Real Complex Research Articles

Analytical Solution to the Generalized Complex Duffing Equation.

Future scientific and technological evolution in many areas of applied mathematics and modern physics will necessarily depend on dealing with complex systems. Such systems are complex in both their composition and behavior, namely, dealing with complex dynamical systems using different types of Duffing equations, such as real Duffing equations and complex Duffing equations. In this paper, we derive an analytical solution to a complex Duffing equation. We extend the Krýlov-Bogoliúbov-Mitropólsky method for solving a coupled system of nonlinear oscillators and apply it to solve a generalized form of a complex Duffing equation.

Numerical modeling of the dam-break flood over natural rivers on movable beds

Abstract In the present work, a modified numerical model was developed to simulate the water flow during a dam break with the mud layer transfer of different heights, consisting of three phases (water, air, and a phase for deposition). To carry out a numerical simulation of this process, a mathematical model based on the VOF (volume of fluid) method was modified, taking into account the movement of the water-free surface, which is carried out by the movement of water flow based on the Newtonian fluid model, and the movement of mud impurities is based on the non-Newtonian fluid model. Validation of the constructed model for the influence of three-dimensional features of the flow on morphological changes is carried out by a modified mathematical model and compared with the results of calculation for two-dimensional (2D) and three-dimensional (3D) models. The proposed method for modeling is applied on a real complex terrain, which was based on the Kargalinka – a river in Almaty and the Almaty region of Kazakhstan, the right tributary of the Kaskelen River. Simulation analysis is carried out for cases with different deposit heights. All results of the numerical simulation can be visually viewed using graphs and illustrations.

Real-time monitoring of mercury(II) in water and food samples using a quinoline-based ionic probe

Herein, a novel ionic fluorescent probe for mercury(II) detection is presented consisting of a functional quinoline-based IL. Interestingly, the probe displayed high sensitivity (0.8 nM) and selectivity through the regulation function of electrostatic attraction, where its performance was significantly superior to that of quinoline probes without negative charge. Furthermore, the probe was found to exhibit two different fluorescent signals and colorimetric signals in the presence of different concentrations of mercury(II), which was consistent with the reaction mechanisms of the generation of large conjugated systems and the formation of anion-mercury(II) complexes. Moreover, this probe could be further loaded on a simple filter paper to serve as a visual paper sensor due to its adequate response time of less than 5 s. This regulation function strategy of electrostatic attraction has excellent potential for use in the precise detection of targeted analytes in real complex samples with improved accuracy and selectivity.

Fluorescent and Colorimetric Ionic Probe Based on Fluorescein for the Rapid and On-Site Detection of Paraquat in Vegetables and the Environment.

Detection of pesticide paraquat (PQ) is of considerable significance to ensure food safety, and its rapid and on-site detection is still a challenge. Aimed at the ion characteristics of PQ, an "enrichment and detection" strategy was proposed to improve the sensitivity through electrostatic attractions, and the ion characteristic of probes was adopted to increase the portability through avoiding aggregation-caused quenching effects in the paper strips. Herein, a novel anion-functionalized ionic liquid (IL) probe with a large conjugated plane and rich π-electrons ([Fluo][P66614]2) was designed as a fluorescent and colorimetric dual-channel probe to sensitively and rapidly detect trace amounts of PQ in vegetables and the environment. The proposed probe exhibited good linearity with a detection limit of 64.0 nM in the PQ concentration range of 0.3-7.0 μM (fluorometry) and 0.1 μM in that of 0.1-8.0 μM (colorimetry), respectively. In addition, it displayed a rapid fluorescence quenching response from green to dark (<5 s) and excellent anti-interference (among 23 other pesticides) due to dual effects of electrostatic attraction and π-π stacking. Most importantly, the lipophilic IL probe could be applied in real vegetables and environmental samples with a satisfying recovery rate of 98-103% and assembled into a handy paper strip that achieved the visual semiquantitative detection of PQ. This ionic probe provides a feasible approach for rapidly and conveniently detecting PQ for ensuring agricultural and food safety and opens a new avenue to detect ion-responsive analytes in real complex samples by an "enrichment and detection" strategy.

Multiscale voter model on real networks

We introduce the Multiscale Voter Model (MVM) to investigate clan influence at multiple scales—family, neighborhood, political party…—in opinion formation on real complex networks. Clans, consisting of similar nodes, are constructed using a coarse-graining procedure on network embeddings that allows us to control for the length scale of interactions. We ran numerical simulations to monitor the evolution of MVM dynamics in real and synthetic networks, and identified a transition between a final stage of full consensus and one with mixed binary opinions. The transition depends on the scale of the clans and on the strength of their influence. We found that enhancing group diversity promotes consensus while strong kinship yields to metastable clusters of same opinion. The segregated domains, which signal opinion polarization, are discernible as spatial patterns in the hyperbolic embeddings of the networks. Our multiscale framework can be easily applied to other dynamical processes affected by scale and group influence.

Deep reinforcement learning in agent-based simulations for optimal media planning

Agent-based models establish a suitable simulation technique to recreate real complex systems, such as those approached in marketing. Reinforcement learning is about learning a behavior policy in order to maximize a long-term reward signal. In this work, we develop a deep reinforcement learning agent that represents a brand in an agent-based model of a market. The goal of the learning agent is to obtain a marketing investment strategy that improves the awareness of its corresponding brand in the marketing scenario. In opposition to conventional marketing investment strategies, the learned strategy is dynamic, so the agent makes its investment decision on-line based on the current state of the market. We choose the Double Deep Q-Network algorithm to train this agent on diverse instances of the model, each of them with different knowledge levels of the brand. First we adjust a subset of the hyperparameters of Double Deep Q-Network on two of the model instances, and then we use the best configuration found to train the agent on all the available instances. The brand agent learns a dynamic policy that optimizes brand’s awareness levels. We perform an expert analysis of the policy obtained, where we observe that the learning brand agent tends to increase investment in media channels with greater awareness impact, but it also invests in other channels according to the situation and the characteristics of the model instance. These results show the benefits of having an on-line dynamic learning environment in a decision support system for media planning in marketing.

A transient solution on potential equation of diffusion type with time-varying coefficients

Research on hydrothermal sulphide ore of marine mineral resources exploration is hot recently. According to the general solution of first order differential equation, Laplace transform is used to transform the linear homogeneous differential equation of second order variable coefficient into first order differential equation of complex domain. The general solution of the first-order differential equations in complex domain is similarly obtained without regard for the difference between real domains and complex domains. Further more, the general solutions are deduced to the second order linear differential equations with variable coefficients. Then it is used to solve the thermoelectric coupling electric field model in oceanic strike exploration; A closed analytical solution with integral form of elementary functions is obtained to the simplified diffusion electric field potential model. To get an explicit solution, with the help of MAPLE software, the general solution of the linear combination of Kummer functions is obtained. This research has theoretical significance for the engineering implementation of the frequency domain induced dipole drag system.

Multi-objective scheduling in the vegetable processing and packaging facility using metaheuristic based framework

This article solves the scheduling problem in vegetable processing and packaging facility, where multiple types of vegetable consignments need to be packed using dissimilar parallel machines. The problem considers the real-life complexities, such as arrival and discharge time of consignments and setup time for the pre-processing requirement between consecutive consignments. The objective is to identify optimal schedules with minimum total cost and minimum makespan by satisfying the associated constraints. This study proposes an optimization framework incorporating metaheuristic techniques and heuristic mechanism to determine optimal solution for the scheduling problem. The heuristic mechanism assists metaheuristic techniques in reducing constraint violations by rescheduling vegetable consignments assigned to the machine. The efficacy of the proposed framework is analyzed by solving a case study using multiple single and multi-objective metaheuristic techniques. All metaheuristic techniques are able to determine a better schedule for optimizing total cost and makespan using the proposed framework than their naïve metaheuristic technique on solving the single objective model. Differential Evolution with Multi-Population Based Ensemble of Mutation Strategies provided a better solution than other techniques for the single objective model of both objectives. Non-dominated Sorting Teaching Learning Based Optimization, with heuristic mechanism, provided strictly dominating solutions compared to the other techniques.

