Ricerca sul ruolo dell’agitazione nel processo di desalinizzazione della ceramica

The porous structure of excavated ancient potteries (especially potteries with a firing temperature below 1000 ̊C) are in general saturated with salts. Desalination is therefore one of the primary steps in the conservation and restoration process of historical porous materials. Since salinity can cause significant damage to ceramic artifacts, they must be subjected to a careful process of desalination. As water-sensitive clay-based ceramics may deteriorate or collapse during long periods of contact with water, alternative desalination methods should be identified to minimize potential damage to the pottery material. New methods such as raising the temperature of the washing water and/or using surfactants have also been suggested as alternative techniques in the desalination process to avoid long periods of contact time. In this study, acceleration of the desalination process based on a unified theoretical formulation is discussed and the role of agitation to enhance the rate of desalination is demonstrated. It was found that the main cause of deficiency in current desalination processes is due to a stagnant liquid layer near the interface of the ancient object and the water, known as concentration polarization. Agitation enhances the efficiency of desalination by minimizing the thickness of the concentration polarization layer.Ceramiche da scavo e ceramiche antiche di solito sono sature di sali all’interno della loro struttura porosa. La desalinizzazione è una delle fasi principali per il restauro di materiali porosi storici. Poiché la salinità può causare danni significativi ai manufatti in ceramica, questi devono essere esposti ad un attento processo di desalinizzazione. Siccome le ceramiche sono sensibili all’acqua e potrebbero deteriorarsi o collassare durante di lungo periodo di contatto con essa, un metodo di desalinizzazione alternativa dovrebbe essere identificato per ridurre al minimo il danno potenziale alle ceramiche. Nuovi metodi come l’aumento della temperatura dell’acqua di lavaggio e / o l’impiego di tensioattivi sono stati suggeriti come tecniche alternative per il processo di dissalazione per evitare il lungo periodo di tempo del contatto. In questa attività, l’accelerazione del processo di dissalazione, basato su una formulazione teorica unificata, è stata discussa ed è stato dimostrato il ruolo dell’agitazione per migliorare il tasso di desalinizzazione. Si è constatato infatti che la principale causa della carenza del processo di dissalazione attuale è dovuto ad uno strato di liquido stagnante vicino all’interfaccia dell’oggetto antico e dell’acqua, così chiamato polarizzazione di concentrazione. L’agitazione migliora l’efficienza della desalinizzazione minimizzando lo spessore dello strato di polarizzazione di concentrazione

Optimal Design of River Groynes using Meta-Heuristic Models

So far, several researchers have conducted many studies on the effective parameters in the design of river breakwaters, which are mostly laboratory-based and are used for limited conditions. Therefore, the aim of the present studywas to optimal design of structure and to present analytical results of Zanjanrood river breakwaters (in terms of length and distance between two consecutive breakwaters) using two optimization meta-heuristic algorithms including the Gray Wolf Algorithm (GWO) and the Election Algorithm (EA). The results were compared with artificial neural network (ANN) method. The data used were randomly divided into two parts: 75% for calibration and 25% for test. The performance of the proposed methods was evaluated using the statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The optimal length of the breakwaters according to the results of GWO and EA algorithms was 19.26 and 18.12 m, respectively. Moreover, the optimal distance between two consecutive breakwaters in the optimal state was calculated to be 52.56 m. On average, according to the results of the optimization, an increase of 28.4 and 35% in length and distance between two consecutive watersheds in Zanjanrood River should be done to be within the recommended design criteria.  In comparison with two methods of EA algorithm and artificial neural network (ANN), based on statistical indicators, the results of GWO algorithm with values ​​of R2 = 0.96, RMSE 0.022 and MAE = 0.016 has a higher efficiency

Application of Meta-Heuristic Algorithms in Reservoir Supply Optimization, Case Study: Mahabad Dam in Iran

