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The storage of CO2 and hydrogen within depleted gas and oil reservoirs holds immense potential for mitigating greenhouse gas emissions and advancing renewable energy initiatives. However, achieving effective storage necessitates a thorough comprehension of the dynamic interplay between interfacial tension and wettability alteration under varying conditions. This comprehensive review investigates the multifaceted influence of several critical parameters on the alterations of IFT and wettability during the injection and storage of CO2 and hydrogen. Through a meticulous analysis of pressure, temperature, treatment duration, pH levels, the presence of nanoparticles, organic acids, anionic surfactants, and rock characteristics, this review elucidates the intricate mechanisms governing the changes in IFT and wettability within reservoir environments. By synthesizing recent experimental and theoretical advancements, this review aims to provide a holistic understanding of the processes underlying IFT and wettability alteration, thereby facilitating the optimization of storage efficiency and the long-term viability of depleted reservoirs as carbon capture and storage or hydrogen storage solutions. The insights gleaned from this analysis offer invaluable guidance for researchers, engineers, and policymakers engaged in harnessing the potential of depleted reservoirs for sustainable energy solutions and environmental conservation. This synthesis of knowledge serves as a foundational resource for future research endeavors aimed at enhancing the efficacy and reliability of CO2 and hydrogen storage in depleted reservoirs. [ABSTRACT FROM AUTHOR]

Accurate estimation of activity durations is a critical project scheduling step. If abundant data are available for activity durations, then distribution fitting is straightforward. However, if sample sizes are small, then choosing an appropriate theoretical distribution for activity duration is challenging. In this study, we propose a methodology for experimenting with the impact of sample size on the accuracy of the estimated duration of a stochastic project network. We synthesized a total of 3,264 stochastic project networks and estimated the true project duration distribution for each network using large-sample Monte Carlo simulation. We varied the sample size per activity and investigated how the choice of theoretical distribution (normal, beta, triangular, and lognormal) for estimated activity durations impacted the accuracy of the estimated project duration distribution. We find that the best choice for a theoretical activity duration distribution highly depends on the sample size. For sample sizes below 50, we find that not much is gained from the common practice of putting extensive effort into selecting best-fit theoretical distributions compared to simply using normal distributions. [ABSTRACT FROM AUTHOR]

Accurate estimation of activity durations is a critical project scheduling step. If abundant data are available for activity durations, then distribution fitting is straightforward. However, if sample sizes are small, then choosing an appropriate theoretical distribution for activity duration is challenging. In this study, we propose a methodology for experimenting with the impact of sample size on the accuracy of the estimated duration of a stochastic project network. We synthesized a total of 3,264 stochastic project networks and estimated the true project duration distribution for each network using large-sample Monte Carlo simulation. We varied the sample size per activity and investigated how the choice of theoretical distribution (normal, beta, triangular, and lognormal) for estimated activity durations impacted the accuracy of the estimated project duration distribution. We find that the best choice for a theoretical activity duration distribution highly depends on the sample size. For sample sizes below 50, we find that not much is gained from the common practice of putting extensive effort into selecting best-fit theoretical distributions compared to simply using normal distributions. [ABSTRACT FROM AUTHOR]

The rapid growth of industrialization has led to the uncontrolled pollution of the environment, and rapid action is needed. This study synthesized Ag/TiO2/polyvinyl alcohol (PVA) nano photocatalyst for promising light-derived photocatalytic removal of heavy metal ions. The design of experiment (DOE) was used to study the effect of important factors (pH, reaction time, and photocatalyst dosage) to maximize the final performance of the photocatalyst. In the optimized condition, the Ag/TiO2/PVA nano-photocatalyst removed more than 94% of Cr6+ in 180 min, and the efficiency was more than 70% for Cu2+, Zn2+, and Ni2+ metal ions. The adsorption of the heavy metal ions on the photocatalyst was described well with the Langmuir isotherm, while the pseudo-second-order linear kinetic model fitted with the experimental data. The nano-photocatalyst's stability was confirmed after maintaining its performance for five successive runs. The enhanced photocatalytic activity for the heavy metal ions removal can be attributed to the presence of metallic silver nanoparticles (electron transfer and plasmonic fields mechanisms) and PVA, which delayed the recombination of electron–hole. The synthesized ternary Ag/TiO2/PVA nano-photocatalyst showed promising performance for the elimination of heavy metal ions and can be used for environmental remediation purposes. [ABSTRACT FROM AUTHOR]

Discharging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO's performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications. [ABSTRACT FROM AUTHOR]

Agrivoltaics power generation is the simultaneous use of agricultural land and photovoltaic panels. In such cases, the panels are placed more sparsely, compared to conventional photovoltaic plants. Furthermore, the panels might be mounted at higher heights, to provide the required space for the farm. Due to this arrangement of the panels in wide areas with crops on the farm, maintenance of the panels is a hard task. The major part of maintenance is to clean the panels, since the accumulation of farm dust and bird excrement can reduce the efficiency of the panels. In Agrivoltaics plants, the panels can be scattered, thus it is not economical for each panel to have a dedicated cleaning robot. This paper presents the initial design and analysis of an automated system for cleaning such power plants. This system consists of a robotic AGV, which transfers a panel cleaner between the photovoltaic panels mounted over the farms. The initial design is presented. Then, to anticipate the required power and control system, the carrier kinetics and kinematics are modeled and validated through simulations. Then, position and trajectory controllers are designed and simulations are presented. The simulations show desired performance for the introduced controlled system. [ABSTRACT FROM AUTHOR]

