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Abstract Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future. This study assesses the projected changes in flood characteristics based on eight downscaled and bias-corrected General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6. The analysis considers two emission scenarios, including SSP2-4.5 and SSP5-8.5 for far-future (2070–2100), mid-term future (2040–2070), and historical (1982–2012) periods. Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. Then, a trivariate copula-based framework assesses flood events in terms of duration, volume, and flood peak in the Kan River basin, Iran. These analyses are carried out using the hierarchical Archimedean copula in three structures, and their accuracy in estimating the flood frequencies is ultimately compared. The results show that a heterogeneous asymmetric copula offers more flexibility to capture varying degrees of asymmetry across different parts of the distribution, leading to more accurate modeling results compared to homogeneous asymmetric and symmetric copulas. Also it has been found that climate change can influence the trivariate joint return periods, particularly in the far future. In other words, flood frequency may increase by approximately 50% in some cases in the far future compared to the mid-term future and historical period. This demonstrates that flood characteristics are expected to show nonstationary behavior in the future as a result of climate change. The results provide insightful information for managing and accessing flood risk in a dynamic environment.

Abstract Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one. Subsequently, we assessed four CMIP6 scenarios for future rainfall analysis. Next, different Machine Learning (ML) algorithms were employed through H2O-AutoML to identify the best model for downscaling future rainfall predictions. Forty distribution functions were then fitted to the future daily rainfall, and the best-fit model was selected to project future Intensity-Duration-Frequency (IDF) curves. Finally, the Soil and Water Assessment Tool (SWAT) model was utilized with sub-daily time steps to make accurate flash flood predictions in the study area. The findings reveal that IITM-ESM is the most effective among GCM models. Additionally, the application of stacked ensemble ML model proved to be the most reliable in downscaling future rainfall. Furthermore, this study highlighted that floods entering urbanized areas could reach 20.33 and 20.70 m³/s under pessimistic scenarios during rainfall events with 100 and 200-year return periods, respectively. This hierarchical comprehensive approach provides reliable results by utilizing the most effective model at each step, offering in-depth insight into future flash flood prediction.

Monitoring water quality, including total dissolved solids (TDS), across various spatial and temporal scales is essential for comprehending water health level. The Karun River's water is a vital resource for drinking water supply and agricultural irrigation, making accurate monitoring of its quality crucial for ensuring public health and sustainable resource management. This study presents a novel hybrid modeling approach that combines piecewise and symbolic regression (SR) models with Landsat imagery to predict TDS levels in river systems. Specific spectral bands, including the red and near-infrared reflectance from Landsat-9, were used as input variables. Field sampling of TDS, as the output variable, was conducted along the Karun River on three dates, synchronized with Landsat satellite images. The innovative use of a fuzzy-based uncertainty analysis, coupled with AHP weighting, allowed for a comprehensive assessment of TDS estimation accuracy and uncertainty. The Composite Uncertainty Index (CUI) approach revealed that the Multivariate Adaptive Regression Splines (MARS) and M5 models performed better than other models, with CUI values of 0.83 and 0.72, respectively. MARS demonstrated higher accuracy under low uncertainty conditions, while M5P excelled in scenarios of elevated uncertainty due to its reduced sensitivity and strong Nash-Sutcliffe coefficient. The hybrid modeling approach presented in this study offers a unique contribution to remote sensing-based water quality monitoring. By leveraging advanced regression techniques and uncertainty quantification, the findings enable more reliable predictions of TDS levels, which are crucial for sustainable river management.

The boundary update approach was proposed by Gandomi and Deb [Computer Methods in Applied Mechanics and Engineering, 363, 112,917, 2020] for constrained optimization problems. The boundary update (BU) method defines a dynamic formulation of an optimization problem in order to eliminate the infeasible search space. This study investigates the concept of variable interaction using a differential grouping algorithm, DG2, to evaluate and visualize variable interaction impacted by the boundary update method. Using multiple social network analysis (SNA) metrics, including Average Betweenness Centrality, Average Closeness Centrality, Network Density, and Clustering Coefficient, this research reveals significant structural simplifications in optimization problems under the BU method. Results show significant improvements in some aspects by applying the BU method. For example, it reduces network density by up to 34 %, closeness centrality by over 67 %, and enhances independence among variables by 45 %, simplifying the optimization landscape. Furthermore, a systematic evaluation using the TOPSIS multi-criteria decision-making (MCDM) approach confirms that BU improves convergence efficiency and solution quality by 20%–30 % compared to without BU methods across various benchmark problems. Network visualizations corroborate these findings, demonstrating reduced complexity and improved clarity in variable relationships. This comprehensive analysis establishes the BU method as a transformative framework, significantly advancing constrained optimization through its ability to streamline variable interactions and enhance algorithmic performance.

