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Extracellular enzymes produced from Streptomyces have the potential to replace toxic chemicals that are being used in various industries. The endorsement of this replacement has not received a better platform in developing countries. In this review, we have discussed the impact of chemicals and conventional practices on environmental health, and the role of extracellular enzymes to replace these practices. Burning of fossil fuels and agriculture residue is a global issue, but the production of biofuel using extracellular enzymes may be the single key to solve all these issues. We have discussed the replacement of hazardous chemicals with the use of xylanase, cellulase, and pectinase in food industries. In paper industries, delignification was done by the chemical treatment, but xylanase and laccase have the efficient potential to remove the lignin from pulp. In textile industries, the conventional method includes the chemicals which affect the nervous system and other organs. The use of xylanase, cellulase, and pectinase in different processes can give a safe and environment-friendly option to textile industries. Hazardous chemical pesticides can be replaced by the use of chitinase as an insecticide and fungicide in agricultural practices.

Aeration is the process of increasing the dissolved oxygen (DO) content of water, which is an important water quality parameter for the survival of aquatic life. In this process, large amounts of air bubbles develop; as a result, contact surface area increases, and hence the water-air-mass transfer accelerates. There are numerous methods for increasing DO concentration in water, including self-aeration, mechanical aeration, chemical aeration, and hydraulic structures. The hydraulic structures are an economical and efficient way of enhancing stream/river water aeration. Even though the water only comes into contact with the structure for a short while, it increases the amount of DO in a river system. In this study, an experimental investigation has been carried out to determine the aeration performance of different types of piano key weir (PKW). To this end, three different types (type-A, type-B, and type-C) of PKW laboratory-scaled models were tested. The results demonstrated that the type-A PKW created maximum oxygen transfer efficiency of the three PKW types. In addition, the results show that the aeration efficiency of all PKW models increases with drop height but decreases with increasing discharge over the weirs.

Energy dissipation across the weir and dam structures is a vital economic and technical solution for designing the downstream morphology of any hydraulic system. Accurately estimating the energy over any hydraulic system using traditional empirical formulas is tedious and challenging. Consequently, employing new and precise techniques still in high demand is crucial. In this study, the authors developed an empirical model for estimating the residual energy downstream of the type-A piano key weir (PKW) using gene expression programming (GEP) by considering six non-dimensional parameters: headwater ratio, magnification ratio, inlet to outlet width ratio, inlet to outlet key bottom slopes, inlet to outlet overhang portions and the number of cycles. The performance of the proposed models has been compared to empirical equations using the statistical factors coefficient of determination (R2), concordance coefficient (CC), and root mean square error (RMSE). The computed relative residual energy values using the proposed models are within ±10% of the observed ones. The proposed GEP model predicted the relative residual energy satisfactorily, with coefficients of determination of R2 = 0.978 for training, 0.980 for testing and root mean square errors (RMSE) of 0.032 and 0.029 for the training and testing datasets, respectively.

Accurate prediction of water surface profile in an open channel is the key to solving numerous critical engineering problems. The goal of the current research is to predict the water surface profile of a compound channel with converging floodplains using machine learning approaches, including gene expression programming (GEP), artificial neural networks (ANNs), and support vector machines (SVMs), in terms of both geometric and flow variables, as past studies were more focused on geometric variables. A novel equation was also proposed using gene expression programming to predict the water surface profile. In order to evaluate the performance and efficacy of these models, statistical indices are used to validate the produced models for the experimental analysis. The findings demonstrate that the suggested ANN model accurately predicted the water surface profile, with coefficient of determination (R2) of 0.999, root mean square error (RMSE) of 0.003, and mean absolute percentage error (MAPE) of 0.107%, respectively, when compared with GEP, SVM, and previously developed methods. The study confirms the application of machine learning approaches in the field of river hydraulics, and forecasting water surface profile of nonprismatic compound channels using a proposed novel equation by gene expression programming made this study unique.

The scour around the bridge piers has been estimated using conventional empirical formulae; however, these formulae are unable to predict the scour depth precisely. The present study is conducted in two parts (a) experimental investigation for evaluating the behaviour of local scour around twin piers positioned in the transverse direction of flow and (b) an empirical equation to estimating scour depth is proposed utilising new evolutionary artificial intelligence technique gene expression programming (GEP). Experimental results for the present study demonstrate the influence of the rate of flow and clear spacing between the piers on the scour depth. Additionally the results of the soft computing technique GEP during testing and training of proposed modelling, fitness function root mean square error is observed as 0.00133 and 0.00113, with the coefficient of determination as 0.950 and 0.955, respectively. Furthermore, in order to find out the role of each variable for scour depth sensitivity, analysis has been conducted. The findings of the sensitivity analysis show that the pier spacing and rate of flow play the most significant role in scour depth estimation. Results of this study demonstrate a good agreement with the proposed GEP model and conclude that it is a better approach for forecasting scour depth.

