Coordination Networks Research Articles

The Link Between Social-Ecological Network Fit and Outcomes: A Rare Empirical Assessment of a Prominent Hypothesis

It is often claimed that the structure of networks influences outcomes in environmental governance. For example, network motifs of social-ecological fit have been linked to positive environmental outcomes, but empirical tests of this link are rare. Social-ecological network fit represents a situation in which actors involved in the governance and management of linked ecological elements coordinate. We empirically analyze how motifs of social-ecological network fit are associated with actors’ outcome assessments in ten cases of Swiss wetlands governance. We combine social networks among organizational actors, networks of interrelated ecosystem management activities, and actors’ assessments of outcomes. Results show that – contrary to the prominent theoretical claim – more fit is linked to worse outcomes. Drawing on the so-called risk hypothesis, we argue that our negative findings likely highlight a complicated causal process between actors’ assessments of outcomes and their adjustment to risks through coordination in networks.

High-Performance Computing Web Search System Based on Computer Big Data

File sharing, streaming media, collaborative computing, and other P2P systems are all unicast to establish the corresponding overlapping network. The superimposed network is generally carried out based on the existing primary network. In this way, the access of each node is random. At the same time, this will cause the topological structure of the upper and lower layers to be inconsistent. This will increase the communication delay between nodes and cause an excellent bandwidth burden to the underlying network. The existing topology matching methods still face problems, such as poor scalability and long node aggregation time. This paper aims to design a topological distributed node aggregation method based on network coordination and distributed hash table (DHT) algorithm. This paper established a two-dimensional mesh model of nodes based on equal-distance concentric circles and divided into two equal areas. The parts of multiple namespaces correspond one by one according to their location. Because nodes are kept close, neighbours can be aggregated through DHT's primary "publish" and "search" primitives. Experimental results show that the TANRA method can match the network's topology under a slight delay and a large number of nodes. The TANRA method can effectively reduce the path delay in structured networks.

RNAIII is linked with the pentose phosphate pathway through the activation of RpiRc in Staphylococcus aureus.

Staphylococcus aureus is a major human pathogen involved in acute and chronic infections. Highly recalcitrant to antibiotic treatment, persistent infections are mostly associated with the loss of RNAIII expression, a master RNA regulator responsible for the switch from colonization to infection. Here, we used the MS2 affinity purification coupled with RNA sequencing approach to identify novel mRNA targets of RNAIII and uncover novel functions. We demonstrate that RNAIII is an activator of the expression of genes involved in the pentose phosphate pathway and is implicated in the adjustment of bacterial fitness as a function of carbohydrate sources. Taken together, our results demonstrate an unprecedented role of RNAIII that goes beyond the knowledge gained so far and contributes to a better understanding of the role of RNAIII in bacterial adaptation expression and the coordination of a complex regulatory network.

Abstract LB046: Treatment of EGFR-mediated tumors via lysosome activity regulation

Abstract The lysosome is a membrane-enclosed organelle that is an essential part of the cell's digestive system. This organelle contains over 60 types of hydrolases that can break down biological polymers such as proteins, carbohydrates, lipids, and nucleic acids. These enzymes require an acidic pH for optimal function, which is achieved by using ATP hydrolysis to pump protons against their electrochemical gradient into the lysosome by the vacuolar H+ ATPase (V-ATPase). The biogenesis of lysosomes is known to be controlled by the coordination of the regulatory gene network and the lysosomal expression, which is governed by the nuclear translocation of transcription factor EB (TFEB). Lysosomes are an essential component of the inner membrane system and participate in numerous cell biological processes, such as macromolecular degradation, antigen presentation, intracellular pathogen destruction, plasma membrane repair, exosome release, cell adhesion/migration, and apoptosis. The primary function of lysosomes is to digest extracellular material that has been internalized by endocytosis and intracellular components that have been sequestered by autophagy. They recycle the unwanted cellular material as energy, providing a nutrient source for maintaining cellular homeostasis. Lysosomal activity affects the tumor microenvironment, making the tumor cells use energy more efficiently, and it is known to be increased compared to neighboring normal tissues. However, recent studies have shown that increased lysosomal activity can lead to downregulating Receptor tyrosine kinase (RTK) activity in cancer cells. RTK activity frequently occurs with mutations in EGFR in various human cancers. Thus, EGFR is currently a target for several cancer therapies. Recently, studies on EGFR-mediated tumors through lysosome regulation have been continuously conducted. According to our previous study, it has been found that lysosome inactivation suppresses the degradation of RKT, such as EGFR, and increases the expression of EGFR.EGFR is expressed in various human tumors, including gliomas and carcinomas of the lung, colon, head and neck, pancreas, breast, ovary, and kidney. Mutations, gene amplification, and protein overexpression of various pathway elements contribute to carcinogenesis and impact prognosis. Alterations within the EGFR signaling cascade, such as gene mutations, gene amplification, and protein overexpression, have been shown to contribute to colorectal carcinogenesis.[4]In this study, we will down-regulate the expression of EGFR by activating the lysosome in DLD-1, a colorectal cancer cell line. Lysosome was activated by overexpression of V-ATPase using the lentiviral system. In vitro experiments confirmed that the degradation of EGFR was actively performed in the lysosome-activated experimental group. It inhibited cancer cells' proliferation, survival, and differentiation. Targeting lysosomes may be a new approach to overcoming EGFR-mediated cancers. Citation Format: Dohyang Kim, Anlin Zhu, Jaewoo Hong. Treatment of EGFR-mediated tumors via lysosome activity regulation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB046.

