Innovation Index Research Articles

DO FINANCIAL DEVELOPMENT AND GREEN TECHNOLOGICAL INNOVATIONS IMPROVE ENVIRONMENTAL QUALITY?

This paper inquires the effects of financial development and green technological innovations on environmental quality between 2009-2021. For this purpose, the effects of financial development, informational globalization, renewable energy use, economic growth, trade openness and patent applications on CO2 emissions are analyzed using the System Generalized Method of Moments (GMM). The findings suggest that; while knowledge-based globalization and economic growth increase CO2, financial development, renewable energy, trade openness and patent applications significantly decrease it. Accordingly, promoting green technology and renewable energy is important for supporting environmental sustainability. Therefore, financial development and green technological innovation indicators will contribute to the support of environmental policies by positively affecting environmental quality.

Temporal and spatial divergence of the integration and innovative development index of the logistics and manufacturing industries: an empirical study from China

ABSTRACT The integration and innovative development of logistics and manufacturing industries (IIDLM) is a notable trend. The Chinese government recognises the importance of IIDLM but encounters two primary challenges: identifying its development trajectory and comprehending its spatial-temporal variability. Thus, this study uses an enhanced TOPSIS method and 2014–2022 provincial data from China to analyze the spatial–temporal divergence of IIDLM. Focusing on the four core characteristics of IIDLM, this study reveals several findings. First, upgrading technological capability is a crucial factor. Second, temporal analysis indicates that the comprehensive index exhibits a W-shaped pattern with significant volatility, specific asset investment displays a V-shape, supply chain optimisation shows an upward trend, technological capability upgrading presents an M-shape, and organisational model transformation follows a W-shape. Third, spatial analysis reveals that IIDLM development is uneven, with the central region leading. However, the eastern region does not rank the highest, and coastal areas outperform their inland counterparts.

Unveiling the drivers of bank profitability: insights from Ethiopian banks

Bank profitability analysis seeks to identify macroeconomic, industry, and bank-specific factors, helping management understand and assess key determinants of profitability. To this end, the objective the current study is to analyze the effects of bank specific, industry-specific and macroeconomic factors on fourteen Ethiopian commercial bank profitability for the period of 2011–2023 using a random effect (RE), generalized methods of moments (GMM), fully modified ordinary least square (FMOLS). The findings reveal that income diversification negatively impacts return on assets (ROA), while gross domestic product (GDP) and government effectiveness have substantial positive effects on return on assets (ROA). Return on equity (ROE) is positively influenced by productivity, government effectiveness, and innovation indexes, but negatively affected by capital adequacy and income diversification. Lastly, profitability evaluated by net interest margin (NIM) is positively affected by productivity, COVID-19, and GDP, whereas bank stability and government effectiveness have negative impacts. This study is the first to explore the influences of the innovation index, government effectiveness, and COVID-19 on the performance of Ethiopian banks. By addressing this novel area, the current study offers valuable insights for policymakers, regulators, bank managers, analysts, and other stakeholders, providing a foundation for strategies to enhance the profitability and performance of banks in Ethiopia

Industrial Green Innovation Efficiency: Spatial Patterns, Evolution, and Convergence in the Yangtze River Economic Belt

This study examines the relationship between technological innovation and economic development in the Yangtze River economic belt context. Specifically, the study employs the SBM-GML model to assess the efficiency of industrial green technology innovation across 110 prefecture-level cities between 2006 and 2022. The study also employs exploratory spatial data analysis (ESDA) and the Spatio-temporal transition method to analyze the spatial evolution pattern of the GML index of industrial green technology innovation. In addition, the study investigates the convergence mechanism using absolute and conditional β convergence models. The findings reveal that the GML index of industrial green technology innovation in the Yangtze River Economic Belt exhibits an upward trend, and technological progress is a key driver. Moreover, the spatial and temporal transition of the GML index of industrial green technology innovation shows substantial spatial dependence and solid spatial stability. The study also finds regional heterogeneity in the absolute and conditional β convergence characteristics and their influencing factors. Considering regional differences, the results suggest differentiated policy recommendations to promote the coordinated development of industrial green technological innovation efficiency in the Yangtze River Economic Belt. The study contributes to the literature on the relationship between technological innovation and economic development, highlighting the importance of spatial considerations and regional heterogeneity in promoting sustainable economic growth.

