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The Thomas Jefferson National Accelerator Facility collaborates with Lawrence Berkeley National Lab to implement a dynamic UDP load balancer (LB) for high-throughput scientific data processing. This study employs a simulation to compare the efficacy of Proportional, Integrative, Derivative (PID) controllers and Q-Learning based controllers for configuring the load balancer. Two cluster configurations, homogeneous and heterogeneous, were examined. The simulation results indicate that PID control is superior in both configurations. In homogeneous clusters, PID achieved a 50% reduction in aggregate queue levels and maintained an even distribution across computational nodes (CNs). In contrast, Q-Learning was less effective in heterogeneous environments, exacerbating queue levels compared to the no-control case and failing to achieve balance across the cluster. Our findings suggest that PID control should be used for the ESnet-JLab FPGA Accelerated Transport (EJFAT) system. [ABSTRACT FROM AUTHOR]

It has become increasingly important for K-12 students to learn how to investigate patterns, correlations, and significance in data. The Berkeley Engineering Research Experiences for Teachers plus Data (BERET+D) pairs undergraduate pre-service teachers and experienced in-service science and mathematics teachers (PSTs and ISTs) to engage in engineering and data science research, exploring and analyzing data sets drawn from a variety of STEM fields and laboratories across the UC Berkeley campus. In addition to conducting independent summer research projects with guidance from university research faculty, the program provides opportunities for: (1) PSTs to develop data science-based lessons inspired by their research and aligned to the Next Generation Science Standards (NGSS), (2) ISTs to create data science-based curricula designed to inspire middle and high school students to see STEM classes as exciting and with real-life applications, and (3) ISTs to collaborate with and mentor PSTs preparing to enter K-12 STEM classrooms. Contributing towards broader impacts, CalTeach recruits a racially and socioeconomically diverse population of PSTs, and all ISTs were recruited from local public schools, in order to educate, prepare, and encourage more minority and female K-12 students to consider higher education and careers in STEM. During the first two summers of this project (2020-2021), participants completed over forty data-science related projects, developed over thirty K-12 data-science related lesson plans in math, science, and engineering, and created six classroom-ready and publicly accessible (teachengineering.org) curricular units showcasing data science. As an example of these curricular units, and as further evidence of the project's broader impact, one IST has developed an ongoing partnership between their classroom and a research laboratory on campus allowing high school physics students to learn data science techniques by analyzing and interpreting distant satellite signals collected by radio telescopes. Preliminary evaluation of this ongoing project revealed that participants viewed data science as important and essential in K-12 curriculum, that data analysis is a critical and useful skill for youth, and that data science aligns closely with the science and engineering practices called forth by NGSS. Though constrained by work-from-home restrictions due to COVID during the first two years, participants described their experience as positive and valuable, particularly in conceiving of ways to engage young learners with data-science through remote instruction. [ABSTRACT FROM AUTHOR]

Over last few decades, education researchers have focused heavily on pedagogy, learning outcomes, academic achievement, retention and graduation, among other host of factors. Instructors and their content delivery techniques and methods (pedagogy) have received a lot of attention and rightly so because instructors have the pivotal role influencing not only the delivery but also the learning outcomes and achievements. Good teaching practices (Classroom skills) ensure conducive learning environment, thereby enhancing the learning experience and the outcomes. In this short manuscript we examine the results of a 40--item survey instrument, originally developed by the Center for Research and Development in Higher Ed., U of CA, Berkeley, and modified subsequently by CU-Denver Office of Teaching Effectiveness. This survey is administered to a junior-level class over a period of six semesters. A subset of twentytwo items is identified. Subsequently, a two-factor structure comprising of 13 items is proposed. [ABSTRACT FROM AUTHOR]

