Medical Device Product Innovation Choices in Asia: An Empirical Analysis Based on Product Space

AI-Driven Microscopy: Cutting-Edge Approach for Breast Tissue Prognosis Using Microscopic Images

Microscopic imaging aids disease diagnosis by describing quantitative cell morphology and tissue size. However, the high spatial resolution of these images poses significant challenges for manual quantitative evaluation. This project proposes using computer-aided analysis methods to address these challenges, enabling rapid and precise clinical diagnosis, course analysis, and prognostic prediction. This research introduces advanced deep learning frameworks such as squeeze-and-excitation and dilated dense convolution blocks to tackle the complexities of quantifying small and intricate breast cancer tissues and meeting the real-time requirements of pathological image analysis. Our proposed framework integrates a dense convolutional network (DenseNet) with an attention mechanism, enhancing the capability for rapid and accurate clinical assessments. These multi-classification models facilitate the precise prediction and segmentation of breast lesions in microscopic images by leveraging lightweight multi-scale feature extraction, dynamic region attention, sub-region classification, and regional regularization loss functions. This research will employ transfer learning paradigms and data enhancement methods to enhance the models' learning further and prevent overfitting. We propose the fine-tuning employing pre-trained architectures such as VGGNet-19, ResNet152V2, EfficientNetV2-B1, and DenseNet-121, modifying the final pooling layer in each model's last block with an SPP layer and associated BN layer. The study uses labeled and unlabeled data for tissue microscopic image analysis, enhancing models' robust features and classification abilities. This method reduces the costs and time associated with traditional methods, alleviating the burden of data labeling in computational pathology. The goal is to provide a sophisticated, efficient quantitative pathological image analysis solution, improving clinical outcomes and advancing the computational field. The model, trained, validated, and tested on a microscope breast image dataset, achieved recognition accuracy of 99.6% for benign and malignant secondary classification and 99.4% for eight breast subtypes classification. Our proposed approach demonstrates substantial improvement compared to existing methods, which generally report lower accuracies for breast subtype classification ranging between 85% and 94%. This high level of accuracy underscores the potential of our approach to provide reliable diagnostic support, enhancing precision in clinical decision-making.

Competency gaps and institutional challenges for translational research in medical devices: insights from Brazilian researchers

BACKGROUND

The translational research approach aims to accelerate the innovation process. In the healthcare sector, this process is highly regulated and requires a broad set of skills. This study with researchers from Brazilian institutions aimed to identify the knowledge, skills, and structures that permeate the process of translating research into medical devices and to what extent they are present in Brazilian research groups working in the area.

METHODS

A structured questionnaire was applied in which the characteristics of the participants and the level of mastery in each skill were analyzed. Fisher's exact test was performed to verify the association between the percentages of knowledge and importance. Pearson's Chi-square test was also performed to verify the association between the sum of knowledge, categorized by the median, and the characteristics of the questionnaire participants. Finally, an exploratory factor analysis (EFA) was performed to validate the questionnaire construct.

RESULTS

One hundred two researchers working in the area of health innovation in Brazil, especially in the medical devices segment, answered the questionnaire. These researchers come from different regions of the country and work in several areas of knowledge, such as engineers (28%), doctors (12%), information technology and connectivity professionals (11%), pharmacists (8%), nurses (7%), and other formations (34%). The research revealed that a small number of these researchers have a good level of knowledge in human factors engineering and usability (23%), in patent legislation and asset management (24%), in pre-clinical and clinical trials (29%), in business plans (30%) and in the requirements of the technology incorporation process in the SUS (31%). The results reveal significant learning gaps and institutional deficiencies in essential skills and structures for translational medical device research.

CONCLUSION

Understanding the necessary skills and gaps to be filled can contribute to the adoption of institutional strategies and the formulation of public policies capable of promoting more effective results for the Brazilian health system.

