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Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

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 ideology of this article is to study the performance concerns of SDN Controllers, with the help of developed SRGM and thereby obtain its optimal testing duration. The effect of undetected uncertainty in the parameter values have also been catered in the proposal.

These uncertainties in the parameter values are studied as the risk of not meeting desired set of requirements, whose removal causes additional cost. Considering these two constructs as attributes of MAUT, the controller's optimal testing duration is obtained.

The article focuses towards obtaining the optimal duration for which the SDN Controllers must be tested. It was observed that the inculcation of risk-attribute has provided the higher utility value as compared to any other existing scenarios.

Plenty of SRGM have been proposed in the literature which talks about the testing stop time determination problems. But, none of them have considered the impact of risk of not meeting the requirements (reliability) along with cost to obtain its testing stop time. Further, validation of the proposed model in presented with the help of two releases versions of SDN controller platform, ONOS, entitled as “Kingfisher” and “Loon” and has acquired promising results.

The purpose of this research is to investigate the cross-level effects and mechanisms of inclusive leadership on employee innovation in team contexts, and further explore the boundary conditions of inclusive leadership.

This study collected data from 237 leader-member dyads in 60 teams of Chinese firms. The research utilized multilevel linear models and multilevel structural equation models in the R language to test the hypothesized model.

The findings suggest that inclusive leadership has a positive impact on both employee incremental and radical innovation. Team psychological safety and employee role breadth self-efficacy mediate the effects. Employee risk avoidance propensity negatively moderates the mediating role of role breadth self-efficacy in the relationship between inclusive leadership and incremental innovation.

Leaders should pay attention to team psychological safety, employee role breadth self-efficacy and employee individual risk avoidance propensity that influence employee innovation to maximize the effectiveness of inclusive leadership.

This research expanded the level of analysis from individual to team, exploring cross-level effects and mechanisms of inclusive leadership on employee innovation in team contexts, and clarified the effectiveness conditions of inclusive leadership.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

This paper aims to provide the security and privacy for Byzantine clients from different types of attacks.

In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.

By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped.

By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

Although the development of public organisations and the continual enhancement of public services depend on the creative behaviour of public employees, it is uncertain from earlier studies how and when inclusive leadership (IL) affects innovative behaviours (IB). This study aims to resolve the inconsistency in the literature by applying social exchange theory to examine the effect of inclusive leadership on employees’ innovative behaviour, while also examining the mediating role of work engagement and the moderating effect of psychological safety within this relationship.

The research model’s analysis draws from a data set of 200 employee–supervisor dyads. Data was collected from employees across diverse public sector organisations in the United Arab Emirates (UAE).

The results demonstrate that IL indirectly (via WE) and directly has a positive influence on employees’ IBs. This influence is enhanced when employees feel safe and do not have to be concerned about negative consequences.

Our study highlights a less-explored sector, unveiling the motivations behind IB among public sector employees. Moreover, this study provides valuable insights within a non-Western context, offering a unique perspective on the intricate relationship between IL, PS, employee engagement and IBs in the UAE public sector.

The smart contract provides an opportunity to improve existing contract management practices in the construction projects by replacing traditional contracts. However, translating the contracts into computer languages is considered a major challenge which has not been investigated. Thus, it is necessary to: (1) identify the obstructing clauses in real-world contracts; and (2) analyze the replacement's technical and economic feasibility. This paper aims to discuss the aforementioned objectives.

This study identified the flexibility clauses of traditional contracts and their corresponding functions through inductive content analysis with representative standard contracts as materials. Through a speculative analysis in accordance to design science paradigm and new institutional economics, the economic and technical feasibility of existing approaches, including enumeration method, fuzzy algorithm, rough sets theory, machine learning and artificial intelligence, to transform respective clauses (functions) into executable codes are analyzed.

The clauses of semantic flexibility and structural flexibility are identified from the contracts. The transformation of semantic flexibility is economically and/or technically infeasible with existing methods and materials. But with more data as materials and methods of rough sets or machine learning, the transformation can be feasible. The transformation of structural flexibility is technically possible however economically unacceptable.

Given smart contracts' inability to provide the required flexibility for construction projects, smart contracts will be more effective in less relational contracts. For construction contracts, the combination of smart contracts and traditional contracts is recommended. In the long run, with the sharing or trading of data in the industry level and the integration of machine learning or artificial intelligence reducing relevant costs, the automation of contract management can be achieved.

This study contributes to the understanding of the smart contract's limitations in industry scenarios and its role in construction project management.