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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.

Perceived benefits of building information modelling (BIM) have been discussed for some time, but cost–benefit benchmarking has been inconsistent. The purpose of this paper is to investigate BIM feasibility and evaluate investment worth to elucidate and develop the current understanding of BIM merit. The aim of the study is to propose a research agenda towards a more holistic perspective of BIM use incorporating quantifying investment return.

An in-depth examination of research patterns has been conducted to identify challenges in the assessment of the investment value and return on investment (ROI) for BIM in the construction industry. A total of 75 research articles were considered for the final literature review. An evaluation of the literature is conducted using a combination of bibliometric analysis and systematic reviews.

This study, which analysed 75 articles, unveils key findings in quantifying BIM benefits, primarily through ROI calculation. Two major research gaps are identified: the absence of a standardized BIM ROI method and insufficient exploration of intangible benefits. Research focus varies across phases, emphasizing design and construction integration and exploring post-construction phases. The study categorizes quantifiable factors, including productivity, changes and rework reduction, requests for information reduction, schedule efficiency, safety, environmental sustainability and operations and facility management. These findings offer vital insights for researchers and practitioners, enhancing understanding of ’BIM’s financial benefits and signalling areas for further exploration in construction.

The ’study’s outcomes offer the latest insights for researchers and practitioners to create effective approaches for quantifying ’BIM’s financial benefits. Additionally, the proposed research agenda aims to improve the current limited understanding of BIM feasibility and investment worth evaluation. Results of the study could assist practitioners in overcoming limitations associated with BIM investment and economic evaluations in the construction industry.

This article aims at - exploring and prioritizing the barriers to adoption of digitization in supply chains (SCs), categorizing them into sustainability triple bottom line (STBL) based upon their direct impact and suggesting digital technologies to address each barrier.

A five-phase methodology is used which consists of an exploration of 44 barriers to the adoption of digitization in SCs, analysis of 44 barriers for mean, standard deviation and Cronbach alpha based on questionnaire-based feedback of 25 experts, extraction of 10 most significant barriers through 05 experts, followed by categorization of the barriers into STBL referring to their direct impact on STBL, prioritization of ten barriers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution and recommendation of digital technologies to address each barrier.

While all the barriers considered in this study significantly impede the adoption of digitization in SCs, lack of top management commitment (B1) is found to be most crucial while lack of culture toward use of information and communication technology required for digitization (B3) has minimum impact. Large investment in digital infrastructure (B6), difficulty in integration of cyber physical systems (CPSs) on varied platforms (B8) and lack of experts having knowledge of digital technologies (B2) are equally important barriers requiring more attention while adopting digitization in SCs.

This study is mainly based on feedback from 25 seasoned experts; a wider cross section of experts will give more insight.

The outcomes are very significant for organizations looking to adopt digitization in their SCs. Simultaneous consideration to all the barriers becomes impractical hence prioritization of same will be useful for the SC managers to benchmark their preparedness and decide strategies for the adoption of digitization with due consideration toward the impact of barriers on STBL. The digital technologies recommended will further aid in planning the digital strategies to address each barrier.

A unique approach to explore, analyze, prioritize and categorize the barriers to adoption of digitization in SCs is used to provide a deeper understanding of factors deterring the same. It implies that a supportive top management along with systematic allocation of finances plays a crucial role. The importance of availability of digital experts for integrating CPSs on a single platform is also highlighted. The digital technologies recommended will further assist the organizations toward adoption of digitization in SCs with due consideration to STBL.

Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.

Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.

1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation

We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.

Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.

To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.

This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

Unsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively.

IFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain.

Experimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets.

Limitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared.

This paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

Since Zuckerberg's announcement to change Facebook's name to Meta Platforms Inc. on October 28, 2021, the concept of the metaverse has gained unprecedented popularity in the business world. Tech giants, SMEs and start-ups across various sectors are making substantial investments in metaverse-related technologies. Despite this, scholarly research in entrepreneurship and strategic management regarding the metaverse remains limited. This paper, grounded in value creation theory, aims to analyze how value is generated in the metaverse era.

This paper conducts a thematic analysis of 895 press releases published by LexisNexis between October 28, 2021, and October 28, 2022. The analysis identifies the primary emerging themes related to value creation in the metaverse age.

The thematic analysis reveals four significant emerging themes concerning value creation in the metaverse age: (1) factors enabling value creation, (2) digital resources contributing to value creation, (3) motives driving value creation and (4) practices of value creation.

