Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction

Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the convolutional operation is used to consider the correlation of the high-dimensional features, and the LSTM network is used to consider the temporal correlation of traffic flow data. Moreover, the multi-head attention mechanism is introduced to use the most relevant portion of the traffic data to make predictions so that the prediction performance can be improved. A traffic flow dataset collected from the Caltrans Performance Measurement System (PeMS) database is used to demonstrate the effectiveness of the proposed method

Application of Value Stream Mapping in E-Commerce: A Case Study on an Amazon Retailer

In recent years, the e-commerce market has grown significantly, and the online retail market has become very competitive. Online retailers strive to improve their supply chain operations to reduce costs and to improve customer satisfaction. Value stream mapping (VSM), a tool created by the lean production movement to identify and reduce errors, losses, and lead time and to improve value-added activities, has been proven to be effective in many manufacturing processes. In this study, we investigate the application of value stream mapping (VSM) in the supply chain of an e-commerce retailer on Amazon. By visualizing the entire supply chain with VSM, the waste that is produced during the delivery process from the retailer to the customer was identified. The five whys method was then applied to find the root cause of the waste. Furthermore, a scoring method was developed to evaluate and compare two different supply chain logistic models to identify a strategy for improvement. This study provides a systematic methodology to understand, evaluate, and improve the entire e-commerce supply chain process utilizing VSM. It was demonstrated that the methodology could improve supply chain management efficiency, customer satisfaction, and cost reduction

Analytical Models for Traffic Congestion and Accident Analysis

In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity

Enhanced microwave dielectric tunability of Ba0.5Sr0.5TiO3 thin films grown with reduced strain on DyScO3 substrates by three-step technique

Tunable dielectric properties of epitaxial ferroelectric Ba0.5Sr0.5TiO3 (BST) thin films deposited on nearly lattice-matched DyScO3 substrates by radio frequency magnetron sputtering have been investigated at microwave frequencies and correlated with residual compressive strain. To reduce the residual strain of the BST films caused by substrate clamping and improve their microwave properties, a three-step deposition method was devised and employed. A high-temperature deposition at 1068 K of the nucleation layer was followed by a relatively low-temperature deposition (varied in the range of 673–873 K) of the BST interlayer and a high-temperature deposition at 1068 K of the top layer. Upon post-growth thermal treatment at 1298 K the films grown by the three-step method with the optimized interlayer deposition temperature of 873 K exhibited lower compressive strain compared to the control layer (−0.002 vs. −0.006). At 10 GHz, a high dielectric tunability of 47.9% at an applied electric field of 60 kV/cm was achieved for the optimized films. A large differential phase shift of 145°/cm and a figure of merit of 23°/dB were obtained using a simple coplanar waveguide phase shifter at 10 GHz. The low residual strain and improved dielectric properties of the films fabricated using the three-step deposition technique were attributed to reduced clamping of the BST films by the nearly lattice-matched substrate

Viral infection of an estuarine Synechococcus influences its co-occurring heterotrophic bacterial community in the culture

Viruses are infectious and abundant in the marine environment. Viral lysis of host cells releases organic matter and nutrients that affect the surrounding microbial community. Synechococcus are important primary producers in the ocean and they are subject to frequent viral infection. In the laboratory, Synechococcus cultures are often associated with bacteria and such a co-existence relationship appears to be important to the growth and stability of Synechococcus. However, we know little about how viral lysis of Synechococcus affects the co-existing bacteria in the culture. This study investigated the influence of viral infection of Synechococcus on co-occurring bacterial community in the culture. We analyzed the community composition, diversity, predicted functions of the bacterial community, and its correlations with fluorescent dissolved organic matter (FDOM) components and nutrients after introducing a cyanophage to the Synechococcus culture. Cyanophage infection altered the bacterial community structure and increased the bacterial diversity and richness. Increased bacterial groups such as Bacteroidetes and Alphaproteobacteria and decreased bacterial groups such as Gammaproteobacteria were observed. Moreover, cyanophage infection reduced bacterial interactions but enhanced correlations between the dominant bacterial taxa and nutrients. Unique FDOM components were observed in the cyanophage-added culture. Fluorescence intensities of FDOM components varied across the cyanophage-infection process. Decreased nitrate and increased ammonium and phosphate in the cyanophage-added culture coupled with the viral progeny production and increased substance transport and metabolism potentials of the bacterial community. Furthermore, increased potentials in methane metabolism and aromatic compound degradation of the bacterial community were observed in the cyanophage-added culture, suggesting that cyanophage infections contribute to the production of methane-related compounds and refractory organic matter in a microcosm like environment. This study has the potential to deepen our understanding of the impact of viral lysis of cyanobacteria on microbial community in the surrounding water

Progress Analysis of International Food Safety Culture Construction and Its Enlightenment to China

In order to explore the construction of a food safety culture suitable for China's national conditions, this paper collects and collates the information of the food safety culture construction work carried out by the Global Food Safety Initiative (GFSI), the World Health Organization (WHO), the Codex Alimentarius Commission (CAC), the European Union, Australia and New Zealand, the United States, the United Kingdom and other organizations and countries. And the analysis of the definition, policies, improtance, evaluation principles, effective measures and evaluation tools about food safety culture is also conducted. Based on the accumulation and inheritance of Chinese traditional culture and the current situation of urban and rural food safety, suggestions on building a food safety culture with Chinese characteristics are put forward, hoping to provide reference and reference for building a food safety culture in line with China's national conditions

Self-Supervised Learning of Whole and Component-Based Semantic Representations for Person Re-Identification

Person Re-Identification (ReID) is a challenging problem, focusing on identifying individuals across diverse settings. However, previous ReID methods primarily concentrated on a single domain or modality, such as Clothes-Changing ReID (CC-ReID) and video ReID. Real-world ReID is not constrained by factors like clothes or input types. Recent approaches emphasize on learning semantics through pre-training to enhance ReID performance but are hindered by coarse granularity, on-clothes focus and pre-defined areas. To address these limitations, we propose a Local Semantic Extraction (LSE) module inspired by Interactive Segmentation Models. The LSE module captures fine-grained, biometric, and flexible local semantics, enhancing ReID accuracy. Additionally, we introduce Semantic ReID (SemReID), a pre-training method that leverages LSE to learn effective semantics for seamless transfer across various ReID domains and modalities. Extensive evaluations across nine ReID datasets demonstrates SemReID's robust performance across multiple domains, including clothes-changing ReID, video ReID, unconstrained ReID, and short-term ReID. Our findings highlight the importance of effective semantics in ReID, as SemReID can achieve great performances without domain-specific designs