Work-Related Stress, Physio-Pathological Mechanisms, and the Influence of Environmental Genetic Factors

Work-related stress is a growing health problem in modern society. The stress response is characterized by numerous neurochemicals, neuroendocrine and immune modifications that involve various neurological systems and circuits, and regulation of the gene expression of the different receptors. In this regard, a lot of research has focused the attention on the role played by the environment in influencing gene expression, which in turn can control the stress response. In particular, genetic factors can moderate the sensitivities of specific types of neural cells or circuits mediating the imprinting of the environment on different biological systems. In this current review, we wish to analyze systematic reviews and recent experimental research on the physio-pathological mechanisms that underline stress-related responses. In particular, we analyze the relationship between genetic and epigenetic factors in the stress response

SIMULATION AND EXPERIMENTAL METHODS FOR IMPROVING ENERGY EFFICIENCY, ENVIRONMENTAL PERFORMANCE AND RESILIENCE OF SINGLE AND CLUSTERED GROUPS OF BUILDINGS

Energy consumption in the building sector is responsible for 36% of the energy use worldwide (corresponding to 39% of the total energy-related CO2 emissions), while at the European level the building sector accounts for a share of the total energy consumption comprised between 25% and 40% (corresponding to about 35% of the overall CO2 emissions throughout Europe). Concerning the Italian context, instead, such figures stand at about 40% and 17.5% for the energy consumption and for the CO2 emissions, respectively. In light of this, much attention has been paid, at global, European and single countries (national) levels on the important aspects regarding the reduction of energy consumption and the related decrease of greenhouse gases emissions in order to improve the environmental performance and the resilience of the building sector, both by the political and legislative bodies and by the scientific community. Despite the effort spent in putting into effect such actions, in recent years, the energy consumption in the building sector has experienced an increase, particularly in Italy. That is why more exertion in advancing the current measures and finding new innovative strategies to improve energy efficiency and resilience of buildings are of paramount importance. The research work carried out during the PhD course, and presented in this doctoral thesis, arises precisely from this context and from the desire to contribute to the question. To this end, strategies and solutions aimed at improving the energy efficiency, environmental performance and resilience of buildings, were assessed in detail by means of both experimental and modeling approaches. Accordingly, a number of case studies were designed and conducted to estimate how the adoption of some proposed interventions could impact the energy consumption, the indoor thermal comfort and contribute to the reduction of the CO2 emissions of buildings. In doing this, two important aspects influencing the afore-mentioned strategies and solutions were also considered, namely, the effect of the climatic conditions characterizeing the considered sites and the spatial scale at which they are applied, from the single building to a wider group of them, and how such perspective may influence the surrounding areas. The outcomes of the carried-out work put in evidence how accurate planning, construction and management of buildings, according to the peculiarities of the sites in which they are located, can contribute to reduce the energy and environmental burden of the building sector and at the same time help in the enhancement of urban resilience. Proper solution sets can, in fact, enable the building resilience against the outdoor stresses and simultaneously guarantee a regenerative indoor environment

Jedan novi postupak estimacije brzine vrtnje vektorski upravljanog asinkronog motora zasnovan na adaptivnom sustavu s referentnim modelom i neuronskim mrežama

This paper proposes a new sensorless technique for induction motor drives based on a hybrid MRAS-neural technique, which improves a previously developed neural MRAS based sensorless method. In this paper the open-loop integration in the reference model is performed by an adaptive neural integrator, enhanced here by means of a speed-varying filter transfer function. The adaptive model is based on a more accurate discrete current model based on the modified Euler integration, with a resulting more stable behaviour in the field weakening region. The adaptive model is further trained on-line by a generalized least squares technique, the MCA EXIN + neuron, in which a parameterized learning algorithm is used. As a consequence, the speed estimation presents an improved convergence with higher accuracy and shorter settling time, because of the better transient behaviour of the neuron. A test bench has been set up to verify the methodology experimentally and the results prove its goodness at very low speeds (below 4 rad/s) and in zero-speed operation.U članku se predlaže novi postupak estimacije brzine vrtnje elektromotornog pogona s vektorski upravljanim asinkronim motorom. Postupak se zasniva na hibridnom adaptivnom sustavu s referentnim modelom (MRAS) i neuronskim mrežama. Takav postupak poboljšava prethodno razvijeni estimacijski postupak također zasnovan na »neuronskom MRAS-u«. U radu je realizirana integracija u otvorenoj petlji u referentnom modelu pomoću adaptivnog neuronskog integratora unaprijeđenog s filtrom čija prijenosna funkcija ovisi o brzini motora. Adaptivni je model zasnovan na točnijem diskretnom strujnom modelu motora dobivenom modificiranom Eulerovom integracijom, što rezultira stabilnijim vladanju pogona u režimu slabljenja polja. Adaptivni je model nadalje on-line obučavan korištenjem poopćene metode najmanjih kvadrata (»MCA EXIN+neuron« postupak) pri čemu se koristi parametrirani algoritam učenja. Zbog boljeg ponašanja neurona u dinamičkim stanjima poboljšava se konvergencija estimacije brzine s većom točnošću i manjim vremenom smirivanja. Za eksperimentalnu provjeru predložene metode izgrađena je laboratorijska maketa. Dobiveni rezultati potvrđuju valjanost metode na veoma niskim brzinama (ispod 4 rad/s) i u režimu nulte brzine

