Search Results (10)

This study proposes a multi-dimensional urban transportation network optimization framework (MTNO-RQDC) to address structural failure risks from aging infrastructure and regional connectivity bottlenecks. Through dual-dataset validation using both the Baltimore road network and PeMS07 traffic flow data, we first develop a traffic simulation model integrating Dijkstra’s algorithm with capacity-constrained allocation strategies for guiding reconstruction planning for the collapsed Francis Scott Key Bridge. Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. The framework introduces three key innovations: (1) a dual-layer regional division model combining K-means geographical partitioning with spectral clustering functional zoning; (2) fault-tolerant network topology optimization demonstrated through 1000-epoch Monte Carlo failure simulations; (3) cross-dataset transferability validation showing 15.7% performance variance between Baltimore and PeMS07 environments. Experimental results demonstrate a 28.7% reduction in road network traffic variance (from 42,760 to 32,100), 22.4% improvement in public transit path redundancy, and 30.4–44.6% decrease in regional traffic load variance with minimal costs. Hyperparameter analysis reveals two optimal operational modes: rapid cooling (rate = 0.90) achieves 85% improvement within 50 epochs for emergency response, while slow cooling (rate = 0.99) yields 12.7% superior solutions for long-term planning. The framework establishes a new multi-objective paradigm balancing structural resilience, functional connectivity, and computational robustness for sustainable smart city transportation systems. Full article

In recent years, the continuous development of deep learning has significantly advanced its application in the field of remote sensing. However, the semantic segmentation of high-resolution remote sensing images remains challenging due to the presence of multi-scale objects and intricate spatial details, often leading to the loss of critical information during segmentation. To address this issue and enable fast and accurate segmentation of remote sensing images, we made improvements based on SegNet and named the enhanced model CSNet. CSNet is built upon the SegNet architecture and incorporates a coordinate attention (CA) mechanism, which enables the network to focus on salient features and capture global spatial information, thereby improving segmentation accuracy and facilitating the recovery of spatial structures. Furthermore, skip connections are introduced between the encoder and decoder to directly transfer low-level features to the decoder. This promotes the fusion of semantic information at different levels, enhances the recovery of fine-grained details, and optimizes the gradient flow during training, effectively mitigating the vanishing gradient problem and improving training efficiency. Additionally, a hybrid loss function combining weighted cross-entropy and Dice loss is employed. To address the issue of class imbalance, several categories within the dataset are merged, and samples with an excessively high proportion of background pixels are removed. These strategies significantly enhance the segmentation performance, particularly for small-sample classes. Experimental results from the Five-Billion-Pixels dataset demonstrate that, while introducing only a modest increase in parameters compared to SegNet, CSNet achieves superior segmentation performance in terms of overall classification accuracy, boundary delineation, and detail preservation, outperforming established methods such as U-Net, FCN, DeepLabv3+, SegNet, ViT, HRNe and BiFormert. Full article

The gas-liquid sulfonation of α-olefin sulfonate (AOS) in falling film reactors faces significant limitations, primarily due to poor mass transfer efficiency and excessive byproduct formation. To overcome these challenges, a novel cross-shaped microchannel reactor was developed for the continuous gas-liquid sulfonation of α-olefin (AO) with gaseous sulfur trioxide (SO₃). The influence of key process parameters, including gas-phase flow rate, reaction temperature, SO₃/AO molar ratio, and SO₃ volume fraction, on product characteristics and their interactions was systematically investigated using the single-factor experiment and response surface methodology (RSM). A high-precision empirical model (coefficient of determination, R² = 0.9882) to predict product content was successfully constructed. To achieve multi-objective optimization considering product active substance content and energy efficiency, a strategy combining a two-population genetic algorithm with the entropy-weighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method was implemented. Optimal conditions were determined as follows: gas-phase flow rate of 228 mL/min, reaction temperature of 52 °C, SO₃/AO molar ratio of 1.27, and SO₃ volume fraction of 4%. Compared to conditions optimized solely by RSM, this multi-objective approach achieved a significant 10% reduction in energy efficiency, with only a marginal 3.8% decrease in active substance content. This study demonstrates the feasibility and advantages of microreactors for the efficient and green synthesis of AOS. Full article

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model. Full article

Identifying critical nodes in the power grid is a crucial aspect of power system security and stability analysis. However, the current methods for identification fall short in fully accounting for the power transfer characteristics between nodes and the consequences of node removal on the security and stability of power grid operation. To enhance the effective and accurate identification of critical nodes in the power grid, a method is proposed. This method is based on improved PageRank algorithm and node-weighted power flow transfer entropy, referred to as IPRA-PFTE. Firstly, based on the power flow and equivalent impedance between nodes, and the introduction of virtual nodes, an improved PageRank algorithm is obtained. Then the node-weighted power flow transfer entropy is derived by considering the uniformity of the transfer power flow distribution in the system following the removal of a node. Finally, the importance of nodes is obtained by combining the improved PageRank algorithm with the node-weighted power flow transfer entropy. The method’s effectiveness and accuracy are validated through simulation using the IEEE 39-bus example and subsequent comparison with existing methods. Full article

