Postponement growth and antioxidative response of Brassica nigra on CuO and ZnO nanoparticles exposure under soil conditions

Due to unique physiochemical properties, nanoparticles (NPs) have acquired substantial attention in the field of research. However, threats of ecotoxicity and phytotoxicity have limited their biological applications. In this study in vivo experiments were performed to determine the effect of CuO (12.5, 25 and 50 mg/kg) and ZnO (200, 400 and 600 mg/kg) NPs on growth, and antioxidant activities of Brassica nigra. The results showed that CuO NPs did not affect the seed germination while presence of ZnO NPs in the soil generated an inhibitory effect. Both CuO and ZnO NPs positively influenced the growth of stem and other physiological parameters i.e. stem height increased (23%) at 50 mg/kg CuO while root length decreased (up to 44%) with an increase in the concentration of NPs. Phytochemical screening of apical, middle and basal leaves showed elevated phenolic and flavonoid contents in the range of 15.3–59 μg Gallic Acid Equivalent (GAE)/mg Dry Weight (DW) and 10–35 μg Querceitin Equivalent (QE)/mg DW, respectively, in NPs‐treated plants. Antioxidant activity was higher in CuO NPs‐treated plants as compared to ZnO and control plants. Results conclude that CuO and ZnO NPs at low concentrations can be exploited as nanofertilisers in agriculture fields.Inspec keywords: biochemistry, enzymes, renewable materials, crops, nanoparticles, soil, nanofabrication, zinc compounds, organic compounds, agricultural products, toxicology, nanobiotechnologyOther keywords: antioxidative response, ZnO nanoparticles exposure, soil conditions, unique physiochemical properties, germination, antioxidant activities, brassica nigra plant, antioxidant activity, CuO NP‐treated plants, control plants, ZnO NPs effect, mass 15.3 mug to 59.0 mug, mass 10.0 mug to 35.0 mug, CuO, ZnO     

Metal accumulation potential of wild plants in tannery effluent contaminated soil of Kasur, Pakistan: field trials for toxic metal cleanup using Suaeda fruticosa

The tannery effluent contaminated lands, adjacent to Depalpur Road, Kasur, Pakistan, have been rendered infertile due to long term effluent logging from the leather industry. The area has been colonized by twelve plant species among which Suaeda fruticosa, Salvadora oleoides and Calatropis procera have been found to be the most common and high biomass producing plants. S. fruticosa was subjected to further experimentation because of its high biomass and phytoextraction capabilities for metals. The pot and field experiments were carried out simultaneously. Pot experiments were conducted using the same field soil in column pots with stoppard bottoms to obtain the leachate. EDTA treatment caused a greater solubility of Cr in the soil pore water. In higher doses more amount of the heavy metal was leached. The increase in the amount of EDTA significantly caused a decrease in the biomass of plants without toxicity symptoms. A higher biomass of plants was observed in the field as compared to the pot experiment. The greatest amount of Na was accumulated by leaves of S. fruticosa followed by stem and roots. Similarly, the greatest amount of Cr was bioaccumulated by leaves of S. fruticosa, but followed by roots and then stem. S. fruticosa can be employed in rehabilitation of tannery effluent contaminated soil using small doses of EDTA.     

A hybrid composite of rGO/TiO2 as a double layer electrode with improved capacitance performance

Reduced graphene oxide (rGO) has unique properties that can revolutionize the performance of the functional devices. rGO hybrids can be designed with transition metal oxides for improved energy storage applications. Herein, a hybrid composite of conductive rGO with titanium dioxide, designed by a simple hydrothermal method, is reported to demonstrate a high double layer capacitance in aqueous electrolyte systems. The mesoporous structure of the composite provides short ion diffusion pathways and the resultant capacitance of the material is 334 F g⁻¹ with ~77% capacitance retention after 7000 charge-discharge cycles.     

Time-dependent hydromagnetic stagnation point flow of a Maxwell nanofluid with melting heat effect and amended Fourier and Fick's laws

An unsteady stagnation point flow of a Maxwell fluid over a unidirectional linearly stretching sheet is studied under the influence of a magnetic field. The parabolic energy equation, which is based on parabolic Fourier law is replaced with a hyperbolic energy equation incorporating the heat flux model of Cattaneo–Christov. The Buongiorno model is used to characterize the properties of nanofluids using thermophoresis and Brownian diffusion coefficients. The phenomenon of melting heat transfer and slip mechanism is also embodied in the present study. Coupled nonlinear differential equations have appeared when the specified similarity transformations are applied. The mathematical problem is tackled via the homotopy analysis method. The impact of important physical parameters on the velocity, concentration, and temperature are highlighted via graphs. To verify our present results, a comparison is given with a limiting case with an already published article. It is witnessed through the graphs that the higher unsteadiness parameter and melting heat coefficient both are responsible for the reduction in the velocity and temperature of the nanofluid. Also, the velocity slip parameter detracts the velocity profile and affiliated boundary layer thickness of the Maxwell nanofluid.     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

Comparison of total ozone column observations from space-borne Ozone Monitoring Instrument with ground-based Dobson Ozone Spectrophotometer measurements at an urban location in Indo-Gangetic Basin

This article presents a comparison analysis of OMIT (Ozone Monitoring Instrument retrieved overpass total ozone column (TOC)), and DOST (Dobson Ozone Spectrophotometer observed TOC) over Delhi during a period from October 2004 to June 2011. Megacity Delhi, located in Indo-Gangetic Basin, is an important site for comparison of ground-based and satellite retrieved TOCs due to significant anthropogenic emissions of ozone precursors, large shift in seasons, and large-scale crop residue burning in the region. DOST and OMIT data show an overall bias of 3.07% and significant correlation with coefficient of determination R² = 0.73. Large seasonal fluctuations in the biases and correlations have been observed ranging from 2.46% (winter) to 3.82% (spring), and R² = 0.84 (winter) to R² = 0.09 (summer), respectively. The large biases are attributed to changes in temperature, cloud cover, pollutants emissions from urban area, and crop-residue burning events. We also find notable variations in correlations between the datasets due to the varying burden of absorbing aerosols from open field crop-residue burning. The R² has changed from 0.67 (for aerosol optical depth, AOD 1.5–3.5) to 0.77 (for AOD 0–0.99). The dependence of the bias on solar zenith angle, cloud fraction, and satellite distance is also discussed. A simple linear regression analysis is applied to check the linkage between DOST and OMIT. The influence of atmospheric air temperature and relative humidity on OMIT at different pressure levels between 1000 and 20 hPa has been discussed.     

Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human gait patterns. In both the MLMCS and MBMCS, we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity (walk and running). Using five state of the art machine learning algorithms, we obtained 44.6% and 64.3% accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm (two angles as features). In the second set of experiments, we used six machine learning algorithms to obtain 65.9% accuracy with the k-nearest neighbor (KNN) algorithm (two angles as features) and 74.6% accuracy with an ensemble algorithm. Also, by increasing features (6 angles), we obtained higher accuracy of 99.3% in MBMCS for person recognition and 98.1% accuracy in MBMCS for activity recognition using the KNN algorithm. MBMCS is computationally expensive and if we re-design the model of OpenPose with more body joint points and employ more features, MLMCS (low-cost system) can be an effective approach for video data analysis in a person identification and activity recognition process.     

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.