Purpose This paper aims to digitally map the dynamic landscape of blue economy research and explore the potentials of bibliometric and data mining methodologies. It analyses the intersection of academic knowledge production and the financial resource allocation through the prisms of innovation and financial intermediation. Design/methodology/approach The study uses a double-methodological framework. The first consists of bibliometric methods using 1,070 publications from Scopus, analyzing co-offering key words, research trends and institutional productivity relating blue economy and finance. The second phase includes a data mining pipeline using linked data methodologies on the EU-funded blue economy projects from the CORDIS database using SPARQL. Stages include preprocessing, clustering, funding analysis and visual exploration of thematic and temporal trends. Findings The results show a strong alignment in the evolution between academic research and public funding priorities. Both analyses revealed an acceleration from 2013 – years on blue economy research, focusing on the topics of sustainable development, marine governance and technological innovation. Some strategic domains in EU projects, e.g. marine shipping, water cleaning and blue biotechnology, demonstrate a similar focus. At the same time, the thematic analyses revealed the imbalances of too strong and too weak thematic clusters, including re-search areas in marine tourism and coastal ecosystems. Research limitations/implications The bibliometric dataset focuses on the Scopus-indexed English publications, leaving out the potentiality of regional or policy-oriented papers. The funding analysis is exclusive for the EU projects, with a potential extension on a global scale. Potential work could include impact evaluations. Originality/value To the best of the authors’ knowledge, this paper is one of the first that systematically applies bibliometric and funding-mapping da-ta mining to explore the Blue Economy research-policy nexus. This paper combines scientific publication trends with EU project funding data to analyze the degree of match between research activity and financial support in relation to blue economy. The results are actionable for the policymakers, financing agencies and researchers willing to align financial instruments with sustainability-driven innovation in marine systems.

For many, video games represent a popular form of entertainment. However, numerous research studies confirm that playing video games is a complex process with a significant educational component in addition to entertainment. Several researchers, including Papert and other authorities, have argued that we can learn a great deal about the learning process through video games, either by playing them ourselves or by observing others play and discussing the processes and thinking strategies involved. The present paper aims to explore the potential of commercial off-the-shelf video games, particularly with regard to developing computational thinking. Five games representing different genres were analysed using standard content analysis. The analysis was based partly on the researchers’ own gameplay, but primarily on observing and interviewing other participants during their gameplay. Four experienced video game players, all adults aged between 26 and 32, were invited to join the study as part of a purposefully formed sample. They were observed while playing and engaged in conversations about their practices and thought processes. The goal was to identify cognitive processes perceived as intrinsically related to computational thinking. The findings support claims made by Papert and Gee, demonstrating that video games can significantly enhance our understanding of computational thinking itself. Based on the analysis, it was concluded that video games offer rich opportunities for the development of certain components of computational thinking, particularly algorithmic thinking, decomposition and evaluation, as well as generalisation and abstraction. The findings are primarily relevant to adult learners, but ideas for school-age students are also discussed. While considering these ideas, we noted another phenomenon that intriguingly aligns with our other area ofresearch, that is, the development of structural thinking within school informatics.1 1 In some countries, informatics is referred to as computer science or computing; however, in Slovakia, it is called informatics and is a mandatory school subject from Year 3 to Year 11, which includes all learners aged between 8 and 17.

<span>According to a research study by the National Institutes of Health, India, amagnetic resonance imaging (MRI) holds 89% diagnostic accuracy for acute stroke, while a computed tomography (CT) holds only 54%. Means there is still 11% area of improvement for accuracy measures required and there is 84% specific in identifying nerve enlargement. The possible solution is to use quantumcomputing; this is new era of technology in advanced design and implementation for computing techniques as compared with that of classical computers. With the goal of improving patient care, this is the area-of research using quantum technology to solve the neurological disorders. MRI and Microsoft’s quantum-inspired algorithms to enhance approach to detecting neurological disorders. To improve accuracy of MRI results in less time, an approach called magnetic resonance fingerprinting (MRF) was explored.This paper mainly focused on optimizing the sequence using Microsoft azure simulator. By generating an optimized pulse sequence and map to the accurate predefined patterns, able to create a solution that improves the diagnostic capability of MRI. Conventional computers will take long time to predict, but accuracy may alter. The proposed quantum-inspired optimization improved MRI diagnostic accuracy up to 92%, with faster sequence optimization compared to classical methods. This simulation-based proof of concept demonstrates potential for enhanced neurological disease detection while acknowledging current limitations such as simulator dependency and limited datasets.</span>

The role of graphene in rechargeable lithium batteries: Synthesis, functionalisation, and perspectives

To analyze the hot words of traditional Chinese medicine (TCM) research and the hotspots of overseas concern in 2020. With the core collection of Web of Science as the data source and 827 TCM-related concepts as the search terms, the research papers related to TCM were obtained through subject search, the authors’ keywords were extracted for frequency statistics, and the top 100 hot words in the field of TCM were obtained. The top 10 overseas hotspots are obtained through cluster analysis. Some suggestions are provided for the hot research areas in the field of TCM.

