Unlabeled Multimodal Datasets for Robust Emotion Recognition

Despite the vast literature on emotion recognition, intra- and inter-subject variability and emotional cultural differences are still outstanding challenges that limit the state-of-the-art model’s generalization ability and robustness to out-of-training distribution data. We argue that potential solution to these problems could be based on the use of unlabeled large-scale datasets available online, in particular those providing multi-modal streams, whose availability is increasing. The aim of this work is to explore the use of multi-modal large datasets, with both EEG and Eye-tracking data streams, to increase the robustness of an emotion recognition downstream task. Three data sets on different scales, with data from different numbers of subjects (117, 47, and 16 subjects) for different pretext tasks (gaze estimation, attention type recognition, and emotion recognition), were used for self-supervised pretraining of a deep learning model and compared with the performance obtained under fully supervised training with a small emotion recognition dataset, SEED-IV (15 subjects). The use of unlabeled multimodal datasets has shown promising results to improve emotion recognition robustness using Eye-related data, although further research is needed to fully benefit from the unprecedented amount of data available in the near future

Convective cold pools and attendant turbulence at the Amazon Tall Tower Observatory (ATTO)

Deep moist convection significantly disturbs the evolution of the planetary boundary layer (PBL) through its convective cold pools (CCPs). This study investigates the statistics of CCPs and their impact on PBL turbulence as observed at the Amazon Tall Tower Observatory (ATTO) site. To obtain a large dataset of CCPs, we adapted an existing CCP-detection algorithm for use in multilevel, micrometeorological data. Application of the algorithm to ATTO observations collected from August 2021 through December 2023 resulted in a dataset consisting of 410 CCPs. CCPs occur nearly every other day at the ATTO site, although they are most common from April through September and are least common from December through March. The occurrence of CCPs is linked to the diurnal cycle of the PBL, with more events around 1800 UTC and the less events around 1100 UTC. The strength of CCP perturbations varies strongly as a function of the distance from the canopy. Above the canopy (approximately 37 m), CCPs exhibit well-defined gust fronts, marked by sharp drops in temperature and humidity, gusting winds, overturning circulations, and pressure rises. During gust front passages, sensible (latent) heat fluxes are briefly enhanced and subsequently invert sign (weaken) in the CCPs, as previously unstable (moister) PBLs become stable (drier). Turbulence is stronger and more anisotropic during gust fronts and just above the canopy due to mechanical drag. Anisotropic turbulence may be transported upward to 200 m by the gust fronts. Below the canopy, CCPs and attendant perturbations are drastically damped, except in high-end cases

Biomarker genomico per la diagnosi della rob1;29 nel bovino

Biomarker genomico per la diagnosi della rob1;29 nel bovin

Unsupervised noisy image segmentation using deep image prior

The so called Deep Image Prior paradigm stands as an exceptional advancement at the intersection of inverse problems and deep learning. By leveraging the inherent regularization properties of deep networks, Deep Image Prior has recently emerged as a landmark approach in addressing various imaging problems, including denoising, JPEG artifacts removal, inpainting, and super-resolution. The aim of this paper is to extend the Deep Image Prior idea to the segmentation of noisy images in order to benefit of both traditional variational models and new deep learning techniques. Indeed the resulting method consists of an unsupervised deep learning approach based on the minimization of very well known variational energies (such as the Mumford–Shah functional and its approximation proposed by Ambrosio and Tortorelli) properly parametrized in terms of the weights of convolutional neural networks. The implicit regularization provided by the network allows to make the traditional variational models more robust with respect to both the noise corrupting the data and the selection of the parameters which balance the role of the regularization terms. Several numerical experiments on noisy segmentation problems show promising results of the suggested approach

Sea-level and paleoenvironmental changes revealed by benthic foraminifera across Oceanic Anoxic Event 2 (OAE 2) at Eastbourne (SE England)

The Cenomanian–Turonian Oceanic Anoxic Event 2 (OAE 2) severely disrupted the global carbon cycle with widespread deposition of organic-rich marine sediments, resulting in a positive carbon isotope excursion. The Eastbourne section in southeastern England offers extensive benthic and planktonic foraminiferal data, revealing four distinct paleoenvironmental intervals across OAE 2. The Grey Chalk interval below OAE 2, which is characterized by the highest species diversity of benthic foraminifera, represents outer neritic-upper bathyal paleodepths, oxygenated environments and low organic fluxes at the seafloor. Deep- and thermocline-dwelling planktonic foraminifera suggest meso-oligotrophic regimes with a well-stratified water column. The onset of OAE 2 in Bed 1a of the Plenus Marl is marked by a sea-level fall supported by the maximum peak in abundance of shallow water agglutinated foraminifera (Ataxophragmium depressum, Arenobulimina, Plectina cenomana) and by the disappearance of bathyal taxa (e.g., Tristix excavata, Kalamopsis grzybowsky). In Bed 1b of the Plenus Marl, corresponding to the onset of the Plenus Cold Event, Eggerellina, Gaudryina, and Textularia replace shallow agglutinated taxa indicating a transgressive phase. This assemblage also coincides with the occurrence of Boreal planktonic foraminifera that suggests the incursion of Boreal waters into the Anglo-Paris Basin. The White Chalk, in the upper part and the interval above OAE 2, is interpreted as a Transgressive and Highstand Systems Tract with a change in the benthic foraminiferal assemblage towards the dominance of Marssonella, Gavelinella, Lingulogavelinella and Tritaxia with warmer and more mesotrophic waters recorded by the dominance of Tethyan planktonic foraminifera

Automated machine learning for bio-oil yield prediction from lignocellulosic biomass pyrolysis

