Statistical computational optimization approach for photocatalytic-ozonation decontamination of metronidazole in aqueous media using CuFe2O4/SiO2/ZnO nanocomposite.

The increasing use of pharmaceuticals and the ongoing release of drug residues into the environment have resulted in significant threats to environmental sustainability and water safety. In this sense, developing a robust and easy-recovered magnetic nanocomposite with eminent photocatalytic activity is very imperative for detoxifying pharmaceutical compounds. Herein, a systematic study was conducted to investigate the photocatalytic ozonation for eliminating metronidazole (MET) from aqueous media utilizing the CuFe2O4/SiO2/ZnO heterojunction under simulated sunlight irradiation. The composite material was fabricated by a facile hydrothermal method and diagnosed by multiple advanced analytical techniques. Modelling and optimization of MET decontamination by adopting the central composite design (CCD) revealed that 90 % of MET decontamination can be achieved within 120 min of operating time at the optimized circumstance (photocatalyst dose: 1.17 g/L, MET dose: 33.20 mg/L, ozone concentration: 3.99 mg/min and pH: 8.99). In an attempt to scrutinize the practical application of the CuFe2O4/SiO2/ZnO/xenon/O3 system, roughly 56.18% TOC and 73% COD were removed under the optimized operational circumstances during 120 min of degradation time. According to the radical quenching experiments, hydroxyl radicals (HO•) were the major oxidative species responsible for the elimination of MET. The MET degradation rate maintained at 83% after seven consecutive runs, manifesting the efficiency of CuFe2O4/SiO2/ZnO material in the MET removal. Ultimately, the photocatalytic ozonation mechanism over the CuFe2O4/SiO2/ZnO heterojunction of the fabricated nanocomposites was rationally proposed for MET elimination. In extension, the results drawn in this work indicate that integrating photocatalyst and ozonation processes by the CuFe2O4/SiO2/ZnO material can be applied as an efficient and promising method to eliminate tenacious and non-biodegradable contaminants from aqueous environments.

On the frontiers of Twitter data and sentiment analysis in election prediction: a review.

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals.

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.

Biomedical and catalytic applications of agri-based biosynthesized silver nanoparticles.

Nanotechnology has been recognized as the emerging field for the synthesis, designing, and manipulation of particle structure at the nanoscale. Its rapid development is also expected to revolutionize industries such as applied physics, mechanics, chemistry, and electronics engineering with suitably tailoring various nanomaterials. Inorganic nanoparticles such as silver nanoparticles (Ag-NPs) have garnered more interest with their diverse applications. In correspondence to green chemistry, researchers prioritize green synthetic techniques over conventional ones due to their eco-friendly and sustainable potential. Green-synthesized NPs have proven more beneficial than those synthesized by conventional methods because of capping by secondary metabolites. The present study reviews the various means being used by the researchers for the green synthesis of Ag-NPs. The morphological characteristics of these NPs as obtained from numerous characterization techniques have been explored. The potential applications of bio-synthesized Ag-NPs viz. Antimicrobial, antioxidant, catalytic, and water remediation along with the plausible mechanisms have been discussed. In addition, toxicity analysis and biomedical applications of these NPs have also been reviewed to provide a detailed overview. The study signifies that biosynthesized Ag-NPs can be efficiently used for various applications in the biomedical and industrial sectors as an environment-friendly and efficient tool.

Sustainable green nanoadsorbents for remediation of pharmaceuticals from water and wastewater: A critical review.

In the last three decades, pharmaceutical research has increased tremendously to offer safe and healthy life. However, the high consumption of these harmful drugs has risen devastating impact on ecosystems. Therefore, it is worldwide paramount concern to effectively clean pharmaceuticals contaminated water streams to ensure safer environment and healthier life. Nanotechnology enables to produce new, high-technical material, such as membranes, adsorbent, nano-catalysts, functional surfaces, coverages and reagents for more effective water and wastewater cleanup processes. Nevertheless, nano-sorbent materials are regarded the most appropriate treatment technology for water and wastewater because of their facile application and a large number of adsorbents. Several conventional techniques have been operational for domestic wastewater treatment but are inefficient for pharmaceuticals removal. Alternatively, adsorption techniques have played a pivotal role in water and wastewater treatment for a long, but their rise in attraction is proportional with the continuous emergence of new micropollutants in the aquatic environment and new discoveries of sustainable and low-cost adsorbents. Recently, advancements in adsorption technique for wastewater treatment through nanoadsorbents has greatly increased due to its low production cost, sustainability, better physicochemical properties and high removal performance for pharmaceuticals. Herein, this review critically evaluates the performance of sustainable green nanoadsorbent for the remediation of pharmaceutical pollutants from water. The influential sorption parameters and interaction mechanism are also discussed. Moreover, the future prospects of nanoadsorbents for the remediation of pharmaceuticals are also presented.

