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Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

This study aims to uncover the influencing mechanism of the tilt angles of the cage pocket walls of the high-speed cylindrical roller bearing on the bearing skidding.

A novel cylindrical roller bearing with the beveled cage pockets was proposed. Using the Hertz contact theory and the elastohydrodynamic and hydrodynamic lubrication formulas, the contact models of the bearing were built. Using the multibody kinematics and the Newton–Euler dynamics theory, a dynamics model of the bearing was established. Using the Runge–Kutta integration method, the dynamics simulations and analysis of the bearing were performed.

The simulation results show that the effects of the tilt angles of the front and rear walls of the pocket on the bearing skidding are remarkable. Under a 5° tilt angle of the front wall of the pocket and a 10° tilt angle of the rear wall, the bearing skidding can be effectively decreased in the rotational speed range of 10,000-70,000 r/min.

In this paper, a novel cylindrical roller bearing with the beveled cage pockets was proposed; a dynamics model of the bearing was established; the influence mechanism of the tilt angles of the front and rear walls of the pocket on the bearing skidding was investigated, which can provide fundamental theory basis for optimizing the pocket.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2020-0035/

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

Recent decade has witnessed exponential growth in e-commerce segment, leading to emergence of various online selling platforms catering to diverse product requirements of customers. Such a development has provided impetus to both existing businesses and newly established ventures to make available their offerings through online selling platforms with a view to improve the reach of their products. This study is an attempt to identify the experience of registered vendors with the online marketplaces. It aims to develop and validate a scale to measure vendor's experience with e-commerce platforms.

As a part of the scale development process, relevant literature sources were scanned to spot the precise knowledge gap and to put in place a sound theoretical background for the study. Thereafter, a scientific approach was adopted for scale creation. First, the scale items were identified through interviews of vendors registered with major online selling platforms and other academic experts pertaining to the marketing domain. Subsequently, major dimensions of seller experience were identified through exploratory factor analysis (EFA) applied on data collected from active vendors by the means of a structured survey instrument. The final data set was subjected to confirmatory factor analysis (CFA) in a bid to validate the scale.

The study’s outcomes reveal that seller experience in an online marketplace can be best captured by a multidimensional scale characterized by six major dimensions. These are “Registration,”; “Product Listing”; “Pricing Autonomy”; “Ease of Pick-up and Delivery”; “Credit of Receivables” and “Vendor Assistance.” A proper emphasis to continually improve upon these dimensions by the e-commerce platforms is expected to enhance the utility and overall experience of vendors from such platforms. Existence of a mutually beneficial relationship between vendors and online marketplaces will help marketplaces to mitigate concerns like nonfulfillment of orders and dispatch of substandard products.

Sustainable long-term relations between vendors and online marketplaces hold the key for such marketplaces to render error-free and delightful service on each individual order received. Seller experience of registering and operating on such e-marketplaces inspite of playing a defining role in vendor–marketplace relations has received scant attention of researchers, both in academia and industry till date. The present research is a seminal attempt to address this gap in marketing literature and offer additional know-how.