CONTAINER LOCALISATION AND MASS ESTIMATION WITH AN RGB-D CAMERA

In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging task. The main challenges consist of occlusions, different filling materials and lighting conditions. The mass of an object constitutes key information for the robot to correctly regulate the force required to grasp the container. We propose a single RGB-D camera-based method to locate a manipulated container and estimate its empty mass i.e., independently of the presence of the content. The method first automatically selects a number of candidate containers based on the distance with the fixed frontal view, then averages the mass predictions of a lightweight model to provide the final estimation. Results on the CORSMAL Containers Manipulation dataset show that the proposed method estimates empty container mass obtaining a score of 71.08% under different lighting or filling conditions

NMDA Receptor Phosphorylation at a Site Affected in Schizophrenia Controls Synaptic and Behavioral Plasticity

Phosphorylation of the NR1 subunit of NMDA receptors (NMDARs) at serine (S) 897 is markedly reduced in schizophrenia patients. However, the role of NR1 S897 phosphorylation in normal synaptic function and adaptive behaviors are unknown. To address these questions, we generated mice in which the NR1 S897 is replaced with alanine (A). This knock-in mutation causes severe impairment in NMDAR synaptic incorporation and NMDAR-mediated synaptic transmission. Furthermore, the phosphomutant animals have reduced AMPA receptor (AMPAR)-mediated synaptic transmission, decreased AMPAR GluR1 subunit in the synapse, and impaired long-term potentiation. Finally, the mutant mice exhibit behavioral deficits in social interaction and sensorimotor gating. Our results suggest that an impairment in NR1 phosphorylation leads to glutamatergic hypofunction that can contribute to behavioral deficits associated with psychiatric disorders

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

Established Numerical Techniques for the Structural Analysis of a Regional Aircraft Landing Gear

Usually during the design of landing gear, simplified Finite Element (FE) models, based on one-dimensional finite elements (stick model), are used to investigate the in-service reaction forces involving each subcomponent. After that, the design of such subcomponent is carried out through detailed Global/Local FE analyses where, once at time, each component, modelled with three-dimensional finite elements, is assembled into a one-dimensional finite elements based FE model, representing the whole landing gear under the investigated loading conditions. Moreover, the landing gears are usually investigated also under a kinematic point of view, through the multibody (MB) methods, which allow achieving the reaction forces involving each subcomponent in a very short time. However, simplified stick (FE) and MB models introduce several approximations, providing results far from the real behaviour of the landing gear. Therefore, the first goal of this paper consists of assessing the effectiveness of such approaches against a 3D full-FE model. Three numerical models of the main landing gear of a regional airliner have been developed, according to MB, "stick," and 3D full-FE methods, respectively. The former has been developed by means of ADAMS® software, the other two by means of NASTRAN® software. Once this assessment phase has been carried out, also the Global/Local technique has verified with regard to the results achieved by the 3D full-FE model. Finally, the dynamic behaviour of the landing gear has been investigated both numerically and experimentally. In particular, Magnaghi Aeronautica S.p.A. Company performed the experimental test, consisting of a drop test according to EASA CS 25 regulations. Concerning the 3D full-FE investigation, the analysis has been simulated by means of Ls-Dyna® software. A good level of accuracy has been achieved by all the developed numerical methods

Ultrasound-assisted green solvent extraction of high-added value compounds from microalgae Nannochloropsis spp.

The aim of this work was to investigate ultrasound (US)-assisted green solvent extraction of valuable compounds from the microalgae Nannochloropsis spp. Individual green solvents (water, ethanol (EtOH), dimethyl sulfoxide (DMSO)) and binary mixture of solvents (water-DMSO and water-EtOH) were used for the extraction procedures. Maximum total phenolic compounds yield (Yp 0.33) was obtained after US pre-treatment (W = 400 W, 15 min), being almost 5-folds higher compared to that found for the untreated samples and aqueous extraction (Yp 0.06). The highest yield of total chlorophylls (Yc 0.043) was obtained after US (W = 400 W, 7.5 min), being more than 9-folds higher than those obtained for the untreated samples and aqueous extraction (Yc 0.004). The recovery efficiency decreased as DMSO > EtOH > H2O. The optimal conditions to recover phenolic compounds and chlorophylls from microalgae were obtained after US pre-treatment (400 W, 5 min), binary mixtures of solvents (water-DMSO and water-EtOH) at 25–30%, and microalgae concentration of 10%

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems

Prone versus supine position for adjuvant breast radiotherapy: a prospective study in patients with pendulous breasts.

