Search

The demand of production is random in the supply chain. Furthermore, it is difficult to measure the demand in the real world. Hence, the interval is more likely to be used to present the demand. The interval can be viewed as a random variable. This paper introduces this random variable as the demand of production into the model of supply chain. We represent uncertain and fuzzy demand by a fuzzy random variable in a supply chain system based on a two-level buyback contract for a newsvendor model with a single cycle. For the sake of computational convenience, the crisp possibilistic mean which has been widely used in supply chain is employed to defuzzify the fuzzy random demand. In this paper, the expected profit is defuzzified using a crisp possibilistic mean value. The optimal order quantities in decentralized and centralized systems are analyzed and the conditions for supply chain coordination are obtained. A realistic example is presented to illustrate the effectiveness of supply chain coordination and the impact of mixed imprecise and uncertain demand on the retailer order quantities and the expected profits for all sides. [ABSTRACT FROM AUTHOR]

In this paper, we first uncover a fact that a partial adiabatic quantum search with $$O(\sqrt{N/M})$$ time complexity is in fact optimal, in which $$N$$ is the total number of elements in an unstructured database, and $$M$$ ( $$M\ge 1$$ ) of them are the marked ones(one) $$(N\gg M)$$ . We then discuss how to implement a partial adiabatic search algorithm on the quantum circuit model. From the implementing procedure on the circuit model, we can find out that the approximating steps needed are always in the same order of the time complexity of the adiabatic algorithm. [ABSTRACT FROM AUTHOR]

Due to the intrinsic similarity between partial adiabatic evolution and global adiabatic evolution, we generalize the partial adiabatic evolution proposed recently to its local adiabatic algorithm version. However, unlike that the local adiabatic evolution can speed up the global adiabatic algorithm quadratically, we prove that this new quantum algorithm presented here just has the same time complexity as the original partial adiabatic evolution. This may imply the optimality of the original partial adiabatic evolution or its generalized version. Additionally, a concrete example is given to further support our conclusion. [ABSTRACT FROM AUTHOR]

Deutsch-Jozsa algorithm has been implemented via quantum adiabatic evolutions by Das et al. (Phys Rev A 65:062310, ) and Wei et al. (Phys Lett A 354:271, ). In the latter literature, the authors have shown a modified version of the adiabatic evolution which can improve the performance of the algorithm of S. Das et al's to constant time. In this paper, we also improve the algorithm of S. Das et al's in a constant time but by using a different construction of adiabatic evolution, i.e., adding ancillary qubits. The algorithm in this paper provides an alternative option to potential users. [ABSTRACT FROM AUTHOR]

We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object. [ABSTRACT FROM AUTHOR]

Abstract: Bacterial cell division occurs in conjunction with the formation of a cytokinetic Z-ring structure comprised of FtsZ subunits. Agents that can disrupt Z-ring formation have the potential, through this unique mechanism, to be effective against several of the newly emerging multi-drug resistant strains of infectious bacteria. 1- and 12-Aryl substituted benzo[c]phenanthridines have been identified as antibacterial agents that could exert their activity by disruption of Z-ring formation. Substituted 4- and 5-amino-1-phenylnaphthalenes represent substructures within the pharmacophore of these benzo[c]phenanthridines. Several 4- and 5-substituted 1-phenylnaphthalenes were synthesized and evaluated for antibacterial activity against Staphylococcus aureus and Enterococcus faecalis. The impact of select compounds on the polymerization dynamics of S. aureus FtsZ was also assessed. [Copyright &y& Elsevier]

Abstract: In this paper, three different approaches for implementing a quantum search algorithm by adiabatic evolution are shown. As expected, either one of them can provide a quadratic speed up as opposed to the classical search algorithm. This implies that adiabatic evolution based quantum computation gives more feasibilities than the quantum circuit model, although the equivalence between them has already been proven in the corresponding literature. [Copyright &y& Elsevier]

Abstract: Short-term combined economic emission hydrothermal scheduling (CEES) is a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. In this paper, quadratic approximation based differential evolution with valuable trade off approach (QADEVT) has been developed to solve the bi-objective hydrothermal scheduling problem. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. In this paper, heuristic rules are proposed to handle the water dynamic balance constraints and heuristic strategies based on priority list are employed to handle active power balance constraints. A feasibility-based selection technique is also introduced to satisfy the reservoir storage volumes constraints. To demonstrate the superiority of the proposed approach, simulation results have been compared with those obtained by differential evolution (DE) and particle swarm optimization (PSO) with same heuristic strategies and the earlier reported methods available in literature. The simulation results reveal that the proposed approach is capable of efficiently providing superior solutions. [Copyright &y& Elsevier]

