During the covid-19 pandemic, a considerable amount of data travels fast worldwide on the net, mainly on the social media platform where people all over the world have constant and easy access to submit materials and posts. A considerable amount of shared news embeds misleading information which acts negatively the cognitive and psychological health of its readers. The present case study focuses on fake news being tweeted during the coronavirus pandemic for the purpose to mislead the targeted population. In this context, this paper exhibits a new approach to detect fake news on Twitter during Covid-19 period. The proposed method consists of a classication approach that uses new tweets features and it is based on natural language processing, machine learning, and deep learning. The method is implemented in a parallel with apache spark. Experimental results show that our approach yields very valuable results once it is used with the random forest algorithm with accuracy equal to 79%. We also demonstrate that the sentiment of tweets plays an important role in the detection of fake news. Indeed, the model we present outperforms those model lacking consideration of new tweets features.

The objective of this work is to obtain the annual phenophasic characterization of Mediterranean serpentine vegetation and its comparison with other studied Mediterranean-type ecosystems. The study was conducted in the serpentine ecosystem of Sierra Bermeja, (Spain) where two plots were stablished (Low and High plot) at different altitudinal level and two endemic scrubland associations (Staehelino baeticae-Ulicion baetici alliance), were studied. A phenophasic study was carried out in both plots studying vegetative and reproductive phenophases. ACP phenophasic index was calculated for the studied plots to obtain the time curse of the phenophasic activity of the vegetation through the whole year. The annual evolution of the phenophases showed that the two communities follow a similar phenophasic pattern, especially regarding to vegetative phenophases. An advance of the reproductive phenophases was observed in the low plot and a delay (of one month) in the flowering time compared to other Mediterranean ecosystems. The APC index showed a period of strong decrease in phenophasic activity up to 30%, which can serve to differentiate the serpentine ecosystem from the other Mediterranean ecosystems where the APC is higher. No significant differences were obtained due to altitude nor vegetation type.

The present study determines antimicrobial activity and permeability of cefixime from optimized formulation by comparing with pure drug using non-everted gut sac technique. Microspheres were prepared by ionic gelation technique with different concentrations of polymers to sustain release of drug for 12hrs.The in-vivo studies are done to evaluate various pharmacokinetic parameters like Cmax, AUC0-∞, AUC0-t, t1/2(Biological half life), Tmax and Kel for the comparison of optimized formulation and pure drug of Cefixime. From the pharmacokinetic evaluation, the optimized formulation of microspheres produced peak plasma concentration (Cmax) of 4.86 ± 1.42 (mcg/ml) at 3 h Tmax 3.00 ± 0.3 at 2 h Tmax. The area under the curve for the control and solid dispersion tablets was 23.69 ± 12.2 and 22.31 ± 11.2(mcg.h/ml)and the mean resident time was 3.99 and 3.68 h, respectively. The 90% confidence intervals for Cmax were 92.99-107.70, for AUC0-t were 93.03-108.10 and for AUC0-∞ were 93.56-118.06.

Ergonomics examines a wide range of working conditions such as lighting, noise, temperature, vibration, workstation design, machine design. This article attempts to explain the noise - human response with the artificial neural network model. Problems related with noise interactions in industrial plants necessitate specific approaches. The term comfort is not usually used when evaluating the effects of noise on the occupants of a workplace, loudness, perceived noisiness and nuisance are also used. Uncomfortable conditions in the workplace. Therefore, workspaces should be designed to address acoustic comfort. Acoustic comfort is the condition where noise and sounds are not found to be distracting, irritating or harmful. A good acoustic environment is necessary for maintaining a high level of satisfaction and morale among workers. Ideally, workplaces should be free of noise. An Artificial neural network (ANN) based on back-propagation algorithm, minimization of the quadratic cost function by tuning the network parameters, was used to modelling interactions between noise and environmental parameters. Field surveys of noise and environmental parameters (temperature and humidity) were conducted at a marble plant throughout a year. Obtained data from the field surveys were transferred to the ANN method for modelling. ANN approach was done to predict the today’s pattern containing of noise and environmental parameters with previous day pattern. As a result ANN modelling was found successful for the prediction of noise and environmental parameters. The results of the model were compared with various statistics (correlation coefficients, max – min, standard deviation, average and coefficient of skewness) and error modes (root mean square error and relative error). The correlation coefficients were significantly high, error modes were very low and the other statistics were very close to the data. This statement indicates the validity of the model. Therefore, the model can be used for noise control in a workplace.