This study explains how the leader-follower relationship and turn-taking could develop in a dyadic imitative interaction by conducting robotic simulation experiments based on the free energy principle. Our prior study showed that introducing a parameter during the model training phase can determine leader and follower roles for subsequent imitative interactions. The parameter is defined as $\mathbf{w}$, the so-called meta-prior, and is a weighting factor used to regulate the complexity term versus the accuracy term when minimizing the free energy. This can be read as sensory attenuation, in which the robot’s prior beliefs about action are less sensitive to sensory evidence. The current extended study examines the possibility that the leader-follower relationship shifts depending on changes in $\mathbf{w}$ during the interaction phase. We identified a phase space structure with three distinct types of behavioral coordination using comprehensive simulation experiments with sweeps of $\mathbf{w}$ of both robots during the interaction. Ignoring behavior in which the robots follow their own intention was observed in the region in which both $\mathbf{w}$s were set to large values. One robot leading, followed by the other robot was observed when one $\mathbf{w}$ was set larger and the other was set smaller. Spontaneous, random turn-taking between the leader and the follower was observed when both $\mathbf{w}$s were set at smaller or intermediate values. Finally, we examined a case of slowly oscillating $\mathbf{w}$ in anti-phase between the two agents during the interaction. The simulation experiment resulted in turn-taking in which the leader-follower relationship switched during determined sequences, accompanied by periodic shifts of $\mathbf{w}$s. An analysis using transfer entropy found that the direction of information flow between the two agents also shifted along with turn-taking. Herein, we discuss qualitative differences between random/spontaneous turn-taking and agreed-upon sequential turn-taking by reviewing both synthetic and empirical studies. Full article

Objective: Converging evidence supporting an effect of transcranial direct current stimulation (tDCS) on postural control and human verticality perception highlights this strategy as promising for post-stroke rehabilitation. We have previously demonstrated polarity-dependent effects of high-definition tDCS (HD-tDCS) on weight-bearing asymmetry. However, there is no investigation regarding the time-course of effects on postural control induced by HD-tDCS protocols. Thus, we performed a nonlinear time series analysis focusing on the entropy of the ground reaction force as a secondary investigation of our randomized, double-blind, placebo-controlled, crossover clinical trial. Materials and Methods: Twenty healthy right-handed young adults received the following conditions (random order, separate days); anode center HD-tDCS, cathode center HD-tDCS or sham HD-tDCS at 1, 2, and 3 mA over the right temporo-parietal junction (TPJ). Using summarized time series of transfer entropy, we evaluated the exchanging information (causal direction) between both force plates and compared the dose-response across the healthy subjects with a Generalized Linear Hierarchical/Mixed Model (GLMM). Results: We found significant variation during the dynamic information flow (p < 0.001) among the dominant bodyside (and across time). A greater force transfer entropy was observed from the right to the left side during the cathode-center HD-tDCS up to 2 mA, with a causal relationship in the information flow (equilibrium force transfer) from right to left that decreased over time. Conclusions: HD-tDCS intervention induced a dynamic influence over time on postural control entropy. Right hemisphere TPJ stimulation using cathode-center HD-tDCS can induce an asymmetry of body weight distribution towards the ipsilateral side of stimulation. These results support the clinical potential of HD-tDCS for post-stroke rehabilitation. Full article

Adult human brains consume a disproportionate amount of energy substrates (2–3% of body weight; 20–25% of total glucose and oxygen). Adenosine triphosphate (ATP) is a universal energy currency in brains and is produced by oxidative phosphorylation (OXPHOS) using ATP synthase, a nano-rotor powered by the proton gradient generated from proton-coupled electron transfer (PCET) in the multi-complex electron transport chain (ETC). ETC catalysis rates are reduced in brains from humans with neurodegenerative diseases (NDDs). Declines of ETC function in NDDs may result from combinations of nitrative stress (NS)–oxidative stress (OS) damage; mitochondrial and/or nuclear genomic mutations of ETC/OXPHOS genes; epigenetic modifications of ETC/OXPHOS genes; or defects in importation or assembly of ETC/OXPHOS proteins or complexes, respectively; or alterations in mitochondrial dynamics (fusion, fission, mitophagy). Substantial free energy is gained by direct O₂-mediated oxidation of NADH. Traditional ETC mechanisms require separation between O₂ and electrons flowing from NADH/FADH₂ through the ETC. Quantum tunneling of electrons and much larger protons may facilitate this separation. Neuronal death may be viewed as a local increase in entropy requiring constant energy input to avoid. The ATP requirement of the brain may partially be used for avoidance of local entropy increase. Mitochondrial therapeutics seeks to correct deficiencies in ETC and OXPHOS. Full article

Neural oscillations reflect rhythmic fluctuations in the synchronization of neuronal populations and play a significant role in neural processing. To further understand the dynamic interactions between different regions in the brain, it is necessary to estimate the coupling direction between neural oscillations. Here, we developed a novel method, termed weighted symbolic transfer entropy (WSTE), that combines symbolic transfer entropy (STE) and weighted probability distribution to measure the directionality between two neuronal populations. The traditional STE ignores the degree of difference between the amplitude values of a time series. In our proposed WSTE method, this information is picked up by utilizing a weighted probability distribution. The simulation analysis shows that the WSTE method can effectively estimate the coupling direction between two neural oscillations. In comparison with STE, the new method is more sensitive to the coupling strength and is more robust against noise. When applied to epileptic electrocorticography data, a significant coupling direction from the anterior nucleus of thalamus (ANT) to the seizure onset zone (SOZ) was detected during seizures. Considering the superiorities of the WSTE method, it is greatly advantageous to measure the coupling direction between neural oscillations and consequently characterize the information flow between different brain regions. Full article

Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system. Full article