Search Results (91)

In 2024, the conservative media outlet Daily Wire produced a documentary film entitled Am I Racist? Created by political commentator and author Matt Walsh and director Justin Folk, the film became one of the highest-grossing documentaries of the last decade. Unlike traditional documentaries, Walsh employs a rhetoric of irony against anti-racist adherents to obstruct their influence and inoculate mostly conservative viewers. His method, however, is unusual and even questionable in conservative Christian circles. The film is analyzed using a Bakhtinian analysis of dialogic opposition wherein Walsh embodies three ironic characters—Rogue, Fool, and Clown—in order to expose the monologue of anti-racism. The analysis demonstrates the dialogization of the anti-racist monologue through rhetorical enactments of anacrisis and syncrisis. Through juxtapositions of anti-racist ideologists and their everyday racist opponents, Walsh obstructs the future effectiveness of the ideology. Even more, by becoming a DEI expert himself, he performatively distorts the monologue to victimize opponents and entertain viewers through the public spectacle. Ultimately, Am I Racist? demonstrates a unique modern turn and strategy in conservative and, more importantly, Christian rhetorical strategies that needs more attention in the future. Full article

Health professionals commonly reference race and ethnicity to inform health care and administrative decisions. However, health researchers (and, arguably, society at large) misapply race and ethnicity when assuming an inherent relationship of these concepts with biological and health outcomes of interest. Misapplication of race potentially results from socially embedded identification predicated upon race essentialism, the belief that people within a racial group share “inherent” biological traits that define them as distinct from other racial groups. This false belief is often associated with implied racial hierarchies obscuring authentic causal disease relationships. Similarly, ethnicity is a socially and politically constructed group descriptor for people from a similar national or regional background who may share common cultural, historical, and social experiences. Thus, as for race, no inherent biological information is contained within such group definitions. This article summarizes the Research Centers for Minority Institutions (RCMI) 2025 Annual Grantee Meeting keynote session on Race and Ethnicity in Medicine. The session described how society originated and subsequently applied/misapplied race and ethnicity in various domains of policy and public health. The keynote session also considered the use of race and ethnicity in describing and envisioning biomedical research, clinical trials, clinical practice, and health services research. The authors summarize a more tenable use of race and ethnicity to advance biomedical research and health by focusing upon social and environmental drivers of health, population representation in clinical trials, and other factors. Associated recommendations from the keynote session are provided. Full article

This paper investigates claims about the typical physical characteristics of Jesus when he is portrayed in film. A number of critics have referred to a recurring blond-haired, blue-eyed Jesus. In reviewing the academic literature, a lack of clarity was found as to any patterns that do exist in this area. An ensuing analysis of the top-grossing films from the last forty years revealed that the recent pattern may be very different from what critics and academics describe. Full article

