Biochar influences nitrogen and phosphorus dynamics in two texturally different soils.

Nitrogen (N) and phosphorus (P) are vital for crop growth. However, most agricultural systems have limited inherent ability to supply N and P to crops. Biochars (BCs) are strongly advocated in agrosystems and are known to improve the availability of N and P in crops through different chemical transformations. Herein, a soil-biochar incubation experiment was carried out to investigate the transformations of N and P in two different textured soils, namely clay loam and loamy sand, on mixing with rice straw biochar (RSB) and acacia wood biochar (ACB) at each level (0, 0.5, and 1.0% w/w). Ammonium N (NH4-N) decreased continuously with the increasing incubation period. The ammonium N content disappeared rapidly in both the soils incubated with biochars compared to the unamended soil. RSB increased the nitrate N (NO3-N) content significantly compared to ACB for the entire study period in both texturally divergent soils. The nitrate N content increased with the enhanced biochar addition rate in clay loam soil until 15 days after incubation; however, it was reduced for the biochar addition rate of 1% compared to 0.5% at 30 and 60 days after incubation in loamy sand soil. With ACB, the net increase in nitrate N content with the biochar addition rate of 1% remained higher than the 0.5% rate for 60 days in clay loam and 30 days in loamy sand soil. The phosphorus content remained consistently higher in both the soils amended with two types of biochars till the completion of the experiment.

Interactive effects of long-term management of crop residue and phosphorus fertilization on wheat productivity and soil health in the rice-wheat.

In the context of degradation of soil health, environmental pollution, and yield stagnation in the rice-wheat system in the Indo-Gangetic Plains of South Asia, an experiment was established in split plot design to assess the long-term effect of crop residue management on productivity and phosphorus requirement of wheat in rice-wheat system. The experiment comprised of six crop residue management practices as the main treatment factor with three levels (0, 30 and 60 kg P2O5 ha-1) of phosphorus fertilizer as sub-treatments. Significant improvement in soil aggregation, bulk density, and infiltration rate was observed under residue management (retention/incorporation) treatments compared to residue removal or residue burning. Soil organic carbon (SOC), available nutrient content (N, P, and K), microbial count, and enzyme activities were also significantly higher in conservation tillage and residue-treated plots than without residue/burning treatments. The residue derived from both crops when was either retained/incorporated improved the soil organic carbon (0.80%) and resulted in a significant increase in SOC (73.9%) in the topsoil layer as compared to the conventional practice. The mean effect studies revealed that crop residue management practices and phosphorus levels significantly influenced wheat yield attributes and productivity. The higher grain yield of wheat was recorded in two treatments, i.e. the basal application of 60 kg P2O5 ha-1 without residue incorporation and the other with half the P-fertilizer (30 kg P2O5 ha-1) with rice residue only. The grain yield of wheat where the rice and wheat residue were either retained/incorporated without phosphorus application was at par with 30 and 60 kg P2O5ha-1. Phosphorus levels also significantly affected wheat productivity and available P content in the soil. Therefore, results suggested that crop residue retention following the conservation tillage approach improved the yield of wheat cultivated in the rice-wheat cropping system.

Soil type and integrated nitrogen nutrient-rice straw residue management techniques affect soil microbes, enzyme activities and yield of wheat crop.

Sporadic burning of rice straw and the particulate air pollution caused consequently have created a pressing need for identification of practical environmentally sound in situ rice residue management methods. However, the agronomic interventions associated with the agri-inputs particularly the type of nitrogen fertilizer source must be worked out for these interventions. In a two-year field study performed at two different locations representing sandy loam and clay loam soil types, zero tillage with application of nitrophosphate (applied as basal dose through drilling) in combination with urea (applied at 1st irrigation + 3 foliar sprays of urea at weekly interval) significantly enhanced the grain and straw yield of wheat. The soil microbial viable cell counts and dehydrogenase and urease enzyme activities were also recorded to be highest in this treatment indicating the occurrence of higher living microbial population. The treatment × response variable Principle component analysis (PCA) biplot depicted relative variation among the residue management treatments/Nitrogen fertilizer sub-treatments and the enzyme activities as response variables. A variation in the soil organic content components was recognized through Fourier transform infra-red spectroscopy (FT-IRS) studies. Irrespective of the soil types under study, the FT-IR spectra exhibited presence of the aromatic carbon functional groups in residue incorporated treatments as compared to the no residue incorporation treatment.

