Fraudulent activities within the U.S. healthcare system cost billions of dollars each year and harm the wellbeing of many qualifying beneficiaries. The implementation of an effective fraud detection method has become imperative to secure the welfare of the general public. In this paper, we focus on the problem of fraud detection using the current year's Medicare claims data from the perspective of utilizing temporal information from the previous years. We group the data into temporal trajectories of the key covariates and base our feature engineering around these trajectories. For effective feature engineering on the temporal data, we propose to use the functional principal component analysis (FPCA) method for analyzing the temporal covariates' trajectory as well as the distributional FPCA for extracting features from the empirical probability density curve of the covariates. Moreover, we introduce the framework of cost-sensitive learning for analyzing the Medicare database to allow for asymmetrical losses in the confusion matrix, such that the classification rule reflects the realistic tradeoff between the fixed cost and the fraud cost. The issue of class imbalance in the database is tackled through the random undersampling scheme. Our results confirm that the trained classifier has a reasonably good prediction performance and a significant percentage of cost savings can be achieved by taking into account the financial cost.
In the 5G era (and the upcoming 6G), Mobile Edge Computing (MEC) has been advocated to serve the massive amount of Internet of Things (IoT) devices by base stations (BSs) and edge data centers (EDCs). Geo-distributed EDCs are generally of much smaller scales as compared to mega DCs and hence of much lower costs, but can have fast response to their users so as to satisfy the demands of real-time applications. As their reliability and availability heavily depend on the electrical power supply, most EDCs are equipped with battery groups as backup power in case of power grid load shedding or outage. In a heterogeneous geo-distributed environment, the QoS of heavily loaded EDCs however can be severely impacted by limited backup power while lightly loaded EDCs may simply waste such precious resources. Moreover, a heavily loaded EDC may suffer from deep discharge of its battery group, which will cause a significant reduction of battery capacity and lifetime. This further aggravates the aforementioned situations should load shedding/outage happen again. In this paper, we carefully analyze the workloads in EDCs and classify them into interactive workloads and batch workloads, respectively. We then develop a novel battery allocation framework with smart workload migration for EDCs, which simultaneously protects interactive workloads from being interrupted and minimizes the waiting time of batch workloads. Our extensive evaluations show that our strategies can optimize all the objectives within a limited overall cost as compared to state-of-the-art practical allocation.
The meshed high-voltage DC network is an effective solution for large-scale new energy consumption and long-distance power transmission. However, when the number of lines is larger than the number of controllable nodes, the converters cannot fully control all line currents. This paper proposes a cascaded multilevel embedded DC current flow controller (DCCFC) to increase the DC current flow control freedom in the meshed high voltage direct current (HVDC) transmission system. It is directly coupled with the main modular multilevel converter (MMC) and maintains power balance through collaborative control. The DCCFC achieves bidirectional control of DC current flow without an additional power supply or isolation transformer. This paper analyzes the working principle of the proposed embedded DC current flow controller. A power balancing control strategy is proposed with the main MMC and the DCCFC operating in concert. This paper also proposes an internal power balancing control strategy considering two circulation optimization methods: common mode DC voltage injection and q-axis AC voltage injection. The experimental results of the down-scaled prototype validate the feasibility and effectiveness of the proposed embedded DC current flow controller and its control strategy.
We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) for both unweighted and weighted terrains, which aims to minimize the coverage time, defined as the maximum travel time of all robots. Specifically, we focus on a reduction from MCPP to Min-Max Rooted Tree Cover (MMRTC). For the first time, we propose a Mixed Integer Programming (MIP) model to optimally solve MMRTC, resulting in an MCPP solution with a coverage time that is provably at most four times the optimal. Moreover, we propose two suboptimal yet effective heuristics that reduce the number of variables in the MIP model, thus improving its efficiency for large-scale MCPP instances. We show that both heuristics result in reduced-size MIP models that remain complete (i.e., guaranteed to find a solution if one exists) for all MMRTC instances. Additionally, we explore the use of model optimization warm-startup to further improve the efficiency of both the original MIP model and the reduced-size MIP models. We validate the effectiveness of our MIP-based MCPP planner through experiments that compare it with two state-of-the-art MCPP planners on various instances, demonstrating a reduction in the coverage time by an average of 27.65% and 23.24% over them, respectively.