Background and Objective: De Quervain's tenosynovitis is one of the common causes of wrist pain. The present study aimed to compare the effectiveness of high-power laser therapy and local corticosteroid injection in the treatment of De Quervain's tenosynovitis. Materials and Methods: In this clinical trial, 30 patients diagnosed with De tenosynovitis were randomly assigned to two groups of 15, A and B. Group A underwent high-power laser therapy, five sessions for three consecutive weeks, and group B received corticosteroid injections of 1 ml triamcinolone and 1 ml lidocaine 2% in the wrist under ultrasound guidance. Tendon thickness, pain intensity, quality of life, and treatment satisfaction were measured and compared in both groups pre-treatment, month one, and month three post-treatment. Results: In groups A and B, respectively, the mean scores of the thickness of extensor abductor pollicis longus and abductor pollicis brevis muscles before the treatment were 7.57±0.63 and 7.25±0.58 (P=0.120). these value were calculated at 6.55±0.61 and 6.54±0.40 one month later (P=0.955). They were 5.71±0.75 and 5.99±0.45 (P=0.239) mm three months post-treatment. In addition, pain scores before the treatment were 8.60±1.18 and 8.47±1.46 (P=0.785). One month later, they were 2.73±1.66 and 4.27±1.16 (C=0.785), and three months post-treatment, they were 1.73±1.33 and 2.47±1.06 (P=0.007). There was no significant difference between the two groups in terms of quality of life and satisfaction with treatment (P

Abstract In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

Abstract 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.

Abstract 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.

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R2 = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R2 = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R2 ranging from 0.79 to 0.95.

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh. To enhance the reliability of the RMS-WQI model, the research incorporated the extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For the assessment of GWQ, the study utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3−), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and iron (Fe). In terms of the GW indicators, concentration of K, Ca and Mg exceeded the guideline limit in the collected GW samples. The computed RMS-WQI scores ranged from 54.3 to 72.1, with an average of 65.2, categorizing all sampling sites' GWQ as “fair.” In terms of model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) in predicting GWQ accurately. Furthermore, the RMS-WQI model exhibited minimal uncertainty (

Abstract 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.

AbstractIntroduction: Effective management in metropolitan planning of urban green space water resources is very important due to the rapid growth of urbanization and the limitation of water resources. The purpose of this research is multi-objective optimization and management of treated wastewater allocation using dynamic system models for urban green space irrigation in Shiraz metropolis.Methods: The factors affecting the supply of water resources and irrigation of the green spaces of Shiraz city have been simulated and according to that, the consumption forecast and the number of available resources and the allocation optimization have been presented. Also, in the current research, effective factors in the current and future conditions that can affect the proposed model have been taken into consideration, and two scenarios have been applied, including increasing the efficiency of green space irrigation and the use of treated wastewater.Results: The results showed that increasing the efficiency of irrigation through management of transfer, storage and distribution of water and also irrigation at the right times has a much greater effect compared to the use of purified wastewater in order to reduce the extraction of limited resources. In such a way, by increasing the irrigation efficiency by 40% and 50%, respectively, 14% and 39% of water shortages will be solved.Conclusion: The simulation results showed that in order to achieve the stability of the system, a combination of scenarios of reducing per capita water consumption in green spaces, increasing irrigation efficiency, using purified wastewater and dry landscaped spaces should be considered in the development of urban spaces.

Abstract Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.

Intermittent water supply (IWS) is one of the effective methods to manage the consumption of urban water networks under water scarcity conditions. However, it is essential to minimize unfair water distribution in this method by defining a proper strategy. This study utilized the EPANET pressure-dependent hydraulic analysis and the gray wolf optimization algorithm to achieve maximum volumetric reliability under different scenarios in a district of the Hamedan urban water distribution network in Iran. The volumetric reliability of the network was evaluated in the IWS condition regardless of justice constraints, with the justice constraint, and by considering the leakage in the IWS network with the justice constraint. The first scenario demonstrated that the reliability decreased by an average of 4.6% for every meter of water level reduction in the tank. The second scenario revealed that the objective function was negligibly affected by the variation of the justice constraint; however, fluctuation of the water level in the tank significantly affected the volumetric reliability. In the third scenario, the objective function value was significantly impacted by leakage, ranging from 0 to 0.3 (representing the absence and presence of leakage in 30% of the nodes, respectively), resulting in an average decrease of about 17%. HIGHLIGHTS A pressure-dependent hydraulic analysis in the intermittent water supply (IWS) under various scenarios.; Volumetric reliability assessment in an IWS network.; Evaluating the justice index in the IWS network.; Investigating the effect of leakage on the performance of the equitable IWS.; Development of simulation-optimization model in IWS.;