Background: Glaucoma is the leading cause of irreversible blindness around the world and is characterized as a group of irreversible optic neuropathies with multiple risk factors such as age, race/ethnicity, sex, and intraocular pressure (IOP), amongst many others that play a role in disease etiology. However, IOP is the only modifiable risk factor, with higher IOP often causing increased damage to the optic nerve, resulting in the vast majority of medical and surgical treatments aiming to reduce IOP. There are a number of interventions available to treat glaucoma including micro-invasive glaucoma surgery (MIGS), whose usage has drastically increased due to its safety and efficacy. Studies also highlight the IOP-reducing effect of cataract surgery, which is the most common procedure performed globally. However, other, more targeted therapies and surgeries have been shown to have a more significant effect on IOP reduction. The objective of this study is to compare the IOP and medication reduction between cataract surgery (CS), MIGS, and MIGS and cataract surgery (MACS) clinical trials. Methods: This analysis consisted of publicly available data on CS, MIGS, and MACS clinical trials from 2005 to 2017 using ClinicalTrials.gov. Data reporting and synthesis adhered to PRISMA guidelines. MIGS interventions studied in this analysis include iStent^®, CyPass^® Micro-Stent, Ex-PRESS^®, Hydrus^®, PRESERFLO^™ MicroShunt, and XEN^® Gel Stent. The main variables of interest are the mean IOP and mean number of glaucoma medications used. The primary outcomes were the baseline, post-procedure, and reduction in IOP and glaucoma medication use. Cohorts were further subdivided by the follow-up period (6, 12, and 24 months), as well as their medicated or unmedicated status for pre-op IOP measurement. PROSPERO CRD42025102892. Results: A total of 21 trials were included in this review, comprising 3330 clinical trial participants: 7 CS trials (N = 570), 13 MIGS trials (N = 1577), and 9 MACS trials (N = 1183). All interventions studied resulted in a decrease in both the IOP and medication usage with varying degrees. At 12 months, the wash-out baseline IOP reduction (mmHg) was 6.9 (27.5%) for CS, 8.8 (34.0%) for MIGS, and 8.2 (32.6%) for MACS. The medication reduction was 0.8 (56.1%) following CS, 1.0 (39.5%) for MIGS, and 1.3 (86.4%) for MACS. At 24 months, the wash-out baseline IOP reduction was 6.3 (25.1%) for CS, 8.4 (33.1%) for MIGS, and 7.6 (30.1%) for MACS. At 24 months, the medication reduction was 0.9 (58.3%) for CS, 1.5 (79.8%) for MIGS, and 1.3 (86.1%) for MACS. Conclusions: The results indicate that CS, MIGS, and MACS all result in a decrease in the IOP and glaucoma medications; however, MIGS and MACS outperform CS in IOP and medication reduction. Adopting MIGS and MACS for patients with ocular hypertension or mild-to-moderate glaucoma will help improve patient outcomes through reducing the IOP and medication burden. Given that glaucoma affects certain populations to a greater degree, future research analyzing racial representation is critical in ensuring the appropriate applicability of clinical trial results toward diverse populations. Full article

Background: Multiple Myeloma (MM) is the most common type of blood cancer among black individuals. CAR-T therapy is crucial, but often inaccessible to many black patients and those from underserved communities. The University of Kansas Health System administers over 100 CAR-T treatments annually and aims to evaluate barriers to CAR-T therapy access related to the social determinants of health in the Midwest area. Methods: This study examined patients with MM referred for CAR-T therapy from January 2021 to December 2023, assessing how race, socioeconomic status, and insurance influenced eligibility for leukapheresis. Data on income and travel were gathered from the 2022 US Census and analyzed using R software. Results: The study included 271 referrals for MM CAR-T therapy involving 179 patients, with a median age of 66 years (51% male). Demographics: 80% white, 16% black, 2.2% other races, 1.8% Asian, with a median income of $70,644. Nearly half lived more than 30 min from the center (Mainly from Kansas, Missouri and Nebraska). Apheresis rates were similar across racial groups: 54% for whites, 54% for blacks, and 50% for others, while none of the three Asian patients proceeded. Nine patients (5%) could not proceed because of caregiver or insurance barriers, and cell collection rates were comparable regardless of distance (34% vs. 35%). Conclusion: This study showed that black representation in CAR-T access matches local demographics, indicating less disparity among minorities. Unlike national reports, distance, income, and insurance do not significantly affect access, suggesting the need for a national study on the social determinants impacting CAR-T access for multiple myeloma. Full article

Background: This systematic review examines the lived experiences of Black students in UK higher education (HE), focusing on their encounters with racism and racial disadvantage, and how institutional and social factors contribute to these experiences. Methods: We conducted a systematic search across seven databases (Academic Search Complete, Education Abstracts, PsycINFO, Race Relations Abstracts, Scopus, Web of Science, and SocINDEX) in April 2023, with periodic updates. The grey literature, which refers to research and information produced outside of traditional academic publishing and distribution channels, was reviewed. This includes reports, policy briefs, theses, conference proceedings, government documents, and materials from organisations, think tanks, or professional bodies that are not commercially published or peer-reviewed but can still offer valuable insights relevant to the topic. Hand searches were also included. Studies were included if they were peer-reviewed, published between 2012 and 2024, written in English, and focused on the experiences of Black students in UK higher education. Both qualitative and quantitative studies with a clear research design were eligible. Studies were excluded if they lacked methodological rigour, did not focus on the UK HE context, or did not disaggregate Black student experiences. Risk of bias was assessed using standard qualitative appraisal tools. Thematic analysis was used to synthesise findings. Results: Nineteen studies were included in the review. Two main themes emerged: (1) diverse challenges including academic barriers and difficulties with social integration, and (2) the impact of racism and institutional factors, such as microaggressions and biased assessments. These issues contributed to mental fatigue and reduced academic performance. Support systems and a sense of belonging helped mitigate some of the negative effects. Discussion: The evidence was limited by potential bias in reporting and variability in study quality. Findings reveal persistent racial inequalities in UK HE that affect Black students’ well-being and outcomes. Institutional reforms, increased representation, and equity-focused policies are needed. Future research should explore effective interventions to reduce the awarding gap and support Black student success Full article

