Average Redundancy Research Articles

Redundancy protects processing speed in healthy individuals with accelerated brain aging.

Recent advancements in computational learning techniques have enabled the estimation of brain age (BA) from neuroimaging data. The difference between chronological age (CA) and BA, known as the BA gap, can potentially serve as a biomarker of brain health. Studies, however, have documented low correlations between BA gap and cognition in healthy aging. This suggests that protective mechanisms in the brain may help counter the effect of accelerated brain aging. Here, we investigated whether redundancy in brain networks may protect cognitive function in individuals with accelerated brain aging. First, we employed deep learning to estimate individual brain ages from structural magnetic resonance imaging (MRI). Next, we associated CA, BA, and BA gap, with cognitive measures and network topology derived from diffusion MRI and tractography. We found that CA and BA were both similarly related to cognitive measures and network topology, while BA gap did not show strong relationships in either domain. Despite observing no strong relationships between brain-age gap (BA gap) and demographic variables, cognitive measures, or topological features in healthy aging, individuals with accelerated aging (BA gap+) exhibited lower average degree and redundancy within the dorsal attention network compared to those with delayed aging (BA gap-). Furthermore, redundancy in the dorsal attention network was positively associated with processing speed in BA gap+ individuals. These results indicate a potential neuroprotective role of redundancy in structural brain networks for mitigating the impact of accelerated brain atrophy on cognitive performance in healthy aging.

Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks

The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40–90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.

Improved Upper Bounds on the Average Redundancy of Optimal RVLC

Improved Upper Bounds on the Average Redundancy of Optimal RVLC

Application of Artificial Intelligence in Computer Neural Network Algorithm Technology in the Age of Big Data

Abstract The arrival of the big data era makes the amount of data explosive growth, which puts forward new challenges and demands for computer network technology, and the integration of big data and network technology has become an important trend. This paper uses the optimization strategy and the elimination mechanism of the genetic algorithm to optimize the inertia weight and particle position speed updating mechanism of the particle swarm algorithm and combines the searching method of the Tennessee whisker algorithm with the sharing mechanism of the particle swarm algorithm to achieve the optimal data searching ability. Finally, the improved artificial intelligence algorithm and MapReduce are combined to improve the performance of the computer neural network algorithm in big data processing. The average data redundancy rate of this paper’s algorithm for big data processing is only 1.18%, and the resource integration checking rate always exceeds 85%, according to simulation experiments. In addition, the algorithm also shows good performance in practical applications, and it can achieve accurate classification of big data labels in big data label classification tasks while maintaining a low energy overhead. Meanwhile, it can accurately recognize electronic medical record data in large medical databases. Big data processing can benefit greatly from the proposed neural network algorithm in this paper.

Optimal Configuration of Voltage Sag Monitoring Points Considering Branch Current based on War Strategy Optimization Algorithm

In recent years, a large number of distributed power sources have been connected to the power grid, and the power quality problem has been deteriorating. In order to comprehensively evaluate the voltage information and current information and realize the guidance of power quality monitoring, this paper considers the global monitoring of fault points and obtains branch current information as much as possible. The concept of average redundancy is added to establish the optimal configuration model of voltage sag monitor. The model is solved by the war strategy optimization algorithm, and the optimal configuration simulation of IEEE39 nodes is carried out by MATLAB. The optimal configuration scheme of the monitor is obtained, and the feasibility and effectiveness of the scheme are verified.

Evolution of the global phosphorus trade network: A production perspective on resilience

Global phosphorus (P) trade, whose resilience is crucial regarding food security globally, involves numerous risks. By combining complex network analysis (CNA) with information-based ecological network analysis (IENA), this study introduces an integrative method for analyzing the evolution of phosphorus trade networks (PTNs) globally from a production perspective, while focusing on the 1990–2020 period; it also provides a conceptual framework and indicators to assess PTN resilience based on that information. Results show that PTN resilience averaged 0.24 and fluctuated over time while being significantly influenced by phosphate fertilizer (PF) trade networks. PTNs exhibited an average redundancy of 4.54 and an average efficiency of 0.55. Various trade paths enabled countries in PTNs to maintain stable sources of import or export. PF had a more diversified trade path and a more balanced connectivity structure than those of phosphate rock (PR). A fluctuating PR share in the core countries’ trade resulted in resilience fluctuations; however, path diversity reduced the impact of shocks emanating from the core countries. Overall, this study highlights that global P management could be improved through more stable national policies of the core countries, improved international cooperation frameworks, and information transparency.