Fall Detection System Using Millimeter-Wave Radar Based on Neural Network and Information Fusion

Falls are fatal for the elderly, and timely detection after falls is crucial. As a contactless device, the radar sensor can monitor users’ falls with the advantage of not revealing their privacy. Today, the use of radar for fall detection has made a significant progress. However, most current methods cannot be used in real complex scenes. They usually collect fewer types of actions, and the ratio of the number of nonfall samples to the number of fall samples is small, which is not consistent with the real-life scene. In addition, the classifiers are usually trained and tested on the same environments and the same people, which cannot be easily extended to new environments and new people. We designed a robust fall detection system based on the frequency-modulated continuous-wave (FMCW) radar to solve these issues. The system detects the moment of human movement and calculates the range–velocity map, range–horizontal angle map, and range–vertical angle map of the radar signals, and creates three neural networks for these three signal maps. The stacking method of ensemble learning is used to fuse the time–space–velocity features extracted by the three neural networks to identify falls. The method was trained and tested on a data set consisting of ten scenes, 21 subjects, 52 nonfall action types, and 12 fall action types. The results show that on the test set containing only new environments and new subjects, the recall is 0.983 and the precision is 0.975.

A Simplified Quantum Walk Model for Predicting Missing Links of Complex Networks.

Prediction of missing links is an important part of many applications, such as friends' recommendations on social media, reduction of economic cost of protein functional modular mining, and implementation of accurate recommendations in the shopping platform. However, the existing algorithms for predicting missing links fall short in the accuracy and the efficiency. To ameliorate these, we propose a simplified quantum walk model whose Hilbert space dimension is only twice the number of nodes in a complex network. This property facilitates simultaneous consideration of the self-loop of each node and the common neighbour information between arbitrary pair of nodes. These effects decrease the negative effect generated by the interference effect in quantum walks while also recording the similarity between nodes and its neighbours. Consequently, the observed probability after the two-step walk is utilised to represent the score of each link as a missing link, by which extensive computations are omitted. Using the AUC index as a performance metric, the proposed model records the highest average accuracy in the prediction of missing links compared to 14 competing algorithms in nine real complex networks. Furthermore, experiments using the precision index show that our proposed model ranks in the first echelon in predicting missing links. These performances indicate the potential of our simplified quantum walk model for applications in network alignment and functional modular mining of protein-protein networks.

A Robust Possibilistic Bi-Objective Mixed Integer Model for Green Biofuel Supply Chain Design under Uncertain Conditions

In recent years, concerns regarding issues such as climate change, greenhouse gas emissions, fossil reserve dependency, and petroleum price fluctuation have led countries to focus on renewable energies. Meanwhile, in developing countries, designing an appropriate biofuel supply chain network regarding environmental competencies is an important problem. This paper presents a new bi-objective mixed integer mathematical model aiming to minimize CO2 emission and total costs in the process of the biofuel supply chain, creating a suitable green supply chain network. In this respect, CO2 emission and biofuel demand are regarded as uncertain data to address the real complex cases. Moreover, the SAUGMECON approach was implemented to construct a single objective model, and the obtained Pareto optimal points were depicted and analyzed. Thereby, a robust possibilistic programming approach was implemented to the proposed model to handle existing imprecise data. Furthermore, the applicability and performance of the proposed model were demonstrated based on an experimental example. In this respect, the obtained results from the proposed robust possibilistic programming model were compared with its crisp form to show the robustness and reliability of the proposed uncertain mathematical model.

The Design of State-Dependent Switching Rules for Second-Order Switched Linear Systems Revisited

This paper focuses on the asymptotic stability of second-order switched linear systems with positive real part conjugate complex roots for each subsystem. Compared with available studies, a more appropriate state-dependent switching rule is designed to stabilize a switched system with the phase trajectories of two subsystems rotating outward in the same direction or the opposite direction. Finally, several numerical examples are used to illustrate the effectiveness and superiority of the proposed method.

