Revolutionary Innovation Research Articles

Unlocking the Riddles of Imperial Greek Melodies: The ‘Lydian’ Metamorphosis of the Classical Harmonic System

Abstract Building upon Lynch 2022a and 2022b, this article offers the first account of the historical evolution of the Greek harmonic system and notation keys (tónoi) that bridges the gap between Classical and Imperial music. This new solution allows us to reconstruct, for the first time, a continuous, if evolving, tradition that stretches from Euripides’ Orestes to late antiquity, reconciling key theoretical insights provided by Ptolemy, Porphyry and others with documentary evidence that illustrates the structure of the Imperial harmonic system and its use in the Imperial musical documents (dDAGM). This approach also enables us to trace the gradual expansion of the Greek notation system from an initial set of symbols (–) to the full array recorded by Aristides and Alypius, mapping its development onto key historical milestones including the revolutionary innovations of the New Musicians and Damon of Oa’s inclusion of the Lydian mode into the Greek modulation system.

Unlocking the potential of photon counting detector CT for paediatric imaging: a pictorial essay.

Recent advancements in CT technology have introduced a revolutionary innovation to practice known as the Photon-Counting detector (PCD) CT imaging. The pivotal hardware enhancement of the PCD-CT scanner lies in its detectors, which consist of smaller pixels than standard detectors and allow direct conversion of individual X-rays to electrical signals. As a result, CT images are reconstructed at higher spatial resolution (as low as 0.2 mm) and reduced overall noise, at no expense of an increased radiation dose. These features are crucial for paediatric imaging, especially for infants and young children, where anatomical structures are notably smaller than in adults and in whom keeping dose as low as possible is especially relevant. Since January 2022, our hospital has had the opportunity to work with PCD-CT technology for paediatric imaging. This pictorial review will showcase clinical examples of PCD-CT imaging in children. The aim of this pictorial review is to outline the potential paediatric applications of PCD-CT across different anatomical regions, as well as to discuss the benefits in utilizing PCD-CT in comparison to conventional standard energy integrating detector CT.

APAAACI 2023 International Conference: the innovation revolution in allergy, asthma, and immunology

APAAACI 2023 International Conference: the innovation revolution in allergy, asthma, and immunology

Magneto‐Responsive Polymeric Soft‐Shell‐Based Capsule Endoscopy for High‐Performance Gastrointestinal Exploration via Morphological Shape Control

Capsule endoscopy is a valuable tool for diagnosis of the gastrointestinal tract. As a result, active‐control capsule endoscopy, which can be controlled via external stimulation, has emerged as a prominent diagnosis tool, unlike traditional passive capsule endoscopy. However, owing to its rigid cover, the high rotating radii limit its observation range at anchor points, such as polyps. There are also persistent challenges with pass‐through in narrow spaces in the intestines. This study proposes a soft‐shell capsule endoscope (SSCE) to improve this limitation. Experiments ranging from virtual phantoms to ex vivo investigations in the stomach, small intestine, and large intestine were conducted to ensure comprehensive coverage of the entire digestive system by the SSCE. Additionally, in the stomach and intestine experiments, we compared the performances of the SSCE and hard‐shell capsule endoscope (HSCE) to highlight the advantages of the SSCE. The results demonstrate that the SSCE achieves a 37.3% increase in the observable angles through bending motions at the anchor point compared to the HSCE, in addition to increased efficiency of pass‐through in the intestines. This revolutionary innovation is expected to significantly impact and promote high efficiency via pass‐through and the observation area in the clinical application of capsule endoscopy.

Artificial intelligence in breast imaging: potentials and challenges.