Plasmonic refractive index sensor of a hexagonal close-packed gold nanodisk array coupled with a gold thin film

The study proposes an innovative surface plasmon refractive index sensor based on a hexagonal close-packed gold nanodisk array coupled with a gold thin film. The composite structure comprises a SiO2 substrate, an Au thin film, a SiO2 spacer layer, and a hexagonal close-packed Au nanodisk array. The study used the finite-difference time-domain method to investigate the surface plasmons excitation characteristics, optical field distribution, and refractive index sensing properties of the composite structure. The composite structure excited three surface plasmon resonance (SPR) modes in a liquid environment. The study investigated the influence of structural parameters on the three SPR modes. The sensing performance of the composite structure was studied in gas environments (n=1.00−1.10) and liquid environments (n=1.33−1.40). The maximum sensitivity (S) and the maximum figure of merit (FOM) could reach 1297 nm/RIU and 260RIU−1 in gas environments, and 1291 nm/RIU and 208RIU−1 in liquid environments. The sensor has the advantages of high sensitivity, a high FOM, a simple structure, and easy processing. This study obtains excellent sensing performance by changing the arrangement of Au nanoparticles, providing a reference for designing surface plasmon refractive index sensors based on the coupling structure of a metal nanoarray and a metal thin film. The sensor can be used in a broad range of biochemical detection applications, such as detection of hemoglobin concentration and temperature sensing.

A Process Tree-Based Incomplete Event Log Repair Approach

The low quality of business process event logs—particularly the widespread occurrence of incomplete traces—poses significant challenges to the reliability, accuracy, and efficiency of process mining analysis. In real-world scenarios, these data imperfections severely undermine the practical value of process mining techniques. The primary research problem addressed in this study is the inefficiency and limited effectiveness of existing Petri-net-based incomplete trace repair approaches, which often struggle to accurately recover missing events in the presence of complex and nested loop structures. To tackle these limitations, we aim to develop a faster and more accurate approach for repairing incomplete event logs. Specifically, we propose a novel repair approach based on process trees as an alternative to traditional Petri nets, thus alleviating issues such as state space explosion. Our approach incorporates process tree model decomposition and innovative branch indexing techniques, enabling rapid localization of candidate branches for repair and a significant reduction in the solution space. Furthermore, by leveraging activity information within the traces, our approach achieves efficient and precise repair of loop nodes through a single traversal of the process tree. To comprehensively evaluate our approach, we conduct experiments on four real-life and five synthetic event logs, comparing performance against state-of-the-art techniques. The experimental results demonstrate that our approach consistently delivers repair accuracies exceeding 70%, with time efficiency improved by up to three orders of magnitude. These findings validate the superior accuracy, efficiency, and scalability of the proposed approach, highlighting its strong potential for practical applications in business process mining.

THE IMPACT OF PROPERTY RIGHTS ON INNOVATION: A RESEARCH ON COUNTRY DATA

The aim of this study is to determine the impact of property rights on innovation. While many studies analyze the topic at the business level, this study aims to determine the role of property rights in improving innovation performance. The research was conducted based on quantitative research methods using secondary data obtained from various organizations, which are publicly published as innovation indices and datasets referred to as property rights. We conducted correlation and regression analyses using the SPSS software package on both variables published for the year 2022. The analyses have identified a linear and significant relationship between property rights development and innovation indices levels. The regression analysis revealed that these linear relationships are significant. It is vital to develop the elements of property rights as driving forces in the advancement of innovation and to ensure trust in these elements.

Environmental exposure assessment in the German National Cohort (NAKO).

Environmental exposure assessment in the German National Cohort (NAKO).

