We present a first-principles theory of resonant relaxation (RR) of a low-mass stellar system orbiting a more massive black hole (MBH). We first extend the kinetic theory of Gilbert to include the ...Keplerian field of a black hole of mass M
•. Specializing to a Keplerian stellar system of mass M ≪ M
•, we use the orbit-averaging method of Sridhar & Touma to derive a kinetic equation for RR. This describes the collisional evolution of a system of N ≫ 1 Gaussian rings in a reduced 5-dim space, under the combined actions of self-gravity, 1 post-Newtonian (PN) and 1.5 PN relativistic effects of the MBH and an arbitrary external potential. In general geometries, RR is driven by both apsidal and nodal resonances, so the distinction between scalar RR and vector RR disappears. The system passes through a sequence of quasi-steady secular collisionless equilibria, driven by irreversible two-ring correlations that accrue through gravitational interactions, both direct and collective. This correlation function is related to a ‘wake function’, which is the linear response of the system to the perturbation of a chosen ring. The wake function is easier to appreciate, and satisfies a simpler equation, than the correlation function. We discuss general implications for the interplay of secular dynamics and non-equilibrium statistical mechanics in the evolution of Keplerian stellar systems towards secular thermodynamic equilibria, and set the stage for applications to the RR of axisymmetric discs in Paper III.
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.
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.
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%.
In a phase 2 study, rucaparib, an inhibitor of poly(ADP-ribose) polymerase (PARP), showed a high level of activity in patients who had metastatic, castration-resistant prostate cancer associated with ...a deleterious
alteration. Data are needed to confirm and expand on the findings of the phase 2 study.
In this randomized, controlled, phase 3 trial, we enrolled patients who had metastatic, castration-resistant prostate cancer with a
,
, or
alteration and who had disease progression after treatment with a second-generation androgen-receptor pathway inhibitor (ARPI). We randomly assigned the patients in a 2:1 ratio to receive oral rucaparib (600 mg twice daily) or a physician's choice control (docetaxel or a second-generation ARPI abiraterone acetate or enzalutamide). The primary outcome was the median duration of imaging-based progression-free survival according to independent review.
Of the 4855 patients who had undergone prescreening or screening, 270 were assigned to receive rucaparib and 135 to receive a control medication (intention-to-treat population); in the two groups, 201 patients and 101 patients, respectively, had a
alteration. At 62 months, the duration of imaging-based progression-free survival was significantly longer in the rucaparib group than in the control group, both in the BRCA subgroup (median, 11.2 months and 6.4 months, respectively; hazard ratio, 0.50; 95% confidence interval CI, 0.36 to 0.69) and in the intention-to-treat group (median, 10.2 months and 6.4 months, respectively; hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001 for both comparisons). In an exploratory analysis in the ATM subgroup, the median duration of imaging-based progression-free survival was 8.1 months in the rucaparib group and 6.8 months in the control group (hazard ratio, 0.95; 95% CI, 0.59 to 1.52). The most frequent adverse events with rucaparib were fatigue and nausea.
The duration of imaging-based progression-free survival was significantly longer with rucaparib than with a control medication among patients who had metastatic, castration-resistant prostate cancer with a
alteration. (Funded by Clovis Oncology; TRITON3 ClinicalTrials.gov number, NCT02975934.).