An Arbitrary Lagrangian-Eulerian formulation has been posed to solve the challenging problem of the three-dimensional Taylor bubble, within a Conservative Level Set (CLS) framework. By employing a domain optimization method (i.e. the moving mesh method), smaller domains can be used to simulate rising bubbles, thus saving computational resources. As the method requires the use of open boundaries, a careful treatment of both inflow and outflow boundary conditions has been carried out. The coupled CLS - moving mesh method has been verified by means of extensive numerical tests. The challenging problem of the full three-dimensional Taylor bubble has then been thoroughly addressed, providing a detailed description of its features. The study also includes a sensitivity analyses with respect to the initial shape of the bubble, the initial volume of the bubble, the flow regime and the inclination of the channel.Postprint (author's final draft

Gyroscopic systems and their properties have been extensively studied as Angular Momentum Devices (AMD), Control Moment Gyros (CMG) or Gyroscopes for various applications such as structure control, stability or energy storage. However, most of the works that have been done are theoretical and do not present experimental implementation. In this work we performed an experimental study of a gyroscope beam system (gyroelastic beam) focused systems on the deflection of cantilever beams or the control of flexural stresses. We first used a simple two-degree of freedom model to better understand the terms governing the design, construction and experimental evaluation of gyroelastic beam systems. We then performed experimental tests at different velocities of the gyroscopic actuator and measured the deflection of the system. The results showed that it is possible to have control of the deflection and the bending forces for this type of configurations which can be exported to helicopter blades or wind turbine blades.

The challenging problem of the full three-dimensional Taylor bubble has been addressed by using a Conservative Level Set method in order to deal with the multiphase flow. A moving mesh is used, aiming to optimize the simulation domain. The mesh is moved as the bubble rises, so the region of study can be limited to the surroundings of the bubble, notably reducing the domain's size. This saving in the computational resources facilitates to face the Taylor bubble problem without the axisymmetric assumption. By doing so, a detailed description of the fluid field is provided, comparing the results against numerical and experimental data.

Background/Objectives: In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for identifying those at risk and predicting psychosis onset. Artificial intelligence tools, particularly natural language processing (NLP), have emerged as effective resources for detecting these language-based indicators. This study aims to synthesize the existing scientific evidence on linguistic markers analyzed through NLP techniques in individuals with ARMS. Methods: A systematic review following the PRISMA 2020 protocol was conducted. Three databases (PubMed, PsycInfo, and Scopus) were searched for published articles from their inception to October 2025. Rayyan software was used to manage references and article downloads. Out of ninety initial search results, fifteen studies involving 1313 participants from diverse groups were included in the review. Results: The findings indicated that alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty differentiated ARMS individuals from healthy controls. Several of these markers, analyzed with NLP methods, predicted the onset of psychosis with accuracy levels ranging from 79% to 100%, although these findings should be interpreted with caution due to the significant methodological heterogeneity and variability in sample sizes across the included studies. Conclusions: NLP techniques offer a powerful approach for detecting language alterations that distinguish ARMS individuals and provide meaningful predictions of psychosis onset, highlighting their potential as a complement to traditional clinical assessments for early identification and prevention.

BACKGROUND In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for identifying those at risk and predicting psychosis onset. Artificial intelligence tools, particularly Natural Language Processing (NLP), have emerged as effective resources for detecting these language-based indicators. OBJECTIVE This study aims to synthesize the existing scientific evidence on linguistic markers analyzed through NLP techniques in individuals with ARMS. METHODS A systematic review following the PRISMA 2020 protocol [1]was done. Three databases (PubMed, PsycInfo, and Scopus) were searched for published articles from their inception to September 2025. Rayyan software was used to manage references and article downloads. RESULTS Thirteen studies met the inclusion criteria. The findings indicated that alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty differentiated ARMS individuals from healthy controls. Several of these markers, analyzed with NLP tools, predicted the onset of psychosis with accuracy levels exceeding 70%. CONCLUSIONS NLP techniques offer a powerful approach for detecting language alterations that distinguish ARMS individuals and provide meaningful predictions of psychosis onset, highlighting their potential as a complement to traditional clinical assessments for early identification and prevention. CLINICALTRIAL PROSPERO CRD420251029527 https://www.crd.york.ac.uk/PROSPERO/view/CRD420251029527