Innovation Networks Research Articles

The Spironolactone Initiation Registry Randomized Interventional Trial in Heart Failure with Preserved Ejection Fraction (SPIRRIT-HFpEF): Rationale and design.

Benefits of mineralocorticoid receptor antagonists (MRAs) in heart failure with preserved and mildly reduced ejection fraction (HFpEF/HFmrEF) have not been established. Conventional randomized controlled trials are complex and expensive. The Spironolactone Initiation Registry Randomized Interventional Trial in Heart Failure with Preserved Ejection Fraction (SPIRRIT-HFpEF) is a unique pragmatic registry-based randomized controlled trial. SPIRRIT-HFpEF is a multicentre, prospective, randomized, open-label, blinded endpoint trial conducted on platforms in the Swedish Heart Failure Registry (SwedeHF) and the United States (US) Trial Innovation Network. Patients with HFpEF/HFmrEF are randomized 1:1 to spironolactone (or eplerenone) in addition to usual care, versus usual care alone. The primary outcome is total number of cardiovascular deaths and hospitalizations for heart failure. Outcomes are collected from Swedish administrative complete coverage registries and a US call centre and subsequently adjudicated. Simple eligibility criteria were based on data available in SwedeHF: heart failure as outpatient or at discharge from hospital, left ventricular ejection fraction ≥40%, N-terminal pro-B-type natriuretic peptide >300 ng/L (in sinus rhythm) or >750 ng/L (in atrial fibrillation), with pre-specified adjustment for elevated body mass index, and chronic loop diuretic use. Power and sample size assessments were based on an event-driven design allowing enrolment over approximately 6 years, and application of hazard ratios from the TOPCAT trial, Americas subset. The final sample size is expected to be approximately 2400 patients. SPIRRIT-HFpEF will be informative on the effectiveness of generic MRAs in HFpEF and HFmrEF, and on the feasibility of conducting pragmatic and registry-based trials in heart failure and other chronic conditions.

Enhancing chip design verification through AI-powered bug detection in RTL code

This paper presents a novel AI-driven approach for enhancing chip design verification through automated bug detection in Register Transfer Level (RTL) code. The proposed method integrates advanced machine learning techniques with domain-specific knowledge of chip design to address the challenges of increasing complexity and time-to-market pressures in modern integrated circuit development. Our system employs a comprehensive data preprocessing pipeline that effectively captures syntactic and semantic features of RTL code, feeding into an innovative attention-based neural network model. The model demonstrates superior bug detection accuracy across diverse design categories and bug types compared to traditional methods and existing AI-assisted approaches. Extensive experimental evaluation on a large-scale dataset of RTL designs, including both open-source projects and industry collaborations, validates the effectiveness of our method. The proposed approach achieves a 95% accuracy in bug detection, with a 28-35% reduction in verification time when applied to real-world chip design projects. The paper addresses the interpretability of AI decisions in the context of chip design, presenting novel visualization techniques that enhance trust and facilitate adoption among RTL designers. While acknowledging current limitations, we discuss future research directions, including integration with formal verification methods and extension to system-level verification scenarios. This work contributes significantly to AI-assisted chip design, paving the way for more efficient and reliable development of complex integrated circuits.

DMMGNet: A discrimination mapping and memory bank mean guidance-based network for high-performance few-shot industrial anomaly detection

For deep learning-based industrial anomaly detection, it is still challenging to get adequate images for model training and achieve cold start for cross-product migration, restricting their practical application in real industrial production. Herein, an innovative few-shot anomaly detection network DMMGNet based on discrimination mapping and memory bank mean guidance strategies are demonstrated, which is trained by a new two-branch data augmentation technique. By separating the features stored in memory bank from the features used for training, the two-branch data augmentation method can significantly improve the robustness of few-shot model training and reduce the redundance of memory bank. In the elaborately designed discrimination mapping module, new negative samples are generated by adding dynamic Gaussian noise to normal samples along the channel dimension in feature space to solve the problem of sample imbalance. Meanwhile, the discrimination mapping module also helps to map the feature distribution of positive samples to the target domain more efficiently and reduce the deviation of feature domain, conducive to a more precise separation of positive and negative samples. In addition, a novel mean guidance approach with an optimized loss function is developed to guide the positive sample feature mapping by specifying the local feature space center to form a clear feature domain contour and enhance the detection accuracy. The multiple experimental results validate that our DMMGNet outperforms the most advanced anomaly detection counterparts on image-level AUROC, showing an increase by 0.3–3 % on both MVTec AD and MPDD benchmarks under several few-shot scenarios.

