Coordination Mechanism Research Articles

Identifying key influencing factors of cross-regional railway infrastructure interconnection: a fuzzy integrated MCDM framework

Cross-regional railway infrastructure (CRI) plays an important role in promoting coordinated regional development. But current understanding on how to strengthen cross-regional railway infrastructure interconnection (CRII) and its influence mechanism is still very limited. This study constructed a conceptual model of CRII system to reveal the logic of CRII operation, and systematically identified 16 influencing factors. Meanwhile, an F-MCDM (F-Multi-criteria Decision Making) model was created to capture the interactions among the influencing factors, identifying eight key influencing factors and four possible countermeasures. The results revealed that policy and institutional innovation, incentives and investment ecology, efficient implementation and coordination mechanisms, and supply chain and technology security capacity are four crucial challenges for CRII. Among them, the cooperation modes between the central government and local governments, as well as among local governments, were identified as the most critical factors. Accordingly, a four-pronged framework of “policy alignment, cooperative incentives, operational excellence, and supply assurance” was developed to better promote CRII. This study contributes to a deeper understanding of CRII, enriches the body of knowledge on cross-regional transportation infrastructure interconnection, and provides theoretical support and decision-making references for policymakers to implement CRII.

Research on a phonocardiogram and electrocardiogram signal classification algorithm based on global group coordinate attention mechanism and multi-scale feature fusion

Purpose In recent years, the incidence of cardiovascular disease has continued to rise, and early screening and prevention are especially critical. Phonocardiography (PCG) and electrocardiography (ECG), as simple, cost-effective and non-invasive tests, are important tools for clinical analysis. However, it is difficult to fully reflect the complexity of the cardiovascular system using PCG or ECG tests alone. Combining the multimodal signals of PCG and ECG can provide complementary information to improve the detection accuracy. Therefore, the purpose of this paper is to propose a multimodal signal classification method based on continuous wavelet transform and improved ResNet18. Design/methodology/approach The classification method is based on the ResNet18 backbone, and the ResNet18 network is improved by embedding the global grouped coordinate attention mechanism module and the improved bidirectional feature pyramid network. Firstly, a data acquisition system was built using a MEMS-integrated PCG-ECG sensor to construct a private data set. Second is the time-frequency transformation of PCG and ECG synchronized signals on public and private data sets using continuous wavelet transform. Finally, the time-frequency images are categorized. Findings The global grouped coordinate attention mechanism and bidirectional feature pyramid network modules proposed in this paper significantly enhance the model’s performance. On public data sets, the method achieves precision, sensitivity, specificity, accuracy and F1 score of 97.96%, 98.51%, 97.58%, 98.08% and 98.23%, respectively, which represent improvements of 3.54%, 3.92%, 4.18%, 4.03% and 3.72% compared to ResNet18. Additionally, it demonstrates a clear advantage over existing mainstream algorithms. On private data sets, the method’s five metrics are 98.15%, 98.76%, 98.08%, 98.42% and 98.45%, further validating the model’s generalization ability. Originality/value The method proposed in this paper not only improves the accuracy and efficiency of the test but also provides an effective solution for early screening and prevention of cardiovascular diseases.

Institutionalizing linkages between informal healthcare providers and the formal health system in Nigeria: what are the facilitating and constraining contextual influences?

With most households in rapidly urbanising LMIC cities using private and informal providers for basic healthcare, the need to establish linkages with the formal sector is paramount in the drive for universal health coverage. Successful and effective linkage of informal healthcare providers to the formal health system requires an understanding of prevailing contextual factors and how they can be modulated to support the linkages. Context plays a pivotal role in shaping the nature and success of any integration efforts. This paper, based on a qualitative study explored the facilitating and constraining contextual influences shaping the linkage of informal healthcare providers into the formal health system in governance, service delivery and data reporting. The research was conducted in Enugu and Anambra states in southeastern Nigeria. In-depth interviews were held with 12 senior healthcare managers, 16 primary healthcare facility managers, 32 informal providers and 16 community leaders. Eight sex-disaggregated focus group discussions were held with health service users. Transcripts were coded in NVivo using a pre-defined coding framework comprising facilitators and constraints at the individual, organisational, and environmental levels. Individual factors that influence linkage of informal providers into the formal health system include personal attitudes towards linkage, capacity of informal providers to deliver quality services, nature of existing relationships between formal and informal providers and trust in the formal health system. Organizational factors include leadership structure, coordination and accountability mechanisms, functional management capacity of the formal health system, and multiple regulatory frameworks. External factors include supportive health policies on integration, sustainable funding for continuous training and supportive supervision, and global agenda/support for integration. This study has provided valuable insights for decision makers and practitioners for harnessing the contextual factors to link informal healthcare providers successfully and effectively to the formal health system to improve access to quality health services in urban slums.

