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Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

This study aims to examine how environmental, social and governance (ESG) initiatives and ISO 14001, which is an internationally agreed standard to set out the requirements for an environmental management system, affect firm performance in the context of the Industry 4.0 supply chain.

The authors develop a new chance-constrained network data envelopment analysis (DEA) in the presence of non-positive data to estimate innovation, operational and profitability performances for three main relation groups (suppliers, partners and customers) in Microsoft's supply chain.

Results of this study show the following: (1) the application of ISO 14001 will reduce profitability but increase overall performance (OP); (2) ESG implementation has a convex U-shaped influence on profitability and OP, which means that firms will benefit when ESG investment goes beyond a particular level; (3) the nonlinear U-shape is presented in the E and G components, but not in the S of the individual ESG initiatives, and (4) only specific subcomponents of S and G in the subcomponent of individual ESG initiatives are nonlinearly connected to OP. Research's results reveal that the customer group has a higher performance value than the other two groups, which suggests that this group will create competitive advantages for Microsoft.

Overall, the authors provide an insightful viewpoint into supply chain management by examining the ESG initiatives, ISO 14001 and performances of Microsoft's supply chain.

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.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

This study investigates the heterogeneous impact of R&D capital market distortions (CMDs) on the quantity and quality of innovation in Chinese firms by exploring key pathways from a dual-arbitrage perspective.

Using data from Chinese A-share listed companies (2015–2021), we employ a multidimensional fixed effects model to validate the research hypotheses. Under the Systems of National Accounts (SNA, 2008) framework, we use the Bureau of Economic Analysis method to estimate enterprise R&D capital stock and the Cobb-Douglas production function to estimate R&D CMDs.

Results show that R&D CMDs drive firms toward strategic innovation, emphasizing quantity over quality. Policy arbitrage, including policy catering and rent-seeking, emerges as a pivotal mechanism under R&D CMDs, encouraging firms to prioritize quantity over quality in innovation. High-technology firms and those in the decline stage are more inclined to spearhead strategic innovation within the context of R&D CMDs.

These findings help policymakers promote high-quality innovation in Chinese enterprises by enhancing patent review mechanisms and shifting policies from quantity-driven to quality-oriented goals.

This study enriches the research on factor market distortions and innovation in emerging markets from the perspective of R&D CMDs, based on the “emerging + transitional” comprehensive framework. Unlike previous studies, which generally use enterprise R&D expenditure flow data, we apply the theory of R&D capitalization accounting to the micro-enterprise level under the SNA (2008) framework, enhancing the accuracy of R&D CMD estimations.

The principal purposes of the research are to empirically investigate three forms of perceived overload on social media and shed light on their associations with users’ passive usage intention by contemplating the mediating influence of social network exhaustion and discontented feelings.

This study employed a cross-sectional methodology to collect statistical data (N = 679) from WeChat users in mainland China. Primitive analysis, confirmatory factor analysis and structural equation modeling were employed to test the corresponding hypotheses.

The findings reveal that three dimensions of perceived overload influence social network exhaustion positively. In addition, communication overload and system feature overload exert positive impacts on the discontented feeling. Furthermore, it is uncovered that social network exhaustion and discontented feeling are related to passive usage intention positively.

Theoretically, this paper offers a conceptual framework to explicate passive usage intention through elucidating social network exhaustion and the discontented feeling that arises from perceived overload in contemporary social media-mediated environments. Practically, the current research has certain realistic implications for WeChat users and SNS operators.

Probing what triggers people’s passive usage intention of social media has been an emerging theme in recent years, yet there is a dearth of discourse that delves into the antecedents of WeChat users’ passive usage intention. The results obtained from the study have enhanced the understanding of the adverse consequences associated with the utilization of social media in mainland China.

Service providers and tourism players have introduced the green hotels concept to mitigate detrimental environmental impact. This study aimed to review the literature on green hotels based on bibliometric analysis.

In analyzing the potential and significant subject of the tourism industry and concern on environmental issues, this study evaluates the themes based on the past, present and future trends in green hotels from a bibliographic database retrieved from the Web of Science (WoS).

Several themes were identified from the role of the theory of planned behavior and predictors of consumers' intention to visit green hotels.

Implications were discussed mainly related to green hotels contribution towards sustainable tourism and its role in shaping the tourism sector's landscape. Among the practical implications include rewards by the authorities in the form of incentives or tax relief to green hotel operators, which will encourage conventional hotel transformation into green hotels. Furthermore, green hotels will be at the forefront of tourism and hospitality brands, requiring substantial green marketing initiatives. Sooner or later, opting for green hotels while traveling will be the norm among travelers.