On acoustic fields of complex scatters based on physics-informed neural networks

This paper proposes a modeling method for scattered acoustic fields under complex structures based on Physics-informed Neural Networks (PINNs), with particular attention to the acquisition of training sets and the embedding of physical governing equations. First, using acoustic simulation softwares to obtain the scattered acoustic field under various models, and select the scattered acoustic field data at several moments as the training sets. Then, according to the characteristics of the simulated model, the corresponding physical equations have been embedded in the loss function of the network. We tested the method by predicting the propagation of ultrasonic waves and the scattering of acoustic fields with various simple scatterers. Furthermore, we also use PINN to simulate the scattered acoustic field of the real complex damaged structure. The results show that the mean square error (MSE) between prediction and ground truth is in the order of 10-4, which illustrate PINN can effectively simulate the propagation and reflection of ultrasonic waves, and can also simulate the scattered acoustic field of complex structures accurately. The meshless and accurate characteristics of PINN provide a reliable alternative for the theoretical prediction of complex and continuous scattered acoustic fields.

Novel complex-valued deep learning applied to automatic classification of heart sounds

Abstract Introduction Cardiovascular disease (CVD) is a problem facing governments around the world. Early detection saves the healthcare system thousands of dollars by allowing prevention and avoiding costly treatments when the disease has progressed to advanced stages. According to the World Health Organization (WHO), cardiovascular diseases represent the leading cause of mortality worldwide (WHO, 2019). That is why, we have decided to develop novel artificial intelligence models for the detection of heart disease, with accuracies that allow us to put the obtained algorithms into production. The revolution of artificial intelligence allows the application Deep Learning techniques of two-dimensional images in the domain of both real numbers and complex value numbers, as classifiers of heart sounds, for the detection of normality or abnormality in the functioning of the heart. Purpose In the present work, we propose the comparison of a novel 2D convolutional neural network (CNN) algorithm in the domain of complex value numbers with its counterpart in the domain of real numbers for the automatic classification of heart sounds into normal or abnormal. Material The database we decided to use for our research is Pascal, which has audio files of cardiac activity, distributed in 351 normal sounds and 129 pathological sounds. Methods The following steps were applied to get the objectives of our work: 1) automatic segmentation of a single heartbeat, 2) conversion of the segmented sound into its associated image scalogram using the Hilbert transform, 3) classification of the sounds into normal and abnormal using the proposed algorithms, and 4) measurement and comparison of the results obtained by performing a two-tailed t-student hypothesis test and cross-validation. Results We present a comparative table between the two proposed models, finding that Accuracy, F1 Score, Precision and Recall metrics obtained using complex-valued convolution networks present significant improvements compared with the real valued one. The following table show us the obtained numbers. For all cases, the t-student test shows us p-values less than 0.05%, giving statistical evidence that the means are significantly different between the two proposed models. Besides, in all cases, the performance of the Complex-valued model is better compared with the Real-valued one. Conclusion Complex-valued neural networks propose a significant advance around Deep learning, since they present a better performance than the traditional counterparts based on real numbers. This proposes an experimental basis for the construction of a new Deep learning paradigm, where information in another numerical domain, is better exploited with the help of mathematical transforms. The latter is a significant advance in health sciences, where the demand is higher, in terms of performance of the proposed models. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): eVIDA research Group

Effect of coupling structure on traffic-driven epidemic spreading in interconnected networks

Interconnected networks, as the good abstraction of many real complex systems, have recently attracted a lot of attentions. In this paper, we propose a traffic-driven epidemic model in interconnected networks to show the effects of interplay induced by the coupled structure. Through simulations on interconnected scale-free networks, we find that in the free flow state, the disassortative coupling can effectively suppress the spreading of epidemics by introducing a more uniform distribution of traffic load, especially for high coupling strength. This result has also been confirmed by our mean-field solution. When the system is congested, the results show that the epidemic threshold is subject to the critical point of the traffic process, which depends on the coupling mode, traffic routing and allocation mechanism of node capacity. We also check our model under different routing protocols, the results show that our findings are robust, indicating that the coupling structure is the determining factor of the dynamics on interconnected networks.