Occurrence and health risk assessment of arsenic and heavy metals in groundwater of three industrial areas in Delhi, India.

Groundwater is a primary natural water source in the absence of surface water bodies. Groundwater in urban environments experiences unprecedented stress from urban growth, population increase, and industrial activities. This study assessed groundwater quality in terms of arsenic and heavy metal contamination in three industrial areas (Shahdara, Jhilmil, and Patparganj), Delhi, India. The water quality was assessed over a 3-year time interval (i.e., 2015 and 2018). The groundwater constituents investigated were As, Fe, Cr, Cd, Ni, Zn, Mn, Cu, and Pb. Metal index and heavy metal pollution indexes were estimated to assess groundwater pollution. The health risk was evaluated in terms of non-carcinogenic and carcinogenic risk assessment. Patparganj industrial area saw increment in concentration for Cu 0.23 mg/L (2015)-0.85 mg/L (2018), Zn 0.51 mg/L (2015)-7.2 mg/L (2018), Fe 0.32 mg/L (2015)-0.9 mg/L (2018), Cr 0.21 mg/L (2015)-0.26 mg/L (2018), Mn 0.14 mg/L (2015)-0.25 mg/L (2018), Ni 0.04 mg/L (2015)-0.34 mg/L (2018), and As 0.01 mg/L (2015)-0.18 mg/L (2018). Cd and Pb concentrations were observed to decrease by 40-90 % and 85-99% for all the three industrial areas. Metal index and heavy metal index values were found to be >1 for all locations. The risk quotient value > 1 was observed for all locations in the year 2015 but was found to increase further to a range of RQ 10-62 in the year 2018, inferring increased non-carcinogenic risk to consumers. The carcinogenic risk was significant with respect to Fe (0.2-0.7), Zn (0.001-0.007), and As (0.002-0.003) for all locations in the year 2015. This study concludes that groundwater in the three industrial areas is highly polluted and is not fit for human consumption. Further studies are required to explore possible control measures and develop methods to mitigate groundwater pollution, sustainable management, and optimized use to conserve it for future generations.

Particulate matter concentration and health risk assessment for a residential building during COVID-19 pandemic in Abha, Saudi Arabia.

Building and its environment are in focus owing to health impact attributed to indoor air quality. This study was carried out to assess indoor air quality in terms of particulate matter (PM) and carbon dioxide in a residential building, during COVD-19 pandemic lockdown from March 25 to April 23, 2020, Abha, Saudi Arabia. The PM concentration range for kitchen, bedroom, and hall were 40,000-81000 µg/m3 (PM0.3), 15,000-26000 µg/m3 (PM0.5), 4000-6000 µg/m3 (PM1), 1100-1500 µg/m3 (PM2.5), 160-247 µg/m3 (PM5), and 60-95 µg/m3 (PM10). The results of this study suggest that bedroom needs to be ventilated as CO2 concentration was reaching 700 ppm during sleep hours. PM concentration was exceeding 300 µg/m3 (unhealthy) for all particle sizes of PM0.3, PM0.5, PM1, and PM2.5 except for PM10 which was also above safe limits (0-50 µg/m3). Also, with influential habit (aromatic smoke), these concentrations increased 2-28 times for PM. The hazard quotient value greater than 1 revealed potential health risk to the inhabitants. Hence, future studies are needed for developing indoor air quality guidelines for residential buildings in Saudi Arabia and better planning and management of energy consumption.

Where we missed? Middle East Respiratory Syndrome (MERS-CoV) epidemiology in Saudi Arabia; 2012-2019.

MERS-CoV first case was reported on 23rd November 2012 in Saudi Arabia, Since, then MERS has remained on World Health Organization (WHO) Blueprint list and declared pandemic. This study was conducted on MERS lab confirmed cases reported to Ministry of Health, Saudi Arabia and WHO for year 2012-2019. The epidemiology was investigated based on infection rate, death rate, case fatality rate, Gender, Age group, and Medical conditions (Comorbid and Symptomatic). The overall median age of infected male was 58 years and of female was 45 years. While average mortality age in male was 60 years and of female was 65 years which is greater than the global average of 50 years. The results also report that specially after age of 40 years in both men and women, chances of infection are more while comorbidities increase the infection rate. The men are more susceptible to infection than women. In case of asymptomatic distribution trend was vice versa with 69.4% women and 30.6% in men. Second, most infected age group was reduced by 20 years in case of men with 47.37% infection for age group of 20-39 years. This was also observed in age-group of 20-39 years for no comorbid cases (men (50%) & women (79%)). This explains MERS-CoV prevalence in Saudi Arabia, as young and healthy population were infected, and acted as carrier and on coming in contact with vulnerable population (Elderly, chronic and comorbid) transferred the infection. Hence, MERS-CoV outbreak kept on happening from time to time over past years. This finding might very well explain the exponential spread of Novel CoV-19 globally, as initial control measures required older people to stay indoors while younger generation brought infection from outside. Further studies are required for epidemiology analysis based on clusters, travel history and specific disease related mortality.

