Scientific Enterprise Research Articles

Predators

Predators

Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine.

Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.

Study on the Evolutionary Game of Cooperation and Innovation in Science and Technology Town Enterprises

Developing science- and innovation-based special towns plays a pivotal role in new urbanization, and enterprise cooperative innovation can help accelerate the development of high-quality science- and innovation-based special towns. A dynamic evolutionary model of enterprise cooperative innovation under two different mechanisms of market law and government regulation is developed for a government-led science and innovation town. The influence of various influencing factors, including willingness to cooperate, on the choice of enterprise cooperative innovation strategy is examined, and MATLAB simulation is used to verify the analysis and provide suggestions for promoting the sustainability of enterprise cooperative innovation. As a result of the study, it is shown that factors influencing the choice of cooperative innovation strategy for enterprises under the market mechanism include the cost and benefits of cooperative innovation, the degree of willingness to cooperate, and the degree of effort. When the market mechanism fails, government subsidies and incentive bonuses are more effective than either alone when it comes to encouraging firms to adopt cooperative innovation strategies. When government penalties are lower than free-rider benefits, they cannot influence firms’ speculative behavior. It is found that only a penalty intensity above the threshold can effectively inhibit the phenomenon of free-riding and motivate enterprises in science- and innovation-oriented characteristic towns to choose a cooperative innovation strategy, and the greater the penalty intensity, the faster the enterprises will tend to cooperate and innovate.

Equity and justice in science education: Toward a pluriverse of multiple identities and onto‐epistemologies

AbstractConcepts in science education such as “science identity” and “science capital” are informed by dominant epistemological and ontological positions, which translate into assumptions about what counts as science and whose science counts. In this theoretical paper we draw on decolonial and antiracist perspectives to examine these assumptions in light of the heterogeneous onto‐epistemological and axiological values, cultural perspectives, and contributions of nondominant groups, and specifically of those who have been historically marginalized based on their gender, race, ethnic, age, and/or social class identity. Building on these arguments, we critique deficit‐based approaches to science teaching, learning, and research, including those that focus on systemic injustice, yet leave intact dominant framings of the scientific enterprise, which are exclusionary and meritocratic. As an alternative, we offer a design of science teaching and learning for the pluriverse—“a world where many worlds fit”. This alternative allows us to reconstruct science and science‐related “outcomes,” such as identity, in the service of cultural, epistemic, and linguistic pluralism. We close the paper with the idea that because mainstream theories reproduce deficit framings and educational injustices, we must engage with decolonial1theories of pluriversality and discuss different onto‐epistemologies to be able to grapple with existing social, racial, environmental injustices, and land‐based devastations.

Scientific and Technological Innovation Rapid Emergency Resource Constraint-Improved Particle Swarm Optimization Project Scheduling Method

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.

CREATION OF A MOBILE INSTALLATION FOR SORTING GRAIN IMPURITIES

The problem of the imperfection of the process of cleaning grain masses in time for refinement to basic quality indicators is considered. It is shown that grain waste contains a significant amount of grain and oilseed admixture. The state of waste generation at the enterprises of the elevator, grain processing and oil and fat industries is described. An analysis was made of the prospects for their processing as raw materials for the production of food products – cereals, vegetable oils and others. It is emphasized that in the conditions of the declared state of war in Ukraine, the allocation of "valuable fractions" from grain waste is financially relevant. A mobile plant for sorting grain waste has been developed and its production tests have been carried out. The rational productivity of such a machine is noted - no more than 48 tons per day (for grain waste). This will ensure the processing of sunflower waste up to 1000 tons of sunflower seeds per day, and the processing of grain crop waste – up to 800 tons of grain per day. It is shown that the developed sorting machine is advisable to use in small farms, where the volume of grain waste is 140-150 kg per hour, and sunflower waste and husks – 100-120 kg per hour. In the conditions of the Limited Liability Company Scientific and Production Enterprise "Sorting Machines" in the city of Dnipro, a study was made of the process of sorting grain waste on a developed mobile unit. The test results confirmed the productivity of the sorting machine. The design of the main unit of a mobile unit for sorting grain masses – a channel for sorting – is described. Improvements in the design of the sorting machine have been proposed and a package of computer programs has been developed for sorting sunflower, wheat, soybean, amaranth, hemp, and rapeseed waste. In the course of research, the necessity of using an anti-adhesive coating and a special form of a vibrating feeder of the developed machine was proved. This allows you to reduce the cleaning time of the feeder, eliminate the compaction of raw materials and eliminate areas of accumulation of grain material. In the program of the sorting unit, a separation channel cleaning module has been developed, which has two modes - the frequency and duration of the channel purge. The description and technical characteristics of the developed mobile machine for sorting grain waste are given.

