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Study region: Rana watershed, located in the mid-Mahanadi River basin in the state of Odisha, India. Study focus: This study attempted to develop a generalizable machine learning (ML)-based streamflow prediction model implementing prediction selection algorithms to the physiographic characteristics, and hydro-meteorological data collected for Rana Watershed. New hydrological insights: The pertinent predictors identified were land use/ land cover (LULC), one and two-day lagged rainfall, one-day lagged PET, and one-day lagged streamflow and its categorized flow regime. The random forest algorithm, which outperformed the other five algorithms evaluated, was trained using identified predictors to develop a streamflow prediction model called “stRFlow”. The mean absolute error, root mean squared error, coefficient of determination, and Nash-Sutcliffe efficiency during training were 0.753 m3/s, 3.584 m3/s, 0.973, and 0.972 and testing were 2.829 m3/s, 10.503 m3/s, 0.855, and 0.851, respectively. The Kling-Gupta efficiency was found to be 0.96 and 0.92 during training and testing, respectively. There was an enhancement to model proficiency with the addition of LULC to temporal predictors. Moreover, the partial auto-correlation factor for the streamflow and examining the categorization of specific lagged flow regimes enhanced the predictive capacities of “stRFlow”. Results depict the potential of stRFlow and the framework in streamflow modeling in similar hydroclimatic regions with applicability for practical and reliable streamflow predictions globally.

The synthesis of Co3O4 nanoparticles (NPs) through biogenic means offers a cost-effective, environmentally friendly, stable, versatile, and biocompatible approach. In this work, we employed Aloe barbadensis Miller leaf extract for the biosynthesis of Co3O4 NPs, leveraging its rich composition of phenolic groups, polysaccharides, enzymes, minerals, vitamins, and various acids. The characterization of the synthesized NPs was performed using XRD, TEM, EDS, FT-IR, TGA-DSC, Raman, XPS, and particle size analysis techniques. Our investigation focuses on evaluating the antioxidant and antimicrobial properties of Co3O4 NPs. Antioxidant activity was assessed through DPPH, ABTS, FRAP, and FICA assays, demonstrating the NPs' ability to scavenge free radicals, reduce ferric ions, and chelate ferrous ions. Antimicrobial testing revealed the effectiveness of NPs against Proteus mirabilis and Staphylococcus aureus. Furthermore, biogenic Co3O4 NPs were utilized as photocatalysts for the degradation of azo dyes (RhB, MB, and CV). Under sunlight exposure for 105 min, biogenic Co3O4 NPs degraded CV dyes by 97.53%. The study also highlighted the remarkable efficiency of azo dye degradation across five cycles of reuse of biogenic Co3O4 NP reuse.

A number of hypothetical risk factors for atherosclerosis and cardiovascular disease have emerged in recent years. Numerous research have been undertaken worldwide looking at the involvement of the risk factors such as infection, inflammation in coronary arteries due to the association of the factors associated with cardiovascular disease. It has been demonstrated that prior interventions can regulate the risk factors to lower the incidence and progression of coronary artery disease. The involvement of traditional risk factors such as smoking, hypertension, obesity in the development of cardiovascular diseases, has been thoroughly established. Recently, it is also discovered that a number of novel risk factors have an impact on the onset of CAD. Finding novel risk factors for atherosclerosis and CAD has kept a lot of attention in recent years. Homocysteine is one of the most recent risk factors whose importance has been proven, however there are still research being conducted to support the significance of other novel risk factors in patients suffering from coronary artery syndrome.

Water Quality Index (WQI) is a comprehensive tool, which is used to assess the water quality status of the streams. In the present study water quality status of the Godavari River at Nashik has been assessed at fourteen water quality monitoring stations WQMS) located at bathing ghats and bridges along the course of the River. For Assessment of the water quality, prevalent National Sanitation Foundation Water Quality Index (NSFWQI) values have been determined at these WQMS using pH,DO, BOD,Turbidity and Total solids parameter values. It is observed that NSFWQI has its inherent limitations and fails to give the representative results of water quality in the region of higher water pollution. To overcome this limitation, Artificial Intelligence techniques like Fuzzy logic, Artificial Neural Network (ANN) and ANFIS are employed. ANFIS approach integrates the merits of Fuzzy logic and ANN approach .In view of this, Adaptive Neuro Fuzzy Inference System (ANFIS) has been employed for the present study, using Triangular, Trapezoidal, Gaussian and Bell membership functions(MF). It has been observed that Gaussian and triangular MF gives better results for Eleven and three number of sampling stations, respectively. Therefore, the Gaussian MF model outperforms other MF models of the ANFIS approach for the study. [ABSTRACT FROM AUTHOR]

BackgroundFew single center studies from resource-poor settings have reported about the epidemiology, clinical feature and outcome of multisystem inflammatory syndrome in children (MIS-C). However, larger data from multi-center studies on the same is lacking including from Indian setting.MethodsThis retrospective collaborative study constituted of data collected on MIS-C from five tertiary care teaching hospitals from Eastern India. Children ≤ 15 years of age with MIS-C as per the WHO criteria were included. Primary outcome was mortality.ResultsA total of 134 MIS-C cases were included (median age, 84 months; males constituted 66.7%). Fever was a universal finding. Rash was present in 40%, and conjunctivitis in 71% cases. Gastro-intestinal and respiratory symptoms were observed in 50.7% and 39.6% cases, respectively. Co-morbidity was present in 23.9% cases. Shock at admission was noted in 35%, and 27.38% required mechanical ventilation. Fifteen (11.2%) children died. The coronary abnormalities got normalized during follow-up in all except in one child. Initial choice of immunomodulation had no effect on the outcomes. Presence of underlying co-morbidity, lymphopenia, thrombocytosis, hyponatremia, increased LDH (>300 U/L), and hypoalbuminemia were the factors significantly associated an increased mortality.ConclusionsMIS-C has myriad of manifestations. Underlying co-morbidity, lymphopenia, thrombocytosis, hyponatremia, increased LDH (>300 U/L), and hypoalbuminemia were associated with an increased mortality. No difference in outcome was noted with either steroid or IVIg or both. Coronary artery abnormalities resolved in nearly all cases.

A saturated health care system with a lack of evidence-based antiviral medicine and ignorance of antimicrobial stewardship during pandemics has prompted clinicians to prescribe a broad-spectrum antibiotic more often. A prospective, cross-sectional study of COVID-infected patients was conducted to gain insight into antibiotic prescribing practices and their impact on antimicrobial resistance. The antibiotic susceptibility test was performed using the disc diffusion method. 318 patients met the study's inclusion criteria, with a mean age of 46 years and 55% (175) of them being males. Antibiotics were prescribed for 93.72% (209) of mild cases, 92.45% (49) of moderate cases, 96.15% (25) of severe cases, and 100% (16) of critical cases of COVID-19. A total of 95 samples were sent in for culture and antibiotic sensitivity testing, with 58.95% (56) confirming growth. The majority of the growth was found to contain E. coli (14). In 54.9% of cases, antibiotics with less than 50% sensitivity to curing bacterial infection were detected. In the study, we found that antibiotics were being used unnecessarily in excessive quantities and that more than half of the antibiotics were less sensitive to isolated bacteria.

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software's features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community's methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "pre-processing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. [ABSTRACT FROM AUTHOR]