View Of System Research Articles

The impact of human-AIGC tools collaboration on the learning effect of college students: a key factor for future education?

PurposeThe maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.Design/methodology/approachIn this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.FindingsThe usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.Research limitations/implicationsBased on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.Originality/valuePrevious studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.

The Problem of Stakeholder Governance

We reply to a comment by Hargrave and Smith on a paper in which we use Ostrom’s design principles to develop theory about stakeholder governance. We agree with most of the issues that Hargrave and Smith raise, but disagree with their statement that the systems view of stakeholder governance that they advocate stands in contrast to our approach. The two approaches are complementary.

A Systematic Local View of the Long-Term Changes of the Atmospheric Energy Cycle

Abstract The global climatology and long-term changes of the atmospheric energy cycle in the boreal winter are analyzed using a local available potential energy framework using the fifth generation ECMWF reanalysis data during 1979 to 2021. In contrast to the classic Lorenz energy cycle, this local framework provides the local information of available potential energy (APE) and its interactions with kinetic energy (KE), allowing the systematic study of barotropic and baroclinic processes. A further systematic decomposition of the APE and KE reservoirs into high- and low-frequency components is conducted to investigate the source and sink terms that account for their spatial variations and long-term changes. The climatology of the local energetics budget terms shows that the interplay between low-frequency APE and KE is mostly over the tropical and polar regions associated with the thermally direct circulation there. Interactions involving high-frequency components are mainly located in the storm track regions. We show that under recent global warming, a prominent dipolar trend of changes is present over the Asian-Pacific region for all energy forms. The long-term changes in the energy budget reveal how high-frequency APE intensification is due to a stronger conversion from low-frequency APE upstream of the storm track, and that this intensification can be compensated by eddy advection away from the storm track, rather than conversion to kinetic energy in the proximity of the mean jet. This provides evidence that a warmer climate substantially affects the energy cycle and, thus, the atmospheric circulation.

Successful Community Infrastructure Risk Management in a Decarbonized Future

It remains uncertain how a decarbonized economy will function and how organizational roles will need to adapt. Irrespective, the climate is forcing a changing risk context, and organizations and communities are in transition, whether actively engaged or not. Managing the emergent risks is critical to a successful transition and community survival. However, it requires a system of systems view. The asset and function-based investment practice does not reflect value. Community transition is complex and persistent efforts to simplify aspects in isolation and project familiar models based on no-longer-valid assumptions that overcomplicate the calculus no longer suffice. Successful risk management of community transition to a decarbonized future requires a shared understanding of the outcome across all stakeholders to build a sense of ownership and partnership. Each step in that transition must follow a risk-sequenced progression that is measurable and transparent, ideally independently validated. Community transition risk management relies on social capital and delivers enhanced economic benefits. This article advocates an infrastructure systems planning approach instead of an asset-based one.

Inference of single-cell network using mutual information for scRNA-seq data analysis

BackgroundWith the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.ResultsWe introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.ConclusionsSINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM.

A conceptual framework for the government big data ecosystem (‘datagov.eco’)

The public sector, private firms, and civil society constantly create data of high volume, velocity, and veracity from diverse sources. This kind of data is known as big data. As in other industries, public administrations consider big data as the “new oil" and employ data-centric policies to transform data into knowledge, stimulate good governance, innovative digital services, transparency, and citizens' engagement in public policy. More and more public organizations understand the value created by exploiting internal and external data sources, delivering new capabilities, and fostering collaboration inside and outside of public administrations. Despite the broad interest in this ecosystem, we still lack a detailed and systematic view of it. In this paper, we attempt to describe the emerging Government Big Data Ecosystem as a socio-technical network of people, organizations, processes, technology, infrastructure, standards & policies, procedures, and resources. This ecosystem supports data functions such as data collection, integration, analysis, storage, sharing, use, protection, and archiving. Through these functions, value is created by promoting evidence-based policymaking, modern public services delivery, data-driven administration and open government, and boosting the data economy. Through a Design Science Research methodology, we propose a conceptual framework, which we call ‘datagov.eco’. We believe our ‘datagov.eco’ framework will provide insights and support to different stakeholders’ profiles, including administrators, consultants, data engineers, and data scientists.

