Real Behavior Research Articles

Prediction of mass adhesive damage based on the Rousselier model: Experimental and numerical analysis

The study of the mechanical strength of adhesives remains an important area of research for researchers. These adhesives must be prepared in the form of mass test pieces to characterize them under different mechanical stresses. However, during the preparation of the test pieces several defects are likely to be present, namely air bubbles, cavities, or impurities. The behavior of the adhesive differs depending on the presence of one of these defects and, in most cases, the real behavior of the adhesive is not precisely known. For this purpose, several tests are necessary to have a close estimate of the adhesive's behavior. To numerically model the behavior of the adhesive it is necessary to consider the presence of these types of defects. This paper proposes a damage criterion based on the Rousselier model, which describes the damage due to crack growth from the presence of cavities in an adhesive, assumed as a ductile material. The proposed damage model was developed and implemented in a user-defined subroutine in the ABAQUS finite element code. Other damage models integrated into ABAQUS were used. In addition, the extended finite element method (XFEM) was used in the numerical simulations to study automatic damage modelling by the appearance and propagation of cracks in highly stressed areas. The main objective of this work is an analysis by the finite element method to determine the elastoplastic behavior coupled with the damage in the mass adhesive, considering the size, position, and shape of the defect (porosities) by the proposed models. Initially, experimental tests were carried out on mass specimens of adhesive to characterize the tensile response and to determine their mechanical properties depending on the position and size of the defect, which may exist in the specimen following its fabrication. The numerical results were validated by uniaxial tensile tests on the mass adhesive. Comparisons with the damage models integrated into ABAQUS have proven their effectiveness in predicting the behavior of the adhesive in the presence of a cavity.

0-D Dynamic Performance Simulation of Hydrogen-Fueled Turboshaft Engine

In the last few decades, the problem of pollution resulting from human activities has pushed research toward zero or net-zero carbon solutions for transportation. The main objective of this paper is to perform a preliminary performance assessment of the use of hydrogen in conventional turbine engines for aeronautical applications. A 0-D dynamic model of the Allison 250 C-18 turboshaft engine was designed and validated using conventional aviation fuel (kerosene Jet A-1). A dedicated, experimental campaign covering the whole engine operating range was conducted to obtain the thermodynamic data for the main engine components: the compressor, lateral ducts, combustion chamber, high- and low-pressure turbines, and exhaust nozzle. A theoretical chemical combustion model based on the NASA-CEA database was used to account for the energy conversion process in the combustor and to obtain quantitative feedback from the model in terms of fuel consumption. Once the engine and the turbomachinery of the engine were characterized, the work focused on designing a 0-D dynamic engine model based on the engine’s characteristics and the experimental data using the MATLAB/Simulink environment, which is capable of replicating the real engine behavior. Then, the 0-D dynamic model was validated by the acquired data and used to predict the engine’s performance with a different throttle profile (close to realistic request profiles during flight). Finally, the 0-D dynamic engine model was used to predict the performance of the engine using hydrogen as the input of the theoretical combustion model. The outputs of simulations running conventional kerosene Jet A-1 and hydrogen using different throttle profiles were compared, showing up to a 64% reduction in fuel mass flow rate and a 3% increase in thermal efficiency using hydrogen in flight-like conditions. The results confirm the potential of hydrogen as a suitable alternative fuel for small turbine engines and aircraft.

Activation of persulfate by a layered double oxide supported sulfidated nano zero-valent iron for efficient degradation of 2,2′,4,4′-tetrabromodiphenyl ether in soil

