Search Results (60)

The scientific goal of this study was to investigate the effects of various parameters on cavitation-induced erosion, with the aim to enhance the understanding and assessment of cavitation resistance in hydraulic systems. Cavitation erosion poses significant challenges to the durability and efficiency of hydraulic components, such as those found in hydropower plants and pumping stations. Prompted by the need to improve the reliability of cavitation testing and material assessment, this research conducted a comprehensive sensitivity analysis of a cavitation jet apparatus (CJA). This study employed an experimental platform that consisted of a vertical cylindrical test tank, a submerged nozzle, and an aluminum sample. By examining a range of orifice diameters, this research identified that smaller diameters led to increased erosion intensity, with the most pronounced effects observed at a diameter of 2 mm. Furthermore, various standoff distances (SoDs) were tested, which revealed that shorter distances resulted in greater erosion, with the highest impact noted at an SoD of 5 cm. This study also evaluated different nozzle geometries, where it was found that a 132° conical sharped edges nozzle, combined with an orifice diameter of 2 mm and an SoD of 5 cm, produced the most severe erosion. Conversely, chamfered edges nozzles and a commercial nozzle (MEG2510) with an SoD of 10 cm or greater showed reduced erosion. These results highlight that by standardizing the testing duration to 1200 s, the CJA could reliably assess the cavitation resistance of materials. This study established a clear relationship between increased pressure and higher impact forces, which led to more severe erosion. The findings underscore the effectiveness of the CJA in evaluating material resistance under various cavitation conditions, thus addressing a critical need for reliable cavitation testing tools. Full article

Water is an irreplaceable commodity with a high economic value. Today, water scarcity is the biggest challenge in the world, and the crises arising from lack of freshwater resources are serious threats to sustainable environmental development and human health and welfare. As the problems grow in complexity and dimensions, it becomes less possible to solve them with conventional optimization methods or explicit computational methods in a proper amount of time and with the currently limited computation memory, making it very difficult to achieve an optimal absolute solution. In this regard, metaheuristic algorithms that are generally inspired by nature are used in complex optimization problems. The Pishin Dam is an important dam in the eastern basin of Iran in the south of Sistan and Baluchestan province. This region faces severe water stress due to very low precipitation and very high evaporation on the one hand and the growing increase in urban, agricultural, and industrial demand on the other hand. The water development plans executed by the Ministry of Energy in the studied region influence water supply and demand profoundly. This research investigated the optimal allocation of water resources of this dam under management scenarios using the metaheuristic technique of the ant colony optimization algorithm (ACO). The results showed that the best value of the objective function was 82.3658 million m³. When applying the scenario of developing the cultivation area, the best value was obtained at 67.1258, which was significantly different from the base state. The results show that the ACO algorithm is suitable for the water resources of the Pishin Dam and can be used in planning and policymaking. Full article

Carbonate reservoirs, influenced by depositional and diagenetic processes and characterized by features like faults and vugs that impact storage capacity, require more than traditional Borehole Imaging logs ($BHIs$) for accurate porosity data. These data are essential for geological assessments, production forecasting, and reservoir simulations. This work aims to address this limitation by developing methods to measure and monitor the sustainability of carbonate reservoirs and exploring the application of sustainability principles to their management. The study integrates $BHIs$ and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation ($BVF$) in the Santos Basin. The methodology involves four steps: (i) analyzing conventional logs; (ii) segmenting BHI logs; (iii) integrating conventional and segmented BHI logs using Self-Organizing Maps ($SOM$); and (iv) interpreting the resulting classes. Matrix porosity values and non-matrix pore sizes categorize the porosity into four facies: (A to D). The results of this research indicate the following: Facies A has high non-matrix porosity with 14,560 small megapores, 5419 large megapores, and 271 gigapores (71.9%, 26.76%, and 1.34% of the 20,250 pores, respectively). Facies B shows moderate non-matrix porosity with 8,669 small megapores, 2642 large megapores, and 33 gigapores (76.42%, 23.29%, and 0.29% of the 11,344 pores, respectively) and medium matrix porosity. Facies C exhibits low non-matrix porosity with 7749 small megapores, 2132 large megapores, and 20 gigapores (78.27%, 21.53%, and 0.20% of the 9901 pores, respectively) and medium matrix porosity. Facies D has low non-matrix porosity with 9355 small megapores, 2346 large megapores, and 19 gigapores (79.82%, 20.02%, and 0.16% of the 11,720 pores, respectively) and low matrix porosity. The results of this research reveal the effectiveness of a semiautomatic methodology that combines $BHI$ and conventional well logs to distinguish between matrix and non-matrix-related pore spaces, thus enabling a preliminary classification of reservoir facies based on porosity. This study advances our understanding of carbonate reservoir sustainability and heterogeneity, thus offering valuable insights for robust, sustainable reservoir characterization and management in the context of global environmental and geological changes. The novelty of this work lies in integrating data from two sources to classify porosity across the presalt reservoir interval, thus serving as a proxy for preliminary lithofacies identification without core data. Full article

