Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach.

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity -as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being -reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting.

Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literature for streamflow forecasting, to our knowledge, the literature lacks a comprehensive comparison of well-known data-driven and model-driven forecasting techniques for runoff evaluation in terms of quality and quantity. This study filled this knowledge gap by comparing the accuracy of runoff, sediment, and nitrate forecasting using four robust data-driven techniques: artificial neural network (ANN), long short-term memory (LSTM), wavelet artificial neural network (WANN), and wavelet long short-term memory (WLSTM) models. These comparisons were performed in two main tiers: (1) Comparing the machine learning algorithms' results with the model-driven approach; In order to simulate the runoff, sediment, and nitrate loads, the Soil and Water Assessment Tool (SWAT) model was employed, and (2) Comparing the machine learning algorithms with each other; The wavelet function was utilized in the ANN and LSTM algorithms. These comparisons were assessed based on the substantial statistical indices of coefficient of determination (R-Squared), Nash-Sutcliff efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). Finally, to prove the applicability and efficiency of the proposed novel framework, it was successfully applied to Eagle Creek Watershed (ECW), Indiana, U.S. Results demonstrated that the data-driven algorithms significantly outperformed the model-driven models for both the calibration/training and validation/testing phases. Furthermore, it was found that the coupled ANN and LSTM models with wavelet function led to more accurate results than those without this function.

Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.

Quantifying the groundwater total contamination risk using an inclusive multi-level modelling strategy.

This paper investigates aggregated risks in aquifers, where risk exposures may originate from different contaminants e.g. nitrate-N (NO3-N), arsenic (As), boron (B), fluoride (F), and aluminium (Al). The main goal is to develop a new concept for the total risk problem under sparse data as an efficient planning tool for management through the following methodology: (i) mapping aquifer vulnerability by DRASTIC and SPECTR frameworks; (ii) mapping risk indices to anthropogenic and geogenic contaminants by unsupervised methods; (iii) improving the anthropogenic and geogenic risks by a multi-level modelling strategy at three levels: Level 1 includes Artificial Neural Networks (ANN) and Support Vector Machines (SVM) models, Level 2 combines the outputs of Level 1 by unsupervised Entropy Model Averaging (EMA), and Level 3 integrates the risk maps of various contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium) modelled at Level 2. The methodology offers new data layers to transform vulnerability indices into risk indices and thereby integrates risks by a heuristic scheme but without any learning as no measured values are available for the integrated risk. The results reveal that the risk indexing methodology is fit-for-purpose. According to the integrated risk map, there are hotspots at the study area and exposed to a number of contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium).

Multi-objective optimization of hydrant flushing in a water distribution system using a fast hybrid technique.

As a critical element in preserving the health of urban populations, water distribution systems (WDSs) must be ready to implement emergency plans when catastrophic events such as contamination events occur. A risk-based simulation-optimization framework (EPANET-NSGA-III) combined with a decision support model (GMCR) is proposed in this study to determine optimal locations for contaminant flushing hydrants under an array of potentially hazardous scenarios. Risk-based analysis using Conditional Value-at-Risk (CVaR)-based objectives can address uncertainties regarding the mode of WDS contamination, thereby providing a robust plan to minimize the associated risks at a 95% confidence level. Conflict modeling by GMCR achieved an optimal compromise solution within the Pareto front by identifying a final stable consensus among the decision-makers involved. A novel hybrid contamination event grouping-parallel water quality simulation technique was incorporated into the integrated model to reduce model runtime, the main deterrent in optimization-based methods. The nearly 80% reduction in model runtime made the proposed model a viable solution for online simulation-optimization problems. The framework's capacity to address real-world problems was evaluated for the WDS operating in Lamerd, a city in Fars Province, Iran. Results showed that the proposed framework was capable of highlighting a single flushing strategy, which not only optimally reduced risks associated with contamination events, but provided acceptable coverage against such threats, flushing 35-61.3% of input contamination mass on average, and reducing average time-to-return to normal conditions by 14.4-60.2%, while employing less than half of the initial potential hydrants.

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA.

