Bridge Network Research Articles

Intermediary-Generated Bridge Network for RGB-D Cross-Modal Re-Identification

RGB-D cross-modal person re-identification (re-id) targets at retrieving the person of interest across RGB and depth image modalities. To cope with the modal discrepancy, some existing methods generate an auxiliary mode with either inherent properties of input modes or extra deep networks. However, such useful intermediary role included in generated mode is often overlooked in these approaches, leading to insufficient exploitation of crucial bridge knowledge. By contrast, in this article, we propose a novel approach that constructs an intermediary mode through the constraints of self-supervised intermediary learning, which is freedom from modal prior knowledge and additional module parameters. We then design a bridge network to fully mine the intermediary role of generated modality through carrying out multi-modal integration and decomposition. For one thing, this network leverages a multi-modal transformer to integrate the information of three modes via fully exploiting their heterogeneous relations with the intermediary mode as the bridge. It conducts the identification consistency constraint to promote cross-modal associations. For another, it employs circle contrastive learning to decompose the cross-modal constraint process into several subprocedures, which provides the intermediate relay during pulling two original modalities closer. Experiments on two public datasets demonstrate that the proposed method exceeds the state-of-the-arts. The effectiveness of each component in this method is verified through numerous ablation studies. Additionally, we have demonstrated the generalization ability of the proposed method through experiments.

An unpaired SAR-to-optical image translation method based on Schrödinger bridge network and multi-scale feature fusion

SAR-to-optical (S2O) translation is able to covert SAR into optical images, which help the interpreter to extract information efficiently. In the absence of strictly matched datasets, it is difficult for existing methods to complete training on unpaired data with a minimum amount of data. By employing the recent Schrödinger bridge-based transformation framework, a multiscale axial residual module (MARM) based on the concept of multi-scale feature fusion has been proposed in this paper. To enable efficient translation of SAR to optical images, the generator and discriminator of the model have been designed. Extensive experiments on the SEN1-2 dataset conducted, and the results show the superiority of the proposed method in terms of the generation quality. Compared with the classical CycleGAN, the proposed method can improve the FID metrics by 42.05%.

Framework for Identification of Bridges in Need of Monitoring Based on Maintenance History

Recently, advanced Internet-of-Things (IoT) technology has been implemented in structural health monitoring to help increase safety of infrastructure and the reliability of maintenance decisions. In this context, preliminary studies are required to identify infrastructure in need of monitoring. This study establishes a basis for the identification of bridges that require monitoring and selects the bridges for measurement in order to build an efficient IoT-based monitoring system for small and medium bridges. The bridges selected based on various maintenance histories and risk factors are expected to be utilized in comprehensively assessing the characteristics of the overall bridge network.

Social Influence in Social-Ecological Systems: Collaborative Capacity and Inter-Organizational Networks in the U.S. Sagebrush.

Collaborative governance has become a prominent, if not dominant, framework for thinking about multi-scalar and cross-jurisdictional environmental management. The literature broadly and consistently suggests that collaborative capacity and inter-organizational networks provide the institutional framework for addressing social-ecological system challenges. Surprisingly little scholarship addresses processes of social influence (or contagion) in social-ecological systems writ large, or more specifically as it relates to collaborative capacity. In this study, we consider the relationship between network position and structure and "collaborative capacity". We use a linear network autocorrelation model to establish a quantifiable, statistical relationship between an organizational-level outcome (collaborative capacity) for organizations that are relationally connected in a network addressing conservation and management issues in the U.S. sagebrush biome. We test three general hypotheses: (1) that social position - operationalized as network centrality - is related to collaborative capacity, (2) that subgroup or community structure - operationalized as modularity - is related to collaborative capacity, and (3) that there is a social contagion effect of collaborative capacity. Our results identify a positive relationship between collaborative capacity and organizations in positions of brokerage as well as evidence of a "collaborative capacity" contagion effect. This work contributes to our understanding of the role of bridging organizations and networks for large-scale environmental management.

Social participation and depression among resettled Syrian refugees: examining a moderated mediation of social support and bonding or bridging social capital.

