Explore the vast range of titles available.

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera Mycena and Marasmius, leveraging their unique ecological and morphological characteristics. The proposed approach integrates a custom convolutional neural network (CNN) with a self-organizing map (SOM) adapted for supervised learning and a Kolmogorov–Arnold Network (KAN) layer to enhance classification performance. The experimental results demonstrate significant improvements in classification metrics when using the CNN-SOM and CNN-KAN architectures. Additionally, advanced pretrained models such as MaxViT-S and ResNetV2-50 achieved high accuracy rates, with MaxViT-S achieving 98.9% accuracy. Statistical analyses using the chi-square test confirmed the reliability of the results, emphasizing the importance of validating evaluation metrics statistically. This research represents the first application of SOM in fungal classification and highlights the potential of deep learning in advancing fungal taxonomy. Future work will focus on optimizing the KAN architecture and expanding the dataset to include more fungal classes, further enhancing classification accuracy and ecological understanding.

PurposeBlockchain is expected to have a significant impact on Systems of Innovation as the new General Purpose Technology. The purpose of this study is to investigate how Blockchain can revolutionise the Systems of Innovation by investigating its overall structure, actors and relationships.Design/methodology/approachThis study used the systematic mapping method to explore and integrate the Blockchain and Systems of Innovation literature for the creation of a new conceptual model of Blockchain-enabled Systems of Innovation. In that scope, 37 Blockchain-related and 32 Systems of Innovation-related papers, besides two major books in the field of Blockchain, have been reviewed and then integrated based on the Systems Thinking approach.FindingsThe key findings for Blockchain-enabled Systems of Innovation are that there is (1) an increased distribution of networks and collaborations, (2) increased trust through the use of reputation systems, (3) an emerging new nature of platform characteristics, (4) a democratisation of entrepreneurship by the new funding landscape and (5) an increased significance of technological drivers, such as energy.Research limitations/implicationsThe study shows new Systems of Innovation-related research implications. Accordingly, a new type of actor, relationship and attribute has been introduced where the boundaries of the role definitions are blurred and more distributed. This is where larger organisations can expect to lose their central position. The different types of actors are replaced by a network of actors as a result of the distributed new Blockchain-based system. The threshold for the Bottom of the Pyramid is expected to be reduced, leading to a more democratised innovation system.Practical ImplicationsBlockchain appears to reduce the effects of distrust in collaborative innovation practices with its consensus mechanisms and the new Blockchain-enabled Systems of Innovation is expected to revolutionise the interactions in the future.Originality/valueThere are very few studies that have been found to integrate innovation management practices with Blockchain. This is the first Blockchain-based Systems of Innovation study enabling the fundamental revision of its structure, types of relationships and actors.

Purpose This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events. Design/methodology/approach This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach. Findings As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges. Research limitations/implications Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings. Practical implications Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data. Social implications The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges. Originality/value The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

Following financial technologies’ rapid growth, innovations and R&D are entering the insurance industry. The development of technologies such as smart sensors, artificial intelligence, and mobile technologies offers potential for disruptive innovations to revolutionise the industry. This paper identifies the key technological adoptions and R&D in the field of InsurTech considering the sources of innovations and actors through patentometrics. It analyses the main areas of granted patents and patent applications in this field. The present literature provides both quantitative and qualitative information about key areas, investments, and further financial data in the field, but a thorough patent analysis is not yet available. This study also contributes to the adoption and innovation theories. It outlines a new angle by examining the sources of innovations and technological adoptions through both academic and professional lenses. Patentometrics helps to identify the technological clusters and main actors globally. Patent applications can reveal potential future focus points. Hence, the analysis of patents can provide a more specific outlook than an extrapolation of historical financial data. In addition, interviews with experts are conducted to justify the results of the patentometrics and to collect further information. The results show how, in a low-tech environment, innovations occur with the support of third parties and industry-specific R&D and organisations. Interestingly, most adopters and innovators are within the insurance industry. Analysing InsurTech patent applications leads to the identification of R&D activities in the vehicle, health, and property insurance areas. These areas can represent the future trends of innovations in the InsurTech industry.

This research aims to analyse inter-organisational collaboration activities and mechanisms in general and also those specific to the nanotechnology field with the purpose of identifying their effectiveness and efficiency in innovation processes. This research proposes and adapts two new frameworks to be used in its analyses and also in future studies. Following these frameworks, this study investigates the nanotechnology patent network to identify many important aspects of it, such as: key actors, network structures and national differences. Some of the research objectives are: 1) to examine the key determinants of collaborative innovation mechanisms that encourage or hinder organisations to involve themselves in collaborative innovation activities; 2) to discover how the collaboration mechanism impacts the innovation process in the nanotechnology field; and 3) to analyse what kind of collaboration mechanisms exist at different stages of the innovation process. This comprehensive study applies a mixed-method approach that combines both patent and interview data analyses, and draws upon an extensive data sample. The patent data covers almost fifty thousand patent documents that are well-optimised for this study, and the interview sample covers the key experts across different regions. The main contributions of this research are theoretical, empirical and practical as well as methodological contributions to the field. The results of patent data analyses identify many key issues in this field, such as collaboration activities for key organisations, nations' competitiveness and innovation networks in the nanotechnology field. The results of interview analyses present a comparative study on the collaboration structures across the UK, the US, Germany and China. In addition, this research provides case studies of where inter-organisational collaborations resulted in innovations to illustrate successful scenarios for the nanotechnology field. The analysis of the nanotechnology innovation networks demonstrates that the proposed network model is a useful means of differentiating types of network structures such as mono-linkages, central-linkages and distributed network. With regard to the various stages of collaboration, new funding systems are found to be a mechanism for encouraging organisations to work together from the initial stage of collaboration. To enhance the collaboration mechanism, nanotechnology centres appear to be an effective place to overcome difficulties related to the multi-disciplinarity of nanotechnology. The results show that large organisations are currently the key organizations for the commercialisation process, especially in the electronics industry where high investments are required. SMEs appear to be the key organisations at the incremental level of innovations, and are essential to the expansion and up-scaling of the number of collaborations within the innovation network. The findings of this study are not only applicable to the nanotechnology field; this study could be beneficial within a wider range of social domains (academia, industry, government, intermediaries, fund providers and policy makers) where active and potential organisations are involved in technological collaboration. Moreover, this study differentiates its findings across the selected regions to show national factors, making this study even more valuable.