Policy scholars can learn a great deal from students of clientelism with respect to the forces at work in the policy process. This is apparent, for example, with respect to scholarship in recent years which has focused attention on the idea of replacing patchworks of public policies in specific issue areas with more coordinated or ‘integrated’ policy strategies (IS). In theory, such strategies should display a judicious mix of policy goals and means which can produce policy outcomes matched to specific large-scale problem contexts. Empirical work on such strategies, however, has shown the resilience of pre-existing policy elements often leading to reform failures and/or sub-optimal outcomes from such efforts. This article examines a case study of large-scale policy reform efforts in the area of Integrated Land Management (ILM) in Western Canada which reveals the role played by policy clientelism in blocking efforts to enhance policy integration in this area. This finding is significant in pointing out more generally the role played by clientelism in preventing successful policy reform.
Canada currently faces the challenge of implementing the UN Declaration on the Rights of Indigenous Peoples, while also managing the shift from fossil fuels to renewable energy. This shift will require massive new mineral extraction projects in ways that will continue to impact both the Global North and the South. The provincial government of British Columbia and the Canadian federal government have ostensibly committed to the current era of “reconciliation.” Despite this, conflicts over Indigenous jurisdiction and resource development persist in the fossil fuel (i.e. Trans Mountain, Coastal Gas Link) and renewable energy sectors (i.e. Site C dam). Extractive bargains occur at local, national, and global scales. With respect to ongoing fossil fuel extraction in Canada, Indigenous peoples and territories have often been sacrificed in favour of the “national interest.” Myriad factors—legal, political, economic—have led to greater Indigenous involvement in mainstream resource extraction projects via revenue-sharing and impact benefit agreements. We argue that these extractive bargains have been largely Faustian in nature. At the heart of the problem is an ongoing denial of self-determination and Indigenous nations’ (in)ability to exercise true free, prior, and informed consent with respect to development projects, fossil fuel or renewable.
This closing chapter is based on a roundtable at the workshop which served as the launchpad for this edited volume. The closing roundtable featured experts from academia, non-governmental organizations, the mining industry, and mining industry watchdogs. The interdisciplinarity of the roundtable features in this chapter and highlights each participant’s different methodological and epistemic perspective on the possibilities and improbabilities of extractive bargains, along with the scale from which they engage with the state-society nexus. As a group, we reflected on and debated the core questions of this book. The following forum-style chapter explores each expert perspective in turn. It concludes by emphasizing that, despite differences, there is a profound interconnectedness among the experts, which is reflective of a world linked together by ecological crisis and aspirations for a climate transition.
The objective of the current study was to identify the authenticity of lime juice and its geographic origin using targeted approaches based on the AIJN guideline and machine learning. Iranian lime juice (Mexican and Persian), sampled from different regions of the country, was analyzed with chemical features for quality and authenticity. To improve the effectiveness of different machine learning techniques, the forward feature selection method was applied to evaluate the applicability of the chemical features in the classification of lime juice based on geographic region. The findings demonstrated that the samples complied with the AIJN standard in terms of the majority of these features, including density, brix, volatile acids, ethanol, HMF, volatile oils, heavy metals, acidity, citric acid, d-isocitric acid, formalin, glucose, fructose, sucrose, ash, NO3, phosphorus, potassium, magnesium, calcium, copper, zinc, and iron. However, sodium (34.6 ± 14.5 mg/l) and malic acid (5.7 ± 2.3 g/l) contents were higher, and sugar-free extract (68 ± 9.9 g/l) was lower than the limit defined by the AIJN. The feature selection method led to SVM with 90%, kNN with 94%, and RF and DT with 100% accuracy in identifying the geographic region of lime juice samples. In all supervised learning techniques except kNN, copper, zinc, and iron were the most vital features in the geographical classification of samples. This is the first time that the authenticity and geographical origin of Iranian lime juice have been well determined using conventional analysis methods and machine learning as an accessible and straightforward identification scheme instead of modern analysis methods.
