Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Probing the degradation of pharmaceuticals in urine using MFC and studying their removal efficiency by UPLC-MS/MS

Nutrient recovery from source-separated human urine has attracted interest as it is rich in nitrogen and phosphorus that can be utilized as fertilizer.However,urine also contains pharmaceuticals,steroid hormones,etc.and their removal is crucial as they have detrimental effects on the environment and human health.The current study focuses on investigating the degradation of pharmaceuticals using a double-chamber microbial fuel cell(MFC).Urine was spiked with four pharmaceuticals(trimethoprim,lamivudine,levofloxacin,and estrone)at a concentration of 2 mg/mL.The MFC was operated for 7 months in batch mode with this spiked urine as feed.The degradation efficiency of the MFC was studied,for which a selective liquid chromatography-tandem mass-spectrometric method was developed for the quantitation of compounds used in the spiking experiments and was validated with a lower limit of quantification of 0.39 ng/mL.The maximum removal rate achieved was 96%±2%.The degradation mechanism involved processes like sorption and anoxic biodegradation.The voltage curve obtained showed that the presence of pharmaceuticals had an initial negative impact on power generation along with increased organic content;however,after the reactor acclimatization,increased power output was achieved with maximum organics removal at 30 h of retention time.This work opens a new perspective for the anoxic biodegradation of pharmaceuticals and can be useful in future bioremediation studies.

Measurement of Hubble constant: were differences in secondary distance indicators apparent as early as the HST Key Project?

Different measurements of the Hubble constant(H_(0))are not consistent,and a tension between the CMB based methods and cosmic distance ladder based methods has been observed.Measurements from various distance based methods are also inconsistent.To aggravate the problem,the same cosmological probe(TypeⅠa SNe for instance)calibrated through different methods also provides different values of H_(0).We compare various distance ladder based methods through the already available unique data obtained from the Hubble Space Telescope(HST).Our analysis is based on parametric(t-test)as well as non-parametric statistical methods such as the Mann-Whitney U test and Kolmogorov-Smirnov test.Our results show that different methods provide different values of H_(0) and the differences are statistically significant.The biases in the calibration would not account for these differences as the data have been taken from a single telescope with a common calibration scheme.The unknown physical effects or issues with the empirical relations of distance measurement from different probes could give rise to these differences.

Investigating the Hubble Tension Through Hubble Parameter Data

The Hubble constant(H_0),which represents the expansion rate of the universe,is one of the most important cosmological parameters.The recent measurements of H0using the distance ladder methods such as type Ia supernovae are significantly greater than the CMB measurements by Planck.The difference points to a crisis in the standard model of cosmology termed Hubble tension.In this work we compare different cosmological models,determine the Hubble constant and comment on the Hubble tension using the data from differential ages of galaxies.The data we use are free from systematic effects as the absolute age estimation of the galaxies is not needed.We apply the Bayesian approach along with the commonly used maximum likelihood method to estimate H_0 and calculate the AIC scores to compare the different cosmological models.The non-flat cosmological model provides a higher value for matter density as well as the Hubble constant compared to the flatΛCDM model.The AIC score is smaller for the flatΛCDM cosmology compared to the non-flat model indicating the flat model a better choice.The best-fit values of H_0 for both these models are 68.7±3.1 km s^(-1)Mpc^(-1)and 72.2±4km s^(-1)Mpc^(-1),respectively.Our results are consistent with the CCHP measurements.However,the flat model result does not agree with the SH0ES result,while the non-flat result is inconsistent with the Planck value.

Improved Energy Management Strategy for Prosumer Buildings with Renewable Energy Sources and Battery Energy Storage Systems

The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits.These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems(BESSs)to deal with the uncertain nature of renewable energy sources.However,because of the high capital investment of BESS and the limitation of available energy,there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS.In this regard,this paper proposes an improved energy management strategy(IEMS)for the prosumer building to minimize the operating cost of MG and degradation factor of BESS.Moreover,to estimate the practical operating life span of BESS,this paper utilizes a non-linear battery degradation model.In addition,a flexible load shifting(FLS)scheme is also developed and integrated into the proposed strategy to further improve its performance.The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic(PV)and BESS-powered AC-DC hybrid MG installed at a commercial building.Moreover,the scenario reduction technique is used to handle the uncertainty associated with generation and load demand.To validate the performance of the proposed strategy,the results of IEMS are compared with the well-established energy management strategies.The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS.Moreover,FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS,thus making the operation of prosumer building more economical and efficient.

Developing a Bayesian belief network model for prediction of R&D project success

The project success is critical to the business performance in the era of fierce competition and globalization.The basis for project success lies in the capabilities of managing risks effectively.Innovation has always been considerably risky;however,managing Research and Development(R&D)project risks has become even more important given today’s tight schedules and limited resources.Risk management has to be an integral part of the development process.The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project.A risk quantification model based on the Bayesian belief network is proposed,which is effective in capturing the interaction between various risk factors.The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.