A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults

The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD), a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring caused by the growing volume of alarm logs. SEMC-AD employs a supervised embedding approach based on deep neural networks, utilizing historical alarm logs and their labels to extract numerical representations for each log, effectively addressing the issue of imbalanced classification due to a small proportion of anomalies in the dataset without employing one-hot encoding. The robustness of the embedding is evaluated by plotting the two most significant principle components of the embedded alarm logs, revealing that anomalies form distinct clusters with similar embeddings. Multivariate normal Gaussian clustering is then applied to these components, identifying clusters with a high ratio of anomalies to normal alarms (above 90%) and labeling them as the anomaly group. To classify new alarm logs, we check if their embedded vectors' two most significant principle components fall within the anomaly-labeled clusters. If so, the log is classified as an anomaly. Performance evaluation demonstrates that SEMC-AD outperforms conventional random forest and gradient boosting methods without embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and XGBoost only detect 86% and 81% of anomalies, respectively. While supervised classification methods may excel in labeled datasets, the results demonstrate that SEMC-AD is more efficient in classifying anomalies in datasets with numerous categorical features, significantly enhancing anomaly detection, reducing operator burden, and improving network maintenance

Efficacy of artichoke leaf extract in non-alcoholic fatty liver disease: A pilot double-blind randomized controlled trial

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide and is potentially treatable, though there are few therapeutic agents available. Artichoke leaf extract (ALE) has shown potential as a hepatoprotective agent. This study sought to determine if ALE had therapeutic utility in patients with established NAFLD. In this randomized double-blind placebo-controlled parallel-group trial, 100 subjects with ultrasound-diagnosed NAFLD were randomized to either ALE 600 mg daily or placebo for a 2-month period. NAFLD response was assessed by liver ultrasound and serological markers including the aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio and AST to platelet ratio index (APRI) score. Ninety patients completed the study (49 ALE and 41 placebo) with no side effects reported. ALE treatment compared with placebo: Doppler sonography showed increased hepatic vein flow (p <.001), reduced portal vein diameter (p <.001) and liver size (p <.001), reduction in serum ALT (p <.001) and AST (p <.001) levels, improvement in AST/ALT ratio and APRI scores (p <.01), and reduction in total bilirubin. ALE supplementation reduced total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and triglyceride concentrations (p =.01). This study has shown beneficial effects of ALE supplementation on both ultrasound liver parameters and liver serum parameters (ALT, AST, APRI ratio, and total bilirubin) in patients with NAFLD. Copyright © 2018 John Wiley & Sons, Ltd

Sargassum macro-algae-derived activated bio-char as a sustainable and cost-effective adsorbent for cationic dyes: A joint experimental and DFT study

Activated bio-char prepared from the pyrolysis and CO2-based physical activation of Sargassum macro-algae was developed as a sustainable and cost-effective adsorbent for malachite green (MG) and methylene blue (MB) adsorption. Prepared activated bio-char was characterized with CHNS, FESEM, Raman, FT-IR and N2 adsorption/desorption methods. Langmuir isotherm and pseudo second order kinetics model best fitted equilibrium and kinetics data. The maximum adsorption capacity for MG and MB were 500 and 204.8 mg/g. The effect of solution pH and temperature on adsorption efficiency was studied and discussed. Acid treatment easily regenerated the adsorbent. After 5 cycles, the MB and MG adsorption efficiency reached 89 % and 78 %, respectively. A Density Functional Theory (DFT) calculation showed that pH-N-(CH3)2 of MB was strongly attracted to the -COOH functional groups in activated bio-char. For MG, the order of preferred interactions between the ph-N-(CH3)2 groups and the functional groups of adsorbent was -CONH2&gt;-COOH&gt;-COH