299645 research outputs found
Sort by
Multielectron Transfer in Halogen Batteries
No full textMultielectron transfer in halogen batteries is a promising solution in pursuing high-energy-density and affordable energy storage systems. Interest in rich chemistries derived from unique valence electron structures of halogens is surging in electrode material design. However, deploying multielectron transfer chemistry comes with challenges, including limited redox reactivity and degrees of electrochemical irreversibility, which contribute to poor charging and cycling. To address these challenges, researchers explore physical/chemical strategies to activate high valence reactions and more electron transfer numbers and fix unstable valence state species through electrolyte and electrode regulation. This Concept presents the basic understanding of multielectron transfer electrochemistry concerning theoretical energy capabilities and electronic configuration evolutions. We divide multielectron transfer into two types: single and multi-redox centers, providing an overview of the current development of multielectron transfer and hoping it will spur more intensive efforts towards a diverse energy future.link_to_subscribed_fulltex
Development of a predictive model for loss of functional and cognitive abilities in long-term care home residents: a protocol
Get PDFIntroduction Long-term care (LTC) residents require extensive assistance with daily activities due to physical and cognitive impairments. Medical treatment for LTC residents, when not aligned with residents' wishes, can cause discomfort without providing substantial benefits. Predictive models can equip providers with tools to guide treatment recommendations that support person-centred medical decision-making. This study protocol describes the derivation and validation of time-to-event predictive models for (1) permanent loss of independence in physical function, (2) permanent severe cognitive impairment and (3) time alive with complete dependence for those with disability starting from the date of onset. Methods and analysis We will use population-based administrative health data from the Institute for Clinical Evaluative Sciences of all LTC residents in Ontario, Canada, to construct the derivation and internal validation cohorts. The external validation cohort will use data from LTC residents in Alberta, Canada. Predictors were identified based on existing literature, patient advisors and expert opinions (clinical and analytical). We identified 50 variables to predict the loss of independence in physical function, 58 variables to predict the loss of independence in cognitive function and 36 variables to predict the time spent in a state of dependence. We will use time-to-event models to predict the time to loss of independence and time spent in the state of disability. Full and reduced models (using a step-down procedure) will be developed for each outcome. Predictive performance will be assessed in both derivation and validation cohorts using overall measures of predictive accuracy, discrimination and calibration. We will create risk groups to present model risk estimates to users as median time-to-event. Risk groups will be externally validated within the Alberta LTC cohort. Ethics and dissemination Ethics approval was obtained through the Bruyère Research Institute Ethics Committee. Study findings will be submitted for publication and disseminated at conferences. The predictive algorithm will be available to the general public.published_or_final_versio
Correspondence between Euler charges and nodal-line topology in Euler semimetals
Get PDFReal multi-bandgap systems have non-abelian topological charges, with Euler semimetals being a prominent example characterized by real triple degeneracies (RTDs) in momentum space. These RTDs serve as “Weyl points” for real topological phases. Despite theoretical interest, experimental observations of RTDs have been lacking, and studies mainly focus on individual RTDs. Here, we experimentally demonstrate physical systems with multiple RTDs in crystals, analyzing the distribution of Euler charges and their global connectivity. Through Euler curvature fields, we reveal that type I RTDs have quantized point Euler charges, while type II RTDs show continuously distributed Euler charges along nodal lines. We discover a correspondence between the Euler number of RTDs and the abelian/non-abelian topological charges of nodal lines, extending the Poincaré-Hopf index theorem to Bloch fiber bundles and ensuring nodal line connectivity. In addition, we propose a “no-go” theorem for RTD systems, mandating the balance of positive and negative Euler charges within the Brillouin zone.published_or_final_versio
Coupling dynamics of urban flood resilience in china from 2012 to 2022: A network-based approach
No full textUrban flooding presents a significant challenge in Chinese cities, necessitating a deeper understanding of the coupling effects of China's urban flood resilience for effective resilience planning. This study introduces a four-component Environment-Institution-Infrastructure-Agent (EIFA) framework and utilizes an updated correlation network approach to analyze the temporal variation of coupling effects of urban flood resilience across 639 Chinese cities from 2012 to 2022. The findings indicate a decline in synergy and increased tradeoffs, primarily due to intensified competition within and between institutional and infrastructural sectors, marginal impacts of infrastructure investments, and socially excessive infrastructure. The study also highlights the agent component's strong internal and inter-component coupling effects, implying the effectiveness of China's people-centered resilience strategies, though risks of decoupling remain. Additionally, it notes a good match between societal urban flood resilience and natural flood risks, while natural vegetation loss due to urban expansion is noteworthy. The study further suggests that refining agent-focused deposit and insurance policies could coordinatively enhance urban flood resilience, as these elements are hubs within the network. The updated network-based framework and its findings offer insights for informing and optimizing urban flood resilience planning in China
Strategic M&A decisions powered by predictive insights : media and AI
No full textThe big data era has revolutionized strategic decision-making by enabling organizations to leverage vast amounts of information for enhanced predictive accuracy. This dissertation explores how human-curated media data and machine-generated artificial intelligence (AI) predictions—two critical products of the big data age—support decision-making in the high-stakes context of mergers and acquisitions (M&A). Focusing on the key task of target selection in M&A, this research examines how these two data sources contribute to reducing uncertainty and improving decision outcomes.
