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Until now, a lot of research has been done and applied to provide security and original data from one user to another, such as third-party auditing and several schemes for securing the data, such as the generation of the key with the help of encryption algorithms like Rivest–Shamir–Adleman and others. Here are some of the related works that have been done previously. Remote damage control resuscitation (RDCR) scheme by Yan et al. (2017) is proposed based on the minimum bandwidth. By enabling the third party to perform the verification of public integrity. Although it supports the repair management for the corrupt data and tries to recover the original data, in practicality it fails to do so, and thus it takes more computation and communication cost than our proposed system. In a paper by Chen et al. (2015), using broadcast encryption, an idea for cloud storage data sharing has been developed. This technique aims to accomplish both broadcast data and dynamic sharing, allowing users to join and leave a group without affecting the electronic press kit (EPK). In this case, the theoretical notion was true and new, but the system’s practicality and efficiency were not acceptable, and the system’s security was also jeopardised because it proposed adding a member without altering any keys. In this research, an identity-based encryption strategy for data sharing was investigated, as well as key management and metadata techniques to improve model security (Jiang and Guo, 2017). The forward and reverse ciphertext security is supplied here. However, it is more difficult to put into practice, and one of its limitations is that it can only be used for very large amounts of cloud storage. Here, it extends support for dynamic data modification by batch auditing. The important feature of the secure and efficient privacy preserving provable data possession in cloud storage scheme was to support every important feature which includes data dynamics, privacy preservation, batch auditing and blockers verification for an untrusted and an outsourced storage model (Pathare and Chouragadec, 2017). A homomorphic signature mechanism was devised to prevent the usage of the public key certificate, which was based on the new id. This signature system was shown to be resistant to the id attack on the random oracle model and the assault of forged message (Nayak and Tripathy, 2018; Lin et al., 2017). When storing data in a public cloud, one issue is that the data owner must give an enormous number of keys to the users in order for them to access the files. At this place, the knowledge assisted software engineering (KASE) plan was publicly unveiled for the first time. While sharing a huge number of documents, the data owner simply has to supply the specific key to the user, and the user only needs to provide the single trapdoor. Although the concept is innovative, the KASE technique does not apply to the increasingly common manufactured cloud. Cui et al. (2016) claim that as the amount of data grows, distribution management system (DMS) will be unable to handle it. As a result, various proven data possession (PDP) schemes have been developed, and practically all data lacks security. So, here in these certificates, PDP was introduced, which was based on bilinear pairing. Because of its feature of being robust as well as efficient, this is mostly applicable in DMS. The main purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research provides an efficient and secure protocol for multiple user data in the cloud, allowing many users to easily share data.

The methodology and contribution of this paper is given as follows. The major goal of this study is to design and implement a secure cloud infrastructure for sharing group data. This study provides an efficient and secure protocol for multiple user data in cloud, allowing several users to share data without difficulty. The primary purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research develops an efficient and secure protocol for multiple user data in the cloud, allowing numerous users to exchange data without difficulty. Selection scheme design (SSD) comprises two algorithms; first algorithm is designed for limited users and algorithm 2 is redesigned for the multiple users. Further, the authors design SSD-security protocol which comprises a three-phase model, namely, Phase 1, Phase 2 and Phase 3. Phase 1 generates the parameters and distributes the private key, the second phase generates the general key for all the users that are available and third phase is designed to prevent the dishonest user to entertain in data sharing.

Data sharing in cloud computing provides unlimited computational resources and storage to enterprise and individuals; moreover, cloud computing leads to several privacy and security concerns such as fault tolerance, reliability, confidentiality and data integrity. Furthermore, the key consensus mechanism is fundamental cryptographic primitive for secure communication; moreover, motivated by this phenomenon, the authors developed SSDmechanismwhich embraces the multiple users in the data-sharing model.

Files shared in the cloud should be encrypted for security purpose; later these files are decrypted for the users to access the file. Furthermore, the key consensus process is a crucial cryptographic primitive for secure communication; additionally, the authors devised the SSD mechanism, which incorporates numerous users in the data-sharing model, as a result of this phenomena. For evaluation of the SSD method, the authors have considered the ideal environment of the system, that is, the authors have used java as a programming language and eclipse as the integrated drive electronics tool for the proposed model evaluation. Hardware configuration of the model is such that it is packed with 4 GB RAM and i7 processor, the authors have used the PBC library for the pairing operations (PBC Library, 2022). Furthermore, in the following section of this paper, the number of users is varied to compare with the existing methodology RDIC (Li et al., 2020). For the purposes of the SSD-security protocol, a prime number is chosen as the number of users in this work.

The objective of this paper is to investigate the impact of the information sharing of the dynamic demand on green technology innovation and profits in supply chain from a long-term perspective.

