Complexity最新文献

Transfemoral amputees rely on prosthetic devices to restore their mobility and maintain their independence. However, the design and performance of these devices can significantly affect the amputee’s gait and overall functionality. In this study, we investigate the knee torque dynamics of a single-leg transfemoral prosthetic during walking using a mathematical model that takes into consideration knee friction force (β₂), the horizontal component of the ground reaction force (F₁), and the vertical component of the ground reaction force (F₂). By carrying out model simulation in the MATLAB tool, along with Simulink, we derived the tracking error between the generated knee torque of the transfemoral prosthesis and the knee torque trajectory for a normal human leg. This error was evaluated using the normalized root mean square error (NRMSE) method. Our results show that when β₂ = 0.3 N.s.m⁻¹, F₁ = 320 N, and F₂ = 800 N, a tracking error of NRMSE = 0.342868% occurs. A comparison between the simulated knee torque and a desired knee torque trajectory for a normal leg study in the literature suggests that the tracking error generated will force the amputee to use higher metabolic energy to minimize discomfort while walking. These findings highlight the importance of optimizing the design and performance of transfemoral prosthetic devices to improve the knee torque dynamics and overall functionality of amputees. Our mathematical model provides a useful tool for future research in this field, which could help develop more effective prosthetic devices for transfemoral amputees.

Supply chain optimization and resource allocation are challenging because of the complex dynamics of flows. We can classify these flows based on whether they perform value-added or nonvalue-added activities in our process. The aim of this article is to present a multilayered temporal network-based model for the analysis of network flows in supply chain optimization and resource allocation. Implementation of a multilayered network distinguishes value-added from nonvalue-added resource flows, enabling a comprehensive view of the flow of resources in the system. Incorporating weighted edges representing the probabilities of time-dependent flows identifies the resource needs and excesses at each supply site, addresses optimal transportation challenges for resource reallocation, and assesses the efficiency and robustness of the system by examining the overlaps in network layers. The proposed method offers a significant extension to the toolsets for network flow analysis, which has the potential to improve decision-making processes for organizations dealing with complex resource management problems. The applicability of the proposed method is demonstrated by analyzing the temporal network extracted from taxi cab flows in New York City. With the application of the method, the results indicate that significant reductions in idle times are achievable.

Taking the marine gas turbine as a research background, a dual-rotor-bearing-casing system model was established considering the bearing nonlinearities and unbalanced excitation. Based on Lagrange’s equation of motion and rotor dynamics theory, the effects of key parameters such as radial clearance of intershaft bearing, rotor mass eccentricity, and rotational speed on the nonlinear characteristics of the system are investigated. The results indicate that the typical parameters have a significant effect on the system’s nonlinearities. To alleviate the vibration jump phenomenon, the radial clearance should be reduced to improve the coupling between the high- and low-pressure rotors. Reducing the mass eccentricity can effectively degrade the resonance peaks, but it will highlight the hard resonance characteristics. The work process should pass through the resonant-speed regions and the low-speed regions of start-stop phase as soon as possible. The research findings contribute to understanding the nonlinear dynamic characteristics of dual-rotor systems, providing a theoretical basis for their stable operation and the optimal design of working speeds.

Ehrlichia chaffeensis is a tick-borne infectious disease transmitted by Amblyomma americanum tick. This infectious disease was discovered in the 1970s when military dogs were returning from the Vietnam War. The disease was found to be extremely severe in German Shepherds, Doberman Pinschers, Belgium Malinois, and Siberian Huskies. In this study, we developed a mathematical model for dogs and ticks infected with Ehrlichia chaffeensis with the aim of understanding the impact of movement on dogs as they move from one location to another. This could be a dog taken on a walk in an urban area or on a hike in the mountains. We carried out a global sensitivity analysis with and without movement between three locations using as response functions the sum of acutely and chronically infected ticks and the sum of infected ticks in all life stages. The parameters with the most significant impact on the response functions are dogs disease progression rate, dogs chronic infection progression rate, dogs recovery rate, dogs natural death rate, acutely and chronically infected dogs disease-induced death rate, dogs birth rate, eggs maturation rates, tick biting rate, dogs and ticks transmission probabilities, ticks death rate, and the location carrying capacity. Our simulation results show that infection in dogs and ticks are localized in the absence of movement and spreads between locations with highest infection in locations with the highest rate movement. Also, the effect of the control measures which reduces infection trickles to other locations (trickling effect) when controls are implemented in a single location. The trickling effect is strongest when control is implemented in a location with the highest movement rate into it.

Governments and law enforcement agencies (LEAs) are increasingly concerned about growing illicit activities in cyberspace, such as cybercrimes, cyberespionage, cyberterrorism, and cyberwarfare. In the particular context of cyberterrorism, hostile social manipulation (HSM) represents a strategy that employs different manipulation methods, mostly through social media, to promote extremism in social groups and encourage hostile behavior against a target. Thus, this paper proposes a framework based on natural language processing (NLP) that detects and inspects supposed HSM actions to support law enforcement agencies (LEAs) in the prevention of cyberterrorism. The proposal integrates different NLP techniques through three models: (i) a similarity model that relates content with similar semantic meaning, (ii) a polarity analysis model that estimates polarity, and (iii) a named-entity recognition (NER) model that recognizes relevant entities. In addition, our proposed framework is evaluated in each of its components through exhaustive experiments and is tested with a particular use case related to violent protests in Ecuador in October 2021. Use case’s results indicate that 3 and 4 clusters are obtained when Spanish and English-translated tweets are used, respectively. An analysis of polarity over English-translated tweets allows us to identify, through two different methods, the most negative cluster (#1). The results of the extraction of the mentions show that our framework is able to identify entities of the type of person that may be at risk with a precision of 89.91%. Knowledge graphs achieved in our use case allow us to identify how nodes that promote HSM are interconnected and work collaboratively. Finally, the computational costs of our proposal are quite favorable as memory consumption of similarity and polarity models is proportional to the number of processed tweets, confirming the feasibility of the solution in a real context.