Chronic Obstructive Pulmonary Disease (COPD), a chronic lung disease that causes breathing difficulties and obstructs airflow from the lungs, has a significant global health burden and affects millions of people worldwide. The use of pharmaceuticals in COPD treatment is aimed to alleviate symptoms, improve lung function, prevent exacerbations, and enhance the overall quality of life for patients. Nanotechnology holds great promise to alleviate the burden of COPD. The main goal of this review is to present the full spectrum of therapeutics based on nanostructures for the treatment and management of COPD, including nanoparticles, polymeric nanoparticles, polymeric micelles, solid-lipid nanoparticles, liposomes, exosomes, nanoemulsions, nanosuspensions, and niosomes. Nanotechnology is just one of the many areas of research that may contribute to the development of more effective and personalized treatment modalities for COPD patients in the future. Future studies may be focused on enhancing the therapeutic effectiveness of nanocarriers by conducting extensive mechanistic investigations to translate current scientific knowledge for the effective management of COPD with little or no adverse effects.

Abstract The paper reflects on the value of linking criminological research on atrocity with that on serious economic crime. The two areas of criminological research are outlined briefly, before common challenges around complexity and interdependence are set out. An example of a criminal career encompassing both atrocity and serious economic criminality is put forward to support claims that atrocity and economic crime can usefully be studied together. Three further examples of research are discussed to show the possible merits of bringing together two criminological strands. Ultimately, studying the two forms of criminality together would respect the lived experience of victims, who see firsthand how atrocity and serious economic crime go hand in hand.

The process of producing the metallic nanoparticles (MNPs) in a sustainable and environment- friendly process is very desirable due to environmental hazards posed by climatic changes. Biomedical one of the fields classified under nanoscience, nanoparticles have a potential synthetic application, which makes it a vast area of research. These particles can be prepared using chemical, physical, and biological methods. One of the methods of synthesis of nanoparticles is by the use of plant extracts, known as green synthesis. Because of its low cost and nontoxicity, it has gained attention in recent times. This review was conducted to find the possible outcomes and uses of metallic nanoparticles synthesized using different parts like gum, root, stem, leaf, fruits, etc. of Azadirachta indica (AI). AI, a popular medicinal plant commonly known as neem, has been studied for the green synthesis of NPs by using the capping and reducing agents secreted by the plant. Various phytochemicals identified in neem are capable of metal ion reduction. Green synthesis of NPs from neem is an eco-friendly and low-cost method. These NPs are reported to exhibit good antimicrobial activity. The review covers the preparation, characterization, and mechanism associated with the antibacterial, anticancer, and neurological diseases of the MNPs. Furthermore, the limitations associated with the existing NPs and the prospects of these NPs are also examined.

Abstract Self-adaptive systems are designed to modify their architecture orbehavior to uphold high-level objectives despite changes in theiroperating environments. A critical aspect of developing such systemsinvolves creating strategies to handle unexpected events inthe operating environments. While this remains an active area ofresearch within the autonomic computing and self-adaptive systemscommunity, one commonly adopted approach is leveragingmachine learning techniques, particularly reinforcement learning,to address unforeseen challenges. In this paper, we conduct experimentsusing the EmergentWeb Server exemplar, a publicly availableself-adaptive web server, to investigate various monitoring metricsand implement a multi-armed bandit reinforcement learningapproach. This approach enables the system to identify the optimalweb server configuration for maximizing performance undervarying workload patterns and operating conditions, enabling thesystem to react to unexpected events that rises from the operatingenvironment with minimum human interference.

Mindfulness and self-compassion are two constructs positively related to well-being and mental health outside sport. Within sport, these constructs are emerging in research, yet the extant work has primarily been conducted with athlete samples. The aim of this scoping review was to provide a broad synthesis of the literature on mindfulness and self-compassion among coaches. Fourteen articles were included, 11 of them published 2019–2022. Of the 14 publications, the concepts studied were mindfulness (n = 10), self-compassion (n = 2), and a combination of both (n = 2). The samples were predominantly male coaches (68.7%), and most of the studies targeted coaches at the elite or competitive level. The most common area studied was developing and testing interventions and programs, followed by depicting relationships of mindfulness or self-compassion with desirable outcomes. This review significantly extends the current knowledge by illuminating critical issues in this rapidly moving area of research; the need for conceptual and contextual clarity of mindfulness and self-compassion; methodological considerations, such as measures that may allow reliable comparison across studies; and the need to further explore the potential benefits of mindfulness and self-compassion for coaches for sustainability and performance.