Climate adaptation offers promising approaches to cope with the detrimental impacts of climate change on rainfed agriculture, intending to mitigate yield losses and safeguard food security worldwide. This is especially critical in the semi-arid tropics, characterized by highly variable and often intensified rainfall regimes. The current study explored the potential contribution of climate adaptation measures, specifically sowing windows and supplemental irrigation (SI), to offset the impacts of rainfall variability on rainfed crop yield in the Thondamuthur block, India. Under the proposed adaptation measures, this study analyzed the relationship between historical rainfall variability (1951–2019) and rice, maize, and sorghum yield anomalies. A seamlessly integrated framework was developed to link rainfall variability analysis with yield response simulations using the AquaCrop model. Rainfall classification analysis indicated an increase in rainfall availability during the Kharif season and a decrease during the Rabi season in Period III compared with the baseline. The late-sown Kharif and early-sown Rabi crops demonstrated effective climate adaptation, with the 27th and 38th SMWs identified as optimal climate-adapted sowing weeks, respectively. Significant decreases of 10% and 12% in SI requirements were observed in early-sown Rabi maize and sorghum, respectively. The results showed that substituting Rabi rice and maize with Rabi sorghum led to reductions of 90% and 75% in seasonal SI requirements, respectively. The early-sown Rabi rice and maize contributed to 88% and 12% relative yield increases, respectively, compared with the normal-sown. This study highlights the effectiveness of climate adaptation measures in enabling farmers to withstand climate-induced anomalies.

Introduction and ObjectiveEcological quality is an inclusive measurement unit of the elements, configuration, and performance of an ecosystem in the temporal and spatial scales. In addition, assessing ecological quality could provide valuable information for experts and managers in the first step of directed planning and comprehensive watershed management. Therefore, the current research was conducted to calculate the ecological quality index (EQI) in one of the upland watersheds of Yamchi Dam, which is not without the effect of human interference.Materials and MethodsNir Watershed as a study area is located in the southwestern part of Ardabil Province. To carry out the current research, first, the essential variables of ecological quality assessment (normalized difference vegetation index (NDVI), fractional vegetation coverage (FVC), leaf area index (LAI), net primary production (NPP), moisture index (IM), land surface temperature (LST), soil erosion, ecological integrity index, and runoff retention index) were collected and calculated. The variables were chosen to represent the three functions of ecosystems maintaining, supporting, and regulating. After standardization, weighting of the variables was done using the advanced multi-criteria, i.e., Projection Pursuit Model (PPM). Afterward, the EQI was calculated based on the sum weight of all variables for 11 sub-watersheds of Nir.Results and DiscussionThe wide range of variability of the variables used in the entire Nir Watershed area was confirmed. In general, sub-watershed 1 had the maximum mean of the first to fourth variables (FVC, NDVI, NPP, and LAI) and the minimum mean LST. While sub-watershed 11 had the opposite situation in terms of these five variables. All five variables reflect the structure and function of vegetation and therefore the ecological quality. The status of other indicators except ecological continuity (EC) was also evaluated in sub-watershed 1 in a medium status. Based on the PPM weighting method, the FVC (0.39) has the highest importance, and moisture and runoff retention indices with an equal weight of 0.01 have been assigned the least priority. In addition, the ecological quality results showed that watersheds 11 (0.10) and 1 (0.90) respectively have the lowest and highest levels of EQI. In general, according to the zoning resultd, it was found that the northeastern and southeastern parts of the watershed have the lowest EQI which covered 29% of the area.Conclusion and SuggestionsThe results can be used as one of the main goals of managing watersheds regarding preserving the ecosystem's health and integrity. Moreover, the priority of rehabilitation budget allocation can be assigned according to the resilience degrees of studied sub-watersheds.

Human activities, climate change, and land-use alterations accelerated soil erosion in recent decades and imposed significant threats to soil fertility and stability worldwide. Understanding and quantifying the spatiotemporal variation of soil erosion risks is crucial for adopting the best management practices for surface soils conservation. Here, we present a novel high-resolution (30 m) soil erosion framework based on the G2 erosion model by integrating satellite and reanalysis datasets and Machine Learning (ML) models to assess soil erosion risks and hazards spatiotemporally. The proposed method reflects the impacts of climate change in 1 h time resolutions and land use in 30 m scales on soil erosion risks for almost 4 decades (between 1985 and 2017). The soil erosion hazardous maps were generated/evaluated using Extreme Value Analysis (EVA), utilizing long-term annual soil erosion estimations/projections to aid policymakers in developing management strategies to protect lands against extreme erosion. The proposed framework is evaluated in the Sultanate of Oman, which lacks soil erosion estimation/assessment studies due to data scarcity. Results indicate that soil erosion has increasing perilous trends in high altitudes of the Sultanate of Oman that may cause substantial risks to soil health and stability.