Aspects of social capital, including social participation and social support, are among the factors influencing refugee mental health after resettlement. However, the mechanisms by which these aspects interact with one another and affect mental health remain unclear. This study investigates whether social participation influences depressive symptoms via social support and whether this influence is more prominent for Syrian refugees participating primarily in coethnic (bonding) networks compared to cross-ethnic (bridging) networks during the early stages of resettlement. Using data from a cohort of 464 Syrian refugees recently resettled in Sweden, a moderated mediation analysis was conducted with multigroup structural equation modelling to investigate the mediatory role of social support in the association between social participation and depressive symptoms as well as the moderating role of bonding networks (social participation with other Syrians) versus bridging networks (social participation with Swedes) in this relationship. Frequent social participation, compared to rare or no participation, was significantly associated with lower depressive symptoms, regardless of whether participation included a broad or limited range of activities. Social support only mediated this relationship for those with primarily bonding networks, indicating that the mediation itself was moderated by network type. While participating in both bridging and bonding networks was associated with lower depressive symptoms, bonding networks amplified the effect of social participation on depressive symptoms via increased social support, resulting in an overall twofold decrease in depressive symptoms compared to those with bridging networks. These findings indicate that the frequency of social participation may be a more important consideration for fostering mental well-being in recently resettled refugees than the specific types of activities. Furthermore, while both bonding and bridging social networks confer mental health benefits, access to coethnic networks in early resettlement appears to provide a particularly crucial source of social support.

Molecular Dynamics (MD) Simulations Provide Insights into the Activation Mechanisms of 5-HT2A Receptors

Recent breakthroughs in the determination of atomic resolution 3-D cryo-electron microscopy structures of membrane proteins present an unprecedented opportunity for drug discovery. Structure-based drug discovery utilizing in silico methods enables the study of dynamic connectivity of stable conformations induced by the drug in achieving its effect. With the ever-expanding computational power, simulations of this type reveal protein dynamics in the nano-, micro-, and even millisecond time scales. In the present study, aiming to characterize the protein dynamics of the 5HT2A receptor stimulated by ligands (agonist/antagonist), we performed 1 µs MD simulations on 5HT2A/DOI (agonist), 5HT2A/GSK215083 (antagonist), and 5HT2A (APO, no ligand) systems. The crystal structure of 5HT2A/zotepine (antagonist) (PDB: 6A94) was used to set up the simulation systems in a lipid bilayer environment. We found the monitoring of the ionic lock residue pair (R3.50-E6.30) of 5HT2A in MD simulations to be a good approximation of the effects of agonists (ionic lock breakage) or antagonists (ionic lock formation) on receptor activation. We further performed analyses of the MD trajectories, including Principal Component Analysis (PCA), hydrogen bond, salt bridge, and hydrophobic interaction network analyses, and correlation between residues to identify key elements of receptor activation. Our results suggest that in order to trigger receptor activation, DOI must interact with 5HT2A through residues V5.39, G5.42, S5.43, and S5.46 on TM5, inducing significant conformational changes in the backbone angles of G5.42 and S5.43. DOI also interacted with residues W6.48 (toggle switch) and F6.51 on TM6, causing major conformational shifts in the backbone angles of F6.44 and V6.45. These structural changes were transmitted to the intracellular ends of TM5, TM6, and ICL3, resulting in the breaking of the ionic lock and subsequent G protein activation. The studies could be helpful in future design of selective agonists/antagonists for various serotonin receptors (5HT1A, 5HT2A, 5HT2B, 5HT2C, and 5HT7) involved in detrimental disorders, such as addiction and schizophrenia.

Assessing the Bridge Structure’s System Reliability Utilizing the Generalized Unit Half Logistic Geometric Distribution

Reliability is now widely recognized across various industries, including manufacturing. This study investigates a system composed of five components, one of which is a bridge network. The components are assumed to follow the generalized unit half logistic geometric distribution (GUHLGD) with equal failure rates over time. The following three improvement methods are considered: reduction, cold duplication, and hot duplication. The reliability function and mean time to failure (MTTF) are employers liability equivalence factors (REFs). Additionally, the λ fractiles of both the original and enhanced systems are obtained. Numerical results illustrate the effectiveness of these techniques, with cold duplication shown to be the most effective, offering higher reliability and MTTF compared to hot duplication. The enhanced system outperforms the original system overall.

