Uncertain Information Research Articles

Selection of Green Recycling Suppliers for Shared Electric Bikes: A Multi-Criteria Group Decision-Making Method Based on the Basic Uncertain Information Generalized Power Weighted Average Operator and Basic Uncertain Information-Based Best–Middle–Worst TOPSIS Model

This study introduces a novel multi-criteria group evaluation approach grounded in the theory of basic uncertain information (BUI) to facilitate the selection of green recycling suppliers for shared electric bikes. Firstly, a comprehensive index system of green recycling suppliers is established, encompassing recycling capacity, environmental sustainability, financial strength, maintenance capabilities, and policy support, to provide a solid foundation for the scientific selection process. Secondly, the basic uncertain information generalized power weighted average (BUIGPWA) operator is proposed to aggregate group evaluation information with BUI pairs, and some related properties are investigated. Furthermore, the basic uncertain information-based best–middle–worst TOPSIS (BUI-BMW-TOPSIS) model incorporating the best, middle, and worst reference points to enhance decision-making accuracy is proposed. Ultimately, by integrating the BUIGPWA operator for group information aggregation with the BUI-BMW-TOPSIS model to handle multi-criteria decision information, a novel multi-criteria group decision-making (MCGDM) method is constructed to evaluate green recycling suppliers for shared electric bikes. Case analyses and comparative analyses demonstrate that compared with the BUIWA operator, the BUIGPWA operator yields more reliable results because of its consideration of the degree of support among decision-makers. Furthermore, in contrast to the traditional TOPSIS method, the BUI-BMW-TOPSIS model incorporates the credibility of information provided by decision-makers, leading to more trustworthy outcomes. Notably, variations in attribute weights significantly impact the decision-making results. In summary, our methods excel in handling uncertain information and complex multi-criteria group decisions, boosting scientific rigor and reliability, and supporting optimization and sustainability of shared electric bike green recycling suppliers.

Enhancing renewable energy evaluation: Utilizing complex picture fuzzy frank aggregation operators in multi-attribute group decision-making

Renewable energy resources are pivotal in addressing global energy challenges and achieving sustainable development goals. In this context, the complex picture fuzzy sets (CPFS) theory provides a robust framework for handling complex and uncertain information. This article presents innovative strategies based on Frank aggregation tools to evaluate and prioritize renewable energy resources. By incorporating the concepts of abstinence value, membership value, and non-membership values in the CPFS framework, the characteristics and capabilities of the CPFS are extended to represent a broader range of information. Specifically, we introduce novel operators such as the CPF Frank weighed average (CPFFWA) and CPF Frank weighed geometric (CPFFWG) operators, effectively handling insufficient and unpredictable information during the aggregation process. Moreover, we explore specialized variants of these operators, such as the CPF Frank order weighed average (CPFFOWA) and CPF Frank order weighed geometric (CPFFOWG) operators, which possess distinct characteristics suitable for specific decision-making scenarios. The proposed methodologies are applied within the framework of multi-attribute group decision-making (MAGDM) to evaluate and identify optimal renewable energy options from a group of alternatives. Through comparative analyses and validation against existing approaches, the advantages and consistency of our developed operators are demonstrated. The findings highlight the effectiveness of the CPF-based Frank aggregation operators in evaluating renewable energy resources, providing decision-makers with a comprehensive and reliable framework for renewable energy planning and decision-making.

Type-3 fuzzy neural networks for dynamic system control

This paper presents a Type-3 Fuzzy Neural Network (T3FNN) for dynamic system control. The structure of the T3FNN controller, which is based on the compositional rule of inference of interval type-3 fuzzy systems, is proposed. TSK-type fuzzy rules, employing three-dimensional membership functions (MFs) in the antecedent part and linear functions with coefficients represented as interval sets in the consequent part, are utilized to design T3FNN. Gaussian functions with uncertain centers are employed to describe the type-3 membership functions. By employing three-dimensional MFs, type-3 fuzzy logic rules can accurately model a high degree of uncertainty and effectively handle uncertain information. The design of the T3FNN was accomplished by integrating the type-3 fuzzy logic system (T3FLS) with neural networks. The learning of the T3FNN was conducted using the genetic algorithms and gradient descent algorithm. As a result, the type-3 rule base of the system was effectively developed. The learning algorithms are presented and the proposed T3FNN structure is employed for the development of dynamic system control. The simulations of the T3FNN system for the control of the nonlinear and time-varying dynamic systems were carried out. The results obtained from the simulations indicate the suitability of using the T3FNN in controlling dynamic systems.