The green hotels have emerged as a way to tackle the environmental issues related to tourism and hospitality while at the same time, allowing the industry to flourish. This research is one of the scant studies that provide a comprehensive overview about green hotel studies and offer future research agendas.

This study aims to investigate the satisfaction of farmers with the compensation policy for wildlife-caused damages and its influencing factors, analyze the current situation of satisfaction with the compensation policy among farmers, identify factors significantly affecting satisfaction, and explore ways to optimize the compensation policy and improve the satisfaction of farmers based on the effects of various influencing factors.

The Xishuangbanna National Nature Reserve in Yunnan Province, China, is selected as the research area for the study. Through field interviews, 370 valid questionnaires were collected to obtain relevant data on farmers' satisfaction with the compensation policy for wildlife-caused damages. The Oprobit model is utilized to explore the factors influencing farmer satisfaction and to analyze their underlying reasons.

The study reveals that farmers in the communities surrounding the Xishuangbanna National Nature Reserve generally experience low satisfaction with the compensation policy, particularly concerning satisfaction with compensation amounts, which tends to be dissatisfied on average. Satisfaction with the compensation policy is significantly influenced by individual characteristics and household labor structure, while the degree of human-wildlife conflict, wildlife conservation attitudes and household income structure have insignificant impact. Among individual characteristics, gender, education level, health status, and ethnicity are highly significant. In household labor structure, the number of agricultural laborers, non-agricultural laborers, and household agricultural labor time are highly significant.

Building on the overall satisfaction of farmers with the compensation policy, this study further decomposes policy satisfaction into satisfaction with compensation amounts, coverage, and procedures. It provides more targeted recommendations for enhancing satisfaction with the compensation policy, which can help effectively mitigate human-wildlife conflicts and achieve harmonious coexistence between humans and nature.

This study examines the interplay between media-induced emotional crisis framing (anger vs sadness) and message sidedness of crisis response on publics’ attribution of crisis responsibility as well as subsequent company evaluation and supportive behavioral intention.

A 2 (emotion: anger vs sadness) x 2 (crisis response: one-sided vs two-sided) online experiment was conducted among 161 participants in the USA.

Results showed that anger-inducing media framing of the crisis elicited higher levels of crisis responsibility attribution and more negative company evaluation, compared with sadness-inducing media framing. One-sided message response was more effective than two-sided message response in lowering attribution of crisis responsibility when sadness was induced, but no difference was found under the anger-induced condition. Attribution of crisis responsibility fully mediated the effects of emotional crisis framing on company evaluation and supportive behavioral intention toward the company.

This study is among the first to examine the interaction effect between emotional media framing and response message sidedness in an ambiguous crisis. Drawing on the interdisciplinary theoretical frameworks, this study integrates the situational crisis communication theory, appraisal-tendency framework and message sidedness in persuasion literature. As such, it contributes to theoretical development in crisis communication and offers communication managers guidance on how to effectively address emotionally framed crises.

Although digital content marketing (DCM) research and industry-wide expenditure is growing very rapidly owing to the positive outcomes associated with this new pull marketing strategy, research has not completely mapped how DCM activities can be optimized in the social media brand community context. This paper seeks to understand how social media DCM activities can be optimized to achieve greater relational and monetary outcomes for different products.

A structural equation modeling procedure was used to analyze 416 survey responses obtained from members of Facebook brand communities in South Africa.

The results reveal that social media DCM consumption motives exert significant differential effects on both relational and monetary marketing outcomes in search and experience product contexts while also demonstrating the mechanism through which social media DCM consumption motives lead to contributing social media engagement behaviors.

The study findings call for the need for firms to understand the motives that drive the consumption of DCM in social media brand communities. Specifically, marketers of search products should deploy more of hedonic contents such as images while simultaneously keeping highly textual DCM to a minimum in Facebook brand communities as this works better for experience products. Finally, more authentic SM-DCM activities that effectively address the authenticity SM-DCM consumption motive can result from the DCM activities of social media opinion leaders and genuine consumer–brand interactions in the context of Facebook brand communities.

This paper broke new grounds in three unique directions in terms of: (1) the relative salience of SM-DCM consumption motives in enhancing WTP and different aspects of SMBE; (2) the contextual influence of product type on SM-DCM activities optimization and (3) the mechanisms that underlie the effects of SM-DCM consumption motives on contributing SMBE in the Facebook brand community context.