Evolution of regional collaborative innovation network by the ‘two-wheel drive' of external proximity and endogenous gravity

ABSTRACT Collaborative innovation is the key support for promoting high-quality regional development. Taking Guangdong, Fujian and Zhejiang coastal city clusters as an example, this paper analyzes the evolution mechanism of collaborative innovation network based on the ‘two-wheel drive' perspective of external proximity and endogenous gravity. The results show that the difference of resource control ability among nodes in the network is decreasing, and the inverted ‘V’ structure with Xiamen and Putian as the main apex has evolved into a more robust ‘dual core’ (Fuzhou-Xiamen) radiation-type spatial structure. In addition, the evolution of regional collaborative innovation network is driven by ‘two wheels’. In external proximity, institutional proximity plays the strongest role in promoting, followed by geographical proximity, and the promoting role of technological proximity is gradually weakened. In the endogenous gravity, the influence of investment in science and education is strengthening, while the influence of the administrative level is declining.

Engineering SuFEx-derived macrocyclic polysulfates for superior dynamic iodine capture at high temperatures

Efficient dynamic capture of radioiodine vapor from reprocessing of spent nuclear fuel under industrial conditions (∼423.15 K) is critically required due to its detrimental environmental and health impacts. However, conventional adsorbents often suffer from reduced binding affinity and structural degradation at elevated temperatures. To address these challenges, we have engineered a series of advanced polysulfate networks using sulfur (VI) fluoride exchange (SuFEx) click chemistry. These innovative networks integrate supramolecular macrocycle calix[4]pyrrole (C[4]P) and nitrogen-rich co-monomers as building blocks, and utilize polar sulfate bonds as important linkers, which provide availability of adsorptive sites and enhance the structural stability, thereby affording unexpected iodine capture performance at high temperatures. Among these SuFEx-derived materials, C[4]P-Azo exhibits exceptional iodine affinity through cooperative interactions involving multiple binding motifs, achieving an impressive iodine uptake capacity of 0.45 g g−1 at 423.15 K under dynamic adsorption conditions, setting a new benchmark in this field. Comprehensive characterizations and DFT calculations elucidate that advanced linker sulfate bonds enabling the marriage of C[4]P and N-rich monomers, play pivotal roles in facilitating iodine adsorption and maintaining structural integrity. These findings may pave the way for designing robust macrocyclic polysulfate-based adsorbents for dynamic radioiodine capture under industrial conditions.

Model of resilience strategies of small and medium-sized tourism enterprises post pandemic crises in marine-based destination

This study investigates the resilience strategies employed by small and medium-sized tourism enterprises (SMTEs) in Tanjung Bira, a marine-based destination in South Sulawesi, Indonesia, to cope with the impact of the COVID-19 pandemic. Duchek's Model of resilience, which conceptualizes resilience as a meta-capability composed of three stages (anticipation, coping, and adaptation), is used as a key framework. An exploratory qualitative approach was employed, involving in-depth semi-structured interviews with seven informants representing various tourism stakeholders. Thematic analysis revealed several main strategies for SMTE resilience: collaboration with stakeholders, government intervention, implementation of digital marketing, strengthening SMTEs legality, and strengthening/diversification of SMTE products. Mapping these strategies onto Duchek's Model, the anticipation stage included strategic planning, risk assessment, and building dynamic capabilities; the coping stage consisted of immediate financial measures and policy support; and the adaptation stage involved diversification, technology and innovation adoption, and collaboration and networking. A key implication is that destination management organizations should create innovative products and leverage online platforms for marketing to increase visitor arrivals and boost SMTE product sales. The findings contribute to understanding SMTE resilience in the context of a marine-based destination in Eastern Indonesia and provide insights for policymakers and practitioners to support SMTE recovery and long-term sustainability post-pandemic.