In this paper, we examine how surface runoff affects public safety and urban infrastructure worldwide and how human activity has significantly altered the frequency and magnitude of these events. We investigate this issue in Ferson Creek, IL, USA. Our study focuses on three specific areas of impact: (1) the primary reasons for a considerable increase in average runoff peaks, using annual maximum runoff discharge and annual maximum precipitation and temperature to evaluate the role of climate variability; (2) the effect of land use change on runoff peaks by coupling dominant land use categories with annual maximum runoff discharge; and (3) the use of return level plots as a reference to explore the watershed's sensitivity to land use change. Our findings indicate that land use change has a greater effect on runoff peak values than climate variability in our region of interest. The agricultural areas of Ferson Creek have been most affected by the rapid transformation of about 20% of their land into developed areas. Although agricultural areas can sometimes intensify runoff peaks, their reduction has led to excessive runoff discharges in Ferson Creek, as they have higher relative infiltration capacity than developed areas. We conclude that each watershed has its own fingerprint in terms of the connection between its land use types and hydrological patterns and that the region is most sensitive to the percentage of forests. These results are essential for improving infrastructure design and risk estimation methods in the region of interest.

Comparison of The Effectiveness of Exercise Therapy and Electroacupuncture in Patients with Forward Head Abnormalities and Myofascial Pain Syndrome: A Randomized Clinical Trial.

Background: Forward Head Posture (FHP), which refers to the head being more forward than the shoulder, is one of the most common postural defects of all ages. Therefore, in this study, we aimed to compare the effectiveness of exercise therapy and electroacupuncture in patients with FHP and myofascial pain syndrome (MPS). Methods: The present study was an open-label randomized clinical trial. A total of 61 patients with FHP and MPS who were referred to the physical medicine clinic of Besat Hospital between 2020 and 2021 were analyzed. Patients in one group were treated with electroacupuncture, and another one was treated with exercise therapy. The primary outcomes were FHP angles (CVA, CA, and shoulder angle), pain intensity (VAS), and quality of life (SF-12). Paired t-test was used to compare the results obtained in the pre-test and post-test. To detect differences over time, the analysis of variance models was used to repeat the observations. If the p-test result is less than the test significance level of 0.05, the null hypothesis is not confirmed. Results: The rate of final CVA and increase in CVA in the exercise therapy group were significantly higher than in the electroacupuncture group (P < 0.001). The average shoulder angle in the exercise therapy group increased from 47.1° ± 3.0° to 51.9° ± 3.3° (P < 0.001) and in the electroacupuncture group from 47.9° ± 3.1° to 51.0° ± 2.8° (P < 0.001). A significant difference was observed between the two groups in terms of pain intensity changes during the study. Conclusion: Overall, the results of this study showed that both exercise therapy and electroacupuncture significantly improved patients' posture, reduced pain intensity, and increased quality of life in FHP patients with MPS; But exercise therapy was more effective in improving FHP angles and electroacupuncture was more successful in reducing patients' pain intensity.

Comparison of the Effectiveness of Local Corticosteroid Injection and Extracorporeal Shockwave Therapy in Patients With Pes Anserine Bursitis: An Open-Label Randomized Clinical Trial.

Background: The success rate of extracorporeal shock wave therapy (ESWT) in treating epicondylitis, plantar fasciitis, rotator cuff tendonitis, Achilles tendonitis, and Jumper knee has been reported to be 60% to 80%. Most published studies have compared focused ESWT at different intensities with local corticosteroid injection (LCI). We only identified a few studies that specifically compared ESWT with LCI in patients with pes anserine bursitis (PAB). This study aimed to compare the effectiveness of ESWT and LCI in patients with PAB. Methods: The present study was a randomized clinical trial. Patients diagnosed with PAB who were referred to the physical medicine and rehabilitation clinic underwent a complete physical examination. They (n = 60 patients) were randomly assigned to the ESWT and LCI groups if they met the study criteria. In the ESWT group, 1 ESWT session was performed weekly for 3 consecutive weeks. In the LCI group, 1 injection was performed under an ultrasonography guide. Pes anserine thickness, pain intensity, and treatment satisfaction were measured with visual analog scale (VAS) and quality of life (Short Form-12). A paired-samples t test was used to compare the results obtained in the pre-and posttests. Analysis of variance for repeated measures was used to detect differences over time. The null hypothesis would not be confirmed if the P value was less than the 0.05 level of significance. Results: Pes anserine thickness and pain intensity decreased significantly during the study in both groups (P < 0.001). However, the mean difference of pes anserine thickness was more in the LCI group the ESWT group [(-0.6; 95% CI, -1.0 to -0.3) than (-0.1; 95% CI, -0.5, -0.2); P = 0.008]. Also, the mean difference of pain intensity was lower in the ESWT group] than the LCI group [(-2.9; 95% CI, -3.7 to -2.1) (1.0; 95% CI, 0.1to 1.8); P < 0.001]. Patients' quality of life in both groups increased significantly during the study period (P < 0.001), but the increase in quality of life in patients in the ESWT group (mean difference, 15.3 [95% CI, 10.6-19.9]) was considerably more than in the LCI group (mean difference, -5.3 (95% CI, -10.0 to -0.6). Conclusion: Overall, the results of this study showed that both local corticosteroid injections and extracorporeal shock wave therapy are safe and effective in PAB patients.

Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA.

Antibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH â‰… 7, optimal conditions for initial CIP concentration, Fe2+ concentration, [H2O2]/[Fe2+] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.

Groundwater sustainability: Developing a non-cooperative optimal management scenario in shared groundwater resources under water bankruptcy conditions.

Groundwater level drawdown changes the hydrological cycle and poses challenges such as land subsidence and reduction of the groundwater quality. In this study, a new approach using a simulation-optimization framework was developed for shared groundwater management under water bankruptcy conditions (where water demand is greater than the allowable discharge capacity of water resources). The novelty of this study lies in using bankruptcy rules and a game model to manage a bankrupted shared groundwater resource considering aquifer sustainability. Accordingly, groundwater flow in the aquifer was numerically simulated by a finite-differences model (MODFLOW). Then, the repeated performance code of the finite-differences model was run for different discharge scenarios, and the results were applied to develop an MLP-ANN meta-model. By coupling the meta-model with a non-dominated sorting genetic algorithm II (NSGA-II)-based multi-objective optimization model, an optimized cultivation pattern under water bankruptcy conditions was achieved. Then, six different bankruptcy methods were utilized to specify groundwater allocation between three stakeholders. To manage the water bankruptcy conditions, different scenarios considering various groundwater extraction rates and cultivation areas were defined, and the optimization model was recoded for each scenario to find the corresponding optimized cultivation pattern. To consider the competition between stakeholders for groundwater extraction, a non-cooperative 3-player game was applied to achieve a compromise for different combinations of management strategies, which maximizes the profit and yields the best cultivation scenario. Applicability of the proposed methodology was investigated in an aquifer system located in Golestan Province, Iran, including three regions (Minudasht, Azadshahr, and Gonbade-kavus). Results show that the proposed method is capable of managing the bankruptcy conditions by generating greater agricultural profit and reducing groundwater drawdowns.

Design of a high-coverage ground-based CO2 monitoring layout using a novel information theory-based optimization model.

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO2 monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory-in terms of transinformation-is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO2 monitoring layout for Iran, the eighth-ranked country emitting CO2 worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns.

A fuzzy multi-objective optimization approach for treated wastewater allocation.

In face of the new climate and socio-environmental conditions, conventional sources of water are no longer reliable to supply all water demands. Different alternatives are proposed to augment the conventional sources, including treated wastewater. Optimal and objective allocation of treated wastewater to different stakeholders through an optimization process that takes into account multiple objectives of the system, unlike the conventional ground and surface water resources, has been widely unexplored. This paper proposes a methodology to allocate treated wastewater, while observing the physical constraints of the system. A multi-objective optimization model (MOM) is utilized herein to identify the optimal solutions on the pareto front curve satisfying different objective functions. Fuzzy transformation method (FTM) is utilized to develop different fuzzy scenarios that account for potential uncertainties of the system. Non-dominated sorting genetic algorithm II (NSGA-II) is then expanded to include the confidence level of fuzzy parameters, and thereby several trade-off curves between objective functions are generated. Subsequently, the best solution on each trade-off curve is specified with preference ranking organization method for enrichment evaluation (PROMETHEE). Sensitivity analysis of criteria's weights in the PROMETHEE method indicates that the results are highly dependent on the weighting scenario, and hence weights should be carefully selected. We apply this framework to allocate projected treated wastewater in the planning horizon of 2031, which is expected to be produced by wastewater treatment plants in the eastern regions of Tehran province, Iran. Results revealed the efficiency of this methodology to obtain the most confident allocation strategy in the presence of uncertainties.