Investigating the application of IoT mobile app and healthcare services for diabetic elderly: A systematic review

As the prevalence of diabetes increases among the elderly population, effective management becomes increasingly crucial. IoT mobile applications offer promising solutions for diabetes care by providing real-time monitoring, medication management, and lifestyle support. This paper aims to investigate the potential applications and challenges of IoT mobile applications in managing diabetes among elderly patients. Three databases including Scopus, Web of Science, and IEEE were systematically searched; 29 articles were screened in the final analysis process. Key results indicate that the application of mobile apps includes blood glucose monitoring, medication adherence, promotion of physical activity, and dietary control. Devices such as continuous glucose monitors and smart pill dispensers significantly improve glycemic control and medication adherence rates, these technologies enable real-time tracking, personalized feedback, and timely interventions, which enhance self-management and communication with healthcare providers. However, technical challenges like interoperability, data security and privacy, usability and involvement of policymakers pose significant barriers to their effective implementation. Collaborative efforts from healthcare providers, device manufacturers, and policymakers are essential to overcome these barriers and fully leverage the benefits of IoT technologies in diabetic elderly care. This review highlights the need for collaborative efforts to develop standardized frameworks that ensure device compatibility and seamless data integration in IoT solutions for diabetic elderly care, enhance data privacy with advanced technology, and design user-friendly apps for the diabetic elderly to improve the generalization and adoption of IoT IoT mobile applications in healthcare fields for elderly.

A comparative analysis of patient satisfaction with various methods of digital smile design and simulation

Background

Digital smile design (DSD) is a technique that utilizes the scientific methods and advanced software to design patients' smiles, presenting the visualized smile map directly to the patient. However, patients may not always find the proposed smile satisfactory or feel a sense of alignment with it. To address this concern, dentists have been integrating the tooth shape with the overall facial shape and other parameters to develop a personalized smile plan for each patient.

Materials and Methods

This study employed a descriptive-analytical, cross-sectional research design conducted during the summer and fall of 2022. This research sought to evaluate patient satisfaction levels associated with three distinct DSD techniques: Visagism, Proportional, and Stepwise Comprehensive. A sample of 20 participants, evenly split between males and females, was selected, all of whom were seeking smile design treatment and did not present with skeletal, jaw, facial, or periodontal complications. Interviews were conducted to analyze personality and temperament, and smile maps were created utilizing the Visagism, Stepwise Comprehensive, and Proportional methods. Subsequently, patients evaluated the designs produced by all three methods and completed a satisfaction questionnaire. Nonparametric statistical tests, namely the Kruskal-Wallis test and post hoc Bonferroni tests, were used to examine the research hypotheses at a significance level of 0.05.

Results

The results indicated a high level of satisfaction with all three DSD methods, with no statistically significant differences observed among them. These results suggest that all three approaches effectively met the patients' expectations and preferences.

Conclusion

The outcomes of this study have practical implications for dental professionals engaged in DSD, potentially enhancing patient experiences and treatment outcomes. Further research in this domain may explore the additional factors that could influence patient satisfaction and refine the DSD process.

A promising fusion: Traditional Chinese medicine and probiotics in the quest to overcome osteoporosis

Botanical drugs and probiotic supplements present safer alternative options for the prevention and treatment of osteoporosis (OP). However, pathological disorders of the gut microbiota and the specific properties of probiotics and traditional Chinese medicine (TCM) significantly limit their therapeutic efficacy. Given the favorable synergistic and complementary effects between probiotics and herbal medicines, a creative combination of these approaches may address the issue of their current limited efficacy. A comprehensive analysis is necessary to provide a detailed review of their potential for combination, the mechanisms behind their synergy, scientific applications, and future developments. There exists a complex relationship between gut microbiota and OP, and the underlying regulatory mechanisms are multidimensional, involving the production of pro-inflammatory metabolites, immune system disruption, and the impairment of the intestinal mucosal barrier. Furthermore, we analyzed the complex mechanisms and potential connections between probiotics, TCM, and their combined applications. We highlighted the principle of complementary gain and the substantial therapeutic potential of their organic combination, which facilitates the release of active substances in TCM, increases the bioavailability of TCM, enhances probiotic delivery efficiency, and exerts synergistic effects. The combined use of probiotics and TCM offers a safe and effective strategy for managing OP and presents an innovative and promising direction for the future development of modern phytomedicine.