Unpacking climate services: Knowledge politics, beneficent humanitarianism and the realpolitik of risk management in China

In the last four decades, climate services (CS) have moved from being limited forecasting tools in their predictive capacity to becoming involved in the shaping of risk assessment instruments with global reach affected to enhance adaptation to climate change. Yet, they have been relatively overlooked by human geographers and critical risk theorists, whose interests have been to document the political processes involved in shaping climate change and the global scientific enterprise it has generated. By looking at the ways in which CS have been developed and exported to countries deemed as climate-vulnerable, the paper sheds light on two simultaneous kinds of knowledge politics that are occurring at the interstices of global human security aspirations and the realpolitik of local practices. The first emerges from the ways in which CS political relevance has been secured by climate scientists in the midst of grand developmentalist and humanitarian ambitions, what we have called beneficent knowledge politics. The second comes from the nitty-gritty of risk management practices in countries to which CS are exported, in this case China, and highlights how a myriad of knowledge and sensitivities involved in shaping risk and science have been overlooked by the superseding ideals underpinning the production of CS and their application to wider climate adaptation agenda. By doing so, the paper contributes to the geographies of risk and emergencies as well as to the geographies of science by enhancing our understanding of the knowledge politics at play in the development of and resistance to technocratic climate governance.

In-Between the Lines and Pixels: Cartography’s Transition from Tool of the State to Humanitarian Mapping of Deprived Urban Areas

Cartography has been, in its pre-modern and modern production of maps, influential in determining how space and territory is experienced and defined. But advancements in telecommunications and geovisualization software, along with geoinformation systems and geoinformation science (GIS), have transformed cartographic practice from a tool of dominantly state apparatus to a scientific, commercial, and humanitarian enterprise. This is exemplified in the use of remote sensing (RS) techniques to acquire, process, and visualize images of the Earth. In the last decade, RS techniques have increasingly incorporated Artificial Intelligence (e.g., Convolutional Neural Networks) to improve the speed and accuracy of feature extraction and classification in remotely sensed images. This paper will investigate the use of CNNs in the classification of deprived urban areas referred to as “slums” and “informal settlements” in the Global South. Using a postphenomenological methodology, this paper shall analyze the role of classification and use of geoinformation in shaping how deprived urban areas are algorithmically classified. This analysis will reveal that besides the technical opportunities and challenges, attention needs to be given to three ethical areas of concern: how deprived area mapping using AI impacts the agency of communities, how there is a potential lack in the democratization of these RS technologies, and how the privacy and data protection of communities being mapped is endangered.

Городской код: политика городского моделирования в эпоху больших данных

A model is "any representation or concept that helps us to understand the world whenever common sense or direct observations are inadequate." Common sense and direct observation often prove inadequate to the complexities of the twenty-first-century cities. Thus, models abound in urban life and governance. However, a model is not only a tool for control but a way of defining a situation. Framing the city so as to render it susceptible to interpretation and intervention is an exercise not merely with scientific or technological value but with rhetorical power. The tradition of comprehensive urban models, beginning with the advent of computers and culminating in the self-analyzing "smart city," I argue, sidelines this rhetorical power in favor of a tone of scientific authority that, while justifiable in technical domains, does not legitimately scale to the level of a political community. Making good on the civic potential of Big Data thus requires recontextualizing properly scientific enterprises within an adequate political philosophy of the city, allowing for the construction of cultural urban models that set human freedom at the core of its inner workings.

Research on the Factors Influencing Business Growth Based on ANN-MLP Algorithm Title Case Taking China Maotai Group as an example

The growth of enterprises is the key to the national economy and even the annual economic recovery. Therefore, it is of great theoretical significance and practical value to construct a scientific and effective enterprise growth evaluation model. Multi-layer perceptron (MLP), as a typical representative of artificial neural network (ANN), has the performance of rapid computation and high fault tolerance. In this paper, we take the enterprise data and social data of Maotai Group from 2001 to 2020 as samples, combine enterprise growth theory and, through the literature research method, initially select seven indicators to evaluate enterprise growth and twenty-seven indicators as factors affecting enterprise growth. Then, Kendall correlation coefficient was used to reduce the dimensions of indicators, and the index system of enterprise growth and influencing factors were obtained. Finally, the MLP algorithm was used and optimized to optimize the robustness of the model. The results show that the final model is highly accurate, with MSE of 0.033 and R2 of 0.942, and return on assets and GDP have the greatest influence on firm growth. In other words, the profitability of an enterprise and the country's overall economic prosperity can largely influence the growth of an enterprise, and the stronger the profitability of an enterprise, the stronger its growth ability will be in the era of continuous national economic development. The model has a certain reference value for enterprises to formulate development strategies and the government to formulate macro policies to promote economic growth.

Deep Learning-Based Optimization Algorithm for Enterprise Personnel Identity Authentication.

Enterprise strategic management is not only an important part of enterprise work, but also an important factor to deepen the reform of management system and promote the centralized and unified management of enterprises. Enterprise strategic management is to study the major problems of survival and development of enterprises in the competitive environment from the overall and long-term point of view. It is the most important function of senior leaders of modern enterprises. Starting from the characteristics of the recognition object, this paper analyzes the individual differences of biometrics through intelligent face image recognition technology to identify biometrics, which can be used to identify different individuals. This paper studies the main problems of personnel identity authentication in the current enterprise strategic management system. Based on identity management and supported by face image recognition technology, deep learning, and cloud computing technology, the personnel management model of the management system is constructed, which solves the problems of personnel real identity authentication and personnel safety behavior control. Experiments show that the model can simplify the workflow, improve the operation efficiency, and reduce the management cost. From the perspective of enterprise system development, building a scientific enterprise strategic management system is of great significance to improve the scientific level of enterprise system management.

Predictors and consequences of intellectual humility.

In a time of societal acrimony, psychological scientists have turned to a possible antidote — intellectual humility. Interest in intellectual humility comes from diverse research areas, including researchers studying leadership and organizational behaviour, personality science, positive psychology, judgement and decision-making, education, culture, and intergroup and interpersonal relationships. In this Review, we synthesize empirical approaches to the study of intellectual humility. We critically examine diverse approaches to defining and measuring intellectual humility and identify the common element: a meta-cognitive ability to recognize the limitations of one’s beliefs and knowledge. After reviewing the validity of different measurement approaches, we highlight factors that influence intellectual humility, from relationship security to social coordination. Furthermore, we review empirical evidence concerning the benefits and drawbacks of intellectual humility for personal decision-making, interpersonal relationships, scientific enterprise and society writ large. We conclude by outlining initial attempts to boost intellectual humility, foreshadowing possible scalable interventions that can turn intellectual humility into a core interpersonal, institutional and cultural value.

Interrupting pathways to health inequities in citizen science health research.

