Global Momentum Research Articles

Overcoming a stumbling block: Challenges of English-medium instruction for Vietnamese tertiary Students

English as a medium of instruction (EMI) is a growing global phenomenon, and non-native English-speaking (NNES) countries have increasingly adopted EMI courses in higher education over the past few decades in response to the momentum of globalization and internationalization. However, implementing EMI initiatives in Vietnam has faced numerous challenges and constraints. While there has been extensive research on the difficulties faced by content teachers in EMI classrooms, there is a lack of scholarly inquiry into the challenges students encounter in EMI programs in Vietnam. Therefore, this study aimed to explore the challenges faced by university students in EMI classrooms. The study used a mixed-methods research approach, gathering quantitative and qualitative data through a questionnaire and semi-structured interviews, respectively. The participants were 120 students enrolled in EMI programs at a public university in the Mekong Delta of Vietnam. The results revealed that students faced significant challenges in their EMI learning course. Precisely, they needed help comprehending course materials, understanding lectures, taking examinations, and participating in classroom discussions. Based on the results of the study, practical implications were discussed to enhance the effectiveness of EMI programs in higher education contexts.

A novel explicit fully-discrete momentum-preserving scheme of damped nonlinear stochastic wave equation influenced by multiplicative space-time noise

In this paper, the evolution laws of global momentum and averaged global momentum for the damped nonlinear stochastic wave equation (DNSWE) influenced by multiplicative space-time noise are derived. We innovatively combine the second-order central finite difference method and discrete gradient method in space, and integrate the splitting method and Störmer-Verlet type method in time. Both the novel spatial semi-discrete scheme and fully-discrete scheme constructed in this way can successfully preserve the corresponding evolution laws for discrete global momentum and discrete averaged global momentum. Numerical experiments on DNSWE with cubic nonlinearity validate the theoretical results.

Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation.

Federated learning (FL) commonly encourages the clients to perform multiple local updates before the global aggregation, thus avoiding frequent model exchanges and relieving the communication bottleneck between the server and clients. Though empirically effective, the negative impact of multiple local updates on the stability of FL is not thoroughly studied, which may result in a globally unstable and slow convergence. Based on sensitivity analysis, we define in this paper a local-update stability index for the general FL, as measured by the maximum inter-client model discrepancy after the multiple local updates that mainly stems from the data heterogeneity. It enables to determine how much the variation of client's models with multiple local updates may influence the global model, and can also be linked with the convergence and generalization. We theoretically derive the proposed local-update stability for current state-of-the-art FL methods, providing possible insight to understanding their motivation and limitation from a new perspective of stability. For example, naively executing the parallel acceleration locally at clients would harm the local-update stability. Motivated by this, we then propose a novel accelerated yet stabilized FL algorithm (named FedANAG) based on the server- and client-level Nesterov accelerated gradient (NAG). In FedANAG, the global and local momenta are elaborately designed and alternatively updated, while the stability of local update is enhanced with help of the global momentum. We prove the convergence of FedANAG for strongly convex, general convex and non-convex settings. We then conduct evaluations on both the synthetic and real-world datasets to first validate our proposed local-update stability. The results further show that across various data heterogeneity and client participation ratios, FedANAG not only accelerates the global convergence by reducing the required number of communication rounds to a target accuracy, but converges to an eventually higher accuracy.

Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

AbstractAs the global momentum for wind power generation accelerates, the industry faces substantial challenges due to premature failures in wind turbine components. These failures, particularly in critical elements like the high‐speed shaft bearing, lead to significant operational losses, including unplanned downtime and elevated maintenance costs. To mitigate these issues, it's crucial to have precise predictions of the remaining useful life (RUL) of these components, enabling timely interventions and more efficient maintenance schedules. This article proposes advanced, data‐driven approaches for estimating the RUL of wind turbine high‐speed shaft bearings, utilizing cutting‐edge techniques such as long short‐term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent units (GRU), and random forest (RF) algorithms. Our analysis leverages vibration data from a 2 MW wind turbine equipped with a 20‐tooth pinion gear, providing a thorough validation and comparison of these methodologies against traditional models. Our results reveal that the LSTM and BiLSTM models excel in both accuracy and computational efficiency for predicting RUL and enhancing system prognosis, surpassing the performance of conventional RF and GRU methods. This research underscores the potential of our innovative data‐driven strategies to develop effective RUL estimation algorithms, significantly advancing wind turbine proactive operation and maintenance operations.

