Search results

This paper aims to solve the problems molybdenum disulfide (MoS₂) nanosheets suffer from inadequate dispersion stability and form a weak lubricating film on the friction surface, which severely limits their application as lubricant additives.

MoS₂/C₆₀ nanocomposites were prepared by synthesizing molybdenum disulfide (MoS₂) nanosheets on the surface of hydrochloric acid-activated fullerenes (C₆₀) by in situ hydrothermal method. The composition, structure and morphology of MoS₂/C₆₀ nanocomposites were characterized. Through the high-frequency reciprocating tribology test, its potential as a lubricant additive was evaluated.

MoS₂/C₆₀ nanocomposites that were prepared showed good dispersion in dioctyl sebacate (DOS). When 0.5 Wt.% MoS₂/C₆₀ was added, the friction reduction performance and wear resistance improved by 54.5% and 62.7%, respectively.

MoS₂/C₆₀ composite nanoparticles were prepared by in-situ formation of MoS₂ nanosheets on the surface of C₆₀ activated by HCl through hydrothermal method and were used as potential lubricating oil additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0321/

The purpose of this study was to determine the influence of environmental pH on production of biofilms and virulence genes expression in Pseudomonas aeruginosa.

Among 303 clinical and environmental samples 109 (61 + 48) isolates were identified as clinical and environmental P. aeruginosa isolates, respectively. Clinical samples were obtained from patients in the Al-Yarmouk hospital in Baghdad city, Iraq. Waste water from Al-Yarmouk hospital was used from site before treatment unit to collect environmental samples. The ability of producing biofilm at various pH levels was examined by microtiter plate and the prevalence of Alg D, Psl A and Pel A was determined by quantitative real time-polymerase chain reaction (qRT-PCR).

This study showed that the ability of clinical and environmental isolates to biofilm development was observed in 86.9% and 85.42% of clinical and environmental isolates, respectively. As well as, the environmental P. aeruginosa isolates showed the highest biofilm production at pH 7. Clinical isolates showed the highest genes expression of Alg D, Psl A and Pel A as compared to environmental isolates with pH change. In general, both clinical and environmental isolates formed biofilm and carried AlgD, PslA and PelA genes. Also, alkaline pH was favored for biofilm production.

There are very few studies done to find out the influence of environmental pH on production of biofilms and virulence genes expression in Pseudomonas aeruginosa. This study is unique as it has highlighted the influence of environmental pH on the ability of clinical and environmental isolates to biofilm development and genes expression.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

This research delineates the interdependencies between e-service quality (e-SQ), product quality (PQ) and food biosafety measures (FBM) in shaping consumer satisfaction and loyalty within the online food delivery services (OFDS) landscape. Anchored by the technology acceptance model (TAM) and the theory of planned behavior (TPB), the study integrates these frameworks to examine how perceived service efficiency, reliability, product appeal and biosafety protocols contribute to overall consumer trust and repurchase intentions.

Surveys were conducted on several 100 online food delivery app users, ages 20 to 64, in major cities in Colombia, which provided data for structural equation modeling analysis.

The analysis revealed that reliable, responsive service and appealing food presentation significantly influence consumer perceptions of behind-the-scenes safety protocols during delivery. Strict standards around mitigating contamination risks and verifiable handling at each point further engender trust in the platform and intentions to repurchase among users. The data cement proper food security as pivotal for customer retention.

Quantitatively confirming biosafety’s rising centrality provides an impetus for platforms to integrate and promote integrity, safety and traceability protection as a competitive differentiator.

The study’s originality lies in its comprehensive exploration of the OFDS quality attributes and their direct impact on consumer loyalty. Besides, it offers valuable insights for both academic and practical implications in enhancing service delivery and marketing strategies.

With the growth in online purchasing, the return of distressed shipments also increased. The return experience of the online shopper has a huge impact on their next purchase decision-making. This explanatory research aims to identify and empirically explain factors related to the online buyer’s return experience that influence the repurchase intention of young buyers.

Primary data were collected from 235 active online young buyers who have experienced returning the goods through a structured questionnaire. Structural equation modeling is used for analyzing the data.

This study reveals that an online return policy leniency strongly supports service recovery quality, expected return convenience, buyer trust and satisfaction, which lead to repurchase intentions. Moreover, return satisfaction positively impacts repurchase intention while mediating young buyer trust.

This study is one of the few relevant pieces of research that would benefit e-tailers to improve their product return policy and compel young buyers’ intention to make a repeat purchase.

This study aims to examine the relationship between contextual factors and career exploration. Career adaptability mediates the relationship between social support, sense of belonging and career exploration.

This quantitative study involved 291 undergraduates recruited from two universities in China. Data were analyzed using structural equation modeling.

The results indicated that social support and sense of belonging were positively related to career adaptability. Career adaptability was found to be a mediator between relational predictors and career exploration. Contrary to our hypothesis, the direct path from sense of belonging to career exploration was insignificant.

This study highlights the importance of career adaptability in connecting contextual indicators and career exploration in the Chinese context.

This study aims to investigate the rarefaction effects on flow and thermal performances of an equivalent sand-grain roughness model for aerodynamic thrust bearing.

In this study, a model of gas lubrication thrust bearing was established by modifying the wall roughness and considering rarefaction effect. The flow and lubrication characteristics of gas film were discussed based on the equivalent sand roughness model and rarefaction effect.

The boundary slip and the surface roughness effect lead to a decrease in gas film pressure and temperature, with a maximum decrease of 39.2% and 8.4%, respectively. The vortex effect present in the gas film is closely linked to the gas film’s pressure. Slip flow decreases the vortex effect, and an increase in roughness results in the development of slip flow. The increase of roughness leads to a decrease for the static and thermal characteristics.

This work uses the rarefaction effect and the equivalent sand roughness model to investigate the lubrication characteristics of gas thrust bearing. The results help to guide the selection of the surface roughness of rotor and bearing, so as to fully control the rarefaction effect and make use of it.

The organizational digital transformation (ODT) in companies presents small and medium-sized enterprises (SMEs) – who remain at the beginning of this transformation – with the challenge of offering digital services based on sensor technologies. Against this backdrop, the present paper identifies ways SMEs can enable digital servitization through sensor technology and defines the possible scope of the organizational transformation process.

Around 21 semi-structured interviews were conducted with experts from different hierarchical levels across the German manufacturing SME ecosystem. Using the Gioia methodology, fields of action were identified by focusing on influencing factors and opportunities for developing these digital services to offer them successfully in the future.

The complexity of existing sensor offerings must be mastered, and employees' (data) understanding of the technology has increased. Knowledge gaps, which mainly relate to technical and organizational capabilities, must be overcome. The potential of sensor technology was considered on an individual, technical and organizational level. To enable the successful implementation of service offerings based on sensor technology, all relevant stakeholders in the ecosystem must network to facilitate shared value creation. This requires standardized technical and procedural adaptations and is an essential prerequisite for data mining.

Based on this study, current problem areas were analyzed, and potentials that create opportunities for offering digital sensor services to manufacturing SMEs were identified. The identified influencing factors form a conceptual framework that supports SMEs' future development of such services in a structured manner.