Sustained economic growth has been high on the list of priorities of many developed and developing nations, and the importance of new technology to sustained economic growth has long been recognized in the literature....
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Sustained economic growth has been high on the list of priorities of many developed and developing nations, and the importance of new technology to sustained economic growth has long been recognized in the literature. Despite the extent of this literature and the importance of innovation to economic outcomes, surprisingly little is known about the effect of women's political empowerment in the research sector. This study proposes filling this gap by examining the effect of women's political empowerment on research and development expenditure. Using data from 66 developed and developing countries and system GMM estimation, we show that women's political empowerment has exerted, on average, a positive effect on research and development investment. Moreover, further study shows that women's level of participation in civil society has the largest effect, followed by women's civil liberties. By analyzing these underlying transmission channels, we find that this effect is partly due to the positive effects of women's political empowerment on the accumulation of human capital and on institutional quality, which in turn improve the level of spending on research and development. Based on these results, economic policy implications are discussed.
The conflict between attaining economic growth and lowering carbon dioxide (CO2) emissions is a significant barrier to sustainable development. While the Environmental Kuznets Curve (EKC) hypothesis predicts that emis...
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The conflict between attaining economic growth and lowering carbon dioxide (CO2) emissions is a significant barrier to sustainable development. While the Environmental Kuznets Curve (EKC) hypothesis predicts that emissions ultimately drop with economic maturity, the timing and direction of this turning point remain inconsistent across countries. Drawing on insights from the Environmental Kuznets Curve (EKC), this study examines the relationship between economic growth and carbon dioxide (CO2) emissions while considering the contingent effect of investment in research and development (R&D) and how it affects the trajectory and the turning point of CO2 emissions over time. Using panel data from a sample of about 525 observations from 25 emerging markets for the period 2000-2020 and fixed effects, and the difference and system generalised method of moments (GMM) method, we find that R&D investments positively moderate the relationship between economic growth and CO2 emissions at the initial stage and negatively at the later stage of development. The analysis also revealed that investment in research activities reduce the turning point of the EKC, supporting the idea that technological progress can decouple economic growth from environmental degradation. We further find that increasing expenditure on R&D activities does not invalidate the EKC, though it changes the trajectory of pollution over time with economic growth. Energy consumption and FDI increase pollution, while renewable energy use reduces it in the emerging economies analysed. Our findings are robust and consistent with different estimation approaches. Policymakers should prioritise funding for R&D for environmental innovation to alleviate the environmental impact of economic growth, especially in the early stages of development.
Drug research and development (drug R&D) is a sophisticated, cost-intensive, and time-consuming procedure with historically low success rates. The advent of Artificial Intelligence (AI) technologies has introduced...
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Drug research and development (drug R&D) is a sophisticated, cost-intensive, and time-consuming procedure with historically low success rates. The advent of Artificial Intelligence (AI) technologies has introduced innovative methods into drug R&D, particularly by leveraging AI capabilities. Large language models (LLMs), a breakthrough in generative AI, have revolutionized drug discovery. With their extensive datasets, numerous parameters, and strong multitasking abilities, LLMs have significantly improved efficiency across various related domains, providing unparalleled support to drug R&D. These models have facilitated a deeper understanding of intricate disease mechanisms and the identification of novel therapeutic strategies, ushering in a new era in drug development and clinical applications. As a result, the advancement of LLMs is poised to drive significant transformations in drug R&D, emphasizing the importance of effectively leveraging this technology. This review provides insights into the architecture and characteristics of LLMs, explores their applications in drug R&D, and highlights their research implications in bioinformatics data, including proteins, genes, and chemical compounds. Furthermore, it investigates the practical strategies of LLMs in drug discovery, drug repositioning, and clinical inquiries, presenting an innovative approach to research and future advancements in this field.
Smoke control technology is crucial in the risk management of fire in underground confined spaces. In this study, the integrated foam smoke reduction device (FSRD) of “foaming-spraying” is proposed in response to th...
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Smoke control technology is crucial in the risk management of fire in underground confined spaces. In this study, the integrated foam smoke reduction device (FSRD) of “foaming-spraying” is proposed in response to the challenging problem of smoke control of fire in underground confined spaces. The overall structure of FSRD device is designed and optimized through the numerical simulation of the internal flow field and experimental research. The optimal basic parameters are as follows: area ratio ( $R_{m}$ ) of 20.25, pitch-to-diameter ratio ( $R$ td ) of 1/3 or 5/9, and throat diameter (d 3 ) of 18 mm. Additionally, the throat-nozzle distance (L t ) is 12 mm, and the operating pressure (Po) is 3.0 MPa. Finally, the smoke reduction experiment verified that the FSRD designed in this study had a better effect of elimination than fine water mist and cylindrical foam jet, with the elimination efficiency reaching 36.73%. This study provides a new device for smoke reduction in controlling fires in underground confined spaces.
This paper analyzes the relationship between capitalized research and development (R&D) expenditures under IFRS and innovation performance measured by patent data. Under IFRS, development expenditures are capitali...
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This paper analyzes the relationship between capitalized research and development (R&D) expenditures under IFRS and innovation performance measured by patent data. Under IFRS, development expenditures are capitalized when the success of an R&D investment is highly likely. Hence, such capitalization could be a leading indicator for future innovation performance. We analyze this question based on a hand-collected sample of R&D capitalization data under IFRS and patent data from the European Patent Office's Worldwide Patent Statistical Database. We find that the capitalization rate of R&D is positively related to future patent applications and citations as measures of future innovation performance. We also find a positive association with measures of future financial performance. The results imply that the rate of R&D capitalization is informative and can be considered a leading indicator for future innovation performance.
Fire has become a serious threat to both human life and the natural environment. Traditional fire alarm systems often face challenges during deployment, especially in large areas or with complex terrain. Some methods ...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
Fire has become a serious threat to both human life and the natural environment. Traditional fire alarm systems often face challenges during deployment, especially in large areas or with complex terrain. Some methods of detecting fire risks apply artificial intelligence integrated into intelligent fire alarm systems on shore. In addition, the application of artificial intelligence on modern ships is increasingly popular for many purposes, such as enhancing maritime safety and minimizing accidents and risks during operations at sea. This paper studies and tests an early fire warning system based on Artificial Neural Network (ANN) to minimize damage related to fires on ships. The system combines temperature, humidity, and Carbon Monoxide (CO) and Carbon Dioxide (CO 2 ) sensors, all collected through a microcontroller. The environmental data collected from these sensors is processed and analyzed using a predictive model built in MATLAB. The research results demonstrate that the model achieves high accuracy and provides rapid response, significantly reducing the time required to detect potential fire hazards and thus minimizing damage.
With the widespread application of loaders in engineering construction and mining operations, the safety of loaders has received increasing attention. Lateral rollover accidents are one of the important issues affecti...
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ISBN:
(数字)9798331529482
ISBN:
(纸本)9798331529499
With the widespread application of loaders in engineering construction and mining operations, the safety of loaders has received increasing attention. Lateral rollover accidents are one of the important issues affecting the safety of loader operations, which may cause casualties and equipment damage in severe cases. To this end, this paper proposes a loader lateral rollover warning technology based on multi-sensor data fusion. Through real-time monitoring of the loader's tilt angle, speed, center of gravity position and other information, combined with mathematical models to predict the rollover risk, and then provide a warning signal. The experimental results show that the technology has high warning accuracy and strong practical application value.
The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed ...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes.
This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweigh...
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ISBN:
(数字)9798331532635
ISBN:
(纸本)9798331532642
This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it.
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