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In this work, a series of thermophysical properties (apparent specific volume, excess molar volume, excess Gibbs energy of activation for viscous flow, coefficient of thermal expansion, excess coefficient of thermal expansion) of poly (ethylene glycol) 600 (PEG600) + methanol, ethanol, 1-propanol, and 2-propanol binary liquid mixtures were determined based on density and viscosity experimental measures at 293.15 K, 298.15 K, and 303.15 K and refractive indices at 298.15 K. After that, the obtained excess parameters were used to analyze the inter-intra molecular interactions in the liquid mixtures. In order to estimate the interaction parameters, the excess molar volume results were fitted to Redlich–Kister polynomial. Moreover, a set of semi-empirical relations were proposed to predict the viscosity of liquid mixtures.

Dry reforming of hydrocarbons, alcohols, and biological compounds is one of the most promising and effective avenues to increase hydrogen (H2) production. Catalytic dry reforming is used to facilitate the reforming process. The most popular catalysts for dry reforming are Ni-based catalysts. Due to their inactivation at high temperatures, these catalysts need to use metal supports, which have received special attention from researchers in recent years. Due to the existence of a wide range of metal supports and the need for accurate detection of higher H2 production, in this study, a systematic review and meta-analysis using ANNs were conducted to assess the hydrogen production by various catalysts in the dry reforming process. The Scopus, Embase, and Web of Science databases were investigated to retrieve the related articles from 1 January 2000 until 20 January 2021. Forty-seven articles containing 100 studies were included. To determine optimal models for three target factors (hydrocarbon conversion, hydrogen yield, and stability test time), artificial neural networks (ANNs) combined with differential evolution (DE) were applied. The best models obtained had an average relative error for the testing data of 0.52% for conversion, 3.36% for stability, and 0.03% for yield. These small differences between experimental results and predictions indicate a good generalization capability.

Improving the performance of optimization algorithms is a trend with a continuous growth, powerful and stable algorithms being always in demand, especially nowadays when in the majority of cases, the computational power is not an issue. In this context, differential evolution (DE) is optimized by employing different approaches belonging to different research directions. The focus of the current review is on two main directions: (a) the replacement of manual control parameter setting with adaptive and self-adaptive methods; and (b) hybridization with other algorithms. The control parameters have a big influence on the algorithms performance, their correct setting being a crucial aspect when striving to obtain optimal solutions. Since their values are problem dependent, setting them is not an easy task. The trial and error method initially used is time and resource consuming, and in the same time, does not guarantee optimal results. Therefore, new approaches were proposed, the automatic control being one of the best solution developed by researchers. Concerning hybridization, the scope was to combine two or more algorithms in order to eliminate or to reduce the drawbacks of each individual algorithm. In this manner, different combinations at different levels were proposed. This work presents the main approaches mixing DE with global algorithms, DE with local algorithms and DE with global and local algorithms. In addition, a special attention was given to the situations in which DE is employed as a local search procedure or DE principles are included in other global search methods.

The incidence of colon, rectal, and colorectal cancer is very high, and diagnosis is often made in the advanced stages of the disease. In cases where peritoneal carcinomatosis is limited, patients can benefit from newer treatment options if the disease is promptly identified, and they are referred to specialized centers. Therefore, an essential diagnostic benefit would be identifying those factors that could lead to early diagnosis. A retrospective study was performed using patient data gathered from 2010 to 2020. The collected data were represented by routine blood tests subjected to stringent inclusion and exclusion criteria. In order to determine the presence or absence of peritoneal carcinomatosis in colorectal cancer patients, three types of machine learning approaches were applied: a neuro-evolutive methodology based on artificial neural network (ANN), support vector machines (SVM), and random forests (RF), all combined with differential evolution (DE). The optimizer (DE in our case) determined the internal and structural parameters that defined the ANN, SVM, and RF in their optimal form. The RF strategy obtained the best accuracy in the testing phase (0.75). Using this RF model, a sensitivity analysis was applied to determine the influence of each parameter on the presence or absence of peritoneal carcinomatosis.

The search for powerful optimizers has led to the development of a multitude of metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom as a source of inspiration and performs an extensive, yet not exhaustive, review of the animal inspired metaheuristics proposed in the 2006–2021 period. The review is organized considering the biological classification of living things, with a breakdown of the simulated behavior mechanisms. The centralized data indicated that 61.6% of the animal-based algorithms are inspired from vertebrates and 38.4% from invertebrates. In addition, an analysis of the mechanisms used to ensure diversity was performed. The results obtained showed that the most frequently used mechanisms belong to the niching category.

Purpose>Differential search algorithm (DSA) is a new optimization, meta-heuristic algorithm. It simulates the Brownian-like, random-walk movement of an organism by migrating to a better position. The purpose of this paper is to analyze the performance analysis of DSA into two key parts: six random number generators (RNGs) and Benchmark functions (BMF) from IEEE World Congress on Evolutionary Computation (CEC, 2015). Noting that this study took problem dimensionality and maximum function evaluation (MFE) into account, various configurations were executed to check the parameters’ influence. Shifted rotated Rastrigin’s functions provided the best outcomes for the majority of RNGs, and minimum dimensionality offered the best average. Among almost all BMFs studied, Weibull and Beta RNGs concluded with the best and worst averages, respectively. In sum, 50,000 MFE provided the best results with almost RNGs and BMFs.Design/methodology/approach>DSA was tested under six randomizers (Bernoulli, Beta, Binomial, Chisquare, Rayleigh, Weibull), two unimodal functions (rotated high conditioned elliptic function, rotated cigar function), three simple multi-modal functions (shifted rotated Ackley’s, shifted rotated Rastrigin’s, shifted rotated Schwefel’s functions) and three hybrid Functions (Hybrid Function 1 (n=3), Hybrid Function 2 (n=4,and Hybrid Function 3 (n=5)) at four problem dimensionalities (10D, 30D, 50D and 100D). According to the protocol of the CEC (2015) testbed, the stopping criteria are the MFEs, which are set to 10,000, 50,000 and 100,000. All algorithms mentioned were implemented on PC running Windows 8.1, i5 CPU at 1.60 GHz, 2.29 GHz and a 64-bit operating system.Findings>The authors concluded the results based on RNGs as follows: F3 gave the best average results with Bernoulli, whereas F4 resulted in the best outcomes with all other RNGs; minimum and maximum dimensionality offered the best and worst averages, respectively; and Bernoulli and Binomial RNGs retained the best and worst averages, respectively, when all other parameters were fixed. In addition, the authors’ results concluded, based on BMFs: Weibull and Beta RNGs produced the best and worst averages with most BMFs; shifted and rotated Rastrigin’s function and Hybrid Function 2 gave rise to the best and worst averages. In both parts, 50,000 MFEs offered the best average results with most RNGs and BMFs.Originality/value>Being aware of the advantages and drawbacks of DS enlarges knowledge about the class in which differential evolution belongs. Application of that knowledge, to specific problems, ensures that the possible improvements are not randomly applied. Strengths and weaknesses influenced by the characteristics of the problem being solved (e.g. linearity, dimensionality) and by the internal approaches being used (e.g. stop criteria, parameter control settings, initialization procedure) are not studied in detail. In-depth study of performance under various conditions is a “must” if one desires to efficiently apply DS algorithms to help solve specific problems. In this work, all the functions were chosen from the 2015 IEEE World Congress on Evolutionary Computation (CEC, 2015).

Background Air pollution is an environmental health issue that has received much attention due to its harmful effects on public health. The aim of this study was to determine the psychometric properties of the self-care behaviors related to air pollution protection. To this mean, questionnaire responses provided by Iranian college students were analyzed and a Health Belief Model (HBM) determined. Methods The self-care behavior related to air pollution protection was determined from responses from 301 university students using test item characteristics, face validity, reliability (internal consistency) and construct validity. Moreover, we carried out an exploratory factor analysis (EFA) to determine the factorial structure and explained variance. Results Based on Eigenvalues of ≥1.00 and factor loadings of ≥0.40, seven factors were extracted. The calculated Kaiser–Meyer–Olkin value was 0.804. Overall, the seven factors explain 66.39% of the variance in the hypothesized model. Cronbach’s alpha for the measured factors: the perceived susceptibility, severity, barriers, benefits, self-efficacy, cues to action and behavior were 0.85, 0.79, 0.86, 0.66, 0.74, 0.83, and 0.75, respectively. Conclusion The analysis of the questionnaire’s reliability and validity properties resulted in good values. The questionnaire is a promising instrument to assess self-care behavior related to air pollution protection from the perspective of college students.