Centrality and bridge connections between cognitive emotion regulation strategies and professional identity among Chinese undergraduate nursing students: A network analysis

AimTo construct a network structure for cognitive emotion regulation among Chinese undergraduate nursing students and identify central nodes and to explore the bridge connections between cognitive emotion regulation strategies and professional identity from the perspective of network analysis. BackgroundUndergraduate nursing students are currently in a poor psychological condition and cognitive emotion regulation strategies can help them use positive approaches to regulate their emotions. There may be a link between cognitive emotion regulation strategies and professional identity. DesignThis was a cross-sectional study that used network analysis. MethodA total of 218 Chinese undergraduate nursing students were selected and surveyed using the Cognitive Emotion Regulation Questionnaire and the Professional Identity Questionnaire. A network analysis model was constructed and the related indices were calculated using R 4.3.0 software. ResultsNetwork analysis showed that the central nodes of undergraduate nursing students’ cognitive emotion regulation strategies were positive reappraisal, refocusing on planning and catastrophising; In the bridge network of cognitive emotion strategies and professional identity, professional self-concept, positive reappraisal, benefits of stay and risk of resignation and refocusing on planning were the nodes with the strongest bridge strength. ConclusionThe salient central and bridge nodes can serve as potential targets for interventions aimed at improving the mental health of undergraduate nursing students. Nursing educators must be trained in cognitive emotion regulation and appropriately guided to use positive emotion regulation strategies in their studies and work. Educators should focus on the relation between cognitive emotion regulation and professional identity to improve the mental health of nursing students and stabilise the nursing workforce.

AI-assisted analytical model of seismic displacement estimation for frictional isolated bridge portfolios under pulse-like ground motions

Accurate bearing displacement assessment is crucial for frictional isolated bridges under pulse-like ground motions. In order to cope with the efficiency requirement brought by large-scale bridge portfolio estimation, the artificial intelligence (AI) method, a powerful tool to address multi-parameter issues, is widely adopted in seismic performance analysis for bridge networks. However, the traditional machine learning (TML) method pursues the overall prediction performance, the prediction effect in sensitive intervals caused by pulse-effect can not be assured. To face this challenge, this study proposed an AI-assisted analytical (AIA) method, which owns the characteristics of high efficiency, high accuracy and clear physical meaning. In the first step of the AIA method, the relationship between primary factor (pulse period) and output (seismic displacement) is established through an analytical model; in the second step, the undetermined parameters of the analytical model are estimated with the assistance of the AI method. The results indicate that the AIA model only requires 0.1 times data for model training and achieves overall predictive performance similar to TML methods. Moreover, the predictive effect of the AIA model in the sensitive range is greatly improved compared to the TML method. Finally, based on AIA method, an effective seismic fragility prediction method is proposed for frictional isolated bridge networks. The fragility curves illustrated by AIA method and FE analysis are in good agreement, which exhibited the satisfactory efficiency of the proposed AIA method.

State-of-art review of resilience of bridges and bridge networks

In recent years, many attempts have been made to incorporate resilience frameworks into decision making, establish the best use of available resources and reduce the impact of hazards on bridges and bridge networks. However, scholarly research in this area is still in its early phases, with minimal exploration of research gaps and future prospects. A state-of-the-art review of bridge and bridge network resilience studies using a science mapping approach is presented here. Research on the topic was obtained from the Scopus literature database. The literature database was then analysed using VOSviewer and Nvivo tools to display the domain-specific body of knowledge. The study then focused on the most prolific researchers and disaster types, bridge resilience assessment approaches, current research trends, future directions and theoretical and practical implications. The research paradigm on the resilience of bridges and bridge networks is changing toward the usage of digital tools and technologies. In this context, a framework id proposed by integrating building information modelling/geographic information systems and digital twin models. The proposed framework can aid bridge owners in accumulating iterative data, creating multi-hazard disaster preparedness policies and designing and enhancing life-cycle management and maintenance tasks of bridges and bridge networks.

Sodium Ion-Induced Structural Transition on the Surface of a DNA-Interacting Protein.