Background: The effect of ozone therapy in reducing inflammation and radicular pain with lumbar transforaminal epidural steroid has not been sufficiently investigated. This study compares the effectiveness of transforaminal steroid injection with or without ozone therapy on radicular (leg) pain. Methods: In a double-blind clinical trial, 40 patients with chronic radicular pain whose pain did not respond to conservative treatments were selected and randomly assigned to two treatment groups of A and B. Group A underwent transforaminal epidural steroid injection with ozone and group B underwent transforaminal epidural steroid injection without ozone. The intensity of pain with the Numerical Rating scale (NRS) scale and the degree of disability with the Oswestry Disability Index (ODI) questionnaire were compared in both groups of patients before treatment, 24 hours after treatment, 1 and 3 months after treatment. Results: Both groups of A and B were similar in terms of age, gender, pain duration, pain intensity and disability before treatment (P>0.05). In treatment groups of A and B the mean of pain score 24 hours after treatment was 6.95±2.50 and 6.15±3.50 (P=0.495), one month after treatment it was 4.10±2.59 and 3.25±2.32 (P=0.355) and the third month of treatment was 3.85±3.01 and 3.55±3.25 (P=0.429). respectively. The mean of ODI 24 hours after treatment was 22.50±10.78 and 18.95±15.24 (P=0.401), one month after treatment it was 19.40±7.76 and 7.95±5.42 (P

The adverse effects of coal and gas energy production with the subsequent rapid increase in energy consumption emphasize the importance for Australia to adopt more renewable energy sources to counteract these dismissive contributions to climate change. This work presents a data mining approach for optimally selecting the best locations for installing a distributed hybrid renewable energy generation system for rural regions in Western Australia. The K-Means and K-Medoids clustering algorithms were used to divide the constructed dataset into clusters. In total, 69 locations were selected for the overall dataset, proceeding with the filtering process. The returned cluster data were graphically rendered on a Western Australia map for the region. The clustering algorithms were evaluated using the Dunn index, such that K-Means performed to a higher degree than K-Medoids, given our dataset's nature. After passing the generated clusters to HOMER software to generate the potential wind and solar energy output for each centroid, K-Medoids produced a set of locations that generated higher solar and wind energy on average. However, due to the reduced internal validation, K-Medoids might not be as valuable as K-Means, it does not cluster the data points very well, and within-cluster location energy requirements are not considered in our study.

Ocean energy technologies are in their developmental stages, like other renewable energy sources. To be useable in the energy market, most components of wave energy devices require further improvement. Additionally, wave resource characteristics must be evaluated and estimated correctly to assess the wave energy potential in various coastal areas. Multiple algorithms integrated with numerical models have recently been developed and utilized to estimate, predict, and forecast wave characteristics and wave energy resources. Each algorithm is vital in designing wave energy converters (WECs) to harvest more energy. Although several algorithms based on optimization approaches have been developed for efficiently designing WECs, they are unreliable and suffer from high computational costs. To this end, novel algorithms incorporating machine learning and deep learning have been presented to forecast wave energy resources and optimize WEC design. This review aims to classify and discuss the key characteristics of machine learning and deep learning algorithms that apply to wave energy forecast and optimal configuration of WECs. Consequently, in terms of convergence rate, combining optimization methods, machine learning, and deep learning algorithms can improve the WECs configuration and wave characteristic forecasting and optimization. In addition, the high capability of learning algorithms for forecasting wave resource and energy characteristics was emphasized. Moreover, a review of power take-off (PTO) coefficients and the control of WECs demonstrated the indispensable ability of learning algorithms to optimize PTO parameters and the design of WECs.

Hybrid energy production from available resources such as wind and solar are identified as an environmentally friendly alternative for power production. Australia, as a developed country, possesses the potential to use renewable energies. Motivated by the lack of comprehensive research dedicated to the economic analysis of hybrid systems in Australia, the objective of the present work is to propose an optimal integrated renewable energy system model in order to fulfill the electrical energy demands in five major Australian cities, namely, Perth, Adelaide, Melbourne, Sydney, and Brisbane. To compare the studied cities, ten types of buildings (hospital, hotel, office, mall, clinic, retail, apartment, supermarket, school, and warehouse) have been analyzed in different climatic zones of Australia. The results have revealed that different viable scenarios for the investigated case studies are obtained. Furthermore, the optimal configuration, along with the cost and carbon emission that is obtained in the study, can be applied as a guideline for designing power systems (with a minimized cost) for the considered communities and other cities with similar characteristics (population and climatic conditions).

In this study, a simulation-optimization model is used for multi-period consequence management of sudden contamination in urban water distribution network. The tools used in the management of consequences in this study are to discharge contaminated water by hydrants, to cut or plug the pipes to control the flow path in the network or to isolate the different areas, as well as to switch the pumps on or off to regulate the water flows pressure into the network. EPANET software is used for simulation and single-objective genetic algorithm is used for optimization. In this study, the three objective functions are to minimize system return time to normal situation, minimize the mass of contamination consumed, and minimize the number of infected nodes. Each of the objective functions mentioned is executed for two different management scenarios. In the first scenario, the status of the hydrants, valves and pumps is constant from the beginning to the end of the management period. In the latter scenario, the status of the hydrants, valves and pumps can be changed periodically from the beginning to the end of the management period. The number of decision variables in this study was 54, which included determining whether the hydrants were open or closed, whether the pipes were closed or connected, and whether the pumps were on or off. The contamination loading is injected from the three candidate nodes into the network. The results show that alternating situations of hydrants, valves and pumps, compared to the situation where the hydrants, valves and pumps are in constant condition, decreases the return time to a normal state between 13 and 26.5 percent, the amount of contaminated water consumed between 12 and 20 percent and the number of infected nodes between 6 and 21 percent. It should be noted that in all scenarios, the minimum water pressure at the nodes of the network is controlled from the minimum permissible value and the increase in the number of management responses actions is controlled from the maximum permissible value. The alternation of the opening and closing of the hydrants and valves, as well as the switching of the pumps on or off, helps to improve the target functions to an acceptable extent.