An AI-Enabled ensemble method for rainfall forecasting using Long-Short term memory.

Rainfall prediction includes forecasting the occurrence of rainfall and projecting the amount of rainfall over the modeled area. Rainfall is the result of various natural phenomena such as temperature, humidity, atmospheric pressure, and wind direction, and is therefore composed of various factors that lead to uncertainties in the prediction of the same. In this work, different machine learning and deep learning models are used to (a) predict the occurrence of rainfall, (b) project the amount of rainfall, and (c) compare the results of the different models for classification and regression purposes. The dataset used in this work for rainfall prediction contains data from 49 Australian cities over a 10-year period and contains 23 features, including location, temperature, evaporation, sunshine, wind direction, and many more. The dataset contained numerous uncertainties and anomalies that caused the prediction model to produce erroneous projections. We, therefore, used several data preprocessing techniques, including outlier removal, class balancing for classification tasks using Synthetic Minority Oversampling Technique (SMOTE), and data normalization for regression tasks using Standard Scalar, to remove these uncertainties and clean the data for more accurate predictions. Training classifiers such as XGBoost, Random Forest, Kernel SVM, and Long-Short Term Memory (LSTM) are used for the classification task, while models such as Multiple Linear Regressor, XGBoost, Polynomial Regressor, Random Forest Regressor, and LSTM are used for the regression task. The experiment results show that the proposed approach outperforms several state-of-the-art approaches with an accuracy of 92.2% for the classification task, a mean absolute error of 11.7%, and an R2 score of 76% for the regression task.

Interactive effect of land use systems on depth-wise soil properties and micronutrients minerals in North-Western, India.

Micronutrients play a vital role in improving growth and performance of different crops. Management of soil micronutrients for better crop production needs sound understanding of their status and causes of variability. Therefore, in order to evaluate the changes in soil properties and micronutrient contents of soils, an experiment was conducted with soil samples from six soil depths i.e. 0-10, 10-20, 20-40,40-60, 60-80 and 80-100 cm of four prominent land-use systems viz. forest, horticulture, crop land and barren land. Amongst these, the maximum contents of OC (0.36%), clay (19.4%), DTPA-Zn (1.14 mg kg-1), Fe (11.78 mg kg-1), Mn (5.37 mg kg-1), Cu (0.85 mg kg-1) and Ni (1.44 mg kg_1) were observed in soils of forest land use system followed by horticulture, crop land and barren land, respectively. Also, soils of forest landpossessed 29.5, 21.3, 58.4, 51.8 and 44.0% higher DTPA-Zn, Fe, Mn, Cu and Ni as compared to crop land use system. Interactive influence of land use systems and soil depths on distribution of DTPA extractable micronutrients was found to be positive with maximum content at 0-10 cm depth of forest land use and lowest at 80-100 cm of barren land use system, respectively. Correlation analysis explicit positive and significant relationship of OC with DTPA Zn (r = 0.81), Fe (r = 0.79), Mn (r = 0.77), Cu (r = 0.84) andNi (r = 0.80), whereas the correlation results among DTPA micronutrients indicated the highest positivecorrelation of Ni with Cu (r = 0.95) and Mn (r = 0.93) followed by Fe with Zn (r = 0.93). Therefore, inclusion of forest and horticulture land use in crop lands or shift of land use from forest based to crop land resulted in renewal of degraded soil which could be beneficial for enhancing agricultural sustainability.

A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI.

During the last few decades, the quality of water has deteriorated significantly due to pollution and many other issues. As a consequence of this, there is a need for a model that can make accurate projections about water quality. This work shows the comparative analysis of different machine learning approaches like Support Vector Machine (SVM), Decision Tree (DT), Random Forest, Gradient Boost, and Ada Boost, used for the water quality classification. The model is trained on the Water Quality Index dataset available on Kaggle. Z-score is used to normalize the dataset before beginning the training process for the model. Because the given dataset is unbalanced, Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Experiments results depict that Random Forest and Gradient Boost give the highest accuracy of 81%. One of the major issues with the machine learning model is lack of transparency which makes it impossible to evaluate the results of the model. To address this issue, explainable AI (XAI) is used which assists us in determining which features are the most important. Within the context of this investigation, Local Interpretable Model-agnostic Explanations (LIME) is utilized to ascertain the significance of the features.