Abstract: This paper presents an improved quantum-behaved particle swarm optimization (IQPSO) for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing emission cost. In this paper, quantum-behaved particle swarm optimization is improved employing heuristic strategies in order to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. To show feasibility and effectiveness of the proposed method, different case studies, such as economic load scheduling (ELS), economic emission scheduling (EES) and combined economic emission scheduling (CEES) in hydrothermal scheduling, are carried out and the test results are compared with those of other methods reported in the literature. It is also very important to note that the proposed method is capable of yielding higher-quality solutions while strictly satisfying all constraints of the test system. [Copyright &y& Elsevier]

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators. [ABSTRACT FROM AUTHOR]

• Apply a modified whale optimization algorithm as multi-level image segmentation. • Memory mechanism and multi-leader are used to enhance exploration ability of WOA. • Self-learning strategy and levy flight are applied to avoid premature convergence. • Using Otsu, Kapur entropy, and Fuzzy entropy as fitness functions to assess agents. • Experimental results show that the proposed approach outperforms other methods. In this paper, a multilevel thresholding image segmentation method base on the enhancement of the performance of the whale optimization algorithm (WOA). The developed method, called the multi-leader whale optimization algorithm (MLWOA), aims to avoid the limitations of traditional WOA during the searching process, such as stagnation at the local optimum. This was achieved by integrating the different tools with WOA, such as memory mechanism, multi-leader method, self-learning strategy, and levy flight method. Each of these techniques has its own task, for example, the memory structure of traditional WOA and add a multi-leader mechanism to enhance the ability of exploration. The superiority of leaders will make more influence in MLWOA by adding a self-learning strategy. Also, it used levy flight trajectory to make the algorithm more robust and avoid premature convergence. To evaluate the performance of the developed MLWOA, a set of experiments are conducted using the CEC2017 benchmark. In addition, it is applied to determine the optimal threshold values to segment a set of images using the Otsu method, fuzzy entropy, and Kapur's entropy as a fitness function. The results of MLWOA are compared with well-known meta-heuristic algorithms inside the experiments. The comparison results indicated that MLWOA provides better performance in CEC2017 benchmark functions and shows high superiority in image segmentation in terms of performance measures. In addition, the MLWOA provides better results using Otsu, followed by the Fuzzy entropy and Kapur in terms of PSNR. In terms of SSIM, fuzzy entropy and Otsu have nearly the same SSIM value, but the fuzzy entropy provides better results. [ABSTRACT FROM AUTHOR]

Solving the feature selection problem is considered an important issue when addressing data from real applications that contain a large number of features. However, not all of these features are important; therefore, the redundant features must be removed because they affect the accuracy of the data representation and introduce time complexity into the analysis of these data. For these reasons, the feature selection problem is considered an NP-complete nonlinearly constrained optimization problem. The rough set (RS) and neighborhood rough set (NRS) are the most powerful methods used to solve the feature selection problem; however, both approaches suffer from high time complexity. To avoid these limitations, we combined the RS and NRS with a new metaheuristic algorithm called the runner-root algorithm (RRA). The spirit of the RRA originated from real-life plants called running plants, which have roots and runners that spread the plants in search of minerals and water resources through their root and runner development. To validate the proposed algorithm, several UCI Machine Learning Repository datasets are used to compute the performance of our algorithm employing two effective classifiers, the random forest and the K-nearest neighbor, in addition to some other measures for the performance evaluation. The experimental results illustrate that the proposed algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures. Additionally, the NRS increases the performance of the proposed method more than the RS as an objective function. • A feature selection (FS) method for classification task is proposed. • Proposed approach improved Runner-Root Algorithm by using its binary version. • Degree of dependency from RS and Neighborhood RS are used as objective functions. • Proposed method used to select an optimal subset of features from twelve datasets. • Our proposed method gives better accuracy with a small number of features. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem that when quantum adiabatic evolution with the most general form of system Hamiltonian will get failed. Here the most general form means that the initial and final Hamiltonians are just designed according to the adiabatic theorem in quantum mechanics. As we will see, even in this most general model of quantum adiabatic evolution, it still exists the possibility that the quantum adiabatic computation can fail totally if some condition is satisfied, which implies the time complexity of the quantum algorithm is infinity. That is, here we propose a rather general criterion for judging whether a quantum adiabatic evolution is successful. This result largely extends the authors' previous research on this topic, and it may be seen as a further important clue for us when designing quantum algorithms in the framework of adiabatic evolution for some practical problems. [ABSTRACT FROM AUTHOR]