Cross-modal scenario generation for stock price forecasting using Wasserstein GAN and GCN

The forecasting of stock price has always been a difficult problem, as the various inputs like company performance, technical innovation, political factors are intricate and often assessed with uncertainty. While most of the existing studies belong to deterministic forecast that focused on either the trend or exact value of future stock price, the forecasted results contain little uncertain information, thus bringing certain risks to investors. In this study, an effective scenario generation method based on wasserstein generative adversarial network (WGAN) is proposed for stock price forecasting, which can characterize the temporally and spatially correlations among different stocks, so as to help investors make proper investment strategies in complicated market environments. To the best of our knowledge, the scenario generation of stock price has been rarely studied by existing publications. Specifically, considering the inherent correlation among stocks, graph convolutional network (GCN) is used to capture the features of related stocks, whereafter the features of target stocks and related stocks are combined. In terms of feature fusion, this paper further uses a pre-trained model to obtain the key information of news headlines as text features, and fully integrates them with historical data through the attention mechanism, thus improving the overall performance of scenario generation. In summary, the proposed model can generate promising scenarios for future stock prices over time based on cross-modal information like historical data and news headlines. Numerical experiments show that our method outperforms other baseline methods in terms of root-mean-square error and average absolute percentage error (the average of several scenarios). For each part of the method, stock correlation is the most helpful for the results, followed by cross-modal information, with scenario generation contributing less to the results. Besides, experiments on a real-life portfolio selection problem also demonstrate that our method brings the highest returns, which proves its effectiveness in practical application.

Spatio-temporal neural networks for monitoring and prediction of CO[formula omitted] plume migration from measurable field data

Carbon capture, utilization, and storage (CCUS) technologies are crucial for mitigating greenhouse gas emissions. The success of CCUS projects hinges on accurate prediction and monitoring of the CO2 plume migration during and after injection. To address the computational burden of traditional numerical simulation methods, previous studies have successfully used neural networks as proxy models to expedite the prediction of the CO2 plume migration. However, these models rely on uncertain inputs, such as the distribution of heterogeneous permeability and porosity maps, which can lead to erroneous predictions and pose a significant hurdle for their adoption in real-world applications. To address this issue, this study introduces a framework for reconstruction and short-term prediction of CO2 plume migration based solely on field measurements, which can directly provide information on CO2 plumes, thus eliminating the dependency on uncertain geological information as model input. The framework trains a spatio-temporal neural network model using simulated data under various geologic scenarios to capture the plume evolution dynamics without constraining it to specific geological scenarios. Once trained, the model integrates global and local field measurements from multiple sources to reconstruct the CO2 plume and predict its spatio-temporal evolution. The effectiveness of the proposed framework is tested using two case studies: one using a synthetic dataset and another with data generated from a model of a real field in the Southern San Joaquin Basin. The results show that the proposed framework can accurately reconstruct and perform short-term predictions of the CO2 plume migration by integrating various forms of field measurements.

“You need to step back when you're contract faculty”: Information Practices and Care in Casual Academic Work

ABSTRACTThis study uses the concepts of information practices and care as theoretical lenses to explore the information practices of casual academic staff, who experience complex information contexts. Semi‐structured online interviews followed by thematic qualitative data analysis revealed that, while these academics care deeply about their contributions to student learning, they experience a lack of institutional support and care. This experience leads to individualized information practices, and dependence on an uncertain and unreliable information landscape, characterized by information marginalization, information precarity, and information vulnerability. Exposing the informational challenges experienced by this large subpopulation of academic laborers can support efforts to improve their information environments and support their work. The study also contributes to contemporary theoretical understandings of information vulnerability within academic contexts.

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

A new complex correlation coefficient in complex evidence theory

ABSTRACT Complex Evidence Theory (CET) is a generalization of D-S Evidence Theory that can represent uncertain information and express uncertainty effectively. One of the hot topics in CET is how to measure the conflict between different pieces of evidence. Recently, a complex correlation coefficient has been proposed to measure the degree of similarity between two sets of evidence, where the similarity reflects the difference between the evidence, i.e. the degree of conflict between the sets of evidence. However, there is still a need to improve the precision of conflict metrics. In this paper, a new complex correlation coefficient called CCOR is proposed to measure the similarity between two sets of evidence. Through some numerical examples, CCOR can be used to measure the conflict between the two sets of evidence well. Finally, the validity and applicability of the method proposed in this paper are further illustrated by comparative experiments on real datasets.