A novel AI-driven approach to greenwashing: breakthroughs in the future fit between domain-specific Islamic enterprises with varying developmental progress and ESG landscapes

This research presents an innovative framework for exploring the phenomenon of greenwashing within the context of domain-specific enterprises that are adapting to diverse institutional landscapes. This is achieved through the deployment of a groundbreaking environmental, social, and governance leadership transition index, specifically designed for climate resilience. The index effectively integrates artificial intelligence and machine learning in conjunction with human cognitive expertise. Additionally, the study utilizes a chrono-convolutional neural network to investigate the dynamics of long-term memory concerning the nexus of innovative green fintech index and sectoral investments, thereby assessing the potential for greenwashing activities. This study also recognizes the varying institutional frameworks and approaches to climate risk management between emerging and developed nations. Adopting the quantile-based method, the long-term total connectedness index is assessed across market states. The analysis incorporates 11 sectoral investment indices from emerging and developed countries, comprising 22 international evolving investments. Temporal convolutional networks are leveraged to evaluate long-term memory under varying market conditions. The investigation highlights significant variances in the accuracy of long-term memory between indices representing emerging and developed markets. Notably, emerging markets exhibit a greater degree of precision about climate-smart initiatives. In particular, the mid-range quantiles of emerging market indices display the highest levels of accuracy across a broad spectrum of investments. These observations imply that developed markets, particularly under extreme economic conditions, may foster more favorable conditions for greenwashing practices. Moreover, a sectoral analysis reveals that, irrespective of market maturity, the energy and utilities sectors demonstrate the lowest propensity for greenwashing, while the information technology sector ranks similarly low in this regard. In contrast, real estate firms reveal a heightened susceptibility to greenwashing within developed markets, whereas their counterpart firms in emerging markets exhibit a markedly lower risk of engaging in such practices. The study offers valuable guidance to policymakers and regulators. Insights can inform targeted interventions promoting climate-resilient investment practices, contributing to sustainable development goals. Fostering reliable, interconnected emerging markets enhances the institutional quality and sustainability transition. Overall, the research provides a crucial perspective for navigating the complex landscape at the intersection of finance, climate resilience, and greenwashing. It illuminates the interplay between market fluctuations, green deception, and sustainable climate investment needs.

Research on the Development Level of Science and Technology Finance in Anhui Province Based on Principal Component Analysis

The purpose of this paper is to study the development level of science and tech-nology finance in 16 prefecture-level cities in Anhui Province in 2021.Firstly,on the basis of the existing data and previous researches, the evaluation indexsys-tem of the development level of science and technology finance was construct-ed. Secondly, the principal component analysis method was used to extract thetwo principal components of innovation and intellectual property index, R&Dinvestment and financial structure index, and the comprehensive score equation was obtained, which comprehensively evaluated the development level of science and technology finance in 16 prefecture-level cities in Anhui Prov-ince. Finally, based on the comprehensive scores of each city, the 16 prefec-ture-level cities were divided into five categories, and it was concluded that the development level of science and technology finance in Hefei and Wuhu was in the leading position in the province, while the development level of science and technology finance in Bozhou, Huainan and Suzhou was relatively backward.

The Impact of the Digital Economy on Urban Energy-Environment Efficiency

This study takes the panel data of 278 prefecture-level cities in China from 2011 to 2022 as samples, constructs a super-efficiency SBM model to measure urban energy-environment efficiency, uses the entropy method to synthesize the digital economy composite index, and introduces the green innovation index as a mediating variable. Breaking through the traditional analysis framework in the empirical method: Firstly, through the fixed-effects model and mediating-effect test, it reveals the direct impact of the digital economy on energy-environment efficiency and the transmission path of green innovation. Secondly, it divides the eastern, central, and western regions and low-carbon pilot cities for heterogeneity tests to identify the moderating effect of policy intervention. Thirdly, it strengthens the robustness of the conclusions by replacing the super-efficiency CCR model, shortening the sample period, and performing winsorization. The study finds that the digital economy significantly improves energy-environment efficiency through green technology innovation. These conclusions provide an empirical basis for optimizing the layout of digital infrastructure and improving the regional collaborative emission-reduction mechanism.