Discharging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications. [ABSTRACT FROM AUTHOR]

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. The different steps of the SABO's implementation are described and then mathematically modeled for optimization tasks. The performance of the proposed SABO approach is tested for the optimization of fifty-two standard benchmark functions, consisting of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results show that the proposed SABO approach effectively solves the optimization problems by balancing the exploration and exploitation in the search process of the problem-solving space. The results of the SABO are compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the simulation results shows that the proposed SABO approach provides superior results for most of the benchmark functions. Furthermore, it provides a much more competitive and outstanding performance than its competitor algorithms. Additionally, the proposed approach is implemented for four engineering design problems to evaluate the SABO in handling optimization tasks for real-world applications. The optimization results show that the proposed SABO approach can solve for real-world applications and provides more optimal designs than its competitor algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the simulations demonstrate that WaOA, due to its excellent ability to balance exploration and exploitation, and its capacity to deliver superior results for most of the benchmark functions, has exhibited a remarkably competitive and superior performance in contrast to other comparable algorithms. In addition, the use of WaOA in addressing four design engineering issues and twenty-two real-world optimization problems from the CEC 2011 test suite demonstrates the apparent effectiveness of WaOA in real-world applications. The MATLAB codes of WaOA are available in https://uk.mathworks.com/matlabcentral/profile/authors/13903104. [ABSTRACT FROM AUTHOR]

4-Chlorophenol pollution is a significant environmental concern. In this study, powdered activated carbon modified with amine groups is synthesized and investigated its efficiency in removing 4-chlorophenols from aqueous environments. Response surface methodology (RSM) and central composite design (CCD) were used to investigate the effect of different parameters, including pH, contact time, adsorbent dosage, and initial 4-chlorophenol concentration, on 4-chlorophenol removal efficiency. The RSM-CCD approach was implemented in R software to design and analyze the experiments. The statistical analysis of variance (ANOVA) was used to describe the roles of effecting parameters on response. Isotherm and kinetic studies were done with three Langmuir, Freundlich, and Temkin isotherm models and four pseudo-first-order, pseudo-second-order, Elovich, and intraparticle kinetic models in both linear and non-linear forms. The synthesized adsorbent was characterized using X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) analyses. The results showed that the synthesized modified activated carbon had a maximum adsorption capacity of 316.1 mg/g and exhibited high efficiency in removing 4-chlorophenols. The optimal conditions for the highest removal efficiency were an adsorbent dosage of 0.55 g/L, contact time of 35 min, initial concentration of 4-chlorophenol of 110 mg/L, and pH of 3. The thermodynamic study indicated that the adsorption process was exothermic and spontaneous. The synthesized adsorbent also showed excellent reusability even after five successive cycles. These findings demonstrate the potential of modified activated carbon as an effective method for removing 4-chlorophenols from aqueous environments and contributing to developing sustainable and efficient water treatment technologies. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible method of extracting associations of SNP-phenotype from content considers their degree of certainty. In this paper, several machine learning methods were developed to extract ranked SNP-phenotype associations from biomedical abstracts and then were compared to each other. In addition, shallow machine learning methods, including random forest, logistic regression, and decision tree and two kernel-based methods like subtree and local context, a rule-based and a deep CNN-LSTM-based and two BERT-based methods were developed in this study to extract associations. Furthermore, the experiments indicated that although the used linguist features could be employed to implement a superior association extraction method outperforming the kernel-based counterparts, the used deep learning and BERT-based methods exhibited the best performance. However, the used PubMedBERT-LSTM outperformed the other developed methods among the used methods. Moreover, similar experiments were conducted to estimate the degree of certainty of the extracted association, which can be used to assess the strength of the reported association. The experiments revealed that our proposed PubMedBERT–CNN-LSTM method outperformed the sophisticated methods on the task. [ABSTRACT FROM AUTHOR]

A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO's mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications. [ABSTRACT FROM AUTHOR]

Valorization of waste materials and byproducts as adsorbents is a sustainable approach for water treatment systems. Pottery Granules (PG) without any chemical and thermal modification were used as a low-cost, abundant, and environmentally benign adsorbent against Pb(II), the toxic metal in drinking water. The porous structure and complex mineral composition of PG made it an efficient adsorbent material for Pb(II). The effect of key physicochemical factors was investigated to determine the significance of contact time, PG dose, pH, solution temperature, and coexisting ions, on the process. Pb(II) removal increased by PG dose in the range of 5–15 g/L, and agitation time from 5 to 60 min. Increasing Pb(II) concentration led to a drop in Pb(II) removal, however, adsorption capacity increased significantly as concentration elevated. Pb(II) removal also increased significantly from ~ 45% to ~ 97% by pH from 2 to 12. A ~ 20% improvement in Pb(II) adsorption after rising the solution temperature by 30˚C, indicated the endothermic nature of the process. The sorption was described to be a favorable process in which Pb(II) was adsorbed in a multilayer onto the heterogeneous PG surface. The qmax of 9.47 mg/g obtained by the Langmuir model was superior among many reported low-cost adsorbents. The Pb(II) adsorption was described well by the Pseudo- first-order kinetic model. Na+, Mg2+, Ca2+, Cd2+, and Zn2+ showed a negligible effect on Pb(II) adsorption. However, the presence of Mn2+ and Fe2+ significantly hindered the process efficacy. In conclusion, the use of waste material such as PG against Pb(II) is a viable option from the economic and effectiveness points of view. [ABSTRACT FROM AUTHOR]

