Wave Of Progress Research Articles

Companion Tests and Personalized Cancer Therapy: Reaching a Glass Ceiling.

The use of companion diagnostics has become a standard in precision oncology in the context of ongoing therapeutic innovation. However, certain limitations make their application imperfect in current practice. This position paper underscores the need to broaden the notion of companion testing, considering the potential of emerging technologies, including computational biology, to overcome these limitations. This wave of progress should impact not only our representation of the analytical tool itself but also the nature of the tumoral sample under analysis (liquid biopsies). The complex inter-relationship between companion test guided-personalized therapy, and health agency policies for new drug agreements will also be discussed.

Accelerating AI for science: open data science for science.

Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI's potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.

Gait-based identification using wearable multimodal sensing and attention neural networks

Advances in sensor technology have sparked a wave of innovation and progress in the domain of wearable devices. However, ensuring the security of these devices remains a critical concern for their users. This study proposes a novel identification framework that employs custom multimodal sensing insoles and deep learning techniques to contribute to the data security of wearable devices in a user-transparent manner. The insole incorporates an inertial sensing unit and ten force sensors to capture kinematic and kinetic information. For this implementation, we propose a lightweight multiclass classification deep model based on depthwise separable convolution and an enhanced attention mechanism, which operates over multiple signal channels. In designing the recognition model, we focused on two pivotal factors: (1) achieving an optimal balance between computational complexity and recognition accuracy, which is essential for models that operate on devices with limited computational power, and (2) introducing an enhanced attention mechanism that ensures high recognition accuracy without the stringent requirement for precise temporal alignment between sensor channels, which can be technically challenging to attain. We conducted extensive experiments to validate the proposed approach, utilizing a walking dataset collected from 82 young subjects. The results indicate that when using data from the multimodal sensing insole, a high recognition accuracy of 97.96 % for person identification is achieved, with a notably low model parameter count of 22,462. The results support the feasibility and effectiveness of sensing footwear-based gait identification. The proposed approach holds significant potential for enhancing user authentication methods across a broad range of scenarios while ensuring transparency and user-friendliness.

Technological Change and Its Effect on Education

This work follows the historical interplay between technological advancements and educational systems and highlights that strengthening education has been a key driving force behind the Second and Third Industrial Revolutions. It seems, however, that the arrival of the Fourth Industrial Revolution --- bringing communication platforms, AI and renewable energy --- caught educational institutions unprepared. Whether we like this or not, this technological shift means the educational landscape is bound to change. This article examines the evolving dynamics of modern education amidst a new wave of technological progress, highlighting the rise of student 'collectives' which result from a distinctive synergy among students, communication platforms, and generative AI technologies. The article expresses scepticism regarding the use of strategies that are becoming practically ineffective, technologically outdated and contradictory to prevalent student practices. It advocates for adopting educational principles appropriate for the modern era and capable of upholding academic integrity in the strictest sense. If modern technology seems incompatible with academic integrity when examined within some traditional educational practices, this can mean only one thing---these practices must change. A significant challenge lies in implementing these changes without overburdening already limited educational resources. The article proposes practical principles and measures to ensure that universities can still be a driving force behind technological progress and innovation while upholding and promoting the highest standards for their research and education.

Unlocking the Power of Molecular Cloning: Revolutionizing Medical Microbiology Procedures

The revolutionary realm of molecular cloning, encompassing the creation of recombinant DNA molecules, has ignited a wave of progress within the life sciences. The advent of potent tools has facilitated the manipulation of DNA, resulting in an extraordinary surge in the versatility and breadth of applications in recombinant DNA technology. The once complex task of cloning genes has now been simplified, triggering a veritable explosion of insights into gene functionality. This has been achieved through the seamless fusion of multiple DNA fragments or the utilization of interchangeable gene cassettes, culminating in a state of unparalleled agility and expediency. In the 1970s, when restriction endonucleases, enzymes that cut DNA molecules selectively were discovered, molecular cloning technology has grown exponentially in application and intricacy, resulting in influential DNA manipulation tools. Recent decades have seen an explosion in our understanding of gene function due to the simplicity and efficiency of molecular cloning. It is expected that emerging technologies will offer superior potentials, such as stitching together multiple DNA fragments in under a few hours and transforming the resulting plasmids into bacteria, or the use of swappable genes, which can be easily moved between different paradigms, maximizing promptness and flexibility. It has been proved that cloning techniques provide a gold standard technique for polymicrobial infection, recombinant cytokines, antimicrobial peptides, epidemiology and gene therapy due to the limitations of culture-based methods. Due to molecular cloning technique, recombinant antigens are now being used to monitor patients against clinical infections. As a result of laboratory techniques that permit in vitro chemical synthesis of any DNA construct specified in silico, molecular cloning will likely undergo a paradigm shift in the coming future. As a result of these advances, DNA clones can be constructed faster and iteratively, which will speed up the growth of new vaccines, gene therapy vectors, and recombinant proteins. Here I present a detailed overview of the latest applications of molecular cloning techniques in medical microbiology.

