Diabetes mellitus is a chronic metabolic disorder that necessitates frequent blood glucose monitoring, usually through painful and inconvenient methods. Volatile organic compounds (VOCs) in breath have been used as biomarkers for diabetes detection in non-invasive, Internet of Things (IoT)-based devices. Nevertheless, the cost, compactness, and mobility challenges of existing devices limit their general adoption. We present DiaBreath, a novel, affordable, non-invasive multi-sensor device for the early prediction of diabetes, solving these challenges. DiaBreath consists of (a) a breath analyzer containing MOS-based sensors, optimally selected via an ablation study to capture VOC responses (b) a feature engineering pipeline to to extract feature set, (c) a machine-learning model for reliable diabetes prediction, and (d) a simple user interface that generates prediagnostic diabetes reports. DiaBreath exhibits superior predictive power, with an accuracy of 97.6%, to enable efficient and scalable early diagnosis in public health centers, especially in resource-constrained settings. DiaBreath’s low cost and compact size make it highly adaptable for implementation in rural and underserved regions, where access to timely diabetes screening is limited. This technology improves non-invasive diabetes monitoring, making early diagnosis more cost-effective and accessible globally.

Landslide early warning systems (EWS) are critical to disaster preparedness but are frequently limited by uncertainty of prediction and variability in user trust. This research presents a new dual-modality method combining virtual reality (VR) simulation and electroencephalography (EEG) to evaluate behavioral and neurophysiological reactions to probabilistic landslide warnings. Eighty drivers experienced a VR driving situation with different warning accuracy (70% vs. 95%) and lighting conditions (day vs. night), collecting behavioral measures (e.g., collisions, speed, trajectory deviance) and EEG-based cognitive measures (e.g., alpha/theta, alpha/gamma, beta/gamma ratios) as dependent measures. Results revealed that decreased warning accuracy caused elevated collision rates, route deviances, and beta/gamma EEG activity, representing higher cognitive stress. Higher ratios of alpha/theta and alpha/gamma were related to performance in driving and were more prominent under higher accuracy and daylight. These results stress the promise that neuroadaptive VR systems hold to improve disaster training by dynamically calibrating feedback according to the cognitive states of users, therefore providing useful insights into the intelligent, human-oriented EWS technology design within the fields of system, man, and cybernetics.

Effective uncertain decision-making involves some balance between safe, familiar options and exploration-permitting deviation. This study examines the interaction between transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC) and cognitive load to modulate this exploration–exploitation balance. Anodal tDCS has been shown to enhance risk-taking and cognitive flexibility, but little is known about the impact of tDCS in combination with cognitive load on modulating curiosity. Three groups were administered (N = 30): (1) tDCS + trivia-induced cognitive load, (2) trivia alone (no tDCS), and (3) a control group with no trivia or stimulation. Participants undertook a 50-trial decision-making task with three options each trial: a certain reward (safe), a probabilistic larger reward (risky), and an information-probing option (exploratory). Curiosity was quantified as the percent of exploratory (Option R) responses. The results revealed significantly elevated risk-taking (M = 0.576), exploratory behavior (M = 0.418), and cognitive flexibility (alternation rate: M = 0.170) in the tDCS + Trivia group compared with the No tDCS + Trivia group (M = 0.290, 0.250, 0.098, respectively). The control group produced intermediate measures (M = 0.390, 0.336, 0.139). The results show that both reduced cognitive load and neuromodulation enhance curiosity, adaptive flexible switching of strategies, and adaptive decision-making. The study provides new evidence that tDCS can counter the inhibitory effects of cognitive load on exploration. The results have practical implications for maximizing competency in cognitively taxing situations such as education, healthcare, and operational training environments.

ABSTRACT Human prakriti—a core Ayurvedic construct—encompasses constitution‐based guidance in the Vatt, Pitt, and Kaph types. While clinically helpful, prakriti assessment is highly reliant on subjectivity, limiting reproducibility and cross‐cultural generality. Here, well‐validated psychometric assessments are integrated with interpretable machine learning to yield objective, scalable classification. Emotional quotient (EQ), risk‐taking, personality, and Raven's progressive matrices (RPM IQ) tests were given to Indian ( N = 202; 76% male) and U.S. ( N = 204; 53% male) samples. Seven models were tested, including logistic regression, decision tree, random forest, SVM, XGBoost, CatBoost, and multi‐layer perceptron, and hand‐tuned stacked ensemble (MR‐CAT). Cross‐cultural validation—training Indian data and validation on U.S. data—produced MR‐CAT to achieve accuracies of 0.97 (India) and 0.99 (U.S.) with high precision, recall, and F1. SHapley additive explanations (SHAP) revealed differential psychometric patterns: for example, Vatt with high resilience and self‐motivation and low collaborative leadership, Pitt with high calm and ethical courage, and Kaph with high emotional stability but low leadership initiative. These patterns conform to, yet go beyond, traditional dosha portraits. The findings demonstrate a reproducible, cross‐culturally stable interface between Ayurvedic theory and computational intelligence, opening the door to scalable personalized preventive care and subsequent mind–body research.