Limitations of NHIC claim code-based surveillance and the necessity of UDI implementation in Korea

The E-Health Big Data Evidence Innovation Network (FeederNet) in Korea, based on the observational medical outcomes partnership (OMOP) common data model (CDM), had 72.3% participation from tertiary hospitals handling severe diseases as of October 2022. While this contributes to the activation of multi-institutional research, concerns about the comprehensiveness of device data persist due to the adoption of national health insurance corporation (NHIC) claim codes as device identifiers in the medical device field. This study critically evaluated the effectiveness and compatibility of NHIC claim codes and unique device identifier (UDI) within FeederNet to identify the optimal identifier for efficient Post-market surveillance (PMS). Specifically, this study addressed three main questions: (1) the number of UDIs classified as NHIC-covered items, (2) the number of UDIs included in each NHIC claim code, and (3) the number of NHIC claim codes each UDI covers. Among the 1,979,655 UDIs registered domestically, only 36.02% (712,983) were classified as covered by National Health Insurance. NHIC-covered medical devices were limited to categories (A) medical devices, (B) medical supplies, and (C) dental materials, excluding most software and in vitro diagnostics (IVD). Multiple UDIs could be registered under a single NHIC claim code, and a single UDI could be registered under multiple NHIC claim codes. Only 32.62% (13,756/42,171) of NHIC claim codes had registered UDIs, with an average of 53 UDIs per claim code. Of the UDIs listed as NHIC covered, 92.39% (659,046/713,341) had one claim code, while 7.25% (51,652) had multiple claim codes. Additionally, 2643 UDIs were listed as NHIC covered but had no registered claim codes. Due to this complex relationship, NHIC claim code-based PMS may pool safe and unsafe models or disperse problematic models across multiple claim codes, leading to a lower problem rate or insignificant differences between claim codes, thus reducing signal detection sensitivity compared to UDI-based PMS. In conclusion, NHIC claim code-based PMS has limitations in granularity and signal detection sensitivity, necessitating the adoption of UDI-based PMS to address these issues. The UDI system can enhance the accuracy of medical device identification and tracking, playing a crucial role in generating real-world evidence (RWE) by integrating data from various sources. Future research should explore specific strategies for integrating and utilizing UDI with NHIC claim codes, contributing to the implementation of a more reliable and comprehensive PMS in Korea’s healthcare system.

How much is too much? Inter-city brokerage in ICT industry innovation networks

ABSTRACT In today’s world, where global connectivity drives economic growth, it is essential to understand how cities can leverage intercity networks for innovation. This study explores the role of strategic network brokerage in enhancing cities’ innovation performance in the Information and Communications Technology (ICT) sector. We propose an inverted U-shaped relationship between inter-city network brokerage and ICT innovation performance, suggesting that optimal brokerage levels can maximize innovation outputs. Furthermore, we anticipate that city ICT specialization and internet maturity will positively moderate this relationship. Our analysis, using negative binomial regression on a panel of 65 cities from 2002 to 2015, largely supports our hypotheses, highlighting that cities with advanced ICT capabilities achieve higher levels of innovation at the same brokerage level. We discuss the implications of these findings for city policymakers and ICT firms.

Are numerical scores important for grant assessment? A cross-sectional study

Background: In the evaluation of research proposals, reviewers are often required to provide their opinions using various forms of quantitative and qualitative criteria. In 2020, the European Commission removed, for the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) funding scheme, the numerical scores from the individual evaluations but retained them in the consensus report. This study aimed to assess whether there were any differences in reviewer comments’ linguistic characteristics after the numerical scoring was removed, compared to comments from 2019 when numerical scoring was still present. Methods: This was an observational study and the data were collected for the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) evaluation reports from the calls of 2019 and 2020, for both individual and consensus comments and numerical scores about the quality of the research proposal on three evaluation criteria: Excellence, Impact and Implementation. All comments were analyzed using the Linguistic Inquiry and Word Count (LIWC) program. Results: For both years, the comments for proposal’s strengths were written in a style that reflects objectivity, clout, and positive affect, while in weaknesses cold and objective style dominated, and that pattern remained stable across proposal status and research domains. Linguistic variables explained a very small proportion of the variance of the differences between 2019 and 2020 (McFadden R2=0.03). Conclusions: Removing the numerical scores was not associated with the differences in linguistic characteristics of the reviewer comments. Future studies should adopt a qualitative approach to assess whether there are conceptual changes in the content of the comments.

