Anomalous Behavior Research Articles

Pseudo-transition between antiferromagnetic and charge orders in a minimal spin-pseudospin model of one-dimensional cuprates

This study rigorously investigates the phenomenon of a pseudo-transition in a minimal spin-pseudospin model, serving as a simplified model of one-dimensional cuprate chains [CuO]∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\\infty $$\\end{document}, by making use of the transfer-matrix method. The studied spin-pseudospin model of one-dimensional cuprate chains [CuO]∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\\infty $$\\end{document} reveals a peculiar pseudo-transition between a charged-ordered phase and an antiferromagnetic phase, which is accompanied with anomalous behavior of magnetic and thermodynamic quantities. While the entropy and short-range correlations undergo near the pseudo-transition steep continuous changes reminiscent of a discontinuous phase transition, the specific heat displays a sizable sharp peak akin to a continuous phase transition.Graphic A density plot of the specific heat of one-dimensional spin-pseudospin model of cuprate chain in the inter-site interaction versus temperature plane, which serves as a pseudo-phase diagram involving charged-ordered (CO) and antiferromagnetic (AF) phases

Effect of laser shock peening on tribological properties of WC-Ni under seawater conditions: Anomalous behavior and solutions

This research investigated the effect of laser shock peening (LSP) on the tribological properties of WC-Ni under seawater lubrication. The experimental findings revealed an anomalous behavior, contrary to the existing literature, indicating that the LSP treatment actually deteriorated the tribological properties of WC-Ni. The mechanism and solutions for anomalous behavior were also examined, and the results indicated that the enhancement of material properties through LSP treatment cannot counterbalance the detrimental effects of increased surface roughness. For improved processes, the sample treated with LSP + Polish exhibits the highest residual compressive stress and the lowest surface roughness, thereby yielding the best comprehensive performance. Therefore, the experimental results demonstrate that LSP + Polish treatment can improve material performance and hydrodynamic effect, leading to reductions in friction and wear.

Thermal Transport Properties of Two-Dimensional Janus MoXSiN2 (X = S, Se, and Te).

The design of Janus materials offers an effective means of regulating both their physical and chemical properties, leading to their application in various fields. However, the underlying mechanism governing the modulation of the thermal transport characteristics through the construction of Janus materials remains unclear. In this work, we introduce VI-group elements into the MoSi2N4 structure, yielding two-dimensional Janus MoXSiN2 (X = S, Se, and Te) and systematically investigate their thermal transport properties based on first-principles calculation methods. Our findings reveal that the lattice thermal conductivities (κl) of MoSSiN2, MoSeSiN2, and MoTeSiN2 are 47.2, 24.3, and 40.6 W/mK at 300 K, respectively, significantly lower than that of MoSi2N4 (224 W/mK). Such low κl values mainly come from the introduction of X atoms, which enhances phonon scattering and reduces phonon vibration frequencies. In addition, MoTeSiN2 exhibits a higher κl compared to MoSeSiN2, contrary to the trend observed in most materials containing VI-group elements, where κl decreases gradually from S to Te. This anomalous behavior can be attributed to the competitive result between its lower phonon vibrational frequency and weaker phonon anharmonicity of MoTeSiN2. This work elucidates the inherent mechanism governing the modulation of thermal transport properties in Janus materials, thereby enhancing the potential application of Janus MoXSiN2 in engineering thermal management.

Redox potentials in ionic liquids: Anomalous behavior?

