Search results

Systematic efforts to study students' use of informal learning spaces are crucial for determining how, when and why students use such spaces. This case study provides an example of an effort to evaluate an informal learning space on the basis of students' usage of the space and the features within the space.

Use of heatmap camera technology and a semi-structured interview with a supervisor of an informal learning space supported the mixed-methods evaluation of the space.

Findings from both the heatmap outputs and semi-structured interview suggested that students' use of the informal learning space is limited due to the location of the space on campus and circumstances surrounding students' day-to-day schedules and needs.

Findings from both the heatmap outputs and semi-structured interview suggested that students' use of the informal learning space is limited due to the location of the space on campus and circumstances surrounding students' day-to-day schedules and needs. These findings are actively contributing to the authors’ institution’s efforts surrounding planning, funding and design of other informal learning spaces on campus.

While most research on instructors' and students' use of space has taken place in formal classrooms, some higher education scholars have explored ways in which college and university students use informal spaces around their campuses (e.g. Harrop and Turpin, 2013; Ramu et al., 2022). Given the extensive time students spend on their campuses outside of formal class meetings (Deepwell and Malik, 2008), higher education institutions must take measures to better understand how their students use informal learning spaces to allocate resources toward the optimization of such spaces. This mixed-methods case study advances the emerging global discussion on how, when and why students use informal learning spaces.

Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature.

This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously.

The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class.

This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.

Currently, in the education data use literature, there is a lack of research and examples that consider the early steps of filtering, organizing and visualizing data to inform decision-making. The purpose of this study is to describe how school leaders and researchers visualized and jointly made sense of data from a common learning management system (LMS) used by students across multiple schools and grades in a charter management organization operating in the USA. To make sense of LMS data, researchers and practitioners formed a partnership to organize complex data sets, create data visualizations and engage in joint sensemaking around data visualizations to begin to launch continuous improvement cycles.

The authors analyzed LMS data for n = 476 students in Algebra I using hierarchical cluster analysis heatmaps. The authors also engaged in a qualitative case study that examined the ways in which school leaders made sense of the data visualization to inform improvement efforts.

The outcome of this study is a framework for informing evidence-based improvement cycles using large, complex data sets. Central to moving through the various steps in the proposed framework are collaborations between researchers and practitioners who each bring expertise that is necessary for organizing, filtering and visualizing data from digital learning environments and administrative data systems.

The authors propose an integrated cycle of data use in schools that builds on collaborations between researchers and school leaders to inform evidence-based improvement cycles.

The aircraft’s tyres are forced to spin up at touchdown. A considerable amount of frictional energy will be converted into heat, raising the tread temperature and leading to thermal wear. This study aims to develop a model to analyse the tread heat and discuss the effectiveness of two wear reduction methods.

The tread temperature is calculated using Laplace’s Equation. The efficiency of pre-rotation and soft landing in reducing tyre heat is studied using a developed three-dimensional heatmap method.

The result indicates that pre-rotation can significantly lower landing gear’s heat generation at touchdown. The soft landing, instead, has an insignificant or counterproductive effect.

The pre-rotation can significantly increase the tyre’s lifespan and cut the replacement cost. The emission of tyre particles into the environment can be reduced to protect the planet and human health.

Few studies have used a theoretical model to estimate the tread temperature. The existing studies have only dealt with the maximum tread temperature or the tread centreline temperature, which is insufficient to discuss the heat across the entire tread. However, the heatmap method in this paper can do the job.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

The purpose of this paper is to investigate the effectiveness of biomotion visibility aids for nighttime bicyclists compared to other configurations via 3D eye-tracking technology in a blind between-subjects experiment.