A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation.

This study proposes a fuzzy multi-stakeholder socio-optimal methodology for joint water and waste load allocation (WWLA) in river systems while addressing upstream flow uncertainty and different social choice rules (SCRs). QUAL2Kw, as the numerical river water quality model, is executed for various scenarios of water and waste loads to construct a comprehensive dataset of plausible settings, which is in turn used to train a meta-model in the form of multivariate linear regressions. The river upstream flow as the main uncertain parameter is assessed by fuzzy transformation method (FTM). Then, for different confidence levels of fuzzy uncertain input, the meta-model is linked with the non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization model to generate trade-off curves among the stakeholders' utility functions. Subsequently, five SCRs are utilized at each confidence level to determine the fuzzy interval solutions for each objective. Next, the possibility degree method is applied to rank the fuzzy interval solutions in each α-cut level. Finally, considering the priorities of all stakeholders, the fallback bargaining method is used to specify the most appropriate SCR in each confidence level. Application of the proposed methodology in Kor River, Iran, shows its efficacy to realize the socio-optimal WWLA scenario(s) among different stakeholders.

River water quality management considering agricultural return flows: application of a nonlinear two-stage stochastic fuzzy programming.

In this paper, a new fuzzy methodology is developed to optimize water and waste load allocation (WWLA) in rivers under uncertainty. An interactive two-stage stochastic fuzzy programming (ITSFP) method is utilized to handle parameter uncertainties, which are expressed as fuzzy boundary intervals. An iterative linear programming (ILP) is also used for solving the nonlinear optimization model. To accurately consider the impacts of the water and waste load allocation strategies on the river water quality, a calibrated QUAL2Kw model is linked with the WWLA optimization model. The soil, water, atmosphere, and plant (SWAP) simulation model is utilized to determine the quantity and quality of each agricultural return flow. To control pollution loads of agricultural networks, it is assumed that a part of each agricultural return flow can be diverted to an evaporation pond and also another part of it can be stored in a detention pond. In detention ponds, contaminated water is exposed to solar radiation for disinfecting pathogens. Results of applying the proposed methodology to the Dez River system in the southwestern region of Iran illustrate its effectiveness and applicability for water and waste load allocation in rivers. In the planning phase, this methodology can be used for estimating the capacities of return flow diversion system and evaporation and detention ponds.

Water and waste load allocation in rivers with emphasis on agricultural return flows: application of fractional factorial analysis.

In this paper, a new methodology is developed to handle parameter and input uncertainties in water and waste load allocation (WWLA) in rivers by using factorial interval optimization and the Soil, Water, Atmosphere, and Plant (SWAP) simulation model. A fractional factorial analysis is utilized to provide detailed effects of uncertain parameters and their interaction on the optimization model outputs. The number of required optimizations in a fractional factorial analysis can be much less than a complete sensitivity analysis. The most important uncertain inputs and parameters can be also selected using a fractional factorial analysis. The uncertainty of the selected inputs and parameters should be incorporated real time water and waste load allocation. The proposed methodology utilizes the SWAP simulation model to estimate the quantity and quality of each agricultural return flow based on the allocated water quantity and quality. In order to control the pollution loads of agricultural dischargers, it is assumed that a part of their return flows can be diverted to evaporation ponds. Results of applying the methodology to the Dez River system in the southwestern part of Iran show its effectiveness and applicability for simultaneous water and waste load allocation in rivers. It is shown that in our case study, the number of required optimizations in the fractional factorial analysis can be reduced from 64 to 16. Analysis of the interactive effects of uncertainties indicates that in a low flow condition, the upstream water quality would have a significant effect on the total benefit of the system.

Performance evaluation of a new sponge-based moving bed biofilm reactor for the removal of pharmaceutical pollutants from real wastewater.

