Broad Network Research Articles (Page 1)

ABSTRACT This study explores the impact of the digital economy on urban innovation quality, using data from 265 Chinese cities over the period 2012–2023. The results show that digital economy initiatives significantly enhance urban innovation quality, with pilot zones outperforming non‐pilot zones by 15%. The mechanism analysis emphasises the mediating role of collaborative innovation. Compared to the breadth of collaborative innovation, the digital economy has a relatively small effect on the depth of collaborative innovation; the depth of collaboration plays a more significant role in improving innovation quality. Further analysis shows that the digital economy's impact on innovation quality is stronger in regions with higher internet penetration and advanced industrial structures. In cities with higher internet penetration, innovation quality improves through deeper collaboration, whilst in cities with lower penetration, it improves mainly through broader networks. In cities with advanced industrial structures, the digital economy boosts both the breadth and depth of collaboration, enhancing innovation quality. This study enhances our understanding of how the digital economy drives urban innovation by unravelling the complex mechanisms behind collaborative innovation.

The carotid body is a peripheral chemoreceptor that consists of clusters of chemoreceptive type I cells, glia-like type II cells, afferent and efferent nerves, and sinusoidal capillaries and arterioles. Cells and nerves communicate through reciprocal chemical synapses and electrical coupling that form a “tripartite synapse,” which allows for the process of sensory stimuli within the carotid body involving neurotransmission, autocrine, and paracrine pathways. In this network there are a variety of neurotransmitters and neuromodulators including adenosine 5′-triphosphate (ATP). Carotid body cells and nerve fibre terminals express ATP receptors, i.e., purinergic receptors. Here we used double immunofluorescence associated with laser confocal microscopy to detect the ATP receptor P2X7 and pannexin 1 (an ATP permeable channel) in the human carotid body, as well as the petrosal and cervical sympathetic ganglia. Immunofluorescence for P2X7r and pannexin 1 forms a broad cellular network within the glomeruli of the carotid body, whose pattern corresponds to that of type II cells. Moreover, both P2X7r and pannexin 1 were also detected in nerve profiles. In the petrosal ganglion, the distribution of P2X7r was restricted to satellite glial cells, whereas in the cervical sympathetic ganglion, P2X7r was found in neurons and glial satellite cells. The role of this purinergic receptor in the carotid body, if any, remains to be elucidated, but it probably provides new evidence for gliotransmission.

The increasing prevalence of opioid use disorder (OUD) represents an important global public health crisis, often referred to as the 'opioid epidemic'. Opioids are known for their potent pain-relieving effects, but also have serious side effects, including OUD and respiratory depression, which can lead to fatal overdoses. To address this growing concern, we require a better understanding of the mechanisms underlying OUD, which typically begins with either medical or recreational opioid use and evolves into a complex and chronic brain disorder. In this Review, we highlight recent advances in our understanding of opioid receptors and the neural circuits in which they operate (including the broad network of circuits involved in reward and relief processing), focusing on the changes that follow long-term opioid exposure, abstinence and withdrawal. Additionally, we discuss recent findings that highlight the importance of the local cellular environment in shaping responses to these drugs. Overall, we aim to provide an updated overview of the field that may give us new insights into the multifaceted landscape of OUD.

ABSTRACT This ethnographic study explores how educational staff in a Swedish rural community address challenges in supporting students’ educational and professional futures. Drawing on Massey’s relational understanding of space and place, the rural school is conceptualised as a dynamic node where social relations, digital flows, and institutional structures intersect. Using Massey’s concepts of relational place, power geometry, and spatial divisions of labour, the analysis interprets classroom practices as enactments of place shaped by intersecting local and global trajectories. Based on long-term fieldwork involving observations, interviews, and school – community initiatives, the results show how educators respond to tensions between locally rooted occupational traditions and globally circulating media and labour-market imaginaries. Rather than reproducing structural constraints, they reconfigure relational geographies of opportunity by linking local attachments with broader networks and imagined futures. The study contributes to rural education research by showing how educators’ work both reflects and reshapes spatial divisions of labour.