In this paper, two optimization methodologies for conjunctive operation from surface and groundwater resources are developed. The first proposed methodology consists of an integrated model that is able to consider and analysis supply and water demand management policies, together. In this model, optimum decisions are determined for reservoir-river-groundwater system simultaneous operation for developing water supply and increasing water demands conditions. Also, different climate change scenarios are considered for evaluating the system in the different situations. In this model, using NSGA-II multi-objective optimization model that surface and groundwater allocation are determined with multi-objective goal programming approach for 10-year periods in different climate. For analyzing optimization model, performance criteria are determined for system in the conjunctive operation structure in order to evaluate and rank operation approaches. Finally, using Borda count model, an appropriate solution is chosen on the obtained trade-off among different objectives. In the second methodology, a simulation-optimization model is developed for conjunctive operation from surface and groundwater resooptimum decisions are determined for reservoir-river-groundwater system simultaneous operation under different climate scenarios for developing water supply and increasing demands system. They are presented in the operation rules framework and rule curves in the monthly and weekly time steps. Finally, using fallback bargaining model, an appropriate solution is determined in obtained trade-off among different objectives of stakeholders. The results show that, aquifer modeling has better estimation from water system situation in the case study and the proposed methodologies can determine solution that can provide the most possible compromise among different stakeholders.

Detention rockfill dams are one of the structural methods that can control flood risks with no impermeable core or membrane. These dams decrease the maximum discharge of flood hydrograph and postpone the time of its occurrence reducing life and financial losses in downstream. In this research study, maybe for the first time, a new methodology based on Multi-Layer Perceptron (MLP) artificial neural network meta-model and genetic algorithm optimization model is proposed for optimum design of detention rockfill dam. Hence, results of experiments done on the rockfill dam model, from Khorramshokouh (1391), are used in order to train and validate the MLP neural network model. The MLP neural network model is in fact a meta-model of simulating the hydraulic performance of detention rockfill dam which is linked to genetic algorithm optimization model to determine the optimal features of detention rockfill dam regarding the relationship between design variables and flood hydrograph passed through the porous media. Results of the proposed methodology depict that for the total discharges of probable floods, the optimum thickness of detention rockfill dam and the mean diameter of rockfills in the porous media are 17.6 cm and 2 cm, respectively. Also, in the optimum state, the peak discharge of flood hydrograph has 47.18% reduction and flood duration increases 39.94%.

A multi-objective linear optimization model has been formulated, which is used for water and crop area allocation in two irrigation and drainage networks of Dorudzan and Karbal, including five farming regions. The developed model is based on four bankruptcy rules of proportional cutback (PC), constrained equal awards (CEL), constrained equal losses (CEL), and adjusted proportional (APR) in terms of the certainty and uncertainty in the water availability. The developed model has four objective functions to reflect the various agricultural and environmental consumptions and is solved for two dry and non-dry conditions using a fuzzy compromise approach. The outputs of the model showed that the regions with higher shares of water receives the most allocated water through the bankruptcy rules of the PC and CEL in dry and non-dry condition, respectively. On the other hand, the most allocated water for the regions with lower shares of water occurs through the bankruptcy rule of the CEA in both hydro-climatic conditions. The outcome of the stability evaluation using the bankruptcy stability index (BASI) indicated that this criterion could not be used to evaluate stability under all bankruptcy situations; thus, one should take the necessary precaution for making a decision according to its output.

For proper groundwater resource management as a vital resource, accurate aquifer parameter determination is required. Existing groundwater management practices, for the sake of simplicity, overlook inherent uncertainties in measurements of pumping test parameters. In the current study a fuzzy simulation-optimization model based on consideration of uncertainties in parameter determination is used. To this regard, the novel fuzzy simulation-optimization model is able to predict the confined aquifer parameter precisely, based on minimizing the deviation between observed and calculated drawdown. The proposed approach is tested on a real pumping test data of a confined aquifer and then the results are compared with graphical solution of Theis method. Comparing several statistical indices based on the results of the proposed method and graphical solution of Theis method, performance of these models are evaluated. As an example, Mean Absolute Relative Error (MARE) of the proposed model and graphical Theis solution is 0.69% and 1.13% respectively which shows the appropriate accurate of the proposed model over the traditional method (graphical Theis solution). Thus, the proposed fuzzy simulation-optimization model may replace the graphical Theis solution. In the second part of the study, by considering pumping rate as an uncertain parameter, a fuzzy optimization model based on fuzzy transformation method is developed. Then, the effect of uncertainty in prediction of aquifer parameters is assessed and ranges of aquifer parameters in various α cuts, are determined. Based on the developed fuzzy results, T is found more sensitive to uncertainty in the pumping rate measurements, as compared to S.