The innovation effect of digital M&As: Evidence from China

Digital mergers and acquisitions (M&As) have gained global momentum. However, whether digital M&As can effectively stimulate corporate innovation awaits further research. This paper evaluates the causal effect of digital M&As on corporate innovation using data from digital M&As and patents of Chinese A-share listed companies. Our findings indicate that digital M&As significantly promote the quantity and quality of acquirers' innovation, which still holds after various robustness tests and exhibits heterogeneity in different dimensions such as transactions, acquirers, and targets. Mechanism tests further reveal that knowledge synergy, financial synergy, and human capital structure upgrading are critical channels through which digital M&As promote the acquirers' innovation.

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We have studied the spin polarization of Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document} hyperons in heavy ion collisions at center-of-mass energies sNN=200\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s_\ extrm{NN}}= 200$$\\end{document} GeV and sNN=5.02\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s_\ extrm{NN}}= 5.02$$\\end{document} TeV carried out at RHIC and LHC colliders. We have calculated the mean spin vector at local thermodynamic equilibrium, including all known first-order terms in the gradients of the thermo-hydrodynamic fields, assuming that the hadronization hypersurface has a uniform temperature. We have also included the feed-down contributions to the polarization of Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document} stemming from the decays of polarized Σ∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma ^*$$\\end{document} and Σ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma ^0$$\\end{document} hyperons. The obtained results are in good agreement with the data. In general, the component of the spin vector along the global angular momentum, orthogonal to the reaction plane, shows strong sensitivity to the initial longitudinal flow velocity. Furthermore, the longitudinal component of the spin vector turns out to be very sensitive to the bulk viscosity of the plasma at the highest LHC energy. Therefore, the azimuthal dependence of spin polarization can effectively constrain the initial hydrodynamic conditions and the transport coefficients of the quark gluon plasma.

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this paper, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent trade-off between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller).

Citizen science supports national reporting of a Sustainable Development Goal indicator: A case study of plastic debris density on beaches

Global momentum to address plastic pollution has led to the development of national processes to monitor plastics into and within the environment. The Sustainable Development Goals (SDGs) provide guidance for reporting on plastic pollution, although compiling national estimates remains a challenge. Data availability remains a key barrier, which could be addressed through engagement with the citizen science community, which collects and maintains data at a resolution suitable for SDG reporting. Here, we demonstrate a process that uses citizen science monitoring of plastic debris on beaches to report on SDGs at the national scale. The study uses the Australian Marine Debris Initiative (AMDI) Database, which is composed of data submissions from over 1400 organisations across Australia. The AMDI was used to provide initial estimates of plastic debris density both nationally and across Natural Resource Management areas (NRMs). Before analysis, the database was filtered for data quality and alignment with guidance for SDG reporting (i.e., SDG Indicator 14.1.1b), resulting in the analysis of 3144 events across 538 sites between 2014 and 2019 (inclusive). The average density of plastic debris on beaches across Australia was between 0.155 and 0.279 counts m−2, with management relevant trends identified across NRMs. This study demonstrates the potential for the citizen science community to support SDG reporting and management processes, which could inform and enable actions to address plastic pollution.

Understanding the spatially heterogeneous relationships between urban green amenities and residential dynamics: Evidence from Wuhan, China

Urban green turn has gained global momentum, with cities worldwide actively developing urban green amenities (UGAs). While considerable attention has been given to how expanding UGAs impact residential dynamics, the role of space in shaping these relationships has been underexplored. Employing global and local regression models, we investigate the associations between UGA characteristics, property prices and sales transactions, using data from 443 neighborhoods in Wuhan, China. Our findings reveal that at the city-wide level, larger UGAs are associated with gentrification trends marked by rising property prices and population shifts. High-quality UGAs are linked to higher risks of gentrification but do not significantly indicate direct displacement. Meanwhile, substantial bodies of water are associated with mitigating the effects of increased property prices and population shifts. At the local scale, these relationships show spatial heterogeneity, particularly between old inner-city areas and traditional industrial zones. Additionally, we analyze the roles of the state and real estate developers in influencing UGA development and the associated social ramifications in China. This study enhances our understanding of UGAs and their impacts on residential dynamics in a non-Western context, enriching the discourse on the social implications of urban green movements and ecological initiatives.