Time-frequency analysis (TFA) technology is an important tool for analyzing non-Gaussian mechanical fault vibration signals. In the complex background of infinite variance process noise and Gaussian colored noise, it is difficult for traditional methods to obtain the highly concentrated time-frequency representation (TFR) of fault vibration signals. Based on the insensitive property of fractional low-order statistics for infinite variance and Gaussian processes, robust fractional lower order adaptive linear chirplet transform (FLOACT) and fractional lower order adaptive scaling chirplet transform (FLOASCT) methods are proposed to suppress the mixed complex noise in this paper. The calculation steps and processes of the algorithms are summarized and deduced in detail. The experimental simulation results show that the improved FLOACT and FLOASCT methods have good effects on multi-component signals with short frequency intervals in the time-frequency domain and even cross-frequency trajectories in the strong impulse background noise environment. Finally, the proposed methods are applied to the feature analysis and extraction of the mechanical outer race fault vibration signals in complex background environments, and the results show that they have good estimation accuracy and effectiveness in lower MSNR, which indicate their robustness and adaptability. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

Autonomous race driving has emerged as a challenging domain for reinforcement learning (RL) applications, requiring high-speed control while adhering to strict safety constraints. Existing RL-based racing methods often struggle to balance performance and safety, with limited adaptability in dynamic racing scenarios with multiple opponent vehicles. The high-dimensional state space and strict safety constraints pose significant challenges for efficient learning. To address these challenges, this paper proposes an integrated RL framework that combines three novel components: (1) a state mapping mechanism that dynamically transforms raw track observations into a consistent representation space; (2) an action mapping technique that rigorously enforces physical traction constraints; and (3) a safe exploration guidance method that combines conservative controllers with RL policies, significantly reducing off-track incidents during training. Extensive experiments conducted in our simulation environment with four test tracks demonstrate the effectiveness of our approach. In time trial scenarios, our method improves lap times by 12–26% and increases the training completion rate from 33.1% to 78.7%. In competitive racing, it achieves a 46–51% higher average speed compared to baseline methods. These results validate the framework’s ability to achieve both high performance and safety in autonomous racing tasks. Full article

Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal’s Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments. Full article

Several studies emphasize that the limited representation of Afro-Latinx communities in Spanish language curricula affects students’ understanding of the diversity in Spanish-speaking societies. However, research has yet to evaluate the impact of a curricular intervention incorporating an Afro-Latinx module into an L2 Spanish language course. The present study addresses two research questions: (1) what the changes in knowledge after implementing an Afro-Latinx module in an L2 Spanish language course are, and (2) how the module impacts students’ understanding of language variation and diversity in Afro-descendant communities. Guided by Critical Race Theory, Critical Language Awareness, and Raciolinguistics, this mixed methods study analyzed pre- and post-tests alongside journal reflections completed by 50 college students. The findings demonstrated significant improvements in students’ knowledge, indicating a strong association between the intervention and the observed increase in knowledge regarding the representation of Afro-Latinx communities. It also deepened students’ understanding of language variation within Afro-descendant communities and illustrated the role of language in deconstructing social hierarchies and enabling collective memory, resistance, and empowerment. Full article

