Adolescents and young adults (AYA) often face mental health challenges and are heavily influenced by technology. Digital health interventions (DHIs), leveraging smartphone data and artificial intelligence, offer immense potential for personalized and accessible mental health support. However, ethical guidelines for DHI research fail to address AYA’s unique developmental and technological needs and leave crucial ethical questions unanswered. This gap creates risks of either over- or under-protecting AYA in DHI research, slowing progress and causing harm. This Perspective examines ethical gaps in DHI research for AYA, focusing on three critical domains: challenges of passive data collection and artificial intelligence, consent practices, and risks of exacerbating inequities. We propose an agenda for ethical guidance based on bioethical principles autonomy, respect for persons, beneficence and justice, developed through participatory research with AYA, particularly marginalized groups. We discuss methodologies to achieve this agenda, ensuring ethical, youth-focused and equitable DHI research for the mental health of AYA.

This paper proposes a grey-box modelling approach to predict marine biofouling growth and its effects on ship performance. The approach combines empirical or experimental-based white-box models with data-driven black-box models. First, a white-box model is built to predict ship resistance considering a bare hull. This prediction is based on calm water resistance, wind, waves, and temperature differences. Subsequently, marine biofouling growth is predicted using an experimental model that estimates the level of roughness on the ship hull. Finally, a deep extreme learning machine is used as a black-box model, employing a feedforward neural network technique. To test the approach, a superyacht case study was selected as a category of vessel heavily exposed to fouling. The study used a 2-year dataset obtained through a collaboration with Feadship. Results showed that the black-box approach outperforms the white-box approach in predictive capabilities. However, when the knowledge encapsulated in the white-box model is included in the grey-box approach, the model shows the highest prediction accuracy achieved by leveraging less historical data. This study demonstrates the potential of the proposed grey-box approach to accurately predict marine biofouling growth and its effects on ship performance, which can benefit ship operators and designers in improving operational efficiency and reducing maintenance costs.

Vagus nerve stimulators currently on the market can treat epilepsy and depression. Recent clinical trials show the potential for vagus nerve stimulation (VNS) to treat epilepsy, autoimmune disease, and traumatic brain injury. As we explore the benefits of VNS, it is expected that more possibilities for a new treatment will emerge in the future. However, existing VNS relies on electrical stimulation, whose limited selectivity (due to its poor spatial resolution) does not allow for any control over which therapeutic effect to induce. We hypothesize that by localizing the stimulation to fascicular level within the vagus nerve with focused ultrasound (US), it is possible to induce selective therapeutic effects with less side effects. A geometrically curve US transducer array that is small enough to wrap around the vagus nerve was fabricated. An experiment was conducted in water, with 48 US elements curved in a 1 mm radius and excited at 15 MHz to test the focusing capabilities of the device. The results show that the geometrical curvature focused the US to an area with a width and height of 110 μm and 550 μm. This will be equivalent to only 2.1% of the cross section of the vagus nerve, showing the potential of focused US to stimulate individual neuronal fibers within the vagus nerve selectively.