Exploring the role of political elites in post-truth communication on social media

This study investigates post-truth messaging and participatory disinformation on Twitter, focusing on the activities of Craig Kelly, a former Australian member of parliament and a key figure previously accused of spreading health misinformation in Australia during the COVID-19 pandemic. We draw on Harsin's conceptualisation of post truth communication to analyse 4317 tweets and 5.2 million interactions with Kelly's account and his network of followers over a six-month period. Our novel empirical approach, combining coordination network analysis with a forensic qualitative approach, explores the participatory nature of online interaction, where fringe actors mobilise around Kelly's tweets. The findings demonstrate how political figures have a privileged and outsized role in public discourse, undermining scientific institutions and promoting anti-deliberative politics. This research underscores the role of participatory disinformation in the post-truth era and suggests that regulators, governments, and social media platforms work collaboratively to develop a whole-of-society framework to tackle misinformation.

Synthesis, magnetic and fluorescence detection properties of four metal-organic frameworks based on imidazole carboxylic acid ligands

Four new metal-organic frameworks (MOFs), namely [M(HL)H2O]n (1–3) [M = Co (1), Ni (2), Cu (3)] and [Cd(HL)]n (4), were successfully structured based on imidazole carboxylic acid ligands [H3L = 5-(((1H-imidazol-2-yl)methyl)amino) isophthalic acid]. X-ray single-crystal diffraction analysis showed that MOFs 1–3 possess the same two-dimensional (2D) planar structure, while MOF 4 exhibited a three-dimensional (3D) coordination network. The magnetic test results showed that MOFs 1–3 have typically antiferromagnetic behavior. The sensing experimental showed that MOF 4 could be considered as a promising bifunctional fluorescent sensor for Cu2+ and Fe3+ ions with excellent selectivity and sensitivity, and the detection limit were 4.11 μM and 4.89 μM, with Ksv values of 1.37 × 104 M−1 and 1.24 × 104 M−1, respectively. The cycling experiment illustrated the good reusability of the material. In addition, the fluorescence quenching mechanism of MOF 4 was further explored in detail.

Integrated transcriptomic and metabolomic analyses reveals the molecular bases of alfalfa regrowth processes of new shoots after cutting under different water and nitrogen availability

Alfalfa regrowth is a complex process that is frequently constrained by deficits in water and N fertilisers after cutting. However, the transcriptomic regulatory networks and primary metabolic changes during regrowth are largely unknown. The alfalfa regrowth biomass responses to water deficiency and water deficiency supplied with N fertilisers and corresponding transcriptional and primary metabolic changes in new leaves and roots were systematically explored at 9 and 14 d after cutting. Water deficiency significantly decreased the alfalfa regrowth biomass. Integrated transcriptomic and metabolomic analysis demonstrated that osmoprotection related genes, sucrose and starch metabolic pathways, hormone biosynthesis and signalling pathways, and key transcription factors in new leaves and roots are responsible for alfalfa regrowth under water deficiency. Furthermore, alfalfa regrowth biomass was significantly improved in the presence of N fertilisers under water deficiency through multigene network coordination and metabolic pathway rearrangement. Interestingly, differentially expressed genes expression occurred predominantly in roots at 9 d after cutting, while it mainly occurred in the new leaves at 14 d after cutting. This study revealed the complex molecular mechanisms of alfalfa regrowth and provided theoretical support for optimal irrigation and N fertiliser strategies that will achieve maximum biomass of alfalfa in dryland farming areas.