Extant research highlights the importance of error sharing for managing errors in organizations, but little work examines what happens to employees who disclose errors. Treating errors as sensitive information, we draw on the self‐disclosure literature to propose that error sharing can influence leaders’ evaluations of employee ability and integrity, which affect leader trust in the employee; error visibility and severity work as contingency factors in the above links. We conducted two field studies and one experimental study to test our hypotheses. We used data collected in China from manufacturing companies (560 employees from 71 teams in Study 1), a high‐reliability organization (359 employees from 104 teams in Study 2), and an online sample (356 participants in Study 3). Results show that error sharing impairs leader trust via the negative evaluation of the employee's ability but enhances trust via the positive evaluation of the employee's integrity; error visibility and severity moderate the relationships between error sharing and leader evaluation of employee integrity and leader trust such that the positive relationships are enhanced when errors are of lower visibility or higher severity. Our study offers a novel perspective to understand the relational consequences of error sharing at work.
This article explores Chinese cross-border wives’ experiences, challenges and coping mechanisms and their interaction with the ecology and algorithm of the platform by focusing on their storytelling and practices on YouTube. Through in-depth, semi-structured interviews, narrative analysis and more than 2 years of virtual ethnography, I observed the family politics and power dynamics presented in the videos of Chinese cross-border wives. I found two types of family politics within the ‘power game’ of interculturally married families. Wives who have experienced significant domestic injustice tend to demonstrate and reaffirm their moral capital, whereas those who are content with their marriage lives tend to demonstrate to subscribers how they play the power game with wisdom. Nevertheless, by focusing on a marginalized group on YouTube, I illustrate the platform’s role in these Chinese cross-border wives’ family politics, the great effort they made to attain popularity, the invisibility they endured and their highly unpredictable platform practice.
We investigate how negative news coverage of borrower’s impacts on climate change affects bank loan contracting. Using a sample of publicly traded US firms for the period 2000–2016, we show that loans initiated following negative news coverage about firm’s adverse climate-related incidents have significantly higher spreads, shorter maturities, more covenant restrictions, and a higher likelihood of collateral security requirements. We find no changes in client firm’s credit fundamentals after such incidents, indicating that lender’s reputational concerns rather than the longer-term environmental impacts of their borrower’s actions are the primary drivers of these changes. This observation highlights the need for increased scrutiny of banks’ lending practices to ensure that they are genuinely committed to sustainability rather than merely engaging in symbolic actions.
Activation of the calf (gastrocnemius and soleus) and tibialis anterior muscles play an important role in blood pressure regulation (via muscle-pump mechanism) and postural control. Parkinson’s disease is associated with calf (and tibialis anterior muscles weakness and stiffness, which contribute to postural instability and associated falls. In this work, we studied the role of the medial and lateral gastrocnemius, tibialis anterior, and soleus muscle contractions in maintaining blood pressure and postural stability in Parkinson’s patients and healthy controls during standing. In addition, we investigated whether the activation of the calf and tibialis anterior muscles is baroreflex dependent or postural-mediated. We recorded electrocardiogram, blood pressure, center of pressure as a measure of postural sway, and muscle activity from the medial and lateral gastrocnemius, tibialis anterior, and soleus muscles from twenty-six Parkinson’s patients and eighteen sex and age-matched healthy controls during standing and with eyes open. The interaction and bidirectional causalities between the cardiovascular, musculoskeletal, and postural variables were studied using wavelet transform coherence and convergent cross-mapping techniques, respectively. Parkinson’s patients experienced a higher postural sway and demonstrated mechanical muscle-pump dysfunction of all individual leg muscles, all of which contribute to postural instability. Moreover, our results showed that coupling between the cardiovascular, musculoskeletal, and postural variables is affected by Parkinson’s disease while the contribution of the calf and tibialis anterior muscles is greater for blood pressure regulation than postural sway. The outcomes of this study could assist in the development of appropriate physical exercise programs that target lower limb muscles to improve the muscle-pump function and reduce postural instability in Parkinson’s disease.
Modeling severe acute respiratory syndrome, Coronavirus 2 (SARS-CoV-2) infection in stem cell-derived organoids has helped in our understanding of the molecular pathogenesis of COVID-19 disease due to their resemblance to actual human tissues or organs. Over the past decade, organoid 3-dimensional (3D) cultures have represented a new perspective and considerable advancement over traditional in vitro 2-dimensional (2D) cell cultures. COVID-19 disease causes lung injury and multi-organ failure leading to death, especially in older patients. There is an urgent need for physiological models to study SARS-CoV-2 infection during the pandemic. Human stem cell-derived organoids can provide insight into understanding the SARS-CoV-2 cell entry molecular mechanism. Identifying such complexities will help to develop the best preventive drug targets.