In arid and semi-arid areas, optimization and strategic planning of water delivery through an optimal and intelligently designed reservoir supply system is a primary task for water resources management. In this regard, the election algorithm (EA) is presented to estimate the optimal storage capacity of the Mahabad dam located in northwest Iran. EA is an intelligent iterative population-based algorithm that has recently been introduced for dealing with different optimization purposes. The capability of EA to address issues of local minimums in the feature search space is employed to yield a globally optimal explanation of the present issue. The data used in this study comprise 7-year (2008-2015) evaporation, rainfall, reservoir storage, reservoir inflows, and outflow. The results obtained from the EA approach are approximated with the continuous genetic algorithm (CGA). Based on the estimated results in the testing phase, an average relatively error (5.65%) is attained in the last implementation of the algorithm. The high efficacy of EA relative to the benchmark models in terms of the NSE and RMSE, MAE is found to be approximately 0.037, 0.41, and 0.74, respectively, which are less than the values of these criteria for the CGA. These error measures, i.e. NSE, MAE, and RMSE, for the CGA were calculated to be 0.66, 0.56, and 0.042, respectively. The obtained accurate results show the high performance of the EA model in estimating the optimal reservoir capacity and its efficiency in water resources management

Effect of Squill Oxymel on Knee Osteoarthritis: A Triple-Blind, Randomized, Controlled Clinical Trial

Osteoarthritis (OA) of the knee is a major health problem in the society. Iranian Traditional Medicine (ITM) or Persian Medicine (PM) as a branch of complementary medicine has been practiced in Iran for many centuries. An herbal medication known as squill oxymel has been used by PM physicians for OA. Our aim is to investigate the effect of squill oxymel on OA of the knee joint. Eighty eight patients were assigned to receive a placebo or squill oxymel syrup (10 ml each morning on empty stomach) for 8 consecutive weeks. Acetaminophen tablets were considered as the rescue medicine. Ultimately, 43 patients in the placebo group and 40 patients in the treatment group completed the trial and were included in the statistical analysis. Patients were followed for 4 weeks after cessation of treatment. The Knee injury and Osteoarthritis Outcome Score (KOOS) questionnaire and Visual Analog Scale (VAS) were considered as the main outcome measures. Laboratory tests including AST, ALT, BUN, Cr plus inflammatory tests including WBC, ESR, and CRP with specific tests i.e. IL6 and SOD at the beginning and the end of intervention were measured. The results showed the positive effect of treatment on the outcome of knee pain (p=0.04) and daily activity (p=0.01) of KOOS after Cessation of treatment. On the other hand, VAS decreased in both treatment and placebo groups while it showed significance intra-group and showed no significance between the two groups. After 4 weeks of cessation of treatment, the positive effect of the squill oxymel on the treatment group continued in some of the subscales of KOOS, including symptoms, knee pain and daily activities, but stopped in the placebo group. In general, both clinically and statistically significant improvement was observed after cessation of treatment. Squill oxymel syrup showed promising results in management of knee OA but future researches with larger sample size and longer duration are necessary

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

Abstract One of the practical and financial solutions to increase the efficiency of weirs is to modify the geometry of the plan and increase the length of the weir to a specific width. This increases the discharge coefficient (C d ) of the weir. In this study, a new weir referred to pseudo-cosine labyrinth weir (PCLW) was introduced. A hybrid machine learning LXGB algorithm was introduced to estimate the C d of the PCLW. The LXGB is a combination of the linear population size reduction history-based adaptive differential evolution (LSHADE) and extreme gradient boosting (XGB) algorithm. Seven different input scenarios were presented to estimate the discharge coefficient of the PCLW weir. To train and test the proposed method, 132 data series, including geometric and hydraulic parameters from PCLW1 and PCLW2 models were used. The root mean square error (RMSE), relative root mean square error (RRMSE), and Nash–Sutcliffe model efficiency coefficient (NSE) indices were used to evaluate the proposed approach. The results showed that the input variables were the ratio of the radius to the weir height (R/W), the ratio of the length of the weir to the weir height (L/W), and the ratio of the hydraulic head to the weir height (H/W), with the average values of RMSE = 0.009, RRMSE = 0.010, and NSE = 0.977 provided better results in estimating the C d of PCLW1 and PCLW2 models. The improvement compared to SAELM, ANFIS-FFA, GEP, and ANN in terms of R 2 is 2.06%, 3.09%, 1.03%, and 5.15%. In general, intelligent hybrid approaches can be introduced as the most suitable method for estimating the C d  of PCLW weirs

Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling

The recent problems of Lake Urmia (LU) are caused by extensive and complex socio-ecological factors that require a comprehensive approach to consider the relationships between users and identify failure factors at the basin level. For this purpose, an agent-based simulation model of farmers’ social interactions and economic interests (ABM) with various support scenarios and random supervision and training by the government agent is developed to evaluate its impact on independent farmers’ decision-making in the form of a complex adaptive system. Finally, a fault tree analysis (FTA) is created in the Cara-FaultTree 4.1. software to identify scenarios that lead to the non-development technology in irrigation management (non-DTIM) in the LU sub-basin. The assessment of the impact of government supervision and training revealed that the main causes of non-DTIM in the LU basin are a lack of demands from farmers and low awareness among residents of the basin, with failure probabilities of 0.90 and 0.86, respectively. Ultimately, the failure probability of the main event (non-DTIM) was 0.50. The paths of proper training and farmers’ requirements for sustainable agricultural water supply should become more stringent. The results confirm that appropriate measures to strengthen government supervision and training, as well as raise farmers’ awareness of the importance of long-term sustainability of water resources, can lead to greater resilience in the DTIM

An Imperialist Competitive Algorithm (ICA)-Based Approach to Optimize the Reservoir Storage of the Kahir Dam

Water scarcity, especially in Iran and during the recent droughts, emphasizes the importance of achieving an optimal operation policy for large dam reservoirs. In the last two decades, the annual optimization of dam reservoirs under controlled conditions, as well as climatic and real conditions, has attracted many researchers and experts. This study proposes a new approach to predict reservoir dam storage. The imperialist competitive algorithm (ICA) is a new approach in the field of evolutionary computation that calculates an optimal solution for different optimization problems. Using mathematical modeling of the social-psychological evolution process, ICA provides a new approach to solve mathematical optimization problems, and compared to other algorithms, it has appropriate speed and high convergence rate in finding an optimal answer. This research used the ICA for the annual optimization of the Kahir reservoir to derive optimal policies. Objective function downstream water issue needs to establish relationships based on continuity were selected. Comparison of ICA model in population 100 showed that the ICA algorithm with average best objective function value of 125, 114.6, and 85.60 with a number of further evaluations of the objective function to achieve higher capacity is the optimum answer. The results showed a 6.1 percent error in the implementation of the ICA algorithm between the observed and predicted storages. The results of applying the ICA to the annual optimization problem demonstrate the capability of the proposed method

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Abstract Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m3, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m3/ha, which suggests a decrease of 5019.20 and 2509.6 m3/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria

Chemical Stabilization of Zinc Tailings via Additives of Lime, Red Mud, Cement and GGBFS

To reduce the solubility of nickel, cadmium, lead, zinc and cobalt of filter cake tailings, resulting from zinc processing, a sample of the mentioned tailings was collected from the accumulation site in Zanjan province. Their chemical properties were measured using XRF and XRD analysis. Then, these tailings were mixed with 0-10%, 0-3%, 0-2% and 0-6% of lime, red mud, cement and GGBFS, respectively, as stabilizers. In order to investigate the reduction of solubility of heavy metals, the extraction process of the samples was performed using 0.05 M EDTA solution, and the heavy metal of these extracts were measured by atomic absorption. The results demonstrate that in samples made with a combination of both lime and red mud, the solubility of all heavy metals except lead was reduced by 45 to 50%. A comparison between the XRD spectra of the control sample and that of the stabilized sample shows that the sulfate form of PbSO4 in the control sample has converted to the carbonate form of PbCO3 in the sample containing lime and red mud, which has more solubility. This change was the main factor in increasing the solubility of lead (87%) in these samples. Cement and slag have been the most effective additives in reducing lead solubility in filter cake. According to the XRD spectrum, the form of PbSO4 in the control sample decreased significantly (100%) after being mixed with cement and slag, which was the reason for the maximum reduction of the solubility of extractable lead in the sample