In this research, the application of multilayer perceptron (MLP) neural networks and support vector machine (SVM) for modeling the hydrodynamic behavior of Permeable Breakwaters with Porous Core has been investigated. For this purpose, experimental data have been used on the physical model to relate the reflection and transition coefficients of incident waves as the output parameters to the width of the breakwater chamber, the ratio of the height of rockfill material to the water depth, the ratio of the width of the chamber to the wavelength, Wave number in water depth and wave steepness. The results indicate that the MPL model has better performance in modeling the hydrodynamic behavior than the SVM model and is largely correlated to real data (R = 0.8689 for reflection coefficient and 0.96629 R = for transient coefficient). In order to reveal the response of reflection and transition coefficients to each input parameter, a parametric study was performed. Also, using the sensitivity analysis, the participation rate of each input parameters in the prediction of reflection and transient coefficients has been studied.

Ocean energy is one potential renewable energy alternative to fossil fuels that has a more significant power generation due to its better predictability and availability. In order to harness this source, wave energy converters (WECs) have been devised and used over the past several years to generate as much energy and power as is feasible. While it is possible to install these devices in both nearshore and offshore areas, nearshore sites are more appropriate places since more severe weather occurs offshore. Determining the optimal location might be challenging when dealing with sites along the coast since they often have varying capacities for energy production. Constructing wave farms requires determining the appropriate location for WECs, which may lead us to its correct and optimum design. The WEC size, shape, and layout are factors that must be considered for installing these devices. Therefore, this review aims to explain the methodologies, advancements, and effective hydrodynamic parameters that may be used to discover the optimal configuration of WECs in nearshore locations using evolutionary algorithms (EAs).

This data article presents a rich original experimental video sources and wide collections of laboratory data on water levels, sediment depths and wave front celerity values arose from different multiphase dam-break scenarios. The required data of dam-break shock waves in highly silted-up reservoirs with various initial up- and down-stream hydraulic conditions is obtained directly from high-quality videos. The multi-layer shock waves were recorded by three professional cameras mounted along the laboratory channel. The extracted video images were rigorously scrutinized, and the datasets were obtained through the images via image processing method. Different sediment depths in the upstream reservoir and dry- or wet-bed downstream conditions were considered as initial conditions, compromising a total of 32 different scenarios. A total of 198 original experimental videos are made available online in the public repository “Mendeley Data” in 8 groups based on 8 different initial upstream sediment depths [1–8]. 20 locations along the flume and 15 time snaps after the dam breaks were considered for data collecting. Consequently, a total of 18,000 water level and sediment depth data points were collected to prepare four datasets, which are uploaded in the public repository “Mendeley Data”. A total of 9600 water level data points could be accessed in [9,10], while 8400 sediment depth data points are available online in [11,12] and could be utilized for validation and practical purposes by other researchers. This data article is related to another research article entitled “Experimental study and numerical verification of silted-up dam-break” [13].

The inconsistency of the Indian monsoon has constantly threatened the country’s food production, especially key food crops such as rice. Crop planning measures based on rainfall patterns during the rice-growing season can significantly improve the sustainable water usage for water-intensive crops such as rice. This study examines the variability of Indian monsoonal rainfall in rainfed and irrigated rice-cultivating regions to improve rainfall utilization and irrigation water-saving practices. Two distinct rice-growing conditions in southern peninsular India are chosen for this study. The preliminary seasonal rainfall analysis (1951–2015) showed anomalies in the Sadivayal (rainfed rice) region compared to the Karaikal (irrigated rice). The dry-spell analysis and weekly rainfall classification suggested shifting the sowing date to earlier weeks for the Thaladi season (September–February) and Kar season (May–September) to avoid exposure to water stress in Sadivayal. Harvesting of excess rainwater during the wet weeks is proposed as a mitigation strategy for Karaikal during the vegetative stage of the Kuruvai season (June–October) and Late Thaladi season (October–February), where deficit rainfall is expected. Results showed that an adaptation strategy of early sowing is the most sustainable measure for rainfed rice cultivation. However, harvesting the excess rainwater is an ideal strategy to prevent water stress during deficient rainfall periods in irrigated rice farming. This comparative study proposes a comprehensive rainfall analysis framework to develop sustainable water-efficient rice cultivation practices for the changing rainfall patterns.