The authors consider a supply chain consisting of a manufacturer and a retailer. The retailer has access to the information of dynamic demand of the green product, whereas the manufacturer invests in green technology innovation. Differential game theory is adopted to establish three models under three different scenarios, namely (1) decentralized decision without information sharing of dynamic demand (Model N-D), (2) decentralized decision with information sharing of dynamic demand (Model S-D) and (3) centralized decision with information sharing of dynamic demand (Model S-C).

The optimal equilibrium results show that information sharing of dynamic demand can improve the green technology innovation level and increase the green technology stocks only in centralized supply chain. In the long term, the information sharing of dynamic demand can make the retailer more profitable. If the influence of green technology innovation on green technology stocks is great enough or the cost coefficient of green technology innovation is small enough, the manufacturer and decentralized supply chain can benefit from information sharing. In centralized supply chain, the value of demand information sharing is greater than that of decentralized supply chain.

The authors used game theory to investigate demand information sharing and the green technology innovation in a supply chain. Specially, the demand information is dynamic, which is a variable that changes over time. Moreover, our research is based on a long-term perspective. Thus, differential game is adopted in this paper.

The orientation behavior of fiber is of great significance in improving the performance of fiber-reinforced polymer products. Generally, the Folgar–Tucker equation can accurately describe the variation of orientation vector of fiber, whereas the stability of numerical algorithms was the major challenge. This paper aims to propose an accurate, stable algorithm to solve the Folgar–Tucker equation for the fiber orientation behavior.

First, the mismatch problem between the strain rate and the pressure field was solved by using the integral transformation method. Then, an accurate, stable algorithm to solve the Folgar–Tucker equation based on the invariant-based optimal fitting method was proposed. The equation was discretized by finite element/finite difference method, and the Lagrange multiplier method was applied to ensure stability.

The proposed algorithm is proven to accurately and steadily coincide with the experimental results for different cases, including the fiber orientation behaviors under combined flow field, rectangular sheet, three-dimensional computed tomography imaging of tensile specimen and box cases.

The fiber orientation behavior during the injection molding can be accurately predicted, which plays a significant role in determining the mechanical properties of products.

China is experiencing an economic revolution focused on reducing carbon emissions (CEs). Various technological research and development (R&D) frameworks also hasten the growth of the digital economy, which then fuels this economic revolution. Nevertheless, several correlation uncertainties in China have been observed between R&D investment and CE reduction with green economic transformation. This phenomenon is attributed to insufficient spatial impact considerations.

Therefore, this article explored the spatial impacts of the digital economy and R&D expenditures regarding environmental quality using Chinese-related panel data between 2012 and 2021. This study uses the Moran I index to test whether there is a spatial relevance between regional carbon emissions in China and assess the digital economic advancement level using the entropy weight approach. In addition, this article analyzes the direct and indirect impacts following the partial differentiation approach, and then creates an interaction term between the digital economy and R&D investment to assess the moderating effect for examining the influence of investing in R&D on reducing CO2 levels of the digital economy.

A positive spatial relevance between the digital economy and CEs was then highlighted from the empirical findings. The digital economy expansion also demonstrated higher local CEs while negatively impacting nearby regions. Notably, the digital economy concurrently lowered and increased local CEs in the Eastern and Central zones, respectively. Overall, a larger R&D investment directly impacted the capacity of the digital economy in decreasing the carbon emission intensity (CEI) at a regional level. An accelerated digital economy expansion and lower CEI were recorded in the Eastern zone owing to more significant R&D investments.

China has gradually shifted its focus from reducing CEs to implementing “dual control of carbon” to achieve the “dual carbon” target. Future studies should then involve additional studies concerning the impact mechanism and path selection related to “dual carbon control.”

Investment in R&D plays a key role in reducing carbon emissions from the digital economy. By fostering innovation and technological advances, R&D investment activities can create more energy-efficient digital infrastructures, develop sustainable practices and optimize resource use. In addition, these R&D investments can facilitate the transition to renewable energy sources, enhance data management systems to minimize waste and promote the adoption of green technologies by businesses and consumers. As the digital economy continues to evolve, prioritizing R&D in this area is critical to achieving long-term sustainable development goals and addressing the pressing challenges of climate change. Stakeholders across industries must therefore recognize the importance of investment in research and development as a strategic approach that not only drives economic growth but also ensures environmental stewardship in an increasingly digital world.

Investments in research and development not only foster innovation and technological progress, but also promote sustainable practices, which can have significant environmental benefits. In addition, they have the potential to create new jobs, improve public health through better air quality and drive economic growth in a manner consistent with climate goals. As society becomes increasingly dependent on digital solutions, it is critical to harness the power of the digital economy to achieve a more sustainable and inclusive society.

Research development investment is critical to all aspects of regulation. Research on R&D investment can provide direction to local governments in formulating digital economy policies and can be beneficial to local governments in considering regional differences in resource availability. The research and technical innovation strategies in the policies for developing the digital economy can substantially expedite carbon neutrality achievement by 2060.

Purpose: Risk is a multifaceted concept, and its identification requires complex approaches that are often misunderstood. The consequence is that decisions are based on limited perception rather than the full value and meaning of what risk is, as a result, the way it is being tackled is incorrect. The individuals are often limited in their perceptions and ideas and do not embrace the full multifaceted nature of risk. Regulators and individuals want to follow norms and checklists or overuse models, simulations, and templates, thereby reducing responsibility for decision-making. At the same time, the wider use of technology and rules reduces the critical thinking of individuals. We advance the automation process by building robots that follow protocols and forget about the part of risk assessment that cannot be programed. Therefore, with this study, the objective of this study was to discover how people define risk, the influencing factors of risk perception and how they behave toward this perception. The authors also determine how the perception differed with age, gender, marital status, education level and region. The novelty of the research is related to individual risk perception during COVID-19, as this is a new and unknown phenomenon. Methodology: The research is based on the analysis of the self-administered purposely designed questionnaires we distributed across different social media platforms between February and June 2020 in Europe and in some cases was carried out as a interview over communication platforms such as “Skype,” “Zoom” and “Microsoft Teams.” The questionnaire was divided into four parts: Section 1 was designed to collect demographic information from the participants; Section 2 included risk definition statements obtained from literature and a preliminary discussion with peers; Section 3 included risk behavior statements; and Section 4 included statements on risk perception experiences. A five-point Likert Scale was provided, and participants were required to answer along a scale of “1” for “Strongly Agree” to “5” for “Strongly Disagree.” Participants also had the option to elaborate further and provide additional comments in an open-ended box provided at the end of the section. 466 valid responses were received. Thematic analysis was carried out to analyze the interviews and the open-ended questions, while the questionnaire responses were analyzed using various quantitative methods on IBM SPSS (version 23). Findings: The results of the analysis indicate that individuals evaluate the risk before making a decision and view risk as both a loss and opportunity. The study identifies nine factors influencing risk perception. Nevertheless, it must be emphasized that we can continue to develop models and rules, but as long as the risk is not understood, we will never achieve anything.

Globally, co-production is of great significance in promoting neighborhood regeneration. However, in the Chinese context, characterized by a governance system with strong government discourse power and a tradition of passive public participation, co-production has faced significant challenges. To address issues, this paper aims to deeply understand the co-production behaviors and strategy choices of local governments and residents in the co-produced neighborhood regeneration.

An evolutionary game approach was utilized as the research method to analyze the interest interactions between the two parties, the differences and similarities in strategy choices and the influencing factors in government and resident-initiated project types, respectively. Chongqing was selected as the case area for empirical analysis, with data derived from project materials and in-depth interviews.

This study revealed dynamic interactions between local governments and residents, significant differences between the two project types regarding co-production levels, the positive role of residents' perceived loss and the effect of marginal benefits on critical influencing factors.

Drawing upon co-production theory, this paper elucidates how different levels of co-production are implemented and highlights the differences between the two types of neighborhood regeneration projects within governance systems characterized by strong state discourse power and a lack of public participation tradition. It addresses current issues and provides critical references for government policymakers and urban planners to make informed decisions and promote co-produced neighborhood rehabilitation projects.

This chapter offers a comprehensive review the literature on authentic leadership (AL). The authors employ a bibliometric approach to identify, classify, visualise and synthesise relevant scholarly publications and the work of a core group of interdisciplinary scholars who are key contributors to the research on AL. They review 264 journal articles, adopting a clustering technique to assess the central themes of AL scholarship. They identify five distinct thematic clusters: authenticity in the context of leadership; structure of AL; social perspectives on AL; dynamism of AL; and value perceptions of AL. Velt and Sinkovics assert that these clusters will help scholars of AL to understand the dominant streams in the literature and provide a foundation for future research.

This paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.

A systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.

The findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.

This paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.

This paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR).

The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate).

The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|R_D) is able to erase the negative influences of error images in R_D.

Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach.

Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.

As most existing computer‐aided design systems do not provide part feature information which is essential for process planning, automatic part feature recognition systems serve as an important link between Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). Attempts to provide a structural framework for understanding various issues related to part feature recognition. Reviews previous work in the field of part feature recognition and classifies known feature recognition systems for the sake of updating information and future research. Briefly introduces about 12 systems. Studies 31 systems and lists them in the Appendix based on 60 references. Comments on future research directions.