26419 research outputs found
Sort by
Examining the Socioemotional Outcomes of Social Justice Efforts on Social Media Users: Evidence from the NFL\u27s Inspire Change Initiatives
No full textDespite the growing interest in social justice activities, there is a lack of empirical evidence regarding their impact on various stakeholders, particularly social media users. We gathered longitudinal data from the NFL\u27s @inspirechange X (former Twitter) account, a designated social justice communication handle, between August 2019 and March 2023. During this timeframe, the account garnered 20,967 comments (including original tweets and retweets) from 11,481 distinct users, but our analysis focused on 5851 comments deemed usable from 3632 unique users. Drawing on the social exchange theory, sentiment analysis, and thematic analysis, this study examines social media users\u27 sentiments toward the NFL\u27s social justice efforts and the types of social justice influence that lead to positive and negative responses. Our findings show increased positive sentiment towards equitable social justice efforts, but racial issues, law enforcement, and transparency concerns led to notable negative sentiments, sparking polarized responses. Understanding specific social justice actions that evoke positive and negative emotions helps sport organizations effectively monitor and improve their social justice programs, enabling data-driven decisions. This study employs social media to comprehend diffuse stakeholder voices and extends the application of positive and negative reciprocity to the context of social media users and their perception of social justice
Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation
No full textIn this study, we address the mounting challenge of monitoring high throughput computing clusters running computationally intensive jobs, which increasingly strains system administrators. We develop autoencoders that analyze traces of Linux kernel CPU metrics to capture salient system features by producing robust compressed embeddings for various downstream tasks. In addition, we employ graph neural networks to incorporate contextual information from surrounding CPUs and assess their performance. We also demonstrate the enhanced job differentiation achieved by increasing the sampling rate of these traces. Our models are evaluated based on their ability to generate meaningful latent representations, detect anomalies, and distinguish between different job types, marking a preliminary step towards self-supervised, large-scale foundation models for computing centers
Characterization of Hemi-Wicking in Micro-Engineered Surfaces Using Micro-PTV and Phase Field Lattice Boltzmann Method
No full textMicro-engineered surfaces have seen extensive applications in a broad range of natural and engineering processes such as thermal management, bioengineering, and petrochemical industries. These porous surfaces offer a major advantage of enhanced flow through narrow conduits via capillary wicking and provide a large surface-area-to-volume ratio. The wicking performance in these systems could be further improved by understanding the microscale flow dynamics and their variation with the micro-engineered surfaces. This study investigates the effect of different structures on wicking enhancement via a combination of numerical, experimental, and analytical studies. The numerical approach is based on the three-dimensional phase-field Lattice Boltzmann method, whereas the experimental validation takes advantage of the microfabrication techniques and 3D micro-PTV to track the wicking flow. The results explore the advancing liquid front in different geometries to model wicking in micro-structured surfaces. Further it analyzes the effects of pitch-to-diameter and height-to-pitch ratios on wicking enhancement. The results are corroborated with analytical correlation developed based on thermodynamic energy balance, showing good agreement with the numerical and experimental data
TMD Factorization in the Hadron Structure Oriented Approach
No full textThe main aspect of factorization relies on both the universality of the distributions as well as their interpretations as describing the internal structure of the hadrons. We present a novel approach, called Hadron Structure Oriented approach (HSO), which is best suited for hadron structure studies as it is built to both satisfy theoretic constrains originating from their operator definitions, as well as to clearly demarcate the perturbative contributions from the nonperturbative ones. Some practical examples in a Drell-Yan phenomenological analysis are studied which point the way toward the achievable improvements in future applications
Review of \u3ci\u3eLacan to the Letter: Reading Ecrits Closely\u3c/i\u3e, by Bruce Fink, 2004
No full textComparing Immersive and NonImmersive Clinical Experiences in Athletic Training Education: Effects on Student Engagement and Confidence
No full textContext
With the shift to a graduate-level professional degree in athletic training, it was hypothesized that immersive clinical experiences (ICEs) would be more effectively integrated into curricula than non-ICEs (N-ICEs) and better prepare students for practice.
Objective
To longitudinally compare clinical engagement opportunities in ICEs versus N-ICEs and assess if these opportunities are associated with changes in student confidence in performing related tasks.
Design
Prospective, longitudinal, time-diary study using a Web-based survey. Patients or Other Participants: Fifty-three first-year, master’s-level athletic training students from 21 programs.
Main Outcome Measure(s)
Participants reported their type of clinical experience (ICE, N-ICE, or none), the setting, and hours spent at clinical each day. They quantified the percentage of time spent on 8 categories of athletic training and patient care tasks and rated their confidence in performing these tasks. Independent samples t tests (P, .05) were used to compare confidence ratings and time spent on activities across all students, and the analysis was repeated within students who participated in both ICEs and N-ICEs.
Results
Most clinical experiences occurred in traditional athletic training settings. Immersive clinical experiences led to more time spent on administrative tasks, waiting, and therapeutic interventions, while N-ICEs involved more time in practice coverage, skills practice, diagnostic labs or tests, and applying protective devices. Within students, N-ICEs showed more time on skills practice, but other outcomes were not significant. Immersive clinical experiences resulted in higher confidence in integrating business practices and communicating with health providers and administrators.