How does the role of complementary and alternative medicine in general practice differ between countries? Interviews with doctors who have worked both in Germany and elsewhere in Europe

BackgroundAvailable data suggest that general practitioners (GPs) in Germany use complementary and alternative medicine (CAM) modalities more frequently than GPs in many other countries. We investigated the country differences perceived by general practitioners who have worked in Germany and in one of four other European countries with regard to the role of complementary and alternative treatments in primary care.MethodsIn this qualitative study we conducted semi-structured interviews with 12 GPs who had worked both in Germany and Italy, the Netherlands, Norway or the United Kingdom (UK; n = 3 for each of the four countries). Participants were asked how they perceived and experienced country differences regarding health system, relevance of CAM modalities, the role of evidence-based medicine (EBM) and science, and how they handle so-called indeterminate situations. For the analysis, we followed a thematic analysis approach according to Braun and Clarke with focus on themes that cover CAM.ResultsParticipants unanimously reported that they perceived CAM to be more relevant in general practice in Germany compared to the other countries. We identified four overarching themes in relation to the perceived reasons for these differences. Firstly, physicians with experiences in countries with a strong EBM and science orientation (Netherlands, Norway and the UK) considered the deeply ingrained view in national healthcare systems and GP communities that CAM modalities are not evidence-based as the main reason for the lower use of CAM by GPs. Secondly, extensive training of communication skills was cited as a reason that reduced the need for CAM in the Netherlands, Norway and the UK. Thirdly, differences in patient expectations and demands were perceived as a factor contributing to greater utilisation of CAM by German GPs compared to the other countries. Finally, country-specific reimbursement mechanisms were considered as a factor influencing the role of CAM in general practice.ConclusionsThe study results point to major differences between countries with regard to the role of CAM in GP care. Differences in basic attitudes in the discipline of general practice, patient expectations and system conditions appear to play an important role here.

Compliance in the public sector from a systemic perspective: a bibliometric analysis

The objective of this bibliometric analysis was to investigate the development of publications on compliance, systemic view, and the public sector, using the Scopus and Web of Science databases. The aim was to identify the productivity of authors, the frequency of keywords, the geographical distribution of publications, and the temporal evolution of studies. The methodological procedures included the definition of databases, selection of constructs and related terms, inclusion and exclusion criteria, data collection, and analysis. The results show that publications on these topics began to gain relevance from 2000 onwards, intensifying between 2019 and 2023. The United States leads the scientific production, followed by China and the United Kingdom. The study also indicates the researchers with the highest number of publications on the topic. The most frequent keywords were "regulate", "governance", "policy", "system", "theory", and "sustainable". The analysis indicates the growth of research in these topics, demonstrating their importance in promoting transparency, responsibility, and efficiency in public administration, as well as the need for effective and ethical public policies.

Assessment of Li Hongzhang's foreign policy from the point of view of the modern political system of the People's Republic of China

Assessment of Li Hongzhang's foreign policy from the point of view of the modern political system of the People's Republic of China

Pathological manifestations in a historic building: post-fire scenario at Casarão dos Fabricantes

The objective of this study is to document the pathological manifestations of the Casarão dos Fabricantes, one of the oldest buildings in Fortaleza, which was severely damaged by a major fire in 2020. Despite its historical significance, there are no records detailing the condition of the building either before or after the fire. The internal walls and two facades were the only construction elements that were not destroyed by the fire. The survey of the pathological manifestations was conducted by visual assessment and infrared thermography, revealing different degradation processes across the evaluated structures and providing a systematic view of the extent of the damage to the building. Mapping this damage provided an integrated approach, offering a scientific basis to support the restoration process.