The first study investigates how media data influences ownership decisions in cross-border M&A. Assessing the value of a foreign target firm in cross-border acquisitions has historically been a challenge for acquirers because of information asymmetry. Although a limited number of M&A studies have suggested that signaling theory provides insights into mitigating information asymmetry, these studies have mainly focused on the signals sent by the acquirer and paid less attention to those released by foreign target firms. In this study, I suggest that the reputation of a foreign target firm represents a visible and credible signal released by that firm regarding its valuation. Using a sample of 5,647 cross-border acquisition deals between 2012 and 2017, and a sentiment analysis of 36,469 news articles pertaining to the target companies involved, I find that acquirers tend to increase their ownership level when the target company's reputation index is higher. I also argue that, despite the value of the reputational signal, its efficacy depends on its credibility, which can be violated by noises from both the foreign target firm and its host country. These noises will influence how much foreign acquirers rely on the reputational signal.
The second study explores the interplay between artificial intelligence (AI) and human decision-making in mergers and acquisitions (M&A), examining whether AI predictions enhance or replace human judgment in shaping post-acquisition performance. Using a dataset of 2,906 M&A transactions from 2000 to 2023, I compare predictions generated by a neural network model with those of human analysts from the Institutional Brokers' Estimate System (IBES). My findings reveal that AI predictions consistently outperform human forecasts, with decisions aligned with AI evaluations leading to significantly higher post acquisition performance. By analyzing the alignment between M&A outcomes and predictions, this study provides empirical evidence on the relative effectiveness of AI-driven versus human-driven decision-making in M&A. These findings contribute to the growing discourse on AI in strategic decision-making and offer practical insights for managers navigating the evolving role of AI in corporate strategy.
Together, these studies underscore the transformative potential of media data and AI in enhancing predictive precision and decision-making reliability in M&A. By integrating traditional strategic management theories with emerging technologies, this dissertation contributes to the growing discourse on leveraging big data for competitive advantage. It also raises critical ethical and practical questions about the integration of predictive tools into decision-making processes, advocating for hybrid frameworks that balance machine-driven insights with human creativity and judgment.published_or_final_versionBusinessDoctoralDoctor of Philosoph
Semi-transparent photovoltaics
No full textSemi-transparent photovoltaics (STPVs) have attracted increasing attention owing to their ability to seamlessly integrate power generation with light transmission. They can complement traditional opaque photovoltaics, significantly broadening their potential applications. Although STPVs have achieved great progress driven by advances in material engineering and device engineering, they still encounter substantial challenges for real-world deployment. This review summarizes the recent progress in STPV technologies, highlights the challenges they face in practical applications, and provides a detailed analysis of the factors affecting their performance improvements. We explore how innovations in active layer manipulation, transparent electrode design, interfacial engineering, optical structures and tandem architectures contribute to enhancing STPV performance. Furthermore, we summarize the emerging applications of STPVs in various fields, such as building integrated photovoltaics, agricultural photovoltaics, bioelectronics, wearable electronics and optical wireless communication. Overall, this review offers valuable insights into materials science, physics and optoelectronics.</p
Search for boosted low-mass resonances decaying into hadrons produced in association with a photon in pp collisions at s = 13 TeV with the ATLAS detector
Get PDFMany extensions of the Standard Model, including those with dark matter particles, propose new mediator particles that decay into hadrons. This paper presents a search for such low mass narrow resonances decaying into hadrons using 140 fb−1 of proton-proton collision data recorded with the ATLAS detector at a centre-of-mass energy of 13 TeV. The resonances are searched for in the invariant mass spectrum of large-radius jets with two-pronged substructure that are recoiling against an energetic photon from initial state radiation, which is used as a trigger to circumvent limitations on the maximum data recording rate. This technique enables the search for boosted hadronically decaying resonances in the mass range 20–100 GeV hitherto unprobed by the ATLAS Collaboration. The observed data are found to agree with Standard Model predictions and 95% confidence level upper limits are set on the coupling of a hypothetical new spin-1 Z′ resonance with Standard Model quarks as a function of the assumed Z′-boson mass in the range between 20 and 200 GeV.published_or_final_versio
On a class of fusion 2-category symmetry: condensation completion of braided fusion category
No full textRecently, many studies have focused on generalized global symmetry, a mixture of both invertible and non-invertible symmetries in various space-time dimensions. The complete structure of generalized global symmetry is described by higher fusion category theory. In this paper, we first review the construction of the fusion 2-category symmetry ΣB where B is a braided fusion category. In particular, we elaborate on the monoidal structure of ΣB, which not only determines the fusion rules but also controls the dynamics of topological operators/defects. We then take ΣsVec as an example to demonstrate how weUbKnZ9XrM8EV0KFIXCLgRteBJwhcalculate fusion rule, quantum dimension and 10j-symbol of the fusion 2-category. With our algorithm, all these data can be effciently encoded and computed in the computer program. The complete program has been uploaded to github1 . Our work can be thought as explicitly computing the representation theory of B, in analogy to, for example, the representation theory of SU(2). The choice of basis bimodule maps is in analogy to the Clebsch-Gordon coeffcients, and the 10j-symbol are in analogy to the 6j-symbol.</p
