Local Integration Research Articles

Many-body-localization protection of eigenstate topological order in two dimensions

Many-body localization (MBL) has been proposed to enable and protect topological order in all eigenstates, vastly expanding the traditional ground-state setting. However, for the most intriguing case of two-dimensional (2D) systems with anyons and topology-dependent degeneracies, the dense many-body spectrum challenges studying this MBL protection numerically. Here we use large-scale full-spectrum variational to demonstrate MBL-protected topological order in the disordered 2D toric code perturbed by magnetic fields. We show that the system has topological local integrals of motion (tLIOMs) for magnetic field strengths below hc≈0.1 times the toric-code coupling scale. Combining tLIOMs with exact diagonalization, we also identify high-energy topological multiplets in the dense many-body spectrum. The phase diagram we find is consistent with toric-code and trivial MBL phases being separated by an intervening thermal phase. Published by the American Physical Society 2024

Workforce Optimization for Quick Commerce Dark Stores: Talent Sourcing and Workforce Modelling Strategies

Quick commerce (Q-commerce) has transformed the retail and e-commerce sectors by enabling ultra-fast delivery of goods through hyper-local logistics and dark stores. The operational success of this model hinges on a structured workforce strategy that supports efficient scaling and agility. This paper explores innovative workforce optimization techniques that focus on organizational design, talent sourcing, and workforce modelling, referencing industry leaders like Flipkart, Zepto, Instamart, and Blinkit. The study identifies the vital roles within dark stores and the approaches to talent acquisition, emphasizing localized recruitment, employee retention, and technology integration. It further presents best practices for scalable workforce modelling to accommodate seasonal demands, high attrition rates, and hyper-growth environments, ensuring sustainable development in the Q-commerce industry. Keywords: Quick Commerce, Workforce Strategy, Talent Sourcing, Dark Stores, Organizational Design, Hyper-Local Delivery, Workforce Modelling

Buddhist participatory communication for rural development: a comparative analysis of case studies in Sri Lanka, Lao PDR, Thailand and lessons learned for Vietnam

ABSTRACT Religion, the foundation of all cultures, can serve as a potential substitute for participatory communication and rural community development. This paper attempts to (1) analyse how the Buddhist approach is integrated into development communication initiatives in three countries, namely Sri Lanka, Lao PDR and Thailand, (2) identify the key principles and values of Buddhist philosophy that inform development communication practices in these countries, (3) examine similarities and differences in the Buddhist-based development initiatives and projects conducted in the three countries and (4) provide insights and recommendations for Vietnam through analysing how Buddhist-inspired approaches to development communication have been conceptualised and implemented in these Asian nations by making use of comparative case studies research method. The study reveals that while these initiatives share a philosophical basis centred on Buddhism and local culture integration where Buddhist monks/nuns play important roles, they vary notably in the methods applied due to differences in cultural context. This comparative analysis aims to inform future endeavours in Vietnam and beyond, advocating for culturally sensitive and spiritually grounded approaches to rural development.

Domain decomposition with local time discretization for the nonlinear Stokes–Biot system

This work presents a domain decomposition method for the fluid-poroelastic structure interaction (FPSI) system, which utilizes local time integration for subproblems. To derive the domain decomposition scheme, we introduce a Lagrange multiplier and define time-dependent Steklov–Poincaré-type operators based on the interface conditions. These operators are employed to transform the coupled system into an evolutionary nonlinear interface problem, which is then solved using an iterative algorithm. This approach provides the flexibility to use different time discretization schemes and step sizes in subdomains, making it an efficient method for simulating multiphysics systems. We present numerical tests for both non-physical and physical problems to demonstrate the accuracy and efficiency of this method.

Full Transformer Framework for Robust Point Cloud Registration With Deep Information Interaction.