PCBNet: positional crossing and broad features network for indoor scene semantic segmentation

In this work, a novel broad learning neural network-based adaptive control (BLNNAC) scheme is designed for a class of lateral thrust/aerodynamic force composited high-speed unmanned aerial vehicles with external perturbations, and unknown uncertainty under the time-varying output constraints. The proposed control strategy incorporates several key innovations. Firstly, an innovative tan-type barrier Lyapunov function is introduced to successfully avoid violations of time-varying output constraints. Secondly, the fast response capability and robustness to external disturbances inherent in the integral sliding mode control (ISMC) scheme are integrated into the strategy, enhancing its overall performance. Finally, a novel broad learning neural network (BLNN) is designed to effectively suppress the detrimental effects of unknown uncertainties, thereby significantly improving the system’s approximation performance. The results indicate that all signals are well-constrained, and the transient states of the output signals satisfy the constraint conditions constantly. Finally, the effectiveness and advantages of the proposed scheme are demonstrated through simulation results.

Abstract Public spaces have a significant impact on social interactions and community dynamics, particularly during times of disruption and change. This study investigates the role of public space in fostering community resilience. It introduces a conceptual framework to identify the attributes and characteristics of public space that support or hinder the social environment, in the context of community resilience. Through an interdisciplinary narrative literature review, the research examines how social dynamics—particularly communal interactions and activities—are influenced by spatial features and how these contribute to a community’s adaptive capacity during crises. The findings highlight that public spaces act as vital community infrastructure, facilitating community networks and fostering connections between people and place, all of which are key components of community resilience. As such, the research highlights the importance of recognizing various spaces within the public domain with these attributes, while emphasizing the significance of diverse public spaces to nurture communities and support their resilience. More so, by integrating public spaces into broader networks, the study suggests that fostering belonging, identification, and reinforcing essential structures enhance community resilience, transcending the role of conventional public spaces concepts.

The ability to generalize previously learned knowledge to novel situations is crucial for adaptive behavior, representing a form of cognitive flexibility that is particularly relevant in language. Humans excel at combining linguistic building blocks to infer the meanings of novel compositional words, such as “un-reject-able-ish”. The neural mechanisms and representations required for this ability remain unclear. To unravel these, we trained participants on a semi-artificial language in which the meanings of compositional words could be derived from known stems and unknown affixes, using abstract relational structure rules (e.g., “good-kla” which means “bad”, where “-kla” reverses the meaning of the stem word “good”). According to these rules, word meaning depended on the sequential relation between the stem and the affix (i.e., pre- vs. post-stem). During fMRI, participants performed a semantic priming task, with novel compositional words as either sequential order congruent (e.g., “short-kla”) or incongruent primes (e.g., “kla-short”), and real words serving as targets that were synonyms of the composed meaning of the congruent primes (e.g., “long”). Our results show that the compositional process engaged a broad temporoparietal network, while representations of composed word meaning were localized in a more circumscribed left-lateralized language network. Strikingly, newly composed meanings were decodable already at the time of the prime in a way that could not be accounted for representations of the prime words themselves. Finally, we found that the composition process recruited abstract rule representations in a bilateral frontoparietal network, in contrast to our preregistered prediction of a medial prefrontal-hippocampal network. These results support the hypothesis that people activate a bilateral frontoparietal circuitry for compositional inference and generalization in language.

This paper presents findings of a qualitative comparative case study of Palazzo Falson (PF) House Museum in the Mediterranean island-state of Malta, and MUSE Science Museum in Trento, northern Italy. It elaborates the contribution of intersectional perspectives to the study of participatory museums, and investigates ways and extents to which the two museums (can) feature intersectionally inclusive agoras, understood as accessible spaces that engage diverse publics with participatory lifelong education. The museums’ community focus, and contrasting profiles featured among reasons for selection, with a view of discerning transferable insights. Findings informed the identification of general learning outcomes (LOs) for professional development initiatives that target capacity-building of museum personnel to mediate the museum to diverse publics as an intersectional lifelong education agora. A critical review of secondary sources and thematic analysis of interviews held with personnel, flagged differing capacities and resources. MUSE emerges as strong on accessibility and EU-linked initiatives, whilst the house setting of PF offers opportunities for critical alternative readings of domestic artefacts. The six proposed LOs include recognition of the lifelong educator dimension of museum personnel roles, and collaboration in a broad and inclusive network for sustainable development. Recommendations include periodical needs-analysis and participatory research and impact assessments, and museums’ uptake of social audit practices.