This study assesses the effects of geometrical variations, i.e., relative width ratio, magnification ratio, and cyclic variation, on the Piano Key Weir energy dissipation performance. In order to assess this, 12 type-A PKW models were tested by conducting an experimental investigation. The analysisincludes results from 280 tests and comprehensive findings of the fluid domain. The results demonstrated that the configuration of the PKWs has a greater influence on energy dissipation. The present study’s findings show that the type-A PKW dissipates energy more efficiently than nonlinear or standard weirs. The energy loss over the PKW decreases as the magnification and relative width ratios increase, although the energy dissipation increases with key cycle numbers. Furthermore, the sensitivity analysis of the weir was examined by making steps at outlet key floors and found that the energy dissipation has been increased by 6.67% at low heads while reduced by 1.59% at higher heads. In addition, the author proposed two empirical projection equations for the optimal relative residual energy configuration downstream of the PKW. The proposed equations are the function of headwater ratio, relative width ratio, and magnification ratio. The results of this investigation are in perfect correlation with earlier studies.

Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.

This study aims to identify the water flux in an earth dam using heat flux due to convection. Sixteen earth dam models were constructed in a hydraulic flume by varying geometrical and flow input parameters to identify heat and water flux. Homogeneous as well as earth dams with clay cores were built in a hydraulic flume. Temperature measurements were done to calculate heat flux in the experimental model. A finite element model of the earth dam using Seep/w was developed to obtain water flux, while temp/w was used to obtain heat flux. These results were used as input in Temp/w and Seep/w in Geostudio 2020. Significant reduction of the heat and water flux was seen while comparing the homogeneous models with central impervious core models. An increase in the heat and water flux was observed on increasing the downstream filter's length, longitudinal slope, and vice versa with the upstream slope and the thickness of the clay core. Comparing fluxes in a homogeneous dam model (model 1) with the clay core model (model 9) with top width 2.4 m and bottom width 18 m in model 9, both water flux and heat flux were reduced by 78.46%. While comparing it with model 10, with bottom core width of 18 m and top core width of 1.9 m, both water flux and heat flux reduced by 77.72%. Heat flux measurements were found to be a valuable alternative to detecting water flux and seepage in an earth dam at a reduced cost.

Assessment of water surface profile in compound channels is essential for flood defence systems. Agriculture and development activities in floodplains affect the floodplain shape over the length, leading in a converging compound channel. Few laboratory investigations proved overbank flow in converging floodplains. Therefore, innovative and precise approaches are still in great demand. In this paper, new approach has been proposed to forecast the water surface profile of various compound channels with converging floodplains using gene expression programming (GEP). The models are constructed utilising pertinent experimental data from past studies. A new equation is devised to compute water surface profile in such channels using non-dimensional geometric and flow parameters such as converging angle, width ratio, relative distance, relative depth, aspect ratio and bed slope. The findings demonstrate that the GEP-derived water surface profile is in good correlation with the experimental data and data from other studies (R2 = 0.99 and RMSE = 0.028 for the training data and R2 = 0.99 and RMSE = 0.027 for the testing data). According to the results of statistically based investigations, the GEP model created for the study of compound channel flow is reliable and can be used in this domain.

Consortia of Streptomyces spp. (colonies 169, 194, 165 and 130) used in this study are an efficient producer of secondary metabolites like chitinases and antifungal compounds, which may help in the protection of surplus food from spoilage. Qualitative screening for chitinase production and taxonomy of these colonies were undertaken in our previous studies. In the current study, GC–MS analysis of extract produced from the consortia of Streptomyces strains was done for the identification of antifungal compounds. Treatment of surplus food with activated consortia of Streptomyces spp. has protected powdered food for a month, whereas fresh food (unpowdered) was preserved for two days. A control sample of surplus food (untreated) was kept to check the contamination, which resulted in the growth of three fungi (FP-1, FG-1, and FB-1). Taxonomic characterization of fungi and identification of toxic compounds produced from them were done by ITS amplification and GC–MS analysis, respectively. The study shows that the secondary metabolites from Streptomyces spp. have the potential to protect the food from mycotoxin contamination. Based on literature reports, this is for the first time that bioactive compounds and chitinases produced from Streptomyces are being used for the protection and management of surplus food.