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A sequential two-step thermochemical process was studied for spent green tea waste (SGTW), involving an initial pyrolysis step followed by thermal or catalytic cracking. This process was carried out in two bench-scale reactors (fixed bed reactor and tubular reactor) serially coupled. At a fixed pyrolysis temperature of SGTW (550 °C), the application of high cracking temperatures (700 and 800 °C) positively affected both the yield and composition of the gas product. Consequently, it has the potential to be used for the production of diverse biofuels and chemicals, or to be partially recycled to optimize the process efficiency. Moreover, the use of inexpensive catalysts, particularly dolomite, was considered advantageous, since the syngas yield (56.5 wt%) and its potential were greatly enhanced, reaching a H2/CO ratio of 1.5. The homogenous biochar obtained, with a calorific value of 26.84 MJ/kg, could be harnessed as good-quality fuel for briquette applications and as a biofuel source for generating stationary power. Furthermore, catalytic cracking pyrolysis was examined for different types of coffee waste, revealing that this process is a simple and clean solution to valorize oxygen-rich lignocellulosic biomass and generate valuable gaseous by-products.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV 2) may cause damage to the cardiovascular system during the acute phase of the infection. However, recent studies reported mid- to long-term subtle cardiac injuries after recovering from acute coronavirus disease 2019 (COVID-19). This study aimed to determine the relationship between the severity of chest computed tomography (CT) lesions and the persistence of subtle myocardial injuries at mid-term follow-up of patients who recovered from COVID-19 infection.MethodsAll patients with COVID-19 were enrolled prospectively in this study. Sensitive troponin T (hsTnT) and chest CT scans were performed on all patients during the acute phase of COVID-19 infection. At the mid-term follow-up, conventional transthoracic echocardiograph and global longitudinal strain (GLS) of the left and right ventricles (LV and RV) were determined and compared between patients with chest CT scan lesions of < 50% (Group 1) and those with severe chest CT scan lesions of greater or equal to 50% (Group 2).ResultsThe mean age was 55 ± 14 years. Both LV GLS and RV GLS values were significantly decreased in group 2 (p = 0.013 and p = 0.011, respectively). LV GLS value of more than −18 was noted in 43% of all the patients, and an RV GLS value of more than −20 was observed in 48% of them. The group with severe chest CT scan lesions included more patients with reduced LV GLS and reduced RV GLS than the group with mild chest CT scan lesions [(G1:29 vs. G2:57%, p = 0.002) and (G1:36 vs. G2:60 %, p = 0.009), respectively].ConclusionPatients with severe chest CT scan lesions are more likely to develop subclinical myocardial damage. Transthoracic echocardiography (TTE) could be recommended in patients recovering from COVID-19 to detect subtle LV and RV lesions.

This paper shows that the grammar formalism can be pushed to be applied for the description and the classification of electrocardiograms signals (ECG). We will describe an ECG signal as a sequence of tokens based on specific vocabularies and a set of grammatical rules. QRS complexes, RR distances, PR and QT intervals will be calculated. This type of work is intended for medical diagnosis assistance of an ECG signal.

Abstract: This study presents a comparative evaluation of lacunarity of 3D grey level models with different types of inhomogeneity. A new method based on the “Relative Differential Box Counting” was developed to estimate the lacunarity features of grey level volumes. To validate our method, we generated a set of 3D grey level multifractal models with random, anisotropic and hierarchical properties. Our method gives a lacunarity measurement correlated with the theoretical one and allows a better model classification compared with a classical approach. [Copyright &y& Elsevier]