This article introduces a new metaheuristic algorithm called the Serval Optimization Algorithm (SOA), which imitates the natural behavior of serval in nature. The fundamental inspiration of SOA is the serval's hunting strategy, which attacks the selected prey and then hunts the prey in a chasing process. The steps of SOA implementation in two phases of exploration and exploitation are mathematically modeled. The capability of SOA in solving optimization problems is challenged in the optimization of thirty-nine standard benchmark functions from the CEC 2017 test suite and CEC 2019 test suite. The proposed SOA approach is compared with the performance of twelve well-known metaheuristic algorithms to evaluate further. The optimization results show that the proposed SOA approach, due to the appropriate balancing exploration and exploitation, is provided better solutions for most of the mentioned benchmark functions and has superior performance compared to competing algorithms. SOA implementation on the CEC 2011 test suite and four engineering design challenges shows the high efficiency of the proposed approach in handling real-world optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving. [ABSTRACT FROM AUTHOR]

This paper introduces a new human-based metaheuristic algorithm called Sewing Training-Based Optimization (STBO), which has applications in handling optimization tasks. The fundamental inspiration of STBO is teaching the process of sewing to beginner tailors. The theory of the proposed STBO approach is described and then mathematically modeled in three phases: (i) training, (ii) imitation of the instructor's skills, and (iii) practice. STBO performance is evaluated on fifty-two benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. The optimization results show that STBO, with its high power of exploration and exploitation, has provided suitable solutions for benchmark functions. The performance of STBO is compared with eleven well-known metaheuristic algorithms. The simulation results show that STBO, with its high ability to balance exploration and exploitation, has provided far more competitive performance in solving benchmark functions than competitor algorithms. Finally, the implementation of STBO in solving four engineering design problems demonstrates the capability of the proposed STBO in dealing with real-world applications. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have a wide range of applications in handling optimization problems. In this study, a new metaheuristic algorithm, called the chef-based optimization algorithm (CBOA), is developed. The fundamental inspiration employed in CBOA design is the process of learning cooking skills in training courses. The stages of the cooking training process in various phases are mathematically modeled with the aim of increasing the ability of global search in exploration and the ability of local search in exploitation. A collection of 52 standard objective functions is utilized to assess the CBOA's performance in addressing optimization issues. The optimization results show that the CBOA is capable of providing acceptable solutions by creating a balance between exploration and exploitation and is highly efficient in the treatment of optimization problems. In addition, the CBOA's effectiveness in dealing with real-world applications is tested on four engineering problems. Twelve well-known metaheuristic algorithms have been selected for comparison with the CBOA. The simulation results show that CBOA performs much better than competing algorithms and is more effective in solving optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving. [ABSTRACT FROM AUTHOR]

The need for fresh water is more than before by population growth, and industrial development have affected the quality of water supplies, one of the important reason for water contamination is synthetic dyes and their extensive use in industries. Adsorption has been considered as a common methods for dye removal from waters. In this study, Acid Red18 removal in batch mode by using Granular Ferric Hydroxide (GFH) was investigated. The GFH characterized by XRD, FESEM and FTIR analysis. Experiments were designed using RSM-CCD method. The maximum removal efficiency was obtained 78.59% at pH = 5, GFH dosage = 2 g/l, AR18 concentration = 77.5 mg/l and 85 min of contact time. Optimization with RSM and Genetic Algorithm carried out and is similar together. The non-linear adsorption Isotherm and kinetic fitted with Freundlich (R2 = 0.978) and pseudo-second-order (R2 = 0.989) models, respectively. Thermodynamic studies showed that the AR18 adsorption is endothermic process and GFH nature was found spontaneous. [ABSTRACT FROM AUTHOR]

MXenes are a prominent group within 2D materials with extensive utilization in various electrochemical applications due to their notable specific capacitance, swift ion intercalation kinetics, and favorable hydrophilicity. Despite this, they suffer from low oxidation resistance and ease of layer restacking. Herein, an effective approach to overcome this inherent weakness of MXenes was employed by embedding MoS 2 nanopetals as pillars into the backbone of Ti 3 C 2 T x MXene by a one-step hydrothermal technique. It not only alleviates the restacking problem of Ti 3 C 2 T x but also shorten the diffusion path of sodium ions. Meanwhile, ammonium bicarbonate as a guest ion agent was utilized to achieve the 1 T phase of MoS 2 with numerous edge-active sites to overcome the low electrical conductivity of the 2 H phase derived from the semiconducting nature. The incorporation of carbon nanotubes (CNTs) has strengthened the structural integrity of the Ti 3 C 2 T x /1 T-MoS 2 /CNTs composite by linking 2D material components. Moreover, it elevates the heterostructure's overall electrical conductivity by establishing multiple routes for efficient electron transfer. As a result, the membrane-free established hybrid capacitive deionization cell with Ti 3 C 2 T x /1 T-MoS 2 /CNTs // activated carbon demonstrates a remarkable salt removal capacity of 25.82 mg.g−1 along with an outstanding high removal rate of 5.25 mg.g−1.min−1 and superior cyclic stability (98.1% desalination capacity retention rate). This work represents a simple approach for rational designing low-dimensional heterostructure materials with high connectivity and straightforward electron transferring for electrochemical desalination. [Display omitted] • A novel interconnected network of Ti 3 C 2 T x /1 T-MoS 2 /CNTs heterostructure was fabricated. • The MoS 2 pillaring effect alleviates the volume expansion of Mxene. • The high coherency and low charge transfer resistance achieved by CNTs incorporation. • The coexistence of intercalation and electrical double-layer behaviors was proved. • Ternary composite exhibited a rapid salt adsorption rate and superior cyclic stability. [ABSTRACT FROM AUTHOR]