A typology of civil society organisation activities: a multi-grounded theory approach to what CSOs do

ABSTRACT Beginning explorations of civil society organisations (CSOs) from a democratic, political, or regulatory perspective potentially overlooks important aspects of CSO activity. Instead, this paper takes CSOs in action as its starting point – specifically, Australian CSOs responding to the needs of people and communities during the crisis of the COVID-19 coronavirus pandemic. Working between reflections by Australian CSO leaders during the pandemic and selected literature, a multi-grounded theory approach is used to produce a novel theoretical contribution – a typology of 10 distinct CSO activities. These 10 activities are divided into three categories: (1) The ‘Big Three’ activities – advocate systemically, deliver service and build capacity; (2) ‘Business As Usual’ activities – engage community, manage organisation and work collaboratively; and (3) ‘Enabling’ activities – conduct research, coordinate network, hold space and provide funding. The final three activities are revealed as being less integrated into the broader CSO literature than their more commonly explored counterparts.

Influence of Humidity on Layer-by-Layer Growth and Structure in Coordination Networks.

Metal-organic frameworks (MOFs) are promising materials because of their high designability of pores and functionalities. Especially, MOF thin films and their properties have been investigated toward applications in nanodevices. Typically, MOF thin films are fabricated by using a bottom-up method such as layer-by-layer (LbL) growth in air. Because the water molecules can coordinate and be replaced with organic linkers during synthesis, humidity conditions will be expected to influence the LbL growth processes. In this study, we fabricated MOF thin films composed of Zn2+, tetrakis-(4-carboxyphenyl)-porphyrin (TCPP), and 4,4'-bipyridyl (bpy) at 10 and 40% relative humidity (RH) conditions. Then, we investigated the humidity effects on chemical compositions of TCPP and bpy, periodic structure, orientation, and surface morphology. At high RH, coordination replacement of water with the organic linkers becomes more competitive than that at low RH, resulting in a different TCPP/bpy composition ratio between the two RH conditions. Also, more frequent coordination replacements of water with the organic linkers at high RH led to the formation of phases other than that observed at low RH, loss of growth orientation, and rough surface. The findings clarified the importance of controlling the RH condition during LbL growth to obtain the desired coordination networks.

Fuzzy k-Winner-Take-All Network for Competitive Coordination in Multirobot Systems

Fuzzy <i>k</i>-Winner-Take-All Network for Competitive Coordination in Multirobot Systems

Three-Fold Coordination of Copper in Sulfides: A Blockade for Hole Carrier Delocalization but a Driving Force for Ultralow Thermal Conductivity.

Copper-rich sulfides are very promising for energy conversion applications due to their environmental compatibility, cost effectiveness, and earth abundance. Based on a comparative analysis of the structural and transport properties of Cu3BiS3 with those of tetrahedrite (Cu12Sb4S13) and other Cu-rich sulfides, we highlight the role of the cationic coordination types and networks on the electrical and thermal properties. By precession-assisted 3D electron diffraction analysis, we find very high anisotropic thermal vibration of copper attributed to its 3-fold coordination, with an anisotropic atomic displacement parameter up to 0.09 Å2. Density functional theory calculations reveal that these Cu atoms are weakly bonded and give rise to low-energy Einstein-like vibrational modes that strongly scatter heat-carrying acoustic phonons, leading to ultralow thermal conductivity. Importantly, we demonstrate that the 3-fold coordination of copper in Cu3BiS3 and in other copper-rich sulfides constituted of interconnected CuS3 networks causes a hole blockade. This phenomenon hinders the possibility of optimizing the carrier concentration and electronic properties through mixed valency Cu+/Cu2+, differently from tetrahedrite and most other copper-rich chalcogenides, where the main interconnected Cu-S network is built of CuS4 tetrahedra. The comparison with various copper-rich sulfides demonstrates that seeking for frameworks characterized by the coexistence of tetrahedral and 3-fold coordinated copper is very attractive for the discovery of efficient thermoelectric copper-rich sulfides. Considering that lattice vibrations and carrier concentration are key factors for engineering transport phenomena (electronic, phonon, ionic, etc.) in copper-rich chalcogenides for various types of applications, our findings improve the guidelines for the design of materials enabling sustainable energy solutions with wide-ranging applications.

