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Evaluating the Impact of External Factors on the Stability of the Ethereum Network
No full textThis thesis investigates the resilience of the Ethereum peer-to-peer network against various external factors through empirical analysis. While previous researches have studied different aspects of the then proof-of-work (PoW) blockchain or the impactof external factors on other communication systems, the impact on the proof-of-stake (PoS) Ethereum blockchain remains unexplored. Our study examines three potential influence vectors: cyber threats, geomagnetic activity, and media coverage. Through a comprehensive analysis of head attestations from Ethereum nodes, complemented by honeypot data, geomagnetic measurements, and news sentiment analysis, we compare security patterns between Ethereum and non-Ethereum hosts. Our methodology employs Dynamic Time Warping and Granger Causality Test to detect correlations and anomalies. The results reveal that while Ethereum nodes face similar attack volumes as non-Ethereum hosts, they exhibit distinct patterns in targeted ports and CVEs exploitation attempts. There are also some differences between regions. Notably, our analysis found no significant correlation between the network performance and any of the examined external factors. While this suggests stability in the network’s operation during normal conditions, further research over longer periods and during major events would be needed to make broader conclusions about the network’s resilience. This research contributes to the understanding of PoS blockchain network resilience and is a starting point for further research into the particularities of the Ethereum network against adversaries
Utilizing Observations and Coral Reconstructions to Analyze the Variability of Sea Surface Temperatures in the Intra-Americas Sea
No full textThe Intra-Americas Sea (IAS) includes the Gulf of Mexico and Caribbean, regions relatively understudied in comparison to the larger Atlantic, considering their importance in moisture transport across the United States, Central and South America. The influence of the IAS on hydroclimate in these regions is substantial and has been connected to various processes including tropical storms, the springtime sea surface temperature anomaly dipole and the Atlantic warm pool. However, observational records are not long enough to fully understand variability in this region, particularly on decadal, multi-decadal, and longer timescales and can disagree on signals. A typical springtime dipole is noted by cooler sea surface temperatures in the Gulf of Mexico and warmer in the Caribbean and has been linked to precipitation anomalies in the eastern US and much of the Amazon region. In boreal summer, the Atlantic warm pool is a phenomenon marked by anomalously warm sea surface temperatures, critical for hurricane development. This study uses observations, coral-based reconstructions, and climate model data from the nineteenth to the twenty-first century to examine the variability of the springtime dipole in the IAS and how it impacts the Atlantic warm pool. Coral data was retrieved from CoralHydro2k with the addition of new records near Tobago, Cuba, and Flower Garden Banks. Each record found enhanced warming in the Caribbean compared to the GOM. The reduction of the Loop Current inhibits warm Caribbean SSTs from warming the GOM, a potential cause of this trend difference between regions. Power spectra analysis found significant periods in each region within the periodicities for El Ni\~no Southern Oscillation and Pacific Decadal Oscillation, in agreement with previous studies. During dipole years, in which the average was approximately 4 per decade, warmer sea surface temperature anomalies dominate the IAS in the following summer months through the end of the Atlantic hurricane season. Following a springtime dipole, the Atlantic warm pool increases in extent and intensity when compared to non-dipole years. Non-dipole years display the opposite pattern with cooler sea surface temperature anomalies dominating the IAS in the summer and hurricane season. Climate projections indicate an increase in intensity of weather events such as extreme precipitation, drought as well as enhanced Atlantic hurricane seasons, all of which are particularly impactful to coastal communities lacking adaptive and resilient infrastructure in the Intra-Americas Region. Through the use of observations, models and proxy data, a better understanding of the variability of sea surface temperatures in the IAS will help these regions prepare for a changing climate
THE ROLES OF AD POSITION, AD TYPE, VALUE CO-CREATION, AND THE MODERATING EFFECTS OF ENGAGEMENT & PARASOCIAL RELATIONSHIPS ON PODCAST AD EFFECTIVENESS
No full textFollowing calls in the literature for more academic research on podcast advertising, the present research is among the first to examine the relationship of advertising form (host-read ad vs. conventional/non-host read ad) and serial ad placement (beginning/pre-roll vs. middle/midroll) on ad-related variables and behavioral intentions such as Purchase Intention, Attitude Toward the Ad, Attitude Toward the Brand, and Information Seeking Intention. The study also examines the potential moderating effects of Parasocial Relationships and Engagement, as well as the mediation of Persuasion Knowledge, within these relationships, and investigates precursors to Parasocial Relationship and Persuasion Knowledge perceptions. Experimental data finds support for main effects of mid-roll serial ad placement outperforming pre-roll position on Purchase Intention, Attitude Toward the Ad, and Information Seeking, and an interaction effect such that among those in the pre-roll condition, greater levels of Attitude Toward the