Network Expansion Research Articles

HOPE: High-Order Polynomial Expansion of Black-Box Neural Networks.

Despite their remarkable performance, deep neural networks remain mostly "black boxes", suggesting inexplicability and hindering their wide applications in fields requiring making rational decisions. Here we introduce HOPE (High-order Polynomial Expansion), a method for expanding a network into a high-order Taylor polynomial on a reference input. Specifically, we derive the high-order derivative rule for composite functions and extend the rule to neural networks to obtain their high-order derivatives quickly and accurately. From these derivatives, we can then derive the Taylor polynomial of the neural network, which provides an explicit expression of the network's local interpretations. We combine the Taylor polynomials obtained under different reference inputs to obtain the global interpretation of the neural network. Numerical analysis confirms the high accuracy, low computational complexity, and good convergence of the proposed method. Moreover, we demonstrate HOPE's wide applications built on deep learning, including function discovery, fast inference, and feature selection. We compared HOPE with other XAI methods and demonstrated our advantages.

Complex network analysis of fossil fuel functional regions in the United States during the period 2017 to 2022

In this paper we use complex network analysis to describe fossil fuel spatial flows among 132 places covering the entire United States in 2017 and in 2022. These spatial flows are for crude petroleum, gasoline, and oil fuels. The analysis shows that all three fuels have different network topology. For all six networks we find major hubs of crude petroleum and its products, gasoline and fuel oils, concentrated in areas with large reserves such as the south-central part of the US. Using modularity, a network cluster identification metric, we show that spatial interactions can be used to delineate functional regions and their differences across fuel types. These functional regions evolve over time in response to the shifting US role as a major producer and net exporter of fossil fuels, expansion of the domestic pipeline network, and increases in fuel production and refinement locations. The modal split of the fuels examined in this paper shows the dominant role pipelines play for crude petroleum, transporting approximately 83 % of tonnage in 2017 and increasing to almost 89 % in 2022. In contrast, gasoline and oil fuels modal split hovers at around 60 % of tonnage transported by tanker truck followed by other modes including pipelines. Our analysis shows geographic clustering of major hubs and their functional regions along the Gulf Coast in Texas and Louisiana. These are in places that are often the locations of natural disasters. This together with the rapid increase of a few hubs as gateways to fossil fuel US exports makes them prime candidates in disrupting fossil fuel supply chains worldwide and amplifies vulnerability of fossil fuel supply chains. The spatial clustering trends shown in this paper provide added evidence of the source of short-term negative impacts in places such as Chicago in Illinois and Corpus Christi in Texas. This offers added information for government intervention.

Can Transportation Networks Contribute to the Sustainable Development of Urban Agglomeration Spatial Structures?

The advancements in global urbanisation have significantly elevated the configuration of transportation networks for the sustainable development of urban agglomeration spatial structures (UASS). Using a comprehensive panel dataset covering 140 counties and districts in the Chengdu-Chongqing Economic Circle from 2014 to 2023, this study empirically examines the impact of transportation networks on the sustainability of UASS. It elucidates the underlying mechanisms using spatial Durbin, spatial spillover and multiple mediation effect models. The findings reveal that transportation networks considerably enhance the sustainability of UASS and generate substantial positive spatial spillover effects. The decay boundary of the spatial spillover effects exhibits a transition from positive to negative, eventually approaching ineffectiveness. The heterogeneity analysis indicates significant variations in terms of transportation network expansion and regional functional division on the sustainability of UASS across regions with various levels of economic development. This outcome suggests that regional development policies should carefully consider regional economic development disparities. The mechanism analysis further reveals that transportation networks enable the sustainability of UASS by accelerating regional hierarchical systems and regional connectivity.

Discovering Explainable Biomarkers for Breast Cancer Anti-PD1 Response via Network Shapley Value Analysis

Background and objectiveImmunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions MethodsTo address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance ResultsWe demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities ConclusionThe proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.

Airports and regional development: the expansion of the Norwegian air network, 1950–2019

Abstract This article studies how airports affect regional growth in population and employment, considering heterogeneity in the circumstances of an airport’s opening. We use synthetic controls with staggered adoption and data on the whole airport system in Norway for 1950–2019. We find positive overall effects of airports on population and employment growth. Addressing heterogeneity, we find relatively strong effects of airports opened in the 1950s, more distant from other airports, with longer runways, or with links to major cities. We also find stronger growth if an airport is opened in a region with a university, college, or hospital.

