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A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).

The authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.

The empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.

The risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.

The authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

Ti6Al4V is a widely used metal for biomedical application due to its excellent corrosion resistance, biocompatibility and mechanical strength. However, a coupling reaction of friction and corrosion is the critical reason for the failure of implants during the long-term service in human body, shortening the life expectancy and clinical efficacy of prosthesis. Hence, this study aims to find a feasible approach to modify the service performances of Ti6Al4V.

Selective laser melting (SLM), as one of the emerging metal-based additive manufacturing (AM) technologies is capable for fabricating patient-specific personalized customization of artificial prosthesis joints, owing to its high adaptability for complex structures. This study is concerned with the tribocorrosion behavior of SLM fabricated Ti6Al4V substrate enhanced by laser rescanning and graphene oxide (GO) mixing. The tribocorrosion tests were performed on a ball-on-plate configuration under the medium of simulated body fluid (SBF). Moreover, the surface morphologies, microstructures, microhardness and contact angle tests were used to further reveal the in-situ strengthening mechanism of GO/Ti6Al4V nanocomposites.

The results suggest that the strengthening method of GO mixing and laser rescanning shows its capability to enhance the wear resistance of Ti6Al4V by improving surface morphologies and promoting the generation of hard phases. The wear volume of R-GO/Ti6Al4V is 5.1 × 10⁻² mm³, which is 25.0% lower than that of pure SLM-produced Ti6Al4V. Moreover, a wear-accelerated corrosion of the Ti6Al4V occurs in SBF medium, leading to a drop in the open circuit potential (OCP), but R-GO/Ti6Al4V has the lowest tendency to corrosion. Compared to that of pure Ti6Al4V, the microhardness and contact angle of R-GO/Ti6Al4V were increased by 32.89% and 32.60%, respectively.

Previous investigations related to SLM of Ti6Al4V have focused on improving its density, friction and mechanical performances by process optimization or mixing reinforcement phase. The authors innovatively found that the combination of laser rescanning and GO mixing can synergistically enhance the tribocorrosion properties of titanium alloy, which is a feasible way to prolong the service lives of medical implants.

This study aims to investigate the factors influencing the adoption of cloud accounting (CA) in Oman’s small and medium enterprises (SMEs). The research model is developed based on relationships between technology, organisational and environmental contexts.

This study used a questionnaire to collect data from a sample of SMEs in Oman’s information and communication sector. In total, 300 enterprises were selected, and the questionnaire was distributed to the executives. The questionnaires valid for analysis were 159. The collected data were analysed using structural equation modelling through analysis of a moment structures software.

This study tested seven factors, namely, support from top management, firm size, infrastructure (technology readiness), security and privacy, compatibility, competitive pressure and relative advantage. The results revealed that compatibility has a significant impact on the adoption of CA.

This study suggests the mangers in SMEs should play a more decisive role in identification of technological, organisational and environmental factors that affect the success of implementing CA in a comprehensive model.

This study constitutes a management strategy that helps the enterprises in light of limited economic resources and concerns about the use of cloud services to make the appropriate decision in adopting CA.

A large number of earthquake damages showed that infill walls have obvious influence on the seismic damage performance of RC frame structures. The purpose of this paper is to study the effect of infill walls on the cumulative plastic deformation energy of RC frame structures, for which four RC frame structures are build and the time-history response analysis under unidirectional seismic action is presented.

The time-history response analysis under unidirectional seismic action is presented. Then the effect of periodic reduction coefficient on the cumulative plastic deformation energy of the structures, the beams and the columns is investigated.

Finally, the quantitative calculation formulas are provided. The results show that the periodic reduction coefficient has an obvious effect on the distribution of the accumulated plastic deformation energy, and the influence rules are presented here.

The effect of infill walls on the cumulative plastic deformation energy of RC frame structures is quantitatively analyzed here.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

By investigating trust in the organization as a mediator and traditionality as a moderator, this study aims to examine the effect perceived organizational exploitation poses on frontline hotel employees’ service performance.