Interaction mode enables user perception recognition and perception optimization: An AI human-computer interaction study

Artificial intelligent (AI) has various ways of human-computer interaction, but most of them overlook the recognition of human perception. If the interaction mode is combined with psychology, the user's mood change can be identified by the user's subtle expression, movement change and voice tone change, so as to provide corresponding services and improve the user experience. Statistical analysis of human responses to different situations in cognitive psychology, incorporating them into human-computer interaction methods. The current human-computer interaction modes in products tend to be standardized, and focusing user experience on user perception will bring special experiences to users. Emotional recognition is a cross disciplinary discipline with broad application prospects, but it has not yet reached a mature stage and requires corpus enrichment, theoretical strengthening, and method innovation. The era of artificial intelligence is leading a new wave of technological progress, and emotion recognition, as an important topic in the field of artificial intelligence, can help computer intelligence recognize human emotions and make human-computer interaction more friendly. In the near future, research on emotion recognition technology will make greater progress and be better applied to practical products.

The Current Status and Future Prospects of Machine Learning in the Chinese Stock Market

This article explores the current status and prospects of machine learning techniques in the stock market of china. It begins by providing an overview of the relevant concepts of machine learning and analyzing its strengths and weaknesses in the stock market context. The article also introduces the current application of machine learning techniques in the gobal stock market. In the methodology section, several machine learning models that have performed well in the stock market are listed. The article then summarizes and analyzes the specific applications of machine learning and its algorithms in the stock market of China, highlighting the advantages and limitations due to the unique characteristics of the market.Finally, the article concludes by summarizing the aforementioned content and providing an outlook on the future development direction of machine learning technology in the stock market of chinese. While the performance of machine learning technology in the stock market has its pros and cons, it is undeniable that it holds an important position in the future development of the financial market, especially within the wave of innovation and progress in the financial industry.

Formation of new narratives in the field of regional economic research

The article studies evolution of main approaches to the study of regional economy. Over the past half a century, the research paradigm in the area of economic sciences has undergone significant changes: the theories of regional economics have changed, significantly evolving, complementing, and mutually reinforcing each other. The original objectives of regional economic research have changed, responding to modern challenges of the world community development and global crisis phenomena. As a result, as shown in the article, in recent years focus of research has shifted significantly towards analyzing strategic aspects and directions of regional development, as a result of which the relevant concepts have become less economics-centric. An important pattern of modernity has also become a new wave of scientific and technological progress, which determined regional innovation systems development and their subsequent digital transformation that also put a significant imprint on the paradigm of regional economy research.

How digital technology affects working conditions in globally fragmented production chains: Evidence from Europe

This paper uses a sample of over 9 million workers from 22 European countries to study the intertwined relationship between digital technology, cross-border production links and working conditions. We compare the social consequences of technological change exhibited by three types of innovation: computerisation (software), automation (robots) and artificial intelligence (AI). To fully quantify work-related wellbeing, we propose a new methodology that amends the information on remuneration by reference to such non-monetary factors as the work environment (physical and social), career development prospects, or work intensity. First, we show that employee wellbeing is related to the type of technological exposure. Employees in occupations with a high degree of software or robot exposure face worse working conditions – contrary to highly AI exposed occupations. Thus, we find that AI technologies differ from previous waves of technological progress - also in relation to workers' wellbeing. Additionally, we show that the relationship between digital technology and working conditions weakly depends on participation in global production chains.