The nano zero-valent iron (nZVI) activated persulfate (PS) is recognized as a promising approach to degrade 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47), which is ubiquitous in the soil at electronic waste sites. However, all the reported studies were performed in liquids, gaps in the real behaviour and microbial contribution to the degradation of BDE-47 in soil media need to be urgently filled. The removal efficiency of BDE-47 is low using traditional nZVI as activator because of its aggregation and corrosion. Herein, we designed a novel layered double oxide supported sulfidated nano zero-valent iron (S-nZVI@LDO) composite and explored the performance of S-nZVI@LDO/PS to remediate BDE-47 contaminated soil. The results showed that S-nZVI@LDO has excellent stability and superior reduction capability. It could couple PS to achieve a rapid and efficient degradation of BDE-47, and the removal efficiency reached 92.31 % (5 mg/kg) within 6 h, which was much higher than that of n-ZVI/PS (53.38 %) or S-nZVI/PS (75.69 %). The kinetic constant of BDE-47 degradation by S-nZVI@LDO/PS was 23.6 and 3.7 times higher than that by single S-nZVI@LDO and nZVI/PS, respectively. It is attributable to the efficient production of SO4•-, •OH, O2•-, and 1O2 in the system, in which SO4•- and •OH dominated. The bioinformatic analysis demonstrate that soil remediation by S-nZVI@LDO/PS significantly enriched aromatic compounds-degrading bacteria and increased the abundance of hydrocarbon degradation functions. Microbial degradation may play important roles in the BDE-47 degradation and soil quality recovery. The identification of degradation pathways suggests that BDE-47 was degraded to very low-toxic products based on GHS toxicity prediction through a series process of debromination, hydroxylation, cleavage central oxygen, and ring opening, or even completely mineralized. The findings may provide significant implications for the in-situ clean-up of brominated flame retardants in contaminated soil using S-nZVI@LDO/PS Fenton-like system.

Deployment of Public Charging Stations for BEVs Using an Agent-Based Modeling Approach

A low utilization rate of public chargers and unmatched deployment of public charging stations (CSs) are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation. This study proposes an optimization methodology for public CS deployment, considering real charging behavior and interactions between battery electric vehicle (BEV) users and CSs. Realistic charging choice behavior is modeled based on surveys, and a dynamic charging decision chain is simulated, allowing interactions between BEV users and CSs through an agent-based modeling (ABM) approach. The charging-related activities are triggered by state of charge (SOC) levels randomly generated from distributions derived from real BEV operating data, including the random SOC levels at the start of a trip, the SOC level that prompts the user to charge the BEV, and the SOC level at which the user stops charging the BEV. A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs, to determine the location and the capacity of new CSs. The objective is to minimize the total time cost per BEV user, including travel time, charging time and waiting time in the queue. An application is conducted, for the deployment of fast CSs in Washington State, USA. The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems. The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs and leads to a more balanced utilization among CSs. The proposed approach is expected to contribute to better planning and deployment of public CSs, satisfaction of the booming charging demand and increased utilization of public CSs.

Temporal Autoregressive Matrix Factorization for High-Dimensional Time Series Prediction of OSS.

Open-source software (OSS) plays an increasingly significant role in modern software development tendency, so accurate prediction of the future development of OSS has become an essential topic. The behavioral data of different open-source software are closely related to their development prospects. However, most of these behavioral data are typical high-dimensional time series data streams with noise and missing values. Hence, accurate prediction on such cluttered data requires the model to be highly scalable, which is not a property of traditional time series prediction models. To this end, we propose a temporal autoregressive matrix factorization (TAMF) framework that supports data-driven temporal learning and prediction. Specifically, we first construct a trend and period autoregressive model to extract trend and period features from OSS behavioral data, and then combine the regression model with a graph-based matrix factorization (MF) to complete the missing values by exploiting the correlations among the time series data. Finally, use the trained regression model to make predictions on the target data. This scheme ensures that TAMF can be applied to different types of high-dimensional time series data and thus has high versatility. We selected ten real developer behavior data from GitHub for case analysis. The experimental results show that TAMF has good scalability and prediction accuracy.