FOSTERING INNOVATION ECOSYSTEMS: THE ROLE OF KNOWLEDGE ECONOMY IN REGIONAL DEVELOPMENT

This study investigates the impact of the knowledge economy on regional economic development, focusing on the role of human capital, innovation, and collaborative networks in fostering growth. Employing a desk research on literature related to the topic, the research highlights the importance of education and skill development in enhancing economic performance. Key findings reveal that knowledge-based strategies, supported by academia-industry-government collaborations, significantly contribute to creating vibrant innovation ecosystems. The research acknowledges limitations, such as the scope of regions studied, and suggests future directions, including exploring the impact of digital technologies and policy interventions on regional disparities. The study's originality lies in its comprehensive approach to integrating the knowledge economy's principles into regional development strategies, offering insights for policy formulation and strategic planning to leverage intellectual assets and innovation for sustainable growth.

Traffic Speed Prediction Based on Dynamic Spatial-Temporal Attention Graph Convolutional Network

Abstract The prediction of traffic speed represents a crucial branch within the broader field of traffic forecasting. Its objective is to forecast the average speed or speed distribution on road segments or within a network for a designated future time period . The prediction results can provide scientific decision support for traffic management departments to effectively control and guide traffic flow. However, given the limitations of urban road network topology and its dynamic temporal shifts, traffic prediction has consistently been deemed a perpetual scientific challenge. To simultaneously encapsulate both spatial and temporal dependencies, we introduce an innovative Dynamic Spatio-Temporal Attention Network (DSTANet). This model integrates spatio-temporal attention with graph convolutional networks and temporal convolution to enhance its analytical capabilities. Specifically, the graph convolutional network is leveraged to learn the intricate topology for capturing spatial relations, while temporal convolution is employed to ascertain the dynamic fluctuations of traffic data, thereby acquiring temporal dependencies. Subsequently, we implement our model to forecast traffic patterns within the framework of road networks. Experimental results demonstrate that our model effectively captures the spatio-temporal dependencies among roadway junctions derived from traffic data, outperforming state-of-the-art benchmarks on real-world traffic datasets in terms of predictive accuracy.

Comparing Sino-Nordic systems of higher education: ideology, institutional logics, subaltern governance, and emergent identities

ABSTRACT China is a key player in global science. Collaboration between Chinese and Western universities is critical for enhancing global networks of research and innovation. However, the potential for cooperative outcomes is currently limited by obstacles to collaboration. At the core of these challenges is the continued misunderstanding of ideology and governance between Chinese and Western higher education. Contributing to increased sub-regional understanding of higher education systems, this research provides an important Sino-Nordic comparative understanding of institutional autonomy, academic freedom, professional academic identity and organizational change. Through an empirical case-study and document analysis of macro-social ideopolitical steering logics, institutional logics, and organizational-level complexity, this research finds that within both China and the Nordics, incongruent macrosocial ideo-political steering logics deprofessionalize/politicize the academic profession through a state-driven procedural strengthening, yet substantive narrowing/weakening of institutional autonomy/academic freedom. The implications of this research should be understood within a contemporary context of limited trust and cooperation between China and the Nordics. Through a bridging view of convergent/divergent ideopolitical steering logics within respective systemic higher education contexts, and their effects on professional academic identity and agentic change, increased understanding of respective HE systems may provide avenue for increased sub-regional academic/scientific engagement and cooperation.

DIGAN: distillation model for generating 3D-aware Terracotta Warrior faces

Utilizing Generative Adversarial Networks (GANs) to generate 3D representations of the Terracotta Warriors offers a novel approach for the preservation and restoration of cultural heritage. Through GAN technology, we can produce complete 3D models of the Terracotta Warriors’ faces, aiding in the repair of damaged or partially destroyed figures. This paper proposes a distillation model, DIGAN, for generating 3D Terracotta Warrior faces. By extracting knowledge from StyleGAN2, we train an innovative 3D generative network. G2D, the primary component of the generative network, produces detailed and realistic 2D images. The 3D generator modularly decomposes the generation process, covering texture, shape, lighting, and pose, ultimately rendering 2D images of the Terracotta Warriors’ faces. The model enhances the learning of 3D shapes through symmetry constraints and multi-view data, resulting in high-quality 2D images that closely resemble real faces. Experimental results demonstrate that our method outperforms existing GAN-based generation methods.