A feature explainability-based deep learning technique for diabetic foot ulcer identification

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impaired blood circulation and neuropathy associated with diabetes, increasing the risk of severe infections and even amputations if untreated. Early detection, effective wound care, and diabetes management are crucial to prevent and treat DFUs. Artificial intelligence (AI), particularly through deep learning, has revolutionized DFU diagnosis and treatment. This work introduces the DFU_XAI framework to enhance the interpretability of deep learning models for DFU labeling and localization, ensuring clinical relevance. The framework evaluates six advanced models-Xception, DenseNet121, ResNet50, InceptionV3, MobileNetV2, and Siamese Neural Network (SNN)-using interpretability techniques like SHAP, LIME, and Grad-CAM. Among these, the SNN model excelled with 98.76% accuracy, 99.3% precision, 97.7% recall, 98.5% F1-score, and 98.6% AUC. Grad-CAM heat maps effectively identified ulcer locations, aiding clinicians with precise and visually interpretable insights. The DFU_XAI framework integrates explainability into AI-driven healthcare, enhancing trust and usability in clinical settings. This approach addresses challenges of transparency in AI for DFU management, offering reliable and efficient solutions to this critical healthcare issue. Traditional DFU methods are labor-intensive and costly, highlighting the transformative potential of AI-driven systems.

Improved MTF Measurement of Medical Flat-Panel Detectors Based on a Slit Model

In the development, evaluation, and application of medical flat-panel detectors, the modulation transfer function (MTF) is crucial, as it reflects the device's ability to restore detailed information. Medical flat-panel detectors encompass both image data acquisition and digitization processes, and detectors with varying pixel sizes exhibit differing capabilities for observing details. Accurately quantifying MTF is a critical challenge. The complexity of MTF calculation, combined with unclear principles and details, may result in erroneous outcomes, thereby misleading research and decision-making processes. This paper presents an improved MTF oversampling method based on the slit model. MTF testing is conducted under various sample conditions and using different focal spot diameters of the X-ray tube to analyze the impact of focal spot size. High-precision tungsten plates and fixtures are designed and fabricated, and MTF results with varying line spread function (LSF) sampling intervals are compared. The results demonstrate that the improved slit model offers distinct advantages, with MTF measurements achieving 92.4% of the ideal value. Compared to traditional tungsten edge and point (aperture) model testing methods, the accuracy of the proposed method is improved by 5-13%. The optimal sampling interval is approximately 1/29 of the pixel pitch, offering a more accurate method for evaluating detector performance.

Communication Tapestry: Health Literacy Mediates Public Trust in Physician Health Information in Pakistani Public Hospitals

BACKGROUND/OBJECTIVES

This study explores the multifaceted factors influencing public trust in healthcare services provided by doctors in public hospitals in Pakistan. The objective is to examine the relationships between various determinants such as doctors' reputation and expertise, patient participation in decision-making, communication clarity, health literacy levels, and trust in prescribed medications to provide actionable insights for improving healthcare trust.

METHODS

A total of 550 patients from public hospitals were surveyed, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This approach enabled the identification of intricate relationships between the key factors influencing trust in healthcare services.

RESULTS

The findings indicate that patient participation in decision-making and transparent communication significantly enhance trust in prescribed medications. Additionally, health literacy emerged as a crucial factor, with higher levels of understanding leading to greater confidence in healthcare services.

CONCLUSIONS

The study highlights the importance of patient-centered care, clear communication strategies, and health literacy initiatives in strengthening public trust in healthcare systems. Practical recommendations are provided for policymakers, healthcare professionals, and researchers to collaboratively improve healthcare service delivery and foster public confidence.

Nanotechnology-based biomedical devices in the cancer diagnostics and therapy

Nanotechnology has significantly transformed the field of cancer diagnostics and therapeutics by introducing advanced biomedical devices. These nanotechnology-based devices exhibit remarkable capabilities in detecting and treating various cancers, addressing the limitations of traditional approaches, such as limited specificity and sensitivity. This review aims to explore the advancements in nanotechnology-driven biomedical devices, emphasizing their role in the diagnosis and treatment of cancer. Through a comprehensive analysis, we evaluate various nanotechnology-based devices across different cancer types, detailing their diagnostic and therapeutic effectiveness. The review also discusses FDA-approved nanotechnology products, patents, and regulatory trends, highlighting the innovation and clinical impact in oncology. Nanotechnology-based devices, including nanobots, smart pills, and multifunctional nanoparticles, enable precise targeting and treatment, reducing adverse effects on healthy tissues. Devices such as DNA-based nanorobots, quantum dots, and biodegradable stents offer noninvasive diagnostic and therapeutic options, showing high efficacy in preclinical and clinical settings. FDA-approved products underscore the acceptance of these technologies. Nanotechnology-based biomedical devices offer a promising future for oncology, with the potential to revolutionize cancer care through early detection, targeted treatment, and minimal side effects. Continued research and technological improvements are essential to fully realize their potential in personalized cancer therapy.