The performance of ozonation for the removal of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) using Escherichia coli and Pseudomonas aeruginosa carrying ARGs from hospital wastewaters was evaluated in this study. Bacterial inactivation was determined using plate count methods and real time PCR for ARG damage (Sul1, blatem, blactx, blavim and qnrS). The reduction rate of bacterial cells and ARGs was increased by different amounts of transferred ozone dose from 11 to 45 mg/L. The concentration of 108 cfu/ml bacteria was reduced to an acceptable level by ozone treatment after a 5 min contact time, Although the removal rate was much higher for concentrations of 106 cfu/ml and 104 cfu/ml bacteria. Overall, the tendency of gene reduction by ozonation from more to less was 16S rRNA > sul1 > blatem > blactx > qnrS > blavim. Given that plasmid-borne ARGs can potentially be transferred to other bacteria even after the disinfection process, our results can provide important insights into the fate of ARGs during hospital wastewater ozonation. [ABSTRACT FROM AUTHOR]

The study of evolutionary dynamics on graphs is an interesting topic for researchers in various fields of science and mathematics. In systems with finite population, different model dynamics are distinguished by their effects on two important quantities: fixation probability and fixation time. The isothermal theorem declares that the fixation probability is the same for a wide range of graphs and it only depends on the population size. This has also been proved for more complex graphs that are called complex networks. In this work, we propose a model that couples the population dynamics to the network structure and show that in this case, the isothermal theorem is being violated. In our model the death rate of a mutant depends on its number of neighbors, and neutral drift holds only in the average. We investigate the fixation probability behavior in terms of the complexity parameter, such as the scale-free exponent for the scale-free network and the rewiring probability for the small-world network. Author summary: In this work, we examine an evolutionary model that considers the effect of competition between the mutated individuals for acquiring more resources. This competition has an effect on the death rate of mutants. The model purposes that the death rate of each mutant depends on the number of its neighbors, while the average death rate in the population is equal to one. The birth rate for all individuals is assumed to be the same and equal to one. This situation is called here the 'neutral drift in average'. We study the dynamics of the model on complex networks to take into account the non-uniformity of the environment. The results show the fixation probability differs from the Moran model. For the construction of this model, we were biologically motivated by the avascular tumour, which consists of a population of normal and cancer cells. The cancer cells likely need more oxygen than normal cells. There is a competition between cells for consuming oxygen, and cancer cells are far more sensitive to the amount of oxygen in the environment than normal cells. This means the death rate of a cancer cell grows by increasing the number of its neighbors. [ABSTRACT FROM AUTHOR]

In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted to isotherm, kinetic and thermodynamic models. The quantification of adsorbent elements was determined using EDX. XRD analysis was utilized to study the crystalline properties of adsorbent. The FT-IR spectra were taken from adsorbent before and after adsorption to study the presence and changes in functional groups. The constituted composition of the adsorbent was determined by XRF. Also, the ionic strength and adsorbent reusability were explored. The influences of operational parameters like pH, initial pesticide concentration, adsorbent dosage and contact time were investigated systematically. ANN-GA and RSM techniques were used to identify the optimal process variables that result in the highest removal. Based on the RSM approach, the optimization conditions for maximum removal efficiency is obtained at pH = 3, adsorbent dosage = 4 g/L, contact time = 30 min, and initial pesticide concentration = 6.2 mg/L. To accurately identify the parameters of nonlinear isotherm and kinetic models, a hybrid evolutionary differential evolution optimization (DEO) is applied. Results indicated that the equilibrium adsorption data were best fitted with Langmuir and Temkin isotherms and kinetic data were well described by pseudo-first and second-order kinetic models. The thermodynamic parameters such as entropy, enthalpy and Gibbs energy were evaluated to study the effect of temperature on pesticide adsorption. [ABSTRACT FROM AUTHOR]

In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box–Behnken design (BBD), and process variables were modeled and optimized using Box–Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R2 = 0.9436), and ANN suggested a three-layer model with test-R2 = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg–Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L−1 and MSW compost ash dosage = 2 g L−1. The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale. [ABSTRACT FROM AUTHOR]

Here, we developed an impedimetric nanosensor by modifying the screen-printed carbon electrode using a nanocomposite of molecularly imprinted polyaniline/gold nanoparticles/graphene oxide to facilitate the charge transfer process and increase the specific surface area of the sensor. The electropolymerization and MIP fabrication process, as well as the optimization of the electrode modification were optimized using a set of experiments. The surface morphology of the modified electrode was characterized by field emission scanning electron microscopy, energy dispersive spectroscopy and electrochemical impedance spectroscopy. The analytical assessments of the bupropion hydrochloride (BUP) electrochemical nanosensor showed a linear range from 2.0 to 990.0 nM with the limit of detection of 0.5 nM. The results of selectivity tests of the nanosensor showed a high specificity towards BUP compared to other similar molecules. Furthermore, the developed sensor was successfully applied to detect BUP in tablets and urine samples with a good recovery percentage. [ABSTRACT FROM AUTHOR]