How do emoticons affect youth social interaction? The impact of emoticon use on youths online interpersonal interactions

Emoticons are non-verbal symbols that are employed during online interactions, and they constitute an integral component of online interpersonal communication. The influence of emoticons on the online interpersonal interactions of young people was investigated through in-depth interviews with 25 young people who utilize emoticons to a moderate or significant extent. The study identified three key aspects through which the impact of emoticons on youth’s online social interactions is manifested. Firstly, this is reflected in the level of emotion and meaning conveyed. The utilization of emoticons by young people has, to a certain extent, rectified the contextual limitation of “embodied absence” in online virtual social interaction. Secondly, this is reflected in young people’s recognition of emoticons. The use of emoticons by young people in online social interactions has led to the formation of a distinct “emoticon community.” This phenomenon not only facilitates the development of a more profound social identity among young people in online social interactions but also contributes to the expansion of the adolescent network, resulting in the emergence of a novel “social divide.” Thirdly, the use of emoticons by young people can be seen to contribute to a sense of alienation. As young people become increasingly reliant on emoticons, their influence is gradually extending from the digital realm to the physical world, impacting the normal social interactions of young people in real life. Emoticons have gradually become a means of facilitating young people’s online socialization, but they have also had the unintended consequence of limiting their normal social interaction. The deterioration of online interpersonal communication among young people is a key factor in the symbolic generalization and alienation of expression in the use of emoticons by this demographic.

Effectiveness of protected areas in the Caucasus Mountains in preventing rangeland degradation.

As land use intensifies globally, it increasingly exerts pressure on protected areas. Despite open, nonforested landscapes comprising up to 40% of protected areas globally, assessments have predominately focused on forests, overlooking the major pressures on rangelands from livestock overgrazing and land conversion. Across the southern Caucasus, a biodiversity hotspot extending over 5 countries, we conducted a broadscale assessment of the extent to which protected areas mitigate land-use pressure on rangelands in them. Using satellite-based indicators of rangeland vegetation greenness from 1988 to 2019, we assessed the effectiveness of 52 protected areas. This period encompassed the collapse of the Soviet Union, economic crises, armed conflicts, and a major expansion of the protected area network. We applied matching statistics combined with fixed-effects panel regressions to quantify the effectiveness of protected areas in curbing degradation as indicated by green vegetation loss. Protected areas were, overall, largely ineffective. Green vegetation loss was higher inside than outside protected areas in most countries, except for Georgia and Turkey. Multiple-use protected areas (IUCN categories IV-VI) were even more ineffective in reducing vegetation loss than strictly protected areas (I & II), highlighting the need for better aligning conservation and development targets in these areas. Mapping >10,000 livestock corrals from satellite images showed that protected areas with a relatively high density of livestock corrals had markedly high green vegetation loss. Ineffectiveness appeared driven by livestock overgrazing. Our key finding was that protected areas did not curb rangeland degradation in the Caucasus. This situation is likely emblematic of many regions worldwide, which highlights the need to incorporate degradation and nonforest ecosystems into effectiveness assessments.

Enhancing Cybersecurity in Energy IT Infrastructure Through a Layered Defense Approach to Major Malware Threats

This study examines the cybersecurity vulnerabilities of energy IT infrastructure in the context of rapid digital transformation and network expansion. While these advancements have improved efficiency, they have also increased susceptibility to cyber threats, particularly from malware such as Industroyer, Triton, NotPetya, and BlackEnergy3. By analyzing these malware threats, this research identifies key weaknesses in energy systems and highlights the need for enhanced anomaly detection, network segmentation, and system integrity checks. To address these vulnerabilities, this study proposes a layered security architecture in which each layer—physical, control, network, application, and security—provides independent protection while integrating into a unified defense strategy. This research emphasizes the necessity of combining technical-, organizational-, and policy-based responses to bolster cybersecurity across all levels of energy infrastructure. Key recommendations include immediate vulnerability patching, improved international cooperation, stronger legal frameworks, enhanced cybersecurity education, and the establishment of a unified command structure. This study provides practical insights and strategies for strengthening the cybersecurity of energy systems through a comprehensive, integrated approach.

