Scientific System Research Articles

DeepKernel: 2D-kernels clustering based mutant reduction for cost-effective deep learning model testing

Mutation testing is a practical approach for evaluating the quality of deep learning (DL) testing datasets. However, the enormous mutants during testing lead to significant testing overhead. Feature clustering is a conventional method that reduces the number of mutants while preserving the mutants’ distribution diversity. This distribution diversity is considered crucial for maintaining the effectiveness of testing assessment ability. DL model relies on convolutional kernels to extract data features and construct logic. Thus, using kernels to measure the differences among DL mutants is a feasible approach. This paper proposes DeepKernel, a convolutional kernel features clustering based reduction method. Specifically, it considers 2D-Kernel sparsity and 2D-Kernel entropy as kernel features. The features are clustered to construct a subset with equivalent testing assessment capability to the original set. Empirical studies on four classical DL models demonstrate that: (1) there is a significant correlation between the distribution diversity of the mutants and their testing assessment ability, as indicated by a Spearman Correlation Coefficient of 0.9689. (2) the reduced set maintains a similar distribution diversity and testing effectiveness as the original set. (3) when preserving the effectiveness of the mutation testing, our method reduces 63.47% of mutants and outperforms random selection.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Can Enhanced Information Systems and Citizen Science Improve Groundwater Governance? Lessons from Morocco, Portugal and Spain

New information and communication technologies have a significant potential to increase the transparency of aquifer management and improve groundwater governance. This research experiments the introduction of a mobile application that allows users to transfer and share information about their groundwater extractions and receive agroclimatic information and groundwater data. It takes place in three different aquifers in Morocco, Portugal, and Spain, each with varied institutional frameworks. This research tests and evaluates the potential of enhanced information systems and citizen science applied to groundwater management and aims to identify some factors that facilitate or hinder their adoption and implementation. To do this, the researchers developed a 4-year plan based on surveys, semi-structured interviews, meetings, participatory workshops, and public round tables with local actors. The main lesson learned from the comparative analysis of these three experiences is that the use of enhanced information systems is more positively perceived, accepted, and adopted when an appropriate social and institutional framework exists and that the more consolidated this framework is, the easier they will be to implement and develop enhanced information systems.

Bias in O-Information Estimation.

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information-theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of n=3 variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller-Maddow method is derived for the O-information. We find that for systems of n=3 variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information.

Bouncing bones-ancient wisdom meets modern science in a new take on locomotion.

Recognizing that conventional understanding of animal and human locomotion is based on a dated and reductionist machine modeling of organisms, we set out to create a theory of locomotion by reasoning from first principles. We center on the constraints necessitated by 1) the 2nd law of thermodynamics, 2) the theory of evolution, 3) a systems science view of organisms, and 4) the laws of motion, but we also look for compatibility these constraints might find in emerging areas of scientific inquiry (ecological psychology, processual biology, soft matter, biotensegrity), and in the wisdom embedded in various movement traditions and ancient philosophy. Applying and synthesizing these, we propose an updated "bouncing bones" (BB) model for walking and running, which corresponds with maximum efficiency and conservation of energy.

The Spatial Variation of Soil Structure Fractal Derived from Particle Size Distributions at the Basin Scale

The accurate characterization of soil structure is fundamental to groundwater science, environmental ecology, and Earth systems science. To address the challenge of quantifying the high spatial variability of large-scale soil structures, this study used a laser particle size analyzer to measure the distribution of soil particle size in 207 samples from ten profiles across the Daqing and Ziya River basins in the North China Plain. The quantified soil structure, expressed as soil fractal dimension D, was derived using monofractal theory. Various spatial analysis techniques, including Moran’s I index, correlation analysis heat maps, the Kolmogorov–Smirnov one-sample test, and geostatistical semivariogram function, were jointly applied to investigate the spatial variability of soil structural fractals across different depths in the piedmont plain–coastal areas of the two river basins. The results indicate the following: (1) Quantitative analysis confirms that under the influence of piedmont alluvial and fluvial dynamics, soil D values homogenize from the piedmont to the coastal areas, with decreasing particle size differences closer to the coast. However, the spatial variability of the soil structural fractals in the Ziya River Basin was greater than that in the Daqing River Basin. (2) The combined effects of climate change, regional differences, and human activity led to greater spatial variability in the soil structural fractals in the Ziya River Basin than in the Daqing River Basin. The correlation between D values and burial depth was strongest in the Xianxian profile (−0.78), whereas the spatial correlation was strongest in the Hengshui and Dacheng profiles (−0.47). (3) The greatest spatial variability in soil D values occurred at depths of 1–2 m, with a coefficient of variation of 23.595%, which was significantly higher than those at depths of 0–1 (14.569%) and 2–3 m (16.284%).