Linkage facilitation for opioid use disorder in criminal legal system contexts: a primer for researchers, clinicians, and legal practitioners

At the intersection of drug policy, the opioid crisis, and fragmented care systems, persons with opioid use disorder (OUD) in the United States are significantly vulnerable to contact with the criminal legal system (CLS). In CLS settings, provision of evidence-based treatment for OUD is variable and often secondary to punitive approaches. Linkage facilitation at every touch point along the CLS Sequential Intercept Model has potential to redirect persons with OUD into recovery-oriented systems of care, increase evidence-based OUD treatment connections, and therefore reduce CLS re-exposure risk. Research in this area is still nascent. Thus, this narrative review explores the state of the science on linkage facilitation across the varied CLS contexts, including general barriers, facilitators, and opportunities for using linkage facilitation for OUD treatment and related services. Following the CLS Sequential Intercept Model, the specific CLS contexts examined include community services, police encounters, the courts (pre- and post-disposition), incarceration (pre-trial detention, jail, and prison), reentry (from jails, prisons, and unified systems), and community supervision (probation and parole). Examples of innovative linkage facilitation interventions are drawn from the Justice Community Opioid Innovation Network (JCOIN). Areas for future research and policy change are highlighted to advance the science of linkage facilitation for OUD services in the CLS.

Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing

With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposing an innovative data network propagation path tracing model, which is constructed based on graph convolutional networks (GCNs) and the BERT model. Firstly, propagation trees are constructed based on the propagation structure, and the key attributes of data nodes are extracted and screened. Then, GCNs are utilized to learn the representation of node features with different attribute feature combinations in the propagation path graph, while the Bidirectional Encoder Representations from Transformers (BERT) model is employed to capture the deep semantic features of the original text content. The core of this research is to effectively integrate these two feature representations, namely the structural features obtained by GCNs and the semantic features obtained by the BERT model, in order to enhance the ability of the model to recognize the data propagation path. The experimental results demonstrate that this model performs well in power data propagation and tracing tasks, and the data recognition accuracy reaches 92.5%, which is significantly better than the existing schemes. This achievement not only improves the power system’s ability to cope with data security threats but also provides strong support for protecting data transmission security and privacy.

Characteristics and influencing factors of the industry-university-research collaborative innovation network in China's new energy vehicle industry

Industry-university-research collaboration is the primary channel of industrial innovation, and China is a common context for research on innovation in the new energy vehicle industry. This study analyzes the industry-university-research collaborative innovation network in China's new energy vehicle industry using various software programs, including Gephi and ArcGIS, based on collaborative invention patent data related to the new energy vehicle industry provided by the global patent database incoPat. According to the findings of this study, the highest number of patents obtained through industry-university-research collaboration was observed in new energy vehicle device and accessory manufacturing, whereas the number of new entrants was significantly higher than the number of incumbents albeit in a downward trend, suggesting that the creation of new collaborative relationships among new entrants has always been the main phenomenon in the process of building an industry-university-research innovation system in the new energy vehicle industry. As private firms have long been playing an integral role in industry-university-research collaboration in the new energy vehicle industry, the number of private firms in the network exhibited an upward trend annually, while the dual heterogeneous connection mode was the primary connection mode in industry-university-research collaboration. There were some differences between the spatial characteristics of intraprovincial and interprovincial collaboration in the industry-university-research collaborative network in China's new energy vehicle industry. The analysis results obtained using GeoDetector revealed that the influence of economic base, level of openness to the outside world, government dominance, market dominance, regional science and technology finance, and entrepreneurial spirit varied from one stage to another.