Private protected areas and ecological connectivity in Chile

AbstractSpecies extinction rates have accelerated despite the global growth of the protected area estate, and maintaining or restoring ecological connectivity across landscapes is a major challenge in conservation. To address shortfalls in statutory protected areas, the designation of private protected areas (PPA) has been encouraged. Here we assess the level of ecological connectivity in the National System of Protected Areas in Chile (SNASPE) and measure the contribution of PPAs to the connectivity of the system, using novel indicators derived from landscape ecology. In so doing, we demonstrate how countries could report progress towards achieving connectivity among protected areas, using three complementary indicators. Our results show that while SNASPE currently has a high level of ecological connectivity, it varies substantially from region to region, with more protected and connected land needed to enhance connectivity. When PPAs are considered alongside statutory protected areas, connectivity is improved slightly, but still falls well short of meaningful levels. We therefore conclude that substantial expansion of the formal protected area network in Chile needs to happen, guided by a scientific and systematic process that enables the strategic designation of new PAs that enhance the performance of the current PA system and achieves international conservation goals. Additionally, new approaches and frameworks to recognize and manage land outside protected areas for connectivity and other ecosystem services are required.

A collaborative planning of PCCs siting and transmission network expansion for super large‐scale offshore wind clusters

A collaborative planning of PCCs siting and transmission network expansion for super large‐scale offshore wind clusters

Optimized demand-based charging networks for long-haul trucking in Europe

Abstract Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination (OD) pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of OD flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).

Joint Network Expansion Planning With Internet Data Center and Charging Stations for EV Sharing System

Joint Network Expansion Planning With Internet Data Center and Charging Stations for EV Sharing System

Study on access to social protection policies by homeless people during the COVID-19 pandemic

Abstract The study analyzed the access of homeless people (HP) to health and social protection policies, including emergency measures, during the COVID-19 pandemic in Belo Horizonte (BH), capital of the state of Minas Gerais, Brazil. Its main objective was to provide data on the profile of HP in the municipality, in addition to evaluating existing public policies focused on HP, based on the time frame of the health emergency. Study with mixed methods design with triangulation of quantitative and qualitative data. It used official databases in parallel with document analysis, interviews and focus groups. As a result, the cartography showed that in the first months of the pandemic the municipality had difficulty reorganizing the health system, which underwent constant protocol updates, consolidating, however, over the months. Important emergency interventions in the municipality of BH involved activities that facilitated HP’s access to the provision of services, including intersectorality between health services, social assistance and the third sector. The temporary offer of various intersectoral services, simultaneously with the offer of daytime shelters by organized civil society, was considered a key factor for the expansion and intensification of care networks for HP during the emergency phase. Itinerant services were among those with the greatest positive evidence of service to HP, with emphasis on the ‘Consultório na Rua’. The study concluded that understanding the profile of homeless people in BH and the intersectoral variables that impact HP contributes to better directing investments in interventions for these individuals and increasing the effectiveness of health and social protection systems, in line with with the work carried out by the third sector. Key messages • Understanding the inter-sector variables that impact Homeless People contributes to better targeting of investments in interventions in protection social. • Knowing the profile of the homeless population makes it possible to organize more effective health care and social assistance.

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.

Investigating the Levels of Schools as a Professional Learning Community

Purpose: The study aims to reveal the levels of schools as being a PLC regarding the opinions of teachers working at the high schools. Design/Methodology/Approach: The research was carried out in accordance with the correlational survey design. Participants were selected by the stratified sampling method, which is one of the random sampling types. It consists of 385 teachers. The participants of the research are from Maltepe, Sultanbeyli, Esenler and Bakırköy districts of İstanbul during 2021-2022 academic year. To get the data, PLC Scale was used and to analyze descriptive statistics and one-way analysis of variance were used. Findings: The level of being a professional learning community is at the level of "agree". When the scale’s sub-dimensions were examined, a significant difference was found between the employment type, length of service and district variables. On the basis, some suggestions are given to policy makers. Highlights: It is important to establish an online platform that allows the expansion of the learning network. In addition, a framework plan for schools to ensure sustainability is planned. Lastly, the implementation of rotation between schools could be considered to transform individual intelligence into collective intelligence.

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes.

Peningkatan Peran Pemilih Pemula dalam Penyelenggaran Pemilihan Kepala Daerah Tahun 2024

On November 27 2024, regional head elections will be held simultaneously. In the Indonesian political system, this is the first simultaneous election. In this election, based on the voter list, many novice voters were involved, namely those who were involved in voting for the first time. New voters are the future of democracy in Indonesia. Therefore, their involvement in elections is encouraged more than just voting but also being involved as election organizers. This is very important for competency development and network expansion in the future. This activity is an outreach regarding registration as an election organizer. The participants in this activity were ITSKKes Muhammadiyah Selong students. As a result of this activity, students were enthusiastic in understanding the voter registration process.

Role of Meaningful Social Participation and Technology Use in Mitigating Loneliness and Cognitive Decline Among Older Adults.