Amin’s progressive internationalism

ABSTRACT In 2018, Samir Amin called for a ‘new wave of revival and progress’ which would replace the system of neoliberal globalisation which is to blame for leaving multiple, interlinked global crises. Five years after his death, this article aims to evaluate if his call has been heeded and how far the world is from a new wave of revival and progress. The article investigates the movement of ‘progressive internationalism’ - as a movement that opposes the perpetuation of the legacy of global imperlialism that has manifested in global power assymetry, producin structural inequality and poverty - as a possible response to Amin’s request. . The core research question is: ‘how far are we from realising Amin’s vision’. To do this, the article will also explore initiatives of the progressive internationalist movement with reference to south-south cooperation thorough the BRICS bloc, structural reform of the international system and the United Nations Security Council, and Active Non-Alignment, among other nascent initiatives.

Toward digital twin of the ocean: from digitalization to cloning

The forthcoming wave of progress in oceanographic technology is the digital twin of the ocean, a concept that integrates marine big data and artificial intelligence (AI). This development is a logical consequence of combining data science and marine science and is considered superior to previous models, such as the digital ocean, transparent ocean, and smart ocean. Amid the swift advancement of next-generation information technology, the conditions are favorable for developing a prototype digital twin of the ocean, which will integrate various functionalities—data fusion, situation presentation, phenomenon mining, autonomous learning, and intelligent prediction. The salient distinction between a digital twin of the ocean and traditional forms of virtual or augmented reality is because of the intelligence beyond digitalization exhibited by the former, primarily facilitated by AI-based cloning. Hence, herein, we initially propose a structured architecture for the generative digital twin ocean, encompassing elements from real-time data pools to key technologies and proof-of-concept applications. The core components of this prototype system include a data pool, an AI-based oceanographic model, and three-dimensional visualization interactions. Future research and objectives for the digital twin ocean will principally focus on the following: four-dimensional (comprising three-dimensional space along with time) digital cloning and real-time mapping of global ocean parameters, cooperative observation coupled with human–computer interactions, and intelligent prediction along with cutting-edge applications. Prospectively, this transformative technology holds the potential to considerably enhance our understanding of the ocean, yielding groundbreaking discoveries that will profoundly influence the marine economy and sustainable development.

COVID-19 pandemic: overview of the fiscal policy response and macroeconomic developments in the euro area and the United States

The COVID-19 pandemic has had a profound impact on the European Union and the United States, with varying waves of severity and divergent progress in vaccination campaigns across regions. To address the short-term costs and potential long-term effects of the crisis, policymakers adopted support measures, particularly fiscal policies. This paper provides an overview of the fiscal support measures implemented, with a focus on the euro area and the United States. It also examines the impact of the pandemic and of support policies on the economies of both regions, as well as the ongoing economic recovery. By analysing the fiscal responses and the macroeconomic developments, this study aims to contribute to a better understanding of the diverse approaches taken by policymakers in combatting the COVID-19 crisis and mitigating its economic consequences.

The impact of artificial intelligence on pollution emission intensity-evidence from China.

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. This study examines the effects and mechanisms of AI on the intensity of pollution emissions, using China as an example. Theoretical analysis demonstrates that the scale expansion effect and the technological innovation effect of AI can reduce the intensity of pollution emissions. In the meantime, AI can have a positive structural influence on reducing the intensity of pollution emissions through the upgrading of industrial structures. Therefore, we use panel data for 30 Chinese provinces from 2006 to 2019 to test the effect of AI on pollution emission intensity using a fixed effects model, employ explanatory variable substitution, endogenous analysis, regression after tailing, and eliminate related policy interference for robustness analysis. The results indicate that AI can significantly decrease the intensity of pollution emissions, with a 6.63% reduction for every 10% increase in AI utilization. We use the mediating effect model to conclude that AI can reduce the intensity of pollution emissions via the rationalization of industrial structure and advanced industrial structure, with the rationalization of industrial structure being the main mechanism. The examination of heterogeneity revealed that the implementation of AI in technology-intensive industries is an effective method for reducing the intensity of pollution emissions and that the positive impact of AI on the intensity of pollution emissions is more pronounced in the western region.

