Complexity

Water, energy, and food are essential resources for social and economic development, which are highly interwoven in the urban metabolic processes. The 2011 Bonn conference first introduced the concept of water-energy-food (WEF) nexus to describe the interconnection of three resources. In this study, taking the Yangtze River Delta region as a case study, we proposed a hybrid framework to quantify WEF consumption based on an environmentally extended multiregional input-output model. Then, various ecological network analyses were adopted to explore system properties and sectoral interaction. The results indicate that embodied WEF consumption in interregional trade is highly interconnected, in which Jiangsu accounts for the largest proportion of hybrid energy network, while Anhui dominates the hybrid water network and the food network. The recycling rate in the water network (14.5%–20.8%) is lower than that in the energy network (16.7%–23.5%) and the food network (17.2%–23.9%). Predation and exploitation relationships are dominated between sectors, and the whole trade network stays in a low positive environment. The nexus impact on water networks is smaller than that on the energy networks. This analysis may help identify leverage points and feasible pathways of crossregional resources’ trade and provide insights for integrated resources management of the Yangtze River Delta region.

People in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the epicenter, this study proposed a strategy of distributing supplies from peripheral areas to the epicenter and gave a supply-side selection model considering the epidemic influence and supplies condition in the candidate supply-side areas. First of all, the epidemic spatial-temporal transmission path (STTP) network describing the geographic spread of disease is obtained using a first-order conditional dependence approximation algorithm in a dynamic Bayesian network (DBN). Then, the structural information of the STTP network and the supplies condition characteristic information are combined using the Bipartite network embedding (BiNE) method. Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. The results show that the highest supplies allocation accuracy reaches 87%. Validation and supremacy of the proposed methodology are provided by applying it to the case in Hubei province. This study considers crossed-areas supplies distribution strategy and contributes to select suitable supply-side areas considering the epidemic and supplies condition in the peripheral areas, which is helpful to both epicenter and peripheral areas.

This work focuses on presenting a control algorithm to investigate nonlinear systems, which contain time-varying powers, inverse dynamics, and uncertainties. First, some appropriate transformations are introduced to obtain a new system. Then, a Lyapunov function, which covers quadratic and high-order components, is recursively constructed for control design. Subsequently, by introducing the neural networks, the uncertain functions encountered during the design are approximated. Based on the inequality techniques, the nonlinear terms are skillfully estimated. By defining the bounds of some unknown parameters and using the adaptive technique, some virtual controllers are selected in each step to dominate the nonlinear functions and guarantee that the derivative of the Lyapunov function satisfies the required form. Finally, a new adaptive controller is constructed and semiglobal practical finite time stability (SGPFS) is guaranteed. The proposed approach is verified with a numerical example.

The outbreak of the COVID-19 epidemic has brought profound changes to all aspects of our society and also reflects the importance of community emergency volunteers actively participating in epidemic prevention and control in the face of unexpected public security events. As a bridge between the implementation of government policies and the masses of the community, community emergency volunteers have the characteristics of high efficiency and low cost, which has a great impact on the advancement of modern social governance. In order to motivate volunteers, the community will introduce incentive mechanisms. How does the evolutionary process of a dynamic game between volunteer engagement and community motivation change? How should communities maximize the service investment of volunteers in the game process? However, the current research rarely focuses on the role of community volunteers in the modernization of Community Governance. In order to clarify this game process, this article constructs a public safety governance incentive game model consisting of communities and emergency volunteers. Based on evolutionary game theory, we obtain the evolutionary stable equilibrium point by solving the replicator dynamic equations of all parties in the dynamic system under different constraints. Finally, some numerical examples were provided to simulate the selection of agents. The research results show that the degree of community public security risk, the degree of active involvement of volunteers, the degree of inactive involvement of volunteers, and the level of community incentives have an important impact on the enthusiasm of volunteer community service investment decision-making behavior. In addition, the choice of community incentive-volunteer service investment strategy is a dynamic process, which can converge to the ideal state under certain conditions after continuous adjustment and optimization. In addition, this study puts forward suggestions and measures conducive to the game between both sides, which can provide valuable guidance for the practice of community public security governance and the improvement of government efficiency in China.