Methyl parathion (MP), an organophosphorus insecticide, is commonly used in agricultural products for food preservation and pest control. Due to the severe threat it poses to food safety and the environment, monitoring MP residues has attracted much attention. Traditional spectroscopic and chromatographic methods have been used successfully to analyze MP in a wide range of samples; however, these approaches have several drawbacks, such as requiring specialized equipment, trained technicians, and extensive sample preparation time. Due to these restrictions, there is a growing demand for analysis methods that can reliably and quickly detect MP at trace quantities while also being quick, sensitive, and selective. Electrochemical sensors have emerged over the past few decades as a viable alternative to more time-consuming and laborious analysis methods for detecting MP. However, the performance of electrochemical sensors has been dramatically improved thanks to recent breakthroughs in nanoscience. This study offers an overview of the creation and operation of carbon nanomaterial-based electrochemical sensors (including carbon nanotubes (CNTs), graphene (Gr), and other carbon nanomaterials) to identify MP residues in waters, fruits, and vegetables. A brief discussion of the potential benefits, drawbacks, and future research prospects of MP electrochemical sensors based on carbon nanomaterials is also offered.

Mandelic acid (MA), an aromatic α-hydroxycarboxylic acid (C 6 H 5 CH(OH)COOH) derived from bitter almonds, is widely utilized in the pharmaceutical, cosmetic, and chemical industries. It serves as a key intermediate in the synthesis of antibiotics and other pharmaceuticals. It is valued in skincare applications for its gentle exfoliating properties, offering a less irritating alternative to glycolic acid in the treatment of acne, wrinkles, and photoaging. This study investigates an innovative method for extracting MA using two hydrophobic ionic liquids (ILs): trihexyl(tetradecyl)phosphonium phenylphosphinate ([P 66614 ][Phos]) and trihexyl(tetradecyl)phosphonium decanoate ([P 66614 ][Dec]) as extractants combined with heptane. Extraction experiments were conducted under different conditions to determine the main factors affecting extraction efficiency. Experimentally, the highest extraction yield of 96.36% was obtained at 25 °C using a solvent mixture of heptane and 160 g/L [P 66614 ][Phos], with an aqueous phase pH of 2 to ensure that MA remains in its non-dissociated form. The organic phase was successfully regenerated at 45 °C with sodium carbonate, enabling efficient recovery of MA and reuse of the ILs extractant. An artificial neural network (NN) model was first developed to simulate and predict extraction performance based on experimental input variables, and the Linear Population Size Reduction Success History-based Adaptive Differential Evolution (L-SHADE) algorithm was applied to optimize the process conditions. The results show that the proposed ILs-based extraction system is a promising, environmentally friendly alternative for efficiently recovering MA, with great potential for use in both industrial and cosmetic manufacturing settings.

Folic acid (vitamin B9) is an essential micronutrient for human health. It can be obtained using different biological pathways as a competitive option for chemical synthesis, but the price of its separation is the key obstacle preventing the implementation of biological methods on a broad scale. Published studies have confirmed that ionic liquids can be used to separate organic compounds. In this article, we investigated folic acid separation by analyzing 5 ionic liquids (CYPHOS IL103, CYPHOS IL104, [HMIM][PF6], [BMIM][PF6], [OMIM][PF6]) and 3 organic solvents (heptane, chloroform, and octanol) as the extraction medium. The best obtained results indicated that ionic liquids are potentially valuable for the recovery of vitamin B9 from diluted aqueous solutions as fermentation broths; the efficiency of the process reached 99.56% for 120 g/L CYPHOS IL103 dissolved in heptane and pH 4 of the aqueous folic acid solution. Artificial Neural Networks (ANNs) were combined with Grey Wolf Optimizer (GWO) for modelling the process, considering its characteristics.