Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images

Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents. We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation. The tabular data extraction feature is embedded with rule-based filtering and restructuring functions that are highly scalable. We annotate a new Financial Documents dataset with table regions for the experiment. The excellent table detection performance of the detection model is obtained from our customized dataset. The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files

Long Non-Coding RNA Urothelial Carcinoma Associated 1 Promotes Proliferation, Migration and Invasion of Osteosarcoma Cells by Regulating microRNA-182

Background/Aims: Previous studies demonstrated the oncogenic roles of lncRNA UCA1 in osteosarcoma. This study aimed to explore the internal molecular mechanism of UCA1 on promoting osteosarcoma cell proliferation, migration and invasion. Methods: qRT-PCR was conducted to measure the expression levels of UCA1, miR-182 and TIMP2. Cell transfection was used to change the expression levels of UCA1, miR-182 and TIMP2. Cell viability, migration, invasion and apoptosis were measured using CCK-8 assay, two-chamber migration (invasion) assay and Guava Nexin assay, respectively. The associations between UCA1, miR-182 and iASPP were analyzed by dual luciferase activity assay. The protein expression levels of key factors involved in cell apoptosis, PI3K/AKT/GSK3β pathway and NF-κB pathway, as well as p53, Rb, RECQ family and iASPP were evaluated by western blotting. Results: UCA1 was highly expressed in osteosarcoma MG63 and OS-732 cells. Knockdown of UCA1 inhibited OS-732 cell viability, migration and invasion, but promoted cell apoptosis. miR-182 was up-regulated in OS-732 cells after UCA1 knockdown and participated in the effects of UCA1 on OS-732 cells. TIMP2 was downstream factor of miR-182 and involved in the regulatory roles of miR-182 on OS-732 cell viability, migration, invasion, apoptosis, as well as PI3K/AKT/GSK3β and NF-κB pathways. UCA1 knockdown up-regulated p53, Rb and RECQL5 levels in OS-732 cells, while down-regulated the expression of iASPP. TGF-β or TNF-α treatment could enhance the expression of UCA1 in OS-732 cells. Conclusion: Our research verified that UCA1 exerted oncogenic roles in osteosarcoma cells by regulating miR-182 and TIMP2, as well as PI3K/AKT/GSK3β and NF-κB pathways

Case Report: A 42-year-old male with IABP developing multiple organ embolism and intestinal necrosis

We report a 42-year-old male patient who was diagnosed with acute myocardial infarction (AMI), and subsequently underwent percutaneous coronary intervention (PCI) for revascularization. The patient was transferred to the cardiac intensive care unit for intra-aortic balloon pump (IABP) due to frequent malignant arrhythmia after PCI. Then the patient experienced the most severe complications of IABP, including multiple organ embolism and intestinal necrosis. This report highlights the rare serious complications of IABP and the challenges encountered in handling this complex case

Analysis and Design of Rack-climbing Robotic Storage and Retrieval Systems

Warehouses are becoming increasingly robotized. Autonomous rack-climbing robots have recently been introduced in e-commerce fulfillment centers. The robots not only retrieve loads from any level in a rack but also, roam the warehouse and bring the loads to order picking stations without using conveyors or lifts. This paper models and analyzes this system under both single and dual commands with different robot assignment (dedicated versus shared) and storage location assignment (class-based and random) policies. We study these policies in the presence of robot congestion. We evaluate the impact of two blocking protocols, a wait-outside-aisle policy and a block-and-recirculate policy, on the order throughput time. The system is modeled using semiopen queuing networks (SOQNs) for the different operating policies. The analytical models are validated using simulation. We also use this model to compare this system with a shuttle-based system. The results show that (1) the choice of the wait-outside-aisle policy or the block-and-recirculate policy mainly depends on the number of the robots in the system and the throughput requirement and that (2) the dedicated robot assignment policy can be an attractive policy, especially for a large system

Warehouses without aisles: Layout design of a multi-deep rack climbing robotic system

In the last decade, different multi-deep rack climbing robotic (MRR) systems have been introduced, particularly in e-commerce warehouses. These systems have great benefits, as aisles are no longer needed, allowing a high storage density on a small footprint. They only need vertical channels through which battery-powered robots can climb the racks, retrieve totes from any desired position, and bring them to a workstation. This paper studies two novel MRR system layouts: the cross and the compact layout. In addition, we compare performance with the more traditional aisle-based layout. The system performance, particularly operational cost and energy consumption, depends on these system layouts. The paper establishes queuing network models to investigate the trade-off between storage capacity and throughput of the system with these three layouts, taking robot blocking prevention into account. We compare the throughput, storage density, horizontal travel time, and energy consumption of the system. The results show that, in most cases, the compact layout outperforms other layouts on throughput. For energy consumption, the choice of layout depends on the footprint. We formulate a model to assist warehouse managers in choosing a layout of minimum annual operational cost, with a required storage and throughput capacity. We also compare the MRR system with an alternative robotic compact storage and retrieval system on operational cost and energy consumption. The MRR system appears to always have lower energy consumption and operational cost

Performance Evaluation of Automated Medicine Delivery Systems

The daily medicine delivery is one of the most important activities in hospitals. The traditional medicine delivery, from hospital warehouses to the patients, typically involves a human delivery team supplying patients by handcarts. The multiple steps in the medicine delivery process impact the efficiency and increase the risk of contamination. Many hospitals are therefore on their way to automate this process. The Telelift-based automated medicine delivery system provides high health safety, low operational cost, and high system efficiency. This paper develops a stochastic model to evaluate and analyze the medicine delivery process by such an automated medicine delivery system. We adopt a two-moment approximation method and an aggregation approximation algorithm to solve the nested queuing model, considering regular and peak demand. We use simulation to validate the analytical model. The numerical experiments show that our analytical model is sufficiently accurate to evaluate the automated medicine delivery process. Our model can help decision makers of hospitals to reduce the patient waiting time and medicine response time. Our method can also be extended to other automated overhead material handling systems

Mechanism modeling and application of Salvia miltiorrhiza percolation process

Abstract Percolation is a common extraction method of food processing industry. In this work, taking the percolation extraction of salvianolic acid B from Salvia miltiorrhiza (Salviae Miltiorrhizae Radix et Rhizoma) as an example, the percolation mechanism model was derived. The volume partition coefficient was calculated according to the impregnation. experiment. The bed layer voidage was measured by single-factor percolation experiment and the internal mass transfer coefficient was calculated by the parameters obtained by fitting the impregnation kinetic model. After screening, the Wilson and Geankoplis, and Koch and Brady formulas were used to calculate the external mass transfer coefficient and the axial diffusion coefficient, respectively. After substituting each parameter into the model, the process of percolation of Salvia miltiorrhiza was predicted, and the coefficient of determination R2 was all greater than 0.94. Sensitivity analysis was used to show that all the parameters studied had a significant impact on the prediction effect. Based on the model, the design space including the range of raw material properties and process parameters was established and successfully verified. At the same time, the model was applied to the quantitative extraction and endpoint prediction of the percolation process