Global Network Research Articles

Unveiling the influence of global innovation networks on corporate innovation: evidence from the international semiconductor industry

In this study, we investigate the influence of global innovation networks (GINs) on the innovation output of semiconductor firms. Utilizing negative binomial regression and network analysis, we assess how network positions, specifically degree, betweenness, and closeness centrality, affect firms’ innovation performance, revealing significant positive impacts. Moreover, our results identify a positive U-shaped relationship between structural holes in GINs and innovation performance, suggesting that while moderate network engagement aids innovation, too much can be detrimental. This research provides key insights into optimizing GIN participation for better innovation results in the competitive semiconductor sector.

IMPACT OF 5G TECHNOLOGY ON SATELLITE COMMUNICATION: A PARADIGM SHIFT IN TELECOMMUNICATIONS WITH A FOCUS ON CYBERSECURITY CHALLENGES AND SOLUTIONS

This review investigates the transformative impact of 5G technology on satellite communication, with a particular focus on the cybersecurity challenges introduced by this integration. An analysis of 50 rigorously selected scholarly articles demonstrates that 5G significantly enhances satellite communication by improving data transmission rates, reducing latency, and increasing network efficiency, thereby facilitating extended coverage that bridges the digital divide by delivering high-speed internet to remote areas. Additionally, 5G supports the deployment of new applications such as enhanced Mobile Broadband (eMBB) and extensive Internet of Things (IoT) connectivity. However, this integration also introduces new cybersecurity challenges, such as increased vulnerability to cyber-attacks and the need for robust encryption methods to secure data transmissions. Technical issues like signal interference, regulatory hurdles involving spectrum sharing, and substantial economic investments for infrastructure development also pose significant challenges. These complexities require ongoing research and strategic planning to mitigate risks and fully harness the potential of 5G-enhanced satellite networks. The study highlights a paradigm shift in satellite communications, propelled by 5G's capabilities to create more inclusive, efficient, and secure global communication networks.

A novel relation‐aware global attention network for sentiment analysis of students in the wisdom classroom

A novel relation‐aware global attention network for sentiment analysis of students in the wisdom classroom

The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.

Bridging senses of place and mobilities scholarships to inform social-ecological systems governance: A research agenda

Uncertainty and change are increasingly commonplace as communities respond to impacts of social-ecological change including climate change, and dangerous levels of pollution. Given the extent of these crises, new approaches are needed to support responses. Here we identify challenges and discuss insights that the nexus of Senses of place (SoP) and mobilities research offers in navigating such uncertainty. We conducted a two-round Delphi, followed by a workshop, and collaborative writing process with a global network of researchers with expertise in either or both SoP and mobilities research. Participants identified five challenges at the place-mobility nexus that emerge when a social-ecological system is disrupted. We use the 2022 Odra River fish die-off to exemplify the identified challenges: 1) accounting for power dynamics, inequalities and motility; 2) doing justice to more-than human actors; 3) integrating multiple and sometimes nested spatial scales; 4) considering temporalities of place and mobilities, and 5) embracing multisensoriality. To address these challenges, we recommend drawing on diverse methods and knowledge co-creation processes that combine more-than-human perspectives, multisensoriality, and engage in the dynamic relations between places to understand people-place disruptions in the face of socio-spatial precarity. Addressing such knowledge gaps requires stronger collaboration of mobilities and place researchers.

Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm

AbstractInferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

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.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer’s disease

BackgroundResearch has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer’s disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse.ObjectiveTo investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression.MethodsWe analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy.ResultsThe small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups.ConclusionThese findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

Global logistics and supply chain integration in the digital era: a focus on China's Belt and Road Initiative

PurposeTo delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.Design/methodology/approachPrior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.FindingsThe proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.Research limitations/implicationsThe case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.Originality/valueThe framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

Screening and genetic engineering of marine-derived Aspergillus terreus for high-efficient production of lovastatin

BackgroundLovastatin has widespread applications thanks to its multiple pharmacological effects. Fermentation by filamentous fungi represents the major way of lovastatin production. However, the current lovastatin productivity by fungal fermentation is limited and needs to be improved.ResultsIn this study, the lovastatin-producing strains of Aspergillus terreus from marine environment were screened, and their lovastatin productions were further improved by genetic engineering. Five strains of A. terreus were isolated from various marine environments. Their secondary metabolites were profiled by metabolomics analysis using Ultra Performance Liquid Chromatography–Mass spectrometry (UPLC–MS) with Global Natural Products Social Molecular Networking (GNPS), revealing that the production of secondary metabolites was variable among different strains. Remarkably, the strain of A. terreus MJ106 could principally biosynthesize the target drug lovastatin, which was confirmed by High Performance Liquid Chromatography (HPLC) and gene expression analysis. By one-factor experiment, lactose was found to be the best carbon source for A. terreus MJ106 to produce lovastatin. To improve the lovastatin titer in A. terreus MJ106, genetic engineering was applied to this strain. Firstly, a series of strong promoters was identified by transcriptomic and green fluorescent protein reporter analysis. Then, three selected strong promoters were used to overexpress the transcription factor gene lovE encoding the major transactivator for lov gene cluster expression. The results revealed that compared to A. terreus MJ106, all lovE over-expression mutants exhibited significantly more production of lovastatin and higher gene expression. One of them, LovE-b19, showed the highest lovastatin productivity at a titer of 1512 mg/L, which represents the highest production level reported in A. terreus.ConclusionOur data suggested that combination of strain screen and genetic engineering represents a powerful tool for improving the productivity of fungal secondary metabolites, which could be adopted for large-scale production of lovastatin in marine-derived A. terreus.

Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy

ObjectiveMesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level.MethodsThis study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy.ResultsWe found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome.SignificanceOur research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.