National Institutes of Health Funding and Patent Innovation in Academic Otolaryngology (2014-2024).

Research productivity, measured through publication and citation indices, is frequently evaluated during the allocation of funding to hospitals, medical departments, or faculty. However, current publication metrics do not sufficiently capture "innovativeness," especially in the realm of health technologies. Patent data, which can serve as markers of tangible innovation, remain underutilized in resource decision-making in otolaryngology. Retrospective study. The aim was to explore the relationship between NIH funding and patent production in academic otolaryngology. The Lens Patent Database (lens.org) was used to obtain patent information for all U.S. A61 patents filed in the past decade, affiliated with otolaryngology faculty at 2023 NIH-funded institutions. Faculty data was algorithmically matched to patents and organized into departmental datasets, which included patent totals, citation statistics, and innovation indices. Departmental attributes were collected from publicly available sources. Univariate and multivariate regression analyses were conducted to identify factors correlated with patent activity. Analysis of data from 48 NIH-funded otolaryngology departments revealed an average NIH funding of $3.646 million, with a median of 16.5 patents and 51.5 patent citations per department over the past decade. The amount of NIH funding did not significantly correlate with the 10-year patent count but did show a significant, positive correlation with 10-year patent citations (p < 0.05). The number and proportion of PhD faculty, residency program size, and department rankings were also positively associated with patent output (p < 0.05). This research underscores the importance of institutional funding, educational background, and residency characteristics in promoting innovation within otolaryngology. N/A.

The relationship of cardiometabolic index with bowel movement frequency: an NHANES-based cross-sectional analysis

BackgroundPrior studies have indicated a notable link between gut health and metabolic syndrome (MetS). The cardiometabolic index (CMI), an innovative indicator of metabolic health, effectively predicts MetS. Bowel movement frequency (BMF) closely reflects gastrointestinal function and is a key sign of gut health. Nonetheless, the relationship between CMI and BMF is still unclear. Our research explores the possible association between these two variables.MethodsThis study employed 2005 to 2010 National Health and Nutrition Examination Survey data. The CMI for each participant was determined by triglycerides, high-density lipoprotein cholesterol, and the waist-to-hip ratio. Multiple regression, smooth curve fitting, and threshold effect analyses were employed to investigate the association between CMI and BMF. The association’s stability across populations was assessed through subgroup analyses and interaction tests.ResultsThe study included 9,678 participants in total. After controlling for potential confounding variables, those in the uppermost CMI quartile had a 0.69 more increase in BMF than the bottom quartile (β = 0.69, 95% CI: 0.34, 1.03). The trend analyses showed that BMF increased steadily with the advancement of the CMI quartiles (P for trend < 0.0001). Associations between CMI and BMF were shown to be nonlinear through smooth curve fitting and threshold effect analyses. Specifically, when CMI ranged from 4.97 to 11.75, a negative connection was observed (β = -0.78, 95% CI: -1.33, -0.23), while positive associations were identified in other ranges. Subgroup analyses and interaction tests indicated significant CMI and BMF association variations when stratified by depression and age categories (P for interaction < 0.05).ConclusionsThis research indicates that CMI is generally associated with an increase in BMF. However, when CMI ranges from 4.97 to 11.75, it is associated with a BMF decrease. Notably, the association of CMI and BMF is more potent in young, middle-aged, and depressed people.

Construction of green technology model based on enterprise digital economy of environmental protection

The deteriorating environmental problems worldwide pose a serious challenge to human society and ecosystems, especially for enterprises that rely on natural resources. This article studies the construction of a green technology model for enterprise digital economy. Firstly, green technology innovation is combined with digital economy to construct a driving model for enterprise digital transformation. Then, regional green technology innovation capabilities are defined from three dimensions: input, output, and support capacity. Finally, advanced statistical methods such as level correlation and coefficient of variation analysis are used to rigorously evaluate and refine the green technology innovation indicator set, and a comprehensive evaluation index system for enterprise digital transformation is constructed. The experimental results show that the cumulative variance contribution rate of the four common factors before 2021 was 89.95%; the cumulative variance contribution rate of the four common factors before 2022 was 88.77%; the cumulative variance contribution rate of the four common factors before 2023 was 86.60%. Every year, the Man–machine Language index of the total GTI fluctuated between 0.980 and 1.128, with an average increase of 2.8%.