In the e-commerce supply chain of agricultural products, there are three parties: farmer cooperative organizations, e-commerce platforms, and consumers. This study aims to investigate how to coordinate farmer cooperative organizations and e-commerce platforms to maximize the overall profits of the supply chains of agricultural products. Based on the Stackelberg game theory, this paper constructs a two-level supply chain decision-making model led by farmers’ cooperatives and followed by e-commerce platforms. It discusses two supply chain decision-making models (decentralized and centralized) with decision variables (selling price and promotion effort). The results show that the overall profit of the supply chain under centralized decision making is higher than the overall profit under decentralized decision making. In order to achieve the coordination of agricultural product sales price and brand promotion efforts and achieve win-win cooperation, this paper puts forward two coordination schemes: (1) coordinating revenue sharing, cost sharing, and wholesale price discounts and (2) coordinating the wholesale price. These two contract coordination schemes are verified by example analysis. Finally, the following strategies are recommended, including strengthening the investment in brand promotion and contract management.

The Rulkov map model is an efficient model for reproducing different dynamics of the neurons. In specific neurons, the electrical activity is regulated by time-delayed self-feedback called autapse. This paper investigates how the dynamics of the Rulkov model change by considering the autaptic current. Both electrical and chemical autapses are considered, and bifurcation diagrams are plotted for different autapse gains and time delays. Consequently, various firing patterns of the model are illustrated. The results represent that the firing pattern is greatly dependent on the values of autapse parameters. Moreover, the average firing frequency is computed and it is shown that the enhanced firing activity is induced by the inhibitory autapse. The synchronous dynamics of coupled Rulkov maps in the presence of autapse is also studied. It is shown that the electrical autapse enhances synchronization in small time delays, while the enhancement is achieved by chemical autapse in any time delay. However, increasing the time delay reduces the synchronization region.

State-owned private equity funds in China currently oversee assets worth more than 12 trillion RMB. Due to the uncertainty in the private equity market and the presence of information asymmetry, these state-owned private equity firms frequently engage in coinvestments with other private equity firms. The coinvestment strategy allows them to mitigate risks and exchange valuable information and resources. Which types of partners do state-owned private equity firms typically collaborate with? The existing literature built coinvestment partner selection models based on the traditional regression models and ignored the complexity of the network structure. This research analyzes cooperative relationships using exponential random graph models, considering both structural effects and node attributes. The empirical study of 4645 private equity firms operating in the Chinese private equity market shows that state-owned private equities are more likely to collaborate with foreign private equities and domestic private-owned private equities compared to collaborating with other state-owned private equities. Furthermore, in markets characterized by high marketization indexes, state-owned private equities demonstrate greater inclinations to partner with foreign and domestic private-owned private equities. When state-owned private equities allocate their investments to high-tech industries, their likelihood of collaborating with foreign private equities increases.

This paper investigates the impact of cross-border investment of domestic venture firms (VCs) on the overseas listings of their domestic portfolio companies. Using a sample of 1,439 domestic VCs’ first-round domestic investment events collected by Crunchbase and PEDATA, we find that the more cross-border investment experience domestic VCs have, the more likely their domestic portfolio companies are to go public outside China. The findings remain robust after using the instrumental variable method to eliminate endogeneity, the Heckman two-stage regression method to eliminate sample selection bias, and the exclusion of a portion of the sample for reregression. In addition, we further find that foreign VCs participating in follow-on financing play a mediating role in the relationship between the cross-border investments of domestic VCs and the overseas listings of their portfolio companies. This paper reveals the critical path for domestic VCs to go out and bring in foreign VCs to promote overseas listings of domestic firms. The findings of this paper are critical to the layout of domestic VCs’ internationalization strategy and the sustainable development of domestic firms.

The concept of a hyper coupled map lattice (CML) is presented in this paper. The complexity of the lattice is increased not by adding another spatial dimension of the lattice but by replacing scalar nodal variables by multidimensional square matrices of iterative variables. The proposed scheme exploits the nonlinear effects of the spatiotemporal divergence induced by nilpotent nodal matrices to generate separate secret images at different discrete moments of time during the evolution of the CML. The time variable plays the primary role in the decoding stage of the scheme. The carrying capacity of the proposed scheme is n − 1 different dichotomous digital images, where n is the dimension of the nilpotent nodal matrices. Computational experiments are used to demonstrate the efficacy of the proposed scheme.