Background: In November 2018, the United States withdrew from the Joint Comprehensive Plan of Action (JCPOA), known commonly as the Iran nuclear deal, and imposed severe sanctions on Iran. This study explores the impact of US sanctions in Iran's health research system. Methods: This phenomenological study interviewed 24 Iranian health science scholars through purposeful sampling to learn about their experiences and thoughts regarding the impact of US sanctions on Iran's health research system. Results: The impact of sanctions on Iran's health research system were classified into five categories: (a) financial issues, (b) difficulty in supplying laboratory materials and (c) equipment, (d) disruption in international research collaboration and activities, and (e) other issues (e.g., increased stress and workload). Conclusion: This study indicated that since research centers in Iran are highly dependent on governmental budgets, sanctions have greatly affected the health research system in Iran. Financial and economic problems, restrictions in transferring funds, and the disruption in political and international relations have created many challenges for supplying medical laboratory materials and equipment for medical and health research centers in Iran. [ABSTRACT FROM AUTHOR]

In this paper, the orthogonal cutting process is studied using Smooth Particle Hydrodynamic (SPH) method by a kinematic rigid cutting tool and two work-piece material models: perfectly elastic-plastic (EPP) model and Johnson-Cook (JC) model. The kinematic tool means that if the cutting tool is assumed a rigid body then the horizontal component speed of work-piece particles at cutting tool region are modified to the cutting speed. The chip shapes of orthogonal cutting process using SPH method with kinematic and kinetic tool models are compared with the experimental results. The chip obtained by the simulation with kinematic tool is more similar to the experimental results. Von-Mises stress distribution at different states of the orthogonal cutting process is investigated. The maximum stress occurs at the shear plane and causes the formation of chip teeth. Comparisons between chips of work-pieces with two material models are investigated including different rake angles of 5, 10 and 17.5° with feed rates of 0.3 and 0.4 mm/rev and the cutting forces of the process are obtained. The cutting force of process with 17.5° rake angle, 0.4 mm/rev feed rate and 800 m/min cutting speed is validated using experimental result. [ABSTRACT FROM AUTHOR]

Supply Chain (SC) Optimization is a key activity in today's industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors. [ABSTRACT FROM AUTHOR]

Background: The human papillomavirus (HPV) infection may affect the miRNA expression pattern during cervical cancer (CC) development. To demonstrate the association between high-risk HPVs and the development of cervix dysplasia, we examined the expression patterns of hsa-miR-194-5p and hsa-miR-195-5p in Pap smear samples from southeast Iranian women. We compared samples that were HPV-positive but showed no abnormality in the cytological examination to samples that were HPV-positive and had severe dysplasia. Methods: Pap smear samples were obtained from 60 HPV-positive (HPV-16/18) patients with histologically confirmed severe dysplasia (cervical intra-epithelial neoplasia (CIN 3) or carcinoma in situ) and the normal cytology group. The expression of hsa-miR-194-5p and hsa-miR-195-5p was analyzed by real-time quantitative PCR, using specific stem-loop primers and U6 snRNA as the internal reference gene. Clinicopathological features were associated with miRNA expression levels. Furthermore, functional enrichment analysis was conducted using in silico tools. The Kaplan–Meier survival method was also obtained to discriminate survival-significant candidate miRNAs in CC, and receiver operating characteristic (ROC) curves were constructed to assess the diagnostic value. Results: Compared to HPV-positive cytologically normal Pap smear samples, hsa-miR-194-5p and hsa-miR-195-5p relative expression decreased significantly in HPV-positive patients with a severe dysplasia Pap smear. Kaplan–Meier analysis indicated a significant association between the miR-194 decrease and poor CC survival. In essence, ROC curve analysis showed that miR-194-5p and miR-195-5p could serve as valuable markers for the development of cervix dysplasia in individuals who are positive for high-risk HPVs. Conclusions: This study revealed that hsa-miR-194-5p and hsa-miR-195-5p may possess tumor suppressor capabilities in the context of cervical dysplasia progression. However, it remains uncertain whether these microRNAs are implicated in the transition of patients with high dysplasia to cervical cancer. We also showed the potential capability of candidate miRNAs as novel diagnostic biomarkers related to cervical dysplasia progression. [ABSTRACT FROM AUTHOR]

Supercapacitors are environmentally friendly and safe energy storage devices for diverse applications. The electrode material's structural, microstructural, and electrochemical properties are crucial determinants of supercapacitor capacity. One effective method to improve electrode performance is introducing doping heteroatoms or synthesizing composite materials. In this study, three-dimensional reduced graphene oxide (3D rGO) doped with Phosphorus, Nitrogen, or both (N/P co-doped) was synthesized for high-efficiency, supercapacitors using a facile, fast, and cost-effective hydrothermal process. The hydrothermal reduction of graphene oxide involved substituting Phosphorus and Nitrogen elements with carbon atoms. Moreover, the effect of dopants on structural and microstructural characteristics of 3D rGO was evaluated using different techniques, and the electrochemical characteristics were evaluated by cyclic voltammetric (CV) analysis, galvanostatic charge/discharge (GCD), and electrochemical impedance spectroscopy (EIS). The specific capacitance of 3DN/PrGO on Ni foam reached 648 F g−1 at 1 Ag−1, in a voltage range of −0.6 –0.6 V (vs. Ag/AgCl), using 6 M KOH as the electrolyte. High energy density and power density of 6.1 wh/kg and 513 w/kg were obtained, respectively. The stability test was also conducted, and appropriate stability over 100 charging and discharging cycles was observed. The considerable performances are mainly due to the combined effect of pseudocapacitive properties of Nitrogen and Phosphorus dopants, improved wettability, and the porous structure of 3D rGO, which provides suitable electroactive sites and simplifies the electron/ion transfer for the electrochemical reactions. [Display omitted] • 3D-reduced graphene oxide (rGO) co-doped with N/P fabrication by hydrothermal process • N/P dopants facilitate the reduction and create more defects and active sites • Hierarchically porous texture with better ions adsorption and capacitive consistency • Combined effects of pseudocapacitive properties of dopants and improved wettability [ABSTRACT FROM AUTHOR]