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.

Coupling coordination relationship between health resource allocation and regional economic development: an empirical study based on five provinces in eastern China

Background

Improving system coordination is a pivotal strategy and a critical pathway for social governance. Chinese society is currently facing a significant challenge in aligning the allocation of health resources with economic development. Evaluating the level of coordinated development within the system can provide valuable insights to support the construction of a more coordinated China and foster high-quality development.

Methods

Based on a systematically constructed indicator framework, our study selected data from five eastern provinces of China to establish a ten-year panel dataset covering the period from 2011 to 2020. The comprehensive evaluation index and the relative development degree were employed to comprehensively evaluate the development level of the system. The coupling coordination degree model was applied to analyze the coupling coordination relationship and spatiotemporal evolution trend of the two systems. Additionally, the fixed effects model was used to identify the driving factors behind the coordinated development of the two systems.

Results

From 2011 to 2020, the comprehensive indices of health resource allocation and economic development in the five eastern provinces of China exhibited a consistent year-on-year increase, and the relative development degree experienced two critical values of 0.8 and 1.2, which changed from the lagging allocation of health resources to the lagging economic development. The system coordination index generally ranged between 0.35 and 0.90, with the coordination phase undergoing a transition from an antagonistic stage to a coordinated stage. The coordination type also gradually shifted from mild imbalance to good coordination. Furthermore, the levels of economic development, economic structure, technological investment, as well as the allocation of health human and material resources, all serve as critical drivers in enhancing the coordinated development of the system.

Conclusion

The coordinated development of eastern China's provinces produces substantial spillover effects, and the realization of a Healthy China initiative must strategically harness their radiative and demonstrative effects. Achieving a superior level of coordination requires urgent efforts to rectify the existing deficiencies in the distribution of grassroots healthcare resources. Furthermore, cultivating innovative drivers of economic growth and enhancing the capacity for economic support are critical to ensuring high-quality and sustainable development.

Blockchain Factors in the Design of Smart-Media for E-Healthcare Management

According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services.

Therapeutic approaches targeting aging and cellular senescence in Huntington's disease

Huntington's disease (HD) is a devastating neurodegenerative disease that is manifested by a gradual loss of physical, cognitive, and mental abilities. As the disease advances, age has a major impact on the pathogenic signature of mutant huntingtin (mHTT) protein aggregation. This review aims to explore the intricate relationship between aging, mHTT toxicity, and cellular senescence in HD. Scientific data on the interplay between aging, mHTT, and cellular senescence in HD were collected from several academic databases, including PubMed, Google Scholar, Google, and ScienceDirect. The search terms employed were "AGING," "HUNTINGTON'S DISEASE," "MUTANT HUNTINGTIN," and "CELLULAR SENESCENCE." Additionally, to gather information on the molecular mechanisms and potential therapeutic targets, the search was extended to include relevant terms such as "DNA DAMAGE," "OXIDATIVE STRESS," and "AUTOPHAGY." According to research, aging leads to worsening HD pathophysiology through some processes. As a result of the mHTT accumulation, cellular senescence is promoted, which causes DNA damage, oxidative stress, decreased autophagy, and increased inflammatory responses. Pro-inflammatory cytokines and other substances are released by senescent cells, which may worsen the neuronal damage and the course of the disease. It has been shown that treatments directed at these pathways reduce some of the HD symptoms and enhance longevity in experimental animals, pointing to a new possibility of treating the condition. Through their amplification of the harmful effects of mHTT, aging and cellular senescence play crucial roles in the development of HD. Comprehending these interplays creates novel opportunities for therapeutic measures targeted at alleviating cellular aging and enhancing HD patients' quality of life.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system's interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system's performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren't needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

Global impact of COVID-19 on food safety and environmental sustainability: Pathways to face the pandemic crisis

The COVID-19 pandemic poses ongoing challenges to the sustainability of various socioeconomic sectors, including agriculture, the food supply chain, the food business, and environmental sustainability. This study employs data obtained from the World Health Organization (WHO), and Food and Agriculture Organization (FAO), as well as scientific and technical research publications, to evaluate the impacts of COVID-19 on agriculture and food security. This article seeks to highlight the profound influence of the COVID-19 pandemic on agriculture, the supply and demand of food, and the overall safety of food. The article also explores the several pathways by which COVID-19 can be transmitted in these areas and the various technologies employed for its detection. The ongoing and post-pandemic ramifications are substantial since they could decrease agricultural output due to limitations on migration, a downturn in international trade, less buying capacity, and disturbances in food production and processing. Therefore, based on this thorough investigation, recommendations are issued for mitigating and controlling the pandemic's effects.

