Large Rate Research Articles

Surface shot peening of cast SiCw/Al composites

ABSTRACT The surface shot peening treatment was carried out on the as-cast SiCw/Al composites, and the distribution of the residual stresses in the surface matrix was measured, the X-ray diffraction method for the yield strength of the surface matrix of the composites was established to explore the strengthening effect of the surface shot peening. The results show that the surface matrix of the composites after shot peening shows a large compressive residual stress state, and the FWHM, that is, the microstructure strengthening effect, is significantly increased., resulting in an increase in the yield strength of the surface matrix of the composites.

Transitioning to sustainable, climate-resilient healthcare: insights from a health service staff survey in Australia

BackgroundMore than 80 countries, including Australia, have made commitments to deliver climate-resilient and low carbon healthcare. Understanding how healthcare workers view their own and their organization’s efforts to achieve sustainable and climate-resilient healthcare practice is vital to inform strategies to accelerate that transition.MethodsWe conducted an online staff survey in a large state government hospital-and-health-service organisation in Queensland, Australia, to ascertain attitudes and practices towards environmentally sustainable, climate-resilient healthcare, and views about the organizational support necessary to achieve these goals in their workplace.ResultsFrom 301 participants showed staff strongly support implementing sustainable and climate-resilient healthcare but require significantly more organizational support. Participants identified three categories of organizational support as necessary for the transition to environmentally sustainable and climate-resilient health services and systems: (1) practical support to make sustainability easier in the workplace (e.g. waste, energy, water, procurement, food, transport etc.); (2) training and education to equip them for 21st century planetary health challenges; and (3) embedding sustainability as ‘business as usual’ in healthcare culture and systems.ConclusionsThe research provides new insight into health workforce views on how organizations should support them to realize climate and sustainability goals. This research has implications for those planning, managing, implementing, and educating for, the transition to environmentally sustainable and climate-resilient health services and systems in Queensland, Australia, and in similar health systems internationally.

Fault-tolerant fusing of repeater graph states and its application

A repeater graph state (RGS) is a class of multipartite entangled states with favourable features for quantum communication, particularly as the enabler of all-photonic quantum repeaters. In this paper, based on an alternative formalism, we show that two RGSs can be fused via a Bell measurement in a fault-tolerant manner. The fusing of multiple RGSs can thus be carried out simultaneously and flexibly, which enables a 2-step rapid generation of an arbitrarily large RGS from relatively small building blocks using heralded but non-deterministic photonic Bell measurements. It then makes possible the distribution of a large multipartite entangled state from multiple sources to many parties via high-loss channels, and hence also a satellite-based entanglement distribution scheme. In such a scheme, stationary memories are only required for ground stations, the growth of memory noise with its scale can be effectively suppressed, and recipients of entangled pairs of qubits may remain anonymous.

"COVID-19 - A Perfect Storm": A Qualitative Exploration of Residential Care Facility Managers Perspectives on the Psychosocial Impacts of COVID-19.

Growing evidence highlights the negative impact of managing the COVID-19 pandemic on the wellbeing of the healthcare workforce, including in the aged care sector. We undertook a qualitative study during the pandemic's third year to explore the psychosocial impacts on nine managers of residential care facilities (RCFs) across metropolitan and rural New South Wales, the largest state in Australia. Four themes were identified: (1) Increased pressure on maintaining aged care services, (2) Increased responsibility on RCF managers, (3) Psychosocial impacts due to accumulating pressures, and (4) Experience of beneficial supports. COVID-19 compounded pre-pandemic sector challenges and added new stressors. While resilient and resourceful, RCF managers experienced workplace stress and burnout, which may affect quality of resident care and impact on staff retention. There is a need for more investment to effectively support staff, and research to identify optimal psychosocial and management supports.

Potential-based reward shaping using state–space segmentation for efficiency in reinforcement learning

Reinforcement Learning (RL) algorithms encounter slow learning in environments with sparse explicit reward structures due to the limited feedback available on the agent’s behavior. This problem is exacerbated particularly in complex tasks with large state and action spaces. To address this inefficiency, in this paper, we propose a novel approach based on potential-based reward-shaping using state–space segmentation to decompose the task and to provide more frequent feedback to the agent. Our approach involves extracting state–space segments by formulating the problem as a minimum cut problem on a transition graph, constructed using the agent’s experiences during interactions with the environment via the Extended Segmented Q-Cut algorithm. Subsequently, these segments are leveraged in the agent’s learning process through potential-based reward shaping. Our experimentation on benchmark problem domains with sparse rewards demonstrated that our proposed method effectively accelerates the agent’s learning without compromising computation time while upholding the policy invariance principle.