Efficiency Management of Electronic Communication Information Storage in Multithreaded Networks

The emergence of the Internet has brought revolutionary changes to modern information technology. The development of electronic communication information storage technology facilitates people to access information anytime and anywhere, effectively achieving resource sharing. However, traditional methods scatter communication information to all nodes, but neglect the extraction of electronic communication information association features, resulting in low information storage efficiency. This article analyzes the efficiency management of electronic communication information storage in multi-threaded networks and sets up a control experiment. Comparing the method proposed in this article with traditional methods, this study focuses on five aspects: information redundancy storage, information space utilization, productivity, data accuracy, and data transmission efficiency. It is concluded that the average information redundancy storage capacity of traditional electronic information storage methods is 8.35 x 107 T which is 6 x 107 T higher than using multi wire network electronic communication information storage methods. It indicates that traditional redundant information occupies a large amount of storage space, and its storage value exceeds the upper limit of electronic communication information storage. This means that the stored information may be overwritten by redundant information, resulting in the loss of stored information. The experimental results show that using the method proposed in this paper for electronic communication information storage results in a high distribution density of storage space and a significant improvement in information storage efficiency.

Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted K-nearest neighbors

Most existing imbalanced data classification models mainly focus on the classification performance of majority class samples, and many clustering algorithms need to manually specify the initial cluster centers and the number of clusters. To solve these drawbacks, this study presents a novel feature reduction method for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors (AWKNN). First, the similarity between samples is evaluated by the difference and smaller value between samples on each dimension, a similarity measure matrix is then developed to measure the similarity between clusters, after which a new hierarchical clustering model is constructed. By combining the cluster center of each sample cluster with its nearest neighbor, new samples are generated. Then, a hybrid sampling model based on similarity measure is presented by putting the generated samples into imbalanced data and removing samples from majority classes. Thus, a balanced decision system is constructed based on generated samples and minority class samples. Second, to address the issues that the traditional symmetric uncertainty only considers the correlation between features, and mutual information ignores the added information after classification, the normalized information gain is introduced to design new symmetric uncertainty between each feature and the other features; then, the ordered sequence and the average of the symmetric uncertainty difference of each feature are provided to adaptively select the k-nearest neighbors of features. Moreover, the weight of the k-th nearest neighbor of features is defined to present the AWKNN density of features and their ordered sequence for clustering features. Finally, by combining the weighted average redundancy with the symmetric uncertainty between features and decision classes, the maximum relevance between each feature and decision classes, and the minimum redundancy among features in the same cluster is presented to select the optimal feature subset from the feature clusters. Experiments applied to 29 imbalanced datasets show that the developed algorithm is effective and can select the optimal feature subset with high classification accuracy for imbalanced data.

Provisioning Intelligent Water Wave Optimization Approach for Underwater Acoustic Wireless Sensor Networks

In the Acoustics channel, it is incredibly challenging to offer data transfer for time-sourced applications in an energy-efficient manner due to higher error rate and propagation delay. Subsequently, conventional re-transmission over any failure generally initiates significantly larger end-to-end delay, and therefore it is not probable for time-based services. Moreover, standard techniques without any re-transmission consume enormous energy. This investigation proposes a novel multi-hop energy-aware transmission-based intelligent water wave optimization strategy. It ensures reduced end-to-end while attaining potential amongst overall energy efficiency end-to-end packet delay. It merges a naturally inspired meta-heuristic approach with multi-hop routing for data packets to reach the destination. The appropriate design of this Meta heuristic-based energy-aware scheme consumes lesser energy than the conventional one-hop transmission strategy without re-transmission. However, there is no hop-by-hop re-transmission facilitated. The proposed model shows only lesser delay than conventional methods with re-transmission. This work facilitates extensive work to carry out the proposed model performance with the MATLAB simulation environment. The results illustrate that the model is exceptionally energy-efficient with lesser packet delays. With 500 nodes, the packet delivery ratio of proposed model is 100%, average delay is reduced by 2%, total energy consumption is 8 J, average packet redundancy is 1.856, and idle energy is 6.9Mwh. The proposed model outperforms existing approaches like OSF, AOR, and DMR respectively.