Text-Based Emotion Recognition Using Deep Learning Approach.

Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.

An AI-enabled pre-trained model-based Covid detection model using chest X-ray images.

The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.

Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks.

Brain cancer ranks tenth on the list of leading causes of death in both men and women. Biopsy is one of the most used methods for diagnosing cancer. However, the biopsy process is quite dangerous and take a long time to reach a decision. Furthermore, as the tumor size is rising quickly, non-invasive, automatic diagnostic equipment is required which can automatically detect the tumor and its stage precisely in a few seconds. In recent years, techniques based on Machine Learning and Deep Learning (DL) for detecting and classifying cancers has gained remarkable success in recent years. This paper suggested an ensemble method for detecting and classifying brain tumor and its stages using brain Magnetic Resonance Imaging (MRI). A modified InceptionResNetV2 pre-trained model is used for tumor detection from MRI image. After tumor detection, a combination of InceptionResNetV2 and Random Forest Tree (RFT) is used to determine the cancer stage, which includes glioma, meningioma, and pituitary cancer. The size of the dataset is small, so C-GAN (Cyclic Generative Adversarial Networks) is used to increase the dataset size. The experiment results demonstrate that the suggested tumor detection and tumor classification models achieve the accuracy of 99% and 98%, respectively.

Tribological Behavior of Carbon-Based Nanomaterial-Reinforced Nickel Metal Matrix Composites.

Carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) with exceptional mechanical, thermal, chemical, and electrical properties are enticing reinforcements for fabricating lightweight, high-strength, and wear-resistant metal matrix composites with superior mechanical and tribological performance. Nickel-carbon nanotube composite (Ni-CNT) and nickel-graphene nanoplatelet composite (Ni-GNP) were fabricated via mechanical milling followed by the spark plasma sintering (SPS) technique. The Ni-CNT/GNP composites with varying reinforcement concentrations (0.5, 2, and 5 wt%) were ball milled for twelve hours to explore the effect of reinforcement concentration and its dispersion in the nickel microstructure. The effect of varying CNT/GNP concentration on the microhardness and the tribological behavior was investigated and compared with SPS processed monolithic nickel. Ball-on-disc tribological tests were performed to determine the effect of different structural morphologies of CNTs and GNPs on the wear performance and coefficient of friction of these composites. Experimental results indicate considerable grain refinement and improvement in the microhardness of these composites after the addition of CNTs/GNPs in the nickel matrix. In addition, the CNTs and GNPs were effective in forming a lubricant layer, enhancing the wear resistance and lowering the coefficient of friction during the sliding wear test, in contrast to the pure nickel counterpart. Pure nickel demonstrated the highest CoF of ~0.9, Ni-0.5CNT and Ni-0.5GNP exhibited a CoF of ~0.8, whereas the lowest CoF of ~0.2 was observed for Ni-2CNT and Ni-5GNP composites. It was also observed that the uncertainty of wear resistance and CoF in both the CNT/GNP-reinforced composites increased when loaded with higher reinforcement concentrations. The wear surface was analyzed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis to elucidate the wear mechanism in these composites.

Enhancement of euryhaline Asterarcys quadricellulare biomass production for improving biogas generation through anaerobic co-digestion with carbon rich substrate.