Lignocellulosic biomass pyrolysis for bio-oil production stands as a promising route for renewable energy, yet predicting bio-oil yield remains challenging due to the complex interplay of biomass properties and process conditions. Conventional Machine Learning (ML) approaches, while effective, require labor-intensive manual algorithm selection and hyperparameter tuning, hindering their scalability and reproducibility. To address this point, we present a systematic comparison of four state-of-the-art Automated Machine Learning (AutoML) frameworks—AutoGluon, Auto-Sklearn, FLAML, and TPOT—for automating bio-oil yield prediction. Relying on a dataset of 329 experimental samples from 34 biomass types and seven input features (cellulose, hemicellulose, lignin content, nitrogen flow, heating rate, temperature, and particle size), we demonstrate that FLAML coupled with XGBoost achieves superior predictive performance (R^2 = 0.890, RMSE = 3.158), thus significantly outperforming both traditional ML models and other AutoML tools. Statistical validation via ANOVA and Tukey’s post-hoc test confirms the robustness of these findings. Our study highlights AutoML’s ability to generate accurate and efficient models for complex pyrolysis systems, substantially reducing reliance on expert knowledge and manual configuration. The developed work establishes AutoML as a scalable solution for optimizing bio-oil production, facilitating more sustainable and data-driven biomass conversion strategies

Regional Inequities in the Distribution of the Nursing Workforce in Italy

Background/Objectives: Inequalities in access to nursing professionals represent a significant challenge to achieving equity in healthcare systems. In decentralized countries like Italy, disparities in the distribution of nurses persist despite a universal national health system. This study investigates the extent and determinants of regional inequality in the distribution of the nursing workforce in Italy. Methods: A retrospective ecological analysis was conducted using administrative data from official national sources (ISTAT, Ministry of Health) concerning the number of nurses and population per region, along with Human Development Index (HDI) data from 2021. Descriptive statistics, the Gini coefficient, Lorenz curve, and Pearson correlation were used to assess inequality and identify influencing factors. Results: The national Gini coefficient was 0.136, indicating a moderate degree of inequality in the distribution of nurses across Italian regions. A strong positive correlation was observed between HDI and nurse-to-population ratio (r = 0.76, p < 0.001), suggesting that more developed regions have higher nursing density. Conclusions: Despite a universal healthcare system, Italy shows persistent regional disparities in nurse distribution. These findings emphasize the need for targeted policies and coordinated planning to reduce inequalities and ensure equitable access to nursing care across all regions

Larch wood waste: a functional fiber for late gestating and lactating sows

In the first part of the study, the bioactive compounds and antioxidant potential of larch fiber waste from five Italian woodworking companies (n=3 each) were evaluated to verify possible differences in their composition. Setting up a cascade extraction process, the consecutive hexane, methanol and water extracts were analysed by UPLC-PDA, to quantify the presence of target flavonoids (taxifolin, TXF; dihydrokaempferol, DHK), and terpenoids (larixol, LX; larixyl acetate, LXA). The antioxidant activity (expressed as EC50) was determined by the ABTS assay. In the hexane extract, LX and LXA were quantified up to 15.7 % and 14.50 % w/w dry extract respectively, while in the methanol extract, TXF and DHK were present up to 23.20 % and 24.00 % w/w dry extract, respectively. The methanol and water extract had particularly rewarding EC50 of 0.002 and 0.012 mg/mL, respectively. In the second part of the study, a preliminary evaluation of larch fiber supplementation in sows during late gestation and lactation was considered

A 10-Year Follow-Up of an Approach to Restore a Case of Extreme Erosive Tooth Wear

Background: In recent years, thanks to the improvement of adhesive techniques, patients affected by tooth wear, related to erosion and/or parafunctional habits, can undergo restoration by adding only what has been lost of their dentition (additive approach). However, since not all clinicians are convinced that dental rehabilitation should be proposed in the early stages of exposed dentin, several treatments are often postponed. It is important to emphasize that, in the early stages, the clinical approach should remain conservative, focusing on dietary counseling, the modification of harmful habits, fluoride application, and risk factor management. Only when these preventive and non-invasive strategies prove insufficient, and the condition continues to progress, should invasive restorative treatments be considered. Unfortunately, epidemiological studies are reporting an increase in the number of young patients affected by erosive tooth wear, and not intercepting these cases earlier could lead to a severe degradation of the affected dentition. In addition, parafunctional habits are also becoming more frequent among patients. The combination of erosion and attrition can be very destructive, and may progress rapidly once dentin is exposed and the risk factors remain unaddressed. The aim of this report was to present a conservative full-mouth rehabilitation approach for severe erosive lesions and to provide a 10-year follow-up assessing the biological, functional, and esthetic outcomes. Methods: In this article, the postponed restorative treatment of a patient, suffering from severe tooth wear, is illustrated. The patient had sought dental treatment in the past; however, due to the already very compromised dentition, a conventional but very aggressive treatment was proposed and refused. Four years later, when the patient finally accepted an alternative conservative therapy, the tooth degradation was very severe, especially at the level of the maxillary anterior teeth. The combination of three different approaches, Speed-Up Therapy, BOPT (Biologically-Oriented Preparation Technique), and the 3 Step Technique, however, improved the capacity to successfully complete the difficult therapeutic task. Results: The biological goals (maintenance of the pulp vitality of all of the teeth and the minimal removal of healthy tooth structure) were accomplished, relying only on adhesive techniques. Conclusions: The overall treatment was very comfortable for the patient and less complicated for the clinician. At 10-year follow-up, biological, functional, and esthetic success was still confirmed