Deep learning based adaptive and automatic measurement of palpebral margin in eyelid morphology

Accurate anatomical measurements of the eyelids are essential in periorbital plastic surgery for both disease treatment and procedural planning. Recent researches in eye diseases have adopted deep learning works to measure MRD. However, such works encounter challenges in practical implementation, and the model accuracy needs to be improved. Here, we have introduced a deep learning-based adaptive and automatic measurement (DeepAAM) by employing the U-Net architecture enhanced through attention mechanisms and multiple algorithms. DeepAAM enables adaptive image recognition and adjustment in practical application, and improves the measurement accuracy of Marginal Reflex Distance (MRD). Meanwhile, it for the first time measures the Margin Iris Intersectant Angle (MIA) as an innovative evaluation index. Besides, this fully automated method surpasses other models in terms of accuracy for iris and sclera segmentation. DeepAAM offers a novel, comprehensive, and objective approach to the quantification of ocular morphology.

Innovation, human capital and economic growth in Turkish cities: do city dynamics play a role?

ABSTRACT This paper explores the role of city dynamics, such as growth rate, technology intensity, and population agglomeration, in the relationship between innovation, human capital, and economic growth in the case of 81 Turkish cities. A synthetic innovation index developed through principal component analysis (PCA) is employed as an innovation indicator. Based on the analysis conducted using annual data for the period 2004–2019, it has been determined that innovation and human capital positively impact economic growth. This positive effect is highest in metropolitan cities characterized by high technological density and population agglomeration. Furthermore, the influence of innovation becomes even stronger in metropolitan areas experiencing higher growth rates. While the relationship between human capital and growth follows a U-shaped pattern in metropolitan cities, no significant relationship is observed in medium-to-small-sized cities, except for those with slower growth rates.

Unveiling CO2 Emission Dynamics Under Innovation Drivers in the European Union

This study explores the complex relationship between innovation and carbon dioxide (CO2) emissions across the primary, secondary, and tertiary sectors within the 27 European Union (EU) member states over the period 2017–2023. Drawing on a comprehensive dataset and grounded in theoretical frameworks, the research investigates how different innovation indicators—including broadband penetration, digital skills, public and business R&amp;D expenditure, ICT training, and SME-driven innovations—affect sectoral CO2 emissions. Using robust regression, the findings reveal a nuanced landscape: while ICT skills training, human resource mobility in science and technology, and SME business process innovations are associated with significant reductions in emissions, certain R&amp;D investments and broadband penetration display positive correlations with emissions in specific service-oriented sectors. These results suggest that the environmental impacts of innovation are highly context-dependent and not uniformly positive. This study highlights the importance of strategically aligning innovation policies with sustainability objectives. Policymakers are encouraged to promote targeted digital training, support environmentally conscious R&amp;D, and foster SME-led innovation practices. The results contribute to the growing discourse on sustainable innovation and provide actionable insights to advance the EU’s green transition.

Ratio of red blood cell distribution width to albumin: a predictive biomarker of In-hospital mortality in heart failure patients

Background The ratio of red blood cell distribution width (RDW) to albumin (ALB), known as RAR, functions as an innovative indicator related to prognosis. However, whether RAR can predict the in-hospital mortality (IHM) for heart failure (HF) patients remains ambiguous. Methods This study included HF patients derived from the Medical Information Mart for Intensive Care III (MIMIC-III) and IV (MIMIC-IV) databases. To examine the association between RAR and IHM, multiple Logistic regression models were conducted, complemented by subgroup analyses. Additionally, to ascertain the optimal threshold for RAR, restricted cubic spline (RCS) regressions were applied. Results In the MIMIC-III (n = 9,413) and MIMIC-IV (n = 18,685) HF cohorts, the incidence of IHM was observed in 1,639 (17.41%) and 1,175 (6.29%) participants. Following adjustment for various covariates, RAR was shown to correlate with IHM (OR, 1.45 [95% CI, 1.08–1.39]). The areas under the curves for RAR were 0.683 (MIMIC-III) and 0.710 (MIMIC-IV), indicating superior predictive value than RDW and ALB. In subgroup analysis, younger HF patients with diabetes and without atrial fibrillation or anaemia showed higher ORs than older patients without diabetes or atrial fibrillation and those with anaemia, respectively. RCS indicated the OR for RAR was non-linear with IHM, and the optimal threshold for RAR prediction was between 4.5 and 5. Conclusion An elevated RAR correlates with an increased risk of IHM in HF patients. Given that RAR can be readily derived from routine laboratory tests, it holds potential as a novel biomarker for identifying high-risk HF patients.