Pharmaceutical pollutants, a group of emerging contaminants, have attracted outstanding attention in recent years, and their removal from aquatic environments has been addressed. In the current study, a new sponge-based moving bed biofilm reactor (MBBR) was developed to remove chemical oxygen demand (COD) and the pharmaceutical compound Ibuprofen (IBU). A 30-L pilot scale MBBR was constructed, which was continuously fed from the effluent of the first clarifier of the Southern Tehran wastewater treatment plant. The controlled operational parameters were pH in the natural range, Dissolved Oxygen of 1.5-2 mg/L, average suspended mixed liquor suspended solids (MLSS), and mixed liquor volatile suspended solids (MLVSS) of 1.68 ± 0.1 g/L and 1.48 ± 0.1 g/L, respectively. The effect of hydraulic retention time (HRT) (5 h, 10 h, 15 h), filling ratio (10%, 20%, 30%), and initial IBU concentration (2 mg/L, 5 mg/L, 10 mg/L) on removal efficiencies was assessed. The findings of this study revealed a COD removal efficiency ranging from 48.9 to 96.7%, with the best removal efficiency observed at an HRT of 10 h, a filling ratio of 20%, and an initial IBU concentration of 2 mg/L. Simultaneously, the IBU removal rate ranged from 25 to 92.7%, with the highest removal efficiency observed under the same HRT and filling ratio, albeit with an initial IBU concentration of 5 mg/L. An extension of HRT from 5 to 10 h significantly improved both COD and IBU removal. However, further extension from 10 to 15 h slightly enhanced the removal efficiency of COD and IBU, and even in some cases, removal efficiency decreased. Based on the obtained results, 20% of the filling ratio was chosen as the optimum state. Increasing the initial concentration of IBU from 2 to 5 mg/L generally improved COD and IBU removal, whereas an increase from 5 to 10 mg/L caused a decline in COD and IBU removal. This study also optimized the reactor's efficiency for COD and IBU removal by using response surface methodology (RSM) with independent variables of HRT, filling ratio, and initial IBU concentration. In this regard, the quadratic model was found to be significant. Utilizing the central composite design (CCD), the optimal operating parameters at an HRT of 10 h, a filling ratio of 21%, and an initial IBU concentration of 3 mg/L were pinpointed, achieving the highest COD and IBU removal efficiencies. The present study demonstrated that sponge-based MBBR stands out as a promising technology for COD and IBU removal.

Efficient implicit constraint handling approaches for constrained optimization problems.

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.

The effect of extracorporeal shock-wave therapy on pain in patients with various tendinopathies: a systematic review and meta-analysis of randomized control trials.

OBJECTIVES: Tendinopathy is a common condition that affects the body's tendon structures, causing discomfort, restricted movement, and reduced functionality. In this study, we looked at how extracorporeal shock wave therapy (ESWT) affected pain levels in individuals with various forms of tendinopathy around the world. DESIGN: This study is a comprehensive review and meta-analysis of previously published randomized controlled trials. To gather relevant data, the researchers performed keyword searches in international databases, including PubMed (Medline), Scopus, Web of Sciences, Cochrane Central Register of Controlled Trials (CENTRAL), Research Registers of ongoing trials (ClinicalTrials.gov), as well as Embase. The search was conducted up until March 2023. The quality of the selected articles was assessed using the Cochrane risk-of-bias method for randomized trials (RoB2). RESULTS: Based on the results of the meta-analysis, which included 45 clinical studies, the use of ESWT was found to have a significant impact on reducing pain in various conditions. The standardized mean difference (SMD) in patients with plantar fasciitis (PF) was reduced by 1.63 (SMD: -1.63, 95% CI: -3.04, -0.21; I2: 77.36%; P heterogeneity: 0.0001). For lateral epicondylitis (LE), the SMD was 0.63 (SMD: -0.63, 95% CI: -1.11, -0.16; I2: 67.50%; P heterogeneity: 0.003). In the case of chronic Achilles tendinopathy, the SMD was 1.38 (SMD: -1.38, 95% CI: -1.66, -1.10; I2: 96.44%; P heterogeneity: 0.0001). Additionally, in individuals with rotator cuff tendinopathy, the SMD for pain reduction was 2.37 units (SMD: -2.37, 95% CI: -3.58, -1.15; I2: 98.46%; P heterogeneity: 0.0001). CONCLUSION: This study suggests that ESWT can be a highly effective therapy option for relieving pain in people with tendinopathy. Nonetheless, it is encouraged to make additional recommendations based on high-quality clinical research and more accurate information in order to define the optimal therapeutic options for each type of tendinopathy.