The E2 nuclear resonance effect in kaonic atoms occurs when the energy of atomic de-excitation closely matches the energy of nuclear excitation, leading to the attenuation of some X-ray lines in the resonant isotope target. This phenomenon provides crucial information on the strong interaction between kaons and nuclei. The only nuclear E2 resonance effect observed so far was in the K− −9842Mo isotope, measured by G. L. Goldfrey, G-K. Lum, and C. E. Wiegand at Lawrence Berkeley Laboratory in 1975. However, the 25 hours of data taking were not sufficient to yield conclusive results. In four kaonic Molybdenum isotopes (9442Mo, 9642Mo, 9842and Mo, and 10042Mo), the nuclear E2 resonance effect is expected to occur at the same transition with similar energy values. To investigate this, the KAMEO (Kaonic Atoms Measuring Nuclear Resonance Effects Observables) experiment plans to conduct research on kaonic Molybdenum isotopes at the DAΦNE e+e− collider during the SIDDHARTA-2 experiment. The experimental strategy involves exposing four solid strip targets, each enriched with one Molybdenum isotope, to negatively charged kaons and using a germanium detector to measure X-ray transitions. In addition, a non-resonant 9242Mo isotope solid strip target will be used as a reference for standard non-resonant transitions. [ABSTRACT FROM AUTHOR]

Upper-division technical electives in undergraduate engineering curricula provide students with an opportunity to learn about various industries within the discipline, which are often aligned well with ongoing research areas conducted by professors in the department. As an undergraduate student, establishing a research area of interest is a first step towards becoming a researcher and is an important part of the pipeline to careers in academia. A semester-long seminar course was offered to undergraduate students in the Mechanical Engineering Department at the University of California, Berkeley to expose students to ongoing research, while providing an opportunity to network with researchers in the department. The course consisted of weekly research talks and discussions. Invited speakers presented a diverse array of research areas and their pathway to research. To assess the course's impact, we surveyed students at the beginning and end of the semester. Approximately half of the students in the course were 1st year students. Survey results showed improved confidence when engaging in research discussions, and better understanding of graduate student research. Additionally, students enjoyed listening to presentations and connecting with graduate students. During the seminar, students learned how researchers and engineers design their experiments and learned about the wide array of applications in mechanical engineering. These preliminary findings can aid faculty and staff developing initiatives to enhance the undergraduate experience in engineering. Future work is needed to understand the long-term impact of participating in such a seminar course (e.g., impact on retention and participation in research). [ABSTRACT FROM AUTHOR]

Within a (3+1)D viscous hydrodynamic model we compute anisotropic flow in small system collisions as performed at the Relativistic Heavy Ion Collider and measured by the STAR and PHENIX Collaborations. We emphasize the importance of the rapidity dependence of the geometry for interpreting the differences encountered in measurements by the two collaborations. [ABSTRACT FROM AUTHOR]

The diffusion wake accompanying the jet-induced Mach cone serves as a distinctive tool for investigating the characteristics of quark-gluon plasma(QGP) in high-energy heavy-ion collisions. This phenomenon results in a reduction of soft hadrons opposite to the direction of the propagating jet. Our study explores the 3D structure of the diffusion wake induced by γ-triggered jets in Pb+Pb collisions at the LHC energy, utilizing the coupled linear Boltzmann transport and hydro model. We identify a valley structure caused by the diffusion wake, superimposed on the initial multiple parton interaction (MPI) ridge in both rapidity and azimuthal angle. This leads to a double-peak pattern in the rapidity distribution of soft hadrons opposite to the jets. In addition, when jet goes through the QGP medium, it will be affected by the flow velocity. So we take a new method to detect the effect of jet-flow coupling in heavy-ion collisions. [ABSTRACT FROM AUTHOR]

Within the Color Glass Condensate framework, we demonstrate that exclusive vector meson production at high energy is sensitive to the geometric deformation of the target nucleus and subnucleon scale fluctuations. Deformation of the nucleus enhances the incoherent cross section in the small |t| region. Subnucleon scale fluctuations increase the incoherent cross section in the large |t| region. In ultra-peripheral collisions (UPCs), larger deformation leads to a wider distribution of the minimal impact parameter Bmin required to produce a UPC. This, together with larger incoherent cross sections for larger deformation, results in smaller extracted radii. Our results demonstrate great potential for future studies of nuclear structure in UPCs and electron-ion collisions. [ABSTRACT FROM AUTHOR]

This contribution outlines the physics program and performance of the Forward Calorimeter (FoCal), which is a planned new detector subsystem for the ALICE experiment in Run 4. The forward pseudorapidity coverage of 3.2 < η < 5.8 allows to study the gluon density in hadronic matter down to x ∼ 10−6 and enables the search for gluon saturation at the LHC. [ABSTRACT FROM AUTHOR]

