Search Results (8)

As a crucial vehicle for green technological innovation, cooperative networks significantly promote resource integration and knowledge sharing. Yet, their dynamic evolution and micro-mechanism remain underexplored. Drawing on data from the joint applications of green invention patents between 2006 and 2021, this study constructed a multi-agent GTCIN involving multiple stakeholders, such as enterprises, universities, and research institutions, and analyzed the topological structure and evolutionary characteristics of this network; an exponential random graph model (ERGM) was introduced to elucidate its endogenous and exogenous driving mechanisms. The results indicate that while innovation connections increased significantly, the connection density decreased. The network evolved from a “loose homogeneity” to “core aggregation” and then to “outward diffusion”. State-owned enterprises in the power industry and well-known universities are located at the core of the network. Preferential attachment and transitive closure as endogenous mechanisms exert strong and continuous positive effects by reinforcing local clustering and cumulative growth. The effects of exogenous forces exhibit stage-specific characteristics. State ownership and regional location become significant positive drivers only in the mid-to-late stages. The impact of green innovation capability is nonlinear, initially promoting but later exhibiting a significant inhibitory effect. In contrast, green knowledge diversity exerts an opposite pattern, having a negative effect in the early stage due to integration difficulties that turns positive as technical standards mature. Geographical, technological, social, and institutional proximity all have a positive promoting effect on network evolution, with technological proximity being the most influential. However, organizational proximity exerts a significant inhibitory effect in the later stages of GTCIN evolution. This study reveals the shifting influence of endogenous and exogenous mechanisms across different evolutionary phases, providing theoretical and empirical insights into the formation and development of green innovation networks. Full article

The emergence of Multimodal Large Language Models (MLLMs) has made methods of artificial intelligence accessible to the general public in a conversational way. It offers tools for the automated visual assessment of the quality of a built environment for professionals of urban planning without requiring specific technical knowledge on computing. We investigated the capability of MLLMs to perceive urban environments based on images and textual prompts. We compared the outputs of several popular models—ChatGPT, Gemini and Grok—to the visual assessment of experts in Architecture, Engineering and Construction (AEC) in the context of a real estate construction project. Our analysis was based on subjective attributes proposed to characterize various aspects of a built environment. Four urban identities served as case studies, set in a virtual environment designed using professional 3D models. We found that there can be an alignment between human and AI evaluation on some aspects such as space and scale and architectural style, and more general accordance in environments with vegetation. However, there were noticeable differences in response patterns between the AIs and AEC experts, particularly concerning subjective aspects such as the general emotional resonance of specific urban identities. It raises questions regarding the hallucinations of generative AI where the AI invents information and behaves creatively but its outputs are not accurate. Full article

This study examines the characteristics of green startups and the progression of their resource utilization patterns during the pandemic emergency, focusing on case studies from the Startup Incubation and Innovation Centre (SIIC) of the Kanpur Indian Institute of Technology (IITK). This study explores how these startups overcame lockdown challenges and identified the resources they utilized throughout various development stages. The findings reveal that the green startups in the sample share characteristics of visionary and inventive startups, often lacking formal business education but possessing strong technical skills and social engagement. They rely heavily on human and social resources in the early stages, leveraging their networks and stakeholder support to define and develop their green innovations. The research also highlights the importance of open innovation strategies, particularly in the product development stage, where startups leverage research labs and expertise within the IITK ecosystem. This study increases the literature on green startups and offers practical recommendations for young green entrepreneurs, emphasizing the importance of operating in familiar industries, building stakeholder networks, and utilizing open innovation strategies for successful green innovation development. Full article

Climate change mitigation is one of the most important challenges facing the modern world. It is necessary to monitor the development of new concepts and technologies and take a stab at identifying disruptive innovations, which have the potential of becoming real climate-friendly game changers. The aim of this paper is to examine the patterns of inventive activity aimed at mitigating climate change in the maritime industry with respect to other transport modes. Appropriate research tools in the area of patent analysis were selected and utilised. A new class of patents related to climate change in maritime transport (CPC-Y02T70/00) was used as a data source. The original value of the study consists of offering a complete picture of the efforts made in patenting activity in climate change mitigation in the maritime transport, with a look at leading applicants and countries, knowledge flows, the most robustly developed and underdeveloped technical fields. A map of technical knowledge flows for climate change mitigation in transport was constructed. The research results show that inventions for the maritime industry are less hermetic than those for air and road transport; however, they are not as much linked with previously developed solutions. The most intensively developed technical fields include the design and construction of watercraft hulls (1) and measures to reduce greenhouse gas emissions related to the propulsion system (2). Among the technologies whose further development merits close attention are solutions related to electrical propulsion and wave energy. At the same time, inventive activity in the area of climate change adaptation dedicated to ports is insignificant and definitely needs more support from the community of scientists and inventors. Building knowledge based on patent information can help universities, research institutions, shipyards, manufacturers of marine equipment and other business entities to identify the technologies of the greatest potential for further development. Full article

A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions. Full article

In recent times, with the development of science and technology, new technologies have been rapidly emerging, and innovators are making efforts to acquire intellectual property rights to preserve their competitive advantage as well as to enhance innovative competitiveness. As a result, the number of patents being acquired increases exponentially every year, and the social and economic ripple effects of developed technologies are also increasing. Now, innovators are focusing on evaluating existing technologies to develop more valuable ones. However, existing patent analysis studies mainly focus on discovering core technologies amongst the technologies derived from patents or analyzing trend changes for specific techniques; the analysis of innovators who develop such core technologies is insufficient. In this paper, we propose a model for analyzing the technical inventions of applicants based on patent classification systems such as international patent classification (IPC) and cooperative patent classification (CPC). Through the proposed model, the common invention patterns of applicants are extracted and used to analyze their technical inventions. The proposed model shows that patent classification systems can be used to extract the trends in applicants’ technological inventions and to track changes in their innovative patterns. Full article

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations. Full article

The broad interdisciplinary nature of biocatalysis fosters innovation, as different technical fields are interconnected and synergized. A way to depict that innovation is by conducting a survey on patent activities. This paper analyses the intellectual property activities of the last five years (2014–2019) with a specific focus on biocatalysis applied to asymmetric synthesis. Furthermore, to reflect the inventive and innovative steps, only patents that were granted during that period are considered. Patent searches using several keywords (e.g., enzyme names) have been conducted by using several patent engine servers (e.g., Espacenet, SciFinder, Google Patents), with focus on granted patents during the period 2014–2019. Around 200 granted patents have been identified, covering all enzyme types. The inventive pattern focuses on the protection of novel protein sequences, as well as on new substrates. In some other cases, combined processes, multi-step enzymatic reactions, as well as process conditions are the innovative basis. Both industries and academic groups are active in patenting. As a conclusion of this survey, we can assert that biocatalysis is increasingly recognized as a useful tool for asymmetric synthesis and being considered as an innovative option to build IP and protect synthetic routes. Full article