Participatory research in multiple forms is rapidly gaining ground and stands to provide valuable benefits in the area of health research. Citizen science, an increasingly popular form of participatory research, aims to involve laypersons in knowledge production and, as such, can offer new insights that may not be readily accessible by the traditional scientific research enterprise. Heralded as an advance in the democratization of science, citizen science stands as a potential player in evidence generation. Yet, while citizen science offers advantages through its emphasis on lay participation in uncovering information likely to be pertinent to improving health outcomes, without deliberate attention to pathways to inequity and oversight, some aspects can operate to perpetuate or exacerbate health inequities. Drawing on health disparities literature to assist in identifying possible pathways, this work aims to provide support for greater attention to 'process', which would include active engagement with intersecting inequities as a way of interrupting pathways to health inequity. If citizen science is to be true to its moniker, it cannot be 'equity-neutral' in a society where structural or systemic inequities are present. It must be equity-promoting. In the health context, the concerns of justice evolve into concerns of differential health impacts. To accomplish this, equity and justice, as well as representation and fair process, become critically important. Accordingly, this paper foregrounds proactive commitment to equity by confronting the reality of social inequalities present in a given society and adapting the project accordingly to minimize downstream health inequities.

Quantifying the rise and fall of scientific fields.

Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies.

Framework Design of Science and Technology Venture Capital Salary Management System Driven by Blockchain Technology

The aim of this article is to study the framework design of science and technology venture capital salary management system driven by blockchain technology. Firstly, it expounds on the background and research significance of enterprise salary management. According to the current theory and research direction of salary management in domestic science and technology enterprises, combined with the mode of salary management in domestic enterprises, it analyzes the current situation and existing problems of enterprise salary management and the relevant algorithms driven by blockchain technology, among which the typical is consensus algorithm. Then, the salary management system is established, and the use cases of the system are analyzed according to the operation and business process of the system. Finally, according to the system requirements analysis, the overall network and software framework of the system are designed, the main business processes of the system are designed, and the database solution is put forward. The system test shows that the system finally completes the basic business of enterprise salary management, the operation interface is simple and convenient, the program runs stably, and can achieve the expected effect of demand analysis.

The search for genetic dark matter and lessons learned from the journey.

In this review, I describe our scientific journey to unearth the impact of RNA metabolism in cancer using the eukaryotic translation initiation factor eIF4E as an exemplar. This model allowed us to discover new structural, biochemical, and molecular features of RNA processing, and to reveal their substantial impact on cell physiology. This led us to develop proof-of-principle strategies to target these pathways in cancer patients leading to clinical benefit. I discuss the important role that the unexpected plays in research and the necessity of embracing the data even when it clashes with dogma. I also touch on the importance of equity, diversity, and inclusion to the success of the scientific enterprise.

Behavioral additionality: the role of cooperation with research institutions in fostering technological maturity of enterprises

Abstract The main aim of the article is to examine how cooperation with research institutions influences technological maturity of enterprises. It is accompanied with the additional aim, which is to assess the role of innovation policy interventions in this process, as captured by the concept of behavioral additionality, which is one of the externalities of public support for R&D. The focus of this article is put on one of the specific types of behavioral additionality—cooperation additionality—which takes place in the situation where public support influences the collaboration behavior of a firm receiving R&D support. The study was conducted with the use of firm-level data collected in a survey on the sample of 464 enterprises operating within national smart specializations in Poland. The results of the research suggest that public funding fosters the cooperation between scientific institutions and enterprises, thus confirms the cooperation additionality.

Exploring Epistemic Humility and its Limits in Therapeutics

Exploring Epistemic Humility and its Limits in Therapeutics Douglas Porter, MD* (bio) I would like to thank Scott Waterman for sharing what must have been very challenging personal circumstances. Waterman's reflections touched upon many complex issues. For my part, I would like to explore how Waterman's experience underscores the importance of epistemic humility regarding the limits of scientific knowledge and our sense of the meaning of scientific knowledge while still recognizing the significance and power of scientific knowledge. The first epistemic challenge presented by Scott Waterman's experience of "patienthood" that demonstrates the limits of scientific knowledge is the ability to know pain. Of course, we have all known pain, but such firstperson knowledge would not qualify as scientific knowledge. To scientifically know pain, we would need to have an intersubjectively verifiable correlate of the pain. This is all well and good so long as scientific knowledge is not embraced with a form of epistemic hubris such that scientific knowledge is regarded as the only true form of knowledge. It is not uncommon for the knowledge associated with first-person experience to be denigrated as inherently dubious and merely subjective. Phenomenologists (see Merleau-Ponty, 1964, for example) have pointed out that ultimately the veracity of scientific knowledge must rely upon the veracity of knowledge gained from lived experience and, consequently, regarding first-person experience as inherently invalid is tantamount to regarding the entire scientific enterprise as invalid. Epistemic hubris regarding scientific knowledge may therefore be regarded as self-contradictory and irrational. But, when epistemic hubris regarding scientific knowledge becomes wed, as it sometimes does, to a certain form of metaphysical thinking then epistemic hubris becomes downright pernicious. The metaphysics I am referring to would regard reality as that which is scientifically knowable and, ipso facto, that which cannot be known scientifically as not really real. Such thinking would regard the experience of pain as merely an epiphenomenon to what is really going on which is, loosely put, some nerve signals firing away. Such thinking leads to a perverse inversion of priorities in therapeutics where the person experiencing pain should be understood as the salient phenomenon and the value of scientific knowledge must ultimately be measured by its relevance for addressing that experience of pain. In this sense I can echo Scott Waterman's assertion that humanism and the concern for scientific rigor should not be regarded [End Page 111] as antithetical or even complementary, but are better regarded as being cut from the same cloth. It is the humanist knowledge of the significance and meaning of suffering, in this case the distress of experiencing chronic pain, that motivates and measures the concern for the tools of science to intervene and relieve that suffering. Science untied to humanist moorings in therapeutics is science gone rogue. Waterman's experience with chronic pain demonstrated the need for epistemic humility regarding the limits of scientific knowledge. I think it also underscores the need for humility involving our sense of the exact meaning of science. A friend of mine who happens to be an anthropologist recently attended a party with members of the physics department and found the status of anthropology as a "science" to be met with some incredulity by some of the physicists in attendance. It seems to me that hubris regarding physics being the paradigmatic science would severely hamper any meaningful scientific assessment of the treatment of pain. Waterman explored the scientific relevance of the purported mechanisms of action for several treatment alternatives for pain. The etymology of the term mechanism refers to machines. As I have never known a machine to experience pain, I immediately questioned the limits of mechanistic thinking regarding this topic. But what has been dubbed "the new philosophy of mechanics" (Craver & Tabery, 2019) has jettisoned some of the cartesian physicalist assumptions originally associated with mechanistic reasoning in science. This new philosophy has embraced a more liberal stance to accommodate the myriad ways in which explanatory mechanisms are actually used in scientific practice. As an example, Illari and Williamson (2012) offered this definition of a mechanism for science, "A mechanism for a phenomenon consists of entities and activities organized in such a way that they are...

Application of Machine Learning in Enterprise Risk Management

With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.

Research on Sustainable Development Strategy of Chinese Small and Medium-sized Enterprises

Small and medium-sized enterprises are an important part of China's national economy. They play an irreplaceable role in the national economy and social development by constructing the main body of China's market economy, promoting economic development, promoting social employment and promoting social stability. At present, small and medium-sized scientific and technological enterprises, as the group with the greatest development potential and innovation vitality, play an important role in the process of building an innovative country in China. However, the short average life span is a common problem in China's small and medium-sized scientific and technological enterprises. After the financial crisis, due to the worldwide economic recession and the changes of China's macro-control policies, small and medium-sized enterprises are facing the problem of financing difficulties. At the same time, China's small and medium-sized enterprises generally have a big gap compared with domestic large enterprises and multinational companies in terms of technology, internal and external system construction and perfection. The sustainable development of small and medium-sized enterprises lies not only in the improvement of technical ability, but also in the establishment of internal and external systems.