Physics-informed neural network classification framework for reliability analysis

Reliability analysis is crucial for quantitatively evaluating structural safety amidst uncertainties, laying the foundation for reliability-based design optimization aimed at augmenting structural reliability and reducing economic expenditures, thereby offering substantial engineering benefits. However, performing reliability analysis on intricate structures, especially those requiring time-intensive finite element models, presents a formidable challenge. This study develops an innovative physics-informed neural network classification (PINNC) model to tackle this issue. In the PINNC model, the loss associated with the structural output state (i.e., safety or failed state) is defined as classification loss. Additionally, the structural output value (i.e., actual structural response) is treated as critical physical information, with its associated loss termed as physical loss. To facilitate a separate calculation of physical loss and classification loss, a parametric sigmoid activation function is used, establishing a link between the structural output value and the structural output state. The total loss is calculated as a weighted sum of both physical and classification losses. Unlike conventional neural network classification models that solely focus on classification loss, the PINNC model integrates both physical and classification losses, markedly improving the model’s efficacy in structural reliability analysis. Moreover, an adaptive framework is developed to incrementally include samples proximal to the limit state surface as new training data, thereby reinforcing the PINNC model’s computational precision. The effectiveness of the proposed PINNC framework in overcoming reliability analysis challenges is demonstrated through various applications.

Social innovation for sustainable and equitable transport: the case of Commons Cargo Bikes

Socially innovative civil society initiatives are seen as promising to address societal and environmental problems such as those encountered in the transport sector. However, many social innovators struggle when it comes to growth and achieving broader system-level impact. These problems are often ascribed to challenges associated with organizational structures. This paper investigates the scaling process and organizational development of a European social innovation in the transport sector, Commons Cargo Bikes. Empirically, the study primarily builds on two quantitative surveys with initiatives and users as well as qualitative interviews with spokespersons of an overarching network. The results indicate that social innovations can provide important complements to technological and market-based innovations by providing alternative solutions that reach geographical regions and users not typically addressed by traditional services. Furthermore, the study highlights that rather formal structures can be necessary for successful social innovation networks at a certain point. As the present case study indicates, tensions associated with organizational changes might, however, be anticipated and addressed proactively by combining different organizational archetypes in one structure.

Global Mobile Inventors

The number of Global Mobile Inventors (GMIs), inventors moving across borders during their career, has increased more than tenfold over the past two decades, and the corridors of mobility have shifted towards a growing presence of emerging markets. We document that GMIs that have patented in a given technology before moving are 70% more likely to be among the pioneering inventors in that technology once they arrive at destination, which we interpret as evidence of knowledge diffusion across borders. Returnees, which are typically inventors from emerging markets that go back after having spent some time in the US and other advanced economies, are twice as likely to file pioneering patents once returned than migrants when arriving abroad. Finally, we find that the more central the GMIs in the network of inventors during the early stages of the technology life-cycle at destination, the faster the technology-specific knowledge is absorbed by local inventors.

Recipients of public assistance with advanced chronic kidney disease: insights into receiving a presentation of kidney replacement therapy options and polypharmacy from Japanese investigators with innovative network about kidney disease study.

The characteristics of patients with advanced chronic kidney disease (CKD) who are recipients of public assistance in Japan, and the adequacy of their medical care have not been reported previously. The records of patients with CKD stage G5 who visited nine facilities in Japan from April to June 2013 were retrospectively reviewed to compare the characteristics and care of recipients of public assistance with those of non-recipients. Receiving a presentation of kidney replacement therapy (KRT) options and polypharmacy were used as indicators of suboptimal medical care. Of the 592 patients included in this analysis (mean age, 69.6years; male, 59.3%), 56 (9.5%) were recipients of public assistance and 536 (90.5%) were non-recipients of public assistance. The prevalence of diabetes mellitus, unmarried status, and living alone were higher in recipients of public assistance. In multivariable logistic regression analysis, compared with non-recipients of public assistance, recipients of public assistance were less likely to receive a presentation of KRT options (adjusted odds ratio [aOR], 0.31; 95% confidence interval [CI], 0.17-0.56), and were more likely to receive ≥ 10 (aOR, 1.92; 95% CI, 1.05-3.51), and ≥ 15 (aOR, 2.78; 95% CI, 1.23-6.26) types of medication. Patients with advanced CKD receiving public assistance were less likely to receive a presentation of KRT options and more likely to receive ≥ 10 and ≥ 15 types of medication, suggesting that recipients of public assistance are more likely to receive suboptimal medical care.