The magnetic chitosan was prepared and characterized using FT-IR, SEM, and XRD techniques. The adsorbent was used for uptake of phenol in water. The sorption was modeled based on central composite design (CCD) - response surface methodology (RSM) method for independent variables pH, time, adsorbent dosage and phenol concentration; suggesting a quadratic polynomial model with p -value < 0.001, R2 = 0.9983 and adjusted R2 = 0.9963. The optimization of input factors was conducted using both CCD-RSM and Algorithm Genetic (GA). The obtained results were the same, where the optimum level of pH, time, adsorbent dosage, and phenol concentration for CCD-RSM technique were 3, 50 min, 0.96 g/L and 15 mg/L, and for GA technique were 3, 50 min, 1 g/L and 15 mg/L. In this condition, maximum removal was 97% for both CCD-RSM and GA. The isotherm and kinetic studies of adsorption process illustrated that adsorption of phenol onto magnetic chitosan corresponded Freundlich isotherm and Intraparticle diffusion kinetic models. The maximum adsorption capacity was approximately 51.68 mg∙g−1. Thermodynamic studies identified exothermic nature of adsorption. It was concluded that magnetic chitosan is a potential and promising adsorbent in adsorption process of phenolic pollutants and similar compounds from aquatic environments. • Economic synthesis of magnetic chitosan • Efficient, fast and reproducible removal of phenol in water • Removal optimization by central composite design, response surface and algorithm genetic methods • Method may be used for effective and inexpensive removal of phenol in water. [ABSTRACT FROM AUTHOR]

This work presents a new SAFT-VR Electrolyte equation of state (SAFT-VRE Morse EOS) that can predict solid-liquid equilibrium and second order derivative properties of electrolyte solutions. In this study, variable range statistical associating fluid theory (SAFT-VR Morse) EOS was extended to electrolyte solutions. In this model short-range interactions were mimicked by SAFT-VR Morse EOS, and long-range and solvation interactions were modeled by Debye-Hückle and Born terms respectively. Global ion parameters were adjusted using single electrolyte mean ionic activity coefficient (MIAC) and solution densities data at 25 °C and 1 atm. Using obtained ion-specific parameters, thermodynamic properties of aqueous electrolyte solutions such as MIAC, osmotic coefficient, vapor pressure, Gibbs free energy of hydration and excess enthalpy of solution were predicted without any adjustable parameter. Then, the model capability was evaluated in phase behavior calculations over a wide range of salt concentrations. In this respect, solid-liquid equilibrium (SLE) phase diagrams for mixed-electrolyte were predicted up to high temperatures. The results show that the model can predict the MIAC, osmotic coefficient, vapor pressure, and SLE of mixed electrolyte solutions up to 100 °C. Obtained results prove that SAFT-VRE Morse can efficiently be utilized for electrolyte solutions. We attribute this capability to utilizing the SAFT-VR Morse EOS as dispersion term. [ABSTRACT FROM AUTHOR]

Tri-functional antibodies, as an effective novel tumor targeting agents, are composed of anti-CD3 rat IgG2b and an anti-tumor antigen antibody. We have intended to develop a novel drug system to induce the apoptosis of HER2-expressing tumor cells, activate and engage cytotoxic T lymphocytes, natural killer cells, and macrophages. In addition, the drug can inhibit programmed death ligand 1 (PDL-1)-expressing tumor cells. We have designed a mammalian vector system suitable to express the trifunctional antibody composed of antiHER2×human-CD80: human-IgG1Fc antibody. This antibody contains four chains of anti-HER2/CL, anti-HER2/CH1-3, B7.1/CL, and B7.1/CH1-e human IgG1 framework and so the vector system will simultaneously express the four chains. The amino acid/nucleotide sequences datasets were retrieved from the GenBank, UniProtKB and PDB databases. The heavy and light chains variable domain framework regions and complementarity determining regions of scA21 antibody were determined by IMGT/V-QUEST and Paratome software. The amino acid sequences of tri-functional antibody chains manually were assembled and converted to DNA sequences by sequence manipulation suite software. The adapting codon usage of these DNA sequences was performed by JCAT software. Finally, the secondary structures of obtained RNAs from the DNAs were individually analyzed by RNAfold program. The Prodigy equilibrium dissociation constant, a ratio of koff/kon, between the antibody and its antigen for hantiHER2VHCH-HER2, hantiHER2VLCL-HER2, CD80CH-CD28, and CD80CL-CD28 were equal to 3.60E-10, 3.10E-9, 1.10E-8 and 2.70E-10; respectively. These findings were confirmed the composition and nano-molar affinity of the respective constructs. A single specific no-cloning expression vector, pHuchiTriomAb, was designed in silico with a desirable length of 8.292 Kbps and bidirectional expression potential for the four chimeric antibody chains. This construct was designed, and gene codon usage adapted to be expressed within CHO cells and secreted a trifunctional antibody into the cell culture medium by interleukin 21 signal peptide. This robust expression system for rapid production of tri-functional antibody has been designed to substitute hybrid hybridoma technology (quadroma and trioma) with the facilitated purification of the trifunctional antibody from the antibody variants. [ABSTRACT FROM AUTHOR]