Beyond the scoreboard: school-based sport experiences and high school student-athlete well-being

ABSTRACT With an estimated seven million high school student-athletes in the United States participating in school-based sports each year, opportunities exist to examine how recent changes in education and sport are affecting this generation of student-athletes. The COVID-19 pandemic, school cancelations, and emergent trends related to recruiting, name, image, and likeness policies, sport specialization, academic pressures, and adolescent mental health concerns are current factors influencing school-based sports. To examine associations between the United States education and sport environment and student-athlete well-being, this qualitative study sought to understand the experiences of high school student-athletes in this era of sport. Seven focus groups were conducted with 50 high school student-athletes in diverse school districts in one large Midwestern state. Emergent themes indicated structural, contextual, and psychosocial factors are shaping student-athlete experiences and affecting multiple domains of their well-being. Notably, student-athletes overwhelmingly wanted to perform at high levels as they returned to school and sport as COVID-19 restrictions were lifting, yet felt they were struggling to meet these demands. Findings contribute to an important conversation about the needs of high school student-athletes in the United States and can inform future interventions, coach training programs, and school policies.

Forced exit from the joint-decision trap: US power and the harmonisation of company taxation in the EU

ABSTRACT EU member states have traditionally opposed the harmonisation of company taxation to preserve national sovereignty over a core state power. Accordingly, legislation and case law were marked by negative integration enforcing the free circulation of capital. Tax avoidance and competition intensified, entrenching interest heterogeneity among small low-tax and large normal-tax member states. In December 2022, however, the Council of the EU introduced an effective minimum tax and harmonised the calculation of taxable corporate profits. Why did member states exit the joint-decision trap in company taxation? I show that external pressure from the US government was decisive. Whereas previous proposals for harmonisation failed in the Council despite impeccable orchestration by the Commission and historically high levels of politicisation, the Directive on effective minimum taxation passed despite declining politicisation and little Commission involvement. Instead, the Biden administration clinched a deal on global minimum taxation with large member states and helped enforce it at EU level through bilateral pressure on opponents. By facilitating EU implementation of global minimum taxation, the Biden administration hoped to foster Congressional adoption of corresponding reforms. Hence, exits from the joint-decision trap can result from external pressure, whereas governments may enforce international agreements to exploit policy feedback.

The territorial logic of an export-led growth strategy: Israel’s regime change after the Second Intifada

In recent decades, neo-mercantilism has become ubiquitous among small and large states. The conventional explanation for the appeal of an export-led growth regime has focused on the material interests of domestic growth coalitions. This article offers an alternative explanation for transition to export-led growth strategies, based on the geopolitical and territorial interests of states. It posits that states embrace a mercantilist export-led growth model because it aligns with their geopolitical objectives. The article demonstrates the geopolitical hypothesis based on the transition of Israel from a consumption-led to an export-led growth strategy after the end of the peace process and the outbreak of the Second Intifada in 2000.

Study of Power Sector Reforms for Madhya Pradesh Development

AbstractThe nuclear power industry in India plays a significant role in meeting the country's energy demands and fostering economic growth. This research study explores the need for research in the context of India's nuclear power industry, specifically focusing on Madhya Pradesh (MP). The aim is to investigate the potential of nuclear energy as a clean and sustainable resource for economic development in the region. The study considers the interplay between economic growth, resource reduction, and energy consumption within the state.Highlights of the article importance of nuclear energy in India's overall energy mix and its potential for driving economic progress. The Indian nuclear power industry encompasses various sectors, including industrial, transmission, and commercial. Nuclear energy is recognized as a clean and vital economic resource, capable of providing substantial benefits to the national economy.The focus of this research is MP, the largest state in terms of nuclear power generation in India. The state's economic growth depends on several factors, including energy consumption. To achieve significant improvements in resource reduction and sustainable development, it is crucial to examine the role of nuclear power within the state's energy sector.The research study presented in this article addresses the electricity reform of the Madhya Pradesh State Electricity Board (MPSEB). By evaluating the infrastructure and recent discussions surrounding the Chutka Nuclear Power Plant project, which aims to support the energy sector in the Mandla region, this study aims to contribute to the understanding of the potential economic benefits of nuclear power in MP.The findings from this research will provide valuable insights into the relationship between nuclear energy, economic growth, and sustainable development in the Indian context. It will help policymakers, industry stakeholders, and researchers in formulating strategies and policies that promote the effective utilization of nuclear power as a catalyst for economic progress while ensuring environmental sustainability. This study ultimately contributes to the knowledge base required for harnessing the full potential of nuclear energy in India's energy landscape.