Point cloud registration is an essential technology in computer vision and robotics. Recently, transformer-based methods have achieved advanced performance in point cloud registration by utilizing the advantages of the transformer in order-invariance and modeling dependencies to aggregate information. However, they still suffer from indistinct feature extraction, sensitivity to noise, and outliers, owing to three major limitations: 1) the adoption of CNNs fails to model global relations due to their local receptive fields, resulting in extracted features susceptible to noise; 2) the shallow-wide architecture of transformers and the lack of positional information lead to indistinct feature extraction due to inefficient information interaction; and 3) the insufficient consideration of geometrical compatibility leads to the ambiguous identification of incorrect correspondences. To address the above-mentioned limitations, a novel full transformer network for point cloud registration is proposed, named the deep interaction transformer (DIT), which incorporates: 1) a point cloud structure extractor (PSE) to retrieve structural information and model global relations with the local feature integrator (LFI) and transformer encoders; 2) a deep-narrow point feature transformer (PFT) to facilitate deep information interaction across a pair of point clouds with positional information, such that transformers establish comprehensive associations and directly learn the relative position between points; and 3) a geometric matching-based correspondence confidence evaluation (GMCCE) method to measure spatial consistency and estimate correspondence confidence by the designed triangulated descriptor. Extensive experiments on the ModelNet40, ScanObjectNN, and 3DMatch datasets demonstrate that our method is capable of precisely aligning point clouds, consequently, achieving superior performance compared with state-of-the-art methods. The code is publicly available at https://github.com/CGuangyan-BIT/DIT.

Care models for coexisting serious mental health and alcohol/drug conditions: the RECO realist evidence synthesis and case study evaluation.

People with severe mental illness who experience co-occurring substance use experience poor outcome including suicide, violence, relapses and use of crisis services. They struggle to access care and treatment due to a lack of an integrated and co-ordinated approach which means that some people can fall between services. Despite these concerns, there is limited evidence as to what works for this population. To undertake a realist evaluation of service models in order to identify and refine programme theories of what works under what contexts for this population. Realist synthesis and evaluation using published literature and case study data. Mental health, substance use and related services that had some form of service provision in six locations in the United Kingdom (five in England and one in Northern Ireland). People with lived experience of severe mental illness and co-occurring substance use, carers and staff who work in the specialist roles as well as staff in mental health and substance use services. Eleven initial programme theories were generated by the evidence synthesis and in conjunction with stakeholders. These theories were refined through focus groups and interviews with 58 staff, 25 service users and 12 carers across the 6 case study areas. We identified three forms of service provision (network, consultancy and lead and link worker); however, all offered broadly similar interventions. Evidence was identified to support most of the 11 programme theories. Theories clustered around effective leadership, workforce development and collaborative integrated care pathways. Outcomes that are meaningful for service users and staff were identified, including the importance of engagement. The requirement for online data collection (due to the COVID-19 pandemic) worked well for staff data but worked less well for service users and carers. Consequently, this may have reduced the involvement of those without access to information technology equipment. The realist evaluation co-occurring study provides details on how and in what circumstances integrated care can work better for people with co-occurring severe mental health and alcohol/drug conditions. This requires joined-up policy at government level and local integration of services. We have also identified the value of expert clinicians who can support the workforce in sustaining this programme of work. People with co-occurring severe mental health and alcohol/drug conditions have complex and multifaceted needs which require a comprehensive and long-term integrated approach. The shift to integrated health and social care is promising but will require local support (local expert leaders, network opportunities and clarity of roles). Further work should evaluate the effectiveness and cost-effectiveness of service models for this group. This study is registered as PROSPERO CRD42020168667. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR128128) and is published in full in Health Technology Assessment; Vol. 28, No. 67. See the NIHR Funding and Awards website for further award information.

Incorporating local content stories into innovative e-book development for high school students in Central Kalimantan, Indonesia

Many studies have shown the enhancement of student engagement by integrating local content into digital learning resources. However, little is known about the interactively and culturally relevant materials in incorporating customizable and engaging content for students. The present study aims to develop an e-book focusing on narrative texts that incorporate local content stories for high school students in Central Kalimantan, Indonesia. This research and development study utilized the ADDIE model and TPACK framework. The research involved 35 students in the eleventh grade at the senior high school. It also involved three English teachers and three external validators. Data were garnered through questionnaires, observations and interviews. The quantitative data were analyzed using descriptive statistics analysis, and qualitative data were analyzed using thematic analysis. Results show that expert validation indicated high-quality media presentation and local content integration. In addition, students’ feedback revealed a remarkable attractiveness, suggesting that the e-book significantly enhances educational outcomes. These findings demonstrate the efficacy of the ADDIE model and TPACK framework in creating relevant learning experiences. The study underscores the importance of integrating local content in digital educational tools, offering broad implications for curriculum development and the creation of engaging culturally relevant educational materials.

