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The present study collected wastewater samples from fourteen (14) full-scale wastewater treatment plants (WWTPs) at different treatment stages, namely, primary, secondary, and tertiary, to understand the impact of WWTP processes on the bacterial community structure, their role, and their correlation with environmental variables (water quality parameters). The findings showed that the bacterial communities in the primary, secondary, and tertiary treatment stages are more or less similar. They are made up of 42 phyla, 84 classes, 154 orders, 212 families, and 268 genera. Proteobacteria, Bacteroidetes, Cloacimonetes, Firmicutes, Euryarchaeota, Verrucomicrobia, Cyanobacteria, Desulfomicrobium, Thauera, Zavarzinia, and Nitrospirae, among others, dominated the bacterial community structure in all treatment stages. The biochemical oxygen demand was 7–12 times, chemical oxygen demand (COD) was 6 times, and total suspended solids (TSS) was 3.5 times higher in the wastewater than what the Central Pollution Control Board (CPCB) in New Delhi, India, allows as standard discharge. The correlation analysis using the Pearson r matrix and canonical correspondence analysis (CCA) also confirmed the fact that these water quality parameters (especially BOD and COD) play a pivotal role in deciphering the community structure in WWTPs. [ABSTRACT FROM AUTHOR]

Concerning ecological integrity, public health, and climate change, air quality is a crucial environmental concern. In emerging nations like India, in particular, rapid industrialization and urbanization have had a major negative impact on the quality of the air. Not an exception to this tendency is Meerut, a well-known city in the state of Uttar Pradesh. This study compares data from various locations, including industrial areas, commercial areas, residential areas, and roadside, to identify trends and potential sources of air pollution. It then evaluates the air quality at a few selected areas in Meerut City. The sampling of the air was performed monthly starting from January 2024 to June 2024 using the high-volume respirable dust sampler (Envirotech-ETC APM 415 BL) with a gaseous attachment. The study focuses on major air pollutants that are known to have negative impacts on both human health and the ecosystem, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10 and PM2.5). The data was processed to calculate the Air Quality Index (AQI) to rank the studied sites based on pollution and to make the data understandable to the common people. The obtained results revealed that at all the studied sites, values of PM10 (107.0-116.6 µg/m³) were found beyond the standard values of National Ambient Air Quality Standards (NAAQ) values of 100 µg/m³, and the values of the rest of the analyzed parameters {PM2.5 (50.6-59.4 µg/m³), NO2 (29.7-33.1 µg/m³), SO2 (9.1-10.3 µg/m³), and CO (0.9 µg/m³)} were found within the NAAQ standard. Among all the parameters, PM10 was considered as the criteria pollutant because the subindex (Si) of PM10 (1.07-1.17) was found highest at all the sites. The subindex (Si) of CO (0.22-0.95) was found to be the lowest among all the studied parameters. Based on the AQI, we found the overall quality of air in the study area to be moderately polluted. [ABSTRACT FROM AUTHOR]

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pretrained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of A pple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise. [ABSTRACT FROM AUTHOR]

The human population and domestic animals rely heavily on agriculture for their food and livelihood. Agriculture is an important contributor to the national economy of many countries. Plant diseases lead to a significant reduction in agricultural yield, posing a threat to global food security. It is crucial to detect plant diseases in a timely manner to prevent economic losses. Expert diagnosis and pathogen analysis are widely used for the detection of diseases in plants. However, both expert diagnosis and pathogen analysis rely on the real-time investigation experience of experts, which is prone to errors. In this work, an image analysis-based method is proposed for detecting and classifying plant diseases using an involution neural network and self-attention-based model. This method uses digital images of plant leaves and identifies diseases on the basis of image features. Different diseases affect leaf characteristics in different ways; therefore, their visual patterns are highly useful in disease recognition. For rigorous evaluation of the method, leaf images of different crops, including apple, grape, peach, cherry, corn, pepper, potato, and strawberry, are taken from a publicly available PlantVillage dataset to train the developed model. The experiments are not performed separately for different crops; instead, the model is trained to work for multiple crops. The experimental results demonstrate that the proposed method performed well, with an average classification accuracy of approximately 98.73% (κ = 98.04) for 8 different crops with 23 classes. The results are also compared with those of several existing methods, and it is found that the proposed method outperforms the other methods considered in this work. [ABSTRACT FROM AUTHOR]

Virus, bacteria and fungi are the most common causes for spreading illness in human and in animals. These are the microorganisms and they can cause epidemic and pandemic diseases. World is passing through many viral epidemics affecting respiratory system since last twenty years. It includes SARS-CoV 2002-2003, H1N1 Influenza 2009, MERS-CoV 2012 to the recent COVID-2019. COVID-19 is a viral pandemic infection this is air borne illness that is spreading through droplet infection. This virus especially affects the respiratory system by doing immunosuppression in person. In Ayurveda there are references of Janapadodhwamsa in Charaka samhita vimansthan. Janapadodhwamsa - is the term coined by Charak which means destruction of population living in same place at the same time because of 4 main reasons i.e. Dushit vayu (Air), Dushit jala (water), Dushit desh (land), Dushit kaal (time) Janapadodhwamsha causes death of individuals in the affected area inflicting huge destruction. [ABSTRACT FROM AUTHOR]

In the present work, an effort has been made to utilize Phyllanthus emblica (PE) fruit stone as a potential biomaterial for the sustainable remediation of noxious heavy metals viz. Pb(II) and Cd(II) from the aqueous solution using adsorption methodology. Further, to elucidate the adsorption potential of Phyllanthus emblica fruit stone (PEFS), effective parameters, such as contact time, initial metal concentration, temperature, etc., were investigated and optimized using a simple batch adsorption method. It was observed that 80% removal for both the heavy metal ions was carried out within 60 min of contact time at an optimized pH 6. Moreover, the thermodynamic parameters results indicated that the adsorption process in the present study was endothermic, spontaneous, and feasible in nature. The positive value of entropy further reflects the high adsorbent–adsorbate interaction. Thus, based on the findings obtained, it can be concluded that the biosorbent may be considered a potential material for the remediation of these noxious impurities and can further be applied or extrapolated to other impurities. [ABSTRACT FROM AUTHOR]

Bilwadi agad is one formulation mentioned in Asthang Hridyam chapter 36/84-85 and Asthang Sangrah chapter 42/87-88. This formulation has been found useful in the treatment of poisonous conditions of toxicity like sarpa, luta, vrischik, mushak dansa, jwar, visuchika, ajeerana, gar visha etc. The present research study was carried out with an objective to search a novel herbal preparation having antibacterial potentials. The aim of the study was to assess the antimicrobial activity by calculating the zone of inhibition on some pathogenic bacterial strains for this two different extract (aqueous and alcoholic) of bilwadi agad were prepared and analysed in lab with different concentration (5%,10%,15%) of extract on different bacterial strains. Antimicrobial study was seen on total 10 bacterial strains i.e Escherichia coli (MTCC: 40), Staphylococcus aureus (MTCC: 3160), Pseudomonas aeruginosa (MTCC: 424), Salmonella typhimurium (MTCC: 3231), Enterobacter aerogenes (MTCC: 2822), Klebsiella pneumoniae (MTCC: 39), Salmonella paratyphi B (ATCC: 10719), Vibrio cholera (MTCC: 3906), Shigella dysenteriaes (ATCC: 13313) and Clostridium botulinum (NCTC: 3815). Antimicrobial activity of Bilwadi agad was performed using “well diffusion method” against human pathogenic bacteria. By observing the samples, the activity index of bilwadi agad was found to be greater than 0.5 and this indicates a significant antimicrobial against defined microbes. Standard antibiotic was used as positive control in this study. [ABSTRACT FROM AUTHOR]

The present study collected wastewater samples from fourteen (14) full-scale wastewater treatment plants (WWTPs) at different treatment stages, namely, primary, secondary, and tertiary, to understand the impact of WWTP processes on the bacterial community structure, their role, and their correlation with environmental variables (water quality parameters). The findings showed that the bacterial communities in the primary, secondary, and tertiary treatment stages are more or less similar. They are made up of 42 phyla, 84 classes, 154 orders, 212 families, and 268 genera. Proteobacteria , Bacteroidetes , Cloacimonetes , Firmicutes , Euryarchaeota , Verrucomicrobia , Cyanobacteria , Desulfomicrobium , Thauera , Zavarzinia , and Nitrospirae , among others, dominated the bacterial community structure in all treatment stages. The biochemical oxygen demand was 7-12 times, chemical oxygen demand (COD) was 6 times, and total suspended solids (TSS) was 3.5 times higher in the wastewater than what the Central Pollution Control Board (CPCB) in New Delhi, India, allows as standard discharge. The correlation analysis using the Pearson r matrix and canonical correspondence analysis (CCA) also confirmed the fact that these water quality parameters (especially BOD and COD) play a pivotal role in deciphering the community structure in WWTPs.