In global Supply Chain Quality Management (SCQM), there are methods to ensure continuity of supply, at appropriate prices while meeting quality standards. But they are limited by the large and fallible role of humans, especially in manufacturing and quality control. Open Manufacturing aims to establish an open network of manufacturers that enables product information sharing among others in the production chain. Realizing the goals of open manufacturing requires processing the large datasets inherent to the production chain partners. But it is difficult to comply with the trust and privacy requirements of the suppliers in a supply chain. This paper presents a comprehensive survey of blockchain and its current and potential use in the industrial context including Industrial Internet of Things (IIoT)-based sensor networks, and the impact of the blockchain technology in manufacturing related areas such as traceability. The focus of this paper is on a potential application of blockchain in SCQM with detailed analysis of its technical feasibility and the methods of transitioning between the current approaches to a blockchain-oriented platform. An innovative use of blockchain to store referral information is also discussed. The survey concludes that blockchain along with IIoT can improve SCQM in an Open Manufacturing model.

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. In the 3D object detection process, classifications are centered on the object’s size, position, and direction. And in 6D pose assumptions, networks emphasize 3D translation and rotation vectors. Successful application of these strategies can have a huge impact on various machine learning-based applications, including the autonomous vehicles, the robotics industry, and the augmented reality sector. Although extensive work has been done on 3D object detection with a pose assumption from RGB images, the challenges have not been fully resolved. Our analysis provides a comprehensive review of the proposed contemporary techniques for complete 3D object detection and the recovery of 6D pose assumptions of an object. In this review research paper, we have discussed several proposed sophisticated methods in 3D object detection and 6D pose estimation, including some popular data sets, evaluation matrix, and proposed method challenges. Most importantly, this study makes an effort to offer some possible future directions in 3D object detection and 6D pose estimation. We accept the autonomous vehicle as the sample case for this detailed review. Finally, this review provides a complete overview of the latest in-depth learning-based research studies related to 3D object detection and 6D pose estimation systems and also points out a comparison between some popular frameworks. To be more concise, we propose a detailed summary of the state-of-the-art techniques of modern deep learning-based object detection and pose estimation models.

Regulatory Technology (RegTech) is an emerging set of computing and network-based information systems and practices intended to enhance and improve regulatory compliance processes. Such technologies rely on collecting exclusive information from the environment and humans through automated Internet of Things (IoT) sensors and self-reported data. The key enablers of RegTech are the increased capabilities and reduced cost of IoT and Artificial Intelligence (AI) technologies. This article focuses on a survey of RegTech, highlighting the recent developments in various sectors. This work identifies the characteristics of existing implementations of RegTech applications in the financial industry. It examines the critical features that non-financial industries such as agriculture must address when using such technologies. We investigate the suitability of existing technologies applied in financial sectors to other industries and the potential gaps to be filled between them in terms of designing information systems for regulatory frameworks. This includes identifying specific operational parameters that are key differences between the financial and non-financial sectors that can be supported with IoT and AI technologies. These can be used by both producers of goods and services and regulators who need an affordable and efficient supervision method for managing relevant organizations.

Systematic reviews (SRs) are a rigorous method for synthesizing empirical evidence to answer specific research questions. However, they are labor-intensive because of their collaborative nature, strict protocols, and typically large number of documents. Large language models (LLMs) and their applications such as gpt-4/ChatGPT have the potential to reduce the human workload of the SR process while maintaining accuracy. We propose a new hybrid methodology that combines the strengths of LLMs and humans using the ability of LLMs to summarize large bodies of text autonomously and extract key information. This is then used by a researcher to make inclusion/exclusion decisions quickly. This process replaces the typical manually performed title/abstract screening, full-text screening, and data extraction steps in an SR while keeping a human in the loop for quality control. We developed a semi-automated LLM-assisted (Gemini-Pro) workflow with a novel innovative prompt development strategy. This involves extracting three categories of information including identifier, verifier, and data field (IVD) from the formatted documents. We present a case study where our hybrid approach reduced errors compared with a human-only SR. The hybrid workflow improved the accuracy of the case study by identifying 6/390 (1.53%) articles that were misclassified by the human-only process. It also matched the human-only decisions completely regarding the rest of the 384 articles. Given the rapid advances in LLM technology, these results will undoubtedly improve over time.

Remote access laboratories (RALs) are online environments for operating instruments and collecting measurement data over the Internet. Such systems are often deployed by universities to support undergraduate students and generally follow the client-server paradigm. This paper discusses a RAL system that enables peer-to-peer (P2P) experimental design and sharing. For this, a modular design is required, which allows participating nodes to create rigs and host those individually at distributed locations. The proposed architecture is generic and can be used with any distributed P2P network control systems over the Internet. In this paper, a distributed remote control framework is presented with regard to a P2P RAL system. The experiments in the RAL require three subsystems handling the user interface, instruction interpretation, and instruction execution which can be organized and operated in different manners depending upon the experiment. The key component for creating and controlling experiments is the microcontrollers that can be easily obtained, configured, and set up for use over the Internet. The most popular microcontrollers are examined for suitability to the distributed control architecture. The basic layout of a message-based network protocol suitable for programming the devices and communication between peers for remote instrumentation and control is discussed, and queuing and flow control mechanisms are compared and tested for the proposed framework.