In this contribution, we use the parity doublet model to investigate the fluctuations of the net-baryon number density. We discuss the systematics of the susceptibilities and their ratios for nucleons of positive and negative parity, as well as their correlator. We demonstrate that the fluctuations of positiveparity nucleon do not reflect the fluctuations of the total net-baryon number at the chiral phase transition. [ABSTRACT FROM AUTHOR]

Event generators play an important role in all physics programs at the Large Hadron Collider and beyond. Dedicated efforts are required to tune the parameters of event generators to accurately describe data. There are many tuning methods ranging from expert-based manual tuning to surrogate function-based semi-automatic tuning, to machine learning-based re-weighting. Although they scale differently with the number of generator parameters and the number of experimental observables, these methods are effective in finding optimal generator parameters. However, none of these tuning methods includes the Monte Carlo (MC) systematic uncertainties. That makes the tuning results sensitive to systematic variations. In this work, we introduce a novel method to incorporate the MC systematic uncertainties into the tuning procedure and to quantitatively evaluate uncertainties associated with the tuned parameters. Tested with a dummy example, the method results in a χ2 distribution that is centered around one, the optimal generator parameters are closer to the true parameters, and the estimated uncertainties are more accurate. [ABSTRACT FROM AUTHOR]

LHC@home was launched as a BOINC project in 2004 as an outreach project for CERN's 50 years anniversary. Initially focused on the accelerator physics simulation code SixTrack, the project was expanded in 2011 to run other physics simulation codes on Linux thanks to virtualisation. Later on the experiment and theory applications running on the LHC@home platform have evolved to use containers and take advantage of the CVMFS file system as well as content delivery networks. Furthermore, a substantial part of the contributed computing capacity nowadays is provided as opportunistic back-fill from data centers with spare capacity, in addition to enthusiastic volunteers. The paper will address the challenges with this distributed computing model, new applications to exploit GPUs and the future outlook for volunteer computing. [ABSTRACT FROM AUTHOR]

Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future. [ABSTRACT FROM AUTHOR]

Background: Community health centers (CHCs) pivoted to remote chronic care services during the COVID-19 pandemic. While care continuity is associated with improved care quality and patients' experiences, telehealth's impact on these relationships is unclear. Objective: We aimed to examine the association among care continuity, telehealth use, and quality of diabetes or hypertension care in CHCs before and during the COVID-19 pandemic. Methods: We collected electronic health record data from a cohort of 20,792 patients with diabetes or hypertension with =2 visits per year from March to December 2019 and 2020 among 166 California CHCs in the OCHIN Accelerating Data Value Across a National Community Health Center Network Collaborative. Logistic regression models estimated the association between care continuity (modified, modified continuity index [MMCI]) and telehealth adoption and blood pressure or hemoglobin A1c (HbA1c) testing. Generalized linear regression models for 2019 and 2020 estimated the association between MMCI and blood pressure or HbA1c, exploring telehealth as a mediator. Results: Patients experienced reduced care continuity (2019: MMCI=0.71, SD 0.28; 2020: MMCI=0.63, SD 0.36; P<.001) and more blood pressure (99.99% vs 99.75%) and HbA1c (53.38% vs 48.99%) assessments in 2019 vs 2020. Telehealth accounted for 0.33% of 2019's visits and 9.55% of 2020's visits. MMCI scores were associated with higher odds of telehealth use in 2020 (odds ratio [OR] 1.96; P<.001). MMCI (2019: OR 1.72, P<.001; 2020: OR 1.66, P<.001) and telehealth use (2019: OR 2.44, P<.001; 2020: OR 6.82, P<.001) were associated with greater HbA1c testing. MMCI was associated with lower HbA1c values in 2020 (-0.40, P=.01) and lower systolic (2019: -1.64, P=.045; 2020: -2.40, P=.001) and diastolic (2019: -1.24, P=.007; 2020: -1.33, P=.001) blood pressure. MMCI and telehealth were not associated with HbA1c values in 2019. In 2020, telehealth mediated the relationship between MMCI and HbA1c testing (percent mediated 59%), but not between MMCI and other study outcomes. Conclusions: Care continuity facilitates telehealth use and enables resilient performance on process measures. Elucidating how care continuity influences telehealth adoption may provide insights about implementing patient-centered innovations. [ABSTRACT FROM AUTHOR]

Background: SMS text messaging interventions are increasingly being used to help people manage mental health symptoms due to the COVID-19 pandemic. Despite the widespread adoption of SMS text messaging interventions, little is known about racial and ethnic differences in effectiveness, feasibility, and implementation factors. Objective: A hybrid type 1 mixed-methods study was conducted to compare the effectiveness and implementation of the StayWell intervention for Latinx and non-Latinx White (White) adults using elements of the RE-AIM framework. Methods: Adults using the StayWell intervention received daily mood inquiries and skills-based text messages for 60 days and reported symptoms via online surveys and the HealthySMS portal. Reach was assessed by the share of adults reached via distinct recruitment methods, and effectiveness was evaluated using the Personal Health Questionnaire (PHQ-8) depression scale and General Anxiety Disorder (GAD-7) scale. Adoption was assessed with user engagement, defined as the share of responses to the (60) daily mood inquiries. Implementation was evaluated based on user feedback on the number/timing of messages, the difficulty of using the program, and the System Usability Scale. Maintenance was assessed with user reports of the likelihood of continuing and recommending the program. Quantitative RE-AIM indicators were assessed using at test for continuous outcomes and a chi-square test for categorical outcomes. Mixed-effects linear regressions examined heterogeneity among Latinx and White users in the outcomes' (ie, PHQ-8 and GAD-7) changes over time. A thematic text analysis of responses to an open-ended question about participant experiences of the program was conducted to help contextualize differences in the effectiveness and implementation of StayWell between Latinx and White users. Results: Among 398 users, 262 (65.8%) responded to the postintervention assessment. Upon completion, depressive (-1.48; P=.001) and anxiety (-1.38; P=.001) symptoms decreased among all users. Compared to White adults, Latinx adults reported an additional -1.45 (P<.05) decline in PHQ-8 scores, adjusting for demographics, recruitment sample, and engagement. Among Latinx adults, StayWell resulted in a greater improvement of depression symptoms (25.8% vs 13.8% reduction; P=.02) but not in anxiety symptoms (21.2% vs 15.9% reduction; P=.23). Despite Latinx adults reporting lower usability (76.8 vs 83.9; P=.001) than White adults, they were more likely to report interest in continuing the program (7.5 vs 6.2; P=.001) and recommend StayWell to a family member or friend (7.8 vs 7.0; P=.013). Repetitive content was a critical barrier to engaging Latinx adults in StayWell; among Latinx adults, support groups and bidirectional messages were considered important program modifications. Conclusions: Although anxiety and depressive symptoms decreased among all StayWell users after receiving 2 messages daily for 60 days, Latinx users experienced greater reductions in depression symptoms compared to White users. Bidirectional text messaging using contextual information and ecological momentary analysis of mood data are promising adaptations to enable the tailoring of messages and improved usability of StayWell, which may be especially effective in improving Latinx user engagement. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) has been among the fast-growing areas in recent years in different fields, like health applications, military applications, the operations of disaster relief, etc. In an unattended, even hostile environment, nodes in WSNs are commonly deployed. What is worse, these nodes are fitted with minimal resources for communication, computation, storage and battery. It is also impossible to guarantee the lifespan of WSNs without reducing their network performance. A massive volume of application-specific data is produced by WSNs. Such data must be processed and sent to the base station by sensor nodes, which is an expensive matter. As the resources of sensor nodes are constrained, the key challenges facing WSNs are effective data processing and energy conservation. To tackle those challenges, data aggregation can be used, which is a popular approach that filters redundant data in-network and speeds up the extraction of knowledge. In this paper, we propose an energy-saving data aggregation method for WSNs based on the extraction of extrema points (DAEP) to greatly minimize the energy consumption of each node and boost the higher lifespan of WSN. The efficiency of the proposed method was measured by performing multiple simulation experiments based on real sensor readings collected in Intel Berkeley laboratories and comparing the results achieved with some work in the literature. The results show the ability of the proposed method to reduce the load on the sensor nodes in terms of reducing the amount of transmitted data by up to 69-80%, energy consumed by 73-77% while maintaining an acceptable level of accuracy compared to some existing works. [ABSTRACT FROM AUTHOR]