Local Knowledges in International Peacebuilding: Acquisition, Filtering, and Systematic Bias

Abstract There is widespread consensus among peacebuilding practitioners and scholars on the importance of integrating local knowledge into the design, planning, and implementation of international peace interventions. However, the concept of local knowledge remains undertheorized, and the dynamics of local knowledge integration in international activities have not yet been fully explored. This paper reconceptualizes “local knowledge” in peacebuilding as local knowledges in the plural, highlighting seven categories of relevant local knowledge and the contestation within each. We then draw on organizational theory to identify the processes by which particular types of local knowledge become more or less likely to be incorporated into internationally led peacebuilding activities. Specifically, we argue that knowledge incorporation consists of two stages: acquisition and filtering. In both, international actors control who is able to contribute knowledges and which knowledges are recognized. Systematic biases result: knowledges that confirm previously held beliefs or that simplify complexity are incorporated more regularly. We illustrate our argument by focusing on the UN, but suggest that our findings apply to other international actors, including non-governmental organizations, and extend beyond peacebuilding.

Mapping the relationship between traffic accidents, road network configuration, and urban land use

Understanding the nature of traffic accidents in relation to urban access networks is crucial for building safer and more resilient cities. This paper examines the issue of traffic accidents through the lenses of urban configurational theory and urban land use. Three data layers were used in the study, including space syntax analysis conducted in Depthmap X, geotagged traffic accidents collected by the police department, and geotagged land-use data. The method involved superimposing these data layers and exploring potential correlations using a geographic information system (GIS). The findings indicate significant correlations between the spatial frequency of traffic accidents and the choice measure (at 2500 m), local integration, and active land use. The findings of this study can help inform planners and policymakers about the best location to implement safety measures to reduce the risk of traffic accidents in urban access networks.

Who is responsible for migrant integration? Contextual political drivers of governance actors’ responsibility frames in small and medium European localities

ABSTRACT This article develops a comparative analysis of ‘responsibility frames’ of migrant integration – i.e. cognitive understandings about who should be responsible for migrant integration – held by actors involved in local integration governance across 36, rigorously selected, European small and medium towns and applies multilevel regression analysis to examine potential contextual and political drivers of the emergence of such frames. Our findings contribute to the existing literature on local integration frames indicating that, first, overall, frames that see integration as a responsibility of ‘migrants only’ prevail over other frames. Second, integration frames are localised, meaning that municipalities provide distinct contexts for the social construction of migrant integration. Third, political factors are crucial at both the individual and contextual level: not only elected local policymakers’ frames depend on their political party affiliation but also frames held by bureaucrats and non-public actors depend on the political affiliation of the local executive of their locality.

AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation

In the field of medical image segmentation, although U-Net has achieved significant achievements, it still exposes some inherent disadvantages when dealing with complex anatomical structures and small targets, such as inaccurate target localization, blurry edges, and insufficient integration of contextual information. To address these challenges, this study proposes the Attention-Fused Full-Scale CNN-Transformer Unet (AFC-Unet), aiming to effectively overcome the limitations of traditional U-Net through multi-scale feature fusion, attention mechanisms, and CNN-Transformer hybrid modules. Specifically, we adopt an encoder–decoder architecture, incorporating full-scale feature block fusion and pyramid sampling modules to enhance the model’s ability to recognize fine to overall structural features by integrating cross-level multi-scale features. We propose the Multi-feature Fusion Attention Gates (MFAG) module, which focuses on and highlights discriminative information of potential lesions and key anatomical boundaries, effectively suppressing irrelevant background interference. We design a module Convolutional Hybrid Attention Transformer (CHAT) that integrates CNN and Transformer to address the shortcomings of traditional single models in handling long-range dependencies and global context understanding. Experimental results on three datasets of different scales demonstrate that the model’s segmentation performance for medical images surpasses state-of-the-art models, showcasing high generalization ability.

Comparison of feed-forward control strategies for simplified vertical hopping model with intrinsic muscle properties

To analyse walking, running or hopping motions, models with high degrees of freedom are usually used. However simple reductionist models are advantageous within certain limits. In a simple manner, the hopping motion is generally modelled by a spring-mass system, resulting in piecewise smooth dynamics with marginally stable periodic solutions. For a more realistic behaviour, the spring is replaced by a variety of muscle models due to which asymptotically stable periodic motions may occur. The intrinsic properties of the muscle model, i.e. preflexes, are usually taken into account in three complexities-constant, linear and Hill-type. In this paper, we propose a semi-closed form feed-forward control which represents the muscle activation and results in symmetrical hopping motion. The research question is whether hopping motions with symmetric force-time history have advantages over asymmetric ones in two aspects. The first aspect is its applicability for describing human motion. The second aspect is related to robotics where the efficiency is expressed in term of performance measures. The symmetric systems are compared with each other and with those from the literature using performance measures such as hopping height, energetic efficiency, stability of the periodic orbit, and dynamical robustness estimated by the local integrity measure (LIM). The paper also demonstrates that the DynIn MatLab Toolbox that has been developed for the estimation of the LIM of equilibrium points is applicable for periodic orbits.

Sustained Performance of Cardiac Arrest Prevention in Pediatric Cardiac Intensive Care Units.

The Pediatric Cardiac Critical Care Consortium (PC4) cardiac arrest prevention (CAP) quality improvement (QI) project facilitated a decreased in-hospital cardiac arrest (IHCA) incidence rate across multiple hospitals. The sustainability of this outcome has not been determined. To examine the IHCA incidence rate at participating hospitals after the QI project ended and discern which factors best aligned with sustained improvement. This observational cohort study compared IHCA data from the CAP era (July 1, 2018, to December 31, 2019) with data from the 2-year follow-up era (March 1, 2020, to February 28, 2022). Data were obtained from pediatric cardiac intensive care units (CICUs) from 17 PC4 CAP-participating hospitals. The CAP practice bundle was designed to facilitate local practice integration, with the intention to implement, adapt, and continue CAP processes beyond the CAP era. A web-based survey was administered 2 years after the end of the project to estimate CAP-specific QI work. Risk-adjusted IHCA incidence rates across all admissions were compared between study eras. The survey generated a novel hospital-specific QI sustainability score, which is generally reflective of the sum of local CAP work performed. There were no clinically important differences in demographic and admission characteristics between the 13 082 CAP era admissions and 16 284 follow-up admissions (total mean [SD] age, 5.1 [8.4] years; 56.1% male). Risk-adjusted IHCA incidences were not different between the CAP vs follow-up eras (2.8% vs 2.8%; odds ratio, 1.03; 95% CI, 0.89-1.19), suggesting sustained prevention improvement. There was also no difference between eras in risk-adjusted IHCA incidence within medical, surgical, or high-risk subgroups. A lower hospital QI sustainability score was correlated with higher odds for IHCA in the follow-up vs CAP era (correlation coefficient, -0.58; P = .02). Five hospitals had increases of 1% or greater in risk-adjusted IHCA rates in the follow-up era; these hospitals had significantly lower QI sustainability scores and were less likely to have adopted sustainability elements during the CAP era or report persistent engagement for CAP-related QI processes during follow-up. In this cohort study of all CICU admissions across 17 hospitals, IHCA prevention was feasible and sustainable; the established reduction in risk-adjusted IHCA rate was maintained for at least 2 years after the end of the CAP project. Both implementation strategies and continued engagement in CAP processes during the follow-up era were associated with sustained improvement.

Construction of pairwise orthogonal Parseval frames generated by filters on LCA groups

The generalized translation invariant (GTI) systems unify the discrete frame theory of generalized shift-invariant systems with its continuous version, such as wavelets, shearlets, Gabor transforms, and others. This article provides sufficient conditions to construct pairwise orthogonal Parseval GTI frames in L2(G) satisfying the local integrability condition (LIC) and having the Calderón sum one, where G is a second countable locally compact abelian group. The pairwise orthogonality plays a crucial role in multiple access communications, hiding data, synthesizing superframes and frames, etc. Further, we provide a result for constructing N numbers of GTI Parseval frames, which are pairwise orthogonal. Consequently, we obtain an explicit construction of pairwise orthogonal Parseval frames in L2(R) and L2(G), using B-splines as a generating function. In the end, the results are particularly discussed for wavelet systems.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