Water quantity-quality equitable allocation model in river-reservoir systems provides the possibility of decision making on the amount of allocated water by considering the hydrological, economical, and environmental effects. Water allocation is done according to three criteria, namely equity, efficiency, and sustainability by considering uncertainties in hydrological parameters. In this method, at first a water quantity-quality allocation model was developed in GAMS optimization model based on the mentioned three criteria. Scenarios were built based on scenario optimization technique by identifying the uncertainties in the model inputs. Water was allocated using the initial model for each sub-scenario. Afterward, water allocation in each scenario was obtained by the tracking model. To assess the applicability of the proposed method, it was applied to Roodbal river basin in south-east of Fars province, south of Iran. After analyzing basic data, upstream inflow and TDS concentration were identified as the uncertain input and the most critical quality parameter respectively. Due to the existing uncertainty, three scenarios of wet, normal and dry year were built. The results of using the proposed model showed that 100 percent of the users’ demands are supplied in wet and normal years and 61 percent in dry years. The volume of reservoir was always more than minimum volume (35 MCM) and TDS concentration was lower than 1000 mg/L (TDS water quality standard). The value of these three criteria were also at their highest possible. Based on the results of the model, the Roodbad River-Reservoir system faces no problem in supplying agricultural water demands in wet and normal years. Therefor, water can be stored in wet and normal years to be used during dry years. In dry years, water is allocated to the users in a way that the mentioned criteria have the maximum values and the reservoir storage doesn’t reach the minimum value except for the last month of the optimization period.

Summary: Some algorithms based on fuzzy set theory (FST) such as fuzzy inference system (FIS) and adaptive-network-based fuzzy inference system (ANFIS) have been successfully applied to significant wave height (SWH) prediction. In this paper, perhaps for the first time, the fuzzy K-nearest neighbor (FKNN) algorithm is utilized to develop a fuzzy wave height prediction model for large lakes, where the fetch length depends on the wind direction. As fetch length (or wind direction) can affect the wave height in lakes, this variable is also considered as one of the inputs of the prediction model.The results of the FKNN model are compared with those of some soft computing techniques such as Bayesian networks (BNs), regression tree induction (named M5P), and support vector regression (SVR). The developed FKNN model is used for SWH prediction in the western part of Lake Superior in North America. The results show that the FKNN and M5P model can outperform the other soft computing techniques. Keywords: Significant wave height prediction, Fuzzy K-nearest neighbor, Bayesian networks, Support vector regression, Regression tree induction

Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

The Karkhe River is one of the most important rivers in Iran and is the third largest river in terms of the discharge volume. In its wild state. the river usually left many damage its wake. In order to reduce these detrimental out comes, the Karkhe Dam was built, which is one of the most important and also the largest dams in Iran and the Middle East. This dam has instigated an economic reforminits basin, such as changes in the land-use, amount of water, vegetation and the urban areas. Some of the major changes occurred ofter the dam construction have been evaluated: The Using Landsat satellite images spanning between 1352 and 1392, maximum likelihood classification identifying 7 classes was conducted on the pre-processed images. The results showed the barren soil decrease of 0.2 percent; the residential area, vegetation and water supply have increased by 2.36, 1.4 and 2.5 percent, respectively. In spite of the logical trend of these results, the accuracy assessment was as an added measure to confirmed the previous results. The evaluation showed a high accuracy almost in all of the classification results. The overall accuracy and the Kappa coefficient estimated from the accuracy assessment are higher than 90% and 0.9, respectively, while the user and producer accuracies are more than 80%. This demonstrates the high performance of the maximum likelihood classification.

The advantage of permeable breakwaters over more traditional types has attracted great interest in the behavior of these structures in the field of engineering. The main objective of this study is to apply 19 well-known machine learning regressors to derive the best model of innovative breakwater hydrodynamic behavior with reflection and transmission coefficients as the target parameters. A database of 360 laboratory tests on the low-scale breakwater is used to establish the model. The proposed models link the reflection and transmission coefficients to seven dimensionless parameters, including relative chamber width, relative rockfill height, relative chamber width in terms of wavelength, wave steepness, wave number multiplied by water depth, and relative wave height in terms of rockfill height. For the validation of the models, the cross-validation method was used for all models except the multilayer perceptron neural network (MLP) and genetic programming (GP) models. To validate the MLP and GP, the database is divided into three categories: training, validation, and testing. Furthermore, two explicit functional relationships are developed by utilizing the GP for each target. The exponential Gaussian process regression (GPR) model in predicting the reflection coefficient (R2 = 0.95, OBJ function = 0.0273), and similarly, the exponential GPR model in predicting the transmission coefficient (R2 = 0.98, OBJ function = 0.0267) showed the best performance and the highest correlation with the actual records and can further be used as a reference for engineers in practical work. Also, the sensitivity analysis of the proposed models determined that the relative height parameter of the rockfill material has the greatest contribution to the introduced breakwater behavior.

