Automation significantly alters human behavior, particularly risk-taking. Previous researches have paid limited attention to the underlying characteristics of automation and their mechanisms of influence on risk-taking. This study investigated how automation affects risk-taking and examined the role of sense of agency therein. By quantifying sense of agency through subjective ratings, this research explored the impact of automation level and reliability level on risk-taking. The results of three experiments indicated that automation reduced the level of risk-taking; higher automation level was associated with lower sense of agency and lower risk-taking, with sense of agency playing a complete mediating role; higher automation reliability was associated with higher sense of agency and higher risk-taking, with sense of agency playing a partial mediating role. The study concludes that automation influences risk-taking, such that higher automation level or lower reliability is associated with a lower likelihood of risk-taking. Sense of agency mediates the impact of automation on risk-taking, and automation level and reliability have different effects on risk-taking.
A recently developed data-driven Kalman filter requires offline measurement of the process disturbance; a requirement that is often unmet for many practical applications. We propose a solution that parametrizes the Kalman filter exclusively using measured input and output data. The key idea is to use the innovations form which naturally accounts for the process disturbance and measurement noise into a single orthogonal stochastic process. Unlike process disturbances, the innovations process can be estimated directly from input-output data via a numerically efficient projection step. The performance of the method is demonstrated using a benchmark simulation.
Critical theory (CT) is a widely used theoretical approach that can be used to describe, understand and justify interventions into scientific and technical research and innovation. It can thus be considered a potential theoretical underpinning of responsible innovation (RI). This article explores the relationship between CT and RI by looking at how CT can influence the practice of researchers who implement RI, typically as part of scientific and technical projects. It proposes a theoretical framework based on three dimensions of criticality: the legitimacy, motivational, and epistemological dimensions. These three dimensions can be used to represent a space of CT where RI scholars can take different roles which influence their RI practice. The article offers some anecdotal evidence to support the validity of the conceptual framework. It proposes a research agenda to validate this framework.
This work demonstrates the application of a birth-death Markov process, inspired by radioactive decay, to capture the dynamics of innovation processes. Leveraging the Bass diffusion model, we derive a Gompertz-like function explaining the long-term innovation trends. The validity of our model is confirmed using citation data, Google trends, and a recurrent neural network, which also reveals short-term fluctuations. Further analysis through an automaton model suggests these fluctuations can arise from the inherent stochastic nature of the underlying physics.
This study focuses on the problem of testing for normality of innovations in stationary time series models.To achieve this, we introduce an information matrix (IM) based test. While the IM test was originally developed to test for model misspecification, our study addresses that the test can also be used to test for the normality of innovations in various time series models. We provide sufficient conditions under which the limiting null distribution of the test statistics exists. As applications, a first-order threshold moving average model, GARCH model and double autoregressive model are considered. We conduct simulations to evaluate the performance of the proposed test and compare with other tests, and provide a real data analysis.
The research focuses on the multiple ways in which technological innovation in the management, governance, and security of renewable energies contributes to making smart cities capable of integrating renewable energy systems that lead to more sustainable, efficient governance, and security resilience for the cities. A literature review of Scopus has been done with the aim of asserting at the juncture of smart cities, technological innovation, and renewable energy integration. Co-occurring and co-authorship analyses have been performed by VOSviewer for mapping the key research themes, collaborative networks, and research gaps. Core research themes include smart cities, IoT, renewable sources of energy, urban planning, AI, big data - lots of them, actually. Key areas for concern comprise cybersecurity, social equity, human-centered design, and adaptation to climate change. International collaborations identified through co-authorship analysis mirrored strong contributions from Pandolfi Alessandra and Galiulo Valentina. There are major knowledge gaps with respect to addressing, for example, how combining the elements of energy efficiency and cybersecurity under one umbrella could be defined as a smart city. Contribute uniquely in this area, and analyze the effects that the aggregate technological innovations and integration of renewable energy have on enhancing urban sustainability, governance, and security. These advances indicate ways in which urban systems can be optimized in order to enhance quality of life and lend to more sustainable and resilient urban environments. This study synthesizes interdisciplinary research in a novel manner in pointing out new areas that smart city development has barely touched upon.
Jarmila Straková, Jaroslav Kollmann, Yaroslava Kostiuk
et al.
This paper presents a procedure for definition and analysis of value-creating corporate activities on an example of a selected engineering company and a newly proposed calculation of riskiness of the production process. The risk calculation is based on an analysis of the value chain or value stream of its primary activities. Three research questions were formulated within the framework of the solution concerning differentiation of the value stream in serial and custom production, significance of individual primary processes for creation of the corporate margin and differentiation of the degree of risk between serial and custom production. The obtained outputs from the model engineering enterprise showed that the intensity of added value creation is lower in serial production than in custom production. In terms of the importance of the primary activities for serial and custom production in the value chain, the production process can be considered dominant from the viewpoint of margin generation, followed by the technical preparation process. In addition to the already mentioned reduced level of generation of added value, the lower average riskiness of the production process was also found for serial production compared to custom production. The graphical presentation at the end of the paper suggests a correlation between the level of added value creation in partial production operations and their respective level of risk. The authors of the article assume that the presented results have general validity for engineering production enterprises, which will be the subject of follow-up case studies on similar topics.