What drives labor force participation rate variability? The case of West Virginia

This study examines the dynamics of labor force participation rates across counties in West Virginia to better understand local labor market integration and the factors influencing fluctuations in participation. Drawing on county-level data from January 1990 to July 2020, the research employs a dynamic factor model to decompose labor force participation rates into latent factors at the state, metropolitan/non-metropolitan, and county levels. The findings reveal a general lack of labor market integration across West Virginia, highlighting potential opportunities for growth through enhanced integration. Further analysis using panel data models identifies key determinants of labor force participation, including personal income, education, infrastructure, and the prominence of industries such as agriculture and natural gas. The results underscore the necessity for targeted county-level policies to bolster employment and promote economic expansion within the state.

Classification and analysis of the types of interaction of regional innovation systems

This article is devoted to a detailed study of the interaction between innovation systems and innovation processes. The application of the regional-national innovation system (RIS) model has been studied, and it has been revealed that it is a targeted strategy aimed at accelerating economic growth through the introduction of scientific developments into the production cycle. The importance of including distinctive regional characteristics in analytical models for a more adequate representation of the real innovative and competitive capabilities of the regions under consideration is substantiated. The need for modification of methodologies for the analysis of regional innovation systems (RIS), adapted to the specifics of local development and external integration, is emphasized. It has been established that an adequate assessment of the innovative capacities and positions of regions in the global economic space is hampered by the presence of divergence in classification standards. The need to include a new type of RIS in the classification is shown, which adequately reflects the initial stages of systems development in the context of unique Russian regional features. An analysis of the dynamics of interaction between key actors within these systems has been obtained, contributing to a deep understanding of the mechanisms of transfer of innovative knowledge among actors, providing tools for managing the development of regions, identifying the drivers of their competitive advantages and focusing on market participants.

Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)

The resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide.

Lessons from the COVID-19 Pandemic: Promoting Vaccination and Public Health Resilience, a Narrative Review.

The COVID-19 pandemic has underscored the critical importance of adaptable and resilient public health systems capable of rapid response to emerging health crises. This paper synthesizes the lessons learned from the COVID-19 vaccination campaign and explores strategies to enhance vaccine uptake in the post-pandemic era. Key challenges identified include logistical, economic, sociocultural, and policy dimensions that impact vaccination efforts, particularly in low-resource settings. The analysis highlights the need for resilient supply chains, effective communication, community engagement, and equitable access to healthcare resources. The rapid development and deployment of mRNA vaccines exemplify the potential of innovative vaccine technologies, though public trust and acceptance remain crucial. Strategies such as partnerships with local leaders, tailored messaging, and integration of digital tools are essential for combating vaccine hesitancy. By applying these insights, future vaccination campaigns can be more efficient, equitable, and resilient, ultimately improving public health outcomes globally. This paper aims to inform policy and practice, ensuring that public health strategies are evidence based and context specific, thus better preparing for future health challenges.

Observation of anisotropy-independent magnetization dynamics in spatially disordered Heisenberg spin systems

An important step towards a comprehensive understanding of far-from-equilibrium dynamics of quantum many-body systems is the identification of unifying features that are independent of microscopic details of the system. We experimentally observe such robust features in the magnetization relaxation dynamics of disordered Heisenberg XX, XXZ, and Ising Hamiltonians. We realize these Heisenberg spin models with tunable anisotropy parameter and power-law interactions in an ensemble of Rydberg atoms by encoding the spin in suitable Rydberg state combinations. We consistently observe stretched-exponential relaxation of magnetization for all considered spin models, collapsing onto a single curve after appropriate rescaling of time. This robust short-time relaxation behavior is explained by a perturbative treatment that exploits the strong disorder in pairwise couplings, which leads to a description in terms of approximately independent pairs of spins. In numerical simulations of small systems, we show that these pairs of spins constitute approximate local integrals of motion, which remain at least partially conserved on a timescale exceeding the duration of the relaxation dynamics of the magnetization. Published by the American Physical Society 2024