We introduce the notion of the p-Schur property (1≤p≤∞) as a generalization of the Schur property of Banach spaces, and then we present a number of basic properties and some examples. We also study its relation with some geometric properties of Banach spaces, such as the Gelfand-Phillips property. Moreover, we verify some necessary and sufficient conditions for the p-Schur property of some closed subspaces of operator spaces. [ABSTRACT FROM AUTHOR]

The experimental liquid – liquid equilibrium (LLE) data for three ternary mixtures containing an ionic liquid (IL) (1-butyl-3-methyl imidazolium thiocyanate [Bmim][SCN]), alkanes ( n -hexane, n -octane and n -decane) and sulfur compound (thiophene) have been measured experimentally. Distribution and selectivity coefficients have been calculated using experimental data. The LLE data have been obtained at 25 °C and ambient pressure. The experimental LLE data of ternary mixtures have been compared with our previous works. It has been shown that the IL with higher molecular weight of cation has higher solute distribution and lower selectivity coefficients. It has been found that the solubility decreases as the cation length decreases, meanwhile ILs cation chain length has a remarkable effect on solubility of alkanes. Moreover, the experimental data have been demonstrated that the selectivity decreases as alkane chain length of alkane decreases while the solubility of alkanes in the ILs increases, hence the removal costs of aromatic sulfur components will be increased. The results show that [Bmim][SCN] has the lowest alkane solubility and highest selectivity coefficient, in the system containing n -decane and thiophene. A comparative study reveals that compared to other ILs, [Bmim][SCN] is a more suitable solvent. Finally, the experimental data have been correlated utilizing non-randomness two liquid (NRTL) model. The binary energy parameters were obtained, it can be seen that the model can correlate the experimental data efficiently. [ABSTRACT FROM AUTHOR]

In this work liquid–liquid equilibrium (LLE) data for aqueous two–phase system (ATPS) containing polyethylene glycol 6000 (PEG–6000) and Na 2 CO 3 were measured at T = (293.15, 303.15 and 313.15) K. Our results showed that increasing temperature expands the two–phase region; meanwhile large two–phase region was seen in this system which makes it applicable for industrial purposes. Experimental data were modeled using UNIQUAC, Extended UNIQUAC (E–UNIQUAC) and e–NRTL models to obtain binary interaction parameters. The model parameters were obtained using Shuffled Complex Evolution (SCE) optimization algorithm. The results showed that all studied models were in good agreement with the experimental data. Average deviation (%Δ w ) of e–NRTL, E–UNIQUAC and UNIQUAC models were 1.26, 1.37 and 3.46%, respectively. Finally in order to check the predictability of the E–UNIQUAC model, liquid–liquid equilibrium in systems containing PEG with different molecular mass (1000, 3350, 4000 and 8000) and Na 2 CO 3 were studied; It was found that liquid–liquid equilibrium data can be predicted satisfactorily using adjusted parameters in this work. [ABSTRACT FROM AUTHOR]

• Sonochemistry as an advanced oxidation process. • Mechanism of remediation/disinfection of microbial hazards in polluted water. • Types and comparison of sonochemical reactors for remediation. • Energy and economic aspects. • Challenges and recommendations for future studies. Water is one of the major sources that spread human diseases through contamination with bacteria and other pathogenic microorganisms. This review focuses on microbial hazards as they are often present in water and wastewater and cause various human diseases. Among the currently used disinfection methods, sonochemical reactors (SCRs) that produce free radicals combined with advanced oxidation processes (AOPs) have received significant attention from the scientific community. Also, this review discussed various types of cavitation reactors, such as acoustic cavitation reactors (ACRs) utilizing ultrasonic energy (UE), which had been widely employed, involving AOPs for treating contaminated waters. Besides ACRs, hydrodynamic cavitation reactors (HCRs) also effectively destroy and deactivate microorganisms to varying degrees. Cavitation is the fundamental phenomenon responsible for initiating many sonochemical reactions in liquids. Bacterial degradation occurs mainly due to the thinning of microbial membranes, local warming, and the generation of free radicals due to cavitation. Over the years, although extensive investigations have focused on the antimicrobial effects of UE (ultrasonic energy), the primary mechanism underlying the cavitation effects in the disinfection process, inactivation of microbes, and chemical reactions involved are still poorly understood. Therefore, studies under different conditions often lead to inconsistent results. This review investigates and compares other mechanisms and performances from greener and environmentally friendly sonochemical techniques to the remediation of microbial hazards associated with water and wastewater. Finally, the energy aspects, challenges, and recommendations for future perspectives have been provided. [ABSTRACT FROM AUTHOR]

In this work, a new statistical associating fluid theory with variable range (SAFT-VR) equation of state (EOS) for the Morse fluids has been developed. Monte Carlo (MC) simulations have been performed to determine the first and second order perturbative contributions of Barker and Henderson (BH) perturbation theory over wide range of density, temperature and potential ranges. In addition to perturbative terms, pressure and internal energy of Morse fluid have been simulated and compared with theoretical results. In development of new EOS, an exact analytic solution of the Percus-Yevick (PY) integral equation for hard sphere radial distribution function (RDF) has been implemented into calculation of the first and second terms of BH theory. Our results showed that there are good agreement between MC simulation data and analytical solution of hard sphere RDF. In the case of BH first order term, excellent agreement was obtained. It is found that, the new EOS is able to predict the perturbative Helmholtz free energy, pressure and residual internal energy of Morse fluids over wide range of thermodynamic conditions. Finally, the new EOS has been utilized to correlate vapor pressure and saturated liquid density of pure components. Obtained results in this work prove that considering Morse as potential function can lead us to achieve an accurate SAFT-based EOS. [ABSTRACT FROM AUTHOR]