Difficulties in various face processing tasks have been well documented in autism spectrum disorder (ASD). Several meta‐analyses and numerous case–control studies have indicated that this population experiences a moderate degree of impairment, with a small percentage of studies failing to detect any impairment. One possible account of this mixed pattern of findings is heterogeneity in face processing abilities stemming from the presence of a subpopulation of prosopagnosic individuals with ASD alongside those with normal face processing skills. Samples randomly drawn from such a population, especially relatively smaller ones, would vary in the proportion of participants with prosopagnosia, resulting in a wide range of group‐level deficits from mild (or none) to severe across studies. We test this prosopagnosic subpopulation hypothesis by examining three groups of participants: adults with ASD, adults with developmental prosopagnosia (DP), and a comparison group. Our results show that the prosopagnosic subpopulation hypothesis does not account for the face impairments in the broader autism spectrum. ASD observers show a continuous and graded, rather than categorical, heterogeneity that span a range of face processing skills including many with mild to moderate deficits, inconsistent with a prosopagnosic subtype account. We suggest that pathogenic origins of face deficits for at least some with ASD differ from those of DP.
This chapter focuses on left-wing radicalism, with special focus on the movement known as Antifa. Over the past few years, this movement has gained widespread notoriety. It featured prominently at the Unite the Right Rally in Charlottesville, Virginia, in August 2017. Although one woman was killed in the fracas, this episode elevated the stature of the movement. In the months that followed, Antifa activists and their fellow-travelers were instrumental in discouraging alt-right activists from giving speeches in venues throughout the country. In the summer of 2020, Antifa activists joined with the Black Lives Matter protest movement. Working in tandem, BLM and Antifa have attained a synergy that has proven to be quite destructive in cities throughout the United States. The resurgence of violent left-wing activism could have far-reaching impacts on politics in the United States. At the present time, mainstream liberals seem loath to distance themselves from the movement. However, this could alienate some of the more moderate liberals, perhaps pushing them into the ranks of the Republican Party. The chapter will also examine some antecedents to Antifa, such as Anti-Racist Action in North America and Anti-Fascist Action in the UK.
Older adults spend significant time by themselves, especially since COVID‐19. Solitude has been associated with positive and negative outcomes. Partners need to balance social connectedness with time for one's own needs. This project examines how individual and partner solitude are associated with daily affect and relationship quality in dyads of older adults and a close other. One‐hundred thirty‐six older adults plus a close other rated their relationship quality and reported affect, solitude, and its characteristics (desired and bothersome) every evening for 10 days. Over and above overall associations, individual and partner effects emerged; when individual desired solitude was up, participants reported more positive affect and their partners less negative affect. When bothersome solitude was up, participants and their partners alike reported more negative affect and less positive affect. Desired solitude was associated with more support, whereas bothersome solitude was associated with less partner support. Findings provide further evidence on the potential benefits of solitude, highlighting the importance of considering the social context of what is often believed to be an individual‐level phenomenon.
Background The increased stress the world experienced with the coronavirus disease (COVID-19) pandemic affected mental health, disproportionately affecting females. However, how perceived stress in the first year affected menstrual and menopausal symptoms has not yet been investigated. Objectives This study evaluates the effect that the first year of the COVID-19 pandemic had on female reproductive and mental health. Methods Residents in British Columbia, Canada, were surveyed online as part of the COVID-19 Rapid Evidence Study of a Provincial Population-Based Cohort for Gender and Sex. A subgroup of participants (n = 4171), who were assigned female sex at birth (age 25–69 years) and were surveyed within the first 6–12 months of the pandemic (August 2020–February 2021), prior to the widespread rollout of vaccines, was retrospectively asked if they noticed changes in their menstrual or menopausal symptoms, and completing validated measures of stress, depression and anxiety. Design This is a population-based online retrospective survey. Results We found that 27.8% reported menstrual cycle disturbances and 6.7% reported increased menopause symptoms. Those who scored higher on perceived stress, depression and anxiety scales were more likely to report reproductive cycle disturbances. Free-text responses revealed that reasons for disturbances were perceived to be related to the pandemic. Conclusion The COVID-19 pandemic has highlighted the need to research female-specific health issues, such as menstruation. Our data indicate that in the first year of the pandemic, almost one-third of the menstruating population reported disturbances in their cycle, which was related to percieved stress, depression and anxiety scores.
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