Effects of uncertain anomalous information on traffic flow of automated vehicles with V2V communication

Automated vehicles (AVs) equipped with vehicle-to-vehicle (V2V) communication can operate by sensing real-time status information through onboard sensors and wireless connections. Nevertheless, under the influence of multifarious random factors in real traffic, this critical information that support the normal movement of such vehicles may be anomalous, raising concerns on their mobility and traffic security. Due to the lack of appropriate analytical model, previous studies have not comprehensively uncovered the effects of uncertain anomalous information on traffic flow of AVs with V2V communication. Therefore, this study aims to bridge this critical gap. Firstly, by introducing a probabilistic parameter (i.e., information anomaly probability), we propose a general model that integrates the normal and compromised models, thereby capturing the longitudinal dynamics of AVs featuring V2V communication in the presence of uncertain anomalous information. To enable the detailed theoretical and experimental analyses, we specify it through the cooperative adaptive cruise control model calibrated with real-car data. Subsequently, we define the concept of pseudo string stability and parameterize the stability condition based on the characteristic equation method, so as to demonstrate the relationship between traffic flow stability and the parameters and probability of information anomaly. Finally, we refine the proposed probabilistic model and conduct extensive numerical experiments. The findings show that uncertain anomalous information could result in sudden or even frequent acceleration and deceleration of AVs, causing traffic oscillation, reduced traffic efficiency, and even collision accidents. In particular, the greater the information anomaly probability, the larger the disturbances experienced by traffic flow. Meanwhile, at the same level of anomaly, the combined impacts of various anomalous information could lead to more severe consequences than the singular impact of any individual anomalous information. Furthermore, the duration of anomalous information directly affects the time it takes for traffic flow to return to normal.

Leveraging online reviews and expert opinions for electric vehicle type prioritization

The rapid expansion of the electric vehicle (EV) market in recent years has presented manufacturers with the challenge of strategically prioritizing the types of EVs in which to invest under uncertain conditions. This study proposes an enhanced multi-attribute decision-making (MADM) framework to address this issue by leveraging online reviews and expert opinions. The proposed framework combines a novel linguistic representation, called calibrated basic uncertain linguistic information (CBULI), to capture uncertainty, a value function from cumulative prospect theory (CPT) with double reference points to model the manufacturers’ psychological preferences, and the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) for prioritizing EVs for investment. It extracts demand attributes from online reviews, applies CBULI to represent uncertain evaluations, and incorporates CPT to capture risk preferences. A case study of an EV manufacturing enterprise in Sichuan, China, was conducted to validate our framework, and the results demonstrated its effectiveness and practicability in identifying the most promising types of EVs for investment. The results of sensitivity and comparative analyses further confirmed the robustness and superiority of the model in comparison with prevalent methods. The work here contributes to methodological advancements in research on the choice of types of EVs in which to invest, and provides valuable insights for EV enterprises to make informed investment-related decisions that are aligned with user demands and enterprise development. The proposed MADM framework supports the strategic development of the EV industry by enabling manufacturers to prioritize investment in the appropriate types of EVs under uncertainty.

The role of uncertain perspective information in recovering 3D symmetrical shapes

The role of uncertain perspective information in recovering 3D symmetrical shapes

Integrating GRA with intuitionistic fuzzy VIKOR model to explore attractive design solution of wickerwork cultural and creative products

ABSTRACT With the popularisation of the concept of sustainable traditional wickerwork, wickerwork cultural and creative products have become the most popular craft production. Kansei consensus on the design of wickerwork lamps has become a key factor influencing the communication between manufacturers and customers. Therefore, the purpose of this paper is to develop a product evaluation model for wickerwork cultural and creative products that meet the emotional satisfaction of users. Firstly, this paper could build a three-level evaluation grid diagram driven by user attractiveness through EGM, and extracts key dimensions in the perceptual vocabulary using FA. Secondly, evaluation indicators are constructed and grey relation analysis (GRA) is used to synthetically assess the priority order of evaluation indicators based on the obtained weight values. Finally, in order to effectively deal with uncertain product evaluation information, the intuitionistic fuzzy VIKOR method was used to evaluate representative product design solutions, and the most perceptually attractive product design solution was preferentially selected. This study selected wickerwork lamps as the design case of cultural and creative products, the results of the study enable designers to accurately grasp customers’ emotional perceptions of wickerwork products and obtain the best wickerwork production design solution.