Introduction: Demographic disparities in musculoskeletal (MSK) health exist in the US. Racial representation in advertising has been shown to influence consumer behavior and buying patterns. Direct-to-consumer advertising that does not target a racially diverse audience may exacerbate MSK disparities by failing to reach minorities. We explore the hypothesis that minorities are underrepresented in direct-to-consumer MSK advertisements in this cross-sectional analysis. Methods: Using magazines from four databases, eight health-related magazine types were selected and advertisement categories were established. Racial distribution was analyzed using Pearson’s Chi-squared and Chi-squared tests. Fisher’s Exact test was used when >20% of cells had expected frequencies <5. Significance was set at α = 0.05. Results: Of the advertisements featuring at least one model, 68.5% featured a white-presenting model, followed by 17.6% with a black model. Further, 92.7% of advertisements were monoethnic or monoracial with an overrepresentation of white models (p < 0.001). Black models were overrepresented as athletes (p < 0.001) and underrepresented in advertisements for pain relief (p < 0.001). Hispanic/Latinx and Asian models were underrepresented across all advertisement categories (p < 0.001). Discussion: The causes of musculoskeletal health disparities are multifactorial. One potential influence is adjacent industries such as MSK health-related advertisements. When controlling for US population demographics, white models were overrepresented and minority race models were underrepresented, demonstrating racioethnic disparities in MSK advertising. Improving the racial and ethnic diversity of models within MSK advertisements may serve to improve patient perceptions of orthopaedic products and services and improve MSK disparities. Full article

Background: Stigma can have a significant impact on the lives of those inflicted, and stigmatisation can occur at any time when individuals commonly label behaviours and characteristics of others as offensive. Many attributes, such as homosexuality, ethnicity, race, and mental illness, have been explored using existing instruments. However, there are currently no standardised measures of stigma towards specific behaviours such as naturism. Naturism is the practice of public nudity without any intention of sexual stimulation. Though a global phenomenon, data suggest that almost seven million individuals in the UK alone identify as naturists. The rising figures of those engaging in stigmatised behaviour and the negative representations of this in the media contribute to the necessity for standardised instruments to measure stigma towards naturism. Method: The Naturism Stigma Scale (NSS) was adapted from the Depression Stigma Scale. This 18-item scale has two subscales which measure personal and perceived stigma. The sample consisted of 449 participants recruited by convenience sampling. Results: The analysis indicated that the scale had high reliability for both subscales (Personal Cronbach’s α = 0.91, Perceived Cronbach’s α = 0.93). It can be concluded that the NSS is a reliable psychometric instrument. Conclusions: Overall, this research assists in further understanding the stigma towards this minority group. Further research is necessary to explore the psychometric properties of NSS among different community samples. Full article

Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging devices like phone cameras, webcams, or surveillance systems. Methods: This study introduces a hybrid deep learning model aimed at estimating heart rate (HR), blood oxygen saturation level (SpO2), and blood pressure (BP) from facial videos. The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. Given the temporal variability of HR and BP, emphasis is placed on temporal resolution during feature extraction. The CNN processes video frames one by one while convLSTM and ViViT handle sequences of frames. These high-resolution temporal features are fused to predict HR, BP, and SpO2, capturing their dynamic variations effectively. Results: The dataset encompasses 891 subjects of diverse races and ages, and preprocessing includes facial detection and data normalization. Experimental results demonstrate high accuracies in predicting HR, SpO2, and BP using the proposed hybrid models. Discussion: Facial images can be easily captured using smartphones, which offers an economical and convenient solution for vital sign monitoring, particularly beneficial for elderly individuals or during outbreaks of contagious diseases like COVID-19. The proposed models were only validated on one dataset. However, the dataset (size, representation, diversity, balance, and processing) plays an important role in any data-driven models including ours. Conclusions: Through experiments, we observed the hybrid model’s efficacy in predicting vital signs such as HR, SpO2, SBP, and DBP, along with demographic variables like sex and age. There is potential for extending the hybrid model to estimate additional vital signs such as body temperature and respiration rate. Full article

Deep learning-based medical image classification models have been shown to exhibit race-, gender-, and age-related biases towards certain demographic attributes. Existing bias mitigation methods primarily focus on learning debiased models, which may not guarantee that all sensitive information is removed and usually targets discrete sensitive attributes. In order to address age-related bias in these models, we introduce a novel method called Mitigating Digital Ageism using Adversarially Learned Representation (MA-ADReL), which aims to achieve fairness for age as a sensitive continuous attribute. We propose controlling the mutual information penalty term to reduce the bias for age as a sensitive continuous attribute, and we seek to enhance the fairness without compromising the accuracy. We also employ the fusion of low- and high-resolution inputs to improve the transferable latent representation of medical images. Our method achieves an AUROC of 0.942, significantly outperforming the baseline models while reducing the bias, with an MI score of 1.89. Our experiments on two skin lesion analysis datasets indicate that MA-ADReL can significantly improve the fairness with respect to age-related bias while maintaining high accuracy. Full article