Emotional stability, awareness, and attention may likely be enhanced by meditation and related techniques. Since meditation practitioners may need focus and engagement, virtual reality (VR) may be helpful. Even though there has been some research on the usefulness of VR for meditation, very few studies have looked at the effectiveness of VR on audible mantra repetition (AuMR). Our research addresses this limitation by investigating the efficacy of AuMR, which is assigned to promote better cognitive health and overall brain well-being in VR. Fortyone individuals were randomly divided into two groups, test and control. The test group was engaged in a ten-minute VR-based AuMR session, while the control group did nothing in the same virtual reality setting for ten minutes. Both groups completed self-reported questionnaires before and after the intervention and electroencephalography (EEG) and heart rate variability (HRV) measurements. We evaluated EEG band power ratios such as alpha-to-beta (AB) ratio and frontal-alpha-to-temporal-theta (FATT) ratio to find the effects of VR-aided meditation. The findings of the ANOVA test demonstrated a substantial decrease in the self-reported stress, anxiety, and depression parameters. Furthermore, comparing the test group to the control group revealed a significant increase in the FATT ratio and a significant decrease in the AB ratio. We also observed significant changes in the HRV values of the test group. The study offers sufficient evidence to suggest the feasibility of AuMR in VR for cognitive wellness.

There is growing interest in developing virtual reality (VR) applications for mental health therapies. However, the investigation of the effectiveness of meditation in VR environments for mental health issues like stress remains mostly unexplored. This study seeks to fill this knowledge gap by investigating the influence of VR-guided Zen meditation (VRZM) on stress levels. 40 individuals were randomly divided into two between-subjects groups: one engaged in VRZM (N = 20), while the other received just a VR immersive environment without the Zen meditation's audio (VR; N = 20). The study explored the impact of VRZM on stress via EEG patterns and the Depression Anxiety Stress Scale - 21 (DASS - 21). The results indicated significantly reduced depression, anxiety, and stress levels in the VRZM group but not in the VR group. Moreover, VRZM induced a pronounced increase in the frontal alpha-to-temporal theta ratio, indicating enhanced relaxation, contrasting with no significant change in the VR group. The results suggested the effectiveness of VRZM meditation in promoting calmness and its potential efficacy in mental health interventions. We highlight the implications of VRZM for alleviating mental health problems like stress.

Social robots enhance human-robot interaction, potentially improving English comprehension skills. Despite their promise, their effectiveness in this area is less known. This study addresses this gap by comparing the effectiveness of the social robot Ohbot with traditional text reading for training English comprehension. Participants were randomly assigned to three groups: Ohbot interaction (N = 20), text reading (N = 20), and a control group (N = 20) with no specific intervention. Both Ohbot and reading groups answered multiple-choice questions based on a poem. Emotional arousal was measured using a thermal camera. Results showed that the Ohbot group experienced an average facial temperature decrease of 0.78°C, indicating reduced stress or increased relaxation, while the reading group had a temperature increase of 1.13°C, suggesting higher cognitive or emotional effort. Despite these physiological differences, quiz performance was similar between the Ohbot and reading groups. Therefore, the Ohbot group, which relied on auditory processing, proved as effective as the reading group, which depended on visual processing, in learning English comprehension. These findings indicate that social robots could effectively complement traditional English education methods like reading.

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS.

In search-and-shoot tasks, the organization of the scene area visible to the decision-maker (field-of-view; FoV) and the ratio of enemy targets to distractors present in the scene (target-distractor base-rate) are likely to influence human decision-making. However, currently, little is known about how these factors influence an individual's decision-making and cognition. In this paper, using a two-dimensional flat screen display (indirect visual display; IVD) and a complex search and-shoot simulation, we investigated the influence of two field-of-views (FoVs; 180° × 2 FoV and 90° × 4 FoV) and two target-distractor base-rates (80%-20% and 20%-80%). A total of 25 participants executed all the four FoV and base-rate scenarios in a random order. In 80%-20% base-rate, performance was better in the 180° × 2 FoV compared to the 90° × 4 FoV. However, in the 20%-80% base rate, performance was better in the 90° × 4 FoV compared to the 180° × 2 FoV. Overall, irrespective of the FoV, the performance was superior in the 80%-20% base-rate compared to the 20%-80% base rate. Furthermore, the FoVs and base-rates influenced self-reported mental demand, frustration level, and effort. We highlight the implications of our results towards training personnel in the IVD technology.

According to the Institute of Medicine of the National Academies, more than 100 million Americans suffer from chronic pain related to diabetes, heart disease, and cancer combined. Adoption of pain medications and safe healthcare practices is a major global policy concern. This adoption process is highly influenced by the interpersonal network of physicians prescribing medications to treat pain. However, existing research into physician networks have been hospital-specific, applied to a smaller number of physicians, and dependent upon physicians' self-reports. In this paper, using big-data and data-mining, we overcome these limitations: By using a case of 30+ hospitals spanning across 2000+ physicians, we create a social network containing physicians' prescription data and adoption behavior of pain medications. The social network assumes that connected physicians work in the same hospital and belong to the same specialty or specialty group. Then, using the centrality measures, degree and eigenvector centrality, we analyze prescription volumes and proportion of adopters of pain medications. We also analyze gender effects. Results revealed that the most influential physicians were not the high-volume prescribers. Male physicians were more influential compared to female physicians; however, females prescribed more volume compared to males. Our results help us identify critical physicians from certain core specialties and specialty groups who may be approached by patients seeking pain relief.

Eye-gaze tracking is traditionally used to analyze ocular parameters for investigating visual psychology, marketing study, behavior analysis, and so on. Currently, eye-gaze trackers are also being used to control electronic interfaces in assistive technology, automobile control, and even consumer electronic products like smartphones and tablets. However, there are not many attempts to combine these two streams of research on active and passive uses of eye-gaze trackers. This article compares a few ocular parameters to estimate users’ cognitive load in eye-gaze-controlled interfaces. It was found that average velocity of a particular type of microsaccadic eye movement called Saccadic Intrusion is most indicative of users’ cognitive load compared to pupil dilation and eye-blink-based parameters. Results from the study can be used to develop new metrics of cognitive load measurement, as well as to design intelligent gaze-controlled interfaces that respond to users’ cognitive load.