Background: Acute lung injury (ALI) is a life-threatening condition characterized by severe invasion of inflammatory cells, lung edema, and the development of intestinal fibrosis. The activation of proinflammatory cytokines like TNF-α, IL-6, and others results in the development of several risk factors for ALI. It has been observed that no viable therapies for lung injuries exist. Therefore, there is a significant need for healthcare requirements. However, few effective nonpharmacological and pharmacological treatments are available, which may have assisted doctors in reducing the likelihood of illness development. Still, not much progress has been made in illness management. Objectives: This review aimed to briefly discuss pharmacological and non-pharmacological approaches for treating ALI. Methods: Nowadays, drug delivery and illness diagnosis are the most advanced areas of modern nanotechnology research, particularly concerning the lungs. So, we focused on various novel approaches, viz., organic nanoparticles, inorganic nanoparticles, metal nanoparticles, and bio nanoparticles, that combat ALI and improve lung functions. This review discussed many studies and the advancement of different nanomaterials as novel drug carriers in the lungs that can influence the immune system, suppressing proinflammatory cytokines and improving lung functions. Results: Another aspect of studying nanotechnology is the release kinetics of nanoparticles and safety when administered to a targeted tissue. Conclusion: The higher uptake of nanomaterials and, thus, the drugs is another advancement in nanotechnology. Herein, we explored different approaches to improving and curing acute lung injury.

Intra-articular calcaneus fractures are associated with a high rate of malunion resulting in hindfoot and ankle impingement, peroneal tendinitis, sural neuritis, arthritis, hindfoot varus or valgus deformity, loss of hindfoot height, and resulting gastrocnemius-soleus complex weakness and gait dysfunction. These issues can arise whether the patient’s injury is treated conservatively with non–weight-bearing precautions, neglected/missed, or treated surgically with open reduction and internal fixation. The sequelae of calcaneal malunions can arise early or late in this process. For patients undergoing subtalar arthrodesis following calcaneus fracture malunion, the use of a bone block wedge can assist with achieving a stable, well-aligned, pain-free hindfoot. Patient selection, preoperative planning, careful surgical technique, and compliance with postoperative care pathways are critical to optimize patient outcomes and healing of this complex reconstruction. Recent evidence supports the utility of weight-bearing CT to assist with preoperative planning for subtalar arthrodesis. Wedges used in subtalar distraction arthrodesis include bulk autograft (tricortical iliac crest), allograft (pre-fashioned or intraoperatively fashioned wedges from femoral head allograft or other source), and metallic wedges. These wedges are typically secured with screws spanning and compressing the arthrodesis site, which is filled with a mixture of concentrated bone marrow aspirate, bone growth factors, and allograft bone. Concomitant procedures may be indicated to ensure the overall success of the procedure, such as extra-articular, calcaneal osteotomy for additional realignment purposes and/or arthrolysis. A review of the literature shows high rates of union following subtalar bone block arthrodesis with improved functional outcomes. Our preferred technique for a bone block distraction subtalar arthrodesis, described herein, includes the use of an allograft wedge. Ongoing advances, such as the development of novel materials and structures for use in subtalar bone block arthrodesis, but most importantly, the incorporation of weight-bearing CT technology and potential for preoperative CT navigation to make this procedure more predictable, are all important areas for further exploration and future research. Level of Evidence: Level—IV.

Abstract: The transformation of ordinary fabrics into self-healing (SH) or intelligent fabric is now emerging as an important research area. SH fabrics are capable of maintaining their structural and functional integrity over an extended period of time, while also offering protection and aesthetic appeal. Similar to the biological mechanism of healing the superficial damages caused to a living being, smart fabrics are created to have sensitivity towards the damage-causing factors and act accordingly to overcome the damages. Polymers, owing to their versatile properties, are a preferred group of materials to serve the demands of various technology domains. Polymer-based SH systems for fabrics are being extensively studied for their highly beneficial applications in the world of fabrics. The current paper analyzes the innovation trends of polymer- based SH fabrics from a patent perspective. These fabrics are used for protective clothing, wearables, and other advanced applications. Newer material systems or designs are adopted to incorporate the auto-repairing ability in fabrics in response to damages. The SH functionality ensures durability and an extended lifespan for the fabrics. Innovation trend analysis indicated a steady positive growth trajectory for these materials holding a promising future.

Abstract: Blockchain technology is a decentralized, peer-to-peer network-based public, en-crypted, and immutable digital federated ledger system with various applications in the digi-tal economy. This paper examines how blockchain technology can strengthen intellectual property rights (IPR) in the digital age and the potential challenges and risks associated with this use. The paper analyses the advantages and disadvantages of blockchain technology for confirming and protecting IPR, such as transparency, security, efficiency, and scalability, as well as issues such as legal uncertainty, interoperability, and governance. The paper also ex-plores the implications of blockchain technology for different types of IPR, such as patents, and copyrights, and how it can facilitate innovation, licensing, and enforcement. The paper concludes by providing recommendations for improving the legal and regulatory framework for IPR management using blockchain technology and identifying some areas for further re-search.