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.

The diffusion-improved ant colony optimization (DIACO) algorithm, as introduced in this paper, addresses the slow convergence speed and poor stability of the ant colony optimization (ACO) in obstacle avoidance path planning for quadruped robots. DIACO employs a nonuniformly distributed initial pheromone, which reduces the blind search time in the early stage. The algorithm updates the heuristic information in the transition probability, which allows ants to better utilize the information from the previous iteration during their path search. Simultaneously, DIACO adjusts the pheromone concentration left by ants on the path based on the map information and diffuses the pheromone within a specific range following the artificial potential field algorithm. In the global pheromone update, DIACO adjusts the pheromone on both the optimal path and the worst path generated by the current iteration, thereby enhancing the probability of ants finding the optimal path in the subsequent iteration. This paper designs a steering gait based on the tort gait to fulfill the obstacle avoidance task of a quadruped robot. The effectiveness of the DIACO algorithm and steering gait is validated through a simulation environment with obstacles constructed in Adams. The simulation results reveal that DIACO demonstrates improved convergence speed and stability compared to ACO, and the quadruped robot effectively completes the obstacle avoidance task using the path planning provided by DIACO in combination with the steering gait.

Optimal graph embeddings represent graphs in a lower dimensional space in a way that preserves the structure and properties of the original graph. These techniques have wide applications in fields such as machine learning, data mining, and network analysis. Do we have small (if possible minimal) k -connected graphs with the property that for any j vertices there is a longest path avoiding all of them? This question of Zamfirescu (1972) was the first variant of Gallai’s question (1966): Do all longest paths in a connected graph share a common vertex? Several good examples answering Zamfirescu’s question are known. In 2001, he asked to investigate the family of geometrical lattices with respect to this property. In 2017, Chang and Yuan proved the existence of such graphs in Archimedean tiling. Here, we prove that the graphs presented by Chang and Yuan are not optimal by constructing such graphs of sufficiently smaller orders. The problem of finding nonconcurrent longest paths in Archimedean tessellations refers to finding paths in a lattice such that the paths do not overlap or intersect with each other and are as long as possible. The complexity of embedding graph is still unknown. This problem can be challenging because it requires finding paths that are both long and do not intersect, which can be difficult due to the constraints of the lattice structure.

The influence of COVID-19 on individuals, businesses, and corporations is indisputable. Many markets, particularly financial markets, have been severely shaken and have suffered significant losses. Significant issues have arisen in supply chain networks, particularly in terms of financing. The COVID-19 consequences had a significant effect on supply chain financing (SCF), which is responsible for finance supply chain components and improved supply chain performance. The primary source of supply chain financing is financial providers. Among financial providers, the banking sector is referred to as the primary source of financing. Any hiccup in the banking operational systems can have a massive influence on the financing process. In this study, we attempted to comprehend the key consequences of the COVID-19 epidemic and how to mitigate COVID-19’s impact on Pakistan’s banking industry. For this, three extended hybrid approaches which consists of TOPSIS, VIKOR, and Grey are established to address the uncertainty in supply chain finance under q-rung orthopair probabilistic hesitant fuzzy environment with unknown weight information of decision-making experts as well as the criteria. The study is split into three parts. First, the novel q-rung orthopair probabilistic hesitant fuzzy (qROPHF) entropy measure is established using generalized distance measure under qROPHF information to determine the unknown weights information of the attributes. The second part consists of three decision-making techniques (TOPSIS, VIKOR, and GRA) in the form of algorithm to tackle the uncertain information under qROPHF settings. Last part consists of a real-life case study of supply chain finance in Pakistan to analyze the effects of emergency situation of COVID-19 on Pakistani banks. Therefore, to help the government, we chose the best alternative form list of consider five alternatives (investment, government support, propositions and brands, channels, and digital and markets segments) by using proposed algorithm that minimize the effect of COVID-19 on supply chain finance of Pakistani banks. The results indicate that the proposed techniques are applicable and effective to cope with ambiguous data in decision-making challenges.