The microalga was isolated from Muktsar, the southwestern zone of Indian Punjab and identified as Asterarcys quadricellulare BGLR5 (MF661929) by 18S rRNA sequence analysis. The optimization of various cultural factors by the Plackett-Burman and central composite (CCD) designs helped in discerning the significant cultural factors for the increased production of biomass and other functional components (chlorophyll, carbohydrate, lipid and protein). The optimal cultural conditions as per the model were pH 9.9, 81 µmol m-2 s-1 light intensity, 22 °C temperature, growth period of 25 days, NaNO3 12 mM, 15 mM NH4Cl, and 7 mM K2HPO4. In comparison to the basal condition biomass (0.886 g L-1), a 0.42-fold increase in biomass yield was attained. Further, the highest yield of biogas (P: 361.81 mL g-1 VS) with enhanced biogas production rate (R m: 8.19 mL g-1 day-1) was achieved in co-digesting paddy straw with Asterarcys quadricellulare biomass in 1:1 ratio compared to their digestion individually. Further, the co-digestion resulted in the positive synergistic effect which increased the observed biogas yield compared to the estimated yield by 11-58% depending upon the amount of algal biomass and paddy straw used. Hence, the present study signifies that the biomass of Asterarcys quadricellulare BGLR5 can be utilized as a co-substrate with paddy straw to enhance the biogas yield. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02792-x.

Mechanical and Tribological Behavior of Mechanically Alloyed Ni-TiC Composites Processed via Spark Plasma Sintering.

Titanium carbide (TiC) reinforced nickel (Ni) matrix composites were processed via mechanical alloying (MA) followed by spark plasma sintering (SPS) process. Mechanical alloying has gained special attention as a powerful non-equilibrium process for fabricating amorphous and nanocrystalline materials, whereas spark plasma sintering (SPS) is a unique technique for processing dense and near net shape bulk alloys with homogenous microstructure. TiC reinforcement varied from 5 to 50 wt.% into nickel matrix to investigate its effect on the microstructure and mechanical behavior of Ni-TiC composites. All Ni-TiC composites powder was mechanically alloyed using planetary high energy ball mill with 400 rpm and ball to powder ratio (BPR) 15:1 for 24 h. Bulk Ni-TiC composites were then sintered via SPS process at 50 MPa pressure and 900-1200 °C temperature. All Ni-TiC composites exhibited higher microhardness and compressive strength than pure nickel due to the presence of homogeneously distributed TiC particles within the nickel matrix, matrix grain refinement, and excellent interfacial bonding between nickel and TiC reinforcement. There is an increase in Ni-TiC composites microhardness with an increase in TiC reinforcement from 5 to 50 wt.%, and it reaches the maximum value of 900 HV for Ni-50TiC composites.

Ag-Cu Bimetallic Nanoparticles as Efficient Oxygen Reduction Reaction Electrocatalysts in Alkaline Media.

The alkaline medium oxygen reduction reaction (ORR) activities of Ag-Cu bimetallic nanoparticles (BNPs), consisting of neighboring Ag and Cu domains, were studied and compared with those of pure Ag and Cu nanoparticles prepared by the same polyol route. Three variations of Ag-Cu BNPs viz. Ag-Cu (4:1), Ag-Cu (2:1), Ag-Cu (1:1) BNPs were considered. The electrocatalytic performances of these nanoparticles were investigated by using different techniques, such as cyclic voltammetry (CV) and linear sweep voltammograms (LSV). The Ag-Cu bimetallics demonstrated synergistic ORR electrocatalytic activity compared to pure Ag or Cu. Optimum values of these parameters were observed for Ag-Cu (4:1) BNPs. According to LSV, the reduction peak position is at lower applied potential and showed higher intensity for the Ag-Cu (4:1) as compared to Ag-Cu (2:1) and Ag-Cu (1:1) BNPs. Density Functional Theory (DFT) calculations show that charge transfer from Cu to Ag (in the bimetallic nanoparticles) results in their stronger oxygen interaction and water activation properties relative to that of pure Ag nanoparticles.

Enhanced electron transfer mediated detection of hydrogen peroxide using a silver nanoparticle-reduced graphene oxide-polyaniline fabricated electrochemical sensor.

