Context Of Networks Research Articles

What Promotes International Sustainable Development of Green Ventures? The Joint Effects of Entrepreneurial Orientation and Home Country-Based Networks

Purpose - Along with the growing awareness of environmental sustainability, international green entrepreneurship is expected to realize international sustainable development (ISD) by introducing ecological innovation results, using fewer resources consumption, and reducing negative environmental impacts. Building upon contingency theory and social network theory, this paper attempts to explore the role of home country-based networks (technology ties and business ties) as contingent factors that might impact the effectiveness of entrepreneurial orientation (EO) in promoting ISD among green ventures.
 Design/methodology - Original data were collected from 127 green ventures in China based on the random sampling technique. These green ventures mainly focus on the lower use of energy or materials, lower CO2 emissions, and higher ecological benefits located in the Fujian province, which highlights green economic growth and economic cooperation with international markets. Moderated hierarchical linear regression analysis was conducted to test our hypotheses concerning the main relationship between EO (innovativeness, proactiveness, and risk-taking) and ISD as well as the moderating effects of home country-based networks (technology ties and business ties) on the main relationship.
 Findings - Empirical results indicate that: first, both innovativeness and proactiveness positively affect ISD; second, both technology ties and business ties at home might significantly strengthen the positive relationship between innovativeness (or proactiveness) and ISD. However, this study did not find significant moderating effects of technology ties or business ties at home on the relationship between risk-taking and ISD.
 Originality/value - Since previous literature often gives more focus on the host country context of social networks, this study shifts to a home country-based network context. Therefore, our research might enrich the international green entrepreneurship literature by empirically investigating the contingent value of home country-based networks in the relationship between EO and ISD in the context of an emerging economy such as China.

Accounting Information and Supply Chain Management Practices in the Era of IR 4.0: The Case of a Japanese Subsidiary in Malaysia

Manuscript type: Research paper Research aims: This study aims to explore the interplay between accounting information and supply chain management (SCM) practices of a Malaysian subsidiary whose shares are largely owned by a Japanese parent company. Design/Methodology/Approach: This study follows a qualitative, case study methodology. The major sources of this study’s data were collected through semi-structured interviews, documentary reviews and informal observations. Research findings: Influenced by its parent company’s management style that concerns aligning business practices with the fourth Industrial Revolution (IR 4.0), the case company has subscribed to a set of accounting systems and SCM practices to manage its complex business activities. As much as accounting systems influence its SCM practices more naturally, SCM practices also influence its accounting practices quite extensively. On the one hand, it has used accounting reports to manage its cashflow, to develop its marketing plan, and to monitor its performance. On the other hand, its SCM practices have shaped the ways pricing and performance measures are set with much negotiation. Theoretical contribution/Originality: Using the Resource Based View (RBV) as a theoretical lens, this study analyses the role of accounting within the context of the supply chain network by exploring how accounting systems influence and/or get influenced by SCM practices. Practitioner/Policy implications: The findings are important in shedding light on the interplay between accounting and SCM in view of their strong networks and mutual roles in relation to the natural supply chain setting of a Japanese subsidiary company by relating to the IR 4.0’s demands. Research limitation/Implications: This study is based on one case study and that the specific accounting information that were reported in the company were not identified. This study has revealed that information sharing has streamlined SCM and influenced performance as members in the supply chain network are able to collaborate in a more integrated and seamless manner to enhance the organisational competitive advantage as highlighted by the RBV theory and as emphasised by IR 4.0. Keywords: Accounting Systems, Case Study, Japanese Subsidiary, SCM Practices, IR 4.0 JEL Classification: M41

Evaluation of the Thermal Environmental Effects of Urban Ecological Networks—A Case Study of Xuzhou City, China

Urban heat islands (UHIs) constitute an important ecological problem in cities. Ecological space has a positive effect on UHI mitigation, which can be effectively organized in the form of ecological networks. In this study, the framework for structural UHI improvement based on ecological networks considering the source-corridor model is proposed to examine the spatial threshold of the thermal effect of ecological network factors. Additionally, the cooling mechanism of each constituent element in the ecological network context is further explored. The results demonstrate that (1) an obvious cold and heat island spatial aggregation distribution exists in the Xuzhou main urban area, and land of the same land use type exhibits the dual thermal environmental properties of cold and heat islands through its spatial distribution and characteristics. Ecological space is the main bearing area of cold islands. (2) The ecological network in the main urban area of Xuzhou city occurs at a moderately complex level, and the overall network efficiency is acceptable; the network connectivity is low, while the network loop distribution is uneven. (3) Ecological networks represent an effective spatial means to improve overall UHI patterns. The ecological source area cooling threshold is 300 m, and the optimal threshold is 100 m, while the ecological corridor width threshold is 500 m and 60 m, respectively. (4) Within the optimal threshold in the context of ecological networks, the temperature of ecological sources in category G land is influenced by NDBI and FVC; ecological corridors are mainly influenced by NDBI. The results can provide a quantitative basis for urban ecological network planning considering UHI improvement and a reference for urban thermal environment research within different ecological substrates and planning and control systems in other countries and regions worldwide.