The existing research studies on adaptive control frequently introduce many parameter estimations and lead to a complicated controller. This paper investigates the robust regulation issue for high-order system and plants to raise a new approach for adaptive control. Specifically, the considered system has odd system power, nontriangular form, and external disturbance. By introducing the transformations of a parameter estimation, the studied system is transformed into a new dynamic system. By employing fuzzy systems and some inequality skills, the appropriate bounds of nonlinear terms are established. Based on the adaptive method and homogeneous control, a recursive control design algorithm is provided to construct a new adaptive controller, which dominates those uncertain bounds and guarantees that the closed-loop system is semiglobally uniformly ultimately bounded (SUUB). The constructed controller employs only one adaptive law and has a much simpler form. Simulation examples verify the validness of the presented method.

The leakage of the ship’s pipeline system will bring great risks to the engine equipment and seriously threaten the vitality of the ship. In this paper, the pipeline leakage detection and localization research are carried out based on the vibration signal generated by pipeline leakage. First, the finite element model of the pipeline is constructed to obtain the variation law of the vibration signal when the pipeline leaks are carried out. Second, the vibration signal is processed based on the variational mode decomposition (VMD) and radial basis function (RBF) neural networks. The wavelet packet threshold noise reduction is conducted before signal decomposition to improve the signal-to-noise ratio. Then, the denoised signal is decomposed by VMD. The effective component is identified by analyzing the correlation coefficient between the component and the denoised signal. The center frequency and energy of the effective component are used as feature vector to train the RBF neural network to identify and locate leakage. Finally, a pipeline leakage test platform is built under laboratory conditions. After processing the data samples collected from the test, the RBF neural network is trained to identify and locate leaks. The test sample identification results show that the leak identification and localization method based on VMD-RBF has a high accuracy.

The majority of people with disabilities in the world have impairments that affect their lower bodies. In most of these cases, it was found that the affected person’s upper body was in good health and capable of carrying out all activities; however, the spinal cord injury results in significant health challenges with body functions like urination, bowel movements, heart rate, and respiratory, cardiovascular, and sexual function, which require prompt medical treatment and mobility aids. This study presents the mechanical layout of a wheelchair that can switch between a sitting and a standing position. The center of gravity must be taken into account when creating an electric standing wheelchair. It is designed specifically for people with disabilities to lessen the need of outside assistance, allowing the disabled person to savour a sense of adoration. In the simulation, the electric standing wheelchair analysis is carried out by loading with a human weight of 40 and 100 kg, and the transformation angle is adjusted between 0 and 90 degrees to compare the center of gravity displacement. SolidWorks and ANSYS are used to design the prototype, assemble the product, and establish the safety factor bounds of the structure and capabilities as required. The research focuses to achieve control of speed deviation and acceleration using a fuzzy control technique. The Arduino oversees the operation of the drive system as a controller, and a linear actuator is utilized for standing and sitting positions. This method is affordable, easily constructed, and highly secure.

Aiming at the improvement of innovation efficiency after enterprise obtaining subsidies, this paper constructs two-stage innovation benefit model about research and development (R&D) and transformation and achieves Nash equilibrium of innovative inputs to solve the objective discrepancy of innovation between government and enterprise. The main conclusions are as follows: there are three kinds of resource allocation structure in the way of achieving Nash equilibrium. The allocation structure is determined by the sensitivity of benefits (differentiated by social benefits and enterprise benefits) to R&D and transformation. After obtaining subsidies, enterprise optimizes resource allocation and results in crowding out effect, which is the inevitable choice for enterprise to seek benefits. Relative to the enterprise budget, when the proportion of government subsidies is few, the way of subsidies does not affect benefits. When the government invests more subsidies, which are designated for R&D, there is the possibility of dual losses of social benefits and enterprise benefits. The conclusion defines the proportion of subsidies to enterprise budgets so as to differentiate the allocation structure of innovative inputs. The practical significance is to provide a precise method of resource allocation from the microlevel of enterprise project, which alleviates the objective discrepancy between the government and enterprise.

Statistical probability distributions are commonly used by data analysts and statisticians to describe and analyze their data. It is possible in many situations that data would not fit the existing classical distributions. A new distribution is therefore required in order to accommodate the complexities of different data shapes and enhance the goodness of fit. A novel model called the new generalized exponentiated Fréchet–Weibull distribution is proposed in this paper by combing two methods, the transformed transformer method and the new generalized exponentiated method. This novel modeling approach is capable of modeling complex data structures in a wide range of applications. Some statistical properties of the new distribution are derived. The parameters have been estimated using the method of maximum likelihood. Then, different simulation studies have been conducted to assess the behavior of the estimators. The performance of the proposed distribution in modeling has been investigated by means of applications to three real datasets. Further, a new regression model is proposed through reparametrization of the new generalized exponentiated Fréchet–Weibull distribution using the log-location-scale technique. The effectiveness of the proposed regression model is also investigated with two simulation studies and three real censored datasets. The results demonstrated the superiority of the proposed models over other competing models.

