The interfacing of soft and hard electronics is a key challenge for flexible hybrid electronics. Currently, a multisubstrate approach is employed, where soft and hard devices are fabricated or assembled on separate substrates, and bonded or interfaced using connectors; this hinders the flexibility of the device and is prone to interconnect issues. Here, a single substrate interfacing approach is reported, where soft devices, i.e., sensors, are directly printed on Kapton polyimide substrates that are widely used for fabricating flexible printed circuit boards (FPCBs). Utilizing a process flow compatible with the FPCB assembly process, a wearable sensor patch is fabricated composed of inkjet‐printed gold electrocardiography (ECG) electrodes and a stencil‐printed nickel oxide thermistor. The ECG electrodes provide 1 mVp–p ECG signal at 4.7 cm electrode spacing and the thermistor is highly sensitive at normal body temperatures, and demonstrates temperature coefficient, α ≈ –5.84% K–1 and material constant, β ≈ 4330 K. This sensor platform can be extended to a more sophisticated multisensor platform where sensors fabricated using solution processable functional inks can be interfaced to hard electronics for health and performance monitoring, as well as internet of things applications.

Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability. CCS Concepts: • Security and privacy → Biometrics; • Computing methodologies → Machine learning approaches; • Applied computing → Psychology;

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Social entrepreneurship is a growing area of interest among practitioners. Social entrepreneurship meets and satisfies social needs and brings social change through innovative ideas. This study aims to investigate the impact of positivity and empathy of individuals on social entrepreneurial intention. This study considers the mediating role of social entrepreneurial self‐efficacy between the relationship of positivity, empathy, and social entrepreneurial intention. In addition, this study investigates perceived social support as a boundary condition between the relationship of social entrepreneurial self‐efficacy and social entrepreneurial intention. Findings show that positivity and empathy positively influence social entrepreneurial self‐efficacy, which subsequently positively influences social entrepreneurial intention. Furthermore, results show that high perceived social support strengthens the relationship between social entrepreneurial self‐efficacy and social entrepreneurial intention. Discussions and implications based on the study findings are reported.

The severe challenges of the skyrocketing healthcare expenditure and the fast aging population highlight the needs for innovative solutions supporting more accurate, affordable, flexible, and personalized medical diagnosis and treatment. Recent advances of mobile technologies have made mobile devices a promising tool to manage patients' own health status through services like telemedicine. However, the inherent limitations of mobile devices make them less effective in computation- or data-intensive tasks such as medical monitoring. In this study, we propose a new hybrid mobile-cloud computational solution to enable more effective personalized medical monitoring. To demonstrate the efficacy and efficiency of the proposed approach, we present a case study of mobile-cloud based electrocardiograph monitoring and analysis and develop a mobile-cloud prototype. The experimental results show that the proposed approach can significantly enhance the conventional mobile-based medical monitoring in terms of diagnostic accuracy, execution efficiency, and energy efficiency, and holds the potential in addressing future large-scale data analysis in personalized healthcare.