Farming and ranching through wildfire: Producers’ critical role in fire risk management and emergency response

Wildfires increasingly threaten California’s agricultural sector, posing serious risks to farming, ranching, and food systems. We conducted a survey of 505 California farmers and ranchers affected by wildfires between 2017 and 2023. Main findings show that wildfires’ impacts on producers are extensive and range from mild to catastrophic, with both short and long-term repercussions, regardless of their exposure level. Producers play a central role in community emergency wildfire risk response and management by reducing fuel loads, creating defensible space, and leveraging their fire management expertise for themselves and their neighbors. Many producers lack a robust financial safety net, particularly among vulnerable populations, which points to the need to increase access to recovery resources, including insurance and disaster assistance programs. We find an urgent need for policy reforms, improved support, targeted extension programs, and integrated coordination mechanisms. Producers are our overlooked allies in building widespread wildfire resilience. Enhanced collaborative efforts among producers, fire professionals, and agricultural support organizations are thus imperative to co-create and implement strategies that ensure the long-term sustainability and economic viability of California’s agricultural communities.

Individualism: the end of social cohesion? The effects of inequality and group identity on cooperation

IntroductionGlobal challenges like pandemics and climate change are fundamentally cooperation problems, where individual interests often conflict with the collective good. Rising economic inequality and individualism are believed to erode social cohesion and cooperation, exacerbating these tensions. Public goods games (PGGs) are widely used to study cooperation in scenarios of unequal resource distribution, but findings on the effects of inequality remain inconsistent, partly due to varied applications of heterogeneity across studies.MethodsThis study introduces a novel research design that directly compares collectivistic and individualistic groups to examine the combined effects of individualism and inequality on cooperation. Groups were exposed to different resource distribution conditions, including both equal and unequal distributions, and their cooperation rates were measured.ResultsThe findings reveal no significant differences in cooperation rates between collectivistic and individualistic groups. However, groups with higher inequality exhibited significantly higher cooperation rates than those with equal resource distributions. Notably, heterogeneous groups showed considerable variation in their success at establishing cooperation. Further analysis indicates that the willingness to reduce initial inequality served as a strong signal, enhancing group identity and fostering cooperation.DiscussionThe results suggest that in contexts where group identity is strong, inequality can act as a coordination mechanism, enabling groups to distribute the burden of collective costs more effectively and enhancing overall cooperation. These findings challenge the assumption that inequality inherently undermines cooperation and highlight the potential for strategically leveraging inequality in contexts characterized by collectivistic norms.

Barriers and enablers towards integrated care for survivors of sexual violence in humanitarian settings: A real-time qualitative Delphi study

ABSTRACT In humanitarian settings with high levels of sexual violence (SV), care is often offered through fragmented silos, exacerbating the burden on the health workforce and survivors. We aimed to identify contextual and health systems barriers and enablers to providing integrated medical and mental health & psychosocial support (MHPSS) in the care for SV survivors in humanitarian settings. Using Valentijn’s framework, a qualitative, real-time Delphi study (RTD) approach was conducted with 17 experts representing seven geographical subregions. Challenges and enablers identified across the participants’ contexts were consistent. Contextual challenges included volatile contexts, collapsed health systems, and insufficient basic infrastructure. Professional-related challenges included lacking expertise among healthcare professionals (HCPs), high staff attrition rates, and compassion fatigue. Health systems-related challenges included poor referral and coordination mechanisms, lack of funding and resources, misaligned donor priorities and low prioritisation of SV comprehensive care. Effective networking, community engagement, capacity building, co-locating services, participatory management, promoting employees’ sense of ownership, establishing a digital information system, and a unified joint patient file were key identified enablers. Further research should be conducted to assess HCPs’ and SV survivors’ perceptions and experiences of how best to integrate MHPSS services, and understand the challenges and opportunities in delivering integrated services.