AI-Driven Data Analysis of Quantifying Environmental Impact and Efficiency of Shape Memory Polymers

This research investigates the environmental sustainability and biomedical applications of shape memory polymers (SMPs), focusing on their integration into 4D printing technologies. The objectives include comparing the carbon footprint, embodied energy, and water consumption of SMPs with traditional materials such as metals and conventional polymers and evaluating their potential in medical implants, drug delivery systems, and tissue engineering. The methodology involves a comprehensive literature review and AI-driven data analysis to provide robust, scalable insights into the environmental and functional performance of SMPs. Thermomechanical modeling, phase transformation kinetics, and heat transfer analyses are employed to understand the behavior of SMPs under various conditions. Significant findings reveal that SMPs exhibit considerably lower environmental impacts than traditional materials, reducing greenhouse gas emissions by approximately 40%, water consumption by 30%, and embodied energy by 25%. These polymers also demonstrate superior functionality and adaptability in biomedical applications due to their ability to change shape in response to external stimuli. The study concludes that SMPs are promising sustainable alternatives for biomedical applications, offering enhanced patient outcomes and reduced environmental footprints. Integrating SMPs into 4D printing technologies is poised to revolutionize healthcare manufacturing processes and product life cycles, promoting sustainable and efficient medical practices.

HIV drug resistance following pre-exposure prophylaxis failure among key populations in sub-Saharan Africa: a systematic review and meta-analysis protocol

Background

Key populations (KP) are highly vulnerable to HIV acquisition and account for 70% of new infections worldwide. To optimize HIV prevention among KP, the World Health Organization recommends the combination of emtricitabine plus tenofovir disoproxil fumarate for pre-exposure prophylaxis (PrEP). However, PrEP failure could be attributed to drug resistance mutations (DRMs) but this is unexplored in sub-Saharan Africa (SSA).

Objectives

We aim to conduct a systematic review that will provide evidence on the prevalence of HIV drug resistance (HIVDR) following PrEP failure among KP in SSA.

Design

This will be a systematic review and meta-analysis of studies conducted in sub-Saharan Africa.

Methods and Analysis

This systematic review will include randomized and non-randomized trials, cohorts, case controls, cross-sectional studies, and case reports evaluating the prevalence of HIVDR following PrEP failure among KP (i.e., gay men and men who have sex with men, female sex workers, transgenders, people who inject drugs, prisoners, and detainees) in SSA. Results will be stratified according to various KP, age groups (adolescents and adults), and geographic locations. Primary outcomes will be "the prevalence of PrEP failure among KP" and "the prevalence of HIVDR after PrEP failure" in SSA. Secondary outcomes would be "the prevalence of DRMs and drug susceptibility" and "the level of adherence to PrEP." A random-effects model will be used to calculate pooled prevalence if data permit and we will explore potential sources of heterogeneity.

Discussion

Our findings will provide estimates of HIVDR following PrEP failure among KP in SSA. In addition, determinants of PrEP failure and driving factors of the emergence of DRMs will also be investigated. Evidence will help in selecting effective antiretrovirals for use in PrEP among KP in SSA.

Registration

PROSPERO: CRD42023463862.