This paper represents the inaugural attempt to analyze the metaverse through a value creation lens. Given the substantial investments and growing academic interest in the metaverse, understanding value creation in this context is a pressing concern. Additionally, this study provides valuable insights and suggests critical questions for future research on the metaverse.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

The main goal of viral marketing is to affect brands positively. But most studies concern the causes of an ad going viral, not its impact on brands. In this sense, this study aims to demonstrate and compare video ads' value drivers on brands and sharing, determining which antecedents maximize results on each, enabling the best ad performance for advertisers.

A survey was conducted with 368 respondents who watched viral video ads from five global companies on YouTube. The proposed model was tested using structural equation modeling in SmartPLS4.

The results of this study demonstrated that product category involvement is essential for viral advertising. Furthermore, the entertainment value is the most relevant antecedent of sharing, but it does not affect brand equity; it is the social value responsible for brand equity.

Marketing managers should create ads that simultaneously generate entertainment and social values, maximizing sharing and branding effects. However, if only one of the two effects (brand/share) is achieved, then the advertiser will fail to obtain maximum performance.

The mainstream of viral marketing research is focused on antecedents of sharing. However, sharing is not enough to provide brand effects and return on investment of advertisement. This study reveals that different consumers’ values drive sharing and brand equity, suggesting that firms should consider a dual value generation strategy regarding the performance of viral video ads. On the other hand, this research conciliates the extant literature about the phenomena with the importance of product category involvement.

El objetivo principal del marketing viral es influir positivamente en las marcas. Pero la mayoría de las investigaciones se refieren a las causas de que un anuncio se vuelva viral, no a su impacto en las marcas. En este sentido, esta investigación tiene como objetivo demostrar y comparar los impulsores de valor de los anuncios de video en las marcas y su viralización, determinando qué antecedentes maximizan los resultados en cada uno, permitiendo el mejor rendimiento publicitario para los anunciantes.

Se realizó una encuesta con 368 participantes que vieron anuncios de video virales de cinco empresas globales en YouTube. El modelo estructural se analizó mediante ecuaciones estructurales basada en mínimos cuadrados utilizando SmartPLS4.

Los resultados demostraron que la participación en la categoría de productos es esencial para la publicidad viral. Además, el valor de entretenimiento es el antecedente más relevante de compartir, pero no afecta el valor de la marca; es el valor social responsable del valor de la marca.

Los gerentes de marketing deben crear anuncios que generen simultáneamente entretenimiento y valores sociales, maximizando los efectos de uso compartido y de marca. Sin embargo, si solo se consigue uno de los dos efectos (marca/participación), el anunciante no conseguirá obtener el máximo rendimiento.

La corriente principal de la investigación de marketing viral se centra en los antecedentes de compartir. Sin embargo, compartir no es suficiente para proporcionar efectos de marca y ROI de publicidad. Este estudio revela que los diferentes valores de los consumidores impulsan el intercambio y el valor de la marca, lo que sugiere que las empresas deberían considerar una estrategia de generación de valor dual con respecto al rendimiento de los anuncios de video virales. Por otro lado, esta investigación concilia la literatura existente sobre los fenómenos con la importancia de la participación de la categoría de productos.

病毒式营销的主要目标是对品牌产生积极的影响。但大多数研究关注的是广告走红的原因, 而不是它对品牌的影响。在这个意义上, 本研究旨在证明和比较视频广告对品牌和分享的价值驱动因素, 确定哪些前因能使每一个因素的结果最大化, 为广告商带来最佳的广告效果。

对368名受访者进行了调查, 他们在YouTube上观看了五家全球公司的病毒视频广告。在SmartPLS4中使用结构方程模型 对提议的模型进行了测试。

结果表明, 产品类别的参与对于病毒式广告来说是至关重要的。此外, 娱乐价值是分享的最相关前因, 但它并不影响品牌资产; 对品牌资产负责的是社会价值。

营销经理应该创造同时产生娱乐和社会价值的广告, 使分享和品牌效应最大化。然而, 如果只实现两种效果（品牌/分享）中的一种, 广告商将无法获得最大的绩效。

病毒式营销研究的主流是关注分享的前因后果。然而, 分享并不足以提供品牌效应和广告的投资回报率。本研究揭示了不同消费者的价值观对分享和品牌资产的推动作用, 表明企业应该考虑关于病毒视频广告表现的双重价值产生策略。另一方面, 本研究将现有的文献与产品类别参与的重要性结合在一起。