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

3D-bioprinted anisotropic bicellular living hydrogels boost osteochondral regeneration via reconstruction of cartilage–bone interface

Reconstruction of osteochondral (OC) defects represents an immense challenge due to the need for synchronous regeneration of special stratified tissues. The revolutionary innovation of bioprinting provides a robust method for precise fabrication of tissue-engineered OCs with hierarchical structure; however, their spatial living cues for simultaneous fulfilment of osteogenesis and chondrogenesis to reconstruct the cartilage-bone interface of OC are underappreciated. Here, inspired by natural OC bilayer features, anisotropic bicellular living hydrogels (ABLHs) simultaneously embedding articular cartilage progenitor cells (ACPCs) and bone mesenchymal stem cells (BMSCs) in stratified layers were precisely fabricated via two-channel extrusion bioprinting. The optimum formulation of the 7% GelMA/3% AlgMA hydrogel bioink was demonstrated, with excellent printability at room temperature and maintained high cell viability. Moreover, the chondrogenic ability of ACPCs and the osteogenic ability of BMSCs were demonstrated invitro, confirming the inherent differential spatial regulation of ABLHs. In addition, ABLHs exhibited satisfactory synchronous regeneration of cartilage and subchondral bone invivo. Compared with homogeneous hydrogels, the neo-cartilage and neo-bone in ABLHs were augmented by 23.5% and 20.8%, respectively, and more important, a more harmonious cartilage-bone interface was achieved by ABLHs due to their well-tuned cartilage-bone-vessel crosstalk. We anticipate that such a strategy of tissue-mimetic ABLH by means of bioprinting is capable of spatiotemporal cell-driven regeneration, offering insights into the fabrication of anisotropic living materials for the reconstruction of complex organ defects.

KKN PEMBERDAYAAN MASYARAKAT MELALUI PENGUATAN LEMBAGA DESA DAN UMKM BERBASIS IPTEK DESA PANDANARUM

Real Work Lectures are the implementation of lectures in the form of community service. Students contribute and empathize with the conditions of society and are required to be able to solve existing problems. Activities carried out in Pandanarum hamlet, Pandanarum Village, Sutojayan District, Blitar Regency, use service methods in the form of counseling, socialization and group mentoring. The 2023 Real Work Lecture focuses on community empowerment, which is an effort to shape and prepare society towards continuous progress, independence, prosperity and social justice. The results obtained show a good level of success because they are in accordance with the level of active community participation, one of which was a positive response, including taking part in student socialization regarding stunting and digital marketing assistance for MSMEs through the village institution BUMDes Arum Jaya. Can obtain: (1) Increased knowledge of one of the village institutions in the form of knowledge to overcome stunting which is packaged in the RIAS (Innovation Revolution to Overcome Stunting) program; (2) Implementation of direct nutritional improvement through a free fishing program for communities, especially those classified as stunting data; (3) Real contribution in advancing the marketing of MSME products in Pandanarum Village through Shopee marketing.

The Evolutionary Characteristics and Interaction of Interdisciplinarity and Scientific Collaboration under the Convergence Paradigm: Analysis in the Field of Materials Genome Engineering

Convergence has been proposed as a revolutionary innovation paradigm that advocates the integration of multidisciplinary knowledge through collaboration to solve complex real-world challenges. From a knowledge perspective, this study examined the evolutionary characteristics and interactions between interdisciplinarity and scientific collaboration in the context of the convergence paradigm using complex networks and bibliometric methods for publications (n = 35,227) in the materials genome engineering (MGE) field in China from 2000 to 2021. The findings are as follows: (1) Under the convergence paradigm, knowledge from five core disciplines forms the skeleton of the multidisciplinary knowledge system in the MGE field. The goal of interdisciplinarity gradually evolves from theoretical exploration to applied research, and the knowledge from various disciplines is increasingly integrated. (2) The development of the scientific collaboration network has gone through three phases: 2000–2009, 2005–2014, and 2015–2021, and its core-periphery structure has been gradually optimized. (3) The evolution of interdisciplinarity is nearly synchronized with the evolution of the scientific collaboration network. (4) The promotion of interdisciplinarity through collaboration is becoming increasingly evident. The proportion of interdisciplinary partnerships increased from 0.66 to 0.87, with the proportion of partnerships involving more than two disciplines increasing from 0.24 to 0.59. (5) Institutions from core and periphery disciplines have diverse partner selection preferences, and disciplinary characteristics related to knowledge similarity and complementarity are important factors influencing scientific collaboration behavior. This study contributes to a more comprehensive understanding of the convergence paradigm and provides insights for better incubating convergence research projects and advancing top-down innovation management in convergence fields.