Conclusions
Immersive clinical experiences may offer more engagement opportunities and increase confidence in specific tasks, while engagement opportunities are influenced more by the student than the type of clinical experience. Both ICEs and N-ICEs have valuable roles in clinical education; each providing different types of engagement opportunities
Symbiodiniaceae Shifts Over the Last Decade on the Hottest Coral Reefs on Earth
No full textCorals in the Persian/Arabian Gulf (PAG) are resilient to various stressors, whose levels exceed those of coral reefs globally. These corals thereby offer insight into mechanisms underlying thermal resilience, e.g., regarding the role of endosymbiotic microalgae in the family Symbiodiniaceae. Previous studies have identified the thermotolerant species Cladocopium thermophilum as broadly associated with corals in the southern PAG. However, algal-host specificity at the within-species level and the temporal stability of these associations are not well understood. Here we sampled two dominant stony corals (Porites harrisoni, n = 119 and Platygyra daedalea, n = 79) at three sites in the southern PAG and the neighboring Gulf of Oman (GO) to explore algal symbiont assemblage and specificity, whereby a prior dataset provided the opportunity to assess symbiont community stability in P. daedalea across a decadal time frame. Using high-throughput ITS2 marker gene sequencing and the SymPortal framework, we identified distinct, largely non-overlapping ITS2 type profiles of C. thermophilum as the dominant symbiotic partners in P. harrisoni and P. daedalea in the southern PAG, highlighting high host fidelity at the subspecies level. Despite this, we observed notable changes in C. thermophilum genotype diversity and an overall decrease over the course of a decade. By comparison, algal symbiont diversity in the neighboring GO corals increased, with formerly prevalent ITS2 type profiles being replaced by novel genotypes. Decadal data on P. daedalea suggest a shift in algal symbiont assemblage signified by the decline of formerly dominant algal type profiles and the emergence of novel genotypes. It is currently unknown whether the respective coral colonies associated with novel algae or became rare or extinct themselves. Understanding long-term algal population dynamics is critical to forecast how algal lineage loss or, alternatively, an increase in algal diversity will impact coral resilience and survival
Transient Transmembrane-Electrostatically Localized Protons and Transmembrane Potential in a Laser Flashed Bacteriorhodopsin Purple Membrane Open Flat Sheet - Summary
No full textThe transmembrane-electrostatically localized protons/cations charges (TELCs, also known as TELPs) model may serve as a unified framework to explain a wide range of bioenergetic phenomena. Transient transmembrane-electrostatically localized protons (TELPs) and transmembrane potential in a laser flash-energized bacteriorhodopsin (bR) purple membrane (PM) open flat sheet are now better analyzed. Under the Heberle et al. (1994) experimental conditions, the number of bR molecules is now calculated to be 8200 per PM open flat sheet with a diameter of 600 nm. With a single-turnover laser flash intensity of 3 mJ/cm² to photoexcite 10% of the bR molecules, the laser flash-induced peak TELCs density is calculated to be 2900 per µm² of PM, which translates to a peak transient transmembrane potential of 50 mV. The bR protonic outlet protrudes into the liquid phase outside the putative “potential well/barrier”. The observation is in line with the TELPs model, but does not support the “potential well/barrier” model. The author encourages research on more relevant protonic capacitor cell systems that have transmembrane potential with TELCs comprising excess positive charges at one side and excess anions at the other side of the membrane
Advances in Battery Modeling and Management Systems: A Comprehensive Review of Techniques, Challenges, and Future Perspectives
No full textEnergy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include digital twins (DTs), cloud computing, and wireless BMSs. Nonetheless, there are still issues with cost optimization, cybersecurity, and computing efficiency. This study presents key advancements in battery modeling and BMS applications, including defect diagnostics, temperature management, and state-of-health (SOH) prediction. A comparison of machine learning (ML) methods for SOH prediction is given, emphasizing how well neural networks (NNs) and transfer learning function with real-world datasets. Additionally, future research objectives are described, with an emphasis on next-generation sensor technologies, cloud-based BMSs, and hybrid algorithms. Distinct from existing reviews, this paper integrates academic modeling with industrial benchmarking and highlights the convergence of hybrid physics-informed and data-driven techniques, multi-physics simulations, and intelligent architecture. For high-performance EV applications, this analysis offers insight into creating more intelligent, adaptable, and secure BMSs by addressing current constraints and utilizing state-of-the-art technologies
The PTP4A3 Inhibitor KVX-053 Reduces Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Virulence, Inflammation, and Development of Acute Lung Injury in K18-hACE2 Mice
No full textSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a global health crisis, marked by high transmissibility and virulence. Despite widespread vaccination efforts, effective treatments for COVID-19, particularly for severe cases leading to Acute Respiratory Distress Syndrome (ARDS), remain limited. This study investigates the efficacy of KVX-053, a protein tyrosine phosphatase type IVA (PTP4A3) small molecule inhibitor, in modulating SARS-CoV-2-induced inflammation and lung injury using in vitro cell models and in vivo K18-hACE2 transgenic mice. KVX-053 reduced in vitro pro-inflammatory cytokine expression, including TNFα, CXCL10, and CXCL11, without impacting viral replication or cell viability. K18-hACE2 mice treated with KVX-053 demonstrated marked improvement in clinical scores and reduced histological evidence of lung injury compared to untreated SARS-CoV-2-infected controls. KVX-053 mitigated the activation of key inflammatory mediators in the lung, including NLRP3 inflammasomes, IL-6, and phosphorylated STAT3, effectively curbing the “cytokine storm” associated with severe COVID-19. Importantly, treatment preserved lung parenchymal integrity, reduced edema, and minimized macrophage infiltration. Our findings highlight PTP4A3 as a potential critical regulator of the inflammatory response during SARS-CoV-2 infection. KVX-053, a potent and selective PTP4A3 inhibitor, emerges as a promising host-directed therapeutic strategy for mitigating ARDS and inflammation-driven lung injury in SARS-CoV-2 and potentially other respiratory viral infections. Future studies are required to optimize dosing strategies, elucidate precise molecular mechanisms, and validate these findings in clinical settings