Introduction: Bisphenol A (BPA), 2,2-bis(4-hydroxyphenyl) propane, a common industrial chemical which has extremely huge production worldwide, is ubiquitous in the environment. Human have high risk of exposing to BPA and the health problems caused by BPA exposure have aroused public concern. However, the biomarkers for BPA exposure are lacking. As a rapidly developing subject, metabolomics has accumulated a large amount of valuable data in various fields. The secondary application of published metabolomics data could be a very promising field for generating novel biomarkers whilst further understanding of toxicity mechanisms.Objectives: To summarize the published literature on the use of metabolomics as a tool to study BPA exposure and provide a systematic perspectives of current research on biomarkers screening of BPA exposure.Methods: We conducted a systematic search of MEDLINE (PubMed) up to the end of June 25, 2017 with the key term combinations of ‘metabolomics’, ‘metabonomics’, ‘mass spectrometry’, ‘nuclear magnetic spectroscopy’, ‘metabolic profiling’ and ‘amino acid profile’ combined with ‘BPA exposure’. Additional articles were identified through searching the reference lists from included studies.Results: This systematic review included 15 articles. Intermediates of glycolysis, Krebs cycle, β oxidation of long chain fatty acids, pentose phosphate pathway, nucleoside metabolism, branched chain amino acid metabolism, aromatic amino acids metabolism, sulfur-containing amino acids metabolism were significantly changed after BPA exposure, suggesting BPA had a highly complex toxic effects on organism which was consistent with existing studies. The biomarkers most consistently associated with BPA exposure were lactate and choline.Conclusion: Existing metabolomics studies of BPA exposure present heterogeneous findings regarding metabolite profile characteristics. We need more evidence from target metabolomics and epidemiological studies to further examine the reliability of these biomarkers which link to low, environmentally relevant, exposure of BPA in human body. [ABSTRACT FROM AUTHOR]

In this paper, an improved version of the Grey Wolf Optimizer (GWO) is proposed to improve the exploration and the exploitation ability of the GWO algorithm. This improvement is performed through using the chaotic logistic map, the Opposition-Based Learning (OBL), the differential evolution(DE), and the disruption operator (DO). Where, the chaotic logistic map and the OBL are used to initialize the candidate solutions and these approaches avoid the drawbacks of the random population and increase the convergence of the algorithm. Then, the DE operators are combined with the GWO algorithm, in which, the DE operators work as a local search mechanism to improve the exploitation ability of the GWO through updating the population. Also, after updating the solutions by using a hybrid between the GWO and the DE, the DO is used to enhance the exploration ability, in which, the DO is used to maintain the diversity of the population. Therefore, the combinations with chaotic logistic map, OBL, DE, and DO, provide the GWO with tools to better balance between the exploration and the exploitation of the search space without affecting the computational time required for this task. The proposed algorithm, called COGWO2D, is compared with other seven algorithms through a set of experimental series that have been performed over two benchmark functions, the classical CEC2005, and the CEC2014. Also, the performance of the proposed algorithm to improve the classification of the galaxy images is evaluated, where it is used as a feature selection method. The aim of this experiment is to select the optimal subset of features from the extracted features of the galaxy images. The experimental results support the efficacy of the proposed approach to find the optimal solutions of the global optimization problem, as well as, increase the accuracy of the classification of the galaxy images. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified. [ABSTRACT FROM AUTHOR]