Brand were indicated for those in the host-read condition vs. the conventional ad condition. Support was also found for Evaluative Persuasion Knowledge as a mediator between the serial ad placement condition as IV and Purchase Intention, Attitude Toward the Brand, Attitude Toward the Ad, and Info Seeking; Engagement’s moderation of the relationship between the IVs and PI, Abrand, InfoSeek, and EvalPK; and Parasocial Relationship’s moderation of the relationship between the IVs and InfoSeek as well as EvalPK. Further, Parasocial Interaction Behaviors were found to positively predict Parasocial Relationship perceptions, and Dispositional Persuasion Knowledge measures were found to positively predict situational persuasion knowledge. Additionally, we find that Value Co-Creation successfully predicts focal DVs to a greater extent than thepreviously examined moderator concepts, and show support for a serial mediation model beginning with Engagement then flowing through Parasocial Relationship perceptions on to Value Co-Creation, which then flow to the focal dependent variables. Zooming out, we thus also introduce a novel conceptual factor or phenomenon, Parasocial Engagement, derived from both the experimental data as a well as a conceptual reconciliation of the concepts of Consumer Engagement, motivation-based Engagement, and Parasocial Relationships, utilizing Service- Dominant Logic as a unifying conceptual framework and pointing to “the market” as the field in which social exchanges of many, and possibly all, types occur
Downhole Camera Runs Validate the Capability of Machine Learning Models to Accurately Predict Perforation Entry Hole Diameter
Get PDFIn the field of oil and gas well perforation, it is imperative to accurately forecast the
casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry
hole diameter enhances the design of both conventional and limited entry hydraulic fracturing,
mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees
the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation,
and reduces issues related to sand production. Implementing machine learning and deep learning
models yields immediate and precise estimations of entry hole diameter, thereby facilitating the
attainment of these objectives. The principal aim of this research is to develop sophisticated machine
learning-based models proficient in predicting entry hole diameter under full downhole conditions.
Ten machine learning and deep learning models have been developed utilizing readily available
parameters routinely gathered during perforation operations, including perforation depth, rock
density, shot phasing, shot density, fracture gradient, reservoir unconfined compressive strength,
casing elastic limit, casing nominal weight, casing outer diameter, and gun diameter as input variables.
These models are trained by utilizing actual casing entry hole diameter data acquired from deployed
downhole cameras, which serve as the output for the X’ models. A comprehensive dataset from
53 wells has been utilized to meticulously develop and fine-tune various machine learning algorithms.
These include Gradient Boosting, Linear Regression, Stochastic Gradient Descent, AdaBoost, Decision
Trees, Random Forest, K-Nearest Neighbor, neural network, and Support Vector Machines. The
results of the most effective machine learning models, specifically Gradient Boosting, Random Forest,
AdaBoost, neural network (L-BFGS), and neural network (Adam), reveal exceptionally low values of
mean absolute percent error (MAPE), root mean square error (RMSE), and mean squared error (MSE)
in comparison to actual measurements of entry hole diameter. The recorded MAPE values are 4.6%,
4.4%, 4.7%, 4.9%, and 6.3%, with corresponding RMSE values of 0.057, 0.057, 0.058, 0.065, and 0.089,
and MSE values of 0.003, 0.003, 0.003, 0.004, and 0.008, respectively. These low MAPE, RMSE, and
MSE values verify the remarkably high accuracy of the generated models. This paper offers novel
insights by demonstrating the improvements achieved in ongoing perforation operations through the
application of a machine learning model for predicting entry hole diameter. The utilization of machine
learning models presents a more accurate, expedient, real-time, and economically viable alternative
to empirical models and deployed downhole cameras. Additionally, these machine learning models
excel in accommodating a broad spectrum of guns, well completions, and reservoir parameters, a
challenge that a singular empirical model struggled to address.Ye
Evaluation of the M-Vac® system for DNA collection from bleach-treated blood stains: analyzing the impact of Bluestar, membrane type, and filtration
Get PDFThe purpose of this experiment is to evaluate the performance of the M-Vac wet vacuum system for deoxyribonucleic acid (DNA) collection from blood-stained substrates that have been treated with bleach, a common cleaning reagent used in forensic investigations. Five variables were explored: blood concentration, bleach concentration, the presence or absence of BlueStar forensic latent bloodstain reagent, the type of membrane filter used – polyethersulfone (PES) and cellulose nitrate (CN), and the filtration product used for DNA extraction and isolation – the filter membrane itself and the filtrate. Carpet substrates were stained with single donor, whole human blood prior to treatment with bleach, which simulated cleaning the bloodstain. Appropriate samples were treated with BlueStar forensic reagent then all samples were collected with the M-Vac. The collected materials were passed over a filter with either PES or CN membrane. DNA from both the filter membranes and the filtrate were extracted, isolated, and quantified. Samples were performed in triplicate and the results, in terms of DNA quantity, underwent statistical analysis. Results indicated that the M-Vac system was capable of collecting quantifiable DNA evidence from bloodstains even after treatment with bleach. Key variances in DNA concentration between PES and CN membranes, presence and absence of BlueStar, and filters and filtrate were identified. Results demonstrated variations in DNA yield and integrity across these parameters. DNA was recovered in samples from each category of blood and bleach dilution, despite the presence of bleach. In general, BlueStar absent, CN membrane, and filter variables were found to contain higher quantities of DNA overall than their respective counterparts – BlueStar present, PES membrane, and filtrate. However, it was found that BlueStar reagent and the membrane types may have interacted with bleach or DNA extraction and quantification reagents that interfere with DNA yield. Further research is required to determine the effect these interactions may have on forensic DNA analysis with regard to quantity and quality for compromised or degraded biological samples
Daphne du Maurier's Rebecca: a cautionary tale against villainous women
Get PDFDaphne du Maurier’s novel Rebecca offers an unsettling examination of how Gothic literature is utilized to portray the female struggle for identity within a patriarchal society. Published in the early twentieth century, the novel focuses on a period of immense change in British society. Challenges to traditional gender roles and sexuality begin to arise with the emergence of the feminist and gay and lesbian movements. Daphne du Maurier explores the anxieties surrounding these social changes. The novel can be analyzed through feminist, queer, and psychoanalytical lenses to gain a greater understanding of du Maurier’s response to the changing roles of women and how they threaten the patriarchal structure. A close examination of the novel’s three female characters reveals that they each represent some facet of the social movements that du Maurier utilizes to villainize them. Rebecca, an independent woman with an ambiguous sexual appetite, refuses to follow the restrictive guidelines set out by Maxim de Winter, and by extension, the patriarchy. While Rebecca’s challenges to patriarchy are partially contained by her death, she is not erased; Mrs. Danvers ensures that Rebecca’s memory lives on in Manderley. Mrs. Danvers exhibits her own embodiments of resistance; her sexuality is in question, her appearance is bizarre, and her interests are obsessive. These characterizations are, of course, filtered through the narrator; she herself partly casts these women as villains. The narrator, then, seems to be the least villainous—and yet she displays behavior that is questionable and could be described as scandalous. She often lies to achieve an elevated role in society, and she is willing to support a murderer to maintain it. Instead of celebrating the challenges these women pose to the heteronormative patriarchy, Daphne du Maurier is providing a warning that, from her perspective, should these social movements succeed, they will bring destruction to society
Ambivalent sexism and the impact of victim attractiveness on believability
Get PDFEvidence suggests that one in five college women will be sexually victimized during their time in higher education. In spite of this prevalence, it is still one of the most underreported crimes. One major factor that prevents victims from coming forward about their experiences is the fear of not being believed. Several different factors may contribute to people's likelihood of believing accounts of sexual assault. Personal perception, rape myth acceptance, and physical appearance can all contribute to believing victims. The presence of ambivalent sexism can also play a role in this. Ambivalent sexism is made up of two separate constructs: benevolence and hostility. The current research investigated the impact of victims' attractiveness on the believability of their sexual assault accounts when moderated by ambivalent sexism. Based on the halo effect, I hypothesized that a more attractive victim may be believed more than a less attractive victim (H1a). Based on victim blaming, I hypothesized that a more attractive victim may be believed less than a less attractive victim (H1b). Additionally, I hypothesized that those with high levels of hostile sexism would be more inclined to believe a more attractive victim than the less attractive victim (H2a), and those with high levels of benevolent sexism would be more likely to believe the less attractive victim compared to the more attractive victim (H2b). Participants (N = 177) read a mock sexual assault testimony paired with a confederate photograph of the supposed victim. These photographs were normed based on perceived attractiveness from the Chicago Face Database. The photographs with the highest and lowest ratings were used for the more and less attractive victim conditions, respectively. Participants then completed a believability questionnaire, the Ambivalent Sexism Inventory, and some demographic information. The entire study was completed online through Qualtrics. Hypotheses 1a and 1b were tested through an independent samples t-test; the results were not significant. Hypotheses 2a and 2b were tested using simple slope analysis. Hypothesis 2a was not supported statistically. The test for Hypothesis 2b reached marginal significance, but in the opposite direction as predicted. Those high in benevolent sexism were more likely to believe the more attractive confederate victim compared to the less attractive victim. These results could hold implications primarily for case solvability and jury selection. Limitations included a possible ceiling effect, limiting the ability to find a statistically significant effect. The population also skewed young with an average age of 25. Future research should include measures for rape myth acceptance, feminism, religiosity, and political affiliation