Purpose: To analyze dosimetric parameters of patients receiving adjuvant breast radiotherapy (RT) in the prone versus supine position. Methods and materials: Forty-one out of 55 patients with pendulous breasts and candidates for adjuvant RT were enrolled in the study after informed consent. They underwent computed tomography (CT)-simulation in both prone and supine position. Target and non target volumes were outlined on CT images. Prescribed dose was 50 Gy delivered by two tangential photon fields followed by 10 Gy electron boost. Target coverage and dose homogeneity to clinical target volume (CTV) and planning target volume (PTV) were assessed by V95, V105 and V107 and dose to lung, heart and left anterior descending coronary artery (LAD) by V5, V10, V20, and mean and maximum dose. Data were analyzed by Student\u2019s t-test. Results: CTV and PTV coverage was significantly better in supine than in prone position. Lung V5, V10, and V20 were significantly lower in prone than in supine position. Heart V5, V10, V20, and LAD mean and maximum dose, in the 17 patients with left breast tumor, were lower in prone than in supine position, but without statistical significance. Based on treatment planning data and on treatment feasibility, 29/41 patients (70.7%) were treated in prone position. Acute and late toxicities of patients treated in prone and in supine position were not statistically different. Conclusion: Prone position is a favorable alternative for irradiation of mammary gland in patients with pendulous breasts and in our series was adopted in 71% of the cases

Effects of losartan treatment on cardiac autonomic control during volume loading in patients with DCM

This study evaluated the effect of angiotensin II receptor blockade on cardiac autonomic control adaptation and urine output in response to acute isotonic volume load in patients with idiopathic dilated cardiomyopathy (DCM) and asymptomatic to mildly symptomatic heart failure. Left ventricular volumes and heart rate variability measurements were assessed at baseline and during intravenous saline load in 14 patients before and after 2 mo of losartan treatment. After losartan treatment, blood pressure values were lower, whereas left ventricular ejection fraction was higher (F = 79, P50 ms) decreased during saline load in untreated patients (F = 3.1, P< 0.05 and F = 6.5, P< 0.01, respectively), but not after losartan. Similarly, a decrease in very low frequency (F = 3.2, P< 0.05), low-frequency (F = 2.9, P< 0.05), and high-frequency power (F = 6.1, P< 0.01) after saline load was observed only in untreated patients. In patients with DCM, losartan treatment improves the cardiac autonomic adaptation and increases urine output in response to volume overload

Foreign-language effects in cross-cultural behavioral research: Evidence from the Tanzanian Hadza

Behavioral research in traditional subsistence populations is often conducted in a non-native language. Recent studies show that non-native language-use systematically influences behavior, including in widely used methodologies. However, such studies are largely conducted in rich, industrialized societies, using at least one European language. This study expands sample diversity. We presented four standard tasks—a “dictator” game, two sacrificial dilemmas, a wager task, and five Likert-risk tolerance measures—to 129 Hadza participants. We randomly varied study languages—Hadzane and Kiswahili—between participants. We report a moderate impact of study language on wager decisions, alongside a substantial effect on dilemma decisions and responses to Likert-assessments of risk. As expected, non-native languages fostered utilitarian choices in sacrificial dilemmas. Unlike previous studies, non-native-language-use decreased risk preference in wager and Likert-tasks. We consider alternative explanatory mechanisms to account for this reversal, including linguistic relativity and cultural context. Given the strength of the effects reported here, we recommend, where possible, that future cross-cultural research should be conducted in participants’ first language