Demand forecasting for new products in the fashion industry has always been challenging due to changing trends, longer lead times, seasonal shifts, and the proliferation of products. Accurate demand forecasting requires a thorough understanding of consumer preferences. This research suggests a model based on machine learning to analyse customer preferences and forecast the demand for new products. To understand customer preferences, the fitting room data are analysed, and customer profiles are created. K-means clustering, an unsupervised machine learning algorithm, is applied to form clusters by grouping similar profiles. The clusters were assigned weights related to the percentage of product in each cluster. Following the clustering process, a decision tree classification model is used to classify the new product into one of the predefined clusters to predict demand for the new product. This demand forecasting approach will enable retailers to stock products that align with customer preferences, thereby minimising excess inventory.

This paper develops two numerical methods for solving a system of fractional differential equations based on hybrid shifted orthonormal Bernstein polynomials with generalized block-pulse functions (HSOBBPFs) and hybrid shifted orthonormal Legendre polynomials with generalized block-pulse functions (HSOLBPFs). Using these hybrid bases and the operational matrices method, the system of fractional differential equations is reduced to a system of algebraic equations. Error analysis is performed and some simulation examples are provided to demonstrate the efficacy of the proposed techniques. The numerical results of the proposed methods are compared to those of the existing numerical methods. These approaches are distinguished by their ability to work on the wide interval [0, a], as well as their high accuracy and rapid convergence, demonstrating the utility of the proposed approaches over other numerical methods.

Back electromotive force (EMF)-based sliding mode observer (SMO) is increasingly employed for interior permanent magnet synchronous machine (IPMSM) sensorless drives due to its high robustness to external disturbance and low sensitivity to system parameter variations. However, its control performance is severely weakened by the inherent chattering and speed iteration operation. In order to effectively resolve these problems, a strategy to design a dual-SMO is proposed in this paper. With the proposed strategy, the combination of the stator-voltage transformation matrix (SVTM) and the low-pass filter is developed to obtain the rotor position information, which greatly alleviates the chattering without any deviations. Meanwhile, three independent equations are constructed and extracted by placing two SVTMs in different locations. By solving these three equations, the rotor position can be calculated directly with zero phase shift, which eliminates the speed iteration operation and improves the system’s dynamic performance. Furthermore, by analyzing the influences of machine parameters’ variations, the suitable virtual q-axis inductance can be selected to quickly achieve the optimal-efficiency sensorless control of the IPMSM. Finally, the experimental results on an IPMSM demonstrate that the rotor position with good steady-state and dynamic performance can be obtained accurately by using the proposed sensorless control strategy.

Problem Statement. Proper and timely relief in the postdisaster phase is very important to minimize victims and casualties. It is necessary to assess the needs of the affected points and provide relief in the shortest possible time without wasting time. For this purpose, it is necessary and vital to determine the location of care centers, temporary accommodation centers, and routing to distribution vital and medical items. The Proposed Approach. In this paper, using a scenario-based stochastic planning mathematical model, postdisaster relief is discussed. Also, attention has been paid to the distribution of vital items by using routing. Contributions. By using the epsilon constraint method, a strong efficient solution has been achieved for model’s objectives, and also a sensitivity analysis has been performed on some of the model’s parameters. Also, an accelerated stochastic benders decomposition algorithm is suggested to solve the problem modeled in this paper. To speed up the convergence of the solution algorithm, valid inequalities are introduced to get better quality lower bounds. Results. The results of the research show that the simultaneous consideration of the relief evacuation and distribution process improves the relief logistics process.

Analyzing the supply chain (SC) of biopharmaceutical drugs can be challenging due to their complexity, the existence of a wide variety of risks, and the dynamics of the system. This paper presents a framework for evaluating the SC risks of Iranian biopharmaceutical companies based on cause-and-effect relationships and fuzzy cognitive maps (FCMs). We first interviewed several biopharmaceutical supply experts to learn about potential SC risks, causal relationships among FCM concepts, FCM structure, and FCM activation cycle. The most critical and relevant risks and significant elements of the SCs, such as cost, time, and quality, were identified as relevant FCM concepts. Then, we used failure mode and effects analysis (FMEA) and the FCM of the SC risks to assess the impacts of the biopharmaceutical SC risks on each other and on the crucial elements of the SCs. The Hebbian learning algorithms were then applied to train the FCM models. We tested different scenarios to evaluate the impacts of FCM concepts on the SC risks. The proposed approach can prioritize risk factors and, more importantly, predict and analyze the effect of each risk factor/risk group on other risks or the outcome of a given risk. The proposed FCM features and the knowledge gained from evaluating them can provide practical and helpful information to pharmaceutical companies to deal with their supply risks more efficiently.