• Developing a new paradigm for the feature selection problem based on the hyperheuristic methodology. • Configuring the combination of the components in to automatically find the most pertinent features for each dataset. • Evaluating the performance of the proposed technique using a comprehensive set of eighteen datasets. • Comparing the results of the best FS combination with the other well-known FS methods. Feature selection (FS) is a crucial step for effective data mining since it has largest effect on improving the performance of classifiers. This is achieved by removing the irrelevant features and using only the relevant features. Many metaheuristic approaches exist in the literature in attempt to address this problem. The performance of these approaches differ based on the settings of a number of factors including the use of chaotic maps, opposition-based learning (OBL) and the percentage of the population that OBL will be applied to, the metaheuristic (MH) algorithm adopted, the classifier utilized, and the threshold value used to convert real solutions to binary ones. However, it is not an easy task to identify the best settings for these different components in order to determine the relevant features for a specific dataset. Moreover, running extensive experiments to fine tune these settings for each and every dataset will consume considerable time. In order to mitigate this important issue, a hyper-heuristic based FS paradigm is proposed. In the proposed model, a two-stage approach is adopted to identify the best combination of these components. In the first stage, referred to as the training stage , the Differential Evolution (DE) algorithm is used as a controller for selecting the best combination of components to be used by the second stage. In the second stage, referred to as the testing stage , the received combination will be evaluated using a testing set. Empirical evaluation of the proposed framework is based on numerous experiments performed on the most popular 18 datasets from the UCI machine learning repository. Experimental results illustrates that the generated generic configuration provides a better performance than eight other metaheuristic algorithms over all performance measures when applied to the UCI dataset. Moreover, The overall paradigm ranks at number one when compared against state-of-the-art algorithms. Finally, the generic configuration provides a very competitive performance for high dimensional datasets. [ABSTRACT FROM AUTHOR]

• Brazil and Russia become hotspots of the COVID-19, and they suffer from high increase of the daily confirmed cases. • Proposed an Artificial intelligence based forecasting model to forecast the number of confirmed cases of COVID-19 in Brazil and Russia. • Enhancing the performance of the ANFIS using an improved marine predators algorithm (MPA), called chaotic MPA (CMPA). • Compare the proposed CMPA-ANFIS model to three models, the original ANFIS, PSO-ANFIS, and the original MPA-ANFIS. COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models. [ABSTRACT FROM AUTHOR]

[Display omitted] COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities. [ABSTRACT FROM AUTHOR]

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method. [ABSTRACT FROM AUTHOR]

In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems. [ABSTRACT FROM AUTHOR]

Bioactive peptides from protein hydrolysates with antihypertensive properties have a great effect in health, which warrants their pharmaceutical use. Nevertheless, the process of their production may affect their efficacy. In this study, we investigate the inhibitory activities of various hydrolysates on angiotensin-converting enzyme (ACE) in relation to the chemical diversity of corresponding bioactive peptides. This depends on the enzyme specificity and process conditions used for the production of hydrolysates. In order to mitigate the uncontrolled chemical alteration in bioactive peptides, we propose a computational approach using the random vector functional link (RVFL) network based on the sine-cosine algorithm (SCA) to find optimal processing parameters, and to predict the ACE inhibition activity. The SCA is used to determine the optimal configuration of RVFL, improving the prediction performance. The experimental results show that the performance measures of the proposed model are better than the state-of-the-art methods. • Present an alternative method to predict ACE Inhibition Activity. • Proposed method a modified random vector functional link (RVFL) network. • Using the sine-cosine algorithm (SCA) to find the optimal configuration of RVFL. • Results show that performance of proposed model is better than other methods. [ABSTRACT FROM AUTHOR]

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD 5 and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD 5 , TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD 5 and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R2, RMSE, and others. The obtained results of R2 and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD 5 and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD 5 and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD 5. [Display omitted] • Neural network was used to predict the effluent quality of wastewater plant. • Optimization of random vector functional link model using manta ray foraging (MRFO). • Higher accuracy was observed using the hybrid prediction model of RVFL-MRFO. • Eight statistical metrics have been employed to evaluate the investigated models. [ABSTRACT FROM AUTHOR]

