Nitrification Inhibitors in Soils
No full textNitrogen use efficiency (NUE) can vary according to soil properties, precipitation, and
soil temperature. Nitrification inhibitors show potential to increase NUE by decreasing the
amount of N lost from leaching and denitrification, keeping N in an ammonium form. The
objective of this study was to evaluate the effectiveness of ammonium thiosulfate (ATS) and
potassium thiosulfate (KTS) products to inhibit nitrification in three soil types: Marvyn loamy
sand, Tujunga loamy sand, and Sable silt loam. Two runs of a 10-week incubation experiment
were performed, and soil ammonium-N (NH4+-N), nitrite-N (NO2--N), and nitrate-N (NO3--N)
concentrations were measured to determine the effectiveness of ATS and KTS to inhibit
nitrification in urea-based fertilizers compared to an untreated control, urea, urea + ammonium
sulfate, and urea + dicyandiamide (DCD) treatments. Incubation results showed that the higher
thiosulfate rate of ATS and KTS reduced nitrification compared to urea alone and urea + DCD in
the Tujunga loamy sand from day 21 to day 63 in the first run and from day 28 to day 56 in the
second run. Within the Sable silt loam soil, the higher thiosulfate rate in ATS and KTS were
similar to urea + DCD in reducing nitrification from day 35 to day 56 in the first run, but no
treatment differences were seen in run two. No treatments reduced nitrification compared to the
untreated urea treatment in the Marvyn loamy sand soil type. Trends for NO2--N concentrations
corresponded to trends for NO3--N concentrations in the Tujunga soil, showing the treatments
inhibiting nitrification are most likely inhibiting the first step in the nitrification process.
Greenhouse studies on corn (Zea mays L.) were conducted to determine effects on plant growth
and soil NH4+-N and NO3--N. All fertilizer treatments in all soil types resulted in higher NH4+-N
compared to the control from day 0 to day 7 in run one and from day 0 to day 14 in run two.
Nitrate-N concentrations in run one tended to be higher in all fertilizer treatments than the control in the Marvyn and Tujunga soil from day 0 until day 14, but no differences were seen in
the Sable silt loam. However, treatment did not influence NO3--N concentrations in the second
run. Overall, ATS, KTS and DCD reduced nitrification compared to urea in the Tujunga loamy
sand in both incubation runs and Sable silt loam in one out of two runs for the incubation studies.
However, similar results were not observed in the greenhouse studies. Both ATS and KTS have
potential to inhibit nitrification, but effectiveness of these inhibitors is dependent on soil type and
climatic conditions
Salmonella Bacteriophage-Resistance Genomic Analysis, Computational Approaches of Environmental Data, and Chicken Embryo Lethality Assay
No full textThis dissertation is a coalescence of three individual projects with Salmonella being the only area that the projects have in common. Bacteriophage (phage) treatment for the reduction of multiple drug-resistant Salmonella Newport in dairy calves has been examined in our lab from a clinical disease and food safety perspective. Previously, our lab has examined the emergence of phage-resistant Salmonella as a potential consequence of phage treatment and the affect phage-resistance has on virulence in Salmonella. We generated a spontaneous mutant resistant to 4 of 5 lytic phages used in our treatment regimen. This study examined the mutation that conferred the mutation of phage-resistance in our Salmonella Newport. We also examined a chicken embryo lethality assay as a model for phage therapy against Salmonella and to investigate the virulence of Salmonella strains. In addition, we examined the application of novel statistical methods and whole-genome sequencing when investigating the prevalence of Salmonella in a multi-species animal facility. Regarding the phage-resistant mutation, we found that short-read sequencing alone was not a valid option to locate single-nucleotide polymorphisms (SNPs) that could be attributed to the phage-resistant phenotype. By using short-read and long-read sequencing with a hybrid assembly, we found a SNP in the rfbM gene that could explain a phage-resistant phenotype. Regarding the chicken embryo lethality assay, differences in the survival of embryos were found between different phage isolates. However, we found that this assay should be used cautiously with the understanding that Salmonella’s virulence and effect on survival can be very dramatic. The third project utilized previous studies on environmental surveillance of Salmonella, with an additional year of surveillance to investigate the prevalence of Salmonella with novel statistical methods and whole-genome sequencing. Comparing supervised machine learning algorithms (logistic regression, random forest analysis, and Markov Chain Monte Carlo (MCMC)), we found that these models may be beneficial to epidemiologists investigating widespread environmental Salmonella contamination. All three models found that bovine, summer, and the dairy barns and pastures were variables of importance for our study of environmental Salmonella prevalence. We found three strains of Salmonella Muenster out of ten isolates sequenced, but all isolates appear to be genetically linked and were derived from a common ancestor. Isolates within our strain cluster have less than eight SNPs, with some clusters spanning timelines up to 1493 days. These studies contained methods that have been used before but have not been applied in the areas of research of our interest. These studies are processes of the application of methods of interest to our questions pertaining to Salmonella