Exploring rapidity-even dipolar flow in isobaric collisions at RHIC

Employing the AMPT transport model, we investigate the response of the rapidity-even dipolar flow ( v1even ) and its associated Global Momentum Conservation (GMC) parameter K to structural disparities within 96Ru and 96Zr nuclei. We analyze Ru + Ru and Zr + Zr collisions at a center-of-mass energy of sNN = 200 GeV. Our analysis demonstrates that the eccentricity ε 1, v1even and K exhibit subtle yet discernible sensitivity to the input nuclear structure distinctions between 96Ru and 96Zr isobars. This observation suggests that measuring v1even and the GMC parameter in these isobaric collisions could serve as a means to fine-tune the comprehension of their nuclear structure disparities and offer insights to enhance the initial condition assumptions of theoretical models.

Boosting the transferability of adversarial attacks with global momentum initialization

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously, adversarial examples exhibit transferability across models, enabling practical black-box attacks. However, existing methods are still incapable of achieving the desired transfer attack performance. In this work, focusing on gradient optimization and consistency, we analyze the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these challenges, we introduce Global Momentum Initialization (GI), providing global momentum knowledge to mitigate gradient elimination. Specifically, we perform gradient pre-convergence before the attack and a global search during this stage. GI seamlessly integrates with existing transfer methods, significantly improving the success rate of transfer attacks by an average of 6.4% under various advanced defense mechanisms compared to the state-of-the-art method. Ultimately, GI demonstrates strong transferability in both image and video attack domains. Particularly, when attacking advanced defense methods in the image domain, it achieves an average attack success rate of 95.4%. The code is available at https://github.com/Omenzychen/Global-Momentum-Initialization.

Mapping Hydrogen Initiatives in Italy: An Overview of Funding and Projects

The global momentum towards hydrogen has led to various initiatives aimed at harnessing hydrogen’s potential. In particular, low-carbon hydrogen is recognized for its crucial role in reducing greenhouse gas emissions across hard-to-abate sectors such as steel, cement and heavy-duty transport. This study focuses on the presentation of all hydrogen-related financing initiatives in Italy, providing a comprehensive overview of the various activities and their geographical locations. The examined funding comes from the National Recovery and Resilience Plan (PNRR), from projects directly funded through the Important Projects of Common European Interest (IPCEI) and from several initiatives supported by private companies or other funding sources (hydrogen valleys). Specific calls for proposals within the PNRR initiative outline the allocation of funds, focusing on hydrogen production in brownfield areas (52 expected hydrogen production plants by 2026), hydrogen use in hard-to-abate sectors and the establishment of hydrogen refuelling stations for both road (48 refuelling stations by 2026) and railway transport (10 hydrogen-based railway lines). A detailed description of the funded initiatives (150 in total) is presented, encompassing their geographical location, typology and size (when available), as well as the funding they have received. This overview sheds light on regions prioritising decarbonisation efforts in heavy-duty transport, especially along cross-border commercial routes, as evident in northern Italy. Conversely, some regions concentrate more on local transport, typically buses, or on the industrial sector, primarily steel and chemical industries. Additionally, the study presents initiatives aimed at strengthening the national manufacturing capacity for hydrogen-related technologies, alongside new regulatory and incentive schemes for hydrogen. The ultimate goal of this analysis is to foster connections among existing and planned projects, stimulate new initiatives along the entire hydrogen value chain, raise an awareness of hydrogen among stakeholders and promote cooperation and international competitiveness.

On wave-driven propulsion

A theory is presented for wave-driven propulsion of floating bodies driven into oscillation at the fluid interface. By coupling the equations of motion of the body to a quasipotential flow model of the fluid, we derive expressions for the drift speed and propulsive thrust of the body which in turn are shown to be consistent with global momentum conservation. We explore the efficacy of our model in describing the motion of SurferBot (Rhee et al., Bioinspir. Biomim., vol. 17, issue 5, 2022), demonstrating close agreement with the experimentally determined drift speed and oscillatory dynamics. The efficiency of wave-driven propulsion is then computed as a function of driving oscillation frequency and the forcing location, revealing optimal values for both of these parameters which await confirmation in experiments. A comparison with other modes of locomotion and applications of our model with competitive water sports is discussed in conclusion.