STUDY OF PRAGMATIC DEVELOPMENT AND KNOWLEDGE DOMAIN THROUGH WEB SERIES AMONG YOUTH

Web series is an innovative revolution of digital world and become a new trend setter in entertainment industry. It‘s displayed over digital platforms and provides opportunity to deliver the structure of new text as well as multi-dimension of media and technology that construct modern network. On comprehensive learning perspective, web series programs are responsible for developing new breed and transferring knowledge, new aesthetic and semiotic that producing pragmatic values towards the viewers. Likewise, pattern of accessing and watching of web series is responsible for knowledge dissemination and pragmatic development. This study highlights the attributes that reflects knowledge domain towards viewers through watching perspective web series. Study is consolidated with primary data sources and executed in Bhopal and Indore, cities planning under Smart Cities Mission Strategy.

Adversarial Attack and Defense in Breast Cancer Deep Learning Systems.

Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women's health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on natural images are vulnerable to attacks that can lead to errors in recognition and classification, raising security concerns about deep systems based on medical images. We used the adversarial attack algorithm FGSM to reveal that breast cancer deep learning systems are vulnerable to attacks and thus misclassify breast cancer pathology images. To address this problem, we built a deep learning system for breast cancer pathology image recognition with better defense performance. Accurate diagnosis of medical images is related to the health status of patients. Therefore, it is very important and meaningful to improve the security and reliability of medical deep learning systems before they are actually deployed.

Fintech and Start-ups: A Systematic Literature Review

Fintech is the application of the technologies of the fourth industrial revolution in the financial field. In recent years, it has aroused growing interest from various financial actors and specialties, thanks to its relevant characteristics such as information security, increased performance, and decentralization. Despite the positive aspects of fintech, there are some trends in its application that require overcoming certain problems such as regulation, technological scalability, and hybridization. Therefore, a systematic overview is considered necessary in which information is collected to provide an overview of the state of the art related to the application of fintech and start-ups to serve as a reference for future research in this field. To this end, a systematic literature review was conducted using the PRISMA protocol in combination with the Tree of Science (ToS) algorithm. Articles published between 2016 and 2021 were found in the Web of Science, Google Scholar, Scopus and IEEE Xplore databases, where the Tree of Science (ToS) algorithm was then applied to identify the most relevant literature on the topic. As a result, 72 articles were analyzed. The analysis of the selected articles provided valuable information and answered the research questions posed in the PRISMA protocol. The results indicate as research trends on the topic: business financing, innovation and financial information, financial technology and finance and regulation. Fintech is a revolutionary innovation that creates opportunities in the market and in the financial world, which is why prototypes are being developed worldwide.

Effects of Inflammatory Cell Death Caused by Catheter Ablation on Atrial Fibrillation.

Atrial fibrillation (AF) poses a serious healthcare burden on society due to its high morbidity and the resulting serious complications such as thrombosis and heart failure. The principle of catheter ablation is to achieve electrical isolation by linear destruction of cardiac tissue, which makes AF a curable disease. Currently, catheter ablation does not have a high long-term success rate. The current academic consensus is that inflammation and fibrosis are central mechanisms in the progression of AF. However, artificially caused inflammatory cell death by catheter ablation may have a significant impact on structural and electrical remodeling, which may affect the long-term prognosis. This review first focused on the inflammatory response induced by apoptosis, necrosis, necroptosis, pyroptosis, ferroptosis and their interaction with arrhythmia. Then, we compared the differences in cell death induced by radiofrequency ablation, cryoballoon ablation and pulsed-field ablation. Finally, we discussed the structural and electrical remodeling caused by inflammation and the association between inflammation and the recurrence of AF after catheter ablation. Collectively, pulsed-field ablation will be a revolutionary innovation with faster, safer, better tissue selectivity and less inflammatory response induced by apoptosis-dominated cell death.

MY ChatGPT TEACHER. TOWARDS A METHODOLOGY FOR USING ARTIFICIAL INTELLIGENCE IN SELF-DIRECTED LEARNING

Language is the distinctive feature separating humans from the rest of the animal world. Therefore, throughout history, inventions and technologies that changed the way we use language have been crucial turning points. These include the invention of writing, which allowed the communication across space and time, the printing press, which facilitated the rapid and widespread dissemination of knowledge, and the advent of the Internet and mobile phone technology, which resulted in an explosion of communications and faster diffusion of knowledge. The latest revolutionary innovation in the field of language processing is ChatGPT, a generative AI model launched by OpenAI in November 2022, which produced spectacular results in natural language processing (NLP). It appears to be one of the most significant milestones in this field and has the potential to produce major changes in numerous domains, particularly in education. Starting from the available literature and from several experiments with ChatGPT, this paper outlines the main methodological requirements for using this instrument as an aid for self-directed learners.

Farmer’s perception of the opportunites offered by drone technology in arable crop production

The incredible scale of development in the world requires constant innovation of technologies used in agriculture. Precision agriculture is also one of the most defining trends of our time in drone technology, which represents a revolutionary innovation in arable crop production. Its continuous monitoring provides many opportunities to assess the specific culture, estimate yields, determine water shortages, nutrient deficiencies, and apply other decision support. Frequent flights provide farmers with up-to-date information so that they can respond to changes in status quickly and cost-effectively. The use of drones is also responsible for improving the competitiveness of agriculture, drones and solutions based on them are spreading rapidly around the world. After reviewing the relevant international literature, publications and materials published by stakeholders involved in drone technology, we conducted a questionnaire survey among farmers based in northern Hungary. Commitment and willingness to precision developments were discovered how open they are to modern agriculture achievements, to what extent are they willing to sacrifice technology, especially spray drones, to what extent do they keep in mind aspects related to the environmental load and with what willingness to invest. overall, the perception of precision elements is positive, farmers 71% considers the use of drones in plant protection, for example, a correct and useful idea, which reduces treading damage, chemical use and soil use.

An overview of Blockchain: Definitions, architecture, versions, applications and future directions

Blockchain technology has been gaining popularity in recent years, with many industries exploring its potential applications. This innovative technology has the potential to revolutionize the way we do business and interact with each other. It has emerged as a revolutionary innovation that promises to transform various industries by enabling secure, transparent, and decentralized transactions. The blockchain is a distributed ledger technology that uses cryptography to ensure the integrity and immutability of data. In this article, we will provide an overview of blockchain, including definitions, architecture, security, applications, and future directions

Emotion Detection with CNN Model and Song Recommendations using Machine Learning Techniques

Music is an exemplary tool to judge a person’s emotional state. It is the language of the soul. What cannot be articulated through words are easily conveyed through a melody. Music not only speaks to a person’s emotional and mental state, but it is also known to have a therapeutic effect on the listener. The traditional method of music recommendation uses collaborative or content-based filtering to recommend songs but a person’s song choices does not depend only on the song they usually listen to but depends mostly on their emotional state. With the fast-paced innovations pertaining to the music application industry, there is still scope of further improvement in the user experience and creating an encompassing application that not only allows the app users to enjoy listening to their favorite songs but also caters to their recommendation based on their emotional state. Thus, an emotion detection system using Convolution Neural Networks has been proposed. The user feeds in a custom playlist containing a mixture of musical genres that are classified into different emotions using K-Means Clustering. The CNN model detects the emotional state of the user and recommends a series of songs from the classified playlist. This interactive interface is a revolutionary innovation for users who need song recommendations that suit their current mind-state.

RENAL7: Unmet Needs In Nephrology: Survey Results to Drive Innovation

Background: Renal Disease is an increasing cause of morbidity and mortality in the U.S. and around the world. Despite advances in diagnosis and therapy, as well as improvements in care technology, long-term outcomes for renal disease patients remain suboptimal. Innovation, as a science and methodology, is focused on developing solutions for advancement beyond the status quo. Central in innovation, is clear identification of opportunities for improvement. In medicine the main driver of innovation is the unmet need. We conducted a survey to identify unmet needs in Nephrology. Here we report on the results. Methods: A multi-question survey was developed by a team of practicing academic nephrologists, biomedical scientists, and innovators. The survey was distributed across the U.S. to practicing academic and non-academic nephrologists. The survey was web-based. The survey collected information on participants’ identification of unmet needs, barriers to achieving solutions to needs, and suggestions for improvement of current therapeutics and technology. Survey questions ranged from more general to specific, also addressing the specific topics of: hemodialysis, interventional nephrology, and transplantation. Results: A broad group of nephrologists responded to the survey from across the U.S. 76 % of respondents felt that Nephrology lags behind other specialties regarding innovation. In the general questionnaire, participants felt that treatment of inflammation and cardiac complications were the most significant unmet needs. The need to develop a bioartificial kidney for patients on hemodialysis was the most significant future need, recognized by 53.5% of respondents. Interestingly, at the same time, respondents felt that technical challenges and lack of investment might limit artificial kidney development. For interventional nephrology, 26% thought creating an AV fistula, which matures fast, is the most significant need. The availability of living donors was the most critical unmet need in transplantation. Conclusion: A broad survey of unmet needs was conducted across the U.S. which revealed a perspective of opportunity. Nephrology was generally felt to have lagged somewhat compared to other medical subspecialties. It was recognized however that there are unmet needs, many of which are ripe for incremental innovation; though some opportunities for revolutionary innovation exist as well. Through further analysis of this data details of specifics will provide starting points for the advance of nephrology into the future.

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

Research on the problems and reform strategies of government statistics based on big data technology

Abstract In the past two years, with the development of big data technology and the revolutionary innovations it has caused within various industries, there has been a boom in the use of big data to transform and optimize government workflow within the field of public administration. In this paper, we build a theoretical framework through government statistics, focusing on the four elements of government statistics. The decision tree algorithm is used to analyze the indicators of the government statistical work problem, mainly from three aspects: grassroots statistical institutions, government revenue, and government forestry construction statistics. The accuracy of the decision tree algorithm is controlled between 83% and 91%, with a variation range value of 8%, which is more accurate than the other two algorithms. The decision tree algorithm helps improve the efficiency and quality of government statistics, thus contributing to the processing of statistical work problems and reform strategies. This study is important for deepening the reform of the Chinese government administration system, improving the construction of a service-oriented government, ensuring the quality of statistical information, and guiding the benign development of statistical work.

Advanced contribution of IoT in agricultural production for the development of smart livestock environments

Many areas of contemporary life need the use of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) for analysis and comprehension. The application of AI and ML in livestock farming has led to an improved understanding of animal behavior and discomfort, the prevention and management of diseases, and the efficiency of the farmer's economic choices. Digital twin technology is a particularly promising field that builds on AI and is already being utilized to increase efficiency and decrease costs in cattle livestock agriculture. A Digital Twin (DT) is a constant-update numerical copy of smart livestock environments. The proposed research is IoT, and Digital Twins in Livestock Farming (DTLF) might expand extensive Precision Livestock Farming (PLF), technology and tools use, and farm animal health and well-being. Global Positioning Satellite (GPS), Radio Frequency IDentification (RFID), AI, and even ML are only some current technologies that may be used for automated tracking of individual animal whereabouts. There is promise in using these methods and innovations to monitor and evaluate animal welfare, but these techniques are difficult. Just like other revolutionary innovations, the utilization of the DT mechanism will improve disease analysis prediction for individual farms by a factor of 92%. The responsiveness of DTLF will be a 92% response rate in analyzing the Heartbeat, a 94% effectiveness rate in analyzing the Temperature Range, and a 94% overall effectiveness in evaluating the Humidity Range.