Contrasting dorsal caudate functional connectivity patterns between frontal and temporal cortex with BMI increase: link to cognitive flexibility.

BACKGROUND: Obesity is associated with brain intrinsic functional reorganization. However, little is known about the BMI-related interhemispheric functional connectivity (IHFC) alterations, and their link with executive function in young healthy adults. METHODS: We examined voxel-mirrored homotopic connectivity (VMHC) patterns in 417 young adults from the Human Connectome Project. Brain regions with significant association between BMI and VMHC were identified using multiple linear regression. Results from these analyses were then used to determine regions for seed-voxel FC analysis, and multiple linear regression was used to explore the brain regions showing significant association between BMI and FC. The correlations between BMI-related executive function measurements and VMHC, as well as seed-voxel FC, were further examined. RESULTS: BMI was negatively associated with scores of Dimensional Change Card Sort Test (DCST) assessing cognitive flexibility (r = -0.14, p = 0.006) and with VMHC of bilateral inferior parietal lobule, insula and dorsal caudate. The dorsal caudate emerged as a nexus for BMI-related findings: greater BMI was associated with greater FC between caudate and hippocampus and lower FC between caudate and several prefrontal nodes (right inferior frontal gyrus, anterior cingulate cortex, and middle frontal gyrus). The FC between right caudate and left hippocampus was negatively associated with scores of DCST (r = -0.15, p = 0.0018). CONCLUSIONS: Higher BMI is associated with poorer cognitive flexibility performance and IHFC in an extensive set of brain regions implicated in cognitive control. Larger BMI was associated with higher caudate-medial temporal lobe FC and lower caudate-dorsolateral prefrontal cortex FC. These findings may have relevance for executive function associated with weight gain among otherwise healthy young adults.

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain.

In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.

Recognition of opinion leaders in blockchain-based social networks by structural information and content contribution.

Exploring the influence of social network users in the blockchain environment and identifying opinion leaders can help understand the information dissemination characteristics of blockchain social networks, direct the discovery of quality content, and avoid the spread of rumors. Members of blockchain-based social networks are given new responsibilities by token awards and consensus voting, which alters how users connect to the network and engage with one another. Based on blockchain theory and the relevant theories of opinion leaders in social networks, this article combines structural information and content contributions to identify opinion leaders. Firstly, user influence indicators are defined from the perspective of network structure and behavioral characteristics of user contributions. Then, ECWM is constructed, which combines the entropy weight method and the criteria importance through intercriteria correlation (CRITIC) weighting method to address the correlation and diversity among indicators. Furthermore, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), called ECWM-TOPSIS, is proposed to identify opinion leaders in blockchain social networks. Moreover, to verify the effectiveness of the method, we conducted a comparative analysis of the proposed algorithm on the blockchain social platform Steemit by using two different methods (voting score and forwarding rate). The results show that ECWM-TOPSIS produces significantly higher performance than other methods for all selected top N opinion leaders.

A System Dynamics Model for Ecological Environmental Management in Coal Mining Areas in China.

In recent years, mounting attention has been paid to ecological environmental management in coal mining areas in China. This paper conducts a system dynamics (SD) model for ecological environmental management in coal mining areas. Firstly, the whole causal loop diagram of the system is built to illustrate the general system. Secondly, five subsystems are presented according to the causal loop diagram. Then, given the stable investment for ecological environmental management in coal mining areas, our objective is to find a better allocation that can get the best ecological environmental quality in coal mining areas. Notably, we present six allocations of the investment for ecological environmental management in coal mining areas. The results show that, in allocation 4, we can get the best ecological environmental quality in coal mining areas. That is, the best improvement of mining environment can be achieved by distributing the treatment cost highly on the proportion of investment in green vegetation.

An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms.

In recent years, with the rapid development of the economy, industrial pollution problems have become more and more serious. This paper constructs an evolutionary game model for industrial pollution between the local governments and enterprises to study the dynamic evolution path of a game system and the evolutionary stable strategy under two punishment mechanisms. The results show that, in a static punishment mechanism (SPM), the strategy between governments and enterprises is uncertain. Moreover, the evolutionary trajectory between governments and enterprises is uncertain. However, under the dynamic punishment mechanism (DPM), the evolution path between governments and enterprises tends to converge to a stable value. Thus, the DPM is more conducive than the SPM for industrial pollution control.