A Qualitative Analysis of Beliefs about Masculinity and Gender Socialization among US Mothers and Fathers of School-Age Boys.

Internalization of culturally dominant masculine gender role norms can have harmful impacts on the physical and emotional health of men and boys. Although parents play an important role in influencing gender-related beliefs in their children, limited research has examined how contemporary parents conceptualize masculinity and their role in gender socialization. The current study conducted 13 focus groups with Black, Latino, and White parents (N = 83) of school-age boys from rural and urban areas in a large southeastern state in the US. Parent beliefs about masculinity existed across a spectrum from "rigid" (representing narrow, culturally dominant masculine norms) to "flexible" (defining a broader set of behaviors and attitudes as masculine). In general, more flexible beliefs were expressed by mothers than fathers, and by White than Black and Latino parents. Most parents reacted positively to messages about potential harms associated with restrictive masculinity norms; however, many saw these issues primarily as parenting challenges (e.g., teaching boys to resist negative peer influences) rather than related to gender socialization. Some unique themes also emerged within racial/ethnic groups, with Black parents noting the impact of racial discrimination on societal expectations for Black men and fathers, and Latino parents describing generational shifts towards more equitable gender role attitudes and parenting practices. These findings highlight the need for more complex and nuanced messages about masculinity norms and their relationship to health and well-being and can help inform the development of interventions to promote healthy masculine gender socialization, increase health equity, and prevent injury and violence.

Towards Efficient Coordination in Multi-Agent Reinforcement Learning through Hybrid Information-Driven Approaches

Abstract: This paper introduces a novel framework to enhance coordination in multi-agent reinforcement learning (MARL) systems by integrating mutual information (MMI) principles with information-driven strategies. Firstly, we propose a variational approach leveraging MMI to promote coordinated behaviors among agents by regulating the cumulative return alongside the simultaneous mutual information between multi-agent actions. Through the introduction of a latent variable inducing nonzero mutual information and the application of a variational bound, a tractable lower bound is derived for the MMI-regularized objective function. This bound combines maximum entropy reinforcement learning with reducing uncertainty in other agents' actions. Subsequently, we present a practical algorithm, Variational Maximum Mutual Information Multi-Agent Actor-Critic (VM3-AC), utilizing policy iteration to maximize the derived lower bound, following a centralized learning with decentralized execution (CTDE) paradigm. Secondly, we explore the challenges of large state spaces and limited computational resources in distributed multi-agent systems, proposing a hybrid information-driven MARL approach. This approach integrates informationtheoretic models as heuristics to aid navigation in sparse state spaces, complemented by information-based rewards within an RL framework to learn higher-level policies efficiently. Our preliminary findings suggest that this hybrid approach could enhance exploration efficiency significantly, demonstrating approximately three orders of magnitude improvement over naive baseline metrics. Although still in its early stages, this work presents a promising direction for future research in achieving efficient coordination in MARL systems

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

Photonic Source of Heralded Greenberger-Horne-Zeilinger States.

Generating large multiphoton entangled states is of main interest due to enabling universal photonic quantum computing and all-optical quantum repeater nodes. These applications exploit measurement-based quantum computation using cluster states. Remarkably, it was shown that photonic cluster states of arbitrary size can be generated by using feasible heralded linear optics fusion gates that act on heralded three-photon Greenberger-Horne-Zeilinger (GHZ) states as the initial resource state. Thus, the capability of generating heralded GHZ states is of great importance for scaling up photonic quantum computing. Here, we experimentally demonstrate this required building block by reporting a polarisation-encoded heralded GHZ state of three photons, for which we build a high-rate six-photon source (547±2 Hz) from a solid-state quantum emitter and a stable polarization-based interferometer. The detection of three ancillary photons heralds the generation of three-photon GHZ states among the remaining particles with fidelities up to F=0.7278±0.0106. Our results initiate a path for scalable entangling operations using heralded linear-optics implementations.

