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Novel thick film strain gauges have been constructed using a z‐axis orientation on insulated stainless steel for a variety of force sensing applications. These devices exhibit high gauge factor and good thermal stability compared with conventional x‐axis devices and offer other mechanical advantages due to their mode of operation. The work reported here investigates the characteristics of different types of stainless steel substrate and different types of insulating material used in the construction of the sensors. Both ferritic and austenitic steels have been investigated, together with different resistive and insulative compositions. The temperature coefficient of resistance of the devices has been shown to be a complex function of device thickness, surface area and the difference between the thermal coefficients of expansion of the various materials employed.

This paper presents results of work aimed at characterising the zero offset stability in novel thick film strain gauges. The devices studied are z‐axis (k₃₃) load sensors fabricated on insulated stainless steel substrates and include examples of novel commercially developed force sensors. Devices loaded with compressive strains using a purpose designed test jig were found to exhibit a significant zero offset shift, which is negative up to a certain level (typically 1,000 micro strains) and then increasingly positive when strained beyond this point. Repeated cycles of loading then produced a certain level of stability until the previous maximum value of applied strain was exceeded. Temperature coefficient of resistance (TCR) measurements showed the devices to exhibit characteristics that depend significantly on the device geometry. The TCR was found to increase positively with increasing device thickness and surface area. The effect of overglazing the devices was found to decrease the TCR.

Results are presented from a programme of research aimed at establishing the mechanisms behind the effects of fabrication parameter variation on the performance of thick film strain gauges on steel substrates. The research is aimed at describing the effect on the repeatability of the device characteristics due to different choices of materials, thicknesses of printed layers, firing regimes and geometry of the gauges. In particular the effects of load and temperature on the offset and gain characteristics of a variety of different sensor constructions have been explored. The sensors described here are of a type where the applied strain is parallel to the measured resistance path but orthogonal to the substrate (k₃₃). It has been found that these devices exhibit different characteristics to conventional thick film strain gauges that can help explain the mechanisms affecting gain and offset changes caused by temperature fluctuations and mechanical deformation.

Social media's role in engaging participants in sports events, particularly during the pandemic, is acknowledged. However, previous studies often utilized sports events for diverse objectives but overlooked brand equity's influence on sports event development. And very limited research explores social media's impact on brand equity in esports events, despite its significance. Therefore, this study aims to explore how social media affects esports event brand equity and participants' satisfaction and engagement with social media through brand equity's influence.

The study focused on League of Legends World Championships (LOLWC) participants, who completed a self-judged questionnaire online. The questionnaire included demographic details and latent constructs. Data analysis involved two steps: exploratory factor analysis (EFA) to assess measurement scale validity and structural equation modeling (SEM) to study relationships between traits.

The results reveal that, within the esports event context, controlled communication exerts a meaningful and dual impact – both directly and indirectly – on the fundamental components of brand equity. This, in turn, serves as a catalyst for increasing participant contentment and their posting intention.

This study applies brand equity theories to the esports domain, exploring participant-based brand equity concepts, user behavior and the influence of social media communication on event branding and engagement. It also recommends strategies for event improvement, emphasizes controlled communication for brand equity and highlights marketing's role in brand awareness, association and participant satisfaction. Additionally, it suggests government regulation to address cyber violence during esports events.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.