National Wealth as an Economic Category

National wealth is one of the most general economic categories and an integral indicator for assessing and comparing the economic potential and capabilities of different countries. The calculation of such important macroeconomic quantities is based on the system of national accounts. In the economic literature, the term wealth refers to the sum of certain elements. In the economic literature, the term "wealth" refers to the sum of certain elements. In this sense, "wealth" is the object of economic science and statistical research. Country, nation, people, social wealth - these categories emerged with the formation of economic science. The first mention of the category "national wealth" in foreign literature is associated with the works of the classical economist W. Petty. In his search for the source of wealth, the author quantified the economic events of his time. In 1664 the scholar calculated the national wealth of England. Two concepts have emerged: national wealth is regarded as a certain fund held by a country in a certain period of time, or as recurrent income, a certain flow of products processed and used by the state, of the country over a certain period of time. The second concept dominated 19th century science. In our view, the definition of national wealth as a certain flow over a certain period of time is more characteristic of the concept of national income or gross product. For a scientifically sound and legitimate characterization of the concept of national wealth it is necessary to combine these two approaches. Only from the middle of the 20th century onwards. National wealth is identified with the accumulated stock of tangible and intangible assets. All elements of national wealth have a material composition, i.e. represent the material wealth of society. But on the wave of scientific and technological progress, information played a big role, in the mid-twentieth century the economy transformed from industrial to neo-industrial, and intangible elements were included in national wealth: human capital and information. If we compare countries by the level of national wealth, it is known that natural resources prevail in the structure of national wealth of low-income countries, and human potential prevails in high-income ones. In order to achieve a certain level of socio-economic well-being, most researchers believe that forms of international division of labor and organization are necessary to enable less developed countries to change the proportions of national wealth. and increase the proportion of human potential. Keywords: Wealth, national wealth, national wealth as an economic category.

Success Factors in Adopting AI in Human Resource Management in UAE Firms: Neutrosophic Analysis

The revolutionary breakthroughs of artificial intelligence (AI) are swiftly and extensively invading many domains of human activity, serving as the central driving force of the next wave of informatization progress and industrial revolution. Although artificial intelligence (AI) technology and apps have been extensively explored, and variables affecting AI acceptance have been found, the influence of success factors on the acceptance of AI is still unclear. Thus, this study presents a methodology to investigate the effects of gadgets, organizations, and ecosystems on the acceptance of AI in Human Resource Management (HRM) in the UAE. This study used a neutrosophic set (NS) to overcome the vague information. The NS is integrated with the AHP method to rank the success factors in adopting AI in HRM in UAE. The AHP method is used to give importance to these factors. This study used 12 factors in UAE to rank it by the N-AHP.

Brief History of Oral and Maxillofacial Pathology in Colombia: Milestones and Current State.

Oral and maxillofacial pathology, the branch of dentistry concerning clinical and histopathological diagnosis of pathologies of the oral and maxillofacial region, started to develop in Colombia in the mid- 19th century. Since 1950s, dedicated dental clinicians from different dental schools played an important role in its inception and subsequent development. The first significant wave of progress occurred in the 1970s with the appearance of the first formally trained oral pathologists. It was sometime in the late 1980s and early 1990s that dentists Ines Velez and Benjamin Herazo had separate initiatives and created the first oral pathology postgraduate programs in Bogotá. It was in the late 80's and early 90's that dentists Ines Velez and Benjamin Herazo had their own initiatives and created the first oral pathology postgraduate programs in the city of Bogotá. A concomitant of their achievements was the establishment of the first on-site oral pathology biopsies units and registers at Colegio Odontãlogico Colombiano, Pontificia Universidad Javeriana and Escuela Colombiana de Medicina dental schools. The programs have trained many dentists in the field over the years and two of them remain active and fully accredited. The past and present contributions and leaderships of some academic figures and graduates have allowed a steady evolution of the specialty nationwide. Currently, the ability to manage and overcome educational and professional challenges is necessary to advance the growth of this specialty in this country.