Analysis of renewable energy resources based on frank power aggregation operators and EDAS method for circular bipolar complex fuzzy uncertainty

The model of circular bipolar complex fuzzy (Cir-BCF) sets computed based on the membership function, non-membership function, and radius among both functions for each value of the universal set. The technique of the Cir-BCF set is the modified or extended form of fuzzy sets, complex fuzzy sets, bipolar fuzzy sets, bipolar complex fuzzy sets, and simple circular bipolar fuzzy sets to cope with uncertain and vague information. In this manuscript, we describe the novel technique of frank operational laws based on Cir-BCF values for frank t-norm and frank t-conorm. Further, we simplify the model of Cir-BCF frank power averaging (Cir-BCFFPA), Cir-BCF frank power weighted averaging (Cir-BCFFPWA), Cir-BCF frank power geometric (Cir-BCFFPG), Cir-BCF frank power weighted geometric (Cir-BCFFPWG) operators, and highlighted their valuable properties, called idempotency, monotonicity, and boundedness. Moreover, analysis of renewable energy resources is very awkward and complicated, but it is natural sources of energy that are refilled constantly or relativeness quickly on a human timescale. Further, solar energy, hydroelectric energy, geothermal energy, biomass energy, and wind energy are five major sources of energy, but it is complex to find which one is the best and which one is worst. For evaluating the above problems, we construct the technique of evaluation based on the distance from the average solution (EDAS) method under the presence of the Cir-BCF numbers (Cir-BCFNs). At the end, we arrange the comparison between proposed and existing techniques based on some numerical examples to describe the validity and proficiency of the initiated information.

Intuitionistic Linguistic EDAS Method with New Score Function: Case Study on Evaluating Universities’ Innovation and Entrepreneurship Education

Intuitionistic linguistic numbers (ILNs) describe expert evaluation information by representing semantic assessment values and reflecting the confidence level and hesitation of decision-makers. ILNs are widely used to handle uncertain and incomplete information. The Evaluation Based on Distance from Average Solution (EDAS) method selects the optimal solution based on the distance of each alternative from the average solution, making it suitable for multi-attribute decision-making with conflicting attributes. This study proposes a new scoring function for ILNs and develops an evaluation method combining ILNs with EDAS (IL-EDAS). Experts’ evaluations of each alternative’s indices are expressed using ILNs, and the EDAS method ranks the alternatives to select the optimal solution. We apply this method to assess innovation and entrepreneurship education capabilities in universities, and compare the results with those from other methods to verify their applicability and practicality.

Sustainable management of polyethylene terephthalate waste flow using a fuzzy frank weighted assessment model

The consequences for the ecosystem of polyethylene terephthalate (PET) waste are becoming increasingly significant and widespread. Companies managing PET waste strive to enhance sustainability in all areas. The development of systematic decision-making approaches and frameworks for PET waste management is strongly needed. This research aims to present a new methodological framework for the categorization of the most efficient PET waste management solutions. The introduced fuzzy Frank weighted sum product assessment (FWESPA) model enables rational and flexible reasoning by nonlinearly processing uncertain information. A nonlinear aggregation function is proposed for the fusion of fuzzy strategic options. It is advantageous in simulating the impact of strategic options on a final decision. An integral part of the introduced fuzzy FWESPA model is a reverse sorting algorithm. This innovative algorithm can improve the performance of traditional normalization techniques. Also, an improved fuzzy Frank ordinal priority approach linear model is formulated to define the significance of evaluation criteria. The comprehensive real-life study demonstrates the proposed decision-analytics-based approach. The results showed the following rankings of considered alternatives: “recycling” (ℤA2 = 0.8565) > “energy recovery” (ℤA1 = 0.7364) > “remanufacturing” (ℤA4 = 0.690) > “incineration” (ℤA3 = 0.6592). Based on the results presented, alternatives “recycling” (A2) and “energy recovery” (A1) represent dominant alternatives with a slight advantage of recycling. Research findings can be used when deciding the appropriate way to enhance PET waste handling. The findings also describe the benefits and limitations of each treatment option for PET waste, as well as highlight the crucial challenges.

Integrated management of electric vehicle sharing system operations and Internet of Vehicles energy scheduling

The sharing of electric vehicles and the Internet of Vehicles both positively impact societal benefits. However, the complexity, uncertainty and multi-directionality of transport–energy coupled systems greatly increase the difficulty of integrated management. This paper proposes a multi-objective robust optimization framework to solve the integrated management issue of electric vehicle sharing system operations and Internet of Vehicles energy scheduling under uncertain information, in which components such as renewable energy, microgrids, autonomous driving and their impacts are fully considered. To enhance the capability of integrated management and the synergy between supply and demand, a charging and discharging scheduling strategy that considers a converged demand response policy is proposed. Numerical analyses using real data provide valuable validation and insights. The proposed method and scheduling strategy enable the implementation of integrated management in photovoltaic microgrids and significantly improve economic and environmental benefits. Meanwhile, the microgrid system can operate in an ”off-grid mode”, which enables a multi-energy complementary mechanism. Additionally, electric vehicles release a great deal of scheduling flexibility through demand-side management, which combined with electric energy storage, can efficiently local consumption of photovoltaic power.