How do behavioral public policy experts see the role of complex systems perspectives? An expert interview study.

Amidst the global momentum of behavioral insights (BI), there has been a shift from mostly nudge-based BI applications to systemic approaches. This is particularly pressing in public health, where interacting issues regularly produce unanticipated consequences. Regardless, little is known about adopting complex systems approaches in behavioral public policy. This study aims to capture current practices of international BI experts on the definition, application, drivers, and hindering factors in adopting complex systems approaches in public policy. Semi-structured individual expert interviews (n = 12) of international BI experts with extensive experience in educating, cooperating with, and/or advising public servants were analyzed with inductive content analysis. While the working definition of BI aligned with published definitions, experts varied in their descriptions of complex systems approaches and its significance for public policy, including socioecological aspects, systematic BI use across policy stages, recognizing intertwined behavior, and lack of ready-made solutions. They emphasized the importance of systems approaches, identifying drivers (e.g. need for a broader focus) and hindrances (e.g. pressure for quick results). Embracing complex systems in behavioral public policy provides a holistic perspective, extending beyond simple nudges, sometimes presumed as universally applicable. While complexity perspectives would align with policymakers' worldview, applications require more work to tailor to local situations and to evaluate. Recognizing that, given their distinct expertise content, BI expertise can be quite different from complex systems expertise. The field would benefit from clear descriptions and specialized training for effective integration and advocacy for these approaches.

Increasing risks of the explosive extratropical cyclones over the North Atlantic storm track: a perspective from their surface wind maxima

Extratropical cyclones play a crucial role in balancing the global momentum, energy, and moisture, and also, they shape the extreme weather events over the extra-tropics. As the strongest category of the extratropical cyclones, the explosive extratropical cyclones (EECs) frequently induce severe disasters through their strong surface winds. During the entire lifespan of an EEC, there is a maximum value of its surface wind (i.e. the maximum surface wind; MSW), which processes the greatest destructive power. After nearly a century of research on the EECs, key features about their MSWs still remain vague. In this study, we systematically investigate the EECs’ MSWs over the North Atlantic storm track (NAST) based on the ERA-Interim reanalysis. It is found that, the average intensity of EECs’ MSWs shows a significant increasing trend of ∼0.3 m s−1 per 10a. More importantly, for the last 20 years, even larger increasing rates of 1.5 m s−1 and 3.5 m s−1 per 10a are found in the average and maximum intensities of the EECs’ MSWs, respectively, implying the EECs’ risks increase notably for the NAST. We further clarify the physical mechanisms governing the production of EECs’ MSWs, and then establish a mechanism-based statistical model, which has the potential to predict the MSWs’ annual average intensity. In summary, our study fills a knowledge gap for the EECs’ MSWs, which would have broad implication of the economics and society.

Food Systems Interventions for Nutrition: Lessons from 6 Program Evaluations in Africa and South Asia

Although there is growing global momentum behind food systems strategies to improve planetary and human health—including nutrition—there is limited evidence of what types of food systems interventions work. Evaluating these types of interventions is challenging due to their complex and dynamic nature and lack of fit with standard evaluation methods. In this article, we draw on a portfolio of 6 evaluations of food systems interventions in Africa and South Asia that were intended to improve nutrition. We identify key methodological challenges and formulate recommendations to improve the quality of such studies. We highlight 5 challenges: a lack of evidence base to justify the intervention, the dynamic and multifaceted nature of the interventions, addressing attribution, collecting or accessing accurate and timely data, and defining and measuring appropriate outcomes. In addition to more specific guidance, we identify 6 cross-cutting recommendations, including a need to use multiple and diverse methods and flexible designs. We also note that these evaluation challenges present opportunities to develop new methods and highlight several specific needs in this space.