Precise inventory prediction is the key to goods inventory and safety management. Accurate inventory prediction improves enterprises’ production efficiency. It is also essential to control costs and optimize the supply chain’s performance. Nevertheless, the complex inventory data are often chaotic and nonlinear; high data complexity raises the accuracy prediction difficulty. This study simulated inventory records by using the dynamics inventory management system. Four deep neural network models trained the data: short-term memory neural network (LSTM), convolutional neural network-long short-term memory (CNN-LSTM), bidirectional long short-term memory neural network (Bi-LSTM), and deep long-short-term memory neural network (DLSTM). Evaluating the models’ performance based on RMSE, MSE, and MAE, bi-LSTM achieved the highest prediction accuracy with the least square error of 0.14%. The results concluded that the complexity of the model was not directly related to the prediction performance. By contrasting several methods of chaotic nonlinear inventory data and neural network dynamics prediction, this study contributed to the academia. The research results provided useful advice for companies’ planned production and inventory officers when they plan for product inventory and minimize the risk of mishaps brought on by excess inventories in warehouses.

Public libraries are popular gathering places, so understanding the factors that contribute to colony-forming unit (CFU) concentrations and how to minimize them is essential. This study aimed to investigate the factors that affect CFU concentrations in a public library, using air sampling (Bioluminescent ATP-assay) and statistical analysis software (SPSS) to collect and analyze data. The findings indicated that the CFU concentration in the library was significantly influenced by the air quality surrounding the building, the number of library visitors, and the hygiene and health of both visitors and employees. Additionally, indoor temperature and humidity were found to be key factors affecting CFU concentration. These findings suggest the need for better ventilation and air filtration systems, as well as regular cleaning and disinfection in public libraries. Furthermore, research is recommended to investigate other potential factors that may impact indoor air quality in public spaces.

In recent years, much energy has been devoted to the study of chaotic models with specific features particularly those with cyclic connection of the variables. Previous ones provide multistability, amplitude control, and so on. Concerning the first phenomenon, models with ring connection of variables presented a coexistence of up to twelve disconnected attractors. In order to emphasize the complexity of circulant chaotic oscillators and their use in the engineering domain, a quintic chaotic model with cyclic connection of variables is considered and studied, which has complex equilibria located on the line x = y = z = w . Therefore, it experiences, amongst other, the phenomenon of offset boosting obtained by introducing four constants into the equations of the model, which has not be done in the past. Multistability is also revealed and the coexistence of eight and sixteen attractors is demonstrated using phase portraits. The system’s dynamics has been investigated considering its two parameters. Nonlinear dynamical tools such as bifurcation diagrams, phase portraits, time evolutions, two-parameter diagram, and Lyapunov exponents help to highlight the important phenomena encountered. The numerical results are confirmed using PSpice and particularly show the double-band chaotic attractor. Moreover, total amplitude control (TAC) is shown, proving that our oscillator can be used as an attenuator or amplifier in the engineering domain. The method of adaptive synchronization has been applied to the considered oscillator to emphasize the possible implication into the secure of communication systems.

Fractional variants of distance-based parameters have application in the fields of sensor networking, robot navigation, and integer programming problems. Complex networks are exceptional networks which exhibit significant topological features and have become quintessential research area in the field of computer science, biology, and mathematics. Owing to the possibility that many real-world systems can be intelligently modeled and represented as complex networks to examine, administer and comprehend the useful information from these real-world networks. In this paper, local fractional strong metric dimension of certain complex networks is computed. Building blocks of complex networks are considered as the symmetric networks such as cyclic networks Cn, circulant networks Cn1,2, mobious ladder networks M2n, and generalized prism networks Gmn. In this regard, it is shown that LSFMD of Cnn≥3 and Gmnn≥6 is 1 when n is even and n/n−1 when n is odd, whereas LSFMD of M2n is 1 when n is odd and n/n−1 when n is even. Also, LSFMD of Cn1,2 is n/2⌈m+1/2⌉ where n≥6 and m=⌈n−5/4⌉.