Influence of an Automated Vehicle with Predictive Longitudinal Control on Mixed Urban Traffic Using SUMO

In this paper, an approach to quantify the area of influence of an intelligent longitudinally controlled autonomous vehicle in an urban, mixed-traffic environment is proposed. The intelligent vehicle is executed with a predictive longitudinal control, which anticipates the future traffic scenario in order to reduce unnecessary acceleration. The shown investigations are conducted within a simulated traffic environment of the city center of Darmstadt, Germany, which is carried out in the traffic simulation software “Simulation of Urban Mobility” (SUMO). The longitudinal dynamics of the not automated vehicles are considered with the Extended Intelligent Driver Model, which is an approach to simulate real human driver behavior. The results show that, in addition to the energy saving caused by a predictive longitudinal control of the ego vehicle, this system can also reduce the consumption of surrounding traffic participants significantly. The area of influence can be quantified to four vehicles and up to 250 m behind.

Effect of Electric Fields on the Decomposition of Phosphate Esters.

Phosphate esters decompose on metal surfaces and form protective polyphosphate films. For many applications, such as in lubricants for electric vehicles and wind turbines, an understanding of the effect of electric fields on molecular decomposition is urgently required. Experimental investigations have yielded contradictory results, with some suggesting that electric fields improve tribological performance, while others have reported the opposite effect. Here, we use nonequilibrium molecular dynamics (NEMD) simulations to study the decomposition of tri-n-butyl phosphate (TNBP) molecules nanoconfined between ferrous surfaces (iron and iron oxide) under electrostatic fields. The reactive force field (ReaxFF) method is used to model the effects of chemical bonding and molecular dissociation. We show that the charge transfer with the polarization current equalization (QTPIE) method gives more realistic behavior compared to the standard charge equilibration (QEq) method under applied electrostatic fields. The rate of TNBP decomposition via carbon-oxygen bond dissociation is faster in the nanoconfined systems than that in the bulk due to the catalytic action of the surfaces. In all cases, the application of an electric field accelerates TNBP decomposition. When electric fields are applied to the confined systems, the phosphate anions are pulled toward the surface with high electric potential, while the alkyl cations are pulled to the surface with lower potential, leading to asymmetric film growth. Analysis of the temperature- and electric field strength-dependent dissociation rate constants using the Arrhenius equation suggests that, on reactive iron surfaces, the increased reactivity under an applied electric field is driven mostly by an increase in the pre-exponential factor, which is linked to the number of molecule-surface collisions. Conversely, the accelerated decomposition of TNBP on iron oxide surfaces can be attributed to a reduction in the activation energy with increasing electric field strength. Single-molecule nudged-elastic band (NEB) calculations also show a linear reduction in the energy barrier for carbon-oxygen bond breaking with electric field strength, due to stabilization of the charged transition state. The simulation results are consistent with experimental observations of enhanced and asymmetric tribofilm growth under electrostatic fields.

Internet searches for ADHD medications surged during the COVID-19 pandemic

On January 2020, the World Health Organization (WHO) declared that Coronavirus Disease (COVID-19) had become a public health emergency of international concern (PHEIC) and was assessed as a pandemic in March 2020. For the next 2–3 years, Americans followed stay-at-home orders, and used virtual technologies, while struggling with pandemic-related stressors (1). This affected mental health (2). Attention-deficit hyperactivity disorder (ADHD) symptoms increased (3), resulting in an uptick in ADHD prescriptions (4). A shortage of Adderall was announced by the U.S. Food and Drug Administration (FDA) in October 2022. As actual prescription usage data were not available on the short time frame of the pandemic, we explored the potential of using internet searches as a proxy for real health behavior, with prevention of future shortages in mind. We used Google Trends (GT) data (5) to estimate public interest in ADHD medications during the pandemic.

Validation of AISI Design of Cold-Formed Steel Beams Using Non-Linear Finite Element Analysis