How can critique in responsible innovation (RI) become generative? The anything-but-neutral relations between science, technology and society, at the core of science and technology studies, have led to the development of different repertoires of critique. None of them fitted the configurations in the biomedical practices we came to study. There, biomedical experts presented us with an analysis of the power relations perpetuated through the mainstream practices in their fields and had built socio-material alternatives to the common forms of practicing biomedicine. The paper suggests conceptualising critical observations voiced by experts embedded into socio-material alternatives as ‘critique from within’ yielding collateral goods and bads. Rather than asking how to foster responsibility conditions in RI, the paper suggests modestly reclaiming critique by articulating already existing forms of responsibility practices developed by experts themselves and analysing the ambivalent effects they engender.
With the rapid advancements in technology, automation has emerged as the future of human endeavors. From simple tasks like attendance management to complex security systems, automation has the potential to revolutionize various aspects of our lives. This research paper explores the implementation of a method aimed at enhancing room security in hostels and automating class attendance using ID cards. In this study, we propose a system that utilizes the unique identity information stored in ID cards for various security and check-in tasks. By integrating RFID (Radio-Frequency Identification) reader technology, GSM modules, Node MCU, and Arduino, we create a comprehensive solution. The RFID reader scans the ID card, extracting the relevant information and verifying the user's identity. The data is then transmitted via the GSM module to a central database, ensuring real-time monitoring and security measures. Moreover, the system also enables the automation of class attendance. By utilizing the same ID cards, students can simply tap their cards on a reader placed in the classroom. This information is recorded automatically, eliminating the need for manual attendance taking and reducing errors and time consumption. This research project highlights the practical implementation of ID card technology to enhance room security in hostels and automate class attendance processes. By leveraging the power of automation, we aim to streamline administrative tasks, improve security measures, and optimize efficiency in educational institutions and other relevant settings.
The innovations algorithm is a classical recursive forecasting algorithm used in time series analysis. We develop the innovations algorithm for a class of nonnegative regularly varying time series models constructed via transformed-linear arithmetic. In addition to providing the best linear predictor, the algorithm also enables us to estimate parameters of transformed-linear regularly-varying moving average (MA) models, thus providing a tool for modeling. We first construct an inner product space of transformed-linear combinations of nonnegative regularly-varying random variables and prove its link to a Hilbert space which allows us to employ the projection theorem, from which we develop the transformed-linear innovations algorithm. Turning our attention to the class of transformed linear MA($\infty$) models, we give results on parameter estimation and also show that this class of models is dense in the class of possible tail pairwise dependence functions (TPDFs). We also develop an extremes analogue of the classical Wold decomposition. Simulation study shows that our class of models captures tail dependence for the GARCH(1,1) model and a Markov time series model, both of which are outside our class of models.
The conducted study addresses the issue of the university reality to the extent that it fosters, in general terms, a scientific attitude, promoting the notion that students could become mere repeaters of knowledge, while university research becomes an institution isolated from the issues and potentialities of each reality. The aim of the article was to determine the levels of scientific attitude among students in a public university in the Puno region (Peru - 2018). A quantitative, descriptive approach was followed. The studied population comprised students from various fields: social sciences, biomedical sciences, engineering, and economics-finance. An instrument was developed to assess levels of scientific attitude, categorizing them into four levels: at the beginning, in progress, as expected achievement, and as outstanding achievement. The results reflect that 62.25% of the students are in progress, 19% have reached the expected achievement, and none have achieved the outstanding achievement. This suggests significant challenges in promoting formative research within the analyzed university.
Dmitry Katalevsky, Natalia Kosmodemianskaya, Arutyun Arutynyan
et al.
Modern Universities play increasingly important role in contemporary society, advancing frontiers of science and transforming regional economies. As funding models of modern universities change, they adopt some features of a business organization. While their ability to attract funding becomes vitally important for universities, especially from private sources (industry), a balance between fundamental and applied research becomes vital. The current research investigates 5-years of activities of the Skolkovo Institute of Science and Technology (Skoltech) and particularly its research portfolio. It is based on the theory and practice of the Research Domain Portfolio Matrix (RDPM) approach considering a University to be a portfolio of R&D technologies in diverse scientific areas and at various stages of technology maturity. It is of utmost importance for Universities to find a balance between basic and applied research while making decisions on launching new projects/programs or modifying the existing projects/programs. The proposed RDPM approach helps to leverage limited resources, establish priorities, monitor risks, and influence outcomes in the short and long-term horizons.
