Exact solutions of the (2+1) Dimensional Dirac equation in a constant magnetic field in the presence of a minimal length

We study the (2+1) dimensional Dirac equation in an homogeneous magnetic field (relativistic Landau problem) within a minimal length, or generalized uncertainty principle -GUP-, scenario. We derive exact solutions for a given explicit representation of the GUP and provide expressions of the wave functions in the momentum representation. We find that in the minimal length case the degeneracy of the states is modified and that there are states that do not exist in the ordinary quantum mechanics limit (\beta -->0). We also discuss the mass-less case which may find application in describing the behavior of charged fermions in new materials like Graphene.Comment: 9 pages, to appear in Physical Review

Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks

In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with the help of a suitably designed Convolutional Neural Network, OilNet40. In order to detect oil leaks more clearly, we dilute the oil in AirTender with a fluorescent dye with an excitation wavelength peak of approximately 390 nm. AirTender is then illuminated with suitable UV LEDs. The whole system is an attempt to design a low-cost detection setup. An on-board device, such as a mini-computer, is placed near the suspension system and connected to a full hd camera framing AirTender. The on-board device, through our Neural Network algorithm, is then able to localize and classify AirTender as normally functioning (non-leak image) or anomaly (leak image)

Air pollutants and daily number of admissions to psychiatric emergency services: evidence for detrimental mental health effects of ozone

Abstract Aims Aim of the current study is to investigate the associations between daily levels of air pollutants (particulate matter, ozone, carbon monoxide, nitrogen dioxide) and daily admissions for mental disorders to the emergency department of two general hospitals in Umbria region (Italy). Methods We collected data about daily admissions to psychiatric emergency services of two general hospitals, air pollutants' levels and meteorological data for the time period 1 January 2015 until 31 December 2016. We assessed the impact of an increase in air pollutants on the number of daily admissions using a time-series econometric framework. Results A total of 1860 emergency department admissions for mental disorders were identified. We observed a statistically significant impact of ozone levels on daily admissions. The estimated coefficient of O3 is statistically significant at the 1% level. All other pollutants were not significantly associated with the number of daily admissions. Conclusions Short-term exposure to ozone may be associated with increased psychiatric emergency services admissions. Findings add to previous literature on existing evidence for air pollution to have an impact on mental health. Ozone may be considered a potential environmental risk factor for impaired mental health

COVID-19-Related Social Isolation Predispose to Problematic Internet and Online Video Gaming Use in Italy

COVID-19 pandemic and its related containment measures have been associated with increased levels of stress, anxiety and depression in the general population. While the use of digital media has been greatly promoted by national governments and international authorities to maintain social contacts and healthy lifestyle behaviors, its increased access may also bear the risk of inappropriate or excessive use of internet-related resources. The present study, part of the COVID Mental hEalth Trial (COMET) study, aims at investigating the possible relationship between social isolation, the use of digital resources and the development of their problematic use. A cross sectional survey was carried out to explore the prevalence of internet addiction, excessive use of social media, problematic video gaming and binge watching, during Italian phase II (May-June 2020) and III (June-September 2020) of the pandemic in 1385 individuals (62.5% female, mean age 32.5 ± 12.9) mainly living in Central Italy (52.4%). Data were stratified according to phase II/III and three groups of Italian regions (northern, central and southern). Compared to the larger COMET study, most participants exhibited significant higher levels of severe-to-extremely-severe depressive symptoms (46.3% vs. 12.4%; p < 0.01) and extremely severe anxiety symptoms (77.8% vs. 7.5%; p < 0.01). We also observed a rise in problematic internet use and excessive gaming over time. Mediation analyses revealed that COVID-19-related general psychopathology, stress, anxiety, depression and social isolation play a significant role in the emergence of problematic internet use, social media addiction and problematic video gaming. Professional gamers and younger subjects emerged as sub-populations particularly at risk of developing digital addictions. If confirmed in larger and more homogenous samples, our findings may help in shedding light on possible preventive and treatment strategies for digital addictions

Depressive mood and circadian rhythms disturbances as outcomes of seasonal affective disorder treatment: A systematic review

