Global System Research Articles

Strategic technological processes in hospitals: Conflicts and personal experiences of healthcare teams.

Global health systems operate amid dynamic factors, including demographic shifts, economic variations, political changes, technological progress, and societal trends that lead to VUCA reality (Volatility, Uncertainty, Complexity, and Ambiguity). To address these challenges, healthcare organizations are increasingly turning to Strategic Technological Processes and digital transformation. Against this background, the current study examined the personal experiences, conflicts, difficulties, and moral dilemmas attendant upon accommodating this digital transformation of healthcare professionals. The study involved 27 healthcare professionals working in Israeli hospitals, whose experiences and perspectives were central to understanding the impact of digital transformation in healthcare settings. The study methodology rested on in-depth interviews, which were analyzed through the prism of the Listening Guide analytical technique. The research obtained pre-approval from the Ethics Committee at the researcher's institution. The study revealed that the healthcare professionals are indeed facing ethical conflicts and personal challenges related to digitalization (such as providing the best quality of care, being the best caregiver, and acting for the betterment of the hospital). It also identified dilemmas resulting from conflicts between the needs and demands of the health system and the healthcare professionals' values and resources. The study findings highlight the impact of ethical and moral challenges accompanying strategic organizational and digital transformation changes implemented by healthcare organizations worldwide. These challenges arise as healthcare institutions adapt to the demands of the 21st century, potentially leading to burnout and moral distress among healthcare professionals, further exacerbating the already stressful reality they face. In conclusion, the study emphasizes the critical necessity for comprehensive support strategies to alleviate stress and burnout among healthcare professionals. As healthcare organizations and personnel navigate significant organizational changes to address contemporary challenges, prioritizing the well-being of healthcare workers through effective support mechanisms becomes imperative.

Endothelial progenitor cells for diabetic cardiac and kidney disease.

The management of diabetes mellitus and its resultant end organ dysfunction represents a major challenge to global health-care systems. Diabetic cardiac and kidney disease commonly co-occur and are significant contributors to the morbidity and mortality of patients with diabetes, carrying a poor prognosis. The tight link of these parallel end organ manifestations suggests a deeper common underlying pathology. Here, we outline the mechanistic link between diabetic cardiac and kidney disease, providing evidence for the role of endothelial dysfunction in both processes and the potential for cellular therapy to correct these disorders. Specifically, we review the preclinical and clinical evidence for endothelial progenitor cell therapy in cardiac, kidney, and cardio-renal disease applications. Finally, we outline novel approaches to endothelial progenitor cell therapy through cell enhancement and the use of extracellular vesicles, discussing published and future work.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN’s vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study’s insights can guide policy interventions to further strengthen the network against future shocks and promote global food security.

Metagenomic sequencing and reconstruction of 82 microbial genomes from barley seed communities

Barley (Hordeum vulgare) is essential to global food systems and the brewing industry. Its physiological traits and microbial communities determine malt quality. Although microbes influence barley from seed health to fermentation, there is a gap in metagenomic insights during seed storage. Crucially, elucidating the changes in microbial composition associated with barley seeds is imperative for understanding how these fluctuations can impact seed health and ultimately, influence both agricultural yield and quality of barley-derived products. Whole metagenomes were sequenced from eight barley seed samples obtained at different storage time points from harvest to nine months. After binning, 82 metagenome-assembled genomes (MAGs) belonging to 26 distinct bacterial genera were assembled, with a substantial proportion of potential novel species. Most of our MAG dataset (61%) showed over 90% genome completeness. This pioneering barley seed microbial genome retrieval provides insights into species diversity and structure, laying the groundwork for understanding barley seed microbiome interactions at the genome level.

Personalized Approaches to Cardiovascular Disease: Insights into FDA-Approved Interventions and Clinical Pharmacogenetics.

Cardiovascular diseases place a considerable burden on global health systems, contributing to high rates of morbidity and mortality. Current approaches to detecting and treating Cardiovascular Diseases (CVD) often focus on symptomatic management and are initiated after the disease has progressed. Personalized medicine, which tailors medical interventions to individual characteristics, has emerged as a promising strategy for improving cardiovascular health outcomes. This article provides an overview of personalized medicine in the context of CVD, with a specific emphasis on FDA-approved interventions. It explores the potential benefits, challenges, and future directions of personalized medicine in cardiovascular disorders. By reviewing the advancements in this field, this article underscores the importance of early detection, intervention, and innovative treatment options in reducing the impact of CVD on individuals and society.

Specific Grammatical Aspects of Translating the Iaea Regulatory Documents into the Russian Language

The article discusses some grammatical features of the IAEA safety requirements and guides in the English language and in their Russian translations. Considering that these documents make a foundation of the global nuclear safety system, and grammar is the backbone of the language and an essential constituent of the communication content, the paper is focused on the analysis of the ways of translating specific grammatical structures, which determines current importance of this study. The aim of the work is to compare typical grammatical constructions used in the texts of the Russian translations of the IAEA publications under consideration with the grammatical features of similar Rostechnadzor’s regulatory documentation. The main tasks of the authors are to compare the ways of translating English modal and passive constructions, English verbals and verbal structures into Russian and to verify the translator’s choice of a certain grammatical structure in the translations of the regulations and guidelines under consideration. Taking research tasks predetermined the choice of the following research methods: comparative analysis; linguistic and statistical analysis; contextual analysis; inductive and deductive analysis. To obtain linguistic and statistical analysis data, the authors applied such software tools as SDL Trados, Adobe Acrobat, Excel and MS Word. In order to ensure the correctness of the results, only the substantive part of the documents under consideration was analyzed.The analysis has brought to the following results: the need to comply with the regulatory requirements narrows the choice of Russian modal constructions in the translation of "should" statements used in IAEA documents. The issue of changing the regulatory requirement that prescribes to use the Russian verb "следует" has to be treated very carefully, considering the established practice of both translating the IAEA safety guides and their understanding by the target users; the translation of English passive constructions and verbals used in the IAEA safety guides gives clear preference to the use of Russian indefinite-personal structures with verbs in the active voice in the 3rd person plural; the grammatical structures used in the translation of the IAEA publications in question generally correspond to the grammatical structures used in the Rostechnadzor regulatory documents, which is predetermined by the information nature and ensures a fairly high similarity of their style.

Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.

Mapping drivers of land conversion among smallholders: A global systems perspective

CONTEXTAgriculture-driven land conversion poses a significant threat to global biodiversity conservation. Balancing agricultural expansion with conservation is a critical challenge for governments and development partners. Sustainability-oriented agriculture programs such as conservation agriculture and land intensification aim to reduce land conversion and deforestation; however, evidence of their effectiveness remains complex and elusive, especially regarding the role of smallholders. OBJECTIVEThis study addresses this knowledge gap by systematically mapping the drivers of smallholder land conversion and their interconnections across three focal biodiverse contexts including sub-Saharan Africa, Latin America, and Southeast Asia as well as from a global scale. METHODSUsing literature that focuses on the nexus of smallholder farmers and agricultural intensification, this study systematically identified the potential of distinct factors and dynamic processes to serve as system leverage points toward improved outcomes for biodiversity, food security, and smallholder livelihoods, using a combination of structural factor analysis, centrality analysis, and feedback loop analysis. RESULTS AND CONCLUSIONSThe results highlight the importance of strengthening land tenure systems, providing technical support to farmers, improving market access, and prioritizing smallholder livelihoods in mitigating land conversion. SIGNIFICANCEContextual differences emphasize the need for context-specific interventions and further research to explore factors driving land conversion more deeply within specific contextual boundaries. Future studies should map proposed policy interventions onto local systems driving land conversion and incorporate the diverse perspectives of local stakeholders to guide effective and sustainable agriculture interventions.

A novel LiDAR-GNSS-INS Two-Phase Tightly Coupled integration scheme for precise navigation

Abstract. Recent advances in precise navigation have extensively utilized the integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), particularly in the domain of intelligent vehicles. However, the efficacy of such navigation systems is considerably compromised by the reflection and multipath disruptions of non-light-of-sight (NLOS) signals. Light Detection and Ranging (LiDAR)-based odometry, an active perception-based sensor known for its precise 3D measurements, has become increasingly prevalent in augmenting navigation systems. Nonetheless, the assimilation of LiDAR odometry with GNSS/INS systems presents substantial challenges. Addressing these challenges, this study introduces a two-phase sensor fusion (TPSF) approach that synergistically combines GNSS positioning, LiDAR odometry, and IMU pre-integration through a dual-stage sensor fusion process. The initial stage employs an Extended Kalman Filter (EKF) to amalgamate the GNSS solution with IMU Mechanization, facilitating the estimation of IMU biases and system initialization. Subsequently, the second stage integrates scan-to-map LiDAR odometry with IMU mechanization to support continuous LiDAR factor estimation. Factor graph optimization (FGO) is then utilized for the comprehensive fusion of LiDAR factors, IMU pre-integration, and GNSS solutions. The efficacy of the proposed methodology is corroborated through rigorous testing on a demanding trajectory from an urbanized open-source dataset, with the system demonstrating a notable enhancement in performance compared to the state-of-the-art algorithms, achieving a translational Standard Deviation (STD) of 1.269 meters.

Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters

AbstractThis study consists of three main sections. The first section delves into a performance analysis centered around modeling PM10, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.

Physics schemes in the first version of NCEP operational hurricane analysis and forecast system (HAFS)

This document summarizes the physics schemes used in two configurations of the first version of the operational Hurricane Analysis and Forecast System (HAFSv1) at NOAA NCEP. The physics package in HAFSv1 is the same as that used in NCEP global forecast system (GFS) version 16 except for an additional microphysics scheme and modifications to sea surface roughness lengths, boundary layer scheme, and the entrainment rate in the deep convection scheme. Those modifications are specifically designed for improving the simulation of tropical cyclones (TCs). The two configurations of HAFSv1 mainly differ in the adopted microphysics schemes and TC-specific modifications in addition to model initialization. Experiments are made to highlight the impacts of TC-specific modifications and different microphysics schemes on HAFSv1 performance. Challenges and developmental plans of physics schemes for future versions of operational HAFS are discussed.

