Entire Network Research Articles

Negative collaboration risk analysis and control in manufacturing service collaboration based on complex network evolutionary game

In the era of the digital economy, manufacturing service collaboration (MSC) effectively addresses user personalization and customization through networked collaboration among service providers. However, opportunistic negative collaboration behaviors within the network can lead to risk generation, risk propagation and potential dysfunction of the entire collaboration network. Traditional risk control strategies are often based on experience and lack predictive validation. To improve the efficiency of risk control strategy formulation and validate the effectiveness of risk control, the article proposes a negative collaboration risk control optimal strategy exploration method. This method can realize effective optimal decision-making for risk control and effectiveness validation by combining the complex network evolutionary game and Q-learning. The experiment results show that this method is able to formulate situation-specific risk control strategies and keep negative collaboration providers below 10%, effectively controlling MSC risk.

Polysiloxane-Based Fluorescent Probes for Visualizing pH and Thiocyanate during Mitochondrial Autophagy.

Mitochondrial autophagy, known as mitophagy, is a vital cellular process that involves the selective degradation of damaged or dysfunctional mitochondria through autophagy, which is critical to the functional integrity of the entire mitochondrial network and determines the survival and death of cells. An abnormal pH may lead to an imbalance in mitochondrial homeostasis and the occurrence of mitochondrial autophagic acidification and dysfunction. SCN- is also an important anion in cellular metabolism, and its abnormal concentration may lead to mitochondrial damage. However, so far, there are few reports on the simultaneous realization of pH and SCN- detection in mitochondria. Therefore, to complement the blank in this area, we developed the polysiloxane-based fluorescent probe P0-CMN that is capable of simultaneously visualizing pH and SCN- fluctuation levels in mitochondria. The probe P0-CMN has the desired mitochondrial-targeting properties and sensitivity to pH and SCN-. It is able to simultaneously monitor pH and SCN- changes in mitochondria in a dual-channel mode. In addition, probe P0-CMN can visualize pH changes during mitochondrial autophagy. This work provides an effective strategy for the design of dual-responsive fluorescent probes and further broadens the application of polysiloxane fluorescent materials.

Determinants of Outcomes for Acute Myeloid Leukemia Patients Treated in a Community-Based Specialized Versus Non-Specialized Hospital Setting.

The treatment setting influences acute myeloid leukemia (AML) outcomes. Most cancer patients receive care in the community, yet few studies have evaluated the effectiveness of clinical programs outside of academic or National Cancer Institute (NCI)-designated cancer centers. This was a multi-level, case-controlled study of real-world outcomes for initial AML treatment in a community-based network for 1,391 patients with AML between 2011 and 2018. We benchmarked survival within our network against the Surveillance, Epidemiology, and End Results (SEER) database. Coarsened exact matching was performed against 17,186 chemotherapy-treated patients in the SEER database. Cox proportional and accelerated failure time multivariable modeling were performed to identify patient, disease, therapy and center characteristics associated with the risk of AML mortality. Within the network, 799 patients were treated at six specialized blood cancer centers and 592 at 63 other hospitals. Patients receiving high-intensity induction at specialized centers had improved median survivals of 31 months versus 18 months [P<0.001] at non-specialized centers. Median survivals were 13 for non-specialized centers versus 10 months for SEER [P<0.001], and 18 for the entire network versus 10 months for SEER [P<0.001]. Multivariable modeling showed significant impacts from age (HR = 1.025), high-intensity induction therapy (HR= .695), diagnosis year (HR= .937), neighborhood income (HR = .997; P<0.01), higher acuity (HR = 1.002) and Charlson comorbidity score (HR = 1.117). AML treatment may be effectively delivered in the community hospital setting, with specialized centers producing better outcomes for higher intensity treatments.

