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Robot adoption and corporate supply chain efficiency: Evidence from China
No full textAmid global climate policy shifts, enhancing supply chain efficiency has become a vital channel for renewable energy manufacturers to maximize financial returns and cultivate sustainable competitive advantages. Despite growing literature on manufacturing automation, limited empirical evidence exists on whether robot adoption affects supply chain efficiency in this strategically important sector. Leveraging micro-level data from China's renewable energy manufacturing industry, this study employs a two-way fixed effects model to examine the impact and mechanisms of robot adoption on corporate supply chain efficiency. The findings reveal that: (1) robot adoption significantly enhances supply chain efficiency in renewable energy manufacturing enterprises; (2) inventory management optimization, technological advancements, and transaction cost reduction serve as the primary mechanisms; (3) the positive effects of robot adoption on supply chain efficiency are more pronounced in large-scale enterprises, export-oriented enterprises, and those located in regions with higher levels of marketization. These findings underscore the financial potential of automation investment in improving operational efficiency and value chain performance. Finally, this paper concludes with targeted policy recommendations to support supply chain optimization and guide smart manufacturing investment decisions in the renewable energy sector
Data privacy in a retail CBDC system built on a public blockchain
No full textThis article examines the data protection and privacy concerns arising from the use of retail central Bank digital currencies (CBDCs), specifically in the context of a system built on a public blockchain, such as Ethereum. It examines the extent to which the risks and concerns can be minimised by building in privacy-enhancing technologies in the governance framework of the retail CBDC as set out in documents and legislative provisions explaining the operation of the digital pound and the digital euro
Socio-ecological risks management dynamic simulation in megaproject development of the Edinburgh Tram Network
Get PDFPurposeThe inherent risks and their interactive impacts in megaproject development have been found in numerous cases worldwide. Although risk management standards have been recommended for the best practice in engineering construction projects, there is still a lack of systematic approaches to describing the interactions. Interactions such as social, technical, economic, ecological and political (STEEP) risks have complex and dynamic implications for megaproject construction. For a better understanding and effective management of megaprojects such as the Edinburgh Tram project, the dynamic interaction of concomitant risks must be studied.Design/methodology/approachA systems dynamic methodology was adopted following the comprehensive literature review. Documentary data were gathered from the case study on Tram Network Project in Edinburgh.FindingsA casual loop of typical evolution of key indicators of risks was then developed. A hypothesised model of social and ecological (SE) risks was derived using the system dynamics (SD) modelling technique. The model was set up following British Standards on risk management to provide a generic tool for risk management in megaproject development. The study reveals that cost and time overruns at the developmental stage of the case project are caused mainly by the effects of interactions of risk factors from the external macro project environment on a timely basis.Originality/valueThis article presented a model for simulating the socio-ecological risk confronting the management and construction of megaprojects. The use of SD provided the opportunity to explain the nature of all risks, particularly the SE risks in the past stages of project development
DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments
No full textAiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce the interference of the complex obstacle environment on robot decision making. Each robot generally chooses the neuron with the largest sum of surrounding activity values among adjacent neurons as its next movement position. Then, we propose a collaborative search mechanism. When a robot falls into a local deadlock state in the complex obstacle environment, the mechanism will guide the robot to quickly find unsearched areas. Finally, we conduct multi-robot area search simulation experiments under different obstacle environments and compare them with three baseline algorithms in this field. The simulation results verify that the proposed algorithm can efficiently guide the multi-robot to complete the area search task in the complex obstacle environment. Note to Practitioners —The motivation of this article arises from the need to develop fast and effective area search algorithms for practical applications such as UAV swarm reconnaissance and multiple mobile robots area search and rescue. The algorithms based on BNN has been widely used in search tasks under unknown environments due to its good scalability and efficiency. However, the efficiency of area search in complex obstacle environments cannot be guaranteed. In order to achieve efficient area search in unknown complex obstacle environments, the DIBNN algorithm is proposed. It utilizes a cooperative search mechanism and achieves better performance. DIBNN can also be applied to multi-robot systems in different scenarios, demonstrating strong scalability
Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing
Get PDFGenerative Artificial Intelligence(GAI) models such as ChatGPT , DALL-E , and the recently introduced Gemini have attracted considerable interest in both business and academia because of their capacity to produce material in response to human inputs. Cognitive computing is a broader field of machine learning that encompasses GAI, which particularly emphasizes systems capable of creating content, such as images, text, or sound, while semantic computing acts as a fundamental element of GAI, furnishing the comprehension of context and significance essential for GAI systems to generate content akin to human-like standards. GAI is becoming a game-changing technology for consumer electronics industry with a variety of applications that improve user experiences and product development. GAI can revolutionise architectural visualisation by facilitating quick prototyping and the investigation of cutting-edge design ideas. By creating unique compositions and graphics for a variety of applications, it also empowers media production and music composition. Our research identifies several applications of GAI in the consumer electronics industry. We analyze how GAI is utilized in augmented reality (AR) applications, optimizing user interactions and immersive experiences. Moreover, we explore the integration of GAI in voice assistants and virtual avatars, enhancing images, natural language understanding and delivering more personalized interactions. We present a novel case study on a Generative Artificial Intelligence-based Framework for answering consumer electronics queries. We have developed and presented the system using various GAI-based tools and integrations. The paper also discusses the challenges in implementing GAI in consumer electronics, such as ethical considerations, data privacy, compatibility with existing systems, and the need for continuous updates and improvements
Determination of singular control in the optimal management of natural resources
Get PDFA method is presented to simplify the determination of solutions of certain optimal control problems which commonly arise in natural resource management and bioeconomic contexts. The method, termed the resource-value balance method, essentially leverages an equivalent formulation of the original optimal control problem and, as described, in certain cases the method obviates the need for classical tools from optimal control theory, such as the Pontryagin Principle. Indeed, in these cases the method reduces the original problem to one solvable with elementary calculus techniques. Further, the solution provided by the resource-value balance method is shown to equal the singular solution of an associated (and more commonly considered) input-constrained optimal control problem, providing insight into the nature of singular control in this context. The theory is illustrated with examples from bioeconomics
Sustainability, energy finance and the role of central banks: A review of current insights and future research directions
Get PDFThe importance of energy finance in sustainable development and particularly within the sustainable finance strand has visibly increased in recent years. In this review article, we focus on the role of central banks in this area. We draw on official documents from regulatory and supervisory institutions, central banks, think tanks, non-governmental organisations and academic publications. First, we discuss the definitions of energy finance, Environmental, Social and Governance (ESG) risks and their transmission channels. Then we describe the mandates of central banks to act in this field. Finally, we discuss key aspects of ESG monetary and macroprudential policies as and central bank communications in this regard. We conclude by proposing topics for further research in this area
Internet of Twins Approach: Digital-Twin-as-a-Platform Architecture
No full textDigital twins (DTs) are becoming integral in sectors such as energy and manufacturing, catalyzing applications from monitoring and analysis to optimization and autonomous management. However, data in different formats, volumes, and qualities require high processing capabilities for integration. Moreover, the functionalities in DTs must work together, bringing interoperability challenges that have necessitated extensive manual programming. To address these, we propose the Internet of Twins, a disaggregated DT deployed in a distributed cloud architecture where interconnections are made using a remote procedure call framework, gRPC. Using this, we develop DT as a platform with knowledge-graph-based orchestration. Here, we implement a recursive execution algorithm to ensure necessary data are processed in the correct sequence autonomously. Then, we implement a wind energy pilot application and evaluate the performance. The results show that our approach lowers data preprocessing and line-of-code overhead up to 20% and 26%, creating a flexible and scalable architecture
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