A three-wave survey that targets 219 supervisor–subordinate dyads from four Chinese hotels was conducted to test the hypotheses. The authors used SPSS 20.0 and AMOS 21.0 to analyze the data and verify the theoretical model.

This study found that perceived organizational exploitation exerts a destructive impact on frontline hotel employees’ service performance. Trust in the organization is a full mediator of the link connecting perceived organizational exploitation to service performance. Furthermore, traditionality weakens perceived organizational exploitation’s impact on trust in the organization and subsequent service performance.

The authors’ findings remind hotels to cease exploiting their employees to avoid compromising service performance. Hotels should also endeavor to instill trust among employees toward the hotel and allocate more attention to employees with lower levels of traditionality.

First, to the best of the authors’ knowledge, this study is among the first to explore the impact of perceived organizational exploitation on frontline hotel employees’ service performance. Second, this study reveals a novel mechanism underlying the connection between perceived organizational exploitation and service performance. Finally, this study identifies frontline hotel employees’ traditionality as a vital moderator that mitigates the negative relationships among perceived organizational exploitation, trust in the organization and service performance.

Nowadays, some online retailing platforms emerge to integrate transport capacity to provide standard distribution service for sellers. Such an integrated form of service is defined as delivery alliance (DA). To have a better understanding of how to price the service, this study aims to fixate on the seller’s problems and builds a series of profit maximization models in accordance with the two-sided market pricing theory within a platform business model.

In the present study, some optimization models are built in the two-sided market type and the optimal solutions are found in a three-dimensional decision space. By using the basic model as the benchmark, some optimization problems of DA in realistic situations are discussed. Particularly, a power-law-distribution model is established to deal with the uncertainty in forecasting. Also, a price-sensitive model and a loss-aversion model are presented to describe the various reactions of sellers to charging modes. Finally, some combined situations are discussed and the strategies are compared under the mentioned models.

By selecting the basic model as the benchmark, the specific pricing strategies are found for each context to yield the optimal profits. The flexibility of pricing strategy in the basic model and rigid pricing strategies in extended models, are discussed. As a result, the guidelines for the online retailing platforms are developed on designing and pricing the DA service.

First, it would be interesting to expand the pricing plan of the platform. For instance, menu pricing and quantity discount have not been considered, which are common in practice. The time discounting has also been ignored. If the time value were calculated, the contract fees would be more critical due to the earliest of collecting money. Finally, those joiners who have huge order sizes are crucial for the ecosystem indeed, but arouse no attention. While in reality, they may have more power to bargain with the platform. Thus, how the platform competition affects the pricing strategies needs future research.

The optimal pricing strategies under these models are analytically found out, and it is shown that the presented models result in the same scale of joiners and profits in optimization. This suggests that DA works well in various behavioral contexts. This also suggests that DA is a significant controller in service quality improvement. Then, the optimal pricing strategies are compared among all the models. During this, it is discovered that the realistic contexts might reduce the profit, whereas an appropriate pricing strategy can pull this back without loss of service quality.

This paper aims to study the mixed after-sales service which simultaneously offers return and replacement services. The authors develop a model to propose what kind of after-sales service the firm should choose and how to make the after-sales service policy to improve the profit. The study aims to extend the literature on the mixed after-sales service and give some support to the managers to make decisions.

In this paper, the authors use the optimization modeling method to describe the situations of a firm offering two exclusive after-sales service policies and a mixed after-sales service policy, respectively. They compare the results in different cases and analyze the impact of different parameters on the boundary values and other results. Finally, the authors include three numerical examples to illustrate the major results.

The authors find that the mixed after-sales service can successfully segment the market, meet various customers’ distinct needs and differentiate the service prices to improve the total profit. Moreover, the authors find the boundary values which indicate the optimal interval for each service. Then, for a certain situation, they can clearly tell which after-sales service dominates and provides the optimal selling price, order quantity and total profit. Besides, the authors show the impact of different parameters on the boundary values and other results.

This paper combines after-sales service into traditional models and provides a new mixed service to segment the market and improve total revenue. It provides some managerial implications for the decision-makers.