This study aimed to understand the factors limiting access to medications for the preventive treatment of migraine and to improve access to evidence-based preventive care. For decades, the effective use of medication for the preventive treatment of migraine was limited by slow onset, slow and complex dose titration schedules, modest benefits, drug interactions, frequent side effects, and very low long-term adherence. The calcitonin gene-related peptide (CGRP) targeted preventive medications mitigate some of these limitations and demonstrated substantial therapeutic benefits in a significant proportion of adults with migraine. The American Headache Society considers these medications among the first-line options for migraine prevention, although access to them remains limited. The American Migraine Foundation hosted a single-day, multidisciplinary expert panel discussion to identify barriers to optimal preventive care and developed recommendations to address them. Participants identified and prioritized barriers and used a modified nominal group technique to achieve consensus on them. A series of moderated discussions in plenary and breakout sessions was used to create possible solutions. Modified nominal group technique was also employed to achieve consensus on the priorities among these barriers and to achieve whole-group consensus on the recommendations. Ethical issues that inform access were discussed. Participants included eight neurologists and board-certified headache specialists, six representatives of reimbursement decision-makers, six employees of life sciences companies, four patient advocates with lived experience with migraine, and a medical ethicist. Among those who have consulted healthcare professionals and received a diagnosis of migraine, we identified four main barriers to accessing preventive treatment: restrictive prior authorization requirements, the perceived lack of real-world evidence and treatment guidelines, the need for clinician education, and the need for patient education. Consensus recommendations for eliminating barriers centered on using new evidence to evaluate policies that restrict the selection of first-line therapies, initiating/improving collaboration among stakeholders, sharing of data and best practices, and increased training. Participants agreed to explore novel definitions of the value of preventive treatment and to establish the Migraine Prevention Network to facilitate ongoing cooperation and collective action. However, due to financial limitations, staffing changes, and time constraints, post-meeting discussions led to a shift from establishing a broad Migraine Prevention Network to forming smaller task forces focused on the top-priority barriers (real-world evidence and The Patient Playbook) identified through collaborative voting among American Headache Society, American Migraine Foundation, and industry stakeholders. Adults with migraine face multiple barriers in accessing novel migraine-specific, CGRP-targeted preventive treatment. Stakeholders in the delivery of care, including clinicians, reimbursement decision-makers, life sciences companies, and patient and clinician advocates, may be able to overcome many of these barriers and improve access by working with and on behalf of patients.

Mr. Talib Al-Rifai embarked on his political journey at an early stage, engaging in Islamic activism at the age of twenty-two in 1953. During this period, he established strong connections with numerous figures and members affiliated with Sunni Islamic parties, which expanded his sphere of influence and social network. Despite his close interaction with these organizations, he was careful to maintain his independence and avoided formal affiliation with any specific party or organized movement. Al-Rifai distinguished himself by his ability to engage with diverse Islamic currents while maintaining his intellectual independence, which provided him with significant flexibility and enabled him to cultivate a broad network of relationships. He was known for preferring to operate within a flexible framework, away from rigid party structures, which enhanced his stature and positioned him as an influential figure beyond conventional organizational boundaries. Although he never officially joined any political organization, his strong ties with leading figures of Sunni Islamic activism made him a significant actor within the broader political landscape. He effectively utilized this extensive network to promote his ideas and advance his causes without being constrained by partisan affiliations. In this way, he succeeded in combining adaptability with steadfast commitment to his principles—earning him wide respect among his contemporaries. (1)

Site neutrality in payment practices has become a salient issue in the US health care debate, as rising prices have brought increased pressure for policy action. Although Medicare has received disproportionate attention, these policies could also apply to commercial insurers, particularly to address payment differentials between hospital outpatient departments (HOPDs) and ambulatory surgical centers (ASCs). Using 2024 Transparency in Coverage data provided by Clarify Health on commercial prices for three insurers (UnitedHealthcare, Cigna, and BlueCross BlueShield), we compared payments for thirteen common procedures across settings. Overall, in 2024, commercial prices were $1,489 (78percent) higher in HOPDs than in ASCs, whereas Medicare prices were $633 (97percent) higher. However, site payment differentials varied substantially across payers: Cigna had the lowest differentials between HOPDs and ASCs ($327), whereas United had the highest ($1,673). Cigna achieved this through provider selection, contracting with only 14percent of HOPDs in applicable markets compared with an average of 76percent for United and BlueCross BlueShield. If United and BlueCross BlueShield paid Cigna's average HOPD rates for these procedures, together they would save approximately $1.4billion a year. Our results suggest that payers can reduce site differentials through provider selection; they also imply that larger insurers with broader networks may continue to reimburse different sites differently in the absence of either government action or a shift in market dynamics.