A total of 40 participants were randomly assigned one of four visibility aid conditions in the form of videos: biomotion (retroreflective knee and ankle bands), non-biomotion (retroreflective vest configuration), pseudo-biomotion (vertical retroreflective stripes on the back of the legs), and control (all-black clothing). Gaze fixations on a screen were measured with a 3D eye-tracking system; coordinate data for each condition were analyzed via one-way ANOVA and Tukey’s post-hoc analyses with supplementary heatmaps. Post-experimental questionnaires addressed participants’ qualitative assessments.

Significant differences in eye gaze location were found between the four reflective clothing design conditions in X-coordinate values (p<0.01) and Y-coordinate values (p<0.05).

This research has the potential to further inform clothing designers and manufacturers on how to incorporate biomotion to increase bicyclist visibility and safety.

This research has the potential to benefit both drivers and nighttime bicyclists through a better understanding of how biomotion can increase visibility and safety.

There is lack of literature addressing the issue of the commonly administered experimental task of recognizing bicyclists and its potential bias on participants’ attention and natural driving state. Eye-tracking has the potential to implicitly determine attention and visibility, devoid of biases to attention. A new retroreflective visibility aid design, pseudo-biomotion, was also introduced in this experiment.

This paper aims to present a natural human–robot teleoperation system, which capitalizes on the latest advancements of monocular human pose estimation to simplify scenario requirements on heterogeneous robot arm teleoperation.

Several optimizations in the joint extraction process are carried on to better balance the performance of the pose estimation network. To bridge the gap between human joint pose in Cartesian space and heterogeneous robot joint angle pose in Radian space, a routinized mapping procedure is proposed.

The effectiveness of the developed methods on joint extraction is verified via qualitative and quantitative experiments. The teleoperation experiments on different robots validate the feasibility of the system controlling.

The proposed system provides an intuitive and efficient human–robot teleoperation method with low-cost devices. It also enhances the controllability and flexibility of robot arms by releasing human operator from motion constraints, paving a new way for effective robot teleoperation.

This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates).

This study applies the GARCH-DECO model and cross-quantilogram framework.

The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term.

For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors.

To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.

The purpose of studying digitization transformation of the supply chain is to understand how digital technologies and processes are changing the way supply chains operate and to identify the opportunities and challenges associated with this transformation. Studying digitization transformation of the supply chain is important because it can help global businesses in identifying the best practices in supply chain management (SCM) systems and enhance supply chain performance. Hence, this research study is contributing in revealing the outcomes of digital inclusiveness in overall SCM for the growth of retail and e-commerce based platforms.

This research is using both descriptive and explanatory research designs to provide a comprehensive understanding of the problems in SCM. Descriptive research provides a detailed description of the characteristics of the population under study, while explanatory research identifies the causal relationships between the variables. Descriptive research has helped us to develop hypotheses about the relationships between variables that can be tested using explanatory research. Explanatory research has been used to validate the findings of descriptive research. By using both descriptive and explanatory research designs, our research design has increased the generalizability of our findings.

According to this study, businesses intend to change their supply chain strategies after the wake of competitive era to make them more robust, sustainable and collaborative with suppliers, customers and stakeholders by investing more in SCM technology like Blockchain, AI, analytics, robotic process automation and data control centers. This study evaluates the impact of digitization on supply chain systems. This includes assessing the benefits of digitization and identifying the factors that contribute to successful implementation. This research is studying the role of data analytics in SCM and how it can be leveraged to improve efficiency, reduce costs and increase transparency.

The study highlights the importance of adopting digitization in supply chain systems to improve supply chain robustness, sustainability and collaboration with stakeholders. This study's emphasis on data analytics in SCM presents an opportunity for businesses to gain insights into their supply chain systems and make data-driven decisions. This can enhance efficiency, reduce costs and improve overall supply chain performance. The study's focus on SCM technology and data analytics may overlook other factors that contribute to successful SCM, such as organizational culture, human resources and supply chain governance.

This study will complement to the existing body of information, management theory and practice and will benefit all. The research work is original and can be implemented worldwide to promote digitization in SCM for smooth transactions in the entire chain of wholesalers, retail distributors and customers.