In this work the partitioning behavior of Hg(II), Zn(II) and Co(II) was measured in a polyethylene glycol based aqueous two phase system (PEG–ATPS) containing Na 2 CO 3 as a phase forming salt. The experiments were performed by mixing equal weights of 30% ( w /w) PEG–6000 solution and 10% ( w /w) Na 2 CO 3 solution. The partitioning of ions was studied at various concentration of iodide as extracting agent, stock salt solution pH, temperature and initial metal ion concentration. The experimental results showed that extraction of metal ions mainly depends on concentration of iodide. In the other words the extraction of metal ions increases as the amount of iodide ions increases. It was observed that the extraction of mercury increases as pH increases, while in the case of zinc and cobalt it was insignificant meanwhile temperature has a minor effect on metal ions extraction. In this work the maximal extraction percentages of Hg(II), Zn(II) and Co(II) were obtained as 99.3%, 98.62% and 58.2%, respectively. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the applicability of the adsorption process for the removal of organophosphorus pesticide malathion 57% from water by using multi-walled carbon nanotubes (MWCNTs). The impact of various experimental conditions such as pH, quantity of adsorbent, concentration of pesticides, contact time and temperature was studied and optimized for the maximum removal of malathion. Unlike conventional optimization, a limited number of experiments (26 steps) were performed in a cost-effective manner for different independent variables such as MWCNTs concentration (0.1–0.5 g/L), the malathion (57%) concentration (6 mg/L and 10 mg/L), contact time (2–30 min) and pH (neutral range). Based on the experimental data obtained in a lab-scale batch study, a three-factor response surface modeling (RSM) approach was implemented in order to optimize the conditions for maximum removal of malathion, and compare experimental results with standardized malathion samples. The optimized conditions to achieve the maximum removal of malathion (100%) were determined to be a malathion concentration of 6 mg/L, an initial MWCNTs concentration of 0.5 g/L, and a contact time of 30 min. Findings of this study clearly indicated that 100% of the malathion could be cost-effectively removed by MWCNTs in conditions predicted by the proposed optimization methodology. [ABSTRACT FROM AUTHOR]

Cubic zeolitic imidazolate framework-8 (ZIF-8), a new class of hybrid adsorbent, was synthesized and investigated for phosphorus (P) removal from aqueous solution. A prediction model for P adsorption was developed by performing the experiments according to central composite design. The adsorption model showed that P adsorption is associated directly with time and ZIF-8 dosage and indirectly with initial P concentration. The removal also increased with decrease in pH until reaching the critical pH of about 2.6. The efficiency of P removal under mechanically stirred increased with agitation speed from 100 to 300 rpm. In contrast to high ultrasonic frequency (130 kHz), sonication under 35 kHz provides excellent dispersive mixing and shortened the equilibrium time. Study of coexisting ions showed that P adsorption hindered considerably in the presence of carbonate. Equilibrium studies indicated that P adsorbed onto ZIF-8 in monolayer and the adsorbent has definite localized sites that are energetically identical. The maximum Langmuir sorption capacity of ZIF-8 for P was 38.22 mg/g in the present study. Analysis of experimental data also revealed that the chemosorption is the rate limiting step in P adsorption by ZIF-8. In conclusion, the present report suggest zif-8 as an efficient and fast removal sorbent for P from aqueous solutions. [ABSTRACT FROM AUTHOR]

This paper deals with three dimensional leader–follower formation control of unmanned aircraft systems. The main contribution of the paper is kinematic formulation of the leader–follower formation based on seeker measurements which omit the need for communication networks. Considering uncertainties in measurement of followers speeds, a robust controller is employed. Then, by proposing an integrated controller–estimator consisting of the robust controller and a disturbance compensator, keeping the formation while the leader is maneuvering is guaranteed. Simulation results verify the accuracy of the integrated controller–estimator system. [ABSTRACT FROM AUTHOR]

A colored effluent entry into the environment gives rise to disruption of the environment and the production of hazardous and toxic byproducts. In order to improve the color removal of methyl orange and methylene blue, a flotation method with poly-aluminum chloride (PAC), granular ferric hydroxide (GFA), alum, ferrous sulfate and also simultaneous application of an advanced oxidation method UV/TiO 2 /H 2 O 2 was used. A reactor with a useful volume of 6 L and an inlet flow of 100 mL/min along with two UV-C (15 W) lamps were applied as a flotation tank. The effect of concentration of PAC, GFA coagulants, alum, and ferrous sulfate in 0.5, 1, 1.5, and 2 mg/L concentrations, TiO 2 in 0.5–2 g/L concentrations, and also H 2 O 2 in 150–400 mL concentrations on the flotation process were put into account. Alum was obtained in the concentration of 1.5 mg/L with an efficiency of 84.6% and also in concentrations of 0.5, 1, and 2 mg/L with efficiencies of 66%, 82%, and 75%. In a flow of 150 mL/min, the color removal efficiency was obtained between 54 and 86.5% and in pH 10; the most removal efficiency was (89%). In the best condition, the removal efficiency of methyl orange (MO) and methylene blue (MB) in AOP/flotation process in pH 10, the alum concentration of 5 mg/L, air flow of 100 mL/min, TiO 2 concentration of 1 g/L, H 2 O 2 concentration of 200 mL were obtained 96–98% and 90–95%, respectively. Results of the present study indicate the effect of flotation method well with coagulants in combination with advanced oxidation methods on removing and reducing the concentration of soluble compounds in water like a color. [ABSTRACT FROM AUTHOR]