Carbon Emission Underreporting: Evidence from Satellite Emission Data
No full textI compare satellite emissions data to company reports filed with the U.S. Environmental Protection Agency (EPA) to identify firms that underreport their carbon emissions. I find that firms are more likely to underreport their emissions to the EPA when they are more publicly visible, face greater shareholder pressure for corporate greening, and are subject to cap-and-trade programs. Firms are less likely to underreport their emissions when they have greater monitoring from the board of directors and are more likely to be affected by environmental disclosure mandates. Next, I examine the relation between firms’ emission intensity and characteristics of their environmental disclosures in 10-Ks. As expected, I find that firms’ emission intensity relates positively to both the number of environmental keywords and optimistic tone used in disclosures. However, these relations weaken for firms that underreport their emissions. The results are consistent with firms attempting to hide their underreporting of emissions and avoid litigation risks. Overall, the results suggest that investors and other stakeholders should be cautious when using self-reported environmental information, and that regulators’ concern about existing climate disclosures in annual reports (10-Ks) not adequately reflecting actual operations is warranted
Teaching about the role of aesthetics in science through a short story based on the history of the concept of energy
Get PDFThe nature of science or, briefly, what science is and how it works, is an essential component of science education. However, it is not typically well understood by neither teachers nor students in K-12 settings. In particular, the complex role of aesthetics, i.e., of experiences of beauty, emotions, and taste, on how scientists engage with the practices and products of science is often misunderstood as being non-existent, or at least ideally so. This misconception has a negative effect on many students’ attitudes towards science, which, in turn, can negatively affect their learning of science.
In response to these concerns, the present study addresses the development and implementation, as well as the assessment of the effects, of an intervention that aimed at improving pre-service elementary teachers’ attitudes towards science by fostering an enhanced understanding of the role of aesthetics in science. The intervention was based on a short story concerned with episodes from the historical development of the concept of energy. It highlights ideas about the role of aesthetics in science through the events narrated in the story and through explicit statements and questions that foster personal reflection about these ideas. The discussion of this story formed the basis of an activity that was performed as part of one lesson in an introductory science methods course.
To assess the effects of the intervention on the participants’ attitudes towards science and understanding of the role of aesthetics in science, survey data was collected before and after the intervention and analyzed using a mixed-methods approach. Additionally, a small number of confirmatory follow-up interviews were performed and used to assess the accuracy of the analysis. The results show a positive effect of the intervention on the attitudes towards science of all the pre-service teachers that participated in the study. Moreover, the results also show a positive, though moderate, improvement in their understanding of the role of aesthetics in science. However, the improvement was not uniform among the three elements of aesthetics, being greatest for the role of emotions and smallest for the role of taste
Cannibalistic Society: Studying social behavior and learning under high social risk
Get PDFRisks of sociality, including competition and conspecific aggression, are particularly pronounced in venomous invertebrates such as arachnids. Spiders show a wide range of sociality, with differing levels of cannibalism and other types of social aggression. To have the greatest chance of surviving interactions with conspecifics, spiders must learn to assess and respond to risk. One of the major ways risk assessment is studied in spiders is via venom metering, in which spiders choose how much venom to use based on prey and predator characteristics. While venom metering in response to prey acquisition and predator defense is well-studied, less is known about its use in conspecific interactions. In Chapter 1, I discuss that due to the wide range of both sociality and venom found in spiders, they are poised to be an excellent system for testing questions regarding whether and how venom use relates to the evolution of social behavior and, in return, whether social behavior influences venom use and evolution. I focus primarily on the widow spiders, Latrodectus, as a strong model for testing these hypotheses, and my hypotheses in Chapter 2. In Chapter 2, I test for the presence of aversion learning, a risk-management tool, in Latrodectus mactans juveniles. Although no aversion learning was evident from the results, there were potential signs of other, nonassociative learning types. The results indicate that aversion learning is not utilized by juvenile L. mactans to reduce the risk of cannibalism in the social environment, leaving the question of how L. mactans maintains sociality while mitigating risk open. Given that successful responses to risk are vital for maintaining sociality, comparative analysis of spider taxa in which venom metering and sociality vary can provide valuable insights into the evolution and maintenance of social behavior under risk