Підвищення ефективності формування сцен доповненої реальності за допомогою нейронних мереж

On the way to the fourth wave of industrial technological progress, visualization and virtualization tools have received a wide range of applications and integration into the multi-industry space. The technology of creating additional visual images is currently used in the medical field, the field of education, the industrial and industrial field, the field of advertising and trade, in the field of modeling and design, in the scientific field, the cultural and entertainment field, etc. The potential of using visualization tools is inexhaustible, because the integration of additional information in the form of graphic objects helps to increase the perception of the data flow of reality and develops analytical capabilities for users of augmented reality technology. Modern means of creating scenes of augmented reality and additional visual images have increased requirements for the consumption of computing power, as they require dynamic adaptive interaction with streams of real data, which actually leads to the formation of extremely complex algorithms and corresponding hardware-analog and software-digital solutions. Optimizing and improving the efficiency of the augmented reality scene creation technology is a scientific problem that needs to be solved, including within the scope of the current research. According to the bibliographic search and analysis of modern trends and profile developments, the potential possibility of using neural network tools to create additional visual objects in augmented reality scenes has been established. Neural networks have a high adaptive capacity for learning and an adequate reaction to external conditions of functioning. Therefore, neural network tools are surprisingly suitable for integration into technological solutions for the functioning of augmented reality technology. Known topological solutions for arranging and organizing the functioning of neural networks, which can be applied to solve a certain scientific problem of optimizing the consumption of computing power and increasing the efficiency of creating augmented reality scenes, have a number of limitations to their application, which prompts the further search for adaptive solutions. A promising solution is the formation of combined-hybrid technologies for constructing the topology of neural networks. Thus, the relevance of the research is outlined, the scientific issues are formed, and the vector of scientific research to solve the identified issues is proposed.

Performance and stability enhancement of mixed dimensional bilayer inverted perovskite (BA2PbI4/MAPbI3) solar cell using drift-diffusion model

A variety of classic photovoltaic technologies, such as multi-crystal silicon and copper indium gallium selenide, have been exceeded by organometal halide perovskite solar cells, which have made significant advancements in the cost-effective manufacturing method as well as the high power conversion efficiency. Meanwhile, the unfavourable operational stability of perovskite solar cells is impeding the commercialization of the technology. When exposed to critical environmental factors such as rising moisture and intense sunlight, perovskite solar cells suffer lattice degradation and lose their ability to harvest energy. As a result, enhancing the stability of perovskite cells is one of the most important variables in determining the next wave of progress for the technology. While there have been numerous techniques reported to improve the stability of perovskite-based photovoltaic cell, the construction of a more than one dimension (2D/3D) perovskite as the light absorbing layer has recently emerged as one of the most reliable strategies to stabilise the perovskite-based device without compromising photo physical performance. Herein, we have computationally simulated p-i-n device configuration composed of ITO/NiO/2D perovskite/3D perovskite/PCBM/Au. The Ruddlesden-Popper (RP) phases of two-dimensional perovskite act as a cover layer, which inhibits the deterioration of 3D perovskite in an open-air environment. Both optical and electrical simulation is performed using solar cell capacitance simulator (SCAPS). Simulation findings showed that the enhancements in overall performance are due to the decrease in the trap state's density and better coordination of the band between the 2D/3D perovskite and the hole-transporting materials. Effects of the interfacial defects (2D perovskite/HTL and 3D perovskite/ETL) on solar cell parameters are also performed. Simulation results reveal that the defects at 3D perovskite/ETL interfacial are more dominating in overall performance degradation. Parametric differences, such as thickness of various layers, standard solar spectra, further refine the proposed architecture. The multidimensional-based architecture proposed in this work resulted in a power conversion efficiency (PCE) of 19.6%.

The State of Human Capital and Innovativeness of Polish Voivodships in 2004–2018

The category of human capital has increased in importance with the emergence of human capital theory in the 1960s. The interest in innovativeness is a result of successive waves of industrial revolutions and technical progress. The article aims to estimate human capital and innovation in Polish voivodeships 2004–2018 as an essential determinant of socio-economic development in emerging economies. The regional dimension related to human capital and innovativeness is rarely studied in a socio-economic context. Additionally, the main contribution of the paper is that we propose an extraordinary set of variables capturing quantitative and qualitative aspects of regional research. To measure these factors, we propose a set of sub-indices describing the state of human capital and innovation. The delimitation of regions was carried out using the method of Czekanowski. The study results confirmed the polarization of voivodeships in Poland, generally according to Eastern and Western Poland. Unfortunately, it turns out that despite the economic growth in the country in recent years, disparities within the human capital of voivodeships are increasing. This makes it challenging to unleash innovation and enter a faster and more sustainable path of growth.

Digging in Crypto-Communities’ Future-Making

Digging in Crypto-Communities’ Future-Making