Redox potentials depend on the nature of the solvent/electrolyte through the solvation energies of the ionic solute species. For concentrated electrolytes, ion solvation may deviate significantly from the Born model predictions due to ion pairing and correlation effects. Recently, Ghorai and Matyushov [J. Phys. Chem. B 124, 3754-3769 (2020)] predicted, on the basis of linear response theory, an anomalous trend in the solvation energies of room temperature ionic liquids, with deviations of hundreds of kJ/mol from the Born model for certain size solutes/ions. In this work, we computationally evaluate ionic solvation energies in the prototypical ionic liquid, 1-butyl-3-methylimidazolium tetrafluoroborate (BMIM/BF4), to further explore this behavior and benchmark several of the approximations utilized in the solvation energy predictions. For comparison, we additionally compute solvation energies within acetonitrile and molten NaCl salt to illustrate the limiting behavior of purely dipolar and ionic solvents. We find that the overscreening effect, which results from the inherent charge oscillations of the ionic liquid, is substantially reduced in magnitude due to screening from the dipoles of the molecular ions. Therefore, for the molten NaCl salt, for which the ions do not have permanent dipoles, modulation of ionic solvation energies from the overscreening effect is most significant. The conclusion is that ionic liquids do indeed exhibit unique solvation behavior due to peak(s) in the electrical susceptibility caused by the ion shell structure; redox potential shifts for BMIM/BF4 are of more modest order ∼0.1V, but may be larger for other ionic liquids that approach molten salt behavior.

Prediction of Errors in the Field of Additive Manufacturing Technology

Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem.

Anomalous behavior of critical current in a superconducting film triggered by DC plus terahertz current

The critical current in a superconductor (SC) determines the performance of many SC devices, including SC diodes which have attracted recent attention. Hitherto, studies of SC diodes are limited in the DC-field measurements, and their performance under a high-frequency current remains unexplored. Here, we conduct the first investigation on the interaction between the DC and terahertz (THz) current in a SC artificial superlattice. We found that the DC critical current is sensitively modified by THz pulse excitations in a nontrivial manner. In particular, at low-frequency THz excitations below the SC gap, the critical current becomes sensitive to the THz-field polarization direction. Furthermore, we observed anomalous behavior in which a supercurrent flows with an amplitude larger than the modified critical current. Assuming that vortex depinning determines the critical current, we show that the THz-current-driven vortex dynamics reproduce the observed behavior. While the delicate nonreciprocity in the critical current is obscured by the THz pulse excitations, the interplay between the DC and THz current causes a non-monotonic SC/normal-state switching with current amplitude, which can pave a pathway to developing SC devices with novel functionalities.

Smart Irrigation with Intrusion Detection

This paper proposes a comprehensive approach to smart irrigation by integrating intrusion detection mechanisms. By combining the functionalities of smart irrigation systems with intrusion detection systems (IDS), the proposed framework offers enhanced security and reliability in agricultural water management. The system employs a network of sensors weather, and soil moisture levels patterns, and other relevant parameters to optimize irrigation scheduling. Concurrently, it utilizes intrusion detection algorithms to identify and respond to unauthorized access attempts or anomalous behaviors within the irrigation infrastructure. The proposed approach represents a noteworthy advancement in the direction of sustainable and secure management of water in agriculture, contributing to improved crop yields, resource conservation, and overall perseverance in the face of emerging challenges. Additionally, by incorporating machine learning algorithms into the intrusion detection system, the framework is able to change and grow over time, enhancing its capacity to identify and neutralize possible threats. Furthermore, the system can more accurately predict when irrigation is needed by utilizing real-time data analysis and predictive modeling approaches. This maximizes water usage efficiency while reducing waste. This all-encompassing strategy encourages a more ecologically sustainable method of managing water resources while also strengthening the resilience of farming operations. Furthermore, the framework enables farmers to take educated decisions in real-time, maximizing productivity and lowering risks, by giving them actionable insights and alerts about security breaches and irrigation needs.

Trustworthy Precision Medicine: An Interpretable Approach to Detecting Anomalous Behavior of IoT Devices.

The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechanisms. We propose an interpretable machine-learning approach to the trustworthy and effective detection of behavioral anomalies within the realm of medical IoT. The discovered anomalies serve as indicators of potential system failures and security threats. Essentially, the detection of anomalies is accomplished by learning a classifier from the operational data generated by smart devices. The learning problem is dealt with in predictive association modeling, whose expressiveness and intelligibility enforce trustworthiness to offer a comprehensive, fully interpretable, and effective monitoring solution for the medical IoT ecosystem. Preliminary results show the effectiveness of our approach.