Substituted carboxylate and dipyridyl assisted Cd(II) coordination network: structural elucidation, photoluminescence, TGA, and DFT interpretation

Substituted carboxylate and dipyridyl assisted Cd(II) coordination network: structural elucidation, photoluminescence, TGA, and DFT interpretation

Conflict or coordination? An analysis of the Southern New England offshore wind - fisheries policy network

Offshore wind represents a growing part of coastal states’ energy portfolios. While it stands on the brink of rapid expansion in the United States, reaction from ocean users is mixed, with commercial fishers representing the most vocal opposition. Recent events suggest that fisheries conflict may represent a barrier to advancing the growth of U.S. offshore wind, yet little is known about the inner workings of this conflict nor how actors interact to try to influence policy outcomes. This research utilized policy networks and the advocacy coalition framework to assess the ways in which fishers, fisheries advocates, and others interacted with each other, government agencies, and wind developers within the context of offshore wind farm decision-making in southern New England, U.S. Data collected through a questionnaire were used to perform a social network analysis. Findings revealed one baseline communication network, and a similarly configured coordination network, rather than multiple advocacy coalitions. Degree and betweenness centrality measures revealed highly influential roles of one fisheries advocacy organization and two wind developers. Information/advice and trust networks were also analyzed; in-degree centrality measures revealed the influence of two federal fisheries offices, as well as the same fisheries organization, as credible and trustworthy. Overall, results reveal that while coordination was taking place, conflict was also fueled through the dominant influence of private industries and the lack of prominence of a neutral bridging organization. Recommendations for practice include integrating a bridging organization and facilitating a more influential role of respected government agencies to mitigate conflict.

A two-dimensional coordination polymer bearing an Evans-Showell-type polyoxometalate: Synthesis, structure and biological insight†

An Evans-Showell-type polyoxometalate (POM) based coordination polymer (CP), [H2(Co2Mo10H4O38)2(Co3(L)3(HL)2(H2O)6)(HL)2]·14H2O (1), {L = 4,4′-bipyridine}, was synthesized under hydrothermal conditions and structurally characterized by infrared spectroscopy (IR), elemental analysis, powder X-ray diffraction (PXRD), diffuse reflectance spectroscopy (DRS) and single crystal X-ray diffraction methods. In 1, two [Co2Mo10H4O38]6− POMs are joined together via two Co-O interactions to form a dimer which is centrosymmetric. Here, the dimers act as repeating units and are expanded by another crystallographically independent Co ion in the structure. Binding of the cations to the O atoms of the POM and the N atoms of organic ligand resulted in the formation of a 2D CP structure. Compound 1 is the first example of an inorganic-organic polymer architecture constructed from Evans-Showell-type POM in which Co cations act as metal nodes. The topology of 1 can be rationalized as a 2D square lattice (sql) coordination network with point symbol {44.62} in which [Co2Mo10H4O38]6− and 4,4′-bipyridine units act as linkers and the Co cations as nodes. The cytotoxicity effects of the POM were evaluated on the cancer CT26 and normal L929 cell lines by an MTT assay from which an IC50 value of cancer CT26 cells of approximately 366 ppm was obtained, but normal L929 cells did not show significant toxicity.

Construction of porphyrinic manganese-organic frameworks based on structural regulation for electrochemical determination of nitrobenzene in water and vegetable samples.

