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Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration.

A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively.

The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other.

First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

The rapid growth of the mobile money industry has been matched by a rise in mobile money fraud. The technology required to apprehend perpetrators of such fraud is nonexistent in most developing countries. Hence, the need for individuals to be willing to pay for insurance against such frauds is crucial. This paper aims to examine individuals’ willingness to pay for insurance against mobile money fraud in Ghana.

The paper uses nationally representative data collected from 4,266 adults (persons 18 years and above) in Ghana. Individuals’ willingness to pay premiums for protection against mobile money fraud was elicited by a single-bound dichotomous choice and open-ended contingent valuation designs.

On average, 24.34% of Ghanaians are willing to pay premiums for insurance against mobile money frauds, with more men (26.37%) being willing than women (22.56%). Similarly, the average monthly premium that men are willing to pay for protection against mobile money fraud is GH¢32.16 (US$8.16), while that of women is GH¢22.5 (US$5.62). Furthermore, the results show that years of schooling, income, previous fraud experience, and using the accounts for saving are all positively associated with willingness to pay. However, using other networks apart from MTN has a negative association with willingness to pay.

To the best of our knowledge, this is the first study that examines willingness to pay for insurance against mobile money fraud. Thus, this is the first that estimate quantitatively how much mobile account holders will pay as premiums for insurance against mobile money fraud.

This paper aims to understand how cultural and creative industries (CCIs) contribute to regional innovation.

This paper explores the process of CCIs contributing to regional innovation and assesses the accumulated outcome of this process.

The authors conclude that CCIs contribute to a city’s innovation involving five dimensions (time, space, tangible, intangible and division) and four phases (people, tool, collaboration and brokerage) and the contributions are accumulated into positive innovation outcome; however, a highly developed economy is relatively unsupportive of CCIs contributing to regional innovation.

The main contributions are that the authors configured the detailed process of CCIs contributing to regional innovation and the authors quantitatively measured the impact of CCIs on regional innovation, using the Porter diamond model and Shannon entropy to construct the CCI index.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

The main purpose of this study is to test for the interaction effect of digital literacy in the relationship between financial technologies (FinTechs) of biometrics and mobile money and digital financial inclusion among the unbanked poor women, youth and persons with disabilities (PWDs) in rural Uganda.

Covariance-based structural equation modeling was used to construct the interaction effect using data collected from the unbanked poor women, youth and PWDs located in the four regions in Uganda as prescribed by Hair et al. (2022).

The findings from this study are threefold: first; the results revealed a positive interaction effect of digital literacy between FinTechs of biometrics and mobile money and digital financial inclusion. Second; the results also confirmed that biometrics identification positively promotes digital financial inclusion. Lastly; the results showed that mobile money positively promotes digital financial inclusion. A combination of FinTechs of biometrics and mobile money together with digital literacy explain 29% variation in digital financial inclusion among the unbanked poor women, youth and PWDs in rural Uganda.

The data for this study were collected mainly from the unbanked poor women, youth and PWDs. Further studies may look at data from other sections of the vulnerable population in under developed financial markets. Additionally, the data for this study were collected only from Uganda as a developing country. Thus, more data may be obtained from other developing countries to draw conclusive and generalized empirical evidence. Besides, the current study used cross sectional design to collect the data. Therefore, future studies may adopt longitudinal research design to investigate the impact of FinTechs on digital financial inclusion in the presence of digital literacy across different time range.

The governments in developing countries like Uganda should support women, youth, PWDs and other equally vulnerable groups, especially in the rural communities to understand and use FinTechs. This can be achieved through digital literacy that can help them to embrace digital financial services and competently navigate and perform digital transactions over digital platforms like mobile money without making errors. Besides, governments in developing countries like Uganda can use this finding to advocate for the design of appropriate digital infrastructures to reach remote areas and ensure “last mile connectivity for digital financial services' users.” The use of off-line solutions can complement the absence or loss of on-line network connectivity for biometrics and mobile money to close the huge digital divide gap in rural areas. This can scale-up access to and use of financial services by the unbanked rural population.

