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Although many scholars have investigated different aspects of the notion of innovation ambidexterity, the conceptualization of examining this concept in a technological setting remained unclear, as no serious attempts have been made to figure out the core concept of innovation ambidexterity in a technological context, which is a critical concept for high-tech firms.

This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

This study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.

The study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.

The study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.

This study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

Supply chain management (SCM) has evolved to fulfill the demands of the dynamic global business environment. The development of the Internet of Things (IoT), which offers unmatched connectivity and real-time data insights, has further transformed SCM. This chapter provides an overview of SCM development and its integration with IoTs. This integration led to improved inventory control, supply chain optimization (SCO), and visibility which further enhances the conventional SCM and provides benefits, such as more accurate real-time tracking and monitoring, improved data analytics, more efficient logistics and transportation management, and reduced costs and wastages. However, despite these benefits, there are various associated challenges and concerns, like privacy and data security, compatibility and interoperability, implementation costs, returns on investment, trained workforce, and training requirements, which are required to be addressed. Additionally, the outcomes of this study and managerial implications are provided along with the future research scope. Overall, this chapter provides valuable insight into the transformative potential of IoT in SCM and practical suggestions on how managers can successfully navigate difficulties and get benefits from the IoT-SCM integration. Organizations can enhance their supply chain operations, efficiency, and innovation by actively confronting challenges and taking advantage of the opportunities provided by IoT technologies. This will ultimately result in the delivery of greater value to both stakeholders and customers.

The purpose of the present study is to synthesize and organize the existing literature on sustainable supply chain management (SSCM) challenges and potential solutions to overcome these challenges. The four-step research method has been used to collect and analyze pertinent literature, define the unit of analysis, select the classification context, collect publications, and evaluate the material. The study found 10 prevalent SSCM challenges and 18 potential solutions to overcome these challenges. By implementing these solutions, organizations can implement SSCM practices and contribute to achieving various Sustainable Development Goals (SDGs). The study significantly contributes to stakeholder theory, triple bottom-line theory, and resource-based view theory. The current study provides insights to managers working in supply chain management on SSCM implementation. Furthermore, the study also has practical implications for academicians and policymakers. This study is the first of its kind to amalgamate the SSCM challenges and solutions to overcome these challenges in a single framework by reviewing the literature.

Stringent environmental regulations and the need for a robust supply chain (SC) network have necessitated organizations to adopt circular economy (CE) practices. With proven impact of CE practices on SC activities, digital technologies are prompting organizations to digitalize SC networks. Yet, the correlation between SC digitalization and CE practices has been less examined. This study aims to identify and evaluate, the critical success factors (CSFs) necessitating SC digitalization and strategies helping in SC digitalization.

An extensive literature review was performed to identify CSFs and strategies for SC 4.0 (SC4.0), and for finalization, experts’ input was obtained with the Delphi approach. An integrated Fermatean fuzzy set – analytic hierarchy process – decision-making trial and evaluation laboratory – combined compromise solution technique was used to evaluate CSFs and strategies.

Smart work environment, performance monitoring and data reliability and relevance were identified as the top three important CSFs for SC digitalization. Enhancement of analytical capability, data-driven process optimization and development of an integrated digital platform were identified as potential SC4.0 transition strategies.

This study helps SC practitioners better understand the CSFs and strategies for the SC4.0 transition. Furthermore, this study explores the integration of CE principles within these digital strategies, emphasizing how sustainability practices can be embedded in the SC4.0 framework to foster a more resilient and environmentally conscious electronics SC in India.

To the best of the authors’ knowledge, this work is the first to analyze CSFs for SC4.0 in the Indian electronics industry.

The purpose of this study is to examine the impact of green bonds on common prosperity in China. Green bonds have gained significant attention as a means to address financial challenges and promote environmental protection. This research aims to investigate the influence of green bonds on common prosperity by utilizing the system-generalized method of moments (SYS-GMM) and analyzing panel data from prefecture-level cities. The study also explores the theoretical mechanisms and heterogeneous relationships between green bonds and common prosperity, providing valuable guidance for advancing economic and social well-being in China.

This study employs a system-generalized method of moments (SYS-GMM) as the methodology to investigate the influence of green bonds on common prosperity in China. Panel data from prefecture-level cities for the period 2014 to 2020 are utilized for analysis. The SYS-GMM approach allows for the examination of dynamic relationships and control of endogeneity issues. By utilizing this methodology, the study aims to provide robust and reliable findings on the impact of green bonds on common prosperity, considering the specific context of China's ecological civilization development and financial challenges faced by energy-saving and environmental protection enterprises.

The findings of this research indicate several important outcomes. Firstly, common prosperity in China experienced substantial growth between 2014 and 2020. Secondly, green bonds have demonstrated a clear and positive impact on common prosperity. They contribute to the enhancement of common prosperity by driving industrial structure upgrading and fostering green technology innovation. Lastly, the study reveals that the positive influence of green bonds on common prosperity is particularly pronounced in the western region of China. These findings highlight the significance of green bonds in promoting sustainable economic development and societal well-being.

This study contributes to the existing literature by examining the impact of green bonds on common prosperity in China, utilizing the system-generalized method of moments (SYS-GMM) and panel data analysis. The research not only adds to the understanding of the relationship between green bonds and economic well-being but also provides insights into the theoretical mechanisms and heterogeneous relationships involved. The findings showcase the positive influence of green bonds on common prosperity, emphasizing their role in addressing financial challenges, promoting environmental protection, and driving sustainable development. The study's conclusions offer valuable guidance for policymakers, financial institutions, and stakeholders in advancing common prosperity in China.

The purpose of this study is to explain the effect of slab and roll initial temperatures on the wear characteristics of the surface of hot roll descaling rolls.

The UMESHMOTION subroutine and the Arbitrary Lagrangian-Eulerian adaptive mesh technique are used to investigate the wear profile of the descale roll surface and to evaluate the effect of the slab and roll’s initial temperature on the wear depth.

Wear is more pronounced at the edges of the roll-slab contact area and less severe in the roll-body’s central region. A rise in the initial slab temperature from 1,337 K to 1,429 K results in a 67% rise in maximum wear depth and 52% in frictional stress. The peak wear region progressively shifted toward the center of the roll body. A rise in the initial roll temperature from 308.15 K to 673.15 K caused a 46% reduction in maximum wear depth and 73% in frictional stress. The location of the peak wear region remained primarily unchanged.

This study used the UMESHMOTIONI subroutine and the Arbitrary Lagrangian-Eulerian adaptive mesh technique in ABAQUS® to evaluate the quantitative correlation between the wear depth of the descaling roll surfaces and the initial temperatures of the slab and rolls. This study offers valuable insights into improving the wear of descaling roll surfaces.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0231/