Control Network Research Articles

Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix.

Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.

Standardizing Attention Process Training-III for a Multisite Clinical Trial of Neuromodulation.

The Control Network Neuromodulation to Enhance Cognitive Training in Complex Traumatic Brain Injury (CONNECT-TBI) study is an ongoing randomized, double-blinded, sham-controlled multisite clinical trial to determine the enhancing effects of noninvasive neuromodulation when paired with cognitive training in military participants (Veterans and active duty) with mild TBI. Attention Process Training-III (APT-III) was selected for its strong evidence base, manualized procedures, and computerized program. However, many aspects of APT-III that make it ideal for personalization make it less ideal for reliable implementation across participants, clinicians/technicians, and sites. The purpose of this feature article is to highlight APT-III procedures that require additional standardization for reliable administration across participants and sites. Ten studies using APT-III were reviewed for methodology of APT-III administration. The manual was also scrutinized; aspects of administration that involved clinical decision-making, subjectivity, flexibility, and/or that were identified by the APT-III developers as areas in need of "empirical evaluation" were flagged by clinicians. Literature and manual review findings were presented to the team for discussion and solution-finding. The authors created and refined a standardized process that would allow participants to move through APT-III training, including task movement algorithms and new materials drafts. Refining of algorithms and drafts continued until there was a consensus from team members. Many gray areas were identified, but we will limit our reporting to focus on (1) dosage, (2) adaptation, (3) metacognitive strategy instruction, and (4) goal attainment scaling. We present APT-III manual details, literature review findings, and CONNECT-TBI decisions and materials for each of these areas of focus. We have highlighted some of the major gray areas of APT-III administration so that fellow researchers can understand the need to take similar steps in clinical trials using APT-III. We provide examples of our standardization process and resultant rules and materials. Our algorithm, based on prior studies using the APT-III and our own iterative adjustments, allows for adjustment of the difficulty and speed of the training tasks (but within certain parameters) in order to achieve the best balance between individualization and consistency across participants and sites. We provide an example of a workflow and reporting process for future studies.

The critical mediating roles of the middle temporal gyrus and ventrolateral prefrontal cortex in the dynamic processing of interpersonal emotion regulation

Interpersonal emotion regulation (IER) is a crucial ability for effectively recovering from negative emotions through social interaction. It has been emphasized that the empathy network, cognitive control network, and affective generation network sustain the deployment of IER. However, the temporal dynamics of functional connectivity among these networks of IER remains unclear. This study utilized IER task-fMRI and sliding window approach to examine both the stationary and dynamic functional connectivity (dFC) of IER. Fifty-five healthy participants were recruited for the present study. Through clustering analysis, four distinct brain states were identified in dFC. State 1 demonstrated situation modification stage of IER, with strong connectivity between affective generation and visual networks. State 2 exhibited pronounced connectivity between empathy network and both cognitive control and affective generation networks, reflecting the empathy stage of IER. Next, a ‘top-down’ pattern is observed between the connectivity of cognitive control and affective generation networks during the cognitive control stage of state 3. The affective response modulation stage of state 4 mainly involved connections between empathy and affective generation networks. Specifically, the degree centrality of the left middle temporal gyrus (MTG) mediated the association between one's IER tendency and the regulatory effects in state 2. The betweenness centrality of the left ventrolateral prefrontal cortex (VLPFC) mediated the association between one's IER efficiency and the regulatory effects in state 3. Altogether, these findings revealed that dynamic connectivity transitions among empathy, cognitive control, and affective generation networks, with the left VLPFC and MTG playing dominant roles, evident across the IER processing.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

BackgroundInternet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. MethodResting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. ResultsIGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. ConclusionThe dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Complementary split ring resonator-inspired multi-HCN for implementation of an efficiency-enhanced inverse class-F2,3 PA