With the increasing difficulty of conventional oil and gas exploration and development, oil and gas resources have developed from conventional to unconventional, and the exploration and development of tight-oil reservoirs are highly valued. In view of the complexity of the influencing factors of oil-water spontaneous seepage after fracturing and the instability of reservoir recovery, this paper takes the tight sandstone reservoir of Yanchang Formation in the southern Ordos Basin as the research object. Based on the micro-nano pore throat characteristics of tight sandstone, the seepage experiment is carried out, and the theoretical model of seepage suction is constructed. The mechanism and influencing factors of suction and oil displacement after fracturing in tight reservoirs are analyzed. Based on the analysis of fluid buoyancy and gravity, a mathematical model of the oil-water spontaneous flow after fracturing was established, and its influencing factors were analyzed. The experimental results show that the pore throats of tight sandstone are mainly in micron- and submicron scale, and the reservoir permeability is related to the pore throat structure, oil-water interfacial tension, and wettability. After fracturing, with the increase of the fracture length, the seepage velocity gradually decreases. With the increase of fracture opening, the influence of buoyancy and gravity on seepage velocity increases. With the increase of the fracture number, seepage velocity also increases. The fracture helps to reduce the adsorption of oil droplets on the core surface and improve the efficiency of spontaneous imbibition and oil displacement of the core. The research results provide theoretical data support for enhancing oil recovery and have important application guiding significance for the operational reliability of manufacturing systems with complex topology and the complexity and operability of production operations in manufacturing systems.

The goal of this study is to suggest a new general triple integral transform known as Gamar transform. Next, we compare the current transform to a number of existing triple integral transforms such as those by Laplace, Sumudu, Elzaki, Aboodh, and Laplace–Aboodh–Sumudu. We outline its essential properties and prove some important results, including linearity property, existence theorem, triple convolution theorem, and derivatives properties. Moreover, the proposed new transform is applied to solve some partial differential equations (PDEs) such as Laplace, Mboctara, and Wave equations. The capacity of general triple integral transforms to change PDEs into simple algebraic equations is demonstrated.

This paper presents the design of an adaptive controller that solves the synchronization control problem of two identical Nwachioma chaotic systems in a master-slave configuration. The closed-loop stability is guaranteed by means of a Lyapunov-like analysis. With the aim of verifying the feasibility and performance of the proposed approach, a comparison with an active control algorithm is developed at the numerical simulation level. Based on such results, the master-slave Nwachioma chaotic system in closed-loop with adaptive control is now being experimentally tested by using two personal computers and two low-cost Arduino UNO boards. The experimental results not only show the good performance of the adaptive control but also that Arduino UNO boards are an excellent option for the experimental setup.

Collective motion models most often use self-propelled particles, which are known to produce organized spatial patterns via their collective interactions. However, there is less work considering the possible organized spatial patterns achievable by non-self-propelled particles (nondriven), i.e., those obeying energy and momentum conservation. Moreover, it is not known how the potential energy interaction between the particles affects the complexity of the patterns. To address this, in this paper, a collective motion model with a pairwise potential energy function that conserved the total energy and momentum of the particles was implemented. The potential energy function was derived by generalizing the Lennard–Jones potential to reduce to gravity-like and billiard-ball-like potentials at the extremes of its parameter range. The particle model was simulated under a number of parameterizations of this generalized potential, and the average complexity of the spatial pattern produced by each was computed. Complexity was measured by tracking the information needed to describe the particle system at different scales (the complexity profile). It was found that the spatial patterns of the particles were the most complex around a specific ratio in the parameters. This parameter ratio described a characteristic shape of the potential energy function that is capable of producing complex spatial patterns. It is suggested that the characteristic shape of the potential energy produces complex behavior by balancing the likelihood for particles to bond. Furthermore, these results demonstrate that complex spatial patterns are possible even in an isolated system.