Metric learning aims to learn a discriminative metric space, where samples of the same class stay close, and those of different classes far apart. Existing classical metric learning methods based on linear transformation have limited learning performance due to the low representation capability. Although deep metric learning learns nonlinear mappings, the training may come across convergence issues and be unstable. Additionally, many classical metric learning algorithms suffer from long computational time for iterative optimization especially when data dimension is high. Deep metric learning also requires high training cost. To learn a metric space more efficiently and effectively, this article proposes a novel broad metric learning (BML) model, which learns the data transformation by training a broad network. BML maps input data to a broad feature space by fast and convenient nonlinear feature mapping based on random weights, and learns a linear transformation to a discriminative output space. Intraclass distance is reduced by minimizing the distance between data and their class-specific reference points in the target space. The hard-triplet distance learning (HDL) is proposed to learn the distance of hard positive and negative sample pairs, which enhances the intraclass compactness and interclass separation. Closed-form solutions are adopted to solve the optimization problems efficiently when learning the linear transformation. Experiments are conducted on nine datasets to verify the efficiency and effectiveness of BML. BML learns fast and achieves high classification and clustering accuracies in the learned data space.

This paper addresses the problem of rapid, precise, and efficient identification and diagnosis of grape leaf diseases by proposing the Deep-Broad Learning Network Model (ABLSS), which combines a Broad Learning network model with deep learning techniques. The model is optimized using the Adam algorithm based on BLS, and incorporates the LTM mechanism, which significantly enhances learning efficiency, stability, and recognition accuracy. Additionally, by integrating deep learning network optimization techniques, a SENet attention mechanism is added between the mapping and enhancement layers of BLS. Furthermore, based on the U-Net segmentation model, the method integrates dilated spatial pyramid pooling and feature pyramid networks. Dilated convolutions with varying dilation rates are used to capture multi-scale contextual information, which providing rich semantic information and high-resolution details during the decoding process. This improves the ABLSS model’s ability to identify small disease spots. Experimental results show that the ABLSS model achieves the highest recognition accuracy for three types of diseases with similar features on grape leaves, with an average accuracy improvement of 7.69% over BLS and 4.48% over deep learning networks. The MIOU of the segmentation model reaches 86.61%, which is a 6.48% improvement over the original U-Net model, and the MPA is 90.23%, a 8.09% improvement over the original U-Net. These results demonstrate that the proposed method significantly improves the algorithm’s recognition accuracy for small and irregular complex images. The ABLSS model recognizes images 0.375 seconds faster than the deep learning network, achieving a 72.12% speed improvement, thereby significantly enhancing the recognition efficiency of fine features. The ABLSS model combines the high recognition accuracy of deep learning with the fast processing speed of Broad Learning, while overcoming the limitations of BLS in recognizing complex images. This study provides valuable support for the development of smart orchard technologies and the optimization of learning network models.

Increasing data volumes, rapid digitisation and the demands for open science and responsible research data management (RDM) — driven by research organisations, funders and policymakers — have created a growing demand for RDM services. This complex and ever-changing field requires that RDM specialists continuously adapt and develop new competencies in order to support scholars practising open science. The National research data management (RDM) Training Network in Finland addresses the specific challenges faced by RDM specialists in the country, where the small population size limits the number of RDM specialists, leading to a reliance on cross-organisational collaboration. Often, these specialists are the only experts in their organisations, tasked with diverse responsibilities across disciplines. Additionally, they face time constraints, high staff turnover and a lack of formal RDM training. Recognising these challenges, Finland's National RDM Training Network was established to support and bridge gaps in resources and expertise, and to have a significant impact on the wider academic and research community by enhancing competencies and reducing disparities between institutions. Another goal is to produce publicly available training materials and resources on platforms such as Zenodo to promote transparency and sharing. Originally a modest assembly under the National Open Science and Research Coordination (AVOTT), the network has matured under a broad cooperation network Finn-ARMA, bringing together professionals from some 30 organisations. It is open to both open science professionals and scholars. The network strengthens the Finnish RDM landscape, offering solutions to meet growing demands and resource limitations, and contributing to a sustainable knowledge ecosystem. In this presentation we will explore the origins, development and activities of the Finnish RDM Training Network and discuss its role in bridging expertise gaps and its impact on the wider research community and how working together with shared resources can speed up the transition to open science.