Functional characterization of co-phosphorylation networks.

Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, the characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases and normal development. We comprehensively investigate the concept of 'co-phosphorylation' (Co-P), defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phosphoproteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzheimer's disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations and pathway annotations to investigate the functional relevance of Co-P. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative Co-P. Specifically, we show that Co-P can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish Co-P as a useful resource for analyzing phosphoproteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation. github.com/msayati/Cophosphorylation. This research used the publicly available datasets published by other researchers as cited in the manuscript. Supplementary data are available at Bioinformatics online.

Abstract LB148: Knowledge graph integration of germline, primary cancer and cancer genetic dependency data prioritizes new target candidates

Abstract Background: Effective target discovery strategies are key to developing new precision medicines against cancer. Large-scale efforts to systematically characterize cancer-associated germline variants, genomic changes in tumors and cancer genetic dependencies via CRISPR and RNAi in cell lines provide valuable resources for machine learning (ML) approaches designed to identify disease-modifying targets. However, fundamental challenges remain in understanding how various data types (including primary tumor and cell line data) can be jointly modeled to decipher the molecular network contexts underpinning the biology and efficacy of new target candidates. Approach: Using a data-driven knowledge graph spanning >2 billion relations over >350,000 biomedical entities, we assembled a comprehensive target prioritization resource with 53 feature types including multi-omics (germline and tumor) and genetic dependency data. Feature categories consisted of raw feature types (e.g. germline genetic, somatic mutation, copy number variation, transcriptomic, proteomic, survival associations, CRISPR/RNAi sensitivity, selectivity, efficacy and essentiality), network-transformed features capturing the underlying molecular network context of each possible target, and multidimensional network integration features combining systems-level information from multiple feature types. Multiple ML models were trained on either omics-based features only or on both omics and genetic dependency features; and using information from each cancer type separately, as well as jointly in multi-disease models. Results: Individual features and the resulting ML models were evaluated using known drug targets in 20 cancer types. We found that a) germline genetic and survival association features along with genetic dependency (efficacy and selectivity) features were the most predictive prior to network transformation; b) network-based feature transformation raised the predictive power of individual features (e.g. increasing mean AUPRC from 0.024 to 0.064 for germline genetic and from 0.018 to 0.048 for cell line sensitivity features); c) network integration of multiple feature types increased mean AUPRC by 21% over the best individual omics features; d) ML models integrating both omics and genetic dependency data outperformed ML omics-only approaches (mean AUPRC 0.219 vs. 0.195); and e) multi-disease ML models provided the best overall performance (mean AUPRC 0.232) underlining the value of multi-dimensional data integration and information transfer across cancer types. Resulting network-based machine learning models provide a highly interpretable view into the top-scoring genes for each cancer, prioritizing established targets, targets recently being evaluated (e.g. FGFR2 and FGFR3 in lung squamous cell carcinoma) and novel target candidates. Citation Format: Janusz Dutkowski, Radosław Bielecki, Karol Nienałtowski, Michał Kukiełka, Roy Ronen. Knowledge graph integration of germline, primary cancer and cancer genetic dependency data prioritizes new target candidates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB148.

Informal network context: deepening the knowledge and extending the boundaries of social network research in international human resource management

While informal networking has been universally regarded as an important feature of expatriate effectiveness, respective network constructs (yongo, wasta, blat/svyazi, etc.) remain weakly understood when taking expatriates’ ability to connect to local networks into account. Drawing on informal institution and social capital theory, we present informal networks as an important contextual factor in international human resource management (IHRM), relevant to the work of expatriates in particular. We define informal networks by pointing out their distinctive nature in contrast to social networks and developing central themes to guide future research in this area. This is achieved by integrating the informal network context into research themes relevant to the IHRM domain. In this article, seven research themes centering on focal research questions are developed, which, taken together, constitute a future research agenda and expand the typical domain of IHRM research in the informal network context.

Towards Green Driving: A Review of Efficient Driving Techniques

The exponential increase in the number of daily traveling vehicles has exacerbated global warming and environmental pollution issues. These problems directly threaten the continuity and quality of life on the planet. Several techniques and technologies have been used and developed to reduce fuel consumption and gas emissions of traveling vehicles over the road network. Here, we investigate some solutions that assist drivers to follow efficient driving tips during their trips. Advanced technologies of communications or vehicle manufacturing have enhanced traffic efficiency over road networks. In addition, several advisory systems have been proposed to recommend to drivers the most efficient speed, route, or other decisions to follow towards their targeted destinations. These recommendations are selected according to the real-time traffic distribution and the context of the road network. In this paper, different high fuel consumption scenarios are investigated over the road networks. Next, the details of efficient driving techniques that were proposed to tackle each case accordingly are reviewed and categorized for downtown and highway driving. Finally, a set of remarks and existing gaps are reported to researchers in this field.