This article introduces a conceptual framework for enhancing the efficiency of an inverse class-F2,3 (F2,3−1) power amplifier (PA) through the incorporation of an innovative metamaterial-based multi-harmonic control network (multi-HCN). In the recommended approach, the multi-HCN comprises two resonant cells inspired by square-shaped complementary split ring resonator (CSRR), which are configured to suppress unwanted harmonics and to obtain the desired waveforms. The primary focus is to showcase how CSRRs can be effectively employed to form a shunt resonator through the etching of the conductive pattern of the proposed multi-HCN. Apart their harmonic tunability, CSRRs offer various noteworthy attributes such as impressive wave restoration capabilities and the capacity to address spatial limitations. To assess the efficacy of the proposed methodology, a class-F2,3−1 PA is devised and constructed utilizing a commercially available 10 W GaN HEMT device. The presented class F2,3−1 PA yields a commendable drain efficiency (DE) ranging from 50.3 % to 84 % and achieves an output power within the range of 38 dBm to 41.2 dBm across the bandwidth of 3.3 GHz to 3.7 GHz. These significant performance metrics serve as compelling evidence, validating the effectiveness of the proposed approach. Furthermore, this achievement establishes the design as a promising path for creating high-performance, affordable PA suitable for emerging communication systems.

Neural mechanisms of inhibitory control in preadolescent irritability: Insights from the ABCD study

ObjectiveElevated pediatric irritability is a transdiagnostic symptom that predicts multiple mental health problems in adolescence and adulthood. Altered top-down regulatory networks, such as inhibitory control networks that suppress an impulse in favor of goal-directed behavior, are thought to contribute to high levels of youth irritability. Nevertheless, little work has examined links between youth irritability and neural processes supporting inhibitory control in large diverse samples, nor have they focused on the key period ramping up to adolescence (i.e., preadolescence). MethodFunctional MRI data from 5380 preadolescents (age M=9.97 years, SD=0.62) in the baseline Adolescent Brain and Cognitive Development (ABCD) Study were analyzed. Parents reported on their preadolescent’s irritability. The stop signal task (SST) was leveraged to probe successful and failed inhibitory control. Activation and functional connectivity with amygdala, ventral striatum, and prefrontal seed regions were calculated during the SST and used in whole brain and region of interest (ROI) group-level analyses evaluating irritability effects. ResultsPreadolescents with higher levels of irritability displayed decreases in functional connectivity among amygdala, ventral striatum, and prefrontal cortex regions during both successful and failed inhibitory control conditions. These results remained after adjusting for co-occurring anxiety, depression, and attention-deficit/hyperactivity symptoms. ConclusionsFindings suggest neural aberrations in inhibitory control play a role in the pathophysiology of preadolescent irritability and associations are not merely due to co-occurring symptoms. Neural mechanisms of inhibitory control associated with irritability may provide novel intervention targets.

Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control

Advanced computational methods are being applied to address traditional guidance problems, yet research is still ongoing regarding how to utilize them effectively and scientifically. A numerical root-finding method was proposed to determine the bias in biased proportional navigation to achieve the impact time control without time-to-go estimation. However, the root-finding algorithm in the original method might experience efficiency and convergence issues. This paper introduces an enhanced method based on neural networks, where the bias is directly output by the neural networks, significantly improving computational efficiency and addressing convergence issues. The novelty of this method lies in the development of a reasonable structure that appropriately integrates off-the-shelf machine learning techniques to effectively enhance the original iteration-based methods. In addition to demonstrating its effectiveness and performance of its own, two comparative scenarios are presented: (a) Evaluate the time consumption when both the proposed and the original methods operate at the same update frequency. (b) Compare the achievable update frequencies of both methods under the condition of equal real-world time usage.

Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Competency level and determinants among infection prevention and control staff in the Middle East and North Africa region

BackgroundCompetency of the staff working in infection prevention and control (IPC) is the cornerstone of successful IPC programs. The objective was to assess competency level and associated factors among IPC staff working in the Middle East and North Africa (MENA) region.MethodsA cross-sectional study was carried out in 2019 among active IPC staff who were members of the Arab Countries Infection Control Network. The questionnaire covered demographic and professional characteristics of IPC staff and characteristics of work facilities and IPC programs. A competency score was created from self-perceived responses to 8 competency domains. The competency score was then transformed into 100-scale and categorized into two groups (≥ median and < median).ResultsA total of 176 participants completed the survey. Participants were mostly female (65.7%), and the mean age was 40.2 ± 8.3 years. The mean competency score was 61.4%. It was slightly variable between domains, being highest with preventing transmission of infection (65%) and lowest with sterilization and disinfection (59%). Higher (≥ median) competency score was associated with having CBIC certification (p < 0.001). It was also associated with facilities having IPC plan (p = 0.005), IPC committee (p = 0.049), regular meetings of IPC committee (p < 0.001), and IPC plan included communications with healthcare workers (p < 0.001). Domain-specific competency scores were significantly associated with receiving same domain-specific training (p < 0.05 for all).ConclusionsThe competency levels of IPC staff in the MENA region is still suboptimal. The current finding calls for more regional and national investment in IPC staff by providing up-to-date customized educational and training opportunities.

TDCS-enhanced cognitive training improves attention and alters connectivity in control and somatomotor networks: A triple blind study

BackgroundExecutive dysfunction such as inattention or forgetfulness can lead to disruptions in a person's daily functioning and quality of life. Objective/HypothesisThis triple-blinded randomized clinical trial assessed the efficacy of bifrontal (over the forehead) transcranial direct current stimulation (tDCS) concurrent with cognitive training to improve cognitive performance in a healthy sample. MethodsFifty-eight participants were randomly assigned to one of three stimulation conditions (2 mA left anode–right cathode, 2 mA right anode–left cathode, or sham), which was administered with cognitive training tasks 3x/week over 12 weeks with assessments at baseline, midpoint (6 weeks), and post-training (12 weeks). We assessed cognitive performance, functional connectivity, and the influence of individual differences in training advancement. ResultsForty participants completed training. We found that at midpoint and post, all groups improved significantly on overall cognitive performance. The left anode group's attention & vigilance score improved significantly at post, but the other two groups did not. Greater attention training advancement predicted attention improvement by post, most notably in the left anode group. Finally, within-network connectivity decreased in the control network and increased in the somatomotor network across all groups. ConclusionsThese results suggest that, given cognitive training, the left anode montage is more effective at improving attention than the right anode montage and sham. Future research may focus on the application of the left anode montage during cognitive training to assess its effectiveness in improving cognition in neuropsychiatric disorders.

The right posterior parietal cortex mediates spatial reorienting of attentional choice bias

Attention facilitates behavior by enhancing perceptual sensitivity (sensory processing) and choice bias (decisional weighting) for attended information. Whether distinct neural substrates mediate these distinct components of attention remains unknown. We investigate the causal role of key nodes of the right posterior parietal cortex (rPPC) in the forebrain attention network in sensitivity versus bias control. Two groups of participants performed a cued attention task while we applied either inhibitory, repetitive transcranial magnetic stimulation (n = 28) or 40 Hz transcranial alternating current stimulation (n = 26) to the dorsal rPPC. We show that rPPC stimulation – with either modality – impairs task performance by selectively altering attentional modulation of bias but not sensitivity. Specifically, participants’ bias toward the uncued, but not the cued, location reduced significantly following rPPC stimulation – an effect that was consistent across both neurostimulation cohorts. In sum, the dorsal rPPC causally mediates the reorienting of choice bias, one particular component of visual spatial attention.

Diagnostic value of structural, functional and effective connectivity in bipolar disorder.

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS. A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria. Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

IoT and machine learning models for multivariate very short‐term time series solar power forecasting

AbstractIn solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors’ study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors.