Background: The present systematic review was aimed at critically summarizing the evidence about interventions focused on circadian rhythms and mood symptoms in seasonal affective disorder (SAD). Methods: A systematic search of the electronic databases PUBMED, PsycINFO and Web of Science was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Original papers reporting data about the effects of treatments on both mood and circadian rhythms disturbances in SAD patients were considered for inclusion. The quality of the evidence provided by the eligible studies was assessed using the Revised Cochrane Risk of Bias Tool (RoB 2.0) and the Cochrane Risk of Bias in Non-Randomized Studies of Interventions Tool (ROBINS-I). Results: Forty papers were deemed eligible for the systematic review. The evidence of treatment outcomes referring to circadian disturbances was not robust. Despite this, bright light therapy (BLT) demonstrates to phase-advance delayed rhythms and to improve sleep-wake disorders. As for mood symptoms, both BLT and selective serotonin reuptake inhibitors (SSRIs) show evidence of efficacy. The possible connection between improvements of mood symptoms and changes in circadian outcomes seems controversial. Limitations: The included studies presented considerable methodological heterogeneity, small sample sizes and non-optimal sample selection. Conclusions: The effectiveness of BLT in depressive symptoms and circadian disturbances of SAD was outlined by the present systematic review. The evidence about other biological and pharmacological treatments, although promising, should be replicated. A multifactorial etiopathogenesis could explain the heterogeneous clinical presentations of SAD and the complex link between mood and circadian symptoms

Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

This paper presents a comparison of different methodologies for monitoring the plants growth in a greenhouse. A 2D measurement based on Computer Vision algorithms and 3D shape measurements techniques (Structured light, LIDAR and photogrammetry) are compared. From the joined 2D and 3D data, an analysis was performed considering health plant indicators. The methodologies are compared among each other. The acquired data are then fed into Deep Learning algorithms in order to detect anomalies in plant growth. The final aim is to give an assessment on the image acquisition methodologies, selecting the most suitable to be used to create the Deep Learning model inputs saving time and resources

A reappraisal of the intertwined association between affective lability and mood reactivity in a post hoc analysis of the BRIDGE-II-MIX study

Objective: This post hoc analysis of the BRIDGE-II-MIX study is aimed at evaluating affective lability (AL) as a possible clinical feature of mixed depression and assessing the relationship with atypical depressive features, particularly mood reactivity (MR). Methods: In the BRIDGE-II-MIX multicenter, cross-sectional study, 2,811 individuals suffering from a major depressive episode (MDE; DSM-IV-TR criteria), in the context of bipolar I or II disorder (BD-I, BD-II, respectively) or major depressive disorder, were enrolled between June 2009 and July 2010. Patients with (MDE-AL, n = 694) and without (MDE-noAL, n = 1,883) AL and with (MDE-MR, n = 1,035) or without (MDE-noMR, n = 1,542) MR were compared through χ2 test or Student t test. Stepwise backward logistic regression models, respectively testing AL and MR as the dependent variable, were performed to differentiate the 2 clinical constructs. Results: AL was positively associated with BD-I (P < .001) and BD-II (P < .001), with DSM-5 mixed (DSM-5-MXS) (P < .001) and atypical (DSM-5-AD) features (P < .001) and negatively associated with MDD (P < .001). In the logistic regression models, MR was the variable most significantly associated with AL and vice versa (P < .001 for both). AL was positively associated with severity of mania and DSM-5-MXS and negatively correlated with severity of depression, while MR was better predicted by atypical symptoms such as hyperphagia, hypersomnia, and leaden paralysis and correlated with both comorbid anxiety disorders and DSM-5-MXS. Conclusions: Mixed and atypical depression may lie on the same continuum. MR and AL could represent the underlying matrix, bridging the gap between mixed and atypical depression

Patterns of response to antidepressants in major depressive disorder: Drug resistance or worsening of depression are associated with a bipolar diathesis

Resistance and worsening of depression in response to antidepressants (ADs) are major clinical challenges. In a large international sample of patients with major depressive disorder (MDD), we aim to explore the possible associations between different patterns of response to ADs and bipolarity. A total of 2811 individuals with a major depressive episode (MDE) were enrolled in the BRIDGE-II-MIX study. This post-hoc analysis included only 1329 (47%) patients suffering from MDD. Patients with (TRD-MDD, n = 404) and without (NTRD-MDD, n = 925) history of resistance to AD treatment and with (n = 184) and without (n = 1145) previous AD-induced irritability and mood lability (AIM) were compared using Chi-square, t-Student's test and logistic regression models. TRD-MDD patients resulted significantly associated with higher rates of AIM, psychotic features, history of suicide attempts, emotional lability and impulsivity, comorbid borderline personality disorder and polipharmacological treatment, compared to NTRD-MDD group. In comparison to NAIM-MDD patients, subjects in the AIM-MDD group showed significantly higher rates of first-degree family history for BD, previous TRD, atypical features, mixed features, psychiatric comorbidities, lifetime suicide attempts and lower age at first psychiatric symptoms. In addition, patients with AIM presented more often almost all the hypomanic symptoms evaluated in this study. Among these latter symptoms, logistic regressions showed that distractibility, impulsivity and hypersexuality were significantly associated with AIM-MDD. In conclusion, in MDD patients, a lifetime history of resistance and/or irritability/mood lability in response to ADs was associated with the presence of mixed features and a possible underlying bipolar diathesis