The detection of communities in complex networks offers important information about the structure of the network as well as its dynamics. However, it is not an easy problem to solve. This work presents a methodology based of the robust coloring problem (RCP) and the vertex cover problem (VCP) to find communities in multiplex networks. For this, we consider the RCP idea of having a partial detection based onf the similarity of connected and unconnected nodes. On the other hand, with the idea of the VCP, we manage to minimize the number of groups, which allows us to identify the communities well. To apply this methodology, we present the dynamic characterization of job loss, change, and acquisition behavior for the Mexican population before and during the COVID-19 pandemic modeled as a 4- layer multiplex network. The results obtained when applied to test and study case networks show that this methodology can classify elements with similar characteristics and can find their communities. Therefore, our proposed methodology can be used as a new mechanism to identify communities, regardless of the topology or whether it is a monoplex or multiplex network.

In this study, a new nonlinear mathematical programming model of mixed integer was presented to formulate the problem of designing a sustainable closed loop supply chain, in which the three aspects of sustainability, i.e., social effect such as job creation, customer satisfaction, and distributors, environmental effects such as reducing air pollution, and economic effects such as reducing supply chain costs, increasing supply chain reliability, quality of returned products by customers, and product routing were considered. In order to solve the proposed model, a new hybrid metaheuristic algorithm based on the distinctive features of gray wolf algorithm and genetic algorithm was proposed in addition to MOPSO and NSGA-II algorithms. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions was tested and evaluated by MID, DM, and SM criteria. The results of statistical analysis of indices indicated that no significant difference between the performance of the three algorithms at 5% error level. In general, GW-NS, NSGA-II and MOPSO algorithms had better performance in terms of MID index, respectively. In addition, GW-NS, NSGA-II, and MOPSO algorithms performed better in terms of DM index. NSGA-II, MOPSO, and GW-NS algorithms performed better in terms of SM index, respectively. In addition, the variability of DM index in all three algorithms was almost the same, but in MID index, GW-NS algorithm, and in SM index, MOPSO algorithm had the highest change and less sustainability.

In this parper, a 4D absolute memristor Jerk chaotic system is proposed. Firstly, complex dynamics are studied by phase diagram, Poincaré section, power spectrum, bifurcation diagram, 0-1 test, and Lyapunov exponent spectrum. Then, the period doubling bifurcation, degradation, and offset boosting are revealed. For the feasibility of practical application, the analog circuit and FPGA digital circuit are designed. Finally, a simplified predefined time synchronization scheme is proposed; comparing with the full control input synchronization scheme, the simplified predefined time synchronization scheme can not only reduce the controller inputs but also predefine the synchronization time.

In a time-frequency biwavelet framework, we analysed the short-, medium-, and long-term impacts of COVID-19-related shocks on ten energy commodities (i.e., Brent, crude oil, coal, heating oil, natural gas, gasoline, ethanol, naphtha, propane, and uranium) from January 2020 to April 2022. We document intervals of high and low coherence between COVID-19 cases and the returns on energy commodities across the short-, medium-, and long-term horizons. Low coherence at high frequencies indicated weak correlation and signified diversification, hedging, and safe-haven potentials in the short term of the pandemic. Our findings suggest that energy markets’ dynamics were highly driven by the pandemic, causing significant changes in market returns, particularly across the medium- and low-frequency bands. Furthermore, the empirical results indicate dynamic lead-lag relationships between COVID-19 cases and energy returns between the medium- and long-term horizons, signifying that diversification could be sought through crossinvestment in different energy commodities. The results have significant implications for market participants, regulators, and practitioners.

A Boolean-valued information system (BIS) is an application of a soft set in which the data are mapped in a binary form and used in making applications not limited to decision-making, medical diagnoses, game theory, and economics. BIS may be lost for several reasons including virus attacks, improper entry, and machine errors. A concept was presented that the entire lost BIS can be regenerated from four aggregate sets through supposition. Based on that concept, this paper presents an algorithm to recalculate the entire BIS through a genetic algorithm (GA), named BISGA which is more general and easy to implement than the supposition method. A solved example is presented which explains how BISGA works. Furthermore, BISGA is implemented in Python and evaluated on both UCI benchmark datasets and randomized datasets for checking its efficiency and accuracy. Results show that the lost BIS is recovered significantly and accurately; however, the efficiency drops when the size of the BIS increases. This novel approach may help practitioners recalculate the entire lost BIS, which in turn helps in the decision-making process and conclusions.

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U = . 50 and A P I o U = . 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively.