Miami University
  • Oxford, Ohio, United States
Recent publications
A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy.
Agricultural and prescribed burning activities emit large amounts of trace gases and aerosols on regional to global scales. We present a compilation of emission factors (EFs) and emission ratios (ERs) from the eastern portion of the Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) campaign in 2019 in the United States, which sampled burning of crop residues and other prescribed fire fuels. FIREX‐AQ provided comprehensive chemical characterization of 53 crop residue and 22 prescribed fires. Crop residues burned at different modified combustion efficiencies (MCE), with corn residue burning at higher MCE than other fuel types. Prescribed fires burned at lower MCE (<0.90) which is typical, while grasslands burned at lower MCE (0.90) than normally observed due to moist, green, growing season fuels. Most non‐methane volatile organic compounds (NMVOCs) were significantly anticorrelated with MCE except for ethanol and NMVOCs that were measured with less certainty. We identified 23 species where crop residue fires differed by more than 50% from prescribed fires at the same MCE. Crop residue EFs were greater for species related to agricultural chemical use and fuel composition as well as oxygenated NMVOCs possibly due to the presence of metals such as potassium. Prescribed EFs were greater for monoterpenes (5×). FIREX‐AQ crop residue average EFs generally agreed with the previous agricultural fire study in the US but had large disagreements with global compilations. FIREX‐AQ observations show the importance of regionally‐specific and fuel‐specific EFs as first steps to reduce uncertainty in modeling the air quality impacts of fire emissions.
This volume contains 30 chapters that provide an up-to-date account of key topics and areas of research in political psychology. In general, the chapters apply what is known about human psychology to the study of politics. Chapters draw on theory and research on biopsychology, neuroscience, personality, psychopathology, evolutionary psychology, social psychology, developmental psychology, cognitive psychology, and intergroup relations. Some chapters address the political psychology of political elites—their personality, motives, beliefs, and leadership styles, and their judgments, decisions, and actions in domestic policy, foreign policy, international conflict, and conflict resolution. Other chapters deal with the dynamics of mass political behavior: voting, collective action, the influence of political communications, political socialization and civic education, group-based political behavior, social justice, and the political incorporation of immigrants. Research discussed in the volume is fueled by a mix of age-old questions and recent world events.
Recent work has demonstrated that temporary crosslinks in polymer networks generated by chemical “fuels” afford materials with large, transient changes in their mechanical properties. This can be accomplished in carboxylic-acid-functionalized polymer hydrogels using carbodiimides, which generate anhydrides with lifetimes on the order of minutes to hours. Here, the impact of the polymer architecture on the mechanical properties of materials was explored. Single networks (SNs) were compared to interpenetrated networks (IPNs). Notably, semi-IPN precursors that give IPNs on treatment with the carbodiimide gave much higher fracture energies (i.e., resistance to fracture) and superior resistance to compressive strain compared to other network structures. A precursor semi-IPN material featuring acrylic acid in only the free polymer chains yields, on treatment with carbodiimide, an IPN with a fracture energy of 2400 J/m2, a fourfold increase compared to an analogous semi-IPN precursor that yields a SN. This resistance to fracture enables the formation of macroscopic complex cut patterns, even at high strain, underscoring the pivotal role of polymer architecture in mechanical performance.
With more and more data related to driving, traffic, and road conditions becoming available, there has been renewed interest in predictive modeling of traffic incident risk and corresponding risk factors. New machine learning approaches in particular have recently been proposed, with the goal of forecasting the occurrence of either actual incidents or their surrogates, or estimating driving risk over specific time intervals, road segments, or both. At the same time, as evidenced by our review, prescriptive modeling literature (e.g., routing or truck scheduling) has yet to capitalize on these advancements. Indeed, research into risk-aware modeling for driving is almost entirely focused on hazardous materials transportation (with a very distinct risk profile) and frequently assumes a fixed incident risk per mile driven. We propose a framework for developing data-driven prescriptive optimization models with risk criteria for traditional trucking applications. This approach is combined with a recently developed machine learning model to predict driving risk over a medium-term time horizon (the next 20 min to an hour of driving), resulting in a biobjective shortest path problem. We further propose a solution approach based on the k-shortest path algorithm and illustrate how this can be employed.
Starting from 2016, big data is widely used. The world has also entered the era of big data. Internet finance is also a topic that has received a lot of attention in recent years. Together with the arrival of COVID, Internet finance is developing rapidly. Internet finance will become more of a big trend in the future. People primarily use third-party payment, online lending, direct fund sales, crowdfunding, online insurance, banking, and other similar services. At the same time, Internet finance also brings more new opportunities to the financial sector, but the new opportunities and innovations also bring some risk factors. Starting from the current situation of Internet and big data, the article finds that how to control financial risk and credit risk is a problem that needs to be addressed vigorously nowadays. Developing a good personal credit system is also a practical action that needs to be put in place. Through SWOT analysis and questionnaire, we analyze the public perception of the Internet and the credit risks in the development of Internet financial innovation, from which we can provide solutions to improve the regulatory power, thus promoting the safe and efficient development of Internet finance. In addition, through literature view, we analyze how to enhance people's privacy and the credibility of Internet finance.
This paper analyzes the case study of the worlds largest nickel producer: Tsingshan Hold-ing Groups $8 Billion trading loss. We examine Tsingshans trading position before and after the surge in nickel prices to better understand the rationale behind their decision to short 200,000 tons of nickel futures mostly on the London Metal Exchange. Moreover, this paper outlines the impact of Tsingshans trading loss on their various stakeholders. For in-stance, the rise of almost 250% in nickel futures resulted in the LME suspending all nickel trading on March 8th 2022 meanwhile creating a large liquidity crisis for the LME. On the other hand, Tsingshans broker China Construction Bank Corp and their largest counterpar-ty JP Morgan Chase provided large sums of loan packages so that Tsingshan could avoid defaulting on meeting their margin calls. We concluded that the case study revealed vari-ous underlying issues. One was the failure of LMEs ability to regulate OTC trading, and the second was Tsingshans poor liquidity management which put them in a vulnerable po-sition.
Social media, an essential part of social connection, is becoming an inevitable global communication tool. The popularity and availability of mobile phones have further fueled the importance of social media. Social media marketing activities carried out by enterprises helps to attract more comprehensive customers and influence customers' purchase behavior. However, with the increasing investment of significant brands in marketing activities on social media platforms, marketing on social media is becoming increasingly competitive in building consumers' awareness about a particular product, customers' purchase behavior, and purchase decisions. Marketing research has focused on using social media to motivate consumers' purchase intentions and maintain consumer loyalty. The previous studies have shed some light on the significant impacts of social media on consumers' behaviors. However, few focus on consumers' purchase intentions and decisions. Through a literature review, this paper studies how consumer behavior changes due to social media. This paper explores how social media can ultimately market consumer behavior by influencing consumer psychology, attitude, and internal motivation for consumption through a literature review. Hopefully, this paper also can provide some ideas for enterprises to better their strategy formulation, optimize marketing plans, and improve brand benefits and corporate earnings in the increasingly fierce social media marketing competition.
This dissertation examines U.S. inflation and unemployment data from January 1, 1990, to June 1, 2022, and pandemic death data from January 27, 2020 to June 1, 2022, the impact of the pandemic on society is studied. Studying this case clarifies the relationship between inflation and unemployment, and the impact of pandemic deaths on inflation and unemployment. Data have been collected from Fred, County of Santa Clara Open Data Portal, U.S. Bureau of Labor Statistics. This dissertation explains why, during a pandemic, wage increases fail to attract workers, what happens when a labor glut doesn't drive down prices, and how to fix the economic problems caused by covid.
We present a complete constructive proof of the classical mountain-climbing theorem for two continuous piecewise monotone profiles. The proof strengthens the classical result, fills gaps omitted by previous proofs, provides information relevant to computational complexity, and illuminates how special properties of the separate profiles may be reflected in the functions that coordinate the movements of the two climbers.
In India and other low-and-middle-income countries, little is known about how intersectional stigma affects MSM engagement in ART. Informed by the Health Stigma and Discrimination Framework, we qualitatively examined how multiple stigmas influence ART engagement among Indian MSM. We conducted 3 focus groups (N = 22) with MSM living with HIV, aged 21–58 years, in Delhi and Hyderabad to identify potential intervention targets and solutions to improve treatment outcomes. Framework analysis and techniques were used to code and analyze translated audio-recordings. Findings revealed enacted stigma, associated with HIV and MSM identity, manifested as familial shame and healthcare discrimination, inhibiting access to support, and decreasing HIV care engagement. Anticipated stigma led to worry about disclosure and societal repercussions. Community-Based-Organizations, ART centers, and family members were primary sources of support, leading to increased ART initiation and retention. Potential solutions included using MSM peer-counselors, increasing social support, and providing HIV education to the general community.
What strategies work best for enforcing sanctions? Sanctions enforcement agencies like the US Office of Foreign Assets Control (OFAC) face resource limitations and political constraints in punishing domestic firms for violating sanctions. Beyond monetary fines, sanctions enforcement actions also serve a “naming and shaming” function that tarnishes violators’ reputations. Larger, higher-profile companies tend have much more at stake in terms of their reputations than smaller or less well-known firms. At the same time, punishing higher-profile companies for sanctions violations is likely to generate more publicity about the risks and potential consequences of not complying with sanctions. We theorize that OFAC should impose larger fines on high-profile companies to draw attention to those cases, make the enforcement actions more memorable, and enhance the reputational costs that they inflict. We conduct a statistical analysis of OFAC enforcement actions from 2010 to 2021 and find support for our theory.
Objective Minimal research has examined teletherapy for group or intensive eating disorder (ED) treatment, particularly partial hospital programme (PHP). This study compared treatment outcomes for individuals treated before and after a pandemic‐driven implementation of virtual PHP. Method Patients received care at ED treatment centres using the Renfrew Unified Treatment for Eating Disorders and Comorbidity. Patients treated with virtual PHP were compared to patients treated with traditional PHP. Measures of ED symptomology and behaviours, depressive symptoms, anxiety severity, anxiety sensitivity, experiential avoidance, mindfulness, and body mass index (BMI; reported for anorexia nervosa [AN] patients only) were collected at intake and discharge. Multiple regression analyses were conducted to examine the effect of treatment group on outcomes, controlling for intake score, comorbidity, discharge status, AN diagnosis, and step‐down status. Results Differences in treatment type were only found for binge eating frequency, with those in virtual PHP reporting significantly lower binge eating episodes at discharge than those in traditional PHP. Body mass index showed significantly less improvement in virtual PHP than in traditional PHP. Conclusions Preliminary results suggest virtual PHP is feasible and effective, potentially increasing access to evidence‐based, intensive ED treatment. However, additional research is needed to establish efficacious support for weight gain among individuals with AN in virtual programs.
It is the concern of policymakers every year in New York City to consider whether or not the enacted rent control policy has a positive effect on the rental market. In order to measure the efficacy of the rent control policy, we aim to study the change in housing quality of people who live in rent controlled homes compared to those in non-rent controlled homes. A housing quality index metric was created in order to study how housing quality changes over time and its relationship to rent control. The impact of rent control on housing quality is analyzed, thus assessing one measure of policy effectiveness. The analysis indicates that rent controlled homes are associated with higher damage rates than non-rent controlled homes, perhaps indicating that the inverse of the intended effect is occurring.
Purpose This mixed-methods research sought to examine the experience of people with aphasia who used text-to-speech (TTS) support to read a novel for virtual book club participation. Method Six people with chronic aphasia used a TTS system to review portions of a novel about which they conversed during eight virtual book club meetings occurring over 5 weeks. During one-on-one interactions prior to each meeting, participants answered comprehension questions and provided feedback about reading experiences. Then, during group meetings, they reviewed and discussed relevant book content and predicted upcoming content. During a structured individual interview, participants reflected on their supported reading and book club experience. Results Participants reported a range of reading confidence prior to study participation, mostly influenced by decreased comprehension or reading speed. After book club participation, four participants expressed increased confidence. Some reported searching for key words and skipping difficult words as strategies additional to TTS support. All reviewed at least some book sections more than once either with or without TTS support. Highly motivated participants expressed low frustration and high reading ease and enjoyment. Perceived comprehension was roughly consistent with actual comprehension across participants. Most believed TTS support promoted faster reading than otherwise possible. Participants liked adjustable features affecting speech output rate, word or sentence highlighting, and font size. Psychosocial benefits included decreased isolation and increased friendship. Conclusions The findings extend previous evidence about perceived and actual benefits associated with TTS support. People with aphasia express positive experiences when given TTS support during book club participation.
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6,338 members
William Berg
  • Department of Kinesiology and Health
Gary Peterson
  • Family Studies and Social work
Saruna Ghimire
  • Department of Sociology and Gerontology
William B. Stiles
  • Department of Psychology
45056, Oxford, Ohio, United States