Search Results (13)

This study proposes a new concept that explains a source of technological change: The invasive behaviour of general purpose technologies that breaks into scientific and technological ecosystems with accelerated diffusion of new products and processes that destroy the usage value of all units previously used. This study highlights the dynamics of the invasive destruction of new path-breaking technologies in driving innovative activity. Invasive technologies conquer the scientific, technological, and business spaces of alternative technologies by introducing manifold radical innovations that support technological, economic, and social change. The proposed theoretical framework is verified empirically in new technologies of neural network architectures, comparing transformer technology (a deep learning architecture having unsupervised and semi-supervised algorithms that create new contents and mimic human ability, supporting Generative Artificial Intelligence) to Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs). Statistical evidence here, based on patent analyses, reveals that the exponential growth rate of transformer technology over a period of five years (2020–2024) is 45.91% more than double compared to the alternative technologies of LSTM (21.17%) and RNN (18.15%). Moreover, the proposed invasive rate in technological space shows that is very high for transformer technology at the level of 2.2%, whereas for LSTM it is 1.39% and for RNN it is 1.22% over 2020–2024, respectively. Invasive behaviour of drastic technologies is a new approach that can explain one of the major causes of global technological change and this scientific examination here significantly contributes to our understanding of the current dynamics in technological evolution of the Artificial Intelligence technology having high industrial impacts on the progress of human society. Full article

The proposed general theory of scientific variability for technological evolution explains one of the drivers of technological change for economic progress in human society. Variability is the predisposition of the elements in systems to assume different values over time and space. In biology, the variability is basic to explaining differences and development in organisms. In economics of technical change, the effects of variability within research fields on evolutionary dynamics of related technologies are unknown. In a broad analogy with the principles of biology, suggested theoretical framework here can clarify a basic driver of technological evolution: the variability within research fields can explain the dynamics of scientific development and technological evolution. The study sees whether statistical evidence supports the hypothesis that the rate of growth of scientific and technological fields can be explained by the level of variability within scientific fields. The validation is based on emerging research fields in quantum technologies: quantum imaging, quantum meteorology, quantum sensing, and quantum optics. Statistical evidence seems in general to support the hypothesis stated that the rate of growth can be explained by the level of scientific variability within research fields, measured with the relative entropy (indicating the dispersion of scientific topics in a research field underlying a specific technology). Nonparametric correlation with Spearman’s rho shows a positive coefficient of 0.80 between entropy measures and rates of growth between scientific and technological fields. The linear model of the relation between rate of growth and scientific variability reveals a coefficient of regression equal to 1.63 (R² = 0.60). The findings here suggest a general law that variability within research fields positively drives scientific development and technological evolution. In particular, a higher variability within research fields can support a high rate of growth in scientific development and technological evolution. The proposed general theory of scientific variability is especially relevant in turbulent environments of technology-based competition to clarify a basic determinant of technological development to design strategies of technological forecasting and management of promising innovations. Full article

One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impcats. Full article

The goal of this study is to analyze associations between COVID-19 transmission and meteorological indicators in cities of the Black Sea region of Turkey, located specifically in the dampest area, with excess rainfall and recurring fog. In particular, the working hypothesis is that the widespread transmission of new coronavirus SARS-CoV-2 (leading to the airborne disease COVID-19) in cities can be explained by specific weather conditions, namely high levels of air humidity. Statistical evidence here does not seem, in general, to support the hypothesis that the accelerated transmission of COVID-19 in the studied cities can be explained by high levels of humidity because different meteorological, environmental, demographic, and socioeconomic factors also plays a critical role in the disease transmission dynamics of the investigated region. The main implications of our findings here are that the demographic structure of the population, climate indicators, organization of the health system, and environmental factors (e.g., air pollution, etc.) should be considered through a systemic approach when designing effective national and regional pandemic plans directed to implement health policies for facing new variants of COVID-19 and/or new airborne diseases, in order to reduce their negative effects on health, social and economic systems. Full article

The goal of this study is to analyze how levels of air pollution changed between countries with their restriction policy of lockdown to cope with the COVID-19 pandemic. The study design compares average changes of CO, NO₂, SO₂, O₃, PM_2.5 and PM₁₀ concentrations based on measurements at ground level in January, February, and March for the years 2019, 2020, 2021, and 2022 (during the COVID-19 pandemic crisis) to average values of a 2015–2018 baseline period (ex-ante COVID-19 pandemic) between 300 cities in 19 countries of five geoeconomic regions. Results reveal that the maximum reduction in air pollutant concentrations is given by: CO (−4367.5%) in France, NO₂ (−150.5%) in China and Australia, SO₂ (−154.1%) in Israel, O₃ (−94.1%) in China, PM_2.5 (−41.4%) in Germany, and PM₁₀ (−157.4%) in Turkey. Findings show that the effects of restriction policies for COVID-19 pandemic on air quality vary significantly between countries, depending on the different geographical, economic, industrial and social characteristics of the countries. These results clarify the critical relationship between control measures for pandemic crises and levels of air pollution in countries that can support best practices of environmental policy for pathways of sustainable development. Full article