Exploring the factors affecting political education in Iran

The transmission of political values and norms through education is crucial for global political systems. It enhances citizens' engagement in politics and society. This paper explores influential factors in the political education of Iranian citizens within an Islamic community society. Focusing on Islamic political education values and objectives, it elaborates on principles impacting Iranian citizens' education. It examines the conceptual framework, nature, goals, applications, and dimensions, while scrutinizing components' role in educating committed citizens. This article employs the qualitative document analysis method. The findings highlight the crucial role of Iranian political culture in developing political education. The political culture of Iranians is a combination of participant and subject political cultures. Iranian political culture significantly influences political education, driven by factors like family, media, education institutions, government, political and religious rituals, and religion. These factors play a critical role in shaping the perspectives, values, and political identities of individuals.

Subnational Environmental Policy: Trends and Issues

Policies relevant to many key sociological processes are often subnational, enacted at the regional, state/provincial, and/or local levels. This applies notably in the politics of the environmental state, where public and private subnational environmental policies (SNEPs) have major consequences for managing climate change, addressing environmental injustices, regulating land uses, greening energy markets, limiting pollution, and much more. While sociologists focus more on national policies, diverse sociological contributions emphasize the importance of SNEPs and their origins, diffusion, implementation, and sources of backlash. We begin by providing a typology of SNEPs. Next, we highlight not only environmental sociology (with its particular attention to climate change and energy) but also the sociologies of social movements, politics, the economy, science, risk, and organizations, which have each offered unique perspectives. Finally, we outline an agenda for how sociologists can further elaborate a distinctive perspective that highlights inequality, valuation, diffusion, scale shifts, and venue-shopping up to national and global policy systems.

Patterns and trends of university-industry research collaboration in Ghana between 2011 and 2020

Purpose The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales. Design/methodology/approach This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020. Findings Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities. Originality/value This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.

Az európai regionális integráció jövője. Kormányzási és elosztási dilemmák

The cornerstone of the global system, as well as the EU, is the principle of the sovereign equality of states. However, states are far from being equal. This paradox is one of the main obstacles to the smooth functioning of international organisations, including the EU. Sovereigntist critics of the EU pretend that national competencies are sacrificed in exchange for co-operation and joint governance at a larger scale. In reality, EU membership has widened the scope of member states’ sovereignty: they are better informed and can take part in decision-making at the European level. However, the EU’s decision-making power has weakened for two main reasons. First, EU competencies have been watered down: exclusive union competencies exist only in five sectors, in parallel with a large and growing number of mixed and shared competencies. As a result, EU decision-making tends towards intergovernmental methods, and consensus seeking opens the way for veto players. Second, after the end of the Cold War, in the 1990s, many new states were formed because of the dissolution of former federal structures. In place of the Soviet Union, Yugoslavia, and Czechoslovakia 24 new states have taken form so far, 19 out of them Europeans, and the majority is attracted by the EU. The EU member states from the former eastern bloc which have Soviet-type political, economic, and social heritage, have ‘imported’ their unsolved – internal and external – conflicts into the EU. After the European elections in 2024 and the renewal of the European Parliament and the European Commission, those institution should undergo a ‘slimming diet’ and speed up decision-making by extending qualified majority voting. This is an absolute precondition of further enlargements.

Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science

Abstract. The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC).

Research on the analysis and management measures of land subsidence caused by groundwater exploitation in Tianjin, China

Since the 20th century, there has been an increasing reliance on groundwater due to the rapid development of agriculture and industry. This has resulted in the exploitation of groundwater in large quantities, leading to global land subsidence problems. Tianjin is one of the cities where this problem first emerged, but few studies and surveys have been conducted. As a result, the lack of management of groundwater extraction has caused the issue of land subsidence to become more prominent in recent years. By investigating and collating global research on land subsidence problems caused by groundwater extraction, it is concluded that mathematical and numerical methods, global information systems, remote sensing, and other technologies can be utilized to monitor and predict groundwater levels. Regional management measures can then be developed based on simulation results. However, the application of such technology in Tianjin is insufficient, and there is a need to strengthen the connection between management measures and research data analysis results.

Democratising complex system modelling

In the contemporary context of an acute need for sustainability and swift response to imminent crises such as global warming, pandemics and economic system disruptions, the focus on responsible decision making, ethical risk assessment and mitigation at all organizational levels is an overarching goal. Our aim is to introduce a deterministic method for investigating the stability of complex systems, in order to find the most important elements of such systems and their impact on different scenarios. The novelty of the current approach lies in its compact format and intuitive nature, designed to accommodate a limited amount of computational resources. The proposed modelling method involves the mapping of complex systems from a diversity of disciplines (economic markets, resource management domain and the community impact of suburbanisation) onto a sequence of chemical reactions and involving a subsequent mathematical analysis. Mapping the results back onto the use cases shows that one can retrieve a considerable amount of detail, making the modelling strategy general enough to be adaptable and scalable while also detailed enough to provide valuable insights for practical scenarios.

Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional downscaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2, and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation systems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting.