Single and Two Stage Anaerobic Digestion of landfill leachate.
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•Assessment of Single and two stage AD of leachate with varying pH and IOL.•Highest VFA yield was obtained at IOL of ...48 g/L at acidic (5.5) & alkaline (11) pH.•Acetic acid was dominant at acidic pH whereas it was butyric acid at alkaline pH.•IOL of 48 g/L and 6 g/L yielded highest methane in single & two stage respectively.•Overall increase of 21% COD removal efficiency can be achieved in two stage AD.
This work aims to evaluate the impact of pH and initial organic load (IOL) in terms of Chemical Oxygen Demand (COD) of landfill leachate for the production of value added products during single and two stage anaerobic digestion (AD). It was observed that at an optimal IOL of 48 g/L, acetic acid was dominant at pH 5.5 whereas it was butyric acid at pH of 5.5–6.0 and 10–11. The yield of Volatile Fatty Acids (VFA) was dependent on IOL and it was in the range of 0.26 to 0.36 g VFA/(g COD removed). Methane was also harvested during single and two stage AD and found that it was varying in the range of 0.21–0.34 L CH4/(g COD removed) and 0.2–0.32 L CH4/(g COD removed) respectively. An overall increase of 21% COD removal was noticed in two stage AD in comparison to single stage.
In late 2019, a novel human coronavirus – severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) – emerged in Wuhan, China. This virus has caused a global pandemic involving more than 200 ...countries. SARS-CoV-2 is highly adapted to humans and readily transmits from person-to-person.
To investigate the infectivity of SARS-CoV-2 under various environmental and pH conditions. The efficacies of various laboratory virus inactivation methods and home disinfectants against SARS-CoV-2 were investigated.
The residual virus in dried form or in solution was titrated on to Vero E6 cells on days 0, 1, 3, 5 and 7 after incubation at different temperatures. Viral viability was determined after treatment with various disinfectants and pH solutions at room temperature (20–25oC).
SARS-CoV-2 was able to retain viability for 3–5 days in dried form or 7 days in solution at room temperature. SARS-CoV-2 could be detected under a wide range of pH conditions from pH 4 to pH 11 for several days, and for 1–2 days in stool at room temperature but lost 5 logs of infectivity. A variety of commonly used disinfectants and laboratory inactivation procedures were found to reduce viral viability effectively.
This study demonstrated the stability of SARS-CoV-2 on environmental surfaces, and raises the possibility of faecal–oral transmission. Commonly used fixatives, nucleic acid extraction methods and heat inactivation were found to reduce viral infectivity significantly, which could ensure hospital and laboratory safety during the SARS-CoV-2 pandemic.
Cloud computing is a preferred option for organizations around the globe, it offers scalable and internet-based computing resources as a flexible service. Security is a key concern factor in any ...cloud solution due to its distributed nature. Security and privacy are huge obstacles faced in its success of the on-demand service as it is easily vulnerable to intruders for any kind of attack. A huge upsurge in network traffic has paved the way to security breaches which are more complicated and widespread. Tackling these attacks has become an inefficient application of traditional intrusion detection systems (IDS) environment. In this research, we developed an efficient Intrusion Detection System (IDS) for the cloud environment using ensemble feature selection and classification techniques. This proposed method was relying on the univariate ensemble feature selection technique, which is used for the selection of valuable reduced feature sets from the given intrusion datasets. While the ensemble classifiers that can competently fuse the single classifiers to produce a robust classifier using the voting technique. An ensemble based proposed method effectively classifies whether the network traffic behavior is normal or attack. The implementation of the proposed method was measured by applying various performance evaluation metrics and ROC-AUC (“area under the receiver operating characteristic curves”) across various classifiers. The results of the proposed methodology achieved a strong considerable amount of performance enhancement compared with other existing methods. Moreover, we performed a pairwise
t
test and proved that the performance of the proposed method was statistically significantly different from other existing approaches. Finally, the outcome of this investigation was obtained with the best accuracy and lowest false alarm rate (FAR).
Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a ...few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using data cleaning and normalization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to improve interpretation of prediction results. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%.
In this study, we compare the classification accuracy achievable with linear support vector machine (L-SVM), K-nearest neighbor (KNN), and multilayer perceptron (MLP) methods for a multi-class EEG ...signal. This can be done in three phases. In phase one, band-pass filtering is applied to raw electroencephalogram (EEG) signals to decompose into five different frequency subbands. In phase two, we extract 10 important features from each subband. In phase three, these extracted feature sets are used as input to L-SVM, KNN, and MLP classifiers which categorize the sample data into three classes namely yoga, meditation, and combined yoga–meditation. Various performance measures for each classifier are evaluated and then compared to know which classifier is effective in the classification of the EEG data into yoga, meditation, and combined yoga–meditation groups. Performance measures such as confusion matrix, accuracy, sensitivity, specificity, precision, and F1 score are used to validate the performance of classifiers. Kruskal–Wallis test has been conducted to compare the classification performance of the linear SVM, KNN, and MLP classifier models. By comparing the classification accuracy between the three classifiers, L-SVM achieved the highest accuracy of 91.67%.
Introduction: Fibrotic scar in the heart is known to act as a substrate for arrhythmias. Regions of fibrotic scar are associated with slowed or blocked conduction of the action potential, but the ...detailed mechanisms of arrhythmia formation are not well characterised and this can limit the effective diagnosis and treatment of scar in patients. The aim of this computational study was to evaluate different representations of fibrotic scar in models of 2D 10 × 10 cm ventricular tissue, where the region of scar was defined by sampling a Gaussian random field with an adjustable length scale of between 1.25 and 10.0 mm. Methods: Cellular electrophysiology was represented by the Ten Tusscher 2006 model for human ventricular cells. Fibrotic scar was represented as a spatially varying diffusion, with different models of the boundary between normal and fibrotic tissue. Dispersion of activation time and action potential duration (APD) dispersion was assessed in each sample by pacing at an S1 cycle length of 400 ms followed by a premature S2 beat with a coupling interval of 323 ms. Vulnerability to reentry was assessed with an aggressive pacing protocol. In all models, simulated fibrosis acted to delay activation, to increase the dispersion of APD, and to generate re-entry. Results: A higher incidence of re-entry was observed in models with simulated fibrotic scar at shorter length scale, but the type of model used to represent fibrotic scar had a much bigger influence on the incidence of reentry. Discussion: This study shows that in computational models of fibrotic scar the effects that lead to either block or propagation of the action potential are strongly influenced by the way that fibrotic scar is represented in the model, and so the results of computational studies involving fibrotic scar should be interpreted carefully.
Platinum-based chemotherapy is standard-of-care first-line treatment for advanced urothelial carcinoma. However, progression-free survival and overall survival are limited by chemotherapy resistance.
...In a phase 3 trial, we randomly assigned patients with unresectable locally advanced or metastatic urothelial cancer who did not have disease progression with first-line chemotherapy (four to six cycles of gemcitabine plus cisplatin or carboplatin) to receive best supportive care with or without maintenance avelumab. The primary end point was overall survival, assessed among all patients who underwent randomization (overall population) and among those with tumors positive for programmed cell death ligand 1 (PD-L1). Secondary end points included progression-free survival and safety.
Among all 700 patients who underwent randomization, the addition of maintenance avelumab to best supportive care significantly prolonged overall survival as compared with best supportive care alone (control). Overall survival at 1 year was 71.3% in the avelumab group and 58.4% in the control group (median overall survival, 21.4 months vs. 14.3 months; hazard ratio for death, 0.69; 95% confidence interval CI, 0.56 to 0.86; P = 0.001). Avelumab also significantly prolonged overall survival in the PD-L1-positive population; overall survival at 1 year was 79.1% in the avelumab group and 60.4% in the control group (hazard ratio, 0.56; 95% CI, 0.40 to 0.79; P<0.001). The median progression-free survival was 3.7 months in the avelumab group and 2.0 months in the control group in the overall population (hazard ratio for disease progression or death, 0.62; 95% CI, 0.52 to 0.75) and 5.7 months and 2.1 months, respectively, in the PD-L1-positive population (hazard ratio, 0.56; 95% CI, 0.43 to 0.73). The incidence of adverse events from any cause was 98.0% in the avelumab group and 77.7% in the control group; the incidence of adverse events of grade 3 or higher was 47.4% and 25.2%, respectively.
Maintenance avelumab plus best supportive care significantly prolonged overall survival, as compared with best supportive care alone, among patients with urothelial cancer who had disease that had not progressed with first-line chemotherapy. (Funded by Pfizer and Merck Darmstadt, Germany; JAVELIN Bladder 100 ClinicalTrials.gov number, NCT02603432.).
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