High-strength aluminum alloy processed by micro laser powder bed fusion (μ-LPBF): Coordination of laser formability, microstructure evolution, and mechanical properties

As the scale of additive manufacturing process (e.g., laser spot size, powder particle size, powder layer thickness) decreases, the application of micro laser powder bed fusion (μ-LPBF) involves novel mechanisms for process, microstructure and performance coordination. This study provides a systematic view of the processing window and performance enhancement of high-strength Al-Mg-Sc-Zr alloy fabricated by μ-LPBF. The effects of μ-LPBF parameters on defect control and densification activity of the printed parts were analyzed, so as to obtain the suitable processing window. The influence of building orientation and heat treatment on microstructural characteristics and mechanical properties of μ-LPBF processed parts was studied. The μ-LPBF Al-Mg-Sc-Zr exhibited sound surface quality (Ra of 6.088 μm) and considerably refined grains with an average size of 1.102 μm, which was related to the high cooling rate (8.6 × 107 K/s) induced by a small-sized laser beam (25 μm) and a tiny powder particle size distribution (2–20 μm) applied in μ-LPBF. After aging treatment (325 °C/4 h), the superior ultimate tensile strength of 590.24 ± 4.75 MPa combined with the sufficiently high elongation of 11.99 ± 1.17 % was achieved. Due to the significantly decreased scale of μ-LPBF production, the anisotropy caused by the variation of building directions was negligible. These enhanced mechanical properties were attributed to the combined effect of the grain size refinement, the higher number density (1.2 × 1024/mm3) of interior precipitates within grains, and the small-sized molten pool size of μ-LPBF. Computational fluid dynamics (CFD) simulation was applied to reveal the molten pool thermodynamics, indicating that a higher thermal temperature gradient (up to 9.8×107 K/m), a smaller molten pool size (with the width of 38.7–69.8 μm and depth of 8.7–30.0 μm) were generated in μ-LPBF. This work presents great potential in processing high-precision metallic components with fine structural feature size and satisfactory manufacturing quality.

Systemic preparation of future mathematics teachers for training schoolchildren to solving Olympiad problems

The article actualizes the problem of developing the readiness of a future mathematics teacher to teach schoolchildren to solve Olympiad problems. Based on the analysis of scientific and methodological sources and the results of mathematics students’ self-assessment of their readiness for this type of activity, the need for a systematic approach to solving the identified problem is substantiated. The study outlines the author's view of the university training system aimed at developing future mathematics teachers with mathematical and methodological competencies that determine their readiness to teach schoolchildren to solve Olympiad problems, and outlines the problems that may arise during its implementation.

Systemic View of the Role of Higher Educational Institutions in the Great Reset

The Great Reset is a concept proposed by the World Economic Forum to rebuild the global economy sustainably following the COVID-19 pandemic, emphasising stakeholder capitalism, greener practices, and more equitable social contracts. However, most of the literature focuses on the role of business and government actors in the Great Reset. At the same time, research that considers the role of higher education institutions (HEIs) is lacking. However, HEIs have the significant capacity to support various aspects of the Great Reset. In this work, we examine different mechanisms by which HEIs could bring change, such as fostering the growth of workforce skills, promoting entrepreneurship and innovation, participating in community development and others. A survey on a sample of university professors, postdoctoral researchers, and research assistants has been conducted with the goal of evaluating the degree to which HEIs have played a role in influencing economic recovery after COVID-19, leveraging the industry 4.0, enhancing regional development, revitalising global cooperation, formulating sustainable business models, restoring environmental well-being, and restructuring social contracts, skills, and employment opportunities. A two-step cluster analysis has been conducted, indicating that HEIs played different roles in the Great Reset process, being the Leaders, Followers and Laggers. We found a significant difference between the various groups of respondents according to their scientific field, position, and region regarding the perception of the role played by their HEIs in influencing economic recovery after COVID-19. The position of the respondents contributed to their perception of the role that their HEIs participated in the process of the Great Reset, with the assistant professors perceiving the most often that their HEIs are Leaders. Additionally, the scientific field and region of the HEI also impacted their role in the Great Reset, with the HEIs from technology and countries outside of Europe being the Leaders most often. On the other hand, Followers and Laggers were equally from social sciences and other fields and European countries.