• Developed a global optimization approach using cooperative meta-heuristic methods. • The proposed method inspired from the natural selection theory. • DE, GWO, WOA, SSA, SCA, and SOS are used to build the proposed method. • Three variants of proposed are developed based on strategy of updating solutions. • Results of proposed method compared with other methods using CEC2014 and CEC2017. This paper presents an alternative global optimization meta-heuristics (MHs) approach, inspired by the natural selection theory. The proposed approach depends on the competition among six MHs that allows generating an offspring, which can breed the high characteristics of parents since they are unique and competitive. Therefore, this leads to improve the convergence of the solutions towards an optimal solution and also, to avoid the limitations of other methods that aim to balance between exploitation and exploration. The six algorithms are differential evolution, whale optimization algorithm, grey wolf optimization, symbiotic organisms search algorithm, sine–cosine algorithm, and salp swarm algorithm. According to these algorithms, three variants of the proposed method are developed, in the first variant, one of the six algorithms will be used to update the current individual based on a predefined order and the probability of the fitness function for each individual. Whereas, the second variant updates each individual by permuting the six algorithms, then using the algorithms in the current permutation to update individuals. The third variant is considered as an extension of the second variant, which updates all individuals using only one algorithm from the six algorithms. Three different experiments are carried out using CEC 2014 and CEC 2017 benchmark functions to evaluate the efficiency of the proposed approach. Moreover, the proposed approach is compared with well known MH methods, including the six methods used to build it. Comparison results confirmed the efficiency of the proposed approach compared to other approaches according to different performance measures. [ABSTRACT FROM AUTHOR]

• A novel enhanced MPA algorithm has been proposed for effective PV modelling. • Static and dynamic PV model parameters have been estimated. • The estimated parameters have been validated via experimental data-sets. • The statistical analysis has been performed for effectiveness of the EMPA method. • The results confirm the EMPA efficiency comparing with state-of-the-art algorithms. Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301 e - 04 and 5.9135 e - 07 . Similarly for double diode are 7.4396 e - 04 and 3.1849 e - 05 , respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505 ± 1.0971 e - 17 , which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505 ± 9.6235 e - 14 and 0.0084505 ± 2.5235 e - 9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563. [ABSTRACT FROM AUTHOR]

Existing approaches for knowledge graph embedding usually represent knowledge as triples of binary relations between entities and embed them into continuous vector space, which are not practical for the complex facts in the form of multi-fold relations in real life. In this work, we address the problem of multi-fold relation embedding in knowledge graph and propose a new framework CoRelatE that learns correlations between entities, facts and relations from the instances. We first model the entity-relation correlation directly via combinational operator, then utilize a graph convolutional network to model the correlation between entities and their related facts. Finally, the facts are forced to embed close to their corresponding relations to learn the fact-relation correlation. We formulate the objective as a joint optimization problem and introduce an efficient algorithm to solve it. We compare the proposed CoRelatE model with several state-of-the-art models including HypE, NaLP and RAE, m-TransH. Experimental results on four datasets of multi-fold relations and two datasets of binary relations for link prediction and instance classification tasks validate the effectiveness and merits of our model. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc . We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed., Competing Interests: The authors declare that they have no competing interests., (© 2024 ur Rehman et al.)

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.)

During normal pregnancy, mothers face a unique physiological challenge in the adaptation of glucose metabolism in preparation for the metabolic stress presented by fetal development. However, the responsible mechanism remains elusive. The purpose of this study is to investigate the mechanism of the metabolic stress of glucose metabolism in pregnant women using metabolomics method.A Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometer-based untargeted metabolomics study was performed to investigate the dynamic urinary signature of the intermediates of glucose metabolism in a longitudinal cohort of 232 healthy pregnant women in their first, second, and third trimesters.Twelve glucose metabolic intermediates were screened out from hundreds of candidate metabolites using partial least squares discriminant analysis models. These 12 markers were mainly involved in the metabolic pathways of insulin resistance, glycolysis/gluconeogenesis, tricarboxylic acid cycle, nonabsorbable carbohydrate metabolism, and N-glycan biosynthesis. In particular, L-acetylcarnitine, a metabolite that is beneficial for the amelioration of insulin resistance, decreased in a time-dependent manner during normal pregnancy. Moreover, thiamine pyrophosphate, an intermediate product of glycolysis/gluconeogenesis, significantly increased in the second trimester, and argininosuccinic acid and oxalosuccinic acid, intermediates involved in the tricarboxylic acid cycle, significantly decreased in the third trimester, suggesting an increased glucose demand in the maternal body during fetal development.These findings provide novel insight into the normal pregnancy-induced elevation of insulin resistance and glycolysis/gluconeogenesis, as well as the observed reduction in the aerobic oxidation of glucose.

In this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures., Competing Interests: Conflict of interest: The authors report no conflicts of interest in this work.