Nitrobenzene (NB) is one of the major organic pollutants that has seriously endangered human health and the environment even in trace amounts. Therefore, it is of great significance to detect trace NB efficiently and sensitively. Herein, a porphyrinic metal-organic framework (MOF) of Mn-PCN-222 (PCN, porous coordination network) was first synthesized by the coordination between Zr6 cluster and tetrakis (4-carboxyphenyl)-porphyrin-Mn (Ⅲ) (MnTCPPCl) ligand. To regulate its structure and the electrochemical properties, a phenyl group was inserted in each branched chain of TCPP to form the TCBPP organic ligand. Then, we used Zr6 clusters and manganese metalloporphyrin (MnTCBPPCl) to synthesize a new porphyrin-based MOF (Mn-CPM-99, CPM, crystalline porous material). Due to the extended chains of TCPP, the rod-shaped structure of Mn-PCN-222 was switched to concave quadrangular bipyramid of Mn-CPM-99. Mn-CPM-99 exhibited higher porosity, larger specific surface area, better electrochemical performances than those of Mn-PCN-222. By using modular assembly technique, Mn-CPM-99 film was sequentially assembled on the surface of indium-tin-oxide (ITO) to prepare an electrochemical sensor (Mn-CPM-99/ITO). The proposed sensor showed excellent electrochemical reduction of NB and displayed three linear response ranges in the wide concentration ranges. The obtained low limit of detection (LOD, 1.3nM), high sensitivity and selectivity, and good reproducibility of the sensor for NB detection fully illustrate that Mn-CPM-99 is an excellent candidate for electrochemical sensor interface material. Moreover, the sensor was successfully applied to the detection of NB in lake water and vegetable samples showing satisfactory recovery of 98.9%-101.8%.

Implementation of NCCARE360, a Digital Statewide Closed-Loop Referral Platform to Improve Health and Social Care Coordination: Evidence from the North Carolina COVID-19 Support Services Program.

Efforts to improve population health by being responsive to patients' social and economic conditions will benefit from care models and technologies that assess and address unmet social needs. In 2019, NCCARE360 launched in North Carolina as the first state-wide digital care coordination network to "close the loop" on referrals between community-based organizations (CBOs), health service providers, and social service agencies. The platform creates a shared network for sending and receiving electronic referrals and track client outcomes. As a case study, we compare NCCARE360 resolution rates for community resource referrals originating from a large integrated health system primarily in Durham County from September 1, 2020, through February 28, 2021. In the first year, COVID-19 Support Ser-vices Program (COVID-SSP) funding was available to reimburse associated CBOs for providing food assistance and case management services. We compared this with the same period the following year after funds had been exhausted. We present frontline implementation experiences and highlight opportunities, challenges, and recommendations for NCCARE360 implementation. Multi-level considerations for individual end users, organizations adopting the platform, and policymakers are presented. Addi-tionally, we find that when COVID-SSP funding was available, more referrals were placed (3,220 cases) and referrals were more likely to be resolved (88% resolution rate) when compared to the same time frame when funds were no longer available (860 cases; 30% resolution rate). These results underscore the importance of reimbursement mechanisms and funding. The examination of referral rates is observational and may not generalize to other contexts. The shift to value-based care is an opportunity to embrace structural solutions to health and social care fragmentation. There is also an opportunity to realize the potential of NCCARE360 and efforts like it to contain costs and improve health outcomes and equity.

Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features

A safe and healthy infrastructure is essential for humanity. Maintenance and repair of roads, which are of great importance, especially in transportation, is essential. Roads can sometimes be damaged due to natural disasters or human factors. In this study, roads damaged as a result of the natural disaster and the 6 February 2023 Gaziantep-Kahramanmaras earthquakes, which had devastating/fatal consequences, were studied. In recent years, the use of machine learning methods in the automatic detection process of road cracks has become widespread. Here, deep learning architectures generally based on multilayer convolutional neural networks have become a popular solution, mainly thanks to their high discrimination power in visual data. In this study, it was aimed to automatically detect cracks on the roads in Gaziantep (Nurdagi) and Adiyaman provinces after the earthquake. The data set used in the experimental process consists of two categories: uncracked and cracked, and was collected by the researchers. ResNet152 deep learning architecture, traditional machine learning algorithms, and an ensemble learning (EL) model were used to classify cracks. Among the machine learning algorithms used in the experiments are support vector machine (SVM), K-nearest neighbors, Adaptive Boosting, and Naive Bayes. In the experimental process, ResNet152 architecture was used for feature extraction, and classification was carried out with machine learning algorithms. In addition, the EL method has been used to improve the success of machine learning algorithms. According to experimental results, the SVM algorithm and EL method gave the most successful results with 98.68% accuracy values. The proposed method can benefit experts in the automatic classification of road cracks. The study can contribute to activating rapid coordination and transportation networks in natural disasters that cause fatal and destructive causes, such as earthquakes.