The Complex Interplay of TGF-β and Notch Signaling in the Pathogenesis of Fibrosis

Fibrosis is a major medical challenge, as it leads to irreversible tissue remodeling and organ dysfunction. Its progression contributes significantly to morbidity and mortality worldwide, with limited therapeutic options available. Extensive research on the molecular mechanisms of fibrosis has revealed numerous factors and signaling pathways involved. However, the interactions between these pathways remain unclear. A comprehensive understanding of the entire signaling network that drives fibrosis is still missing. The TGF-β and Notch signaling pathways play a key role in fibrogenesis, and this review focuses on their functional interplay and molecular mechanisms. Studies have shown synergy between TGF-β and Notch cascades in fibrosis, but antagonistic interactions can also occur, especially in cardiac fibrosis. The molecular mechanisms of these interactions vary depending on the cell context. Understanding these complex and context-dependent interactions is crucial for developing effective strategies for treating fibrosis.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Multi-period operation of integrated electricity and gas systems with hydrogen blending considering gas composition dynamics

Green hydrogen from renewable sources can be blended with natural gas and serves as a potentially feasible measure for contributing to the net zero energy sector. The time-varying nature of hydrogen injection, influenced by stochastic renewable generations, can result in fluctuations in gas concentrations in the entire network. It poses a potential threat to the secure regulation of integrated electricity and gas systems (IEGS). For managing the operating condition and guaranteeing the security of IEGS during operation, a multi-period operation framework with alternative gas (e.g., hydrogen) blending is developed. First, a convex gas security range is derived using the Dutton method. Then, the multi-period operation framework is devised to mitigate the impacts of alternative gas injection on gas security over the entire operational period. Both the dynamics from gas composition and gas flow are modelled, accurately describing the real-time travel of alternative gas concentrations. The dynamics in the gas mixture properties (e.g., relative density) are fully revealed with time-varying gas concentrations. To tackle the high non-convexities in the optimization problem, second-order-cone relaxation is well-tailored and firstly used in the case of varying gas compositions, making the motion equations and advective transport equations more tractable. An advanced second-order-cone sequential programming is devised to drive the relaxation tight more efficiently. Finally, our operation strategy is illustrated in IEEE and Belgium Electricity and Gas Systems. Results indicate that hydrogen concentrations take about 12.5 h to travel from the injection point to the end of the pipeline route in the Belgium gas system. By incorporating this unique characteristic into the model, operational scheduling and dispatch in IEGS can be more practical when integrating hydrogen in the future.

Monitoring roadkill in Amravati, India: A Citizen Science project

Roadkill is a global conservation problem, and India is no exception. Animal-vehicle accidents on roads are a leading cause of death for a variety of animal species both within and near protected areas. However, roadkills are rarely documented due to field limitations and the unavailability of dedicated IT platforms. The lack of documentation and unavailability of traffic fatality data pose major limitations for government agencies and conservationists. In 2018, as part of a citizen science program to collect opportunistic data, we launched a free "ArRM" mobile app and website, www.arrm.org.in. In 2022, we conducted a systematic random survey of 10,500 km covering the entire road network in Amravati district, Maharashtra, India. Through citizen science and our efforts, 364 roadkills (336 wild, 28 domestic) from 70 species were recorded. Our project aims to overcome the non-documentation of roadkills in the Amravati district and provide data on an interactive map that is freely accessible in the public domain. This will help wildlife authorities, conservationists, and policymakers plan or implement effective measures to reduce the number of wildlife killed on roads.

The innovated national noise monitoring network in The Netherlands

Over ten years ago, the National Institute for Public Health and the Environment started monitoring noise emissions from major roads and railways in The Netherlands. Today, this monitoring program is still going strong. The measured data is used to validate the yearly reported noise production levels calculated by the Dutch road and railway authorities, to monitor trends in noise production, and to check the validity of the Dutch noise calculation method. Measurements in the entire network are performed at an altitude of four meters close to fifty meters from the major road or railway in free field as these are the requirements for the reported calculated levels. The network therefore, has to operate stand-alone, resulting in challenges in terms of power supply, datastorage and communication. Ten years ago, we started with a battery-powered network of simple and cost-effective dataloggers. Recently, we made innovations by transitioning to a solar-powered network of modern sound level meters with remote data communication. In this presentation I will give a brief history of this monitoring network, how it developed, and show you the possibilities of the present-day innovated network.