This study examines the adoption process of advanced manufacturing technologies (AMTs) and their relationship to different innovation types. Whilst the literature on AMT adoption is well established, there is a lack of studies examining the relationship between the AMT adoption process and innovation types as outcomes of this process. Our findings provide several contributions to the AMT literature. Firstly, we uncovered that manufacturers typically consider three main sub-stages during the evaluation stage of the adoption process. Secondly, through a qualitative method we describe the nature of barriers and enablers and their relevance to each adoption stage. Thirdly, we uncover the collaborative nature of the AMT adoption process where adopter firms may be involved in relationships with ecosystems of technology providers and other actors, suggesting the need to further examine these relationships. Lastly, we describe the differences of the AMT adoption process by the enabled innovation types and overall project outcomes.

In this research, which started with the need to examine the effects of enriching museums by using learning environments in terms of students' understanding of learning and their approaches to assimilation of new information, it was tried to measure how the visits to museums and museum education activities they participated in had an effect on the learning skills of secondary school students.In the research, one group is both pre-test post-test model, in one of the experimental research methods. This was applied by using the repeated measurements design. The situation of the significant difference in the approaches of the group to which the research was applied and examined.One of the most important structures on the basis of learning is making connections. It has been observed that the ability to relate and learn between the artifacts, tools and products created with the technological possibilities. we are developed according to the findings obtained from the students.According to the scale data, it was observed that the museum education increases the students' inner feelings and motivations such as interest, curiosity, and desire to research, and positively affected their learning skills.This situation increases the interest of individuals in the works and makes them feel the need to enrich their knowledge with different experiences.With the enrichment of teaching methods and techniques, the rate of using museums as an educational environment should be increased, as more permanent and qualified learning takes place.

Objectives Head and neck cancers (HNCs) represent a significant global health concern due to high morbidity and mortality rates. Despite therapeutic advances, the prognosis for advanced or recurrent cases remains challenging. Nivolumab obtained approval for recurrent or metastatic HNC based on the Phase III CheckMate 141 trial. This study aimed to evaluate the real-world outcomes of nivolumab in patients with non-nasopharyngeal HNC. Design In this multicenter retrospective study, we analyzed 124 patients with recurrent or metastatic non-nasopharyngeal HNC who received nivolumab in the second-line setting and beyond. Data were collected from 20 different cancer centers across Turkey. The effectiveness and safety of the treatment and survival outcomes were evaluated. Results Nivolumab exhibited favorable clinical responses, yielding an objective response rate of 29.9% and a disease control rate of 55.7%. Safety assessments revealed a generally well-tolerated profile, with no instances of treatment discontinuation or mortality due to side effects. Survival analysis disclosed a median overall survival (OS) of 11.8 (95% CI 8.4–15.2) months. Multivariate analysis revealed that ECOG-PS ≥ 1 (HR: 1.64, p = 0.045), laryngeal location (HR: 0.531, p = 0.024), and neutrophil-to-lymphocyte ratio > 3.5 (HR: 1.97, p = 0.007) were independent predictors of OS. Conclusions Nivolumab is an effective and safe treatment option for patients with recurrent or metastatic non-nasopharyngeal HNC in real-world settings. Further studies are needed on factors affecting response to treatment and survival outcomes.

The aim of this study is to investigate the effects of islet cells and mesenchymal stem cells transferred together in the amniotic membrane (AM) in order to preserve the viability and functionality of islet cells on the success of islet transplantation in diabetes mellitus-induced rats. A total of 80 male Wistar albino rats, aged 3.5-4 months, were included in this study. While 40 Wistar Albino rats were used for the process of islet cell isolation, 40 Wistar Albino rats were used to establish experimental groups. These rats were assigned to five experimental groups including eight rats in each. These groups were AM, amniotic membrane + mesenchymal stem cell (AM + MSC), amniotic membrane + islet cell (AM + IC), amniotic membrane + islet cell + mesenchymal stem cell (AM + IC + MSC), and sham groups. The study was concluded for 28 days. Although there was no significant difference between AM + IC and AM + IC + MSC groups in terms of mean blood glucose levels, both groups had statistically different values compared to the sham group. A significant difference was observed between the AM + IC and AM + IC + MSC groups in the c-peptide levels before and after transplantation. Immunohistochemical staining illustrated the presence of insulin-positive cells in both AM + IC and AM + IC + MSC groups. Moreover, BrDU (+) cells were determined in AM + IC and AM + IC + MSC groups using BrDU staining. The study results indicated that transplanting islet cells into the omentum by being packaged in AM preserved their viability and function, leading to significant effects on blood glucose and c-peptide levels.