Detecting malicious proxy nodes during IoT network joining phase

During the joining phase of an IoT network, when a node plays the role of a proxy node, it becomes responsible for forwarding Join Requests and Join Responses between the joining node and the network coordinator. If a proxy node is malicious, it has the potential to prevent new nodes from joining the network or direct them towards another entity impersonating the coordinator. Therefore, the joining phase is a critical stage for ensuring network security.In this paper, a robust system for detecting malicious proxy nodes during the joining phase is presented. This solution involves the coordinator maintaining a log table that records the participation frequency of each node as a proxy node. Following each joining phase, the coordinator receives a lightweight end-to-end encrypted packet from the joining node, containing information about any malicious proxy nodes encountered during the joining process. Having these data, the system calculates the number of legitimate proxy node participation for each node. The detection system utilizes these factors, along with a range of tunable parameters, to categorize nodes as either malicious or honest. Furthermore, this solution takes into account various potential attacks on the detection process, originating from both proxy nodes and joining nodes.This solution was integrated with a previously proposed consensus-based authentication mechanism for the 6TiSCH protocol. The evaluation includes both theoretical analysis and simulations, taking into consideration diverse parameters and attack scenarios. The results from the theoretical analysis align with the simulation outcomes, confirming the efficacy of our detection system in identifying malicious nodes and its resilience against potential attacks.

Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height

Accurate forecasting of significant wave heights (SWH) plays an important role in the generation and conversion of ocean wave energy, being a promising clean energy source, while a challenging task since the SWH is produced by a complex natural process. Additionally, some deep forecasting models integrate parallel and recursive structures via unexplained ways to present intelligent fitting, leading to insufficient inheritance of hybrid advantages. Since biological multisensory systems automatically cope with complex time series, this paper explores a bio-multisensory-inspired gate-attention coordination network (BGC-Net) for forecasting SWH. Specifically, inspired by the multisensory cognitive process, the BGC-Net includes sensory capture, brain memory, and forecasting components, and their collaboration offers a clear multi-level handling process. In internal implementations, through mimicking that multiple senses independently capture external stimuli, the first component adopts several activation functions for suppressing data fluctuations and detecting the complex nonlinear and linear features. Through mimicking that the left and right hemispheres interact and collaborate electrical impulses, the second component integrates both gate-driven recursive and self-attention-driven parallel structures to extract multivariate and temporal dependencies at the feature level. Through mimicking that a higher cerebral cortex fuses extracted information for the final cognition, the third component proposes three feature fusion ways of concatenate, attention, and gated fusion, and takes the best one to generate SWH forecasts. In summary, the BGC-Net not only has interpretability at the biological level but also effectively integrates the advantages of parallel and recursive structures. Five experiments and six analyses all indicate that the BGC-Net exceeds 17 baselines, has root mean squared error (RMSE) average improvements with 22.30% and 19.22% for two datasets, and provides references to support SWH forecasting.

Robust Stabilizing Control of Perturbed Biological Networks via Coordinate Transformation and Algebraic Analysis.

This article investigates robust stabilizing control of biological systems modeled by Boolean networks (BNs). A population of BNs is considered where a majority of BNs have the same BN dynamics, but some BNs are inflicted by mutations damaging particular nodes, leading to perturbed dynamics that prohibit global stabilization to the desired attractor. The proposed control strategy consists of two steps. First, the nominal BN is transformed and curtailed into a sub-BN via a simple coordinate transformation and network reduction associated with the desired attractor. The feedback vertex set (FVS) control is then applied to the reduced BN to determine the control inputs for the nominal BN. Next, the control inputs derived in the first step and mutated nodes are applied to the nominal BN so as to identify residual dynamics of perturbed BNs, and additional control inputs are selected according to the canalization effect of each node. The overall control inputs are applied to the BN population, so that the nominal BN converges to the desired attractor and perturbed BNs to their own attractors that are the closest possible to the desired attractor. The performance of the proposed robust control scheme is validated through numerical experiments on random BNs and a complex biological network.