Uncovering emerging technologies in intelligent manufacturing via graph classification of community characteristics

Advancing the identification of emerging technology trends is pivotal for informed design decisions and risk mitigation. Our research introduces a novel methodology, centered on technology subgraphs within a patent knowledge graph, utilising graph neural networks combined with data augmentation techniques. This approach deviates from traditional methods, which primarily focus on individual patent nodes or the entire patent network. Instead, it segments the patent knowledge graph into subgraphs, analyzing them based on structural features to identify emerging technologies. Our method is further strengthened by employing an expert-curated emerging technology tag library, enabling a quantitative, rather than solely qualitative, assessment of the subgraphs’ relevance to emerging technologies. Additionally, we integrate comparative learning to enhance subgraph data quality, thereby boosting the reliability and accuracy of our approach. The results demonstrate a significant improvement in the accurate identification of emerging technologies, underscoring the effectiveness of our proposed model.

Atypical venous drainage system and distinct vascular characteristics in pediatric intracerebral hemorrhage caused by multiple micro arteriovenous malformations.

Hemorrhagic brain micro-arteriovenous malformations (micro-AVMs) are considered to constitute a relatively significant portion of pediatric AVMs, though they are often associated with a low bleeding rate, as seen in hereditary hemorrhagic telangiectasia, which frequently involves multiple micro-AVMs. We present a rare case of a 10-year-old girl with multiple hemorrhagic micro-AVMs. Intraoperative findings during the emergency operation for hematoma evacuation and post-operative superselective angiography highlighted the unique angioarchitecture of three micro-AVMs (two lesions in the superficial areas and one lesion in the deep-seated area) and the atypical bleeding source due to the complex congestive venous drainage system. One micro-AVM was successfully occluded by a transarterial emboliozation, and remaining two micro-AVMs underwent gamma knife irradiation as a salvage therapy. Superselective angiography is crucial for detecting micro-AVMs, offering detailed insights into small, localized abnormal vascular drainage systems, and guiding therapeutic strategy. Additionally, micro-AVM-associated unique vascular hypersensitivity, such as vasospasm, requires careful consideration, as invasive procedures may significantly alter the visibility of the entire micro-AVM network.

When two chains meet: Optimizing the design of blockchain-enabled supply chain networks

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

Reviving collapsed plant-pollinator networks from a single species.

Mutualistic ecological networks can suddenly transition to undesirable states due to small changes in environmental conditions. Recovering from such a collapse can be difficult as restoring the original environmental conditions may be infeasible. Additionally, such networks can also exhibit a phenomenon known as hysteresis, whereby the system could exhibit multiple states under the same environmental conditions, implying that ecological networks may not recover. Here, we attempted to revive collapsed mutualistic networks to a high-functioning state from a single species, using concepts from signal propagation theory and an eco-evolutionary model based on network structures of 115 empirical plant-pollinator networks. We found that restoring the environmental conditions rarely aided in recovery of collapsed networks, but a positive relationship between recovering pollinator density and network nestedness emerged, which was qualitatively supported by empirical plant-pollinator restoration data. In contrast, network resurrection from a collapsed state in undesirable environmental conditions where restoration has minimal impacts could be readily achieved by perturbing a single species or a few species that controls the response of the dynamical networks. Additionally, nestedness in networks and a moderate amount of trait variation could aid in the revival of networks even in undesirable environmental conditions. Our work suggests that focus should be applied to a few species whose dynamics could be steered to resurrect entire networks from a collapsed state and that network architecture could play a crucial role in reviving collapsed plant-pollinator networks.

Bayesian pathway analysis over brain network mediators for survival data.

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity, and time to disease onset with maximum information extraction, we propose a Bayesian approach to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural model which includes a symmetric matrix-variate accelerated failure time model for disease onset and a symmetric matrix response regression for the network-variate mediator. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Simulations are carried out to confirm the advantages of our proposed method over existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies.

Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.

Compared to artificial neural networks (ANNs), spiking neural networks (SNNs) present a more biologically plausible model of neural system dynamics. They rely on sparse binary spikes to communicate information and operate in an asynchronous, event-driven manner. Despite the high heterogeneity of the neural system at the neuronal level, most current SNNs employ the widely used leaky integrate-and-fire (LIF) neuron model, which assumes uniform membrane-related parameters throughout the entire network. This approach hampers the expressiveness of spiking neurons and restricts the diversity of neural dynamics. In this paper, we propose replacing the resistor in the LIF model with a discrete memristor to obtain the heterogeneous memristive LIF (MLIF) model. The memristance of the discrete memristor is determined by the voltage and flux at its terminals, leading to dynamic changes in the membrane time parameter of the MLIF model. SNNs composed of MLIF neurons can not only learn synaptic weights but also adaptively change membrane time parameters according to the membrane potential of the neuron, enhancing the learning ability and expression of SNNs. Furthermore, since the proper threshold of spiking neurons can improve the information capacity of SNNs, a learnable straight-through estimator (LSTE) is proposed. The LSTE, based on the straight-through estimator (STE) surrogate function, features a learnable threshold that facilitates the backward propagation of gradients through neurons firing spikes. Extensive experiments on several popular static and neuromorphic benchmark datasets demonstrate the effectiveness of the proposed MLIF and LSTE, especially on the DVS-CIFAR10 dataset, where we achieved the top-1 accuracy of 84.40 .

Production overrun optimization considering supply chain network reliability

Production overrun is a common practice in manufacturing to meet the demand by increasing the number of qualified products and compensating for manufacturing defects. While production overrun can improve Supply Chain Network Reliability (SCNR), it leads to higher material costs. In this paper, a model is proposed to evaluate SCNR by incorporating the reliabilities of inbound logistics, operations, and outbound logistics. Based on the proposed SCNR model, we study the optimal production overrun of each manufacturing site in a supply chain network and identify the production plan that satisfies the reliability requirement with minimum total production overrun penalty. By analyzing the monotonicity of the objective and constraint functions of the formulated problem, an algorithm based on linearization is developed to solve this optimization problem. Numerical examples across various scales are presented to illustrate the developed model and method. The impact of the penalty coefficient of the production overrun on the result is investigated. The results from the numerical examples provide managerial insights on allocating resources in the entire supply chain network and improving the supply chain reliability and competitiveness.

A nonlinear spectral core–periphery detection method for multiplex networks

Core–periphery detection aims to separate the nodes of a complex network into two subsets: a core that is densely connected to the entire network and a periphery that is densely connected to the core but sparsely connected internally. The definition of core–periphery structure in multiplex networks that record different types of interactions between the same set of nodes on different layers is non-trivial since a node may belong to the core in some layers and to the periphery in others. We propose a nonlinear spectral method for multiplex networks that simultaneously optimizes a node and a layer coreness vector by maximizing a suitable non-convex homogeneous objective function by a provably convergent alternating fixed-point iteration. We derive a quantitative measure for the quality of a given multiplex core–periphery structure that allows the determination of the optimal core size. Numerical experiments on synthetic and real-world networks illustrate that our approach is robust against noisy layers and significantly outperforms baseline methods while improving the latter with our novel optimized layer coreness weights. As the runtime of our method depends linearly on the number of edges of the network, it is scalable to large-scale multiplex networks.

The Study of Echocardiography of Left Ventricle Segmentation Combining Transformer and Convolutional Neural Networks.

Accurate prediction of echocardiographic parameters is essential for diagnosis and treatment of cardiac disease, especially for segmentation of the left ventricle to obtain measurements such as left ventricular ejection fraction and volume. However, manually outlining left ventricle on echocardiographic images is a time-consuming and physician experience-dependent task. Therefore, it is crucial to develop an accurate and efficient automatic segmentation tool. Therefore, we aimed to explore a model to perform echocardiography of left ventricle segmentation by combining transformer and convolutional neural networks (CNN).ResNet-50 was used in CNN branch. The encoder-decoder architecture was used for transformer branch, which was fused to the corresponding feature maps of the CNN branches. Fusion module was used to effectively combine feature information from the CNN and transformer. Bridge attention used to increase sensitivity and prediction accuracy of model. The entire network was trained end-to-end using the binary cross-entropy with logits loss L.In this work, we propose an automatic left ventricular (LV) segmentation model based on Transformer and CNN that efficiently captures global dependencies and spatial details and create a fusion module using CBAM that fuses Transformer and CNN features. In addition, attention is also computed using multi-level fusion features to obtain the final attention segmentation map. The model was trained and evaluated on a large cardiac image dataset, EchoNet-Dynamic, with test dice coefficient of 92.4%.The results show that our model can better segment left ventricle. We also tested our model on clinical patient ultrasound images, and visualization results proved effectiveness of the model.

Applications of ancestral sequence reconstruction for understanding the evolution of plant specialized metabolism.

Studies of enzymes in modern-day plants have documented the diversity of metabolic activities retained by species today but only provide limited insight into how those properties evolved. Ancestral sequence reconstruction (ASR) is an approach that provides statistical estimates of ancient plant enzyme sequences which can then be resurrected to test hypotheses about the evolution of catalytic activities and pathway assembly. Here, I review the insights that have been obtained using ASR to study plant metabolism and highlight important methodological aspects. Overall, studies of resurrected plant enzymes show that (i) exaptation is widespread such that even low or undetectable levels of ancestral activity with a substrate can later become the apparent primary activity of descendant enzymes, (ii) intramolecular epistasis may or may not limit evolutionary paths towards catalytic or substrate preference switches, and (iii) ancient pathway flux often differs from modern-day metabolic networks. These and other insights gained from ASR would not have been possible using only modern-day sequences. Future ASR studies characterizing entire ancestral metabolic networks as well as those that link ancient structures with enzymatic properties should continue to provide novel insights into how the chemical diversity of plants evolved. This article is part of the theme issue 'The evolution of plant metabolism'.

BootCellNet, a resampling-based procedure, promotes unsupervised identification of cell populations via robust inference of gene regulatory networks.

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As measurement techniques evolve, there has been an increasing need for data analysis methodologies, especially those focused on cell-type identification and inference of gene regulatory networks (GRNs). We have developed a new method named BootCellNet, which employs smoothing and resampling to infer GRNs. Using the inferred GRNs, BootCellNet further infers the minimum dominating set (MDS), a set of genes that determines the dynamics of the entire network. We have demonstrated that BootCellNet robustly infers GRNs and their MDSs from scRNA-seq data and facilitates unsupervised identification of cell clusters using scRNA-seq datasets of peripheral blood mononuclear cells and hematopoiesis. It has also identified COVID-19 patient-specific cells and their potential regulatory transcription factors. BootCellNet not only identifies cell types in an unsupervised and explainable way but also provides insights into the characteristics of identified cell types through the inference of GRNs and MDS.

Data-Driven Relationship for Reduction Factor of Ballasted Railway Bridge Deflections Due to Load Distribution Within Track

Increasing the operating speed of the trains on modern networks necessitates performing dynamic analyses to assess the performance of bridges under passage of trains. The detailed investigation of their responses requires constructing complex computational models capable to take the train–track–bridge interaction effects into account. Such models have successfully been developed; however, employing those elaborated models for practical engineering applications, or to perform studies that require a large number of analyses may become infeasible. Among such situations are conducting probabilistic investigations, screening of entire networks, or sensitivity analyses. These concerns have been addressed by employing simplified models mostly relying on moving load modeling strategy which disregards the train–track–bridge interaction effects. Those neglected contributions can be compensated by implementing additional correction factors. The distribution of loads within track is one of those disregarded effects where a reduction factor is recommended by design guidelines to take its contribution into account. It has been shown that the existing relationship for these reduction factors delivers an acceptable performance for vertical accelerations, while showing a less favorable performance for displacements. Then, a data-driven strategy is adopted in this study to propose easy-to-apply relationships for reduction factors of deflections, due to load distribution within the track. In this context, three different distributive lengths of triangular load footprints have been considered, namely 2.0, 2.5 and 3.0[Formula: see text]m. The procedure employed has trained and tested for more than 1[Formula: see text]200 train configurations, comprising conventional, articulated and regular vehicles, and including several tens of thousand data points for each distributive length. The performance observed in the new models revealed a considerable improvement with respect to the existing relationship.