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In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.

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.

This paper considers carbon abatement in a competitive supply chain that is composed of a manufacturer and two retailers under vertical shareholding. The authors emphasize the equilibrium decision problem of stakeholders under vertical shareholding and different power structures.

A game-theoretic approach was used to probe the influence of power structure and retailer competition on manufacturers' carbon abatement under vertical shareholding. The carbon abatement decisions, environmental imp4cacts (EIs) and social welfare (SW) of different scenarios under vertical shareholding are obtained.

The findings show that manufacturers are preferable to carbon abatement and capture optimal profits when shareholding is above a threshold under the retailer power equilibrium, but they may exert a worse negative impact on the environment. The dominant position of the held retailer is not always favorable to capturing the optimal SW and mitigating EIs. In addition, under the combined effect of competition level and shareholding, retailer power equilibrium scenarios are more favorable to improving SW and reducing EIs.

This paper inspects the combined influence of retailer competition and power structure on manufacturers' carbon abatement. Distinguishing from previous literature, the authors also consider the impact of vertical shareholding and consumer preferences. In addition, the authors analyze the SW and EIs in different scenarios.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

This paper investigates school library services in the digital age for students with dyslexia and explores the impact of current library services on students’ learning.

A qualitative study with semi-structured interviews was designed according to the LAFE (Look and listen, Assistance and accessibility, Format and fit, and Environment) framework for learners with dyslexia and the 5E instructional model and conducted with 11 school librarians.

Results indicated that participants lacked knowledge of dyslexia for appropriate library services. Awareness, IT skills, school administration, funding and parental attitudes would influence the library’s tailored services to dyslexic children, despite the rich resources in these participants’ libraries, including paperbacks, digital resources and electronic devices. Adaptations are necessary to provide accessible services, especially by applying digital technologies, and school libraries can positively impact students’ reading interests, promote knowledge inquiry and strengthen information literacy skills.

While students with dyslexia spend significant time in schools, limited studies focus on school library services in the digital age, especially in Asia. This study fills the gap by systematically exploring the issue with the 5E instructional model.

This research uses a multifaceted approach to develop an MPA/MPP curriculum to support a data science track within the existing MPA/MPP programs by identifying the core and elective areas needed.

The approach includes (1) identifying a suitable structure for MPA/MPP programs which can allow the program to develop its capacity to train students with the data science and general public administration skills to solve public policy problems and leave explicit space for local experimentation and modification; (2) defining bridging modules and required modules for the MPA/MPP programs; and (3) developing of data science track thought to make suggestions for the inclusion of suitable data science modules into the data science track and benchmarking the data science modules suggested with the best practices developed by other professional bodies. The authors review 46 NASPAA-accredited MPA/MPP programs from 40 (or 22.7%) schools to identify the suitable required modules and some potential data science and analytics courses that MPA/MPP programs currently provide as electives.

The proposal includes a three-course (six–nine credits, not counted in the program but as prerequisites) bridging module, a nine-course (27 credits) required module and a five-course (15 credits) data science track/concentration.

This work can provide a starting point for the public administration education community to develop graduate programs focusing on data science to cater to the needs of both public managers and society at large.

With the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.

We conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.

Our results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.

Few studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.