Surveys on the optimum seismic design of structures reveal that many investigations focus on minimizing initial costs while satisfying performance constraints. Although reducing initial costs while complying with earthquake design codes significantly ensures occupant safety, it may still cause considerable economic losses and fatalities. Therefore, calculating potential earthquake damages over the structure’s lifetime is essential from an optimal Life-Cycle Cost (LCC) design perspective. LCC analysis evaluates economic feasibility, including construction, operation, occupancy, maintenance, and end-of-life costs. The population-based, meta-heuristic Ideal Gas Molecular Movement (IGMM) algorithm has proven effective in solving highly nonlinear mono- and multi-objective engineering problems. This paper investigates the LCC-based mono- and multi-objective optimum design of a 3D four-story concrete building structure using the Endurance Time (ET) method, which is employed for its efficiency in estimating structural responses under varying seismic hazard levels. The novelty of this work lies in integrating the ET method with the IGMM algorithm to comprehensively address both economic and performance criteria in seismic design. The results indicate that the proposed technique significantly reduces minor injury costs, rental costs, and income costs by 22%, 16%, and 16%, respectively, achieving a total reduction of 10% in all structural Life-Cycle Costs, which is considered significant. Full article

This research presents an innovative data fusion model that utilizes Monte Carlo simulations (MC) and the Gray Analytic Hierarchy Process (G-AHP) to address the complexity and uncertainty in decision-making processes, particularly in selecting sustainable wastewater treatment systems. The study critiques and extends the Dempster–Shafer and Yager’s theories by incorporating a novel MC algorithm that mitigates the computational challenges of large numbers of experts and sensors. The model demonstrates superior performance in synthesizing diverse expert opinions and evidence, ensuring comprehensive and probabilistically informed decision-making under uncertainty. The results show that the combined MC algorithm produces satisfactory results, and thus, offers wide applicability in decision-making contexts. To determine its effectiveness, an extensive empirical study was conducted to identify an appropriate wastewater treatment system for the busy city of Tehran, incorporating the insights and perspectives of respected experts in the field. The selection was based on three technical, economic, and environmental–social criteria. Due to the large dimensions of each of the defined criteria, sub-criteria were also defined to achieve better results for each of the criteria. The in-depth analysis conducted revealed that enhanced aeration activated sludge (EAAS) emerged as the best choice for Tehran’s most urgent needs among various competitors, with a remarkable priority rating of 34.48%. Next, the Gray Analytic Hierarchy Process (G-AHP) was used to determine the most important sub-criterion, based on which resistance to hydraulic shock is most important in the enhanced aeration activated sludge system. Due to its versatility in different fields and industries, this method is a powerful tool for managers to optimize system efficiency and identify defects and risks and eventually to minimize costs. Full article

The growing increase in demand for water and the lack of balance between water supply and demand have led to conflicts among the downstream stakeholders of the international Hirmand River. This river is shared between Iran and Afghanistan and is located in the southeast of Iran, in the Sistan region. The Vardkhaneh is divided into two branches, Parian and Sistan, and it is the only main source of water in the Sistan region of Iran. The inner part of Hirmand catchment is considered bankrupt concerning its water resources, so there is a need to take the current status and resulting issues into account in order to resolve conflicts. In Hirmand catchment, four different games of bankruptcy theory, namely proportional (Pr), adjusted proportional (AP), constrained equal award (CEA), and constrained equal losses (CEL), were developed as optimization models based on genetic algorithms. For this purpose, the catchment was simulated with 26 scenarios using the WEAP 2022 version software for an average time period. The results of the bankruptcy game modeling showed that water could be effectively allocated to resolve conflicts among stakeholders. It is therefore recommended to use such a model to resolve fights and optimally allocate resources, even in bankrupt catchments. Full article