From hippocampal neurons to broad spiking neural networks

A Novel Framework for Few-Shot RF Fingerprint Identification Using Signal Recurrence Plot and Convolutional Broad Learning Network

Movement and maintenance of posture involve many interacting factors that rely on a broad neural network. These factors include proprioception, body schema, and peripersonal and extrapersonal space, all of which may be affected in individuals with neurocognitive disorders. Frontal release signs are a common clinical finding in such individuals, resulting from the disruption of the related neural networks. In this case series, we discuss the potential clinical relevance of midline hand drift (MHD). MHD is a rarely documented physical exam finding in which one or both hands move toward the midline when the individual is seated upright with their eyes open, arms outstretched, and palms facing upwards. Upon examination, 24 individuals with a chief complaint of worsening memory were found to have MHD. All 24 individuals with MHD had at least mild cognitive impairment, and 22 were also diagnosed with a neurodegenerative disorder. The most common diagnoses among these individuals were Parkinson disease dementia, Lewy body dementia, and vascular dementia. Nine of the 24 patients demonstrated no frontal release signs upon examination. MHD may be a useful clinical finding that aids in the diagnosis of an underlying neurocognitive disorder. Assessing for MHD may be most beneficial for individuals who are in the earlier stages of neurocognitive decline. They may show mild impairment on the Mini-Mental State Examination or the Montreal Cognitive Assessment, but no other characteristic findings (eg, frontal release signs).

This study examines the influence of Kiyai Haji Ahmad Dahlan (1868-1923) in the history of Islamic reform in Indonesia and its impact on modern movements and thought. This study focuses on Muhammadiyah, an Islamic organization founded by Ahmad Dahlan on November 18, 1912, in the context of religious reform and purification. The main objective of this study is to reveal the philosophy and struggle brought by Ahmad Dahlan, a pioneer of modern awareness and awakening in Indonesia. The method used is descriptive-historical with a qualitative approach, combining scientific and empirical reviews. The study results indicate that Ahmad Dahlan's thoughts have greatly influenced the aspirations of modern Islam through Muhammadiyah, which created a broad network of activists and preachers. His contribution to developing rational streams and awareness from the traditions of madhhab and puritanism is very significant. Recommendations include the need for further research on Ahmad Dahlan's influence in a global context and the adjustment of traditional values ​​in contemporary Islamic education to increase the relevance of education in the modern era.

Paclitaxel, a compound that targets microtubules in cancer cells and inhibits mitosis, has been used as a first-line treatment for recurrent or metastatic breast cancer for over three decades. Research into paclitaxel resistance has yielded thousands of articles. However, this body of research has not been analyzed systematically and quantitatively. This study employs bibliometric analysis and visualisation techniques using VOSviewer and CiteSpace software to elucidate the contributions from countries and institutions, collaboration patterns, journal publication trends, research evolution, and current hotspots. Our findings reveal that leading research institutions in the United States, Europe, and East Asia hold significant advantages in this field. Specifically, the United States contributed the most to this field, with MD Anderson Cancer Center being the most prominent research institution and having a broad collaboration network. Prestigious oncology journals serve as primary platforms for disseminating key findings, with Cancer Research publishing the most relevant articles and having the highest co-citation frequency. We identified the main research branches by analysing keyword co-occurrence and keyword bursts, including treatment methods, drug delivery, mechanisms, and cellular response. Current research focuses on unveiling resistance mechanisms and exploring new therapeutic strategies. Paclitaxel resistance in breast cancer, particularly in TNBC and metastatic cases, remains an active and critical research area. Integrating nanotechnology into drug development and therapeutic strategies is a current focal point and promises to be a significant future direction. These insights highlight a strategic direction for future research, including strengthening international partnerships, deepening understanding of resistance mechanisms, and applying engineered therapeutics for targeted breast cancer treatments.