Enterprise risk management in supply chain operation: a fuzzy risk prioritization approach

PurposeTraditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.Design/methodology/approachWe propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.FindingsThe proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.Practical implicationsThe adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.Originality/valueThis study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Dominance-Based Rough Set Model in Hesitant Fuzzy Information Systems

Hesitant fuzzy information systems have been widely applied in decision-making due to their ability to handle uncertain information. In addition, dominance relationships are often taken into account in many practical decision-making problems. Therefore, it is of great significance to conduct research on hesitant fuzzy information systems involved with dominance relations. In this study, we introduce dominance relations in a hesitant fuzzy information system to make it a dominance-based hesitant fuzzy information system, which can provide a solid new idea for comparing hesitant fuzzy elements. Furthermore, a hesitant fuzzy dominance-based rough set model is constructed, and an attribute reduction method is designed to simplify the dominance-based hesitant fuzzy information system. Further, we propose lower and upper approximation discernibility matrices in the dominance-based hesitant fuzzy decision information system to extract decision rules. In addition, two numerical examples are given to demonstrate the effectiveness of the proposed attribute reduction methods.

A Study of Multiple Comparison Tests with Imprecise and Uncertain Information

Objectives: This study aims to demonstrate the application of multiple comparison tests in situations involving imprecise, incomplete, and uncertain observations. Such data arises in many practical situations. Methods: Five post hoc multiple comparison tests viz. Fisher's LSD test, Tukey's HSD, Bonferroni ( ), Scheffe, and Hochberg ( ) method is utilized within a one-way design framework, with a significance level of 5%. Simulation studies are applied to the empirical level and power calculation. Findings: The study shows that Tukey's HSD ( ) shows the highest power in the determinant part, not the indeterminant part, compared to other tests. For, e.g., For (5, 5, 5, 5) with location parameter (0, .5, 1, 0), the power interval shows [.3953, .3584] where [ ]. Overall, and shows a prominent result and are preferable compared to other tests. From application, NANOVA provided the P-value is [<.001, <.001], i.e., tests are statistically significant. The P-value of the tests for pairs [1, 1] and [1, 1] accepted (greater than .05) means the average daily ICU occupancy of corona patients of more than 55 years and between 35 and 55 age groups are not equal. Similarly, other tests also show the same. Novelty: Neutrosophic Multiple comparisons are still developing, and computer programs are still unavailable to support them. We have applied a simulation study to check the performance of the tests. Keywords: Multiple comparisons, NANOVA, Neutrosophic Statistics, Indeterminacy, Covid-19

Optimality of multisensory integration while compensating for uncertain visual target information with artificial vibrotactile cues during reach planning

BackgroundPlanning and executing movements requires the integration of different sensory modalities, such as vision and proprioception. However, neurological diseases like stroke can lead to full or partial loss of proprioception, resulting in impaired movements. Recent advances focused on providing additional sensory feedback to patients to compensate for the sensory loss, proving vibrotactile stimulation to be a viable option as it is inexpensive and easy to implement. Here, we test how such vibrotactile information can be integrated with visual signals to estimate the spatial location of a reach target.MethodsWe used a center-out reach paradigm with 31 healthy human participants to investigate how artificial vibrotactile stimulation can be integrated with visual-spatial cues indicating target location. Specifically, we provided multisite vibrotactile stimulation to the moving dominant arm using eccentric rotating mass (ERM) motors. As the integration of inputs across multiple sensory modalities becomes especially relevant when one of them is uncertain, we additionally modulated the reliability of visual cues. We then compared the weighing of vibrotactile and visual inputs as a function of visual uncertainty to predictions from the maximum likelihood estimation (MLE) framework to decide if participants achieve quasi-optimal integration.ResultsOur results show that participants could estimate target locations based on vibrotactile instructions. After short training, combined visual and vibrotactile cues led to higher hit rates and reduced reach errors when visual cues were uncertain. Additionally, we observed lower reaction times in trials with low visual uncertainty when vibrotactile stimulation was present. Using MLE predictions, we found that integration of vibrotactile and visual cues followed optimal integration when vibrotactile cues required the detection of one or two active motors. However, if estimating the location of a target required discriminating the intensities of two cues, integration violated MLE predictions.ConclusionWe conclude that participants can quickly learn to integrate visual and artificial vibrotactile information. Therefore, using additional vibrotactile stimulation may serve as a promising way to improve rehabilitation or the control of prosthetic devices by patients suffering loss of proprioception.