A Pyridine Amine Extractant for Separation of La3+ and Ce3+ in HNO3 Solution.

The separation of lanthanum and cerium is of great significance for improving the material properties and promoting scientific and technological progress. In this paper, a pyridine amine extractant, (1R,2S)-N1,N2-bis((5-methylpyridin-2-yl)methyl)cyclohexane-1,2-biamine (cis-BMPyMChBA), was designed for the separation of lanthanum and cerium. The aim is to use this extractant to catalyze the oxidation of tri- to tetravalent cerium and to increase the separation efficiency between lanthanum and cerium. The effects of the temperature, diluent, extractant concentration, and pH were evaluated to the extraction and separation performance of lanthanum and cerium. The experimental results demonstrate that cis-BMPyMChBA exhibits high selectivity for cerium, with a separation factor for lanthanum and cerium reaching 122.9. The coordination mechanism between cis-BMPyMChBA and cerium ion was clarified through the combined use of extraction isotherm, Job's curve, spectroscopic methods, and density functional theory (DFT) calculation. It involved the interaction between the secondary amine N atom and the pyridine N atom and the metal ion in which the cerium ion underwent a valence change from trivalent to tetravalent before extraction. This was found to be more conducive to the separation of lanthanum and cerium.

The influencing factors of the participation intention and behavior in organized scientific research of young teachers in modern industrial colleges—based on the theory of planned behavior and normative activation theory

Organized scientific research is vital to implementing organized technological innovation in modern industrial colleges. Young teachers’ deep participation determines success. Based on the theory of planned behavior and normative activation, a survey of 309 young teachers from modern industrial colleges participating in organized scientific research found that personal norms, subjective norms, behavioral attitudes, and perceived behavioral control significantly impact their intention to participate. Awareness of consequence, ascribed responsibility, and subjective norms indirectly positively impact their participation intention, and the participation intention and perceived behavioral control positively affect their participation behavior. Policy support has a positive mediating effect between participation intention and behavior. There are group differences in gender, education level, professional title, and subject type in demographic characteristics in five aspects: subjective norms, personal norms, perceived behavioral control, participation intention, and participation behavior. This article proposes the following measures: improving the internal interest coordination mechanism of scientific research organizations to enhance young teachers’ sense of scientific research achievement; reconstructing the organized scientific research evaluation model to enhance young teachers’ teamwork awareness; improving the support system for organized research policies to stimulate young teachers’ self-efficacy; concerning young teachers’ group differences and creating a research environment that tolerates failure.

Single cobalt atoms with unconventional dynamic coordination mechanism for selective ammonia sensor

Abstract Developing gas sensors that can simultaneously achieve high sensitivity and selectivity for the detection of a single-type gas remains a significant challenge. Herein we demonstrate a cobalt (Co) single atom with unconventional dynamically changing coordination structure that could be used as NH3 sensing material with superior sensitivity and selectivity. According to the steric effect of 2-methylimidazole (2MI) molecule and carbonyl group on graphene, the Co single atom is evolved into bidentate coordinated structure (Co-2MI-G). The in-situ characterization and theoretical simulation reveal that the sensing mechanism of Co-2MI-G is the specific chemical adsorption between unsaturated coordinated Co single atoms and NH3 molecules, causing a reversible switching of coordination number from 2 to 4, a valence state transfer from Co2+ to Co3+ of Co single atoms, and a band gap width from 0.14 eV to 0.50 eV. Consequently, Co-2MI-G based gas sensor presents a sensing response of 67.598% for 1 ppm NH3 and the limit of detection is 2.67 ppb, at least 1.8 times higher than that of state-of-the-art NH3 sensors, together with robust stability and reproducibility. This work provides an innovative perspective on utilizing single atoms for ultra-selective gas sensing by coordination regulation.

RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion

The paper proposes a model based on receptive field enhancement and cross-scale fusion (RFCS-YOLO). It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. First, an efficient feature extraction module (EFEM) is created. It reconfigures the backbone network. This helps to make the receptive field better and improves its ability to extract features of targets at different scales. Next, a cross-scale fusion module (CSF) is introduced. It uses the receptive field coordinate attention mechanism (RFCA) to fuse information from different scales well. It also filters out noise and background information that might interfere. Also, a new Focaler-Minimum Point Distance Intersection over Union (F-MPDIoU) loss function is proposed. It makes the model converge faster and deals with issues of leakage and false detection. Experiments were conducted on the expanded Vehicle Detection in Adverse Weather Nature dataset (DWAN). The results show significant improvements compared to the conventional You Only Look Once v7 (YOLOv7) model. The mean Average Precision (mAP@0.5), precision, and recall are enhanced by 4.2%, 8.3%, and 1.4%, respectively. The mean Average Precision is 86.5%. The frame rate is 68 frames per second (FPS), which meets the requirements for real-time detection. A generalization experiment was conducted using the autonomous driving dataset SODA10M. The mAP@0.5 achieved 56.7%, which is a 3.6% improvement over the original model. This result demonstrates the good generalization ability of the proposed method.

Study Policies of Two Ministries in Implementing the Education Curriculum in Indonesia

This study aims to analyze the policies of two ministries, namely the Ministry of Religious Affairs (Kemenag) and the Ministry of Education, Culture, Research and Technology (Kemendikbudristek) at the elementary and junior high school levels, in implementing the education curriculum in Indonesia. This study is qualitative with a Systematic Literature Review (SLR) approach. Data in the form of scientific articles collected from 2015–2024. The data collection technique used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure. The study results show significant differences in the curriculum's structure, focus, and approach implemented by the two ministries. The Ministry of Religious Affairs curriculum emphasizes integrating religious values ​​with science. In contrast, the curriculum under the Ministry of Education and Culture focuses more on developing general competencies based on science and technology. However, there is potential to synergize the policies of the two ministries to create a more holistic curriculum, which not only builds intellectual intelligence but also strengthens religious character and national insight. This study recommends harmonizing inter-ministerial policies through more intensive coordination mechanisms and adapting inclusive curricula according to the needs of the multicultural society in Indonesia. This study focuses on policy analysis at the ministerial level and does not directly evaluate school or district-level curriculum implementation. This study contributes by comparatively analyzing the education policies of two ministries in Indonesia, which have not been widely discussed in previous studies. This study also highlights the importance of policy synergy in building an integrative and adaptive curriculum to the challenges of education in a culturally and religiously diverse society.

COVID-19 policy response: Perspectives of key stakeholders in Mexico's health system and implications for resilience

PurposeThis study aims to analyze the perspectives of key stakeholders within Mexico's health system regarding the policy response to COVID-19 in 2020 to inform future policy formulation. MethodsThis qualitative study is based on a secondary data analysis (SDA) derived from a broader primary research on governance in Mexico during the COVID-19 pandemic. The analysis adopted a grounded theory approach to examine 15 semi-structured interviews with prominent figures in the health system between June and November 2020. The interviews were audio-recorded and analyzed through the lens of the Health Policy Triangle which includes context, key actors, and the Mexican health policy process. Moreover, the analysis was enriched by incorporating the framework outlined in the World Health Organization's COVID-19 Strategic Preparedness and Response Plan (SPRP). ResultsThe findings indicate that key stakeholders perceived a centralized approach to policymaking with a strong emphasis on cross-sector communication, expert panels, equity-focused measures, accessibility to healthcare services (promoting portability), and improvements in the health information system. ConclusionKey stakeholders recognized the response to COVID-19 was complex, involving multiple dimensions and levels of action. The importance of preparedness and response in building health system resilience is emphasized. Recommendations include explicitly integrating resilience into national and local policy agendas, addressing systemic structural deficiencies, promoting equity in the pandemic response, improving risk governance, strengthening coordination mechanisms, ensuring supply chain resilience, improving personnel training, and enhancing local diagnostic capacity to better prepare for future public health emergencies.

