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Interpersonal touch plays a crucial role in human communication, development, and wellness. Mediated interpersonal touch (MIT), a technology to distance or virtually simulated interpersonal touch, has received significant attention to counteract the negative consequences of touch deprivation. Studies investigating the effectiveness of MIT have primarily focused on self-reporting or behavioral correlates. It is largely unknown how MIT affects neural processes such as interbrain functional connectivity during human interactions. Given how users exchange haptic information simultaneously during interpersonal touch, interbrain functional connectivity provides a more ecologically valid way of studying the neural correlates associated with MIT. In this study, a palm squeeze task is designed to examine interbrain synchrony associated with MIT using EEG-based hyperscanning methodology. The phase locking value (PLV) index is used to measure interbrain synchrony. Results demonstrate that MIT elicits a significant increase in alpha interbrain synchronization between participants' brains. Especially, there was a significant difference in the alpha PLV indices between no MIT and MIT conditions in the early stage (130-470 ms) of the interaction period (t-test, p < 0.05). Given the role that alpha interbrain synchrony plays during social interaction, a significant increase in PLV index during MIT interaction seems to indicate an effect of social coordination. The findings and limitations of this study are further discussed, and perspectives on future research are provided.

IntroductionThermal feedback technologies have been explored in human-computer interaction to provide secondary information and enhance the overall user experience. Unlike fast-response haptic modalities such as vibration and force feedback, the human brain's processes associated with thermal feedback are not fully understood.MethodsIn this study, we utilize electroencephalography (EEG) brain imaging to systematically examine the neural correlates associated with a wide range of thermal stimuli, including 9, 15, 32, and 42°C, during active touch at the fingertip. A custom experimental setup is developed to provide thermal stimulation at the desirable temperature levels. A total of 30 participants are recruited to experience the four levels of thermal stimulation by actively touching a thermal stimulation unit with the index finger while recording brain activities via EEG. Time-frequency analysis and power spectral density (PSD) of the EEG data are utilized to analyze the delta, theta, alpha, beta, and gamma frequency bands.ResultsThe results show that the delta, theta, and alpha PSDs of 9 and 15°C stimuli are significantly higher than the PSDs of 32 and 42°C in the right frontal area during the early stage of the stimulation, from 282 ms up to 1,108 ms (One-way ANOVA test, Holm-Bonferroni correction, p < 0.05). No significant differences in PSDs are found between 9 and 15°C thermal stimuli or between 32 and 42°C thermal stimuli.DiscussionThe findings of this study inform the development of thermal feedback system in human-computer interaction.

Climatic conditions intricately influence agriculture, impacting optimal crop growth and reproduction through environmental factors. This study introduces a technological solution leveraging the Internet of Things (IoT), with Raspberry Pi 3 as the central controller. The system systematically collects crucial data on temperature, humidity, and soil moisture, ensuring the quality of crops. Subsequently, this information is used to formulate systematic crop watering plans. By efficiently using Raspberry Pi, the system regulates and monitors crops, contributing to water conservation by directing it precisely to the crops. This technology offers a practical and efficient approach to crop management, especially during challenging agricultural periods.

The ubiquity of Internet of Things (IoT) devices has prompted security concerns, particularly in the face of evolving botnet attacks. This paper investigates the impact of botnet attacks on IoT devices and proposes a network-based detection and prevention system employing signature and anomaly-based mechanisms. Notably, our methodology extends beyond traditional detection, focusing on proactively impeding bot creation. Leveraging a Linux-based distributed system, Security Information and Event Management (SIEM) tools, and custom rules, our approach encompasses distinct phases Preprocessing, Network Security Monitoring, Rule-based IDS System, and Analysis. Experimental results with diverse PCAP files demonstrate the efficacy of custom rules, significantly enhancing alert counts for various security aspects, including network trojan detection and privacy violations. The significant finding is the substantial increase in alert counts after the integration of custom rules, exemplified in the 1.1 GB PCAP file scenario. Network trojan detection surged from 585 to 988, emphasizing the heightened efficacy of rule-based measures. Privacy breaches and bad traffic alerts also experienced significant increments, showcasing the system’s improved sensitivity and responsiveness. This finding reinforces the pivotal role of custom rules in fortifying IoT network security comprehensively.