Mapping the Masked Region of the A1 Domain in Full-Length VWF

Von Willebrand factor (VWF) is the largest protein in plasma and promotes the early hemostatic response following vascular injury. The hemostatic response of VWF relies on the exposure of its A1 domain, which recognizes its receptor GPIbα on the platelet surface. To avoid binding and activating platelets in the absence of injury, the A1 domain is dormant in full-length VWF through a mechanism that is not completely understood. We recently characterized two recombinant A1 fragments (longA1, residues 1238-1472; and shortA1, residues 1261-1472) with differential GPIbα-binding affinities by hydrogen-deuterium exchange (HDX) mass spectrometry. We found that N- and C-terminal residues flanking the A1 domain (residues 1238-1271 and 1459-1472) cooperatively constitute an autoinhibitory module (AIM) that masks a region in A1 and impedes its binding to GPIbα[1]. To determine if the A1 domain in full-length VWF is “masked” in the same manner, we have characterized human plasma-derived VWF using the same HDX approach.On-column peptic fragmentation was performed following HDX from 10 to 10,000 sec. We obtained 946 sequenced peptides covering 96.4% of the VWF sequence with an average redundancy of 6 per residue. Among them, 113 peptides covered the A1 domain, allowing HDX analysis of A1 at high resolution. We found that residues around the 1272-1458 disulfide bond, helix α1, loops α1β2 and β3α2 in the A1 domain, which are protected by the AIM in the longA1 fragment (residues 1238-1472), are deeply protected in full-length VWF, suggesting that AIM in full-length VWF likely masks A1 in a similar pattern. Additionally, sheet β3, helix α3, and loops β4α3 in the A1 domain of full-length VWF reported lower HDX rates than their counterparts in the shortA1 fragment. These regions, along with those protected by AIM, form a contiguous surface that significantly overlaps with the GPIbα-binding interface (Fig. 1). Residues in loop β6α6, helix α6 and helix α1 also reported lower HDX rates, but they are separate from the regions protected by AIM and the GPIbα-binding interface.In summary, our results suggest that AIM and likely other domains in full-length VWF are in direct contact with the A1 domain and regulate the hemostatic properties of VWF.Figure 1: Difference in HDX between isolated VWF A1 domains and full length VWF.1 Deng W, Wang Y, Druzak SA, Healey JF, Syed AK, Lollar P, Li R. A discontinuous autoinhibitory module masks the A1 domain of von Willebrand factor. Journal of thrombosis and haemostasis : JTH . 2017. 10.1111/jth.13775. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Topology optimization of microvascular composites for active-cooling applications using a geometrical reduced-order model

In this study, we develop a design methodology with a basis in gradient-based topology optimization and a geometrical reduced-order thermal/hydraulic model for actively cooled microvascular composite panels. The proposed method is computationally very efficient owing to the suggested simplifications while preserving the required accuracy. The analytical sensitivity for the topology optimization scheme is derived. Several numerical examples are solved to demonstrate the applicability of the proposed method for active-cooling applications. Using topology optimization, the maximum temperature of the composite panel is reduced by up to 59% compared to a benchmark design. The optimization framework is compared to hybrid topology/shape (HyTopS) and shape optimization (SO) methods based on several measures such as maximum and average temperatures, temperature uniformity, network redundancy, and manufacturability. The solution obtained from the proposed TO scheme outperforms the other approaches in terms of the aforementioned measures.

Variability in Responding to Joint Attention Cues in the First Year is Associated With Autism Outcome.

With development, infants become increasingly responsive to the many attention-sharing cues of adults; however, little work has examined how this ability emerges in typical development or in the context of early autism spectrum disorder (ASD). This study characterized variation in the type of cue needed to elicit a response to joint attention (RJA) using the Dimensional Joint Attention Assessment (DJAA) during naturalistic play. We measured the average redundancy of cue type required for infants to follow RJA bids from an experimenter, as well as their response consistency, in 268 infants at high (HR, n= 68) and low (LR, N= 200) familial risk for ASD. Infants were assessed between 8 and 18 months of age and followed up with developmental and clinical assessments at 24 or 36 months. Our sample consisted of LR infants, as well as HR infants who did (HR-ASD) and did not (HR-neg) develop ASD at 24 months. We found that HR and LR infants developed abilities to respond to less redundant (more sophisticated) RJA cues at different rates, and that HR-ASD infants displayed delayed abilities, identifiable as early as 9 months, compared to both HR-neg and LR infants. Interestingly, results suggest that HR-neg infants may exhibit a propensity to respond to less redundant (more sophisticated) RJA cues relative to both HR-ASD and LR infants. Using an approach to characterize variable performance of RJA cue-reading abilities, findings from this study enhance our understanding of both typical and ASD-related proficiencies and deficits in RJA development.

Research on Adaptive Updating Method of Education Resource Index Based on Mobile Computing

When using the current method to update the index of educational resources, there is no analysis of the hierarchical association structure between educational resources. There are some problems such as too long index construction time, high query time and low query accuracy. Therefore, this paper introduces mobile computing to study the adaptive updating method of education resource index. This paper analyzes the structure of the index system of educational resources. On the basis of mobile computing, the hierarchical association structure between educational resources is analyzed by LDA (Latent Dirichlet Allocation) topic modeling and topic hierarchical clustering. The initial query is expanded by selecting extension words by local co-occurrence method. The weight of the extended query is allocated by genetic algorithm to realize the adaptive updating of the index of educational resources. The experimental results show that when the number of nodes is 25 × 104, the total query time of this method is only 3.5 s, and the average index redundancy rate is only 0.22%, indicating that the resource index update performance of this method is better.

Supervised feature selection using integration of densest subgraph finding with floating forward–backward search

In this paper, a novel approach of supervised feature selection is proposed based on the principle of dense subgraph discovery. To exploit dense subgraph discovery for the purpose of feature selection, the dataset is initially mapped to an equivalent weighted graph notation by considering the set of all features as its vertex set and the mutual dependency between each pair of features as the weight of the corresponding edge. The proposed feature selection algorithm proceeds in a two-phase manner. In the first stage, a dense sub-graph is first discovered so that the features within it become maximally non-redundant among each other and the averaged class relevance as well as averaged standard deviation of all these features are obtained as maximal as possible. In this regard, a novel induced degree is also defined for each feature by incorporating the aforesaid three important objectives of feature selection. In this phase, a modified version of an existing approximation algorithm is also used to find dense subgraph module. Finally, in the second stage, a floating forward–backward search is performed on the dense subgraph so obtained to reveal a better feature subset. In both stages, an existing version of the normalized mutual information score is employed to compute both the class relevance and redundancy. The main contribution of this paper is proposing a feature selection strategy by which the reduced features have the characteristics like maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. The experimental results demonstrate that the proposed approach is competent with several conventional as well as state-of-art algorithms of supervised feature selection.

Indicators for assessing the robustness of metapopulations against habitat loss

Habitat loss and fragmentation resulting from environmental changes are main drivers of global biodiversity loss, as the survival of metapopulations relies on the ability of individuals to disperse among suitable habitat patches. To prioritize conservation efforts, methods are needed for evaluating the robustness of metapopulations against habitat loss. We therefore investigate this robustness for different degrees of habitat loss, for different types of habitat loss (random, peripheral, and contagious) and of habitat networks, and for species differing in their local-extinction risks and dispersal ranges. In particular, we analyse several standard network types (with random, regular, small-world, or scale-free structure) and compare them with several alternative network types derived from real-world two-dimensional habitat landscapes (with random, clustered, or contiguous habitat allocation). Furthermore, we investigate how well 29 different graph-theoretic metrics of habitat networks can serve as indicators of metapopulation robustness against habitat loss – as this approach, where feasible, allows replacing complex simulation-based predictions with simple indicator-based predictions. We find that responses of species to habitat loss on the considered landscape-based habitat networks qualitatively differ from those on the considered standard habitat networks. This suggests that results obtained for the latter, albeit widely examined in the literature, can be unrepresentative and misleading. As expected, species with high risks of local extinction and short dispersal ranges are particularly vulnerable to habitat loss, across all considered types of habitat loss and habitat networks. The graph-theoretic network metric that best explains the robustness of metapopulations against habitat loss depends on the considered types of species, habitat networks, and habitat loss. None of the examined metrics give consistently reliable predictions under all circumstances. For sensitive species, characterized by high local-extinction risks and short dispersal ranges, a network’s average clique size, redundancy, average degree, connectance, clustering coefficient, and average closeness centrality are the best indicators of metapopulation robustness. For landscape-based habitat networks, a network’s average clique size, beta coefficient, clustering coefficient, redundancy, and cyclomatic number work best. For contagious habitat loss, the network type has a particularly strong impact on the species-specific robustness against habitat loss. In summary, our study introduces a method for evaluating the robustness of metapopulations against habitat loss and shows that a network’s clustering coefficient, under a wide range of circumstances, is a particularly reliable indicator of this robustness.

Impact of passive and negative sentences in automatic generation of static UML diagram using NLP

In this research work, we propose a rule based approach for the automatic extraction of UML diagram from the unstructured format of software functional requirements. The existing work provides decent results for active sentences and positive sentences but the challenge in our work is to automatic extract class diagram elements from passive voice type sentences and negative sentences. Furthermore, there is scope to do more research in extraction process using multi-word terms. Thus, we have endeavored to automatic extract the class diagram elements by overcoming these challenges. The methodology uses the Stanford CoreNLP Tools along with Java for the practical implementation of formulated rules. Our approach has proved that without supplant the human being and their decision making, one could reduce the human effort while designing functional requirements. Several case studies were performed to compare class diagrams generated by our methodology to the ones created by experts. Our methodology outperforms the existing work and provides impressive Average completeness (0.82), Average correctness (0.92) and Average redundancy (0.15). Results show that class diagram elements extracted by our methodology are precise as well as accurate and hence, in practice, such class diagrams would be a good preliminary diagram to converge towards to precise and comprehensive class diagrams.

Identifying redundant features using unsupervised learning for high-dimensional data

In the digital era, classifiers play a vital role in various machine learning applications such as medical diagnosis, weather prediction and pattern recognition. The classifiers are built by classification algorithms using data. Nowadays, the data are high dimensional in nature since the data are massively generated due to advancements in information and communication technology. The high-dimensional space contains irrelevant and redundant features; both of them reduce the classification accuracy and increase space and building time of the classifiers. Redundancy and relevancy analysis mechanisms of the feature selection process remove the irrelevant and redundant features. Identifying the irrelevant features is a simple task since that only considers the relevancy between each feature and the target class of a dataset using any one of the statistical or information theoretic measures. Identifying the redundant features from a dataset is quite difficult, especially in high-dimensional space since it needs to consider the relevancy among the features. This leads to more computational complexity and an inappropriate relevancy measure that can degrade the classification accuracy. In order to overcome these problems, this paper presents an unsupervised learning-based redundancy analysis mechanism for feature selection by evaluating various clustering techniques in terms of average redundancy rate and runtime.

A Variable Neighborhood Search-Based Hybrid Multiobjective Evolutionary Algorithm for HazMat Heterogeneous Vehicle Routing Problem With Time Windows

Heterogeneous vehicle routing problem with time windows (HVRPTW) aspect of hazardous material (HazMat) transportation is studied in this article. Given the multiobjective nature of HazMat transportation, a three-objective optimization model is defined for HVRPTW in HazMat transportation. The objective is to determine fleet size and routes so as to meet all given constraints as well as to minimize the objectives of the total traveling cost, the transportation risk, and the average vehicle redundancy. A load-variant HazMat transportation risk assessment model considering vehicle type and waiting time is presented to describe the transportation risk. A variable neighborhood search-based hybrid multiobjective evolutionary algorithm (VN-HMOEA) is proposed for solving the problem. The proposed VN-HMOEA integrates a two-phase push forward insertion heuristic (TP-PFIH) for initial population construction, specialized evolutionary operators for optimizing different objectives and a VNS metaheuristic for local search exploitation. The algorithm is tested on the modified Solomon benchmark instances for HVRPTW. Experimental results show that the proposed VN-HMOEA is competitive in terms of convergence and diversity. We also find that multiobjective fashion is of great significance for transportation risk mitigation to provide a set of nondominated solution rather than a single solution.

Measuring rank robustness in scored protein interaction networks

BackgroundProtein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds.ResultsWe propose three measures—rank continuity, identifiability, and instability—to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges.ConclusionDue to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.

Establishment of the ecological relationships and properties of the Lhasa River Basin water resources system, China

Rapid socioeconomic development poses a critical challenge to sustainable management of water resources. Further development of systems-oriented approaches and objective tools is required to allow the improved evaluation of system-level properties within water resources systems. Using the Lhasa River Basin as a case study, we categorized the water resources system into nine compartments with 22 flows, following which ecological network analysis was used to calculate a comprehensive utility matrix for the flow between compartments from 2000 to 2016 and the corresponding ecological relationships among compartments were identified. More specifically, we examined changes to the evolution of the network structure through calculating system-level indices involving total system throughput, average mutual information, development capacity, ascendency, overhead, redundancy and Finn cycling index. The results indicated that the system is dominated by exploitation relationships whereas the degree of synergism degenerated. Resilience was shown to be stronger than efficiency since 2004; therefore, the efficiency of internal flow transfer requires improvement. External pressure on the system decreased, but increasing water consumption resulted in decreasing system maturity. Such analysis provides scientific support for water resources management to improve the sustainability of water resources systems.