Recent accounting research has indicated that not only do auditors form relational bonds with their clients, but they also tend to acquiesce to their client’s perspective because of that bond. As a result, professional skepticism is often compromised. Accounting research has suggested auditor rotation as a potential solution with mixed results. This may be explained by psychology research showing how quickly bonds can form. Using Social Identity Theory, I predict and find that increasing accountability as operationalized by increasing the salience of any potential client bond before the auditor makes an audit judgment can mitigate the impact of this bond on the auditor’s likelihood to acquiesce to the client’s point of view. This accountability mitigation could be implemented in practice regardless of auditor tenure or auditor rotation. This research provides an intervention that helps to maintain auditor independence while being both cost-effective and practical as it does not require the auditor to discontinue working at their client’s office.

No description available.

Introduction: This thesis hypothesized that synchronous feedback which is supportive in nature would have a positive impact on the learning and transfer of the skills of visual and auditory cue identification in osteopathic diagnostic procedures. It was believed that the positive impact of supportive feedback would be evident through accurate identification of both visual and auditory cues. The categories of cues were visually identifiable asymmetrical motion, visual or auditory expressions of pain, and visually identifiable cues of a possible soft tissue tear or motor nerve issue. Methods: All participants received the same video-based learning resource which was optimized for content (what/how) and cognition (why) followed by the same instructions for the practice phase. During the practice phase all participants were shown a video of a previously learned diagnostic procedure which contained visual and auditory cues. Between videos, participants were all asked the same questions in the same order. In the Supportive Feedback with Specific Content (SC) group participants would receive supportive comments regardless of accuracy of answers and, if they had identified an appropriate physical phenomenon but ascribed it to an incorrect category, they would receive feedback to correct the categorization error. The Supportive Feedback (S) group would receive supportive comments regardless of accuracy of answers but no feedback in relation to categorization errors. The No Feedback (NF) group would receive no supportive comments or feedback in relation to categorization errors. Responses were coded as accurate detection of cues, or categorization errors (detection of cues that were not there, or incorrect categorization of cues). Results: All groups performed similarly with respect to accurate identification of auditory and visual cues such that there was no identifiable impact in relation to group condition during both the practice and transfer phases. The SC group did commit less categorization errors (11.43%) when compared to the S (28.21%) and NF (31.43%) groups. Conclusion: The experimental findings supported the hypothesis that supportive feedback enhanced learning outcomes. While not demonstrated through accuracy of cue identification, this was demonstrated through a reduction in cue categorization errors. An additional hypothesis generated from the results of this thesis is that educational designs that allow for the commission of errors by learners followed by correction in the form of direct feedback or group lecture may predict faster attainment of expertise as noted in the reduction of errors. / Thesis / Master of Science (MSc) / Synchronous feedback has the ability to aid learning. It was hypothesized in this thesis that synchronous feedback that was supportive in nature would improve learning and transfer for learning the skill of visual and auditory cue identification in osteopathic diagnostic procedures. All participants received the same initial learning material, the same instructions for the practice phase of the experiment, and the same videos of a previously learned diagnostic procedure that they identified visual and diagnostic cues from. During the practice phase the three groups were the Supportive Feedback with Specific Content (SC), Supportive Feedback (S), and No Feedback (NF). The differences between groups were evident between diagnostic videos subsequent to the collection of answers for identified cues. The material differences were the delivery of supportive comments regardless of accuracy of answers (SC and S groups), the delivery of specific feedback when accurate cues were identified but placed in the wrong category (SC group only), or the absence of any commentary (NF group). All groups identified cues at similar levels such that the supportiveness of feedback showed no impact on performance. There was a notable difference between groups in relation to the commission of categorization errors where the SC group made less categorization errors with the S group and NF group committing errors at similar rates. The primary benefit of synchronous feedback in this experiment is that the instructor is able to identify errors and provide insight for correction.

No description available.