Study on the performance of electric double-layer capacitors based on lignocellulose gel polymer electrolytes with different pH values for Zn2+-lignocellulose coordination mechanism

Study on the performance of electric double-layer capacitors based on lignocellulose gel polymer electrolytes with different pH values for Zn2+-lignocellulose coordination mechanism

Objective transformation-based and niche-based many-objective evolutionary algorithm with a two-step coordination mechanism

Objective transformation-based and niche-based many-objective evolutionary algorithm with a two-step coordination mechanism

Automatic Detection of War-Destroyed Buildings from High-Resolution Remote Sensing Images

Modern high-intensity armed conflicts often lead to extensive damage to urban infrastructure. The use of high-resolution remote sensing images can clearly detect damage to individual buildings which is of great significance for monitoring war crimes and damage assessments that destroy civilian infrastructure indiscriminately. In this paper, we propose SOCA-YOLO (Sampling Optimization and Coordinate Attention–YOLO), an automatic detection method for destroyed buildings in high-resolution remote sensing images based on deep learning techniques. First, based on YOLOv8, Haar wavelet transform and convolutional blocks are used to downsample shallow feature maps to make full use of spatial details in high-resolution remote sensing images. Second, the coordinate attention mechanism is integrated with C2f so that the network can use the spatial information to enhance the feature representation earlier. Finally, in the feature fusion stage, a lightweight dynamic upsampling strategy is used to improve the difference in the spatial boundaries of feature maps. In addition, this paper obtained high-resolution remote sensing images of urban battlefields through Google Earth, constructed a dataset for the detection of objects on buildings, and conducted training and verification. The experimental results show that the proposed method can effectively improve the detection accuracy of destroyed buildings, and the method is used to map destroyed buildings in cities such as Mariupol and Volnovaja where violent armed conflicts have occurred. The results show that deep learning-based object detection technology has the advantage of fast and accurate detection of destroyed buildings caused by armed conflict, which can provide preliminary reference information for monitoring war crimes and assessing war losses.

Exploring the impact of telemedicine in chronic patients from diverse socioeconomic contexts: systematic review of qualitative studies

BackgroundTelemedicine has a positive impact on improving health literacy and disease management ability of chronic disease patients and reducing the medical burden. However, telemedicine still has many problems in chronic disease management. We should actively solve the problems and maximize the functions of telemedicine.ObjectiveTo explore the application and improvement of telemedicine in chronic disease self-management to provide important insights from functional module analysis for stakeholders, healthcare professionals, and policymakers to promote the development of telemedicine in chronic disease management.DesignWe conducted a systematic review and qualitative synthesis of five English databases including PubMed, Embase, Web of Science, Cochrane Database of Systematic Reviews, and Scopus, as well as three Chinese databases: China National Knowledge Infrastructure (CNKI), WanFang and VIP database. Databases were searched from inception until November 12, 2024.MethodsThis review is reported in accordance with guidelines for Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ). Two reviewers independently performed study selection, data extraction, and quality assessment using the Joanna Briggs Institute's key assessment tools for qualitative research. Thematic analysis was used for data synthesis.ResultsA total of 35 studies were included, and the contents were refined and summarized into 8 new subthemes. Then, four themes were synthesized: Reminder and supervisor, Access to knowledge, Transition in medical treatment mode, and Emotional support platform.ConclusionBy improving information quality, developing diverse functions, and constructing multidisciplinary coordination mechanisms to meet the needs of patients with chronic diseases, improve the medical service system, maximize the function of telemedicine, and enhance the stickiness of patients to use telemedicine.

MAFF-Net: PCB defect detection via feature-enhancement fusion

Abstract Defect detection of printed circuit board (PCB) is of significant practical importance to ensure quality control in the production process. However, traditional defect detection methods suffer from limitations such as low detection accuracy and poor generalization ability. To tackle these issues, we propose a novel deep learning-based defect detection method for bare PCBs through multi-attention adaptive feature-enhancement fusion (AFF). First, we utilize ResNext101 as the backbone for feature extractor and embed a normalization-based attention mechanism in a residual structure, aiming at improving the feature extraction capability of the network. Second, we introduce an AFF module, which leverages multi-scale feature extraction and feature fusion to facilitate information interaction and enhance the correlation of feature information between channels. Finally, we incorporate the coordinate attention mechanism into AFF to highlight the target area for boosting detection accuracy. The experimental results demonstrate the effectiveness of the proposed method, which achieves a mean accuracy precision (mAP) of 99.01 % on a publicly available PCB defect dataset.