This study investigates the optimal and safe operation of pumping stations in water distribution systems (WDSs) with the aim of reducing the environmental footprint of water conveyance processes. We introduced the nonlinear chaotic honey badger algorithm (NCHBA), a novel and robust optimization method. The proposed method utilizes chaotic maps to enhance exploration and convergence speed, incorporating a nonlinear control parameter to effectively balance local and global search dynamics. Single-objective optimization results on a WDS show that NCHBA outperforms other algorithms in solution accuracy and convergence speed. The application of the proposed approach on a water network with two variable-speed pumps demonstrated a significant 27% reduction in energy consumption. Expanding our focus to the multi-objective optimization of pump scheduling programs in large-scale water distribution systems (WDSs), we employ the non-dominated sorting nonlinear chaotic honey badger algorithm (MONCHBA). The findings reveal that the use of variable-speed pumps not only enhances energy efficiency but also bolsters WDS reliability compared to the use of single-speed pumps. The results showcase the potential and robustness of the proposed multi-objective NCHBA in achieving an optimal Pareto front that effectively balances energy consumption, pressure levels, and water quality risk, facilitating carbon footprint reduction and sustainable management of WDSs. Full article

Anti-inflammatory drugs are used to relieve pain, fever, and inflammation while protecting the cardiovascular system. However, the side effects of currently available medications have limited their usage. Due to these adverse effects, there is a significant need for new drugs. The current trend of research has shifted towards the synthesis of novel anthranilic acid hybrids as anti-inflammatory agents. Phenyl- or benzyl-substituted hybrids exerted very good anti-inflammatory effects in preventing albumin denaturation. To confirm their anti-inflammatory effects, additional ex vivo tests were conducted. These immunohistochemical studies explicated the same compounds with better anti-inflammatory potential. To determine the binding affinity and interaction mode, as well as to explain the anti-inflammatory activities, the molecular docking simulation of the compounds was investigated against human serum albumin. The biological evaluation of the compounds was completed, assessing their antimicrobial activity and spasmolytic effect. Based on the experimental data, we can conclude that a collection of novel hybrids was successfully synthesized, and they can be considered anti-inflammatory drug candidates—alternatives to current therapeutics. Full article

This study was conducted to test the hypothesis that supplementary blue light, feeding with selenium and iodine can improve the biochemical quality, macro- and micro-elements in the edible parts of fenugreek and, with the accumulation of selenium and iodine in plant tissue, the production of a biofortified crop. For this purpose, the effect of selenium (0, 2, and 4 mg L⁻¹), iodine (0, 2, and 4 mg L⁻¹), and supplementary blue light (no-blue light and blue light treatment) was tested in the form of a three-way factorial experiment based on a completely randomized design. The results showed that supplementary light treatment and feeding with iodine (2 mg L⁻¹) increased the content of phenol, total protein, and vitamin C. The interaction of iodine (4 mg L⁻¹) and blue lighting increased the content of iron and vitamin C. Irrespective of lighting conditions, iodine improved the iodine content. The combination of blue light and 4 mg L⁻¹ selenium increased the nitrogen, iron, phosphorus, and magnesium compared to other treatments. The best conditions for the accumulation of selenium were a combination of blue light and 4 mg L⁻¹ of selenium. Increasing the concentration of feeding with iodine and selenium was beneficial for the accumulation of flavonoids, carbohydrates, protein, and vitamin C. Although the triple effects of feeding with selenium and iodine under blue supplementary light affected some traits, it is difficult to elicit obvious results from them. In general, the application of iodine and selenium (4 mg L⁻¹) under blue light is recommended to achieve the study objectives. Full article

The partial Gini coefficient measures the strength of dispersion for uncertain random variables, while controlling for the effects of all random variables. Similarly to variance, the partial Gini coefficient plays an important role in uncertain random portfolio selection problems, as a risk measure to find the optimal proportions for securities. We first define the partial Gini coefficient as a risk measure in uncertain random environments. Then, we obtain a computational formula for computing the partial Gini coefficient of uncertain random variables. Moreover, we apply the partial Gini coefficient to characterize risk of investment and investigate a mean-partial Gini model with uncertain random returns. To display the performance of the mean-partial Gini portfolio selection model, some computational examples are provided. To compare the mean-partial Gini model with the traditional mean-variance model using performance ratio and diversification indices, we apply Wilcoxon non-parametric tests for related samples. Full article