The principal goal of this study is to analyze the evolution of sensor research and technologies from 1990 to 2020 to clarify outlook and future directions. This paper applies network analysis to a large dataset of publications concerning sensor research covering a 30-year period. Results show that the evolution of sensors is based on growing scientific interactions within networks, between different research fields that generate co-evolutionary pathways directed to develop general-purpose and/or specialized technologies, such as wireless sensors, biosensors, fiber-optic, and optical sensors, having manifold applications in industries. These results show new directions of sensor research that can drive R&D investments toward promising technological trajectories of sensors, exhibiting a high potential of growth to support scientific, technological, industrial, and socioeconomic development. Full article

The existence of independent, yet time correlated, Extensive Air Showers (EAS) has been discussed over the past years, with emphasis on possible physical mechanisms that could justify their observation. The detector network of the Extreme Energy Events (EEE) Collaboration, with its approximately 60 cosmic ray telescopes deployed over the Italian territory, has the potential to search for such events, employing different analysis strategies. In this paper, we have analyzed a set of EEE data, corresponding to an approximately five month observation period, searching for multi-coincidence events among several far telescopes, within a time window of 1 ms. Events with up to 12 coincident telescopes have been observed. Results were compared to expectations from a random distribution of events and discussed with reference to the relativistic dust grain hypothesis. Full article

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact. Full article

Coronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which appeared in late 2019, generating a pandemic crisis with high numbers of COVID-19-related infected individuals and deaths in manifold countries worldwide. Lessons learned from COVID-19 can be used to prevent pandemic threats by designing strategies to support different policy responses, not limited to the health system, directed to reduce the risks of the emergence of novel viral agents, the diffusion of infectious diseases and negative impact in society. Full article

The pandemic caused by novel coronavirus disease 2019 (COVID-19) is generating a high number of cases and deaths, with negative effects on public health and economic systems. One of the current questions in the contemporary environmental and sustainability debate is how high air pollution and reduced use of renewable energy can affect the diffusion of COVID-19. This study endeavors to explain the relation between days of air pollution, wind resources and energy, and the diffusion of COVID-19 to provide insights into sustainable policy to prevent future epidemics. The statistical analysis here focuses on a case study of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. The results reveal two main findings: (1) cities with high wind speed and high wind energy production have a lower number of cases of COVID-19 in the context of a more sustainable environment; (2) cities located in hinterland zones with high air pollution, low wind speed and less wind energy production have a greater number of cases and total deaths. The results presented here suggest that the pandemic caused by novel coronavirus (SARS-CoV-2) and future epidemics similar to COVID-19 cannot be solved only with research in medicine but the solution also needs advanced capabilities and technologies for supporting sustainable development based on the reduction of air pollution and increase of production in renewable energy to improve air quality and as a consequence public health. Full article

Head and neck squamous cell carcinoma (HNSCC) is one of the main neoformations of the head–neck region and is characterized by the presence of squamous carcinomatous cells of the multi-layered epithelium lining the oral cavity, larynx, and pharynx. The annual incidence of squamous cell carcinoma of the head and neck (HNSCC) comprises approximately 600,000 new cases globally. Currently, the 5-year survival from HNSCC is less than 50%. Surgical, radiotherapy, and chemotherapy treatments strongly compromise patient quality of life. MicroRNAs (miRNAs) are a family of small noncoding endogenous RNAs that function in regulating gene expression by regulating several biological processes, including carcinogenesis. The main upregulated microRNAs associated with oral carcinoma are miR-21, miR-455-5p, miR-155-5p, miR-372, miR-373, miR-29b, miR-1246, miR-196a, and miR-181, while the main downregulated miRNAs are miR-204, miR-101, miR-32, miR-20a, miR-16, miR-17, and miR-125b. miR-21 represents one of the first oncomirs studied. The present systematic review work was performed based on the preferred reporting items for systematic review and meta-analysis (PRISMA) protocol. A search was carried out in the PubMed and Scopus databases with the use of keywords. This search produced 628 records which, after the elimination of duplicates and the application of the inclusion and exclusion criteria, led to 7 included articles. The heterogeneity of the studies according to the odds ratio was high, with a Q value of 26.616 (p < 0.001), and the I² was 77.457% for specificity. The heterogeneity was high, with a Q value of 25.243 (p < 0.001) and the I² was 76.231% for sensitivity. The heterogeneity of data showed a Q value of 27.815 (p < 0.001) and the I² was 78.429%. Therefore, the random-effects model was selected. The diagnostic odds ratio was 7.620 (95% CI 3.613–16.070). The results showed that the sensitivity was 0.771 (95% CI 0.680–0.842) (p < 0.001) while, for specificity, we found 0.663 (95% CI 0.538–0.770) (p < 0.001). The negative likelihood ratio (NLR) was 0.321 (95% CI 0.186–0.554), and the positive likelihood ratio (PLR) was 2.144 (95% CI 1.563–2.943). The summary ROC plot demonstrates that the diagnostic test presents good specificity and sensitivity, and the area under the curve (AUC), as calculated from the graph, was 0.79. Full article