The current study aims at the development of an electrochemical sensor based on a silver nanoparticle-reduced graphene oxide-polyaniline (AgNPs-rGO-PANI) nanocomposite for the sensitive and selective detection of hydrogen peroxide (H2O2). The nanocomposite was fabricated by simple in situ synthesis of PANI at the surface of rGO sheet which was followed by stirring with AEC biosynthesized AgNPs to form a nanocomposite. The AgNPs, GO, rGO, PANI, rGO-PANI, and AgNPs-rGO-PANI nanocomposite and their interaction were studied by UV-vis, FTIR, XRD, SEM, EDX and XPS analysis. AgNPs-rGO-PANI nanocomposite was loaded (0.5 mg cm-2) on a glassy carbon electrode (GCE) where the active surface area was maintained at 0.2 cm2 for investigation of the electrochemical properties. It was found that AgNPs-rGO-PANI-GCE had high sensitivity towards the reduction of H2O2 than AgNPs-rGO which occurred at -0.4 V vs. SCE due to the presence of PANI (AgNPs have direct electronic interaction with N atom of the PANI backbone) which enhanced the rate of transfer of electron during the electrochemical reduction of H2O2. The calibration plots of H2O2 electrochemical detection was established in the range of 0.01 µM to 1000 µM (R 2 = 0.99) with a detection limit of 50 nM, the response time of about 5 s at a signal-to-noise ratio (S/N = 3). The sensitivity was calculated as 14.7 µA mM-1 cm-2 which indicated a significant potential as a non-enzymatic H2O2 sensor.

Evaluation of ECG Abnormalities in Patients with Asymptomatic Type 2 Diabetes Mellitus.

INTRODUCTION: Diabetes Mellitus (DM) is the most common chronic disease. DM is considered a Cardiovascular Disease (CVD) risk equivalent. Its macrovascular complications are associated with two-fold increased risk of premature atherosclerotic CVD. Most of the diabetics with cardiovascular involvement are asymptomatic. Electro Cardio Graph (ECG) abnormalities are found to be predictors of silent ischaemia in asymptomatic persons. An abnormal ECG response is associated with statistically significant high risk for cardiac mortality and morbidity. AIM: The aim of the study was to evaluate ECG changes in asymptomatic Type 2 DM patients. MATERIALS AND METHODS: A cross-sectional comparative study was conducted in a tertiary care hospital in North India. One hundred diabetics presenting to Medicine OPD/IPD were included in the study who had no symptoms of heart disease and no diabetic complications. Fifty person with age and sex matched controls were included in the study. Relevant history and physical examination findings were recorded in a protocol. The variables studied were: gender, age, smoking, physical activity, and waist circumference, Body Mass Index (BMI) and blood pressure. Resting ECG was recorded. RESULTS: Mean age of asymptomatic diabetic patients was 50.3±11.90 years (age range 25-75 years). In this study, none of the control group had ECG abnormality whereas, 26% asymptomatic diabetics had ECG abnormalities. Most of the asymptomatic cases with ECG changes had 5-10 year of duration of diabetes mellitus; 70% patients with ECG changes had poor glycaemic control, increased triglyceride and decreased High Density Lipoprotein (HDL) levels. Most common abnormality observed was ST-T changes, followed by Left Atrial Enlargement (LAE), Left Ventricular Hypertrophy (LVH), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). CONCLUSION: ECG changes are present in quarter of asymptomatic Type 2 DM patients. However, nonspecific ST-T changes, LVH and LAE are common.

Retrograde cerebral perfusion for brain protection in aortic aneurysm surgery.

INTRODUCTION: Retrograde cerebral perfusion is used as an adjunct to deep hypothermic circulatory arrest (DHCA) for cerebral protection while dealing with complex aortic lesions. PATIENTS AND METHODS: Sixty-six patients, operated for aneurysms of the aorta using DHCA, were studied. In 52 patients, retrograde cerebral perfusion was used as an adjunct to DHCA for cerebral protection. Forty patients were subjected to surgical correction of ascending aorta lesions, 10 were operated for ascending aorta and arch lesions, eight had distal arch aneurysm repair and eight had surgery for thoracoabdominal aortic aneurysms. RESULTS: Neurologic dysfunction was reported in 6% of patients. No neurologic complications were reported in any patient who had retrograde cerebral perfusion during the circulatory arrest period. CONCLUSION: A major limitation of DHCA is the time constraint imposed, beyond which DHCA in isolation may not be safe. Considering the simplicity and safety involved, more liberal use of retrograde cerebral perfusion as an adjunct to DHCA is advocated.