The telegraph equation is a hyperbolic partial differential equation that has many applications in symmetric and asymmetric problems. In this paper, the solution of the time-fractional telegraph equation is obtained using a hybrid method. The numerical simulation is performed based on a combination of the finite difference and differential transform methods, such that at first, the equation is semi-discretized along the spatial ordinate, and then the resulting system of ordinary differential equations is solved using the fractional differential transform method. This hybrid technique is tested for some prominent linear and nonlinear examples. It is very simple and has a very small computation time; also, the obtained results demonstrate that the exact solutions are exactly symmetric with approximate solutions. The results of our scheme are compared with the two-dimensional differential transform method. The numerical results show that the proposed method is more accurate and effective than the two-dimensional fractional differential transform technique. Also, the implementation process of this method is very simple, so its computer programming is very fast. Full article

The present article focuses on the synthesis and biological evaluation of a novel anthranilic acid hybrid and its diamides as antispasmodics. Methods: Due to the predicted in silico methods spasmolytic activity, we synthesized a hybrid molecule of anthranilic acid and 2-(3-chlorophenyl)ethylamine. The obtained hybrid was then applied in acylation with different acyl chlorides. Using in silico analysis, pharmacodynamic profiles of the compounds were predicted. A thorough biological evaluation of the compounds was conducted assessing their in vitro antimicrobial, cytotoxic, anti-inflammatory activity, and ex vivo spasmolytic activity. Density functional theory (DFT) calculation, including geometry optimization, molecular electrostatic potential (MEP) surface, and HOMO-LUMO analysis for the synthesized compounds was conducted using the B3LYP/6–311G(d,p) method to explore the electronic behavior, reactive regions, and stability and chemical reactivity of the compounds. Furthermore, molecular docking simulation along with viscosity measurement indicated that the newly synthesized compounds interact with DNA via groove binding mode. The obtained results from all the experiments demonstrate that the hybrid molecule and its diamides inherit spasmolytic, antimicrobial, and anti-inflammatory capabilities, making them excellent candidates for future medications. Full article

Fenugreek (Trigonella foenum-graecum) is an annual plant belonging to the family Fabaceae and has fodder, medicinal and spice uses, and is also used as an organic fertilizer. A total of 18 treatments including the combination of two light environments (with and without supplementary blue light), three concentrations of potassium iodate (0, 2 and 4 mg L⁻¹) and four concentrations of sodium selenate (0, 2 and 4 mg L⁻¹) were organized in a three-way factorial experiment to evaluate the growth characteristics, pigments, trigonelline and seed yield of fenugreek in a greenhouse. The application of 4 mg L⁻¹ of Se resulted in the highest carotenoid, anthocyanin, plant length, fresh weight, chlorophyll and relative water content. The fresh and dry weight of the shoot and the anthocyanin increased with the 2 h supplementation of sunlight with a blue spectrum; however, the fresh root decreased. The interaction of blue light with 0 mg L⁻¹ of Se significantly reduced the plant length. The content of trigonelline was significantly improved with the application of blue light supplementation without negatively affecting the seed yield. In general, 2 h supplementing of sunlight with blue light and feeding with 4 mg L⁻¹ of selenium and iodine are recommended to improve various traits, including trigonelline content. Full article

Anticoagulants prevent the blood from developing the coagulation process, which is the primary cause of death in thromboembolic illnesses. Phenindione (PID) is a well-known anticoagulant that is rarely employed because it totally prevents coagulation, which can be a life-threatening complication. The goal of the current study is to synthesize drug-loaded Ag NPs to slow down the coagulation process. Methods: A rapid synthesis and stabilization of silver nanoparticles as drug-delivery systems for phenindione (PID) were applied for the first time. Results: Several methods are used to determine the size of the resulting Ag NPs. Additionally, the drug-release capabilities of Ag NPs were established. Density functional theory (DFT) calculations were performed for the first time to indicate the nature of the interaction between PID and nanostructures. DFT findings supported that galactose-loaded nanostructure could be a proper delivery system for phenindione. The drug-loaded Ag NPs were characterized in vitro for their antimicrobial, cytotoxic, and anticoagulant activities, and ex vivo for spasmolytic activity. The obtained data confirmed the drug-release experiments. Drug-loaded Ag NPs showed that prothrombin time (PT, sec) and activated partial thromboplastin time (APTT, sec) are approximately 1.5 times longer than the normal values, while PID itself stopped coagulation at all. This can make the PID-loaded Ag NPs better therapeutic anticoagulants. PID was compared to PID-loaded Ag NPs in antimicrobial, spasmolytic activity, and cytotoxicity. All the experiments confirmed the drug-release results. Full article

In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m³ and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs. Full article