DeepTSP: Deep traffic state prediction model based on large-scale empirical data

Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatio-temporal data to predict the traffic state of large-scale traffic systems. In this study, we first summarize the three challenges faced by large-scale traffic state prediction, i.e., scale, granularity, and sparsity. Based on the domain knowledge of traffic engineering, the propagation of traffic states along the road network is theoretically analyzed, which are elaborated in aspects of the temporal and spatial propagation of traffic state, traffic state experience replay, and multi-source data fusion. A deep learning architecture, termed as Deep Traffic State Prediction (DeepTSP), is therefore proposed to address the current challenges in traffic state prediction. Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states

Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language

With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR). However, despite the commonalities of input format and task goal, there are huge gaps between the behavioral and textual information, which obstruct thoroughly modeling SR as language modeling via PLM. To bridge the gap, we propose a novel Unified pre-trained language model enhanced sequential recommendation (UPSR), aiming to build a unified pre-trained recommendation model for multi-domain recommendation tasks. We formally design five key indicators, namely naturalness, domain consistency, informativeness, noise & ambiguity, and text length, to guide the text-item adaptation and behavior sequence-text sequence adaptation differently for pre-training and fine-tuning stages, which are essential but under-explored by previous works. In experiments, we conduct extensive evaluations on seven datasets with both tuning and zero-shot settings and achieve the overall best performance. Comprehensive model analyses also provide valuable insights for behavior modeling via PLM, shedding light on large pre-trained recommendation models. The source codes will be released in the future

Facilitating granule cell survival and maturation in dentate gyrus with baicalin for antidepressant therapeutics

Baicalin isolated from Scutellaria baicalensis possesses antidepressant abilities through its relation to hippocampal neurogenesis. Current research has found that baicalin can promote the proliferation of hippocampal granule cells, however, the detailed mechanism of baicalin on the survival and maturation of hippocampal granule cells has yet to be sufficiently explored. The purpose of this study was to evaluate whether baicalin could facilitate the survival and maturation of hippocampal granule cells, and to explore its potential mechanism. The chronic corticosterone (CORT)-induced mouse model of depression was used to assess antidepressant-like effects of baicalin and to illuminate possible molecular mechanisms by which baicalin affects hippocampal neurogenesis. The survival and maturation of granule cells were measured by immunohistochemistry, immunofluorescence and Golgi staining. The expression of Phosphatidylinositol 3-kinase (PI3K)/Protein kinase B (AKT)/glycogen synthase kinase-3β (GSK3β)/β-catenin pathway related proteins were measured by western blot analysis. PI3K inhibitor LY292002 and AKT inhibitor Perifosine were administered to HT-22 cells to explore the relationship between the PI3K/AKT/GSK3β/β-catenin pathway and baicalin. The results of the study illustrated that baicalin significantly decreased chronic CORT-induced depressive-like behaviors and reduced serum corticosterone levels. In addition, baicalin (administered at 60 mg/kg) reversed chronic CORT-induced lesions on hippocampal granule cells. Moreover, baicalin significantly increased the phosphorylation rate of PI3K, AKT, GSK3β, and total β-catenin. The study found that administration of LY292002/Perifosine counteracted the effects of baicalin in HT-22 cells. These results demonstrate that baicalin can alleviate chronic CORT-induced depressive-like behaviors through promoting survival and maturation of adult-born hippocampal granule cells and exhibiting protective effect on hippocampal neuron morphology. We propose the underlying mechanisms involve the activation of the PI3K/AKT/GSK3β/β-catenin pathway

Passive Homodyne Phase Demodulation Technique Based on LF-TIT-DCM Algorithm for Interferometric Sensors

A passive homodyne phase demodulation technique based on a linear-fitting trigonometric-identity-transformation differential cross-multiplication (LF-TIT-DCM) algorithm is proposed. This technique relies on two interferometric signals whose interferometric phase difference is odd times of π. It is able to demodulate phase signals with a large dynamic range and wide frequency band. An anti-phase dual wavelength demodulation system is built to prove the LF-TIT-DCM algorithm. Comparing the traditional quadrature dual wavelength demodulation system with an ellipse fitting DCM (EF-DCM) algorithm, the phase difference of two interferometric signals of the anti-phase dual wavelength demodulation system is set to be π instead of π/2. This technique overcomes the drawback of EF-DCM—that it is not able to demodulate small signals since the ellipse degenerates into a straight line and the ellipse fitting algorithm is invalidated. Experimental results show that the dynamic range of the proposed anti-phase dual wavelength demodulation system is much larger than that of the traditional quadrature dual wavelength demodulation system. Moreover, the proposed anti-phase dual wavelength demodulation system is hardly influenced by optical power, and the laser wavelength should be strictly limited to lower the reference error

Large-Dynamic-Range and High-Stability Phase Demodulation Technology for Fiber-Optic Michelson Interferometric Sensors

A large-dynamic-range and high-stability phase demodulation technology for fiber-optic Michelson interferometric sensors is proposed. This technology utilizes two output signals from a 2 × 2 fiber-optic coupler, the interferometric phase difference of which is π. A linear-fitting trigonometric-identity-transformation differential cross-multiplication (LF-TIT-DCM) algorithm is used to interrogate the phase signal from the two output signals from the coupler. The interferometric phase differences from the two output signals from the 2 × 2 fiber-optic couplers with different coupling ratios are all equal to π, which ensures that the LF-TIT-DCM algorithm can be applied perfectly. A fiber-optic Michelson interferometric acoustic sensor is fabricated, and an acoustic signal testing system is built to prove the proposed phase demodulation technology. Experimental results show that excellent linearity is observed from 0.033 rad to 3.2 rad. Moreover, the influence of laser wavelength and optical power is researched, and variation below 0.47 dB is observed at different sound pressure levels (SPLs). Long-term stability over thirty minutes is tested, and fluctuation is less than 0.36 dB. The proposed phase demodulation technology obtains large dynamic range and high stability at rather low cost

Type A personality, sleep quality, and cerebral small vessel disease: investigating the mediating role of sleep in a community-based study

PurposeType A behavior pattern (TABP) is a personality type characterized by rapid speech, impatience, competition, and hostility. Asymptomatic cerebral small vessel disease (CSVD) is often endemic in older adults. Individuals with TABP commonly experience suboptimal sleep quality, and a correlation exists between sleep disturbances and CSVD. We investigated the relationship between TABP and CSVD markers and further explored the mediating role of sleep quality in the relationship between TABP and CSVD.MethodsA cross-sectional survey included 764 community-dwelling adults aged 55–85 years. The TABP Scale and the Pittsburgh Sleep Quality Index (PSQI) were used to assess personality and sleep quality, respectively. Linear and logistic regression analyses were used to examine relationships between variables of interest. In addition, mediation analyses with bootstrapping were used to test whether sleep quality mediated the relationship between TABP and CSVD.ResultsOf the 764 participants [median age 65 (61–69) years, 59.9% female], the population with type A personality accounted for 44.8%. After adjusting for covariates, TABP scores (p = 0.03) and PSQI scores (p < 0.001) were significantly correlated with CSVD. In addition, sleep quality partially mediated the association between type A behavior and CSVD, and the mediating effect was 10.67%.ConclusionThis study showed that type A behavior was a risk factor for CSVD among older community-dwelling adults and that sleep quality mediated the relationship between type A behavior and CSVD. Changing type A behavior may help improve sleep quality, which may in turn reduce the prevalence of CSVD