A survey of sustainable development of intelligent transportation system based on urban travel demand

This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development

Effect of a combination of omeprazole and high-dose proton pump inhibitors on the treatment of patients with liver cirrhosis complicated with upper gastrointestinal hemorrhage

Purpose: To determine the effect of a combination of omeprazole and high-dose proton pump inhibitor (PPI) on the treatment of patients with liver cirrhosis complicated with upper gastrointestinal hemorrhage.Methods: A total of 100 patients with liver cirrhosis and upper gastrointestinal hemorrhage who were admitted to Qingdao Chengyang District People’s Hospital from January 2019 to September 2020 were matched and randomly assigned to a control group and a study group. Patients in both groups received a high-dose PPI treatment, while those in the study group were given omeprazole in addition to highdose PPI. Total treatment effectiveness, incidence of adverse reactions, bleeding volume, hemostasis time, liver function after treatment, Quality of Life Index (QLI) scores, Visual Analogue Scale (VAS) scores, and bleeding (%) at 1, 2 and 4 weeks after treatment were compared for the two groups of patients.Results: Omeprazole-PPI combination produced a much more favorable outcome than treatment with only high-dose PPI, in terms of effectiveness, QLI scores and liver function (p < 0.05). The study group had significantly lower incidence of adverse reactions, bleeding volume, VAS scores, and degree of bleeding at 1, 2 and 4 weeks after treatment, as well as shorter hemostasis time, than the control group (p < 0.05).Conclusion: A combination treatment of omeprazole and high-dose PPI produces better therapeutic effect than high-dose PPI alone, in patients with liver cirrhosis and upper gastrointestinal hemorrhage

Decentralized Control for Discrete-time Mean-Field Systems with Multiple Controllers of Delayed Information

In this paper, the finite horizon asymmetric information linear quadratic (LQ) control problem is investigated for a discrete-time mean field system. Different from previous works, multiple controllers with different information sets are involved in the mean field system dynamics. The coupling of different controllers makes it quite difficult in finding the optimal control strategy. Fortunately, by applying the Pontryagin's maximum principle, the corresponding decentralized control problem of the finite horizon is investigated. The contributions of this paper can be concluded as: For the first time, based on the solution of a group of mean-field forward and backward stochastic difference equations (MF-FBSDEs), the necessary and sufficient solvability conditions are derived for the asymmetric information LQ control for the mean field system with multiple controllers. Furthermore, by the use of an innovative orthogonal decomposition approach, the optimal decentralized control strategy is derived, which is based on the solution to a non-symmetric Riccati-type equation

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.Comment: Accepted by SIGIR 2020, full paper with 10 pages and 5 figure

Time Optimal Control of a Thermoelastic System

This paper considers the numerical approximation for the time optimal control problem of a thermoelastic system with some control and state constraints. By the Galerkin finite element method (FEM), the original problem is projected into a semidiscrete optimal control problem governed by a system of ordinary differential equations. Then the optimal time and control parameterization method is applied to reduce the original system to an optimal parameter selection problem, in which both the optimal time and control are taken as decision variables to be optimized. This problem can be solved as a nonlinear optimization problem by a hybrid algorithm consisting of chaotic particle swarm optimization (CPSO) and sequential quadratic programming (SQP) algorithm. The numerical simulations demonstrate the effectiveness of the proposed numerical approximation method

TiC 0.5

TiCN-based cermets with varied fractions of Si3N4 nanopowder (0–5 wt.%) were prepared by spark plasma sintering. The microstructural and mechanical properties of these cermets were investigated. In general, with increasing addition amount of Si3N4 nanopowder the relative density as well as mechanical properties of the as-prepared TiCN cermets increased first and then decreased. The samples containing 2 wt.% Si3N4 nanopowder presented the best performance with the relative density of about 98%, bending strength of 1000 MPa, and Vickers microhardness of about 1810 HV10

MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification

In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an easy-to-implement, simple but effective backbone for automatic speaker verification based on the Convolution-augmented Transformer (Conformer). The architecture of the MFA-Conformer is inspired by recent state-of-the-art models in speech recognition and speaker verification. Firstly, we introduce a convolution sub-sampling layer to decrease the computational cost of the model. Secondly, we adopt Conformer blocks which combine Transformers and convolution neural networks (CNNs) to capture global and local features effectively. Finally, the output feature maps from all Conformer blocks are concatenated to aggregate multi-scale representations before final pooling. We evaluate the MFA-Conformer on the widely used benchmarks. The best system obtains 0.64%, 1.29% and 1.63% EER on VoxCeleb1-O, SITW.Dev, and SITW.Eval set, respectively. MFA-Conformer significantly outperforms the popular ECAPA-TDNN systems in both recognition performance and inference speed. Last but not the least, the ablation studies clearly demonstrate that the combination of global and local feature learning can lead to robust and accurate speaker embedding extraction. We will release the code for future works to do comparison.Comment: submitted to INTERSPEECH 202

Temporally integrated transcriptome analysis reveals ASFV pathology and host response dynamics

African swine fever virus (ASFV) causes a lethal swine hemorrhagic disease and is currently responsible for widespread damage to the pig industry. The pathogenesis of ASFV infection and its interaction with host responses remain poorly understood. In this study, we profiled the temporal viral and host transcriptomes in porcine alveolar macrophages (PAMs) with virulent and attenuated ASFV strains. We identified profound differences in the virus expression programs between SY18 and HuB20, which shed light on the pathogenic functions of several ASFV genes. Through integrated computational analysis and experimental validation, we demonstrated that compared to the virulent SY18 strain, the attenuated HuB20 quickly activates expression of receptors, sensors, regulators, as well as downstream effectors, including cGAS, STAT1/2, IRF9, MX1/2, suggesting rapid induction of a strong antiviral immune response in HuB20. Surprisingly, in addition to the pivotal DNA sensing mechanism mediated by cGAS-STING pathway, infection of the DNA virus ASFV activates genes associated with RNA virus response, with stronger induction by HuB20 infection. Taken together, this study reveals novel insights into the host-virus interaction dynamics, and provides reference for future mechanistic studies of ASFV pathogenicity

Bearing Fault Diagnosis using Multi-sensor Fusion based on weighted D-S Evidence Theory

This paper has presented a novel method for bearing fault diagnosis using a multi-sensor fusion approach based on an improved weighted Dempster-Shafer (D-S) evidence theory combined with Genetic Algorithm (GA). Vibration measurements are collected from an industrial multi-stage centrifugal air compressor using three wireless acceleration sensors. Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE) is applied to extract the complexity changes of vibration data sets. Then, the extracted feature vectors produced by F2CMPE via multiple scales are fed into Back Propagation Neural Network (BPNN) for fault classification. The normalized probability outputs of BPNN are considered now as inputs of the proposed weighted D-S evidence theory for multi-sensor information fusion. The measurements collected from real industrial equipment are analyzed using the proposed diagnosis method, and the experimental validation has demonstrated its efficiency to identify rolling bearing conditions, the results of which have also shown higher accuracy compared to those using individual sensor signal analysis