Copper (II) removal in a column reactor using electrocoagulation: Parametric optimization by response surface methodology using central composite design.

In the present work, electrocoagulation was applied for copper removal from aqueous solution employing iron electrodes in a cylindrical reactor. A four-factorial central composite design (CCD) based on response surface methodology (RSM) was applied to study the effect of various process parameters on removal efficiency and energy consumption as the responses. On optimization, maximum removal efficiency up to 95% was attained with energy consumption as 0.903 W-hour per gram removal of Cu (II) at applied current 0.26 A, initial copper concentration of 27.8 ppm, application time of 5.4 min and pH 7. The interaction between the process variables was evaluated by using the obtained 3-D plots. The models generated were validated by analysis of variance (ANOVA). Studies carried on Cu (II) removal rate showed adsorption suited pseudo-Ist order kinetics best. Overall, the electrocoagulation process proved efficient, low cost and a promising alternative to conventional treatment procedures in removing Cu (II). PRACTITIONER POINTS: Adsorption over hydroxide/polyhydroxide complexes of Fe assisted in enhanced removal of Cu (II) by EC. Higher concentrations treated at lower current but longer duration reduces energy. pH was found to be the deterministic factor for coagulation. CCD-based optimization reduced energy consumption substantially.

FBCSP-based Multi-class Motor Imagery Classification using BP and TDP features.

Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment. Filter Bank Common Spatial Pattern (FBCSP) algorithm is a promising feature extraction technique to deal with subject-specific behavior in Motor Imagery classification. Using FBCSP on EEG we have developed an accurate but less computationally expensive approach by making use of Time Domain Parameters (TDP) and Band Power (BP) features to form a combined feature set. The novelty of our approach is also the use of optimal time segmentation to overcome non-stationary state behavior of Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS) over time. We analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV, the highest reported for FBCSP approaches, along with the lowest inter-subject variation.

DeepMI: Deep Learning for Multiclass Motor Imagery Classification.

In Brain-Computer Interface (BCI) Research,Electroencephalography (EEG) has obtained great attention for biomedical applications. In BCI system, feature representation and classification are important tasks as the accuracy of classification highly depends on these stages. In this paper, we propose a model in which Common Spatial Pattern (CSP) is used to discriminate inter-class data using co-variance maximization and Fast Fourier Transform Energy Map (FFTEM) is used for feature selection and mapping of 1D data into 2D data (energy maps). Convolutional Neural Network is used for classification of multi-class Motor Imagery (MI) signals. Further, this paper investigates near-optimal parameter selection for feature mapping, frequency bands selection, and temporal segmentation. It is shown that our proposed method outperformed the reported methods by achieving 0.61 mean kappa value.

Differentiation and quantification of xylene isomers by combining headspace solid-phase microextraction/gas chromatography and self-ion molecule reaction in an ion trap tandem mass spectrometry.

A headspace solid-phase microextraction (HS-SPME) method using polydimethylsiloxane (PDMS)-coated fiber coupling with GC/self-ion molecule reaction (SIMR)/MS/MS (GC/SIMR/MS/MS) has been developed for the differentiation of o-, m-, and p-xylene isomers. The optimization parameters of this method are: extraction time 20 min, stirring rate 1200 rpm, sampling temperature 28 degrees C, and salt concentration 5%. The LODs were found to be in the range of 3-9 ng/mL under the SIM mode of GC/MS. The RSDs were below 1% (n=5). The linear dynamic range of the calibration curve was from 0.05 to 10.0 microg/mL with correlation coefficient (r2)>0.9089. The advantages of this technique are sensitive, selective, ease of operation, simple device, solvent free, and extremely little sample consumption. This technique is the first example using SIMR combined MS/MS applied in the GC/MS and it can be applied to other volatile compounds for future application.

Validated spectrophotometric methods for the determination of amiodarone hydrochloride in commercial dosage forms using p-chloranilic acid and 2,3-dichloro-5,6-dicyano-1,4-benzoquinone.

Two simple, sensitive and economical spectrophotometric methods have been developed for the determination of amiodarone hydrochloride in pure form and commercial dosage form. These methods (A and B) are based on the reaction of amiodarone base as n-electron donor with p-chloranilic acid and 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) as pi-acceptors to give highly colored complex species which absorb maximally at 535 and 570 nm, respectively. Beer's law is obeyed in the concentration ranges 10.0 - 360.0 and 2.0 - 65.0 microg ml(-1) for methods A and B, respectively. Application of the proposed methods to commercial pharmaceutical tablets are presented.