Objectives: Ankle Foot Orthoses (AFOs) are widely utilized to improve walking ability in hemiplegic patients. The present study aimed to evaluate the effect of Rocker bar Ankle Foot Orthosis (RAFO) on functional mobility in post-stroke hemiplegic patients. Methods: Fifteen hemiplegic patients (men and women) who were at least 6-months poststroke and able to walk without an assistive device for at least 10 meters voluntarily participated in this study. The patients were examined for three conditions: shoes only, with SAFO and with RAFO. Their functional mobility was evaluated through 10-meter walk test and Timed Up and Go (TUG) test. In addition, paired t-test was used to analyze the obtained data. Results: When the patients used RAFO, their gait speed significantly increased (P

Objectives: Ankle Foot Orthoses (AFOs) are widely utilized to improve walking ability in hemiplegic patients. The present study aimed to evaluate the effect of Rocker bar Ankle Foot Orthosis (RAFO) on functional mobility in post-stroke hemiplegic patients. Methods: Fifteen hemiplegic patients (men and women) who were at least 6-months post-stroke and able to walk without assistive device for at least 10 meters voluntarily participated in this study. The patients were examined with and without RAFO. Their functional mobility was evaluated through 10-meter walk test and Timed Up and Go (TUG) test. Also, paired t-test was used to analyze obtained data. Results: When patients used RAFO, their gait speed significantly increased (P

Wildfire danger is often ascribed to increased temperature, decreased humidity, drier fuels, or higher wind speed. However, the concurrence of drivers—defined as climate, meteorological and biophysical factors that enable fire growth—is rarely tested for commonly used fire danger indices or climate change studies. Treating causal factors as independent additive influences can lead to inaccurate inferences about shifting hazards if the factors interact as a series of switches that collectively modulate fire growth. As evidence, we show that in Southern California very large fires and ‘megafires’ are more strongly associated with multiple drivers exceeding moderate thresholds concurrently, rather than direct relationships with extreme magnitudes of individual drivers or additive combinations of those drivers. Days with concurrent fire drivers exceeding thresholds have increased more rapidly over the past four decades than individual drivers, leading to a tripling of annual ‘megafire critical danger days’. Assessments of changing wildfire risks should explicitly address concurrence of fire drivers to provide a more precise assessment of this hazard in the face of a changing climate.

Methods for evaluatingthe quality of groundwater resources and recognition of appropriate locations for urban and agricultural water demand has been known as an important item in water resources planning. The main focus of this paper has been addressed a new water quality mapping based on coupling of fuzzy approximation and water quality indexing. In this paper, after indexing water quality in each monitoring well, based on fuzzy reasoning a new global fuzzy indexing has been presented. In this regard, 12 water quality parameters for 217 monitoring wells in the province of Ghazvin in the north have been used. In the final step, map of fuzzy evaluation over the area of interest has been provided based on an optimized new spatial approximation via Genetic Algorithm (GA). The results showed the capability of the proposed methodology for groundwater quality mapping. The most important contribution of this paper is successful combination of fuzzy reasoning and water quality indexing as a continuous function for evaluating groundwater quality mapping.

In recent decades, river quality management has received enormous attention by researchers as an important water resources management issue. The main reason for this is saving in wastewater treatment costs by optimal allocation of the assimilative capacity of the river system to dischargers. Regarding the unidirectionality of the river flow toward the lowest level, the Trading Ratio System (TRS) and Bayesian Networks are utilized in this paper to develop new, real-time operating policies for discharge permit trading in rivers. TRS is used in a Monte Carlo Analysis to provide the required data for training and validating a Bayesian Network (BN). The trained BN are then used for real time river water quality management to provide probability distribution functions of treatment levels and trading discharge permit policies. The methodology is successfully applied to a case study and its results are compared with those of the TRS. The comparisons show the usefulness of the methodology as a cost-effective and probabilistic decision-making tool in real-time river water quality management.

Background: In this systematic review, the aim is to investigate different VHI-30 versions between languages regarding their validity, reliability and their translation process. Methods: Articles were extracted systematically from some of the prime databases including Cochrane, googlescholar, MEDLINE (via PubMed gate), Sciencedirect, Web of science, and their reference lists by Voice Handicap Index keyword with only title limitation and time of publication (from 1997 to 2014). However the other limitations (e.g. excluding non-English, other versions of VHI ones, and so on) applied manually after studying the papers. In order to appraise the methodology of the papers, three authors did it by 12-item diagnostic test checklist in "Critical Appraisal Skills Programme" or (CASP) site. After applying all of the screenings, the papers that had the study eligibility criteria such as; translation, validity, and reliability processes, included in this review. Results: The remained non-repeated articles were 12 from different languages. All of them reported validity, reliability and translation method, which presented in details in this review. Conclusion: Mainly the preferred method for translation in the gathered papers was " Brislin‘s classic back-translation model (1970), although the procedure was not performed completely but it was more prominent than other translation procedures. High test-retest reliability, internal consistency and moderate construct validity between different languages in regards to all 3 VHI-30 domains confirm the applicability of translated VHI-30 version across languages.