This paper sheds more light on the importance of digital literacy in the ever complex and dynamic global FinTech ecosystem in the presence of rampant cyber risks. To the best of the authors' knowledge, limited studies currently exist that integrate digital literacy as a moderator in the relationship between FinTechs and digital financial inclusion, especially among vulnerable groups in under-developed digital financial markets in developing countries. This is the novelty of the paper with data obtained from the unbanked poor women, youth and PWDs in rural Uganda.

This paper aims to derive a reduced-order model for the heat transfer across the interface between a millimetric thermocapillary liquid bridge from silicone oil and the surrounding ambient gas.

Numerical solutions for the two-fluid model are computed covering a wide parametric space, making a total of 2,800 numerical flow simulations. Based on the computed data, a reduced single-fluid model for the liquid phase is devised, in which the heat transfer between the liquid and the gas is modeled by Newton’s heat transfer law, albeit with a space-dependent Biot function Bi(z), instead of a constant Biot number Bi.

An explicit robust fit of Bi(z) is obtained covering the whole range of parameters considered. The single-fluid model together with the Biot function derived yields very accurate results at much lesser computational cost than the corresponding two-phase fully-coupled simulation required for the two-fluid model.

Using this novel Biot function approach instead of a constant Biot number, the critical Reynolds number can be predicted much more accurately within single-phase linear stability solvers.

The Biot function for thermocapillary liquid bridges is derived from the full multiphase problem by a robust multi-stage fit procedure. The derived Biot function reproduces very well the theoretical boundary layer scalings.

This study aims to investigate how trauma team members perceive technological equipment and tools in the trauma room (TR) environment and to identify how the technological equipment could be optimized in relation to the TR’s space.

A total of 21 focus group sessions were conducted with 69 trauma team members, all of whom worked in Level I TRs from six teaching hospitals in the USA.

The collected data was analyzed and categorized into three parent themes: imaging equipment, assistive devices and room features. The results of the study suggest that trauma team members place high importance on the availability and versatility of the technological equipment in the TR environment. Although CT scans are a usual procedure necessity in TRs, few facilities were optimized for easy access to CT-scanners for the TR. The implementation of cameras and screens was suggested as an improvement to accommodate situational awareness. Rapid sharing of data, such as imaging results, was highly sought after. Unorthodox approaches, such as the use of automatic doors, were associated with slowing down the course of actions.

This study provides health-care designers with the knowledge they need to make informed decisions when designing TRs. It will cover key considerations such as room layout, equipment selection, lighting and controls. Implementing the strategies will help minimize negative patient outcomes.

Level I TRs are a critical element of emergency departments and designing them correctly can significantly impact patient outcomes. However, designing a TR can be a complex process that requires careful consideration of various factors, including patient safety, workflow efficiency, equipment placement and infection control. This study suggests multiple considerations when designing TRs.

The purpose of this paper is to present current state of understanding on jet electrodeposition manufacturing; to compare various experimental parameters and their implication on as deposited features; and to understand the characteristics of jet electrodeposition deposition defects and its preventive procedures through available research articles.

A systematic review has been done based on available research articles focused on jet electrodeposition and its characteristics. The review begins with a brief introduction to micro-electrodeposition and high-speed selective jet electrodeposition (HSSJED). The research and developments on how jet electrochemical manufacturing are clustered with conventional micro-electrodeposition and their developments. Furthermore, this study converges on comparative analysis on HSSJED and recent research trends in high-speed jet electrodeposition of metals, their alloys and composites and presents potential perspectives for the future research direction in the final section.

Edge defect, optimum nozzle height and controlled deposition remain major challenges in electrochemical manufacturing. On-situ deposition can be used as initial structural material for micro and nanoelectronic devices. Integration of ultrasonic, laser and acoustic source to jet electrochemical manufacturing are current trends that are promising enhanced homogeneity, controlled density and porosity with high precision manufacturing.

This paper discusses the key issue associated to high-speed jet electrodeposition process. Emphasis has been given to various electrochemical parameters and their effect on deposition. Pros and cons of variations in electrochemical parameters have been studied by comparing the available reports on experimental investigations. Defects and their preventive measures have also been discussed. This review presented a summary of past achievements and recent advancements in the field of jet electrochemical manufacturing.