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This study aims to investigate the temperature rise characteristics of vibrating rolling bearings under the influence of the polarization force of unbalanced eccentric blocks. A thermal-fluid-solid mechanics coupled finite element model is established to analyze the effects of different loads and rotational speeds on bearing temperature to prevent overheating, wear and thermal damage.

A thermal-fluid-solid mechanics coupled finite element model of the vibrating rolling bearing is developed based on the principles of heat transfer. Finite element analysis software is used to conduct numerical simulations and study the temperature distribution of the bearing system under different loads and speeds. The model’s accuracy is verified by experimentally measuring the actual temperature of the bearing under the same working conditions.

This study successfully established a thermal-fluid-solid mechanics coupled finite element model of a vibrating rolling bearing, verifying its accuracy and reliability. The research results provide an essential reference for optimizing bearing design, preventing overheating and extending service life.

By analyzing the temperature rise characteristics under various load and rotational speed conditions, the law governing the internal temperature distribution of bearings is revealed. This finding offers a theoretical foundation for comprehending the thermal behavior of bearings.

This study offers a scientific foundation for the maintenance and fault diagnosis of shaker rolling bearings, aiding in the timely identification and resolution of thermal damage issues. Through the optimization of bearing design and usage conditions, the equipment’s lifespan can be prolonged, maintenance expenses can be minimized and production efficiency can be enhanced.

A thermal-fluid-solid mechanics coupled finite element model of a vibrating rolling bearing was established, considering the interaction of multiple physical fields. The influence of the polarization force from the unbalanced eccentric block on the bearing temperature is analyzed in detail, which is close to the actual working conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0396/

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

The objective of this study is to investigate how the coronavirus disease 2019 (COVID-19) pandemic affects firms' financial management in China's manufacturing sector. In addition, the authors analyze the changes in various financial indicators before and during the COVID-19 pandemic. Further, the authors make a cross-country comparison of the COVID-19's impact on financial management between China and Romania.

The study uses the balanced panel data of 2,272 manufacturing listed companies from 2019 to 2020, and applies the t-test method and multiple regression method.

The results show that firms' financial performance in most manufacturing sub-sectors decreased during the observed period. In addition, the authors find that equity financing, proper liquidity management and an expanded firm scale can improve firms' financial performance. The authors further compare the results with the Romanian results, and find that the negative impact of debt-to-equity ratio on firms' financial performance in Romania is greater than that in China and the positive impact of financial autonomy ratio and working capital ratios is greater in China than that in Romania.

The findings can help corporate managers make the best financial management decision in response to crisis.

This study is one of the pioneers that analyze how manufacturing companies carried out their financial management during the COVID-19 crisis in the Chinese context, and provides a cross-country analysis of corporate financial management practices in China and Romania.

This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed.

An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF.

Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm.

The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

This paper aims to examine the impact of a board-level governance bundle (i.e. size, independence, expertise, meetings, gender diversity and multiple directorships) on the non-financial performance of National Health Service (NHS) hospitals – and, separately, by hospital type (i.e. trusts hospitals and foundation trusts hospitals).

A logit regression for panel data is used for a sample of 128 NHS trusts and foundation trusts across England from 2014 to 2018. The data was hand-collected from NHS hospitals’ annual reports and Care Quality Commission reports. The cancer waiting time target (i.e. 62-day cancer referral and treatment target) is used to measure non-financial performance.

The main findings for NHS hospitals indicate that multiple directorships positively and significantly affect non-financial performance. However, board expertise and gender diversity have a negative and significant influence. When the sample is partitioned, the results remain the same for the NHS foundation trusts hospitals. For NHS trust hospitals, except for multiple directorships having a positive and significant effect, all remaining governance attributes have an insignificant impact.

The findings have implications for policymakers and practitioners as they move to implement measures to improve hospital performance against the cancer waiting time targets in the English NHS.

To the best of the authors’ knowledge, this is the first study to examine the impact of corporate governance on cancer waiting time targets in public hospitals. Overall, this paper contributes to the corporate governance literature, especially in the context of public hospitals, and has significant practical and theoretical implications.

Identifying the criteria that effectively drive innovation in universities is critical to assessing their innovation maturity level, and hence, planning for the improvements required to reach a target level. This paper aims to propose a three-phase approach to develop a multidimensional maturity assessment framework used by university decision-makers to determine their level of innovation readiness.

First, a systematic collection of evaluation criteria from the literature is conducted. The results are mapped into different categories in a hierarchical and multidimensional way, and validated by experts. The second phase aims to identify the critical factors and their priorities, which are determined using analytic network process (ANP). To facilitate that, a panel of thirteen experts is formed and questionnaires are sent to rank the importance of the criteria and their elements. Finally, a maturity assessment tool is developed to complement the framework, allowing decision-makers to determine the level of innovation maturity with respect to each dimension and the overall position.

Results revealed three clusters, eight criteria and 26 subcriteria related to innovation in universities. The findings about the relative importance of the various attributes are reflected in the developed assessment tool and taken into consideration in the maturity indices computation approach.

To the best of the authors’ knowledge, this is the first attempt to develop a comprehensive list of innovation success drivers in universities and to use this list to design an innovation maturity assessment framework

Green hospitality studies have not adequately focused on the diffusion of eco-innovative hotels amongst visitors. This study aims to fill this gap by identifying green hotel attributes that influence visitors’ adoption of eco-friendly hotel and their intentions to partake in green initiatives.

The paper uses a mixed-method approach to explore the drivers of customers’ green hotel adoption and consumption. In the qualitative phase, data were collected via 20 open-ended interviews and analyzed to derive a measurement scale. The scale was then tested through a survey comprising 500 respondents using structural equation modelling.

The study results elucidate how guests’ visit intentions and green consumption behavior is built through their perception of newness and uniqueness of eco-innovative attributes. Findings shed light on how green hotel’s sustainable communication and corporate social responsibility outreach efforts positively influence guest visit intentions.

Study results reveal perceived eco-innovativeness as an important antecedent of visit intentions. Based on guest’s preferences, green hotels striving to increase its visitors’ base could begin by expanding their eco-innovative attributes.

Contrasting previous studies that have exclusively used the theory of planned behavior constructs, this study argues that diffusion of innovation constructs also offer valuable insights into guests’ visit intentions. While existing studies have covered limited number of eco-innovative attributes, this study adds to the literature by presenting a comprehensive set of attributes including trustworthiness of communication and observability of its social impacts.

Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.

This paper aims to prove the expediency and effectiveness of magnetic textiles use obtained by adding nanopowder synthesized on the basis of oxides of divalent and trivalent iron oxides, taking into account bacteriostatic, magnetotherapeutic and compressive properties.

The research includes methods of synthesis of nanoelements of bivalent and trivalent iron, methods of the theory of elasticity for determining the pressure between compression clothing and a limb, methods of creating an annular magnetic field with determination of its voltage, methods of determining the growth dynamics of mold bacteria and methods of approximation of experimental data.

On the base of the determination of the forces arising from the interaction of magnetic nanotextiles with a magnetic field, the expediency of using these materials in the creation of compression clothing has been proven. An additional medical value of magnetic textiles is the bacteriostatic effect. The content of magnetic nanoelements in the textile composition of 0.2% almost completely suppresses mold infections

Cotton samples with the addition of nanocomponents based on ferric and ferric oxides were studied.

Magnetotextile materials can be used in magnetotherapy, compression clothing, in textile products that provide bacteriostatic properties.

The use of magnetic textile materials is a perspective direction for the creation of medical textile products with complex properties.