Heralded Three-Photon Entanglement from a Single-Photon Source on a Photonic Chip.

In the quest to build general-purpose photonic quantum computers, fusion-based quantum computation has risen to prominence as a promising strategy. This model allows a ballistic construction of large cluster states which are universal for quantum computation, in a scalable and loss-tolerant way without feed forward, by fusing many small n-photon entangled resource states. However, a key obstacle to this architecture lies in efficiently generating the required essential resource states on photonic chips. One such critical seed state that has not yet been achieved is the heralded three-photon Greenberger-Horne-Zeilinger (3-GHZ) state. Here, we address this elementary resource gap, by reporting the first experimental realization of a heralded 3-GHZ state. Our implementation employs a low-loss and fully programmable photonic chip that manipulates six indistinguishable single photons of wavelengths in the telecommunication regime. Conditional on the heralding detection, we obtain the desired 3-GHZ state with a fidelity 0.573±0.024. Our Letter marks an important step for the future fault-tolerant photonic quantum computing, leading to the acceleration of building a large-scale optical quantum computer.

Double Dissociation of Spontaneous Alpha-Band Activity and Pupil-Linked Arousal on Additive and Multiplicative Perceptual Gain.

Perception is a probabilistic process dependent on external stimulus properties and one's internal state. However, which internal states influence perception and via what mechanisms remain debated. We studied how spontaneous alpha-band activity (8-13 Hz) and pupil fluctuations impact visual detection and confidence across stimulus contrast levels (i.e., the contrast response function, CRF). In human subjects of both sexes, we found that low prestimulus alpha power induced an "additive" shift in the CRF, whereby stimuli were reported present more frequently at all contrast levels, including contrast of zero (i.e., false alarms). Conversely, prestimulus pupil size had a "multiplicative" effect on detection such that stimuli occurring during large pupil states (putatively corresponding to higher arousal) were perceived more frequently as contrast increased. Signal detection modeling reveals that alpha power changes detection criteria equally across the CRF but not detection sensitivity (d'), whereas pupil-linked arousal modulated sensitivity, particularly for higher contrasts. Interestingly, pupil size and alpha power were positively correlated, meaning that some of the effect of alpha on detection may be mediated by pupil fluctuations. However, pupil-independent alpha still induced an additive shift in the CRF corresponding to a criterion effect. Our data imply that low alpha boosts detection and confidence by an additive factor, rather than by a multiplicative scaling of contrast responses, a profile which captures the effect of pupil-linked arousal. We suggest that alpha power and arousal fluctuations have dissociable effects on behavior. Alpha reflects the baseline level of visual excitability, which can vary independent of arousal.

Loss-tolerant architecture for quantum computing with quantum emitters

We develop an architecture for measurement-based quantum computing using photonic quantum emitters. The architecture exploits spin-photon entanglement as resource states and standard Bell measurements of photons for fusing them into a large spin-qubit cluster state. The scheme is tailored to emitters with limited memory capabilities since it only uses an initial non-adaptive (ballistic) fusion process to construct a fully percolated graph state of multiple emitters. By exploring various geometrical constructions for fusing entangled photons from deterministic emitters, we improve the photon loss tolerance significantly compared to similar all-photonic schemes.

Large Polarization Near 50 μC/cm2 in a Single Unit Cell Layer SrTiO3.

The generally nonpolar SrTiO3 has attracted more attention recently because of its possibly induced novel polar states and related paraelectric-ferroelectric phase transitions. By using controlled pulsed laser deposition, high-quality, ultrathin, and strained SrTiO3 layers were obtained. Here, transmission electron microscopy and theoretical simulations have unveiled highly polar states in SrTiO3 films even down to one unit cell at room temperature, which were stabilized in the PbTiO3/SrTiO3/PbTiO3 sandwich structures by in-plane tensile strain and interfacial coupling, as evidenced by large tetragonality (∼1.05), notable polar ion displacement (0.019 nm), and thus ultrahigh spontaneous polarization (up to ∼50 μC/cm2). These values are nearly comparable to those of the strong ferroelectrics as the PbZrxTi1-xO3 family. Our findings provide an effective and practical approach for integrating large strain states into oxide films and inducing polarization in nonpolar materials, which may broaden the functionality of nonpolar oxides and pave the way for the discovery of new electronic materials.