Objective. To assess the relationship between a hospital's prioritization of substance use within their community benefit documents and its offerings of substance use services in its clinical setting. Data Sources and Study Setting. This study of a national sample of U.S. hospitals utilizes secondary data sourced from the 2021 American Hospital Association (AHA) Annual Survey and data collected from publicly available Community Health Needs Assessments (CHNAs) and Implementation Strategies (ISs) produced by hospitals between the years 2018-2021. Study Design. This cross-sectional study employs descriptive statistics and multivariable analysis to assess relationships between prioritization of substance use on hospital ISs and the services offered by hospitals, with consideration of community and hospital characteristics. Data Collection/Extraction Methods. Hospital CHNA and IS documents were collected by research team members from hospital websites or by contacting hospitals directly and were then coded to identify whether the substance use needs were prioritized by the hospital. The collected data were incorporated into a data set along with the secondary data sourced from the 2021 AHA Annual Survey. Principal Findings. Multivariable analysis found a significant and positive relationship between the prioritization of substance use as a community need on a hospital's implementation plan and the number of the services included in this analysis offered by the hospital. Significant and positive relationships were also identified for five of the six specific service categories assessed, as well as for hospital size. . Conclusions.The availability of service offerings is related both to a hospital's prioritization of substance use as a community need and to its size, indicating that these factors are likely all inter-related in regard to a hospital's sense of its ability to address substance use as a community need. . Policymakers should be attuned to why hospitals may not prioritize a need prevalent within their community, such as whether the organization feels like it lacks the resources or infrastructure to take such steps. This study also highlights the value of the assessment and implementation strategy process as a way for hospitals to engage with community needs. [ABSTRACT FROM AUTHOR]

Background: In response to the significant stressors on health care delivery created by COVID-19, CITRIS Health and partner organizations developed 2 telehealth solutions through a rapid co-design process: Lighthouse and ACTIVATE. These programs were developed to support providers serving underserved and vulnerable populations who lack the tools and resources to support patients with chronic illness or who are isolated. These challenges were exacerbated by the COVID-19 pandemic, which increased the need for resources to support vulnerable patients who could not come into a clinic in person or were isolated and lacked access to services. ACTIVATE and Lighthouse apply 2 different telehealth strategies to reach vulnerable populations. Objective: This paper presents lessons learned from the design, development, and implementation of 2 innovative telehealth programs developed to improve health care delivery, access to care, digital literacy, and patient outcomes: ACTIVATE and Lighthouse. Methods: ACTIVATE is a comprehensive digital health pathway for community health centers that care for those who are medically underserved. ACTIVATE is an innovative, evidence-based, and sustainable telehealth program, designed to benefit vulnerable populations in rural and agricultural communities in the California Central Valley who experience significant health disparities. Lighthouse focuses on connecting older adults in congregate affordable housing, which are settings where residents are particularly vulnerable to isolation, the lack of health care resources, and limited to no access to the internet. Lighthouse provides digital literacy training as well as access to broadband internet with the goal of increasing communication, engagement, and access to health care. Results: This presentation will discuss successful design and implementation strategies as well as organizational and policy barriers to program operations. Conclusions: In addition to reviewing program and implementation outcomes, strategies for replication and sustainability will be discussed. Although developed in response to COVID-19, the ultimate success of Lighthouse and ACTIVATE is dependent upon its successful scaling beyond the pandemic. [ABSTRACT FROM AUTHOR]

In this work we analyzed the sensitivity of crystal orientational analysis using linear dichroism in x-ray microscopy. A calcite sample was investigated at the COherent Scattering and MICroscopy (COSMIC) beamline at the Advanced Light Source (ALS) using conventional scanning transmission x-ray microscopy and ptychography, and data processing was performed with varying numbers of polarization angles using a Python based fitting algorithm to determine the required number of angles needed for orientational analysis with high precision and high angular resolution. [ABSTRACT FROM AUTHOR]

Tuned Mass Dampers (TMDs) have been studied for years in many aspects ranging from elastic to inelastic structures, with linear and non-linear absorbers. Other features such as the variability of the frequency content and magnitude of the ground motion have also been investigated. Most research has been made on structures with fixed base, but it is known that soil-structure interaction can negatively affect the seismic performance of TMDs if not considered in design. In this study a coupled analysis of the entire soil-structure-TMD system is presented by referring to an existing building implemented in OpenSees. The structure is equipped with a non-conventional TMD characterized by a high mass ratio, realized as a superelevation of the existing structural layout. The TMD effectiveness under seismic conditions is evaluated through nonlinear dynamic analyses on the full soil-structure model, varying the TMD mass ratio and comparing the results with the ones obtained by means of the classical sub-structure approach. [ABSTRACT FROM AUTHOR]