Kiwa is a bacterial membrane-embedded defence supercomplex activated by phage-induced membrane changes.

Bacteria and archaea deploy diverse, sophisticated defence systems to counter virus infection, yet many immunity mechanisms remain poorly understood. Here, we characterise the Kiwa defence system as a membrane-associated supercomplex that senses changes in the membrane induced by phage infection and plasmid conjugation. This supercomplex, comprising KwaA tetramers bound to KwaB dimers, as its basic repeating unit, detects structural stress via KwaA, activating KwaB, which binds ejected phage DNA through its DUF4868 domain, stalling phage DNA replication forks and thus disrupting replication and late transcription. We show that phage-encoded DNA mimic protein Gam, which inhibits RecBCD, also targets Kiwa through KwaB recognition. However, Gam binding to one defence system precludes its inhibition of the other. These findings reveal a distinct mechanism of bacterial immune coordination, where sensing of membrane disruptions and inhibitor partitioning enhance protection against phages and plasmids.

Biodiversity Policy Integration at the sub-national level: Insights from the German Länder in the context of sub-national biodiversity strategies and action plans

ABSTRACT The uptake of biodiversity along different political levels and into sector policies remains a major global challenge. Sub-national governments hold crucial institutional competencies for enabling this Biodiversity Policy Integration (BPI). However, these political levels are largely overlooked in international agreements and the BPI literature. Using the example of the German states (Länder), this study aims to widen the debate through a sub-national perspective. Therefore, 16 interviews with representatives of all German state environmental ministries have been conducted about the implementation of sub-national biodiversity strategies and action plans. The study identifies key barriers and levers to BPI from the perspective of these actors and relates them to underlying causes. State biodiversity policies appear to be trapped in institutional lock-ins, manifested in weak coordination and evaluation mechanisms. Policy change is inevitably linked to the integrative leadership of individual policy-makers or the external interference of public or European initiatives. To avoid merely symbolic policy-making, national and international processes should emphasize joint, cross-level coordination, as incoherencies at higher political levels can only be resolved to a limited extent at lower administrative levels.

YOLOv8‐ESW: An Improved Oncomelania hupensis Detection Model

ABSTRACTTraditional Oncomelania hupensis detection relies on human eye observation, which results in reduced efficiency due to easy fatigue of the human eye and limited individual cognition, an improved YOLOv8 O. hupensis detection algorithm, YOLOv8‐ESW(expectation–maximization attention [EMA], Small Target Detection Layer, and Wise‐IoU), is proposed. The original dataset is augmented using the OpenCV library. To imitate image blur caused by motion jitter, salt and pepper, and Gaussian noise were added to the dataset; to imitate images from different angles captured by the camera in an instant, affine, translation, flip, and other transformations were performed on the original data, resulting in a total of 6000 images after data enhancement. Considering the insufficient feature fusion problem caused by lightweight convolution, We present the expectation–EMA module (E), which innovatively incorporates a coordinate attention mechanism and convolutional layers to introduce a specialized layer for small target detection (S). This design significantly improves the network's ability to synergize information from both superficial and deeper layers, better focusing on small target O. hupensis and occluded O. hupensis. To tackle the challenge of quality imbalance among O. hupensis samples, we employ the Wise‐IoU (WIoU) loss function (W). This approach uses a gradient gain distribution strategy and improves the model convergence speed and regression accuracy. The YOLOv8‐ESW model, with 16.8 million parameters and requiring 98.4 GFLOPS for computations, achieved a mAP of 92.74% when tested on the O. hupensis dataset, marking a 4.09% improvement over the baseline model. Comprehensive testing confirms the enhanced network's efficacy, significantly elevating O. hupensis detection precision, minimizing both missed and false detections, and fulfilling real‐time processing criteria. Compared with the current mainstream models, it has certain advantages in detection accuracy and has reference value for subsequent research in actual detection.