Ocean protection quality is lagging behind quantity: Applying a scientific framework to assess real marine protected area progress against the 30 by 30 target

AbstractThe international community set a global conservation target to protect at least 30% of the ocean by 2030 (“30 × 30”) to reverse biodiversity loss, including through marine protected areas (MPAs). However, varied MPAs result in significantly different conservation outcomes, making MPA coverage alone an inadequate metric. We used The MPA Guide framework to assess the the world's largest 100 MPAs by area, representing nearly 90% of reported global MPA coverage and 7.3% of the global ocean area, and analyzed the distribution of MPA quality across political and ecological regions. A quarter of the assessed MPA coverage is not implemented, and one‐third is incompatible with the conservation of nature. Two factors contribute to this outcome: (1) many reported MPAs lack regulations or management, and (2) some MPAs allow high‐impact activities. Fully and highly protected MPAs account for one‐third of the assessed area but are unevenly distributed across ecoregions in part because some nations have designated large, highly protected MPAs in their overseas or remote territories. Indicators of MPA quality, not only coverage, are needed to ensure a global network of MPAs that covers at least 30% of the ocean and effectively safeguards representative marine ecosystems from destructive human activities.

MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.

Structural network disruption of corticothalamic pathways in cerebral small vessel disease.

Generalized fractional anisotropy (GFA) can eliminate the crossing fiber effect, which may be more reflective of brain tissue changes in patients with cerebral small vessel disease (CSVD). This study aimed to explore the alterations of structural networks based on GFA and its relationship with cognitive performance in CSVD patients. We recruited 50 CSVD patients which were divided into two groups: cognitive impairment (CSVD-CI) and normal cognition (CSVD-NC), and 22 healthy controls (HCs). All participants underwent the Montreal Cognitive Assessment (MoCA) and MRI examinations. The structural topological properties were compared among the three groups. The correlation between these structural alterations and MoCA was analyzed. Compared with HCs, significantly decreased nodal efficiency and connectivity were detected in the corticothalamic pathways in both patient groups, of which some were significantly decreased in CSVD-CIs compared with CSVD-NCs. Moreover, both patient groups exhibited global network disruption including decreased global efficiency and increased characteristic path length compared with HCs. Furthermore, the nodal efficiency in the right pallidum positively correlated with MoCA in CSVD-NCs controlling for nuisance variables (r = 0.471, p = 0.031). The alterations in corticothalamic pathways indicated that the brain structural network underwent extensive disruption, providing evidence for the consideration of CSVD as a global brain disease.

DATA SECURITY IN COMMUNICATION USING BLOCKCHAIN AND KEY BASED PROTOCOL

Satellite communications play an important role in the development of global communications networks. Recently, satellite networking has been receiving a lot of attention as a solution to alleviate the limitations of terrestrial networks due to low stability and coverage. It has many disadvantages, including low processing power, storage space, and limited data security. Illegal access has become a problem. Data processing power is minimal, storage space and data security are limited due to regional power availability and physical satellite limitations, and data can be modified or accessed by malicious users. As the importance of satellite communication increases in the development of global communication networks, concerns about the security of satellite communication are also growing. This project proposes a satellite communication network using blockchain technology and QKD protocol based on authentication and personal information protection. To achieve this goal, an architecture was built consisting of traditional and constrained devices connected to the blockchain over a wireless heterogeneous network. Communication uses registration, authentication, and cancellation. The satellite transmits the received data to a base station on the ground, which stores all key parameters in the decentralized blockchain and removes all invalid node certificates from the blockchain. The proposed blockchain and QKD satellite system provides a high level of security for future 6Gandbeyond networks, Internet of Things, autonomous vehicles and other rapidly developing applications.

Investigating global mobile network traffic dynamics and drivers

Investigating global mobile network traffic dynamics and drivers

Coupled connectivity in the global complex network: the case of United Kingdom (1880–1925)

The main objective of this research is to analyse the connectivity of cities in a coupled network composed of planar (railways) and non-planar (maritime) topologies. It examines the state of the network during the 1880–1925 period, namely in the context of the first globalization wave (1880–1914), when trade and urban development were closely tied to progress in communications systems and steam propulsion especially. Edges represent intercity physical infrastructure on land, and inter-port ship voyages at sea. We tested several hypotheses in terms of inter-network specialisation and urban hierarchies with an application in the United Kingdom. The main results reveal that the networks are highly interdependent, whereas combined centrality is closely associated with city size and urban growth. We discuss the key results in light of network science, spatial science, maritime history, and transport research.

Scenario analysis on CO2 emission reductions in hinterland transport of Japan through intermodal logistics network simulation

Because transport in the port hinterlands heavily depends on roads with negative externalities, much social interest has been in modal shifts (MSs) to rail and shipping with lower environmental impact. Currently, leading industry sectors to ambitiously embrace technological innovations to decarbonize—consequently, in line with these changes, the significance of MSs should change; however, no previous studies have comprehensively presented the overall CO2 emissions and transport mode shares for future regional cargo transport. This paper presents a scenario analysis of CO2 emission reductions in the Japanese domestic hinterland of international maritime container transport based on future scenarios of technological innovation and change in shipper behavior. The simulation results using the global logistics intermodal network simulation model showed that up to 87% CO2 reduction is possible, and the primary driver for this is technological innovation. Moreover, the significance of MSs fluctuates depending on the advancement of technological innovation, suggesting that MSs hold a degree of import as a decarbonization measure during the transitional phase toward carbon neutrality (CN) as technological innovation advances. The results of this study, which fluctuated considerably by scenario and timing, have policy implications with respect to the path to be followed toward CN, including whether to promote MSs.

Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.

While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.