Balancing fiscal expenditure competition and long-term innovation investment: Exploring trade-offs and policy implications for local governments

The mobility of economic factors across jurisdictions has led to increased fiscal competition among decentralized subnational governments. This study examines the relationship between fiscal competition and long-term investment in innovation at the local government level. Panel data analysis, encompassing expenditures, taxes, and innovation inputs from 18 municipalities over a 10-year period, is employed using fixed effects regression. The results reveal a negative correlation between fiscal competition and expenditure on innovation, indicating that intensified competition for mobile capital diverts resources away from essential long-term investments crucial for knowledge-driven growth. Even after controlling for economic and institutional factors, a one standard deviation increase in competition corresponds to an average decline of 25% in per capita innovation investment. These findings highlight the unintended trade-off resulting from heightened competition and underscore the need for policy frameworks that promote localized flexibility while curbing uncoordinated competition that undermines innovation capacity. While fiscal decentralization aims to foster competitive governance, this study provides empirical evidence that short-term expenditure incentives often displace long-term innovation objectives without sufficient coordination. The insights contribute significant empirical evidence on the concealed costs of fiscal competition for regional development. Consequently, a re-evaluation of conventional perspectives on decentralization and competition is warranted, emphasizing the importance of developing cooperative policy solutions that strike a delicate balance between decentralized decision autonomy and strategic coordination. Adopting such an approach is essential to fully leverage the advantages of competitive governance while simultaneously nurturing innovation ecosystems.

A novel ensemble approach for estimating the competency of bank telemarketing

Having a reliable understanding of bank telemarketing performance is of great importance in the modern world of economy. Recently, machine learning models have obtained high attention for this purpose. In order to introduce and evaluate cutting-edge models, this study develops sophisticated hybrid models for estimating the success rate of bank telemarketing. A large free dataset is used which lists the clients' information of a Portuguese bank. The data are analyzed by four artificial neural networks (ANNs) trained by metaheuristic algorithms, namely electromagnetic field optimization (EFO), future search algorithm (FSA), harmony search algorithm (HSA), and social ski-driver (SSD). The models predict the subscription of clients for a long-term deposit by evaluating nineteen conditioning parameters. The results first indicated the high potential of all four models in analyzing and predicting the subscription pattern, thereby, revealing the competency of neuro-metaheuristic hybrids. However, comparatively speaking, the EFO yielded the most reliable approximation with an area under the curve (AUC) around 0.80. FSA-ANN emerged as the second-accurate model followed by the SSD and HSA with respective AUCs of 0.7714, 0.7663, and 0.7160. Moreover, the superiority of the EFO-ANN is confirmed against several conventional models from the previous literature, and finally, it is introduced as an effective model to be practically used by banking institutions for predicting the likelihood of deposit subscriptions.

Spatial network and driving factors of low-carbon patent applications in China from a public health perspective

Introduction

The natural disasters and climate anomalies caused by increasing global carbon emissions have seriously threatened public health. To solve increasingly serious environmental pollution problems, the Chinese government has committed itself to achieving the goals of peak carbon emissions and carbon neutrality. The low-carbon patent application is an important means to achieve these goals and promote public health.

Methods

This study analyzes the basic situation, spatial network, and influencing factors of low-carbon patent applications in China since 2001 at the provincial and urban agglomeration levels using social network analysis based on data from the Incopat global patent database.

Results

The following findings are established. (1) From the number of low-carbon patent applications, the total number of low-carbon patent applications in China increased year by year, while the number of applications in the eastern region was larger than those in the central and western regions, but such regional differences had been decreasing. (2) At the interprovincial level, low-carbon patent applications showed a complex and multithreaded network structure. In particular, the eastern coastal provinces occupied the core position in the network. The weighted degree distribution of China's interprovincial low-carbon patent cooperation network is affected by various factors, including economic development, financial support, local scientific research level, and low-carbon awareness. (3) At the urban agglomeration level, the eastern coastal urban agglomerations showed a radial structure with the central city as the core. Urban innovation capability, economic development, low-carbon development awareness, level of technology import from overseas, and informatization level are highly correlated with the weighted degree of low-carbon cooperation networks of urban agglomerations.

Discussion

This study provides ideas for the construction and governance of low-carbon technology innovation system and perspectives for theoretical research on public health and high-quality development in China.