How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity

AbstractLarge dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream‐downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate‐to‐large dams representing >83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network‐level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network‐level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High‐impact dams were often located in sub‐watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam‐free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins.

Machine Learning in NextG Networks via Generative Adversarial Networks

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> spectrum sharing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> detecting anomalies, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii)</i> mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and nonrepeatable. Second, they enable pre-training classifiers by using semi-supervised data. Third, they facilitate increased resolution. Fourth, they enable the recovery of corrupted bits in the spectrum. The paper provides the basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)–iii)</i> above, and future research directions. As a use case of GAN for NextG communications, we show that a GAN can be effectively applied for anomaly detection in signal classification (e.g., user authentication) outperforming another state-of-the-art ML technique, an autoencoder.

Computational properties of multi-compartment LIF neurons with passive dendrites

Mixed-signal neuromorphic computers often emulate some variant of the LIF neuron model. While, in theory, two-layer networks of these neurons are universal function approximators, single-layer networks consisting of slightly more complex neurons can, at the cost of universality, be more efficient. In this paper, we discuss a family of LIF neurons with passive dendrites. We provide rules that describe how input channels targeting different dendritic compartments interact, and test in how far these interactions can be harnessed in a spiking neural network context. We find that a single layer of two-compartment neurons approximates some functions at smaller errors than similarly sized hidden-layer networks. Single-layer networks with with three compartment neurons can approximate functions such as XOR and four-quadrant multiplication well; adding more compartments only offers small improvements in accuracy. From the perspective of mixed-signal neuromorphic systems, our results suggest that only small modifications to the neuron circuit are necessary to construct more computationally powerful and energy efficient systems that move more computation into the dendritic, analogue domain.

Choosing a Proper Control Strategy for Multipath Transmission in Industry 4.0 Applications

The industrial applications, especially in the context of communication-intensive Industry 4.0, require high throughput, good robustness, and low latency in the data exchange among the system components. These objectives can be achieved through multipath transfer. In order to overcome the restrictions of the primary networking protocols with respect to engaging multiple network interfaces, a new version of transmission control protocol (TCP) has recently been standardized—multipath TCP (MPTCP). Still though, only one type of congestion control algorithm is used to manage the flow of data despite various choices available in the modern operating systems, which leads to nonoptimal transfer dynamics. In this article, the influence of TCP congestion control algorithms, operating at the path and MPTCP level, on the transmission quality in industrial applications is examined. The conducted experiments involving physical devices and real, public networks lead to a set of recommendations for the algorithm selection so that the premises of robustness and efficiency are met. The default algorithm configuration is found ill-suited, yielding priority to nonhomogeneous settings. Only a few algorithms can maintain the benefits of multiplicity for an on-going transmission when the transfer conditions at one or more paths deteriorate. Motivated by the deficiency of common solutions, a new MPTCP congestion control algorithm has been developed. While retaining their good properties in the general networking context, this new–robust–algorithm yields improved performance in the uncertain, industrial communication environment.

Dynamic treatment regimes with interference

AbstractPrecision medicine describes health care where patient‐level data are used to inform treatment decisions. Within this framework, dynamic treatment regimes (DTRs) are sequences of decision rules that take individual patient information as input data and then output treatment recommendations. DTR estimation from observational data typically relies on the assumption of no interference: i.e., the outcome of one individual is unaffected by the treatment assignment of others. However, in many social network contexts, such as friendship or family networks, and for many health concerns, such as infectious diseases, this assumption is questionable. We investigate the DTR estimation method of dynamic weighted ordinary least squares (dWOLS), which boasts of easy implementation and the so‐called double‐robustness property, but relies on the assumption of no interference. We define a network propensity function and build on it to establish an implementation of dWOLS that remains doubly robust under interference associated with network links. The method's properties are demonstrated via simulation and applied to data from the Population Assessment of Tobacco and Health (PATH) study to investigate cigarette dependence within two‐person household networks.

A Robust Inference Method for Decision-Making in Networks

Social network data collected from digital sources is increasingly being used to gain insights into human behavior. However, while these observable networks constitute an empirical ground truth, the individuals within the network can perceive the network’s structure differently—and they often act on these perceptions. As such, we argue that there is a distinct gap between the data used to model behaviors in a network, and the data internalized by people when they actually engage in behaviors. We find that statistical analyses of observable network structure do not consistently take these discrepancies into account, and this omission may lead to inaccurate inferences about hypothesized network mechanisms. To remedy this issue, we apply techniques of robust optimization to statistical models for social network analysis. Using robust maximum likelihood, we derive an estimation technique that immunizes inference to errors such as false positives and false negatives, without knowing a priori the source or realized magnitude of the error. We demonstrate the efficacy of our methodology on real social network datasets and simulated data. Our contributions extend beyond the social network context, as perception gaps may exist in many other economic contexts.