Differential brainstem connectivity according to sex and menopausal status in healthy men and women.

Brainstem nuclei play a critical role in both ascending monoaminergic modulation of cortical function and arousal, and in descending bulbospinal pain modulation. Even though sex-related differences in the function of both systems have been reported in animal models, a complete understanding of sex differences, as well as menopausal effects, in brainstem connectivity in humans is lacking. This study evaluated resting-state connectivity of the dorsal raphe nucleus (DRN), right and left locus coeruleus complex (LCC), and periaqueductal gray (PAG) according to sex and menopausal status in healthy individuals. In addition, relationships between systemic estrogen levels and brainstem-network connectivity were examined in a subset of participants. Resting-state fMRI was performed in 50 healthy men (age, 31.2 ± 8.0 years), 53 healthy premenopausal women (age, 24.7 ± 7.3 years; 22 in the follicular phase, 31 in the luteal phase), and 20 postmenopausal women (age, 54.6 ± 7.2 years). Permutation Analysis of Linear Models (5000 permutations) was used to evaluate differences in brainstem-network connectivity according to sex and menopausal status, controlling for age. In 10 men and 17 women (9 premenopausal; 8 postmenopausal), estrogen and estrogen metabolite levels in plasma and stool were determined by liquid chromatography-mass spectrometry/mass spectrometry. Relationships between estrogen levels and brainstem-network connectivity were evaluated by partial least squares analysis. Left LCC-executive control network (ECN) connectivity showed an overall sex difference (p = 0.02), with higher connectivity in women than in men; however, this was mainly due to differences between men and pre-menopausal women (p = 0.008). Additional sex differences were dependent on menopausal status: PAG-default mode network (DMN) connectivity was higher in postmenopausal women than in men (p = 0.04), and PAG-sensorimotor network (SMN) connectivity was higher in premenopausal women than in men (p = 0.03) and postmenopausal women (p = 0.007). Notably, higher free 2-hydroxyestrone levels in stool were associated with higher PAG-SMN and PAG-DMN connectivity in premenopausal women (p < 0.01). Healthy women show higher brainstem-network connectivity involved in cognitive control, sensorimotor function, and self-relevant processes than men, dependent on their menopausal status. Further, 2-hydroxyestrone, implicated in pain, may modulate PAG connectivity in premenopausal women. These findings may relate to differential vulnerabilities to chronic stress-sensitive disorders at different life stages.

Examining the relationship between functional connectivity and broader autistic traits in non-autistic children

ABSTRACT In the current study, we used functional near-infrared spectroscopy (fNIRS) to examine functional connectivity (FC) in relation to measures of cognitive flexibility and autistic features in non-autistic children. Previous research suggests that disruptions in FC between brain regions may underlie the cognitive and behavioral traits of autism. Moreover, research has identified a broader autistic phenotype (BAP), which refers to a set of behavioral traits that fall along a continuum of behaviors typical for autism but which do not cross a clinically relevant threshold. Thus, by examining FC in relation to the BAP in non-autistic children, we can better understand the spectrum of behaviors related to this condition and their neural basis. Results indicated age-related differences in performance across three measures of cognitive flexibility, as expected given the rapid development of this skill within this time period. Additionally, results showed that across the flexibility tasks, measures of autistic traits were associated with weaker FC along the executive control network, though task performance was not associated with FC. These results suggest that behavioral scores may be less sensitive than neural measures to autistic traits. Further, these results corroborate the use of broader autistic traits and the BAP to better understand disruptions to neural function associated with autism.

Cerebellar Metabolic Connectivity during Treadmill Walking before and after Unilateral Dopamine Depletion in Rats.

Compensatory changes in brain connectivity keep motor symptoms mild in prodromal Parkinson's disease. Studying compensation in patients is hampered by the steady progression of the disease and a lack of individual baseline controls. Furthermore, combining fMRI with walking is intricate. We therefore used a seed-based metabolic connectivity analysis based on 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) uptake in a unilateral 6-OHDA rat model. At baseline and in the chronic phase 6-7 months after lesion, rats received an intraperitoneal injection of [18F]FDG and spent 50 min walking on a horizontal treadmill, followed by a brain PET-scan under anesthesia. High activity was found in the cerebellar anterior vermis in both conditions. At baseline, the anterior vermis showed hardly any stable connections to the rest of the brain. The (future) ipsilesional cerebellar hemisphere was not particularly active during walking but was extensively connected to many brain areas. After unilateral dopamine depletion, rats still walked normally without obvious impairments. The ipsilesional cerebellar hemisphere increased its activity, but narrowed its connections down to the vestibulocerebellum, probably aiding lateral stability. The anterior vermis established a network involving the motor cortex, hippocampus and thalamus. Adding those regions to the vermis network of (previously) automatic control of locomotion suggests that after unilateral dopamine depletion considerable conscious and cognitive effort has to be provided to achieve stable walking.

ICU patients receiving remifentanil do not experience reduced duration of mechanical ventilation: a systematic review of randomized controlled trials and network meta-analyses based on Bayesian theories.

The purpose of this network meta-analysis (NMA) was to evaluate the efficacy of intravenous opioid μ-receptor analgesics in shortening the duration of mechanical ventilation (MV) in ICU patients. Randomized controlled trials comparing the efficacy of remifentanil, sufentanil, morphine, and fentanyl on the duration of MV in ICU patients were searched in Embase, Cochrane, Pubmed, and Web of Science electronic databases. The primary outcome was MV duration. The Bayesian random-effects framework was used to evaluate relative efficacy. In total 20 studies were included in this NMA involving 3,442 patients. Remifentanil was not associated with a reduction in the duration of MV compared with fentanyl (mean difference (MD) -0.16; 95% credible interval (CrI): -4.75 ~ 5.63) and morphine (MD 3.84; 95% CrI: -0.29 ~ 10.68). The secondary outcomes showed that, compared with remifentanil, sufentanil can prolong the duration of extubation. No regimen significantly shortened the ICU length of stay and improved the ICU mortality, efficacy, safety, and drug-related adverse events. Among these analgesics, remifentanil did not appear to be associated with a reduction in MV duration. Clinicians should carefully titrate the analgesia of MV patients to prevent a potentially prolonged duration of MV due to excessive or inadequate analgesic therapy. https://www.crd.york.ac.uk/prospero/, CRD42021232604.

SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems

SecuriDN v. 0.1 is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study.

A lightweight security defense algorithm for multimodal attack blocking in industrial control networks based on momentum enhanced feature graphs

AbstractAs the infrastructure of modern industry, the safe and stable operation of lightweight industrial control network is crucial for industrial production. Multi‐mode attack is a kind of highly customized and low resource consumption network attack method, which can target the specific vulnerabilities and configuration weaknesses of lightweight industrial control network. In order to improve the defense capability of industrial control network against multimodal attacks, a lightweight security defense algorithm based on momentum‐enhanced feature graph is proposed. The lightweight industrial control network operation mode is determined by abstract relationship, and the ontology structure of industrial control network multi‐mode attack is established; with reference to the ontology structure, the multi‐mode attack map of industrial control network is defined; the normal behavior and lightweight attack behavior are divided, and the convolutional network is used to enhance the characteristics of lightweight attack; and the momentum‐enhanced feature map technology is used to correspond to the enhancement process, and to block and defend against the multi‐mode attack of industrial control network. The results show that the studied algorithm can realize accurate prediction of attack behaviors with different attack behaviors as test objects, and ensure accurate blocking of all kinds of attack behaviors with better security defense effect.

TopMarker: Computational screening biomarkers of hepatocellular carcinoma from transcriptome and interactome based on differential network topological parameters

Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.