Background: With the rise of digitalization, there has been an increasing needfor secure and effective identification solutions, particularly in the realm of votingsystems. Facial biometric technology has emerged as a potential solution to combat fraud and improve the transparency and security of the voting process. Two well known facial identification algorithms, Local Binary Pattern Histograms (LBPH) and Eigenfaces, have been extensively used in computer vision for facial identification.However, their effectiveness in the context of a smart voting system is still a matter of debate. Objectives: The aim of this project is to compare the effectiveness of LBPH and Eigenfaces algorithms in the development of a smart voting system using the Haar cascade for face detection. The objective is to identify the more suitable approach between the two algorithms, considering factors such as lighting conditions and the facial expressions of the individuals being identified. The goal is to evaluate the algorithms using various metrics such as accuracy, precision, recall, and F1 score. Methods: The project involves the comparison of facial identification algorithms using the Haar cascade for face detection. Both the LBPH and Eigenfaces algorithms are implemented and evaluated in a complex environment that is similar to a polling station. The algorithms are trained and tested using a dataset of facial images with varying lighting conditions and facial expressions. The evaluation metrics, including accuracy, precision, recall, and F1 score, are used to compare the performance of thetwo algorithms. Results: The results of the project indicate that the LBPH algorithm performs better than Eigenfaces in terms of accuracy and performance. The algorithms havebeen tested with faces and objects in low-light conditions. Their accuracy and performance are also measured. Conclusions: The comparison of LBPH and Eigenfaces algorithms using the Haarcascade for face detection reveals that LBPH is a more suitable approach. The comparison of facial identification-based algorithms can significantly contribute to the voting process, thereby ensuring integrity of the voting process. The findings of this project can contribute to the development of a more reliable and secure voting system, and the evaluation metrics used in this project can be applied to future research in the field of facial identification purposes.

This research uncovers a novel pathway in precision agriculture, emphasizing the utilization of advanced supervised and self-supervised deep learning approaches for an innovative solution to weed detection and crop yield prediction. The study focuses on key weed species: Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, which are troublesome weeds in the United States. One of the most innovative components of this research is the debut of a self-supervised learning approach specifically tailored for soybean yield prediction using only unlabeled RGB images. This novel strategy presents a departure from traditional yield prediction methods that consider multiple variables, thus offering a more streamlined and efficient methodology that presents a significant contribution to the field. To address the monitoring of Italian ryegrass in wheat cultivation, a bespoke Convolutional Neural Network (CNN) model was developed. It demonstrated impressive precision and recall rates of 100% and 97.5% respectively, in accurately classifying Italian ryegrass in the wheat. Among three hyperparameter tuning methods, Bayesian optimization emerges as the most efficient, delivering optimal results in just 10 iterations, contrasting with 723 and 304 iterations required for grid search and random search respectively. Further, this study examines the performance of various classification and object detection algorithms on Unmanned Aerial Systems (UAS)-acquired images at different growth stages of soybean and Palmer amaranth. Both the Vision Transformer and EfficientNetB0 models display promising test accuracies of 97.69% and 93.26% respectively. However, considering a balance between speed and accuracy, YOLOv6s emerged as the most suitable object detection model for real-time deployment, achieving an 82.6% mean average precision (mAP) at an average inference speed of 8.28 milliseconds. Furthermore, a self-supervised contrastive learning approach was introduced for automating the labeling of Palmer amaranth and soybean. This method achieved a notable 98.5% test accuracy, indicating the potential for cost-efficient data acquisition and labeling to advance precision agriculture research. A separate study was conducted to detect common ragweed in soybean crops and the prediction of soybean yield impacted by varying weed densities. The Vision Transformer and MLP-Mixer models achieve test accuracies of 97.95% and 96.92% for weed detection, with YOLOv6 outperforming YOLOv5, attaining an mAP of 81.5% at an average inference speed of 7.05 milliseconds. Self-supervised learning-based yield prediction models reach a coefficient of determination of up to 0.80 and a correlation coefficient of 0.88 between predicted and actual yield. In conclusion, this research elucidates the transformative potential of self-supervised and supervised deep learning techniques in revolutionizing weed detection and crop yield prediction practices. Its findings significantly contribute to precision agriculture, paving the way for efficient and cost-effective site-specific weed management strategies. This, in turn, promotes reduced environmental impact and enhances the economic sustainability of farming operations. / Master of Science in Life Sciences / This novel research provides a fresh approach to overcoming some of the biggest challenges in modern agriculture by leveraging the power of advanced artificial intelligence (AI) techniques. The study targets key disruptive weed species, such as, Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, all of which have the potential to significantly reduce crop yields. The studies were first conducted to detect Italian ryegrass in wheat crops, utilizing RGB images. A model is built using a complex AI system called a Convolutional Neural Network (CNN) to detect this weed with remarkable accuracy. The study then delves into the use of drones to take pictures of different growth stages of soybean and Palmer amaranth plants. These images were then analyzed by various AI models to assess their ability to accurately identify the plants. The results show some promising findings, with one model being quick and accurate enough to be potentially used in real-time applications. The most important part of this research is the application of self-supervised learning, which learns to label Palmer amaranth and soybean plants on its own. This novel method achieved impressive test accuracy, suggesting a future where data collection and labeling could be done more cost-effectively. In another related study, we detected common ragweed in soybean crops and predicted soybean yield based on various weed densities. AI models once again performed well for weed detection and yield prediction tasks, with self-supervised models showcasing high agreement between predicted and actual yields. In conclusion, this research showcases the exciting potential of self-teaching and supervised AI in transforming the way we detect weeds and predict crop yields. These findings could potentially lead to more efficient and cost-effective ways of managing weeds at specific sites. This could have a positive impact on the environment and improve the economic sustainability of farming operations, paving the way for a greener future.