Individualized discovery of rare cancer drivers in global network context.

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become 'hard currency' of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations' co-occurrence in each tumour genome - as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.

What exactly is “social” about social networks?: Accounting for socio-cultural context in networks of human interaction

What exactly is “social” about social networks?: Accounting for socio-cultural context in networks of human interaction

OMEN: network-based driver gene identification using mutual exclusivity.

Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed. We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark dataset derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways. The source code is freely available for download at www.github.com/DriesVanDaele/OMEN. The dataset is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764. Supplementary data are available at Bioinformatics online.

Determinants of Network Learning - A Conceptual Study

Attention of researchers in the strategic management literature has been drawn to the phenomenon that a firm which develops an ability to learn offers an important source of competitive advantage to it. Thus the concept has been understood from a variety of angles like, levels of learning in organizations, process of learning, outcomes and barriers to learning. On the other hand, firms are increasingly being looked at from the network perspective, since it has been accepted that no organization can function in isolation and rather its existence is affected by the myriad of relationships with other firms, in which it is embedded. This gives rise to a need to understand learning in the network context. Very few studies however, are being carried out in this direction. This study aims to generate variable influencing the learning of network members and generates propositions for future research.

IFC BIM Model Enrichment with Space Function Information Using Graph Neural Networks

The definition of room functions in Building Information Modeling (BIM) using IfcSpace entities is an important quality requirement that is often not fulfilled. This paper presents a three-step method for enriching open BIM representations based on Industry Foundation Classes (IFC) with room function information (e.g., kitchen, living room, foyer). In the first step, the geometric algorithm for detecting and defining IfcSpace entities and injecting them into IFC models is presented. After deriving the IfcSpaces, a geometric method for calculating the graph of connections between spaces based on accessibility is described; this information is not explicitly stored in IFC models. In the final step, a graph convolution-based neural network using the accessibility graph to classify the IfcSpace entities is described. Local node features are automatically extracted from the geometry and neighboring elements. With the help of a Graph Convolutional Network (GCN), the connection and spatial context information is utilized by the neural network for the classification decision, in addition to the local features of the spaces which are more commonly used. To evaluate the classification accuracy, the model was tested on a set of residential building IFC models. A weighted version of the common GCN was implemented and tested, resulting in a slight improvement in the classification accuracy.

Quantifying systemic risk in US industries using neural network quantile regression

The study quantified the systemic risk spillovers between top 10 US industries using conditional value-at-risk in a network context by calibrating the marginal effects of a quantile regression process and coving period from January 3, 2007-May 28, 2021 and found significant variations in the risk measured using a nonlinear process. Neural networks identified the manufacturing industry as the center of risk spillovers with the disconnected telecommunication industry in a system-wide neural network portraying its diversification potential. The systematic fragility index, which ranks industries with a high exposure to tail risk in a system, revealed the utilities industry as being the most vulnerable to economically fragile periods. By contrast, the systematic hazard index, which measures the risk contribution of an industry, showed the manufacturing industry as the principal risk contributor. With this tail risk assessment, particularly during distress periods, we stipulate several implications for policymakers, regulators, investors, and financial market participants.

Layered feature representation for differentiable architecture search

Differentiable architecture search (DARTS) approach has made great progress in reducing the computational costs of designing automatically neural architectures. DARTS tries to discover an optimal architecture module, called as the cell, from a predefined super network containing all possible network architectures. Then a target network is constructed by repeatedly stacking this cell multiple times and connecting each one end to end. However, the repeated design pattern in depth-wise of networks fails to sufficiently extract layered features distributed in images or other media data, leading to poor network performance and generality. To address this problem, we propose an effective approach called Layered Feature Representation for Differentiable Architecture Search (LFR-DARTS). Specifically, we iteratively search for multiple cell architectures from shallow to deep layers of the super network. For each iteration, we optimize the architecture of a cell by gradient descent and prune out weak connections from this cell. Meanwhile, the super network is deepen by increasing the number of this cell to create an adaptive network context to search for a depth-adaptive cell in the next iteration. Thus, our LFR-DARTS can obtain the cell architecture at a specific network depth, which embeds the ability of layered feature representations into each cell to sufficiently extract layered features of data. Extensive experiments show that our algorithm solves the existing problem and achieves a more competitive performance on the datasets of CIFAR10 (2.45% error rate) , fashionMNIST (3.70%) and ImageNet (25.5%) while at low search costs.