Statistical physics and emergent behaviour: from spin models to cultural complexity. In memory of Ralph Kenna (27.08.1964-26.10.2023)

It’s been sixty years since Ralph’s birth day and soon it will be one year since he left us. Initially, this collection of papers was being prepared as a Festschrift - a birthday greeting, and now it is published in his memory. It is difficult to write this foreword, because Ralph is not only a fellow physicist and a close collaborator, he is a very close friend. It is also difficult to write because, in a short text, it is hard to reflect the versatility of his gifts: from statistical physics to field theory, from the science of complex systems to quantitative methods in social sciences and humanities, from scientometrics to history of physics. This is not a complete list of areas that he enriched thanks to his talent and hard work. What’s more, his activities in scientific research are inseparable from his public activities, be it the protection of historical heritage or the restoration of historical memory in Ireland, his motherland, or the collection of funds for the fight against serious diseases in the UK, where he worked, or his participation in the organisation and evaluation of research work on a pan-European scale. The integrity of his personality is one of those features that first come to mind when we think about him, repeatedly.

Analyzing the innovation ecosystem of China's major science and technology projects: From the ecological and systems science perspective

The National Major Science and Technology Projects in China are characterized by government leadership, interdisciplinary collaboration, and international cooperation. However, challenges such as insufficient innovation capacity and inadequate commercialization of scientific achievements are becoming increasingly prominent. This paper adopts the conceptual framework of natural ecology to examine these projects. By utilizing theories and methods from ecology and systems science, we draw an analogy between the innovation ecosystem of China's national major science and technology projects and the structure of ecological systems, analyzing the interrelationships among the components of the innovation ecosystem and establishing the four-level model. The findings indicate that collaboration and policy guidance among the enterprise, research, and auxiliary innovation layers promote scientific output and innovation, while the external environmental layer provides robust support and safeguards, collectively driving the development of the innovation ecosystem. We study the operational mechanisms of the innovation ecosystem in depth, identifying mechanisms of competitive symbiosis, coordination, and sharing. These mechanisms work together throughout the project management process, offering new insights for enhancing the scientific and effective management of these projects. The national innovation ecosystem and high-quality development mutually reinforce and complement each other. As a critical pathway to high-quality development, our study provides new approaches to addressing the challenges of insufficient innovation capacity and the difficulty in translating scientific achievements into practice, contributing to the optimization of resource allocation, and the deep integration of technological innovation with economic and social development.

The impact of narcissistic leadership on organizational support: A study based on a sample of strategic emerging industries

AbstractWith the development of new science and technology, more and more strategic emerging industries have grown up close to the development of science and technology. Companies in these industries no longer emphasize the unilateral loyalty and commitment of employees to the organization but focus on building mutual loyalty and trust between the organizations and employees. Based on the leadership‐member exchange theory and organizational support theory, the study establishes a model of the influence mechanism of narcissistic leadership on organizational support and puts forward hypotheses. Using the questionnaire survey method, from the eight major fields involved in high and new technologies: electronic information, biology and new medicine, aerospace, new materials, high‐tech services, new energy and energy saving, resources and environment, advanced manufacturing and automation, and conducts empirical research on the 228 valid questionnaires recovered in a group manner. The relationship between narcissistic leadership and organizational support is explored at different power distance levels. Empirical results show that at low power distances, narcissistic leadership is negatively correlated with leadership identification. At high power shifts, narcissistic leadership is positively correlated with leadership identification. Regardless of the level of power distance, leadership identification will positively affect organizational support, and leadership identification mediates the relationship between narcissistic leadership and organizational support. This study has contributed to the study of narcissistic leadership and organizational support and has given relevant recommendations in combination with management practices. This study can help to understand the internal operation mode of the organization and provide theoretical support for system science. Besides, it guides the development of intelligent systems to evaluate the impact of leaders' behaviours accurately.

Spherical Magnetic Vector Forwarding of Isoparametric DGGS Cells with Natural Superconvergent Points

With the rapid advancement of satellite remote sensing technology, many scientists and organizations, including NASA, ESA, NAOC, and Roscosmos, observe and study significant changes in the geomagnetic field, which has greatly promoted research on the geomagnetic field and made it an important research direction in Earth system science. In traditional geomagnetic field research, tesseroid cells face degradation issues in high-latitude regions and accuracy limitations. To overcome these limitations, this paper introduces the Discrete Global Grid System (DGGS) to construct a geophysical model, achieving seamless global coverage through multi-level grid subdivision, significantly enhancing the processing capability of multi-source and multi-temporal spatial data. Addressing the challenges of the lack of analytical solutions and clear integration limits for DGGS cells, a method for constructing shape functions of arbitrary isoparametric elements is proposed based on the principle of isoparametric transformation, and the shape functions of isoparametric DGGS cells are successfully derived. In magnetic vector forwarding, considering the potential error amplification caused by Poisson’s formula, the DGGS grid is divided into six regular triangular sub-units. The triangular superconvergent point technique is adopted, and the positions of integration points and their weight coefficients are accurately determined according to symmetry rules, thereby significantly improving the calculation accuracy without increasing the computational complexity. Finally, through the forward modeling algorithm based on tiny tesseroid cells, this study comprehensively compares and analyzes the computational accuracy of the DGGS-based magnetic vector forwarding algorithm, verifying the effectiveness and superiority of the proposed method and providing new theoretical support and technical means for geophysical research.

Moving beyond simplistic representations of land use in conservation

AbstractLand use is both a major cause of the biodiversity crises and a potential solution to it. Decisions about land use are made in complex social–ecological systems, yet conservation research, policy, and practice often neglect the diverse and dynamic nature of land use. A deeper integration of land system science and conservation science provides major opportunities in this context, through a transfer of concepts, data, and methodologies. Specifically, a closer exchange between land‐use data developers and users will enable common terminology and better data use, allowing to move beyond coarse land‐cover representations of land use. Similarly, archetyping and regionalization approaches can help to embrace, rather than oversimplify, the diversity of land‐use actors and practices. Finally, systematically linking land‐use actors to portfolios of pressures on biodiversity, beyond their direct impact on habitat, can better represent and map co‐occurring and interacting threats. Together, this will enable conservation policymakers and planners to recognize the often‐complex and wicked nature of conservation challenges related to land, allowing for more context‐specific conservation policymaking and planning, and more targeted conservation interventions.

Research on the Multi level Training Model for Graduate Students Adapting to the New Positioning of Higher Education Institutions

With the rapid development of higher education and the diversification of talent demand in society, the types and positioning of higher education institutions are becoming increasingly diverse. As an important component of higher education, graduate education also needs to adjust and optimize its training mode accordingly. From the perspective of systems science, this article regards the entire process of cultivating professional degree graduate students as a system, from overall analysis to local research, opens up the graduate student training environment, maintains effective collaboration with various educational resources outside the training subject, focuses on the demand for high-level applied specialized talents in coal mining machinery equipment design and manufacturing in the development of the coal industry in Lvliang District, innovates the training mode, and proposes a collaborative training mode for mechanical degree graduate students in Lvliang University. The aim is to explore a multi-level training model for graduate students that adapts to the new positioning of higher education institutions, analyze the problems existing in the current graduate training model, and propose corresponding optimization strategies, in order to provide reference for the reform and development of graduate education in higher education institutions.

System behaviour of water and environmental law

Rules and procedures of mechanical things might be revealed by a piece by piece, linear analysis, while large unorganised masses can be approached by means of statistics. However, there is a vast middle layer of entities with complex structure and hardly calculable non-linear procedures that offer themselves for system science. System thinking makes possible that we understand wicked problems, perfect storms and other similar phenomena that otherwise resist the old, positivist, silo-thinking. In these cases, we learn that elements of a system form structures, develop procedures through which they together produce such results that could have been unimaginable solely by the elements alone. Is it possible that with the help of system thinking we will be able to better grasp the essence of laws, moreover, we can design and implement laws with higher effectiveness? Is law a system? Or rather a complex tiered system of sub-systems, embedded into larger social-economic systems? In order to test these questions we analyse two intriguing sets of legal problems, to learn if we can describe and interpret them as systems that are – like systems generally – protecting their integrity with accommodation and resilience. The first example is the development of the segment of water laws that regulates drilling of small size wells for farms, where we see a 140 years process off gradual, more and more successful accommodation to the double social needs of protecting the interests of small farmers and sustainable development in respect to underground waters. This system resisted successfully an abrupt change that would have not fit to its goals. The second example is climate law as such – a relatively new development in the short history of modern environmental law, which can be hardly called a success story, because of the stiff resilience of the relevant branches of law. Indeed, resilience of law, as a complex, flexible and effective system does not always work according to our wishes.