Sketched Newton Value Iteration for Large-Scale Markov Decision Processes

Value Iteration (VI) is one of the most classic algorithms for solving Markov Decision Processes (MDPs), which lays the foundations for various more advanced reinforcement learning algorithms, such as Q-learning. VI may take a large number of iterations to converge as it is a first-order method. In this paper, we introduce the Newton Value Iteration (NVI) algorithm, which eliminates the impact of action space dimension compared to some previous second-order methods. Consequently, NVI can efficiently handle MDPs with large action spaces. Building upon NVI, we propose a novel approach called Sketched Newton Value Iteration (SNVI) to tackle MDPs with both large state and action spaces. SNVI not only inherits the stability and fast convergence advantages of second-order algorithms, but also significantly reduces computational complexity, making it highly scalable. Extensive experiments demonstrate the superiority of our algorithms over traditional VI and previously proposed second-order VI algorithms.

Abstract 4797: Impact of COVID-19 pandemic on breast cancer screening and diagnosis stage in a large midwestern United States academic medical center

Abstract Background: Access to breast cancer screening mammogram services decreased in association with the COVID-19 pandemic. There is also evidence for increases in late-stage breast cancer diagnoses. Our objectives were to determine: 1) the COVID-19-affected period on mammogram screening, 2) the proportion of pandemic-associated missed or delayed mammogram screening visits overall and by race/ethnicity and age group, and 3) evidence for pandemic-associated shifts in diagnosis stage. Methods: Screening mammogram encounter data between 1-1-2019 and 12-31-2022 were extracted from EPIC for females ≥ 40 years old for the screening analysis. We used Bayesian state space models to describe weekly screening mammogram counts, modeling an interruption that phased in and out from 3-1-2020 to 9-1-2020. We used the posterior predictive distribution to simulate differences between a predicted, uninterrupted process and the observed screening mammogram counts. Breast cancer diagnoses at ≥ 21 years from the tumor registry between 12-1-2018 and 11-30-2021 were included in the stage analysis. We used logistic regression models to estimate late-stage diagnosis odds comparing matched three-month periods during the pandemic to before the pandemic. Results: A total of 319,492 encounters among 146,644 women were included. Model-estimated screening mammograms dropped by 98.8% (95% CI 95.1 to 100) between 3-15-2020 and 5-24-2020, returning to pre-pandemic levels or higher after this period. Drops in screening mammogram encounters did not vary significantly by race/ethnicity or age group (p > .75). Among 4,669 breast cancer diagnoses, we found no significant differences in the odds of late-stage diagnoses for any period vs. the same period before the pandemic (Table). Conclusions: These data suggest a short-term pandemic effect on screening mammograms. Evidence for increases in late-stage diagnoses is limited. These results may inform future pandemic planning. OR (95% CI) Period comparison 1.19 (0.74 to 1.91) 3/2020 to 5/2020 vs. 3/2019 to 5/2019 1.05 (0.69 to 1.60) 6/2020 to 8/2020 vs. 6/2019 to 8/2019 1.14 (0.73 to 1.78) 9/2020 to 11/2020 vs. 9/2019 to 11/2019 0.92 (0.60 to 1.41) 12/2020 to 2/2021 vs. 12/2019 to 2/2020 1.08 (0.71 to 1.65) 3/2021 to 5/2021 vs. 3/2019 to 5/2019 0.89 (0.59 to 1.33) 6/2021 to 8/2021 vs. 6/2019 to 8/2019 1.31 (0.84 to 2.04) 9/2021 to 11/2021 vs. 9/2019 to 11/2019 Citation Format: Kimberly J. Johnson, RJ Waken, Caitlin P. O'Connell, Derek Brown. Impact of COVID-19 pandemic on breast cancer screening and diagnosis stage in a large midwestern United States academic medical center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4797.