Artificial intelligence (AI)-powered mental health chatbots have evolved quickly as scalable means for psychological support, bringing novel solutions through natural language processing (NLP), mobile accessibility, and generative AI. This systematic literature review (SLR), following PRISMA 2020 guidelines, collates evidence from 25 published, peer-reviewed studies between 2020 and 2025 and reviews therapeutic techniques, cultural adaptation, technical design, system assessment, and ethics. Studies were extracted from seven academic databases, screened against specific inclusion criteria, and thematically analyzed. Cognitive behavioral therapy (CBT) was the most common therapeutic model, featured in 15 systems, frequently being used jointly with journaling, mindfulness, and behavioral activation, followed by emotion-based approaches, which were featured in seven systems. Innovative techniques like GPT-based emotional processing, multimodal interaction (e.g., AR/VR), and LSTM-SVM classification models (greater than 94% accuracy) showed increased conversation flexibility but missed long-term clinical validation. Cultural adaptability was varied, and effective localization was seen in systems like XiaoE, okBot, and Luda Lee, while Western-oriented systems had restricted contextual adaptability. Accessibility and inclusivity are still major challenges, especially within low-resource settings, since digital literacy, support for multiple languages, and infrastructure deficits are still challenges. Ethical aspects—data privacy, explainability, and crisis plans—were under-evidenced for most deployments. This review is different from previous ones since it focuses on cultural adaptability, ethics, and hybrid public health incorporation and proposes a comprehensive approach for deploying AI mental health chatbots safely, effectively, and inclusively. Central to this review, symmetry is emphasized as a fundamental idea incorporated into frameworks for cultural adaptation, decision-making processes, and therapeutic structures. In particular, symmetry ensures equal cultural responsiveness, balanced user–chatbot interactions, and ethically aligned AI systems, all of which enhance the efficacy and dependability of mental health services. Recognizing these benefits, the review further underscores the necessity for more rigorous academic research into the development, deployment, and evaluation of mental health chatbots and apps, particularly to address cultural sensitivity, ethical accountability, and long-term clinical outcomes.

Evaluation of team performance in naturalistic contexts has gained popularity during the last two decades. Among other human factors, physiological synchrony has been adopted to investigate team performance and emotional state when engaged in collaborative team tasks. A variety of methods have been reported to quantify physiological synchrony with a varying degree of correlation with the collaborative team task performance and emotional state, reflected in the inconclusive nature of findings. Little is known about the effect of the choice of synchrony calculation methods and the level of analysis on these findings. In this research work, we investigate the relationship between outcomes of different methods to quantify physiological synchrony, emotional state, and team performance of three-member teams performing a collaborative team task. The proposed research work employs dyadic-level linear (cross-correlation) and team-level non-linear (multidimensional recurrence quantification analysis) synchrony calculation measures to quantify task performance and the emotional state of the team. Our investigation indicates that the physiological synchrony estimated using multidimensional recurrence quantification analysis revealed a significant negative relationship between the subjectively reported frustration levels and overall task performance. However, no relationship was found between cross-correlation-based physiological synchrony and task performance. The proposed research highlights that the method of choice for physiological synchrony calculation has direct impact on the derived relationship of team task performance and emotional states.

Assessment of team performance has become increasingly important in recent years, prompting the exploration of innovative approaches to enhance our understanding of the underlying cognitive and neural processes involved. This review examines the application of neuroimaging techniques such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and other brain imaging techniques in assessing team performance. It specifically emphasises the investigation of team aspects using neuroimaging techniques and their relationship to teamwork. By conducting a thorough analysis of the literature, the review highlights the unique capabilities, advantages, and limitations of brain imaging techniques. It also explores different research paradigms, including simulated tasks and real-world team interactions, to provide insights into the methodological landscape of studying team performance using neurophysiological measures. Moreover, the review underscores the significance of team aspects such as cooperation, workload, engagement, and decision-making, which have been investigated through neuroimaging techniques. By synthesising existing research, the review identifies associations between neurophysiological measures and specific indicators of team performance, shedding light on the underlying neural mechanisms that contribute to effective teamwork. Overall, this review highlights the value of neurophysiological measures in assessing team performance, emphasising the exploration of team aspects using neuroimaging techniques and identifying future research directions to advance our understanding of team dynamics and optimise performance in various domains.

This paper presents a single balanced mixer for ultra-wideband (UWB) applications. The proposed mixer utilized a compact branch line coupler as a balun to enhance the overall performance over a wider bandwidth. The mixer design operated with a Local Oscillator (LO) power level of 10 dBm and Intermediate Frequency (IF) of 100 MHz with the balun characteristic of 90° phase shift over UWB frequency. The well-balanced wideband performance of branch line balun matched to diodes results in the enhancement of the conversion loss and isolation. The simulated and measured results of the proposed mixer exhibit conversion loss of less than 19 dB and a port-to-port isolation of greater than 55 dB over a UWB frequency range of 3.1-10.6 GHz.

A coupled-line balun for ultra-wideband single-balanced diode mixer