Mechanism of eccentricity influence on 3-DOF aerodynamic stability: New insights into instability evolution, energy harvesting, and vibration control

The inconsistency of mass center and elastic center, i.e., eccentricity, widely exists in various engineering structures and components, including iced conductors, wind turbine blades, airfoils, and cable-supported bridges, and can also be extended to the application of energy harvesting and vibration control. Although the eccentricity influence on aerodynamic stability has already been recognized as important, the underlying mechanism remains rather unclear. This study established a comprehensive framework of explicit eigenvalue solutions, offering in-depth insights into the aerodynamic stability mechanism in a vertical-horizontal-torsional 3-degree-of-freedom linear system under all possible frequency scenarios. These solutions elucidated the coupling mechanism of eccentricity, aerodynamic damping, aerodynamic stiffness, and structural frequencies. Remarkably straightforward criteria to predict the promoting/suppressing effect of eccentricity were proposed, consisting of eccentric angle and aerodynamic forces, which not only align with the traditional principles but also offer a broader application range in terms of structure types and wind speeds. Validations of the proposed solutions were performed for all frequency cases via numerical studies. For instability tendency evaluation, numerical studies on a thin plate showed that eccentricity has a significant impact due to the strong aerodynamic effect and large ratio of width/gyration radius, highlighting the importance of considering small eccentricity errors during experiments. Examination for energy harvesting revealed that a small eccentricity can dramatically enhance power generation efficiency, and the explicit solutions successfully explained the rules governing performance changes against frequency ratio, mass center position, rotation center position, etc. The analysis of a bundled conductor showed that the double pendulum can control galloping because it diminishes the eccentricity effect by shifting the eccentric angle. The proposed explicit solution framework provides a systematic view and new insights into the aerodynamic instability mechanism influenced by eccentricity, serving as a reference for the design and studies of various engineering structures, energy harvesting, and vibration control.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BackgroundAutism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.MethodsWe used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.ResultsWe found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.LimitationsOptical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.ConclusionsThis study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

The evolution of plasticity in the neuroscientific literature during the second half of the twentieth century to the present

ABSTRACT In the neurosciences, concepts play an important role in the conception and direction of research. Among the theoretical notions and direction of research, plasticity stands out because of the multiple ways in which scientists use it to describe and interpret how the nervous system changes and adapts to different requirements. The occurrence of different conceptualizations of plasticity in the scientific literature during the second half of the twentieth century and up to the present was investigated using bibliometric methods. Throughout the period analyzed, synaptic plasticity has remained the dominant conceptualization of plasticity. However, scientists have continued to introduce novel plasticity concepts reflecting the scientific advances they have made in understanding the dynamic nature of the nervous system. The conceptual evolution of plasticity documents that the view of the adult nervous system as immutable has been replaced by an understanding of the nervous system as capable of lifelong change and adaptation.

Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.

Can the digital economy transform financial inclusion in rural communities? A gendered lens

Rural communities in Africa face various challenges related to infrastructure, limited economic activities and poverty. The COVID-19 pandemic, which aggravated some of the rural areas’ socioeconomic conditions, has illuminated the vast divide in accessing digital platforms for social services and financial access. Rural communities’ socioeconomic burdens can be alleviated through participation in the digital economy; however, this is yet to be a reality for these communities. While financial digital technologies have helped with financial access elsewhere, the same cannot be said for Africa’s rural areas. This exclusion is because location plays a significant role in the availability of digital solutions; hence areas with limited infrastructure will not have access to products such as digital financing, which is supposed to reach those that are far from financial institutions. The study employed a systematic literature view to understand the extent of financial inclusion in Africa. Content and thematic analysis were used to analyse the data. The findings highlighted a myriad of challenges that affect the participation of the rural population in the digital economy, stemming from access to and stable supply of electricity, the poor or limited infrastructure required for accessing digital products and gender-related issues where women’s gendered roles limit them from harnessing digital products. The study revealed that there is a shortage of digital products to reach all that needs access to finance. This is due to infrastructural challenges that are making access difficult for digital products such as mobile money to reach rural communities. Therefore, policymakers must focus on digital transformation for rural areas and support the usage of financial digital technologies to build the rural digital economy. In this way they will be promoting digital inclusion and reducing digital inequalities.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity.

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration.

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.