The power of feedback.

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular riskThe Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS)

Coronary artery disease (CAD) is a pathological process characterized by atherosclerotic plaque accumulation in the epicardial arteries, whether obstructive or non-obstructive. This process can be modified by lifestyle adjustments, pharmacological therapies, and invasive interventions designed to achieve disease stabilization or regression. The disease can have long, stable periods but can also become unstable at any time, typically due to an acute atherothrombotic event caused by plaque rupture or erosion. However, the disease is chronic, most often progressive, and hence serious, even in clinically apparently silent periods. The dynamic nature of the CAD process results in various clinical presentations, which can be conveniently categorized as either acute coronary syndromes (ACS) or chronic coronary syndromes (CCS). The Guidelines presented here refer to the management of patients with CCS. The natural history of CCS is illustrated in Figure 1.

/pdf/2019-esc-guidelines-for-the-diagnosis-and-management-of-5b7mv2v506.pdf

2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes.

The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Climate change 2014 - Mitigation of climate change

The concern about air pollution in urban areas has substantially increased worldwide. One of its main components, particulate matter (PM) with aerodynamic diameter of ≤2.5 µm (PM2.5), can be inhaled and deposited in deeper regions of the respiratory system, causing adverse effects on human health, which are even more harmful to children. In this sense, the use of deterministic and stochastic models has become a key tool for predicting atmospheric behavior and, thus, providing information for decision makers to adopt preventive actions to mitigate air pollution impacts. However, stochastic models present their own strengths and weaknesses. To overcome some of disadvantages of deterministic models, there has been an increasing interest in the use of deep learning, due to its simpler implementation and its success on multiple tasks, including time series and air quality forecasting. Thus, the objective of the present study is to develop and evaluate the use of four different topologies of deep artificial neural networks (DNNs), analyzing the impact of feature augmentation in the prediction of PM2.5 concentrations by using five levels of discrete wavelet transform (DWT). The following types of deep neural networks were trained and tested on data collected from two living lab stations next to high-traffic roads in Guildford, UK: multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolutional neural network (1D-CNN) and a hybrid neural network composed of LSTM and 1D-CNN. The performance of each model in making predictions up to twenty-four hours ahead was quantitatively assessed through statistical metrics. The results show that wavelets improved the forecasting results and that discrete wavelet transform is a relevant tool to enhance the performance of DNN topologies, with special emphasis on the hybrid topology that achieved the best results among the applied models.

/pdf/particulate-matter-forecasting-using-different-deep-neural-1uwsvnai.pdf

Particulate Matter Forecasting Using Different Deep Neural Network Topologies and Wavelets for Feature Augmentation

Quantitative estimation of cardiopulmonary mortality due to fine particulate matters: a case study on Delhi city

The last decade in India has seen a rapid deterioration in the air quality in its major cities. This
has led to increased interest from the general public to their exposure to ambient air quality
primarily because of the effects of such air pollutants on human health. In this context, the air
quality indices (AQI) is often used by the local authorities to signify the levels of the seriousness
of air pollution to the common public. The use of air quality indexing for assessment of
existing air quality standards has been widely used for different cities in India and the world.
The paper presents the application of air quality indices for assessing the existing air quality
standards in an Indian city, Shimla. The indices have been calculated using the methodology
described by the US Environmental Protection Agency (USEPA), which is adopted by the
Central Pollution Control Board (CPCB) in India. An alternative method for determination of air
quality indices is also utilized (referred to as AQIam for the Indian context. The estimates air
quality indices are applied to two monitoring sites (Tekka Bench, Ridge and ISBT bus stand) in
Shimla city over the study period (2004-2015) on the pollutants: Sulphur dioxide (SO2), oxides
of nitrogen (NOx), suspended particulate matter (SPM) and respirable suspended particulate
matter (RSPM). The annual air quality indices results for the study period showed that the air
quality was 'good' for Tekka Bench monitoring station for the entire study period and for the
ISBT bus stand for all the years, except 2011 when it was in ‘moderate' category. The annual
air quality indices predicted using the alternative methodology indicated the level of air quality
to be 'good' for the entire study period, except 2013 when it was classified as ‘satisfactory'
for the monitoring site at Tekka Bench. Similarly, the annual air quality was classified as
'moderate’ for the years 2011, 2013-2015 for the monitoring station at ISBT bus stand site
with the remaining years of the study period being classified as 'good'. These categorizations
of existing air quality interpret the expected health effects of exposure to surrounding ambient
air. Higher the value of air quality indices more severe is the categorization and thereby more
harmful are the human health effects being exposed to ambient air conditions. Similar such
seasonal variations of air quality indices were also observed during the study period at both
the monitoring sites.

The Air Quality Assessment of Northern Hilly City in India.

Homophilic interaction and deformation of E-cadherin and cadherin 7 probed by single molecule force spectroscopy.

The effect of nickel doping on the structural and magneto-rheological properties of Fe3O4 based nanomagnetic fluids (NMFs) having the composition NixFe3-xO4 (0 ≤ x ≤ 0.8) has been studied. The structural and compositional analysis confirmed the nanoscale regime (∼10 nm) of samples prepared by chemical co-precipitation method and structural analysis was performed by XRD pattern to ascertain the effect of Ni doping. From the UV–Visible spectroscopy studies, the energy band gap was found to be in the range 2.2–3.0 eV with increase in Ni concentration. The magnetic measurements reveal the lower values of coercivity and remanence. The magneto-rheological measurements confirm that in all samples shows a non-Newtonian shear thinning behavior and the increase of viscosity and dipolar interactions while increasing the magnetic field. Whereas in absence of field, higher grain boundaries enhance the interlayer friction and higher viscosity with Ni doping. 

Prashant Kumar

Papers

Particulate Matter Forecasting Using Different Deep Neural Network Topologies and Wavelets for Feature Augmentation

Quantitative estimation of cardiopulmonary mortality due to fine particulate matters: a case study on Delhi city

The Air Quality Assessment of Northern Hilly City in India.

Homophilic interaction and deformation of E-cadherin and cadherin 7 probed by single molecule force spectroscopy.

Tunning of rheological and magnetic properties of Ni doped magnetite based magnetic nanofluid