Research on the evolution of the Chinese urban biomedicine innovation network pattern: An analysis using multispatial scales

This paper addresses the spatial pattern of urban biomedicine innovation networks by separately using four scales, i.e., the national scale, interregional scale, urban agglomeration scale, and provincial scale, on the basis of Chinese biomedicine patent data from the incoPat global patent database (GPD) (2001-2020) and using the method of social network analysis (SNA). Through the research, it is found that (1) on the national scale, the Chinese biomedicine innovation network becomes denser from west to the east as its complexity continuously increases. Its spatial structure takes the form of a radial network pattern with Beijing and Shanghai as its centers. The COVID-19 pandemic has not had an obvious negative impact on this network at present. (2) On the interregional scale, the strength of interregional network ties is greater than that of intraregional network ties. The eastern, central and western biomedicine innovation networks appear to be heterogeneous networks with regional central cities as the cores. (3) At the urban agglomeration scale, the strength of intraurban-agglomeration network ties is greater than that of interurban-agglomeration network ties. The three major urban agglomerations have formed radial spatial patterns with central cities as the hubs. (4) At the provincial scale, the intraprovincial networks have poor connectivity and low internal ties strength, which manifest as core-periphery structures with the provincial capitals as centers. Our research conclusion helps to clarify the current accumulation of technology and offer guidance for the development of China's biomedicine industry.

Does the creation of healthy cities promote municipal solid waste management? Empirical research in 284 cities in China

In the context of the COVID-19 pandemic, the creation of healthy cities has become an important measure to deal with global public diseases and public health emergencies, and has had a profound impact on the management of municipal solid waste (MSW). This study exploits the Healthy Cities pilot (HCP) program established in 2016 as a natural experiment, and evaluates its impact on MSW management using the difference-in-difference (DID) method. The estimates show that the collection amount and harmless treatment capacity of MSW were increased by 15.66 and 10.75%, respectively, after the cities were established as pilot healthy cities. However, the harmless treatment rate was decreased by 3.544. This conclusion remains valid in a series of robustness tests, including parallel trend test, placebo test, propensity score matching (PSM)-DID, eliminating the interference of other policies, and eliminating the non-randomness of the policy. Mechanism analysis shows that the HCP program increased the collection amount and harmless treatment capacity of MSW by increasing the expenditure on MSW treatment. However, after a city was established as a pilot healthy city, the unsustainable high expenditure of local government on municipal sanitation led to the decrease in the harmless treatment rate of MSW. Moreover, heterogeneity analysis shows that the HCP program had a stronger impact on MSW management in cities with higher administrative levels, more obvious location advantages, and a larger size. Therefore, it is advisable to use the creation of healthy cities as an important tool to gradually improve MSW management, so as to realize the coordinated development of city construction and human health.

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of “high in the east and low in the west.” Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.

Firm innovation activities and consumer brand loyalty: A path to business sustainability in Asia

Background

In recent years, technological advancements have increased the importance of innovation activities. Therefore, firms invest millions of dollars in innovation activities to ensure long-term business sustainability. Similarly, consumer concerns have increased dramatically over the past years. Thus, brand loyalty has become a top priority for firms and consumers. In this background, this research examines how firms' innovation activities translate into consumer brand loyalty to assure business sustainability in Asian markets, particularly China, Pakistan, and Indonesia.

Objectives

This study's specific objectives are to comprehend the concept of firms' innovation activities and their effect on the brand prototype. Examine the effect of the brand prototype on global brand preference, recommendation, and loyalty among Asian consumers. Find out the impact of brand preference on brand recommendations and the influence of brand recommendations on brand loyalty among Asian consumers.

Materials and methods

A total of 814 consumers from Asian countries (China, Pakistan, and Indonesia) participated in this study, and structural equation modeling was used to analyze the data.

Results

The findings indicate that firms' innovation activities, such as processes, products, and store environment, positively influenced the brand prototype, thereby increasing consumer brand knowledge. Likewise, brand prototype contributes to developing brand preference, brand recommendation, and brand loyalty among Asian consumers. Lastly, consumer brand preference significantly influenced brand recommendation, which positively improves consumer brand loyalty in Asia.

Conclusion

This study concluded that Asian (Chinese, Pakistani, and Indonesian) consumers have favorable perceptions of firms' innovation activities (i.e., process, product, and store environment innovation), which influences their ability to develop brand prototypes to increase consumer brand knowledge. Similarly, brand prototype fosters brand preference, recommendation, and loyalty. Likewise, favorable brand preference encourages consumers to recommend the brand to others, strengthening brand loyalty. Thus, firms should invest in innovation activities to strengthen consumer brand loyalty in Asian markets. Consequently, this study may assist multinational corporations in increasing their business volumes and market shares in Asia.

Managerial recommendations

This study provides important managerial recommendations. The findings revealed that global managers can develop and implement several branding strategies for sustaining their businesses in the Asian environment.