Search Results (7)

Papaya ringspot virus (PRSV) is a significant threat to global papaya cultivation, causing ringspot disease, and it belongs to the species Papaya ringspot virus, genus Potyvirus, and family Potyviridae. This study aimed to assess the occurrence and severity of papaya ringspot disease (PRSD) in major papaya-growing districts of Karnataka, India, from 2019 to 2021. The incidence of disease in the surveyed districts ranged from 50.5 to 100.0 percent, exhibiting typical PRSV symptoms. 74 PRSV infected samples were tested using specific primers in RT-PCR, confirming the presence of the virus. The complete genome sequence of a representative isolate (PRSV-BGK: OL677454) was determined, showing the highest nucleotide identity (nt) (95.8%) with the PRSV-HYD (KP743981) isolate from Telangana, India. It also shared an amino acid (aa) identity (96.5%) with the PRSV-Pune VC (MF405299) isolate from Maharashtra, India. Based on phylogenetic and species demarcation criteria, the PRSV-BGK isolate was considered a variant of the reported species and designated as PRSV-[IN:Kar:Bgk:Pap:21]. Furthermore, recombination analysis revealed four unique recombination breakpoint events in the genomic region, except for the region from HC-Pro to VPg, which is highly conserved. Interestingly, more recombination events were detected within the first 1710 nt, suggesting that the 5’ UTR and P1 regions play an essential role in shaping the PRSV genome. To manage PRSD, a field experiment was conducted over two seasons, testing various treatments, including insecticides, biorationals, and a seaweed extract with micronutrients, alone or in combination. The best treatment involved eight sprays of insecticides and micronutrients at 30-day intervals, resulting in no PRSD incidence up to 180 days after transplanting (DAT). This treatment also exhibited superior growth, yield, and yield parameters, with the highest cost–benefit ratio (1:3.54) and net return. Furthermore, a module comprising 12 sprays of insecticides and micronutrients at 20-day intervals proved to be the most effective in reducing disease incidence and enhancing plant growth, flowering, and fruiting attributes, resulting in a maximized yield of 192.56 t/ha. Full article

The objective was to explore the patterns of contribution from vitamin D metabolites (D₂ and D₃) to total vitamin D concentrations in Indian families. This cross-sectional study was carried out in slum-dwelling families residing in Pune city. Data on demography, socio-economic status, sunlight exposure, anthropometry, and biochemical parameters (serum 25OHD₂, 25OHD₃) via the liquid chromatography–tandem mass spectrometry method were collected. The results are presented for 437 participants (5 to 80 years). One-third were vitamin-D-deficient. Intake of foods containing vitamin D₂ or D₃ was rarely reported. Irrespective of gender, age, and vitamin D status, the contribution of D₃ to total 25OHD concentrations far exceeded that of D₂ (p < 0.05). The contribution of D₂ ranged from 8% to 33% while that of D₃ to 25OHD concentrations ranged from 67% to 92%. 25OHD₃ is a major contributor to overall vitamin D concentrations, and the contribution of 25OHD₂ was found to be negligible. This implies that sunlight and not diet is currently the major source of vitamin D. Considering that lifestyle and cultural practices may lead to insufficient sunlight exposure for large sections of the society, especially women, dietary contribution to vitamin D concentrations through fortification may play an important role in improving the vitamin D status of Indians. Full article

The present study is an attempt to model the stormwater quality of a stream located in Pune, India. The city is split up into twenty-three basins (named A to W) by the Pune Municipal Corporation. The selected stream lies in the haphazardly expanded peri-urban G basin. The G basin has constructed stormwater drains which open up in this selected open stream. The runoff over the regions picks up the non-point source pollutants which are also added to the selected stream. The study becomes more complex as the stream is misused to dump trash materials, garbage and roadside litter, which adds to the stormwater pollution. Experimental investigations include eleven distinct locations on a naturally occurring stream in the G basin. Stormwater samples were collected for twenty-two storm events, for the monsoon season over four years from 2018–2021, during and after rainfall. The physicochemical characteristics were analyzed for twelve water quality parameters, including pH, Conductivity, Turbidity, Total solids (TS), Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Bio-chemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Phosphate, Ammonia and Nitrate. The Water Quality Index (WQI) ranged from 46.9 to 153.9 and from 41.20 to 87.70 for samples collected during and immediately after the rainfall, respectively. Principal Component Analysis was used to extract the most significant stormwater quality parameters. To understand the non-linear complex relationship of rainfall characteristics with significant stormwater pollutant parameters, a Support Vector Regression (SVR) model with Radial Basis Kernel Function (RBF) was developed. The Support Vector Machine is a powerful supervised algorithm that works best on smaller datasets but on complex ones with the help of kernel tricks. The accuracy of the model was evaluated based on normalized root-mean-square error (NRMSE), coefficient of determination (R²) and the ratio of performance to the interquartile range (RPIQ). The SVR model depicted the best performance for parameter TS with NRMSE (0.17), R² (0.82) and RPIQ (2.91). The unit increase or decrease in the coefficients of rainfall characteristics displays the weighted deviation in the values of pollutant parameters. Non-linear Support Vector Regression models confirmed that both antecedent dry days and rainfall are correlated with significant stormwater quality parameters. The conclusions drawn can provide effective information to decision-makers to employ an appropriate treatment train approach of varied source control measures (SCM) to be proposed to treat and mitigate runoff in an open stream. This holistic approach serves the stakeholder’s objectives to manage stormwater efficiently. The research can be further extended by selecting a multi-criteria decision-making tool to adopt the best SCM and its multiple potential combinations. Full article

Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work. Full article

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates. Full article

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O₃). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO₂, and O₃, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants. Full article

Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones. Full article