Efficacy of interventions for myopia control in children: Asystematic review with network meta-analyses.

To determine the effectiveness of various interventions in reducing myopia progression in children. Literature databases were searched on December 2, 2023: PubMed, Embase, the Cochrane Central, Web of Science Core Collection, BIOSIS Previews, Current Contents Connect, Data Citation Index, Derwent Innovations Index, KCI-Korean Journal Database, Preprint Citation Index, ProQuest™ Dissertations and Theses Citation Index and SciELO Citation Index. PRISMA guidelines and the Cochrane Handbook recommendations were followed. All unique interventions were analyzed individually in order to generate clinically applicable results. The main outcome was axial length progression. Secondary outcomes were incident corneal infiltrates, photophobia, development of an allergic response towards the intervention, visual acuity at near and distance and drop-out from allocated intervention/control. We identified 74 RCTs involving 12 154 participants aged 6-18 years. Network meta-analysis compared axial length after 1 year between 45 interventions and placebo or single-vision spectacles. The most effective interventions reported in weighted mean difference and 95% confidence interval were low-level red-light (-0.33 mm (-0.40, -0.25)), ortho-K with 5 mm treatment zone (-0.32 mm (-0.41, -0.24)), ortho-K with aspheric base curve (-0.29 mm (-0.37, -0.22)), atropine 1.0% (-0.28 mm (-0.30, -0.26)), combined atropine 0.01% and ortho-K (-0.24 mm (-0.37, -0.11)), spectacles with highly aspherical lenslets (-0.23 mm (-0.26, -0.19)), ortho-K with increased compression factor (-0.23 mm (-0.28, -0.17), atropine 0.05% (-0.21 mm (-0.30, -0.13)) and defocus incorporated multiple segments spectacles (-0.21 mm (-0.27, -0.15)). Photophobia and reduced near-visual acuity were reported for atropine, and lower adherence to treatment was found for atropine at 1.0%. There was no significant association between any interventions and corneal infiltrates or allergic reactions. Over 70% of the studies were conducted in Asian populations. This systematic review and network meta-analysis highlights the efficacy of various interventions, including orthokeratology lenses, atropine, highly aspherical lenslets and defocus incorporated multiple segments spectacles in slowing axial elongation in children. Low-level red-light therapy also slowed axial length progression, but further research is needed to assess the potential side effects. Future studies should include diverse populations and standardized methodologies to enhance the applicability and comparability of results.

Financial potential analysis of rooftop rainwater harvesting systems in different regions of China

AbstractTo assess the financial viability of rooftop rainwater harvesting (RWH) systems nationwide, simulations were conducted on the financial performance of the proposed RWH system for 59 sample buildings in 36 cities across China. An innovative indicator, namely catchment fraction, has been introduced to evaluate the financial efficiency of the RWH systems. The minimum recommended catchment fractions were derived for each city. A graphical representation of the economic potential of different cities and an isopleth map dividing mainland China into 10 sub‐regions based on threshold catchment fractions were created to facilitate practical application. The results indicate that implementing RWH systems can be economically viable in buildings with catchment fractions exceeding the city‐specific recommended values. These findings aid investors and owners in making informed decisions regarding RWH projects and provide valuable insights for water‐related authorities to formulate legislation encouraging the adoption of the RWH system in China.