No description available.

Most research investigating vocal expressions of emotion has focused on one or more of three questions: whether there exist unique acoustic profiles of individual encoded emotions, whether the nature of emotion expression is universal across cultures, and how accurately decoders can identify expressed emotions. This dissertation begins to answer a fourth question, whether there exist unique patterns in the types of acoustic properties persons focus on to identify vocalized emotions. Three hypotheses were tested: first, whether acoustic patterns are interpreted idiographically or nomothetically as reflected in a comparison of individual vs. group lens model identification ratios; second, whether there exists a decoder by emotion interaction for scores of accuracy; and third, whether such an interaction is mediated by the acoustic properties of the vocalized emotions. Results from hypothesis one indicate there is no difference between individual and group identification ratios, demonstrating that vocalized emotions are decoded nomothetically. Results from hypothesis two indicate there is not a significant decoder by emotion interaction on scores of accuracy, demonstrating that decoders who are generally good (or bad) at identifying some vocalized emotions tend to be generally good (or bad) at identifying all vocalized emotions. There are, however, significant main effects for both emotion and decoder. Anger and happiness are more accurately decoded than fear and sadness. Perhaps most importantly, multivariate results from hypothesis three indicate strong and consistent differences across the four emotions in the way they are identified acoustically. Specifically, decoders identify anger by primarily focusing on spectral characteristics, fear by primarily focusing on frequency (F0), happiness by primarily focusing on rate, and sadness by focusing on both intensity and rate. These acoustic mediation differences across the emotions are also shown to be nomothetic, that is, they are surprisingly consistent across decoders.

In theory, RNA expression patterns, including the presence and relative abundance of particular RNA species, provide cell and tissue specific information that could be of use to forensic scientists. An mRNA based approach could allow the facile identification of the tissue components present in a body fluid stain and conceivably could supplant the battery of serological and biochemical tests currently employed in the forensic serology laboratory. Some of the potential advantages include greater test specificity, and the ability to perform simultaneous analysis using a common assay format for the presence of all body fluids of forensic interest. In this report, the recovery and stability of RNA in forensic samples was evaluated by conducting an in-depth study on the persistence of RNA in biological stains. Stains were prepared from blood, saliva, semen, and vaginal secretions, and were exposed to a range of environmental conditions so that the affects of different light sources, temperatures, and environments could be assessed. Using the results from quantitation and sensitivity studies performed with pristine forensic stains, the RNA stability of samples which were collected over a period of 1 day to 1 year for blood, saliva, and vaginal secretion stains and for up to 6 months for semen stains were analyzed. The extent of RNA degradation within each type of body fluid stain was determined using quantitation of total RNA and reverse transcriptase polymerase chain reaction (RT-PCR) with selected housekeeping and tissue-specific genes. The results show that RNA can be recovered from biological stains in sufficient quantity and quality for mRNA analysis. The results also show that mRNA is detectable in samples stored at room temperature for at least one year, but that heat and humidity appear to be very detrimental to the stability of RNA.

