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Loneliness and Support for Political Violence Within the United States
No full textAlthough loneliness is a growing public health issue in the United States, its political effects remain understudied. This paper seeks to understand the relationship between loneliness and the support of political violence by utilizing the Chapman Survey of American Fears (CSAF Wave 11). This survey, which is a component of the 2025 Chapman Survey of American Fears, includes the three-item UCLA Loneliness Scale, as well as two separate items specifically measuring political violence. Using a national survey (N=1,015), I address the following questions: (1) Are lonely individuals more likely to support violence on political grounds? (2) Does strongly identifying with a political party correlate with higher or lower support for violence? and (3) Is loneliness more consequential for political violence in younger adults? Contrary to the original hypotheses, logistic regression does not support the idea that loneliness is a predictor of support for political violence. In fact, loneliness decreases the personal willingness to engage in political violence by damaging property. On the other hand, younger adults are more likely to support political violence and display a greater level of approval for violent political actions compared to older adults, reflecting a clear generational difference. Partisan identification also does not appear to be a strong predictor of violence in attitudes for non-White and non-Christian individuals. These findings alter the current stereotypes of a “lonely radical” and suggest that loneliness, aggression, and violence are more tied to the restrictions of social behavior than loss of control. This study further highlights the necessity to better define how psychosocial distress interacts with group identity within a social democracy
Solutions or Punishment? Determinants of Attitudes Towards Homelessness
No full textAs the homeless population continues to rise in the United States, it is imperative to look into public opinion, fear, and support for policies regarding perceptions of homelessness. I will examine the relationship between several key determinants—political ideology, socioeconomic status, fear perceptions, and public attitudes toward homelessness policies—within the United States, using data from the Chapman Survey of American Fears, a representative national sample of U.S. adults. Among the key findings is a significant relationship between political party identification and attitudes toward homeless individuals. There is strong, consistent agreement across demographic groups that adequate shelter space should be made available for people experiencing homelessness. In contrast, the analysis shows no meaningful relationship between education level and support for this shelter policy, indicating that support remains high regardless of educational attainment. By analyzing the impact of fears surrounding the homeless and tent encampments, this study reveals how such fear-driven attitudes can steer public preferences toward punitive measures rather than supportive, solution-based policies. Although understanding public perceptions will not solve the homelessness crisis on its own, it is essential for developing effective and informed solutions
Multi-Satellite Image Matching and Deep Learning Segmentation for Detection of Daytime Sea Fog Using GK2A AMI and GK2B GOCI-II
No full textTraditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products
Explainable Machine Learning Using EMG and Accelerometer Sensor Data Quantifies Surgical Skill and Identifies Biomarkers of Expertise
No full textTraditional evaluations of surgical skill rely heavily on subjective assessments, limiting precision and scalability in modern surgical education. With the emergence of robotic platforms and simulation-based training, there is a pressing need for objective, interpretable, and scalable tools to assess technical proficiency in surgery. This study introduces an explainable machine learning (XAI) framework using surface electromyography (sEMG) and accelerometer data to classify surgeon skill levels and uncover actionable neuromuscular biomarkers of expertise. Twenty-six participants, including novices, residents, and expert urologists, performed standardized robotic tasks (suturing, knot tying, and peg transfers) while sEMG and motion data were recorded from 12 upper-extremity muscle sites using Delsys® Trigno™ wireless sensors. Time- and frequency-domain features, along with nonlinear dynamical measures such as Lyapunov exponents, entropy, and fractal dimensions, were extracted and fed into multiple supervised machine learning classifiers (SVM, Random Forest, XGBoost, Naïve Bayes). Classification performance was evaluated using accuracy, F1-score, MCC, and AUC. To ensure interpretability, SHAP and LIME were employed to identify and visualize key features distinguishing skill levels. Ensemble models (XGBoost and Random Forest) outperformed others, achieving classification accuracies above 72%, with high F1-scores for all classes. Nonlinear features, particularly Mean_Long_Lyapunov exponent, Correlation Dimension, Approximate Entropy, and Hurst exponent, consistently ranked among the top predictors. Expert surgeons exhibited higher movement complexity and temporal consistency, reflected in higher entropy and correlation dimension, and lower Lyapunov exponents compared to novices. XAI methods revealed that different classes were driven by distinct feature sets: entropy measures best identified novice patterns, while fractal and stability features were more predictive of expert performance. SHAP and LIME enabled both global and instance-specific interpretation of classifier decisions, enhancing transparency and enabling targeted feedback. This study demonstrates the feasibility and utility of combining multimodal wearable sensor data with explainable machine learning to assess robotic surgical skill. The identified biomarkers capture nuanced aspects of motor control such as adaptability, complexity, and stability that distinguish novice, intermediate, and expert surgeons. Beyond classification, the explainable framework offers interpretable insights into why specific skill levels were assigned, providing a pathway for personalized surgical feedback and training. This approach advances the development of objective, transparent, and clinically meaningful assessment tools in surgical education
Children are (Still) Communicators Too: Assessing and Advocating for Child-Focused Communication Scholarship
No full textChildren have long been overlooked in communication scholarship. Content analysis of research published 14 communication journals (n = 8662) revealed that child representation remains below 4%, with the majority of these publications utilizing adolescent rather than school-age and younger samples. Findings in the current study underscore the importance of valuing children’s communication processes. We present a renewed call for children’s communication research by arguing for the need to (1) address discipline barriers to child-focused research, (2) pay attention to younger children, (3) expand research methodologies beyond surveys, (4) explore topic-based opportunities for children’s communication beyond media and health contexts, and (5) develop and revise communication theories to specifically address children’s unique communication capacities, behaviors, and competencies. By addressing these implications, communication scholars can enrich our understanding of human communication across the lifespan and recognize the significant role of children as communicators
Hot Ground State Cooling Following Ultrafast Photoisomerization: Time-Resolved Infrared Spectroscopy
No full textThe ultrafast photophysics of many isomerizing molecules involves subpicosecond formation of a twisted hot ground state, which transfers energy to the environment through vibrational relaxation (cooling) over several picoseconds. In time-resolved infrared (TR-IR) spectroscopy, hot ground state transients show frequency shifts and band reshapings, which cannot be described through kinetic models that assume static spectral functions. We report a simple anharmonic cascade framework, which uses a single adjustable parameter associated with scaling the probability of vibrational energy transfer to the environment, for describing hot ground state cooling (HGSC) in TR-IR spectroscopy. The model is demonstrated against measurements on the cyan fluorescent protein chromophore. To best describe HGSC band shape evolution, the model utilizes ab initio data on anharmonic vibrational structure and nonadiabatic molecular dynamics trajectories of S1→ S0 internal conversion for realistic vibration occupation numbers of the nascent hot ground state. The modeling framework is readily extended to include mode-specific rates for intermolecular energy transfer and can be applied to any ultrafast isomerizing molecule for which anharmonic vibrational properties can be computed
Still Problematic, Even Post-Settlement: Florida’s “Don’t Say Gay” Law and the Federal Constitution
Get PDFFlorida’s “Don’t Say Gay” Law, officially part of the Parental Rights in Education Act, came into force in 2022. As amended in 2023, this law prohibits classroom instruction on sexual orientation or gender identity for children in pre-kindergarten through the eighth grade, and forbids any instruction on sexual orientation or gender identity that is not “age-appropriate or developmentally appropriate” for children in any grade.
From the start, this law was controversial and was challenged in court as a violation of the U.S. Constitution. In March 2024, a settlement agreement was reached in a lawsuit challenging the law, providing clarification on various aspects, including what constitutes forbidden conduct under the law.
This Article argues that although the settlement agreement helps resolve many of the problematic aspects of the “Don’t Say Gay” Law, the law still violates the Constitution. This Article contends that this law violated and still violates the First Amendment’s protection of freedom of speech because of its chilling effect on protected speech and by promoting a particular religious viewpoint in schools. Additionally, it violates the Due Process and Equal Protection Clauses of the Fourteenth Amendment as it is an overbroad and vague law that was enacted with discriminatory animus against the LGBTQ+ community, and it discriminates based on sexual orientation and gender identity. This Article concludes that the courts should strike down this law and others like it as violative of the U.S. Constitution
We Lived the River Through Our Bodies : Environmental Care, Intergenerational Relations, and Sustainable Peacebuilding in Colombia
Get PDFThis chapter offers an ethnographic account3 of youth peacebuilding practices in Montes de María. I place Jose’s pressing question, \u27who am I?\u27 as a central starting point to examine the foundational role that identity work plays in youth processes of \u27provoking\u27 peace in Montes de María, Colombia. I center Jose’s critique of the harm enacted when analytic attention focuses solely on youth who have migrated to urban cities. Instead, I turn my attention to the lives of youth who, against all odds, have stayed. I argue that the struggle to reclaim a sense of self, place, and belonging is at the very heart of JOPPAZ’s daily work to build territorial peace – one that requires intergenerational solidarity. While much of the peace studies literature focuses on transformation, I draw on Indigenous theories of resurgence and multispecies relations to argue that in a context of dispossession and mass violence, social reproduction plays an equally vital role in campesino claims to land and futures (Alfred 2005; Corntassel 2012; Daigle 2018; Hatala et al. 2019; Ruiz Serna 2017; Todd 2017). JOPPAZ did not emerge as an isolated movement, but instead forms the youth wing of the wider campesino movement known as the Peaceful Movement of Reconciliation and Integration of the Alta Montaña (Peaceful Process). In becoming the relevo generacional, Jose – and the 600 young members of JOPPAZ – are engaged in a \u27regenerative struggle\u27 for territorial peace (Alfred 2005, 20). Through the daily work of building peace \u27from and for the territory,\u27 youth imagine and bring into being campesino futures of dignified life (vida digna).https://digitalcommons.chapman.edu/peace_books/1008/thumbnail.jp
Moderate Physical Perspectivalism
Get PDFRecent developments in the foundations of physics have given rise to a class of views suggesting that physically meaningful descriptions must always be relativized to a physical perspective. In this article, I distinguish between strong physical perspectivalism, which maintains that all facts must be relativized to a perspective, and moderate physical perspectivalism, which maintains that all empirically meaningful descriptions must be relativized to a perspective. I argue that scientific evidence and philosophical considerations support moderate physical perspectivalism over strong physical perspectivalism. In particular, motivations connected to epistemic humility and the social nature of science are more compatible with the moderate approach