In the local building industry of Pakistan, pre-engineered steel building manufacturers mainly employ their own self-developed software and Excel sheets. These systems are based on empirical formulas mentioned in the AISI manual. Under this scenario, a need was found to validate AISI flow charts using commercial software like CUFSM 5.04 and ABAQUS R2019x. This study presents a validation of the CUFSM software and the American Iron and Steel Institute (AISI) Direct Strength Method (DSM) results of channel section flexural members using the non-linear finite element method employing ABAQUS. In this study, eight standard cold-formed channel-section (C-section) steel members were modeled and analyzed using ABAQUS to simulate realistic behavior under four-point loading conditions. The non-linear finite element models incorporated material and geometric non-linearities to capture the actual response of the steel elements. The results obtained from ABAQUS were compared with those predicted by the CUFSM and DSM, focusing on critical parameters such as nominal strength, buckling modes, and deformation patterns. During this study, it was observed that out of the selected sections, the AISI charts predict conservative and even unsafe flexural capacities in some of the cases concerning other methods, with a maximum difference of 14.03%. The differences obtained using DSM and ABAQUS when compared with the results of the AISI charts varies on both the plus and minus sides. This study will not only affect the industry in terms of innovative designs for efficient structures but also the community in regards to low-budget construction.

Enhancing Deep Excavation Optimization: Selection of an Appropriate Constitutive Model

To minimize the impact on nearby structures during deep excavations, choosing an appropriate soil constitutive model for analysis holds significant importance. This study aims to conduct a comparative analysis of various constitutive soil models—namely, the Mohr–Coulomb (MC) model, the hardening soil (HS) model, the hardening soil small strain (HSS) model, and the soft soil (SS) model—to identify the most suitable model for the lacustrine deposit. To implement these models, the soil’s index properties and mechanical behavior were evaluated from undisturbed soil samples. The numerical simulation and verification of these properties were carried out by comparing the laboratory test results with the outcome of the finite element method; the most suitable constitutive soil model for the soil was identified as the HSS model. Upon analyzing the wall deflection and ground settlement profiles obtained from respective constitutive models, it was observed that the HS and HSS models exhibit similar characteristics and are well-suited for analyzing typical lacustrine soil. In contrast, the MC and SS models yield overly optimistic results with lower wall deflection and ground settlement and fail to predict realistic soil behavior. As a result, this research highlights the significance of selecting the appropriate constitutive soil model and refining the parameters. This optimization process contributes significantly to the design of support systems, enhancing construction efficiency and ensuring overall safety in deep excavation projects.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Existence and Approximate Controllability of Random Impulsive Neutral Functional Differential Equation with Finite Delay

This study investigates second-order neutral functional differential equations with delays, prevalent in various scientific and engineering fields. These equations, characterized by their neutral nature and delays, present unique challenges within Banach spaces. The research focuses on the existence and approximate controllability of solutions, using advanced mathematical tools like cosine family theory and the Leray-Schauder theorem to establish rigorous solution conditions. These theoretical results are empirically validated through practical examples, enhancing understanding of real-life behavior and bridging theory with practice. The study’s findings advance the understanding of delayed feedback systems, facilitating effective control strategies and practical engineering solutions, thereby contributing significantly to dynamical systems and control theory.

Towards emotion-aware intelligent agents by utilizing knowledge graphs of experiences

Because of the increasing presence of intelligent agents in various aspects of human social life, social skills play a vital role in ensuring these systems exhibit acceptable and realistic behavior in social communication. The importance of emotional intelligence in social capabilities is noteworthy, so incorporating emotions into the behaviors of intelligent agents is essential. Therefore, some computational models of emotions have been presented to develop intelligent agents that exhibit emotional human-like behaviors. However, most current computational models of emotions neglect the dynamic learning of the affective meaning of events based on agents’ experiences. Such models evaluate the events in the environment according to emotional aspects without considering the context of the situations. Also, these models capture the emotional states of agents by using predefined rules determined according to psychological theories. Therefore, they disregard the data-driven methods that can obtain the relationships between appraisal variables and emotions based on natural human data with fewer assumptions on the nature of such relationships. To address these issues, we proposed a novel and unified affective-cognitive framework (EIAEC) to facilitate the development of emotion-aware intelligent agents. EIAEC uses appraisal theories to acquire the emotional states of the agent in various situations. This paper contains four main contributions: 1- We have designed an efficient episodic memory that uses events and their conditional contexts to store and retrieve knowledge and experiences. This memory facilitates emotional expressions and decision-making adapted to the situations of the agent. 2- A novel method has been proposed that learns context-dependent affective values associated with events by using the agent’s experiences in various contexts. Subsequently, we acquired appraisal variables using the elements and related meta-data in episodic memory. 3- We have proposed a new data-driven method that maps appraisal variables to emotional states. 4- Moreover, a method has been developed to update the activation values regarding actions by using the emotional states of the agent. This method models the influence of emotions on the agent’s decision-making. Finally, we simulate a driving scenarios in our proposed framework to manifest the generated emotions in different situations and conditions. Moreover, we show how the proposed method learns the affective meaning of events and actions used in appraisal computing.

Exploring explainable ensemble machine learning methods for long-term performance prediction of industrial gas turbines: A comparative analysis

In today's modern life, where electricity demand is one of the fundamental necessities, gas turbines play a pivotal role in meeting this demand. As such, it is imperative to address the challenges faced in the field. Current models often rely on simplifying assumptions, neglecting the intricate relationships between variables. This limitation leads to reduced accuracy and reliability, ultimately affecting the overall efficiency of gas turbine systems. Furthermore, the complexity of gas turbine behavior, coupled with the scarcity of comprehensive datasets, exacerbates the problem.To address these challenges, this research aimed to develop an advanced model capable of accurately forecasting real gas turbine behavior. The proposed approach leveraged ensemble decision trees, robust preprocessing techniques, and rigorous evaluation using an extensive dataset spanning from 2011 to 2015. The training and validation phases were conducted on data from 2011 to 2014, with the 2015 dataset reserved for evaluation.The results demonstrated that the bagging structure outperformed the boosted structure, exhibiting lower complexity and higher reliability. Remarkably, the bagging approach with only 30 estimators achieved a superior root mean square error of 1.4176, outperforming the boosted trees with 200 learners. The model effectively captured the overall gas turbine performance, though it encountered limitations in certain specific operating ranges.To further investigate the model's behavior, an evaluation was conducted to assess the effects of the input variables on the output power. While the interpretability of the results posed some challenges, the overall findings were deemed acceptable and provide valuable insights for optimizing gas turbine performance. The significance of this research lies in its potential to inform decision-making and enhance the efficiency of gas turbine systems, ultimately contributing to the reliable and sustainable supply of electricity.

Comparison of feed-forward control strategies for simplified vertical hopping model with intrinsic muscle properties

To analyse walking, running or hopping motions, models with high degrees of freedom are usually used. However simple reductionist models are advantageous within certain limits. In a simple manner, the hopping motion is generally modelled by a spring-mass system, resulting in piecewise smooth dynamics with marginally stable periodic solutions. For a more realistic behaviour, the spring is replaced by a variety of muscle models due to which asymptotically stable periodic motions may occur. The intrinsic properties of the muscle model, i.e. preflexes, are usually taken into account in three complexities-constant, linear and Hill-type. In this paper, we propose a semi-closed form feed-forward control which represents the muscle activation and results in symmetrical hopping motion. The research question is whether hopping motions with symmetric force-time history have advantages over asymmetric ones in two aspects. The first aspect is its applicability for describing human motion. The second aspect is related to robotics where the efficiency is expressed in term of performance measures. The symmetric systems are compared with each other and with those from the literature using performance measures such as hopping height, energetic efficiency, stability of the periodic orbit, and dynamical robustness estimated by the local integrity measure (LIM). The paper also demonstrates that the DynIn MatLab Toolbox that has been developed for the estimation of the LIM of equilibrium points is applicable for periodic orbits.

Analysis of Cyber Attacks Using Honeypot

In the cybersecurity world, the concept of a honeypot is generally referred to as trap systems that have real system behaviors, intentionally leave a security gap, and aim to collect information about cybercriminals who want to access them. It is a computer system that sets itself as a target to attract cyberattacks like bait. It is used to imitate a target such as cyberattackers and to learn about attack attempts, ways of working, or to distract them from other targets. In this study, a VoIP-based honeypot was used to determine the profiles of cyberattacks and attackers. A network environment was created using a low-interaction honeypot to analyze the behavior of cyberattackers and identify the services frequently preferred by these individuals. The honeypot in the network environment was monitored for a period of 90 days. 105,308 events were collected regarding protocols such as Telnet, SIP, SSH, SMB, and HTTP. According to the results obtained, the proposed method performed a detailed analysis of the services from which cyberattacks came and the behaviors of the people who carried out these attacks. In addition, the highest level of understanding of user interaction was achieved thanks to the VoIP-based honeypot.