Study on Non-Point Source Pollution Prevention and Control System in Nansi Lake Basin Based on System Dynamics Approach

Agriculture, as an important activity on which human beings depend for their livelihood, brings serious environmental problems while meeting the needs of human survival, among which agricultural non-point source (NPS) pollution is one of the most urgent environmental problems. This study quantitatively assessed the loading characteristics spatial and temporal evolution patterns of two agricultural NPS pollutants, chemical oxygen demand (COD) and ammonia nitrogen (NH3-N), from 2010 to 2020 in the Nansi Lake Basin as an example, and constructed a system dynamics (SD) simulation model to simulate and analyze agricultural NPS pollution under different development and treatment scenarios, based on an investigation of the regional prevention and control strategy of agricultural NPS pollution and the technological system. The results show that the current status of agricultural NPS pollution load in the Nansi Lake Basin is poor, and the level of pollution load is high, showing obvious geographical differences. In terms of temporal changes, the pollution loads of the two pollutants showed a decreasing trend from 2010 to 2020, among which the pollution load of NH3-N showed the largest change. Spatially, the spatial distribution of each type of pollutant has some similarities, with smaller pollution loads in Jining and Zaozhuang and relatively larger pollution loads in Heze and Ningyang. The main source of COD pollution in the Nansi Lake Basin is rural life, with an emission proportion of 52.85%, and the main sources of NH3-N pollution from agricultural NPS pollution in the area are rural life and livestock and poultry farming, with emission proportions of 47.55% and 35.36%, respectively. Under the status quo continuum scenario, the pollution load values for COD are consistently higher than those for NH3-N, so the relative impact of COD is greater. In this study, the principles and methods of SD in system science are adopted to deal with the agricultural NPS pollution of Nansi Lake Basin, and the evolution of its behavioral characteristics are simulated, forecasted, and predicted, and policy experiments are conducted, with a view to providing references for the prevention and control of agricultural NPS pollution in Nansi Lake Basin and further research.

Exploring the determinants of community-oriented health professions education: A narrative review

Background: Community-oriented education is increasingly prioritized in health professions curricula, but evidence on determinants for effective implementation is dispersed. This study aimed to synthesize the key determinants to guide curriculum design, implementation and evaluation. Methods: This narrative review searched PubMed and Scopus databases for relevant studies which were screened against eligibility criteria. The main search terms that were utilized: (community* or “communityoriented” or “community-oriented curriculum” AND “health professions” OR “health professions education”). Determinants were extracted, analyzed thematically, and synthesized narratively. A concept framework was developed to visualize relationships between determinants. Results: Of 2789 records screened, 88 studies were included. Determinants were organized into eight themes: community needs´ relevance, priority health problems, integration level, community involvement, cultural sensitivity, social accountability, health systems science, and collaboration with organizations. Determinants centered on aligning education with local contexts and priorities through engaged partnerships. The relationships between determinants were suggested. Discussion: This study presents a preliminary framework of determinants crucial for effective community oriented education in health professions curricula. The expected hurdles were discussed and mitigating actions were suggested. Eight key themes were synthesized from disparate literature sources that underscore the importance of aligning educational initiatives with local contexts and emphasizing partnerships with communities. While this proposed framework provides a valuable starting point, further rigorous inquiry and validation through systematic reviews are necessary to establish definitive determinants.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Development of East system science of China: A decadal prospect

Development of East system science of China: A decadal prospect

Higher-Order Cellular Automata Generated Symmetry-Protected Topological Phases and Detection Through Multi Point Strange Correlators

In computer and system sciences, higher-order cellular automata (HOCA) are a type of cellular automata that evolve over multiple time steps and generate complex patterns, which have various applications, such as secret-sharing schemes, data compression, and image encryption. In this paper, we introduce HOCA to quantum many-body physics and construct a series of symmetry-protected topological (SPT) phases of matter, in which symmetries are supported on a great variety of subsystems embbeded in the SPT bulk. We call these phases HOCA-generated SPT (HGSPT) phases. Specifically, we show that HOCA can generate not only well-understood SPTs with symmetries supported on either regular (e.g., linelike subsystems in the two-dimensional cluster model) or fractal subsystems, but also a large class of unexplored SPTs with symmetries supported on more choices of subsystems. One example is that has either fractal and linelike subsystem symmetries simultaneously or two distinct types of fractal symmetries simultaneously. Another example is in which chaotic-looking symmetries are significantly different from and thus cannot reduce to fractal or regular subsystem symmetries. We also introduce a new notation system to characterize HGSPTs. We prove that all possible subsystem symmetries in a square lattice can be locally simulated by an HOCA-generated symmetry. As the usual two-point strange correlators are trivial in most HGSPTs, we find that the nontrivial SPT orders can be detected by what we call . We propose a universal procedure to design the spatial configuration of the multi point strange correlators for a given HGSPT phase. Specifically, we find deep connections between multi point strange correlators and the spurious topological entanglement entropy (STEE), both exhibiting long-range behavior in a short-range entangled state. Our HOCA approaches and multi point strange correlators pave the way for a unified paradigm to design, classify, and detect phases of matter with symmetries supported on a great variety of subsystems, and also provide potential useful perspective in surpassing the computational irreducibility of HOCA in a quantum mechanical way. Published by the American Physical Society 2024