Subsidizing the Shift to Renewable Energy in Korea: A Levelized Cost Analysis and Computation of Required Subsidies

This study addresses the pivotal challenge of transitioning from nuclear to renewable energy sources, considering the distinctive energy landscape of South Korea characterized by high land costs and high dependence on nuclear energy. Amidst global momentum toward sustainable and carbon-neutral energy, South Korea’s Renewable Energy 3020 Implementation Plan aims to achieve 20% of power generation from renewables by 2030. Shifting away from nuclear energy, however, may lead to rising energy costs, supply disruptions, and environmental ramifications. Furthermore, achieving grid parity between renewable energy and nuclear power by the late 2030s is unlikely, in contrast to earlier projections. This is primarily due to the comparatively lower production cost of nuclear energy. To expedite the shift toward renewable energy, our study suggests transitioning from the Renewable Energy Portfolio Standard (RPS) to a Feed-in-Tariff (FiT) system. This change would offer voluntary incentives for investment in renewable energy. Additionally, we compute Feed-in-Tariff subsidies required to achieve grid parity by 2030, concluding that the amount of subsidies required to reach grid parity by 2030 is 54% of retail electricity price. This research offers valuable insights for policy formulation and energy transition planning.

FedSBS: Federated-Learning participant-selection method for Intrusion Detection Systems

Federated Learning (FL) is a decentralized machine learning approach in which multiple participants collaboratively train a model. Participants keep data locally, train their local models, and aggregate them in a single global model in a federated server. Collaborative FL-based Intrusion Detection Systems face challenges on an uneven statistical distribution of data and malicious participants trying to subvert the learning process. The statistical hurdles associated with imbalanced data and malicious participants pose a risk of skewing the training with biased or random data. The inability to effectively manage these statistical inconsistencies may degrade system performance, leading to false intrusion detection or opening avenues for cybersecurity breaches. To overcome these challenges, we propose a training method that employs score-based participant selection and utilizes global momentum for model aggregation. Our method improves the global model performance while mitigating the risks posed by malicious participants. The proposal incorporates a scoring system based on an information gain variant to evaluate each participant’s contribution. The scoring system and an epsilon greedy selection method ensure robust participant selection in each aggregation round. Furthermore, incorporating a global momentum term helps preserve previous knowledge at each aggregation round, contributing to model stability and overall learning. The proposed solution has demonstrated superior performance, delivering 80% F1-Score and 90% accuracy on experiments even in the presence of malicious participants, revealing the robustness and effectiveness of the proposal in mitigating statistical challenges. Consequently, the proposed method significantly enhances the performance of federated learning models, leading to more secure and efficient collaborative intrusion detection systems.

The White Gold Rush and The Pursuit of Natural Hydrogen

The buzz around natural hydrogen – dubbed white or gold hydrogen – is gaining global momentum as a potential gamechanger in the hunt for cost-effective, low-carbon energy sources. Rystad Energy research shows that at the end of last year, 40 companies were searching for natural hydrogen deposits, up from just 10 in 2020. Currently, exploratory efforts are underway in Australia, the US, Spain, France, Albania, Colombia, South Korea and Canada.

Formative Spaces of Empire: Masculinities and Outdoor Experiences ca. 1860–1960

ABSTRACT This special issue integrates gender analysis into the global history of outdoor activities in the Age of Empire by focusing on masculinities, a field that has received renewed attention from scholars. The premise of the special issue is that social constructions of masculinities in imperial settings functioned twofold. They operated simultaneously as methods to spread Western-colonial hegemonic values and as a means to expand territorial domains into far-off lands. In examining outdoor experiences, without taking the dominance of ‘imperial’ men over non-Europeans for granted, the contributions here presented develop an intersectional understanding of the conditions in which fashioning and self-perception of masculine roles were constantly contested and negotiated. Outdoor experiences, as seen in this special issue, were formative spaces of empire: while made possible thanks to the wide imperial networks in the colonial world, they existed on the margins of imperial rule. At the ‘frontier’ and in colonial battle-grounds, but also as leisure or free-time activities in transcultural contexts, outdoor experiences served to transform boys into men, and for men to test and perform hegemonic ideas of manhood and hence of imperial power. The essays are in two sections that highlight the dual processes of being and becoming ‘manly’ in the imperial outdoors. The first four contributions focus on archetypical roles of adult men in empires: the mountaineer, the hunter, the sportsman and the soldier. The second section approaches cases of scouting as formative spaces for boys in contexts of decolonisation in the early 1900s. The case-studies included in this special issue cover multiple imperial formations from the American Midwest, the Middle East to the British and Dutch Indies. These diverse cases serve to open up often Anglo-centric historiographies of gender and empire by emphasising the global momentum of new masculinities that were embedded in a trans-imperial fashion between ca. 1860 and 1960.