Paspalum notatum Grass-waste-based Adsorbent for Rhodamine B Removal from Polluted Water

The potential of Paspalum notatum grass waste to adsorb Rhodamine B dye from aqueous phase is reported in this research. The grass waste was activated and characterized through various techniques to analyze the chemical (FTIR), morphological (SEMEDX), and thermal (TGA) changes incorporated through the activation process. The pollutant removal efficiency of the raw and modified adsorbents was studied by varying different process parameters in a batch process. The maximum capacity of adsorption which was observed for grass waste and activated grass waste was 54 mg g–1 and 72.4 mg g–1 respectively. Among the various kinetic models, the pseudo-second order model gives the best regression results. However, the intraparticle diffusion-adsorption model showed that the diffusion within pores controlled the adsorption rate. Thermodynamic analysis of this process revealed that Rhodamine B adsorption was endothermic and spontaneous in nature. The results of this study show that grass waste has the potential to be used as an adsorbent for the treatment of colored water. This work is licensed under a Creative Commons Attribution 4.0 International License

Energy-aware Theft Detection based on IoT Energy Consumption Data

With the advent of modern smart grid networks, advanced metering infrastructure provides real-time information from smart meters (SM) and sensors to energy companies and consumers. The smart grid is indeed a paradigm that is enabled by the Internet of Things (IoT) and in which the SM acts as an IoT device that collects and transmits data over the Internet to enable intelligent applications. However, IoT data communicated over the smart grid could however be maliciously altered, resulting in energy theft due to unbilled energy consumption. Machine learning (ML) techniques for energy theft detection (ETD) based on IoT data are promising but are nonetheless constrained by the poor quality of data and particularly its imbalanced nature (which emerges from the dominant representation of honest users and poor representation of the rare theft cases). Leading ML-based ETD methods employ synthetic data generation to balance the training the dataset. However, these are trained to maximise average correct detection instead of ETD. In this work, we formulate an energy-aware evaluation framework that guides the model training to maximise ETD and minimise the revenue loss due to mis-classification. We propose a convolution neural network with positive bias (CNN-B) and another with focal loss CNN (CNN-FL) to mitigate the data imbalance impact. These outperform the state of the art and the CNN-B achieves the highest ETD and the minimum revenue loss with a loss reduction of 30.4% compared to the highest loss incurred by these methods

Evaluation of Records of Oral and Maxillofacial Surgery Cases Reported at Abbasi Shaheed Hospital and Karachi Medical and Dental College, Pakistan

Background: Oral and Maxillofacial Surgery department is a diverse field in dentistry. Record maintenance has been established as one of the key factors in the success and integrity of health care institutes.Objective: The objective of the study was to evaluate the records of oral and maxillofacial surgery casesreported to oral and maxillofacial surgery department, Abbasi Shaheed Hospital and oral surgery OPD ofKarachi Medical and Dental College.Methods: Cross sectional study was conducted in at ASH and KMDC from July 2019 to September 2019.The data from January 2017 to July 2019 was retrospectively noted through electronic surgical recordof ASH and records of the Oral Surgery OPD of KMDC. Inclusion criteria was patients records of bothgenders of 5–70 years age, having complaint of any oral or dental pathology or pathologies, trauma andimpactions. Data was calculated manually by calculating frequencies and percentages for the trauma,impaction and pathology cases of patients.Results: In 2017, 239 cases were treated under general anesthesia from which trauma 11. 45% (n=11),followed by 48. 11% (n=115) cases of oral pathologies, total 11.7% (n=28) cases of complicated exodontias. In2018, among 211, 51.1% (n=108) cases were trauma followed by 39.3% (n=83) cases of oral pathologies,whereas, total 9.4% (n=20) complicated exodontias cases were observed. During 2019 (January to July),168 cases 36.2% (n=62) cases were diagnosed as trauma, in oral pathology, overall 36.2% (n=62) caseswere surgically excised. Total 23.2% (n=39) complicated exodontias. In 2017, 25122 cases were reported in Surgery OPD of Karachi Medical and Dental College. Total 36.2% (n=9097) teeth were extracted from which 1.93% (n=486) cases were surgical impaction. On the other hand, 1.65% (n=416) patients were treated through minor oral surgeries. In 2018, 29008 cases were reported in Surgery OPD. Total 42.7% (n=12377) teeth were extracted from which 0.92% (n=268) cases were surgical impaction. On the other hand, 0.71% (n=208) patients were treated through minor surgeries. In 2019, January till July 13028 cases were reported in Surgery OPD. Total42.6% (n=5559) teeth were extracted from which 0.66% (n=87) cases were surgical impaction. On the other hand, 0.68% (n=89) patients were treated through minor surgeries.Conclusion: It has been concluded that evaluation of the records of oral and maxillofacial surgery casesreported to oral and maxillofacial surgery department, Abbasi Shaheed Hospital and oral surgery OPD ofKarachi Medical and Dental College were high and appropriate measures should be taken in order tomanage these problems timely and effectively

Deep learning-based meta-learner strategy for electricity theft detection

Electricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers’ electricity consumption (EC) data. Based on the collected data, it is possible to identify the normal and malicious behavior of consumers by analyzing the data using machine learning (ML) and deep learning methods. This paper proposes a deep learning-based meta-learner model to distinguish between normal and malicious patterns in EC data. The proposed model consists of two stages. In Fold-0, the ML classifiers extract diverse knowledge and learns based on EC data. In Fold-1, a multilayer perceptron is used as a meta-learner, which takes the prediction results of Fold-0 classifiers as input, automatically learns non-linear relationships among them, and extracts hidden complicated features to classify normal and malicious behaviors. Therefore, the proposed model controls the overfitting problem and achieves high accuracy. Moreover, extensive experiments are conducted to compare its performance with boosting, bagging, standalone conventional ML classifiers, and baseline models published in top-tier outlets. The proposed model is evaluated using a real EC dataset, which is provided by the Energy Informatics Group in Pakistan. The model achieves 0.910 ROC-AUC and 0.988 PR-AUC values on the test dataset, which are higher than those of the compared models

Lipid Lowering Efficacy of Pennisetum glaucum Bran in Hyperlipidemic Albino Rats

The objective of the study was to determine lipid lowering efficacy of Pennisetum (P) glaucum (Pearl millet, locally known as bajra), bran in hyperlipidemia albino rats. Simvastatin, (Tablet survive®), was used as cholesterol lowering synthetic drug. The period of 0-15 days was considered as a lead-in period to induce hyperlipidemia with atherogenic diet in albino rats. P. glaucum bran at dose rate of 2, 4 and 6 g/kg BW showed lipid lowering efficacy in hyperlipidemic rats at post-treatment days 30, 45 and 60. At the level of 6 g/kg, P. glaucum bran was able to produce a significant (P<0.05) increase in HDL- cholesterol (47%) and fall in other lipid profile parameters i.e. total lipids (41%), triglycerides(48%), total cholesterol (39%) and LDL- cholesterol (55%). P. glaucum 6 g/kg also reduced total cholesterol in liver tissue and increased fecal bile acid secretion. The results of present study suggest that 6 g/kg P. glaucum bran and 0.6 mg/kg Simvastatin were equally effective in treating hyperlipidemia in albino rats. Moreover, the potency of P. glaucum for stimulating fecal bile acid secretion in albino rats may safely be conceived, at least, as a part of mechanisms for its antihyperlipidemic efficacy