Curriculum Development and Teachers for Cultivating OECD Student Agency in Japan: From an Examination of Educational Thought, Educational System, History, and Contemporary Science and Mathematics Education Practices

Curriculum Development and Teachers for Cultivating OECD Student Agency in Japan: From an Examination of Educational Thought, Educational System, History, and Contemporary Science and Mathematics Education Practices

Social trust and COVID-19 mortality in the United States: lessons in planning for future pandemics using data from the general social survey

BackgroundThe United States has lost many lives to COVID-19. The role of social capital and collective action has been previously explored in the context of COVID-19. The current study specifically investigates the role of social trust at the county level and COVID-19 mortality in the US, hypothesizing that counties with higher social trust will have lower COVID-19 mortality rates.MethodsWe used cross-sectional data from the General Social Survey (GSS). We collected COVID-19 mortality data from the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University until October 31, 2021. We obtained county characteristics from the 2019 American Community Survey and supplemented this data source with additional publicly available county-level data, such as measures of income inequality and political leanings. We measured social trust as a single item from the GSS and calculated mean social trust in a county by pooling responses from 2002 to 2018. We then modeled the relationship between mean social trust and COVID-19 mortality.ResultsResults indicate that counties with higher social trust have lower COVID-19 mortality rates. Higher values of mean social trust at the county level are associated with a decrease in COVID-19 mortality (b= -0.25, p-value < 0.001), after adjustment for confounding. The direction of association is consistent in a sensitivity analysis.ConclusionsOur findings underscore the importance of investment in social capital and social trust. We believe these findings can be applied beyond the COVID-19 pandemic, as they demonstrate the potential for social trust as a method for emergency preparedness.

Building a solid foundation: advancing evidence synthesis in agri-food systems science

Enhancing the reliability of literature reviews and evidence synthesis is crucial for advancing the transformation of agriculture and food (agri-food) systems as well as for informed decisions and policy making. In this perspective, we argue that evidence syntheses in the field of agri-food systems research often suffer from a suite of methodological limitations that substantially increase the risk of bias, i.e., publication and selection bias, resulting in unreliable and potentially flawed conclusions and, consequently, poor decisions (e.g., policy direction, investment, research foci). We assessed 926 articles from the Collaboration for Environmental Evidence Database of Evidence Reviews (CEEDER) and recent examples from agri-food systems research to support our reasoning. The analysis of articles from CEEDER (n = 926) specifically indicates poor quality (Red) in measures to minimize subjectivity during critical appraisal (98% of all reviews), application of the eligibility criteria (97%), cross-checking of extracted data by more than one reviewer (97%), critical appraisal of studies (88%), establishment of an a priori method/protocol (86%), and transparent reporting of eligibility decisions (65%). Additionally, deficiencies (Amber) were found in most articles (&amp;gt;50%) regarding the investigation and discussion of variability in study findings (89%), comprehensiveness of the search (78%), definition of eligibility criteria (72%), search approach (64%), reporting of extracted data for each study (59%), consideration and discussion of the limitations of the synthesis (56%), documentation of data extraction (54%) and regarding the statistical approach (52%). To enhance the quality of evidence synthesis in agri-food science, review authors should use tried-and-tested methodologies and publish peer-reviewed a priori protocols. Training in evidence synthesis methods should be scaled, with universities playing a crucial role. It is the shared duty of research authors, training providers, supervisors, reviewers, and editors to ensure that rigorous and robust evidence syntheses are made available to decision-makers. We argue that all these actors should be cognizant of these common mistakes to avoid publishing unreliable syntheses. Only by thinking as a community can we ensure that reliable evidence is provided to support appropriate decision-making in agri-food systems science.