Community social participation aids dementia prevention and alleviates loneliness among older adults. Incorporating occupational therapy using information and communications technology (ICT) could potentially delay dementia onset and reduce loneliness. To quantify how meaningful social participation, participation frequency, ICT use, and expanded social networks influence cognitive function and loneliness among socially active older Japanese adults. Cross-sectional exploratory study using structural equation modeling. Meetings organized by older adults at seven community gathering places in Osaka Prefecture, Japan. One hundred thirteen healthy older adult cohort members. Cognitive function assessed via Mini-Cog; loneliness assessed via the condensed UCLA Loneliness Scale. The final model demonstrated excellent fit, χ2(23) = 28.291, p = .205 (root mean square error of approximation = .045, 90% confidence interval [.000, .094]; confirmatory factor index = .995; Tucker-Lewis Index = .993). ICT use directly affected social networks (β = 0.472), which directly influenced participation frequency (β = 0.324) and meaningful social participation (β = 0.381). The indirect effect of meaningful participation significantly improved cognitive function (β = 0.237). The only indirect effect of meaningful interpersonal participation was a reduction in loneliness (β = -0.235). ICT use contributes to the expansion of social networks among elderly people. Furthermore, the frequency of social participation and the meaningfulness of such participation are related to reduction in loneliness and maintenance of cognitive function. Although the frequency of social participation was not directly related to these outcomes, the results suggest that meaningful social participation may play an important role in reducing loneliness and maintaining cognitive function. Plain-Language Summary: The promotion of social participation among older people is a global phenomenon, driven by the recognition of its positive relationship with cognitive function and the alleviation of loneliness. Nevertheless, older people's social participation is constrained by a combination of physical and social factors. To address this issue, there has been a push to promote social participation based on information and communications technology (ICT). However, no specific study has been conducted on occupational therapists' perspective in capturing social participation and the use of ICT. The findings of this study show that using ICT has the potential to enhance opportunities for social interaction for older adults, thereby improving the quality and quantity of social participation. The quality of social participation was identified as the sole factor that had a positive impact on cognitive function and loneliness. This study suggests the need for occupational therapists to consider means of facilitating the use of ICTs among older adults as well as interventions that use occupational therapy theory to enhance the meaningfulness of existing social participation opportunities.

Performance Evaluation for the Expansion of Multi-Level Rail Transit Network in Xi’an Metropolitan Area: Empirical Analysis on Accessibility and Resilience

As the main form of new urbanization, the coordinated development of cities in metropolitan areas requires reliable and efficient rail transit skeleton support. However, in the rapid development of metropolitan areas, the layout and analysis of multi-level rail transit systems have a certain lag. Taking the Xi’an metropolitan area as an example, this study analyzes the comprehensive accessibility and resilience of the multi-level rail transit network, and proposes an expansion plan accordingly. The traffic analysis zone (TAZ) is divided by towns and streets, and the relationship between points of interest (POIs) and the regional average level is analyzed using DEA. The improved weighted average travel time model is built with the analysis results as regional weights; a site selection model based on multiple construction influencing factors is proposed, and four expansion plans, namely, economic optimal, environmental optimal, transport optimal, and integrated optimal, are designed. The peak passenger flow scenario and the “failure–reparation” scenario during the entire operation period are designed to analyze the resilience of four plans, and the resilience is quantified by the elasticity curve of the maximum connected subgraph ratio (MCSR) changing over time. The research results show that the transport optimal plan has the best comprehensive accessibility and resilience, reducing travel costs in Houzhenzi Town, which has the worst accessibility, by 34%. The expansion model and evaluation method in this study can provide an empirical example for the development of other metropolitan areas and provide a reasonable benchmark and guidance for the development of multi-level rail transit networks in future urban areas.

Identifying climate‐smart tropical Key Biodiversity Areas for protection in response to widespread temperature novelty

AbstractKey Biodiversity Areas (KBAs) are a cornerstone of 21st‐century area‐based conservation targets. In tropical KBAs, biodiversity is potentially at high risk from climate change, because most species reside within or beneath the canopy, where small increases in temperature can lead to novel climate regimes. We quantify novelty in temperature regimes by modeling hourly temperatures below the forest canopy across tropical KBAs between 1990 and 2019. We find that up to 66% of KBAs with tropical forests have recently transitioned to novel temperature regimes. Nevertheless, 34% of KBAs are providing refuge from novelty, 58% of which are not protected. By conducting the first pan‐tropical analyses of changes in below‐canopy temperature conditions in KBAs, we identify KBAs that are acting as climate refugia and should be considered for expansion of the conservation network in response to the post‐2020 Global Biodiversity Framework target to conserve 30% of land area by 2030.