Sixth-generation (6G) networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency, and $μ$s-scale latency. To this end, an original three-layer 6G network architecture is designed to realise uniform full-spectrum cell-free radio access and provide task-centric agile proximate support for diverse applications. The designed architecture is featured by super edge node (SEN) which integrates connectivity, computing, AI, data, etc. On this basis, a technological framework of pervasive multi-level (PML) AI is established in the centralised unit to enable task-centric near-real-time resource allocation and network automation. We then introduce a radio access network (RAN) architecture of full spectrum uniform cell-free networks, which is among the most attractive RAN candidates for 6G TK$μ$ extreme connectivity. A few most promising key technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz wireless access and spatiotemporal two-dimensional channel coding are further discussed. A testbed is implemented and extensive trials are conducted to evaluate innovative technologies and methodologies. The proposed 6G network architecture and technological framework demonstrate exciting potentials for full-service and full-scenario applications.
It is important for leaders to ensure that their employees do their best in order to reach organizational goals. One way to achieve this is by implementing motivational systems. An effective leader must be aware of what motivates employees to perform. Motivation gives human behavior a direction and intensity which results in certain rewards that are valuable to the individual. The objective of our research was to analyze the effects of motivational factors. In the Slovakian economy, nearly 60 percent of employees are employed by small and medium-sized enterprises, which makes them highly significant. They play an important role in reducing unemployment and the development of the local economy both on a national and on a regional level. In addition, we feel there has only been a small number of studies conducted regarding the factors of internal motivation of small and medium-sized enterprises employees. With this objective in mind, based on the data we collected, we would like to set up a model for the organizations concerned that is easy to comprehend and enables the leaders to get a comprehensive picture about which motivating factors have a relevant impact on the motivation of their employees.
In the race to achieve climate goals, many governments and organizations are encouraging the local development of Renewable Energy Technology (RET). The spatial innovation dynamics of the development of a technology partly depends on the characteristics of the knowledge base on which this technology builds, in particular the analyticity and cumulativeness of knowledge. Theoretically, greater analyticity and lesser cumulativeness are positively associated with more widespread development. In this study, we first empirically evaluate these relations for general technology and then systematically determine the knowledge base characteristics for a set of 14 different RETs. We find that, while several RETs (photovoltaics, fuel-cells, energy storage) have a highly analytic knowledge base and develop more widespread, there are also important RETs (wind turbines, solar thermal, geothermal and hydro energy) for which the knowledge base is less analytic and which develop less widespread. Likewise, the technological cumulativeness tends to be lower for the former than for the latter group. This calls for regional and country-level policies to be specific for different RETs, taking for a given RET into account both the type of knowledge it builds on as well as the local presence of this knowledge.
Dario Ortega Anderez, Eiman Kanjo, Amna Amnwar
et al.
Criminal activity is a prevalent issue in contemporary culture and society, with most nations facing unacceptable levels of crime. Technological innovation has been one of the main driving forces leading to the continuous improvement of crime control and crime prevention strategies (e.g. GPS tracking and tagging, video surveillance, etc.). Given this, it is a moral obligation for the research community to consider how the contemporary technological developments (i.e. Internet of Things (IoT), Machine Learning, Edge Computing)might help reduce crime worldwide. In line with this, this paper provides a discussion of how a sample of contemporary hardware and software-based technologies might help further reduce criminal actions. After a thorough analysis of a wide array of technologies and a number of workshops with organisations of interest, we believe that the adoption of novel technologies by vulnerable individuals, victim support organisations and law enforcement can help reduce the occurrence of criminal activity.
This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and short selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and short selling will encourage enterprises to engage in green technology innovation activities. Firstly, our research results show that after the implementation of the margin trading and short selling, the green technology innovation behavior of pilot companies will increase significantly. We believe that the short selling threat and pressure brought by short selling to enterprises are the main reasons for pilot enterprises to engage in green technology innovation. Secondly, the empirical results show that the implementation of margin trading and short selling will significantly promote the quantity of green technology innovation of pilot enterprises, but will not significantly promote the quality of green technology innovation of pilot enterprises. Furthermore, we analyze the difference of the impact of margin trading and short selling on the quantity of green technology innovation of pilot enterprises in different periods. Finally, we find that the performance decline, yield gap between financial assets and operating assets, the risk of stock price crash, management shareholding, institutional shareholding ratio, product market competition, short selling intensity, margin trading intensity and formal environmental regulation intensity will affect the role of policy in promoting green technology innovation of pilot enterprises.
As the roadside perception plays an increasingly significant role in the Connected Automated Vehicle Highway(CAVH) technologies, there are immediate needs of challenging real-world roadside datasets for bench marking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. In this paper, we firstly introduce a challenging BAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations. By providing a real complex urban intersections and highway scenes, we expect the BAAI-VANJEE roadside dataset will actively assist the academic and industrial circles to accelerate the innovation research and achievement transformation in the field of intelligent transportation in big data era.