Protein surfaces have pivotal roles in interactions between proteins and other biological molecules. However, the structural dynamics of protein surfaces have rarely been explored and are poorly understood. Here, the surface of a single-stranded DNA (ssDNA) binding protein (SSB) with four DNA binding domains that bind ssDNA in binding site sizes of 35, 56, and 65 nucleotides per tetramer is investigated. Using oligonucleotides as probes to sense the charged surface, NaCl induces a two-state structural transition on the SSB surface even at moderate concentrations. Chelation of sodium ions with charged amino acids alters the network of hydrogen bonds and/or salt bridges on the surface. Such changes are associated with changes in the electrostatic potential landscape and interaction mode. These findings advance the understanding of the molecular mechanism underlying the enigmatic salt-induced transitions between different DNA binding site sizes of SSBs. This work demonstrates that monovalent salt is a key regulator of biomolecular interactions that not only play roles in non-specific electrostatic screening effects as usually assumed but also may configure the surface of proteins to contribute to the effective regulation of biomolecular recognition and other downstream events.

Network-level optimisation approach for bridge interventions scheduling

This paper introduces a novel extension of the multi-system optimisation method, known as the 3C concept, tailored for optimising budget allocation for bridge interventions at the network level. This extended methodology accounts for the interdependencies among bridges due to their spatial proximity within the network. It incorporates direct and user costs, bridge performance indicators, and a bridge deterioration model. A real-world case study involving a portfolio of 555 bridges demonstrates the practicality of the methodology, efficiently determining the optimal intervention sequence. Over an 18-year analysis period, the proposed methodology achieved a 23% reduction in total costs by combining repairs for bridges with high to severe damage and maintenance for the others. This represents a significant improvement compared to the traditional approach, used by bridge management agencies, which relies exclusively on maintenance. The optimised procedure outperforms human intuition in managing complex bridge networks, particularly over extended periods. This methodology can assist transportation agencies in implementing and exploring various scenarios by adjusting the time between consecutive interventions and budget constraints, supporting comprehensive analysis and informed decision-making.

Comprehensive resilience assessment of bridge networks using ensemble learning method

The assessment of seismic resilience in bridge networks holds significant importance for urban disaster prevention and mitigation efforts. Unlike individual bridges, there has been limited efficiency in assessing bridge networks. A seismic resilience assessment methodology for bridge networks using ensemble learning methods is proposed in this paper. Initially, a comprehensive resilience index is proposed, integrating both structural and functional aspects of bridge networks. Using 3 ensemble learning methods, 9 parameters related to network structure and traffic characteristics are chosen as input variables for predicting the seismic resilience index. Finite element models of 18 bridges are constructed and combined to generate 3500 sets of virtual bridge networks for model training. The predictive accuracy of models trained using the 3 ensemble methods exceeds 89 %, and the expected values of peak ground acceleration (PGA) and functional loss rate are the most influential features. The methodology offers insights into the application of ensemble learning for bridge network seismic resilience assessment.

Online volunteering and subjective well-being in China

This study aims to examine the relationship between online volunteering and subjective well-being (SWB) and to explore the mediation effect of online bridging networks. The data used in this study came from the 2019 Chinese General Social Survey (CSS). In this study, individuals who responded to survey questions about their participation in online volunteering activities and their subjective well-being(SWB) were included for analysis (N = 5556). The results showed a positive relationship between online volunteering and online bridging networks and indicated that online volunteering affects subjective well-being through the mediating role of online bridging networks. The implications of this study are relevant to policy-makers in public health and social service organizations. Based on the findings, policy-makers and nonprofit organizations are called on to support this type of volunteering which is an important means of improving public health. Nonprofit organizations can establish online social networks for potential volunteers. In addition, nonprofit organizations need to invest more resources to enhance the digital literacy of employees and volunteers, in order to enhance the quality of online volunteering services.

Differential Behavior of Conformational Dynamics in Active and Inactive States of Cannabinoid Receptor 1.