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring unfair decision-making, the AI community has concentrated efforts on correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI . In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy that builds upon previous proposals but is tailored for deep learning research to better organize the literature on debiasing methods for fairness. We review all important neural-based methods and evaluation metrics while discussing the current challenges, trends, and important future work directions for the interested researcher and practitioner.

Rheumatoid arthritis (RA) is an autoimmune disease with no known cure that results in joint deformities and dysfunction, significantly impacting the quality of life of patients. The abnormal NF-κB signaling pathway in RA has emerged as a crucial research area for the development of RA therapies, with non-coding RNAs (ncRNAs) serving as a potentially meaningful avenue to regulate it. Thus, understanding the role of ncRNAs in RA and the identification of new therapeutic targets have become pressing issues in the field. In this review, we aim to summarize recent studies on ncRNAs that regulate the NF-κB signaling pathway in RA, including miRNAs, lncRNAs, and circRNAs, as well as the mechanisms by which drugs modulate NF-κB activity. By highlighting these recent advances, we hope to promote further research into targeted RA therapy and provide novel directions and ideas for researchers in the field.

The study is devoted to the consideration of cybersecurity as a guideline for methods and reflection of current trends in ensuring economic security. To achieve the purpose of the article, modern trends in information protection and methods of ensuring cybersecurity are analyzed. Through the prism of signals and indicators of economic security, the contour of the influence of its components on the process of diagnosis and provision, attention is focused on the methodological and organizational features of solving existing problems in the subject area of re search.

Drug development is a complex and expensive process that involves extensive research and testing before a new drug can be approved for use. This has led to a limited availability of potential therapeutics for many diseases. Despite significant advances in biomedical science, the process of drug development remains a bottleneck, as all hypotheses must be tested through experiments and observations, which can be timeconsuming and costly. To address this challenge, drug repurposing has emerged as an innovative strategy for finding new uses for existing medications that go beyond their original intended use. This approach has the potential to speed up the drug development process and reduce costs, making it an attractive option for pharmaceutical companies and researchers alike. It involves the identification of existing drugs or compounds that have the potential to be used for the treatment of a different disease or condition. This can be done through a variety of approaches, including screening existing drugs against new disease targets, investigating the biological mechanisms of existing drugs, and analyzing data from clinical trials and electronic health records. Additionally, repurposing drugs can lead to the identification of new therapeutic targets and mechanisms of action, which can enhance our understanding of disease biology and lead to the development of more effective treatments. Overall, drug repurposing is an exciting and promising area of research that has the potential to revolutionize the drug development process and improve the lives of millions of people around the world. The present review provides insights on types of interaction, approaches, availability of databases, applications and limitations of drug repurposing.

Computational and In vitro Elucidation of Indolenine-barbituric Acid Zwitterions as Potential Chemotherapeutical Agents

Graph-based semisupervised learning can explore the graph topology information behind the samples, becoming one of the most attractive research areas in machine learning in recent years. Nevertheless, existing graph-based methods also suffer from two shortcomings. On the one hand, the existing methods generate graphs in the original high-dimensional space, which are easily disturbed by noisy and redundancy features, resulting in low-quality constructed graphs that cannot accurately portray the relationships between data. On the other hand, most of the existing models are based on the Gaussian assumption, which cannot capture the local submanifold structure information of the data, thus reducing the discriminativeness of the learned low-dimensional representations. This article proposes a semisupervised subspace learning with adaptive pairwise graph embedding (APGE), which first builds a -nearest neighbor graph on the labeled data to learn local discriminant embeddings for exploring the intrinsic structure of the non-Gaussian labeled data, i.e., the submanifold structure. Then, a -nearest neighbor graph is constructed on all samples and mapped to GE learning to adaptively explore the global structure of all samples. Clustering unlabeled data and its corresponding labeled neighbors into the same submanifold, sharing the same label information, improves embedded data's discriminative ability. And the adaptive neighborhood learning method is used to learn the graph structure in the continuously optimized subspace to ensure that the optimal graph matrix and projection matrix are finally learned, which has strong robustness. Meanwhile, the rank constraint is added to the Laplacian matrix of the similarity matrix of all samples so that the connected components in the obtained similarity matrix are precisely equal to the number of classes in the sample, which makes the structure of the graph clearer and the relationship between the near-neighbor sample points more explicit. Finally, multiple experiments on several synthetic and real-world datasets show that the method performs well in exploring local structure and classification tasks.