Developing diagnostic modalities that utilize nanomaterials and miniaturized detectors can have an impact in point-of-care diagnostics. Diagnostic systems that (i) are sensitive, robust, and portable, (ii) allow detection in clinical samples, (iii) require minimal sample preparation yielding results quickly, and (iv) can simultaneously quantify multiple targets, would have a great potential in biomedical research and public healthcare. Bacterial infections still cause pathogenesis throughout the world (Chapter I). The emergence of multi-drug resistant strains, the potential appearance of bacterial pandemics, the increased occurrence of bacterial nosocomial infections, the wide-scale food poisoning incidents and the use of bacteria in biowarfare highlight the need for designing novel bacterial-sensing modalities. Among the most prominent disease-causing bacteria are strains of Escherichia coli, like the E. coli O157:H7 that produces the Shiga-like toxin (Stx). Apart from diarrheagenic E. coli strains, others cause disease varying from hemolytic uremic syndrome and urinary tract infections to septicemia and meningitis. Therefore, the detection of E. coli needs to be performed fast and reliably in diverse environmental and clinical samples. Similarly, Mycobacterium avium spp. paratuberculosis (MAP), a fastidious microorganism that causes Johne's disease in cattle and has been implicated in Crohn's disease (CD) etiology, is found in products from infected animals and clinical samples from CD patients, making MAP an excellent proof-of-principle model. Recently, magnetic relaxation nanosensors (MRnS) provided the first applications of improved diagnostics with high sensitivity and specificity. Nucleic acids, proteins, viruses and enzymatic activity were probed, yet neither large targets (for instance iv bacterial and mammalian cells) nor multiple bacterial disease parameters have been simultaneously monitored, in order to provide thorough information for clinical decision making. Therefore, the goal of this study was to utilize MRnS for the sensitive identification of multiple targets associated with bacterial pathogenesis, while monitoring virulence factors at the microorganism, nucleic acid and virulence factor levels, to facilitate improved diagnosis and optimal treatment regimes. To demonstrate the versatility of MRnS, we used MAP as our model system, as well as several other pathogens and eukaryotic cell lines. In initial studies, we developed MRnS suitable for biomedical applications (Chapter II). The resulting MRnS were composed of an iron oxide core, which was caged within a biodegradable polymeric coating that could be further functionalized for the attachment of molecular probes. We demonstrated that depending on the polymer used the physical and chemical properties of the MRnS can be tailored. Furthermore, we investigated the role of polymer in the enzyme-mimicking activity of MRnS, which may lead to the development of optimized colorimetric in vitro diagnostic systems such as immunoassays and small-molecule-based screening platforms. Additionally, via facile conjugation chemistries, we prepared bacterium-specific MRnS for the detection of nucleic acid signatures (Chapter III). Considering that MAP DNA can be detected in clinical samples and isolates from CD patients via laborious isolation and amplification procedures requiring several days, MRnS detected MAP's IS900 nucleic acid marker up to a single MAP genome copy detection within 30 minutes. Furthermore, these MRnS achieved equally fast IS900 detection even in crude DNA extracts, outperforming the gold standard diagnostic method of nested Polymerase Chain v Reaction (nPCR). Likewise, the MRnS detected IS900 with unprecedented sensitivity and specificity in clinical isolates obtained from blood and biopsies of CD patients, indicating the clinical utility of these nanosensors. Subsequently, we designed MRnS for the detection of MAP via surface-marker recognition in complex matrices (Chapter III). Milk and blood samples containing various concentrations of MAP were screened and quantified without any processing via MRnS, obtaining dynamic concentration-dependent curves within an hour. The MAP MRnS were able not only to identify their target in the presence of interferences from other Gram positive and Gram negative bacteria, but could differentiate MAP among other mycobacteria including Mycobacterium tuberculosis. In addition, detection of MAP was performed in clinical isolates from CD patients and homogenized tissues from Johne's disease cattle, demonstrating for the first time the rapid identification of bacteria in produce, as well as clinical and environmental samples. However, comparing the unique MAP quantification patterns with literatureavailable trends of other targets, we were prompted to elucidate the underlying mechanism of this novel behavior (Chapter IV). We hypothesized that the nanoparticle valency – the amount of probe on the surface of the MRnS – may have modulated the changes in the relaxation times (ΔΤ2) upon MRnS – target association. To address this, we prepared MAP MRnS with high and low anti-MAP antibody levels using the same nanoparticle formulation. Results corroborated our hypothesis, but to further bolster it we investigated if this behavior is target-size-independent. Hence utilizing small-moleculeand antibody-carrying MRnS, we detected cancer cells in blood, observing similar detection patterns that resembled those of the bacterial studies. Notably, a single cancer vi cell was identified via high-valency small-molecule MRnS, having grave importance in cancer diagnostics because a single cancer cell progenitor in circulation can effectively initiate the metastatic process. Apart from cells, we also detected the Cholera Toxin B subunit with valencly-engineered MRnS, observing similar to the cellular targets' diagnostic profiling behavior. Finally, as bacterial drug resistance is of grave healthcare importance, we utilized MRnS for the assessment of bacterial metabolism and drug susceptibility (Chapter V). Contrary to spectophotometric and visual nanosensors, their magnetic counterparts were able to quickly assess bacterial carbohydrate uptake and sensitivity to antibiotics even in blood. Two MRnS-based assay formats were devised relying on either the Concanavalin A (Con A)-induced clustering of polysaccharide-coated nanoparticles or the association between free carbohydrates and Con A-carrying MRnS. Overall, taking together these results, as well as those on pathogen detection and the recent instrumentation advancements, the use of MRnS in the clinic, the lab and the field should be anticipated.