Cannabinoid receptor 1 (CB1) is a G protein-coupled receptor that regulates critical physiological processes including pain, appetite, and cognition. Understanding the conformational dynamics of CB1 associated with transitions between inactive and active signaling states is imperative for developing targeted modulators. Using microsecond-level all-atom molecular dynamics simulations, we identified marked differences in the conformational ensembles of inactive and active CB1 in apo. The inactive state exhibited substantially increased structural heterogeneity and plasticity compared to the more rigidified active state in the absence of stabilizing ligands. Transmembrane helices TM3 and TM7 were identified as distinguishing factors modulating the state-dependent dynamics. TM7 displayed amplified fluctuations selectively in the inactive state simulations attributed to disruption of conserved electrostatic contacts anchoring it to surrounding helices in the active state. Additionally, we identified significant reorganizations in key salt bridge and hydrogen bond networks contributing to the CB1 activation/inactivation. For instance, D213-Y224 hydrogen bond and D184-K192 salt bridge showed marked rearrangements between the states. Collectively, these findings reveal the specialized role of TM7 in directing state-dependent CB1 dynamics through electrostatic switch mechanisms. By elucidating the intrinsic enhanced flexibility of inactive CB1, this study provides valuable insights into the conformational landscape enabling functional transitions. Our perspective advances understanding of CB1 activation mechanisms and offers opportunities for structure-based drug discovery targeting the state-specific conformational dynamics of this receptor.

Social capital and successful aging among senior park golfers

Park golf, which evolved from golf, is one of South Korea’s fastest-growing sports. As park golf has become increasingly popular, specifically among older adults, its advantages and disadvantages must be identified. This study aims to explore the experiences of older adults engaged in park golf, specifically focusing on how playing park golf fosters social capital and contributes to successful aging among senior park golfers. Twenty senior park golfers participated in detailed interviews. The qualitative data analysis shows that this study’s participants constituted a formal group. Five main themes were identified from the data analysis: (a) reciprocity, (b) bonding networks, (c) bridging networks, (d) social participation, and (e) health. Moreover, our findings provide empirical evidence that playing park golf can create social capital related to successful aging among seniors.

Space-borne DInSAR measurements exploitation for risk classification of bridge networks

Space-borne DInSAR measurements exploitation for risk classification of bridge networks

Fabrication of Porous Adsorbents Templated from Capillary Foam Stabilized with Chlorella for Highly Efficient Removal of Cationic Dyes.

The thermodynamic instability of conventional aqueous foam-stabilized surfactants is a critical bottleneck in the construction of porous materials. Herein, a novel strategy is proposed for preparing a capillary foam based on Chlorella and utilizing it as a template for constructing porous materials with high-efficiency adsorption. The capillary foam was stabilized by Chlorella particles enclosed within a gel network of oil bridges connecting the particles (capillary suspension). Chlorella particles, which act as stable particles, form oil bridges and are distributed at the phase interface of the capillary foam. These particles exhibited resistance to shear forces, allowing the formation of a long-term stable capillary foam. Using this foam as a template, a porous material with outstanding adsorption performance for Methylene Blue (MB) and Brilliant Green (BG) dyes was successfully constructed. Additionally, the material exhibited a sustained high adsorption performance even after five thermal regeneration-adsorption cycles. In conclusion, this study presents a green and straightforward method for constructing capillary foams with high stability, which is a promising approach for developing porous materials with exceptional adsorption and regeneration properties for dyes.

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD.

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD. We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network. EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively). EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Social capital in academia: Measuring researchers’ collaboration habits versus preferences

Abstract Many studies concerning social capital in academia have used social network theory and social network analysis as an approach. Social network analysis focuses on a boundary set of actors in a network and what it reveals as an outcome of social capital. However, social capital is also a precursor or catalyst for cooperative work. Here, we investigate researchers’ perceptions of social capital based on the hypothesis that what academics do when they collaborate (as a habit), may not relate to what they actually prefer. We have piloted a questionnaire-survey to test this, focusing on the goal-seeking behavior of publishing new research. Data were collected from 1,092 academics, across 6 faculties at the University of Copenhagen: Health and Medical Science, Science, Social Sciences, Humanities, Law, and Theology. The survey of collaboration habits first revealed significant differences at the level of gender, academic position, years active in publishing, and faculty. Collaboration preferences, interpreted from social capital theory, were also measured according to three interrelated dimensions—i.e. cognitive, relational, and structural. Survey respondents tended to prefer the cognitive-relational aspects of collaboration (i.e. reciprocity and obligation; shared understandings), over certain structural determinants (i.e. linking to networks). Few habits and preferences correlated and did not correlate strongly. Amongst the researchers who indicated a preference for bridging networks (i.e. working with people who have different types of expertise), few confirmed this as a collaboration habit, particularly with experts from organizations outside academia (i.e. the business/public sector).