Search

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO 2 emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments – through a software capable of emulating a quantum system – to data over the period of 1990–2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany. • An innovative Machine Learning algorithm (the Quantum model) has been constructed and applied. • The empirical findings support the powerful role of biomass energy in reducing CO 2 emissions. • GDP represents an important determinant of environmental pollution in Germany. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM 10 , PM 2.5, and NO 2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM 2.5 and NO 2 are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM 2.5 to Deaths, NO 2 to Deaths, and economic growth to both PM 2.5 and NO 2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM 2.5 to Deaths, NO 2 to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM 2.5 and NO 2) in New York state. [Display omitted] • We assess the effect of economic growth, PM 2.5 and NO 2 variations on COVID-19 deaths. • We employ two experimental approaches based on Deep Machine Learning. • PM 2.5 and NO 2 are the most significant air pollutant responsible for COVID-19 deaths. • Increasing levels of economic growth spur air pollution in New York state. [ABSTRACT FROM AUTHOR]

This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO 2) concentrations and COVID-19-related deaths in France. The concentration of NO 2 linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO 2 in spreading the epidemic. The underlying hypothesis is that NO 2 , as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO 2 connected to COVID-19 range between 15.8 μg/m3 for Lyon, 21.8 μg/m3 for Marseille and 22.9 μg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 μg/m³ imposed by Directive 2008/50/EC of the European Parliament. • Machine Learning approach based on ANNs techniques is proposed on NO 2 concentrations and COVID-19 deaths. • Hourly data (41 consecutive days) for three French cities we use for our analysis. • Analysis on the effects of three different threshold levels of NO 2 compared to COVID-19 deaths. • New threshold values on NO 2 concentrations are proposed to policy makers. [ABSTRACT FROM AUTHOR]

Cardiorenal syndrome encompasses a spectrum of disorders involving heart and kidney dysfunction, and sharing common risk factors, such as hypertension and diabetes. Clinical studies have shown that patients with and without diabetes may benefit from using sodium-glucose cotransporter 2 inhibitors to reduce the risk of heart failure and ameliorate renal endpoints. Because the underlying mechanisms remain elusive, we investigated the effects of dapagliflozin on the progression of renal damage, using a model of non-diabetic cardiorenal disease. Dahl salt-sensitive rats were fed a high-salt diet for five weeks and then randomized to dapagliflozin or vehicle for the following six weeks. After treatment with dapagliflozin, renal function resulted ameliorated as shown by decrease of albuminuria and urine albumin-to-creatinine ratio. Functional benefit was accompanied by a decreased accumulation of extracellular matrix and a reduced number of sclerotic glomeruli. Dapagliflozin significantly reduced expression of inflammatory and endothelial activation markers such as NF-κB and e-selectin. Upregulation of pro-oxidant-releasing NADPH oxidases 2 and 4 as well as downregulation of antioxidant enzymes were also counteracted by drug treatment. Our findings also evidenced the modulation of both classic and non-classic renin-angiotensin-aldosterone system (RAAS), and effects of dapagliflozin on gene expression of ion channels/transporters involved in renal homeostasis. Thus, in a non-diabetic model of cardiorenal syndrome, dapagliflozin provides renal protection by modulating inflammatory response, endothelial activation, fibrosis, oxidative stress, local RAAS and ion channels. [Display omitted] [ABSTRACT FROM AUTHOR]