Jedan novi postupak estimacije brzine vrtnje vektorski upravljanog asinkronog motora zasnovan na adaptivnom sustavu s referentnim modelom i neuronskim mrežama

This paper proposes a new sensorless technique for induction motor drives based on a hybrid MRAS-neural technique, which improves a previously developed neural MRAS based sensorless method. In this paper the open-loop integration in the reference model is performed by an adaptive neural integrator, enhanced here by means of a speed-varying filter transfer function. The adaptive model is based on a more accurate discrete current model based on the modified Euler integration, with a resulting more stable behaviour in the field weakening region. The adaptive model is further trained on-line by a generalized least squares technique, the MCA EXIN + neuron, in which a parameterized learning algorithm is used. As a consequence, the speed estimation presents an improved convergence with higher accuracy and shorter settling time, because of the better transient behaviour of the neuron. A test bench has been set up to verify the methodology experimentally and the results prove its goodness at very low speeds (below 4 rad/s) and in zero-speed operation.U članku se predlaže novi postupak estimacije brzine vrtnje elektromotornog pogona s vektorski upravljanim asinkronim motorom. Postupak se zasniva na hibridnom adaptivnom sustavu s referentnim modelom (MRAS) i neuronskim mrežama. Takav postupak poboljšava prethodno razvijeni estimacijski postupak također zasnovan na »neuronskom MRAS-u«. U radu je realizirana integracija u otvorenoj petlji u referentnom modelu pomoću adaptivnog neuronskog integratora unaprijeđenog s filtrom čija prijenosna funkcija ovisi o brzini motora. Adaptivni je model zasnovan na točnijem diskretnom strujnom modelu motora dobivenom modificiranom Eulerovom integracijom, što rezultira stabilnijim vladanju pogona u režimu slabljenja polja. Adaptivni je model nadalje on-line obučavan korištenjem poopćene metode najmanjih kvadrata (»MCA EXIN+neuron« postupak) pri čemu se koristi parametrirani algoritam učenja. Zbog boljeg ponašanja neurona u dinamičkim stanjima poboljšava se konvergencija estimacije brzine s većom točnošću i manjim vremenom smirivanja. Za eksperimentalnu provjeru predložene metode izgrađena je laboratorijska maketa. Dobiveni rezultati potvrđuju valjanost metode na veoma niskim brzinama (ispod 4 rad/s) i u režimu nulte brzine

Immagini letterarie di Parigi fine secolo

L'articolo si propone di descrivere l'impatto della metropoli haussmaniana sulla sensibilità di alcuni scrittori della fine secol

Deep Learning algorithms for automatic COVID-19 detection on chest X-ray images

Coronavirus disease (COVID-19) was confirmed as a pandemic disease on February 11, 2020. The pandemic has already caused thousands of victims and infected several million people around the world. The aim of this work is to provide a Covid-19 infection screening tool. Currently, the most widely used clinical tool for detecting the presence of infection is the reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less sensitive and requires the resource of specialized medical personnel. The use of X-ray images represents one of the latest challenges for the rapid diagnosis of the Covid-19 infection. This work involves the use of advanced artificial intelligence techniques for diagnosis using algorithms for classification purposes. The goal is to provide an automatic infection detection method while maximizing detection accuracy. A public database was used which includes images of COVID-19 patients, patients with viral pneumonia, patients with pulmonary opacity, and healthy patients. The methodology used in this study is based on transfer learning of pre-trained networks to alleviate the complexity of calculation. In particular, three different types of convolutional neural networks, namely, InceptionV3, ResNet50 and Xception, and the Vision Transformer are implemented. Experimental results show that the Vision Transformer outperforms convolutional architectures with a test accuracy of 99.3% vs 85.58% for ResNet50 (best among CNNs). Moreover, it is able to correctly distinguish among four different classes of chest X-ray images, whereas similar works only stop at three categories at most. The high accuracy of this computer-assisted diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis

Factors Associated With Follow-Up Compliance among Clients Referred By a Local Health Department for HIV Pre-Exposure Prophylaxis

Background: Pre-exposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) is a public health strategy to limit HIV infection among at-risk populations. Local health departments (LHDs) promote PrEP initiation by referring patients to private and academic specialty care centers. However, low follow-up compliance remains a challenge in this setting. Between January 2016 and September 2018, Douglas County Health Department, a LHD in Omaha, Nebraska, externally referred 126 clients for PrEP at an academic specialty care center, and only 20 (15%) clients completed a PrEP initiation follow-up appointment. The purpose of this study is to describe the characteristics of clients referred by Douglas County STD Clinic to an academic specialty care center for HIV PrEP services and to identify factors associated with follow-up compliance within this group. Study objective and goals: The goal of this study is to characterize the client population at Douglas County STD clinic that is externally referred for HIV pre-exposure prophylaxis at UNMC. The primary study objective is to describe demographic and behavioral characteristics of clients who were referred to University of Nebraska Medical Center (UNMC) Specialty Care Center by Douglas County STD Clinic for HIV PrEP between January 2016 and September 2018.The second study objective is to identify factors that are associated with PrEP follow-up compliance among clients referred to UNMC Specialty Care Center by Douglas County STD Clinic within this study group. Methods: This was a cross-sectional study of clients referred to UNMC Specialty Care for HIV PrEP between January 2016 and September 2018 by Douglas County STD Clinic (n=126). Surveillance records were retrospectively queried and analyzed for this study. The primary outcome was successful follow-up compliance to PrEP initiation visit at UNMC. Continuous variables were recoded as categorical variables and between-group comparisons were made using Fisher’s exact tests. Estimated odds ratios for PrEP follow-up were evaluated using univariate logistic regression models with 95% confidence intervals (CI) and p \u3c 0.05 was considered significant. Results: A total of n=126 surveillance records were analyzed. Demographic characteristics were similar between individuals who were follow-up compliant (n=20) versus those who were not. In both groups, most individuals were male (100% compliant group versus 89% noncompliant group, p=0.21) with a median age of 28 years (p=0.75) and who identified as white (65% compliant versus 60% noncompliant, p=0.3). Frequencies of social and sexual behavioral characteristics were similar between both groups. History of confirmed positive STI test(s) was significantly associated with PrEP initiation follow-up compliance (p=0.03), and history of a sexual partner’s positive STI screening was associated with PrEP initiation follow-up (p=0.02). Race- and age-adjusted odds ratios (aOR) for follow-up compliant individuals with a sexual partner who had a history of confirmed STI(s) was 4.08 (95% CI: 1.42-11.76), and for those with a personal history of STI infection was 3.72 (95% CI: 1.30-10.64). Impact: The intended public health impact of this study is to reduce the number of new HIV infections among at-risk populations by improving HIV PrEP uptake and access in Douglas County, Nebraska

Covering the Gap for an Effective Energy and Environmental Design of Green Roofs: Contributions from Experimental and Modelling Researches

Green roofs are components of the building envelope that have become increasingly popular in urban contexts because other than providing numerous environmental benefts they are also capable of reducing building energy consumption, especially in summer. However, despite all these advantages, green roofs are still affected by some limitations. Specifcally, there are some gaps affecting the energy modelling consisting in the absence of a proper database, information (growth stage, leaf area index, and coverage ratio) relative to the different green roof plant species, which technicians could use in case of lack of actual feld data to perform energy analysis of buildings equipped with green roofs. These gaps concern also environmental and economic assessments of such technology. In fact, the currently available green roof LCA and LCC studies seem to underestimate the role of the substrate on the overall environmental impact and the role of the disposal phase on the life cycle cost of the green roof. In this chapter, all these aspects are addressed, and contributions to their solution, which arose from both experimental and modelling research, carried out by the authors are presented

Incidencia de sobrepeso en niños en edad escolar

El presente trabajo de Investigación tiene como objetivo determinar, comparar y establecer las causas de la incidencia de sobrepeso en niños en edad escolar pertenecientes al Colegio Santa Rosa de Lima y Escuela Adolfo Tula, instituciones educativas ubicadas en la ciudad de la Consulta, Tunuyán, Mendoza. Dentro de las propuestas superadoras se desea beneficiar el desarrollo de la enfermería escolar como vía de acción para intervenir sobre las circunstancias favorecedoras de esta problemática y por consiguiente mejorar el estado de salud de los niños.Fil: Araguna, Mariela. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas. Escuela de Enfermería..Fil: Cirrincione, María A.. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas. Escuela de Enfermería.

Induction Machine Stator Fault Tracking using the Growing Curvilinear Component Analysis

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally