Search

The introduction of computerized medical records in hospitals has reduced burdensome activities like manual writing and information fetching. However, the data contained in medical records are still far underutilized, primarily because extracting data from unstructured textual medical records takes time and effort. Information Extraction, a subfield of Natural Language Processing, can help clinical practitioners overcome this limitation by using automated text-mining pipelines. In this work, we created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Transformers-based model. Moreover, we collected and leveraged three external independent datasets to implement an effective multicenter model, with overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "low-resource" approach. This allowed us to establish methodological guidelines that pave the way for Natural Language Processing studies in less-resourced languages., Comment: 2 figures, 6 tables, Supplementary Notes included

In extreme values theory, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution. The GEV distribution is a family of continuous probability distributions, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. However, the GEV distribution is not suitable to model extreme bimodal data. In this paper, we propose an extension of the GEV distribution that incorporate an additional parameter. The additional parameter introduces bimodality and to vary tail weight, i.e., this proposed extension is more flexible than the GEV distribution. Inference for the proposed distribution were performed under the likelihood paradigm. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. Finally, the proposed distribution is applied to environmental data sets, illustrating their capabilities in challenging cases in extreme value theory., Comment: 21 pages, 7 figures

The Gumbel model is a very popular statistical model due to its wide applicability for instance in the course of certain survival, environmental, financial or reliability studies. In this work, we have introduced a bimodal generalization of the Gumbel distribution that can be an alternative to model bimodal data. We derive the analytical shapes of the corresponding probability density function and the hazard rate function and provide graphical illustrations. Furthermore, We have discussed the properties of this density such as mode, bimodality, moment generating function and moments. Our results were verified using the Markov chain Monte Carlo simulation method. The maximum likelihood method is used for parameters estimation. Finally, we also carry out an application to real data that demonstrates the usefulness of the proposed distribution., Comment: 23 pages, 17 figures

When a mixture of propylene glycol and water is deposited on a clean glass slide, it forms a droplet of a given apparent contact angle rather than spreading as one would expect on such a high-energy surface. The droplet is stabilized by a Marangoni flow due to the non-uniformity of the components' concentrations between the border and the center of the droplet, itself a result of evaporation. These self-contracting droplets have unusual properties such as absence of pinning and the ability to move under an external humidity gradient. The droplets' apparent contact angle is a function of their concentration and the external humidity. Here we study the motion of such droplets sliding down slopes, how they deform when moving at large speeds, and compare the results to normal non-volatile droplets. We precisely control the external humidity and explore the influence of the volume, viscosity, surface tension, and contact angle. We find that the droplets suffer a negligible pinning force so that for small velocities the capillary number ($\mathrm{Ca}$) is directly proportional to the Bond number ($\mathrm{Bo}$): $\mathrm{Ca}=\mathrm{Bo} \sin\alpha$ with $\alpha$ the angle of the slope. When the droplets move at larger velocities they deform when Ca exceeds a threshold, and deposit smaller droplets when $\mathrm{Ca}$ reaches twice this threshold.

A continuing challenge in material science is how to create active materials in which shape changes or displacements can be generated electrically or thermally. Here we borrow principles from hydraulics, in particular that confined geometries can be used to focus expansion into large displacements, to create solid materials with amplified shape changes. Specifically, we confined an elastomeric poly(dimethylsiloxane) sheet between two more rigid layers and caused focused expansion into embossed channels by local resistive heating, resulting in a 10x greater relative displacement than the unconfined geometry. We used this effect to create electrically controlled microfluidic valves that open and close in less than 100 ms, can cycle >10,000 times, and operate with as little as 20 mW of power. We investigate this mechanism and establish design rules by varying dimensions, configurations, and materials. We show the generality of elastomeric focusing by creating additional devices where local heating and expansion are generated either wirelessly through inductive coupling or optically with a laser, allowing arbitrary and dynamic positioning of a microfluidic valve along the channels., Comment: 9 pages, 4 figures, and supplemental material

Marangoni self-contracted droplets are formed by a mixture of two liquids, one of larger surface tension and larger evaporation rate than the other. Due to evaporation, the droplets contract to a stable contact angle instead of spreading on a wetting substrate. This gives them unique properties, including absence of pinning force and ability to move under vapor gradients, self- and externally imposed. We first model the dynamics of attraction in an unconfined geometry and then study the effects of confinement on the attraction range and dynamics, going from minimal confinement (vertical boundary), to medium confinement (2-D vapor diffusion) and eventually strong confinement (1-D). "Self-induced" motion is observed when single droplets are placed close to a vapor boundary toward which they are attracted, the boundary acting as an image droplet with respect to itself. When two droplets are confined between two horizontal plates, they interact at a longer distance with modified dynamics. Finally, confining the droplet in a tunnel, the range of attraction is greatly enhanced, as the droplet moves all the way up the tunnel when an external humidity gradient is imposed. "Self-induced" motion is also observed, as the droplet can move by itself towards the center of the tunnel. Confinement greatly increase the range at which droplets interact as well as their lifetime and thus greatly expands the control and design possibilities for applications offered by self-contracted droplets.