Fatigue life prediction of a L-PBF component in Ti-6Al-4V using sample data, FE-based simulations and machine learning

Laser Powder Bed Fusion (L-PBF) is a widely-used additive manufacturing (AM) technique for producing complex metal parts used for a variety of dynamically loaded applications. Fatigue performance of standardized L-PBF samples is at present fairly well understood, while the knowledge on the fatigue behaviour of real-life complex shaped components is often lacking. This work presents insight, methods and results on predicting L-PBF component fatigue life using FE-based simulations, stress-based sample fatigue data and machine learning respectively. A realistic end-use part with representative geometry for many industrial applications was selected and produced in Ti-6Al-4V by L-PBF along with many standardized samples under different building orientations and with different types of heat treatments and surface finishing steps. The results indicate that the developed tool for component fatigue life prediction can accurately predict both the failure location and the number of cycles to failure.

INFUS Conference

INFUS (http://infus.itu.edu.tr) is an acronym for Intelligent and Fuzzy Systems. It is an international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing for applied research in general and for complex engineering and decision support systems. The principal mission of INFUS is bridging the gap between fuzzy and intelligence systems and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together.

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification.

To solve the problems of weak generalization of potato early and late blight recognition models in real complex scenarios, susceptibility to interference from crop varieties, colour characteristics, leaf spot shapes, disease cycles and environmental factors, and strong dependence on storage and computational resources, an improved YOLO v5 model (DA-ActNN-YOLOV5) is proposed to study potato diseases of different cycles in multiple regional scenarios. Thirteen data augmentation techniques were used to expand the data to improve model generalization and prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. Based on this, the features extracted from all network layers are visualized, and the extraction of features from each network layer can be distinguished, from which an understanding of the feature learning behavior of the deep model can be obtained. The results show that in the scenario of multiple complex factors interacting, the identification accuracy of early and late potato blight in this study reached 99.81%. The introduced data augmentation technique improved the average accuracy by 9.22%. Compared with the uncompressed YOLO v5 model, the integrated ActNN runs more efficiently, the accuracy loss due to compressed parameters is less than 0.65%, and the time consumption does not exceed 30 min, which saves a lot of computational cost and time. In summary, this research method can accurately identify potato early and late blight in various scenarios.

Towards a New Arab Moment

Towards a New Arab Moment

A Portable Electrochemical Sensor Based on Manganese Porphyrin-Functionalized Carbon Cloth for Highly Sensitive Detection of Nitroaromatics and Gaseous Phenol.

Organic pollutants (OPs) have garnered a considerable amount of attention in recent times due to their extreme toxicity toward humans and the ecosystem. The need for an inexpensive yet robust, sensitive, selective, and easy-to-operate method for detecting OPs remains a challenge. Herein, a portable electrochemical sensor is proposed based on manganese porphyrin-functionalized carbon cloth (CC). To explain the electrochemical performance of the sensor, cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were employed. The presence of manganese(III) ion in the center of the porphyrin ligand acted as an agent for the transfer of electrons and enhanced sensitivity toward analyte-specific redox catalysis. Moreover, it allowed for the concurrent detection of multiple analytes in a complex environment. The modified CC electrode can selectively detect nitroaromatic and phenolic compounds with accessible data collected through wireless means onto a smartphone device. The as-synthesized electrode demonstrated a higher sensitivity toward the detection of nitrobenzene (NB) and aqueous phenol with a limit of detection (LOD) found to be 5.9268 × 10-10 M and 4.0178 × 10-10 M, respectively. Additionally, our proposed portable electrochemical sensor demonstrates a high selectivity and reproducibility toward nitroaromatic and phenolic compounds, which can be employed in real complex water samples. With regard to the sensor's remarkable electrochemical performance, it is envisaged that such a sensor could pave the way for environmental point of care (POC) testing.