Identification of eupneic breathing using machine learning.

The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals. In awake animals, considerable heterogeneity in the electromyographic (EMG) activity of the DIAm reflects varied ventilatory and nonventilatory behaviors. Experiments in awake animals are an essential component to understanding the neuromotor control of breathing, which has especially begun to be appreciated within the last decade. However, insofar as the intent is to study the control of breathing, it is paramount to identify DIAm EMG activity that in fact reflects breathing. Current strategies for doing so in a reproducible, reliable, and efficient fashion are lacking. In the present article, we evaluated DIAm EMG from awake animals using hierarchical clustering across four-dimensional feature space to classify eupneic breathing. Our model, which can be implemented with automated threshold of the clustering dendrogram, successfully identified eupneic breathing with high F1 score (0.92), specificity (0.70), and accuracy (0.88), suggesting that it is a robust and reliable tool for investigating the neural control of breathing.NEW & NOTEWORTHY The heterogeneity of diaphragm muscle (DIAm) activity in awake animals reflects real motor behavior diversity but makes assessments of eupneic breathing challenging. The present article uses an unsupervised machine learning model to identify eupneic breathing amidst a deluge of different DIAm electromyography (EMG) burst patterns in awake rats. This technique offers a scalable and reliable tool that improves efficiency of DIAm EMG analysis and minimizes potential sources of bias.

Flow curve, necking and Finite Elements

Abstract Reliable flowcurves are essential for many FE models. The flowcurves are usually determined via tensile tests. Simple formulas applied to raw data obtained from tensile tests can only be considered reliable until necking. After the onset of necking stress and strain fields become three-dimensional and the assumption of stress uniaxiality is not valid anymore. At the University of Dunaújváros experiments were carried out to reveal the real behaviour of specimens during tensile tests by using specimens with rectangular cross-section having regular grid marks. By post-processing the photos of the deforming specimens taken during tests, using the Choung-Cho correction method the approximation of post-necking behaviour has been improved considerably over the not corrected case. Onset of the necking has been studied via FE models for cylindrical specimens using the previously determined flowcurve. At the onset of the necking the Considère criterion is equivalent to the independence of the axial force from cross-section surface. Based on this criterion we created theoretical “flowcurves” used by FE analyses, resulting in identification of typical behaviours.

Synthetic data generation using Copula model and driving behavior analysis

In this study, the generation of synthetic driving data that can reflect real behavior well using the Copula model was investigated. To see the difference in behavior patterns in the generated synthetic driving data, a feature correlation comparison was made. The difference in driving behavior was provided with the K-means based classification model. It was shown that with a Random Forest model trained with synthetic data and having high accuracy, the privacy of real data could be protected by 98.55%. At the end of the study, it was seen that the Copula model could obtain synthetic driving data with sufficient accuracy with CAN bus data without additional sensor support.

Efficient Generation of Hidden Outliers for Improved Outlier Detection

Outlier generation is a popular technique used to solve important outlier detection tasks. Generating outliers with realistic behavior is challenging. Popular existing methods tend to disregard the “multiple views” property of outliers in high-dimensional spaces.The only existing method accounting for this property falls short in efficiency and effectiveness. We propose Bisect , a new outlier generation method that creates realistic outliers mimicking said property. To do so, Bisect employs a novel proposition introduced in this article stating how to efficiently generate said realistic outliers. Our method has better guarantees and complexity than the current method for recreating “multiple views”. We use the synthetic outliers generated by Bisect to effectively enhance outlier detection in diverse datasets, for multiple use cases. For instance, oversampling with Bisect reduced the error by up to 3 times when compared with the baselines.