Effects Comparison of Different Resilience Enhancing Strategies for Municipal Water Distribution Network: A Multidimensional Approach

Water distribution network (WDN) is critical to the city service, economic rehabilitation, public health, and safety. Reconstructing the WDN to improve its resilience in seismic disaster is an important and ongoing issue. Although a considerable body of research has examined the effects of different reconstruction strategies on seismic resistance, it is still hard for decision-makers to choose optimal resilience enhancing strategy. Taking the pipeline ductile retrofitting and network meshed expansion as demonstration, we proposed a feasible framework to contrast the resilience enhancing effects of two reconstruction strategies—units retrofitting strategy and network optimization strategy—in technical and organizational dimension. We also developed a new performance response function (PRF) which is based on network equilibrium theory to conduct the effects comparison in integrated technical and organizational dimension. Through the case study of municipal WDN in Lianyungang, China, the comparison results were thoroughly shown and the holistic decision-making support was provided

AWTE-BERT:Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling

Intent classification and slot filling are two core tasks in natural language understanding (NLU). The interaction nature of the two tasks makes the joint models often outperform the single designs. One of the promising solutions, called BERT (Bidirectional Encoder Representations from Transformers), achieves the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize each input token into multiple sub-tokens, which causes a mismatch between the tokens and the labels lengths. Previous methods utilize the hidden states corresponding to the first sub-token as input to the classifier, which limits performance improvement since some hidden semantic informations is discarded in the fine-tune process. To address this issue, we propose a novel joint model based on BERT, which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby generating the context features that contribute to slot filling. Specifically, we encode the hidden states corresponding to multiple sub-tokens into a context vector via the attention mechanism. Then, we feed each context vector into the slot filling encoder, which preserves the integrity of the sentence. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on two public benchmark datasets. The F1 score of the slot filling in particular has been improved from 96.1 to 98.2 (2.1% absolute) on the ATIS dataset

Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data

This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay. Various clustering algorithms were compared, and an end-to-end multivariate time series clustering system called Time2Feat, combined with K-Means, was chosen as the most effective method to cluster patients in the ICU. In clustering analysis, data of 8,080 patients admitted between 2008 and 2016 was used for model development and 2,038 patients admitted between 2017 and 2019 for model validation. By analyzing the differences in clinical mortality prognosis among different categories, varying risks of ICU mortality and hospital mortality were found between different subgroups. Furthermore, the study visualized the trajectory of vital signs changes. The findings of this study provide valuable insights into the potential use of multivariate time-series clustering systems in patient management and monitoring in the ICU setting.Comment: Proceedings of Beijing Health Data Science Summit (HDSS) 202

Diversifying of Chemical Structure of Native Monascus Pigments

Red Yeast Rice, produced by solid state fermentation of Monascus species on rice, is a traditional food additive and traditional Chinese medicine. With the introduction of modern microbiology and biotechnology to the traditional edible filamentous fungi Monascus species, it has been revealed that the production of red colorant by fermentation of Monascus species involves the biosynthesis of orange Monascus pigments and further chemical modification of orange Monascus pigments into the corresponding derivates with various amine residues. Further study indicates that non-Monascus species also produce Monascus pigments as well as Monascus-like pigments. Based on the chemical modification of orange Monascus pigments, the diversification of native Monascus pigments, including commercial food additives of Red Monascus Pigments® and Yellow Monascus Pigments® in Chinese market, was reviewed. Furthermore, Monascus pigments as well as their derivates as enzyme inhibitors for anti-obesity, hyperlipidemia, and hyperglycemia was also summarized

Cell-Type-Specific Afferent Innervation of the Nucleus Accumbens Core and Shell

The nucleus accumbens (NAc) is clearly implicated in reward processing and drug addiction, as well as in numerous neurological and psychiatric disorders; nevertheless, the circuit mechanisms underlying the diverse functions of the NAc remain poorly understood. Here, we characterized the whole-brain and monosynaptic inputs to two main projection cell types – D1 dopamine receptor expressing medium spiny neurons (D1R-MSNs) and D2 dopamine receptor expressing medium spiny neurons (D2R-MSNs) – within the NAc core and NAc shell by rabies-mediated trans-synaptic tracing. We discovered that D1R-MSNs and D2R-MSNs in both NAc subregions receive similar inputs from diverse sources. Inputs to the NAc core are broadly scattered, whereas inputs to the NAc shell are relatively concentrated. Furthermore, we identified numerous brain areas providing important contrasting inputs to different NAc subregions. The anterior cortex preferentially innervates the NAc core for both D1R-MSNs and D2R-MSNs, whereas the lateral hypothalamic area (LH) preferentially targets D1R-MSNs in the NAc shell. Characterizing the cell-type-specific connectivity of different NAc subregions lays a foundation for studying how diverse functions of the NAc are mediated by specific pathways

Optimization of network structure to random failures

Network's resilience to the malfunction of its components has been of great concern. The goal of this work is to determine the network design guidelines, which maximizes the network efficiency while keeping the cost of the network (that is the average connectivity) constant. With a global optimization method, memory tabu search (MTS), we get the optimal network structure with the approximately best efficiency. We analyze the statistical characters of the network and find that a network with a small quantity of hub nodes, high degree of clustering may be much more resilient to perturbations than a random network and the optimal network is one kind of highly heterogeneous networks. The results strongly suggest that networks with higher efficiency are more robust to random failures. In addition, we propose a simple model to describe the statistical properties of the optimal network and investigate the synchronizability of this model.Comment: 11 pages, 6 figures, accepted by Physica