Evidences for local non-centrosymmetricity and strong phonon anomaly in EuCu2As2: a Raman spectroscopy and lattice dynamics study

Phonon modes and their association with the electronic states have been investigated for the metallic EuCu2As2 system. In this work, we present the Raman spectra of this pnictide system which clearly shows the presence of seven well defined peaks above 100 cm−1 that is consistent with the locally non-centrosymmetric P4/nmm crystal structure, contrary to that what is expected from the accepted symmorphic I4/mmm structure. Lattice dynamics calculations using the P4/nmm symmetry attest that there is a commendable agreement between the calculated phonon spectra at the Γ point and the observed Raman mode frequencies, with the most intense peak at ∼232 cm−1 being ascribed to the A 1g mode. Temperature dependent Raman measurements show that there is a significant deviation from the expected anharmonic behaviour around 165 K for the A 1g mode, with anomalies being observed for several other modes as well, although to a lesser extent. Attempts are made to rationalize the observed anomalous behavior related to the hardening of the phonon modes, with parallels being drawn from metal dichalcogenide and allied systems. Similarities in the evolution of the Raman peak frequencies with temperature seem to suggest a strong signature of a subtle electronic density wave instability below 165 K in this compound.

Beta Brownian motion

Brownian motion (BM) is the paradigmatic model of diffusion. Transcending from diffusion to anomalous diffusion, the principle Gaussian generalizations of BM are Scaled BM (SBM) and Fractional BM (FBM). In the sub/super diffusivity regimes: SBM is characterized by aging/anti-aging, and FBM is characterized by anti-persistence/persistence. BM is neither aging/anti-aging, nor persistent/anti-persistent. Within the realm of diffusion, a recent Gaussian generalization of BM, Weird BM (WBM), was shown to display aging/anti-aging and persistence/anti-persistence. This paper introduces and explores the anomalous-diffusion counterpart of WBM—termed Beta BM (BBM) due to its inherent beta-function kernel structure—and shows that: the weird behaviors of WBM become even weirder when elevating to BBM. Indeed, BBM displays a rich assortment of anomalous behaviors, and an even richer assortment of combinations of anomalous behaviors. In particular, the BBM anomalous behaviors include aging/anti-aging and persistence/anti-persistence—which BBM displays in both the sub and super diffusivity regimes. So, anomalous behaviors that are unattainable by the prominent models of SBM and FBM are well attainable by the BBM model.

A survey on social network’s anomalous behavior detection

The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure’s impact. Second, detecting anomalies within a network’s topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network’s topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research.

Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks

Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks

Blockchain self-update smart contract for supply chain traceability with data validation

Abstract A sustainable supply chain management strategy reduces risks and meets environmental, economic and social objectives by integrating environmental and financial practices. In an ever-changing environment, supply chains have become vulnerable at many levels. In a global supply chain, carefully tracing a product is of great importance to avoid future problems. This paper describes a self-updating smart contract, which includes data validation, for tracing global supply chains using blockchains. Our proposal uses a machine learning model to detect anomalies on traceable data, which helps supply chain operators detect anomalous behavior at any point in the chain in real time. Hyperledger Caliper has been used to evaluate our proposal, and obtained a combined average throughput of 184 transactions per second and an average latency of 0.41 seconds, ensuring that our proposal does not negatively impact supply chain processes while improving supply chain management through data anomaly detection.

Anomalous exchange bias behavior of NiFe/NiO bilayers induced by high-energy Xe+ ion irradiation

The alteration of the microstructure and magnetic performance of an exchange bias system, induced by ion irradiation, adversely affects the practical application of spintronic/storage devices in extreme environments. Here, we report systematically the correlation between static and dynamic magnetism and microstructure changes in NiFe/NiO exchange-biased bilayers after high-energy Xe+ ion irradiation. The effect of cascade collision induced by irradiation on exchange bias is studied through Monte Carlo simulations. It is distinguished from the traditional modification caused by keV-level ion irradiation. At low doses, the transition from amorphous to recrystallization occurs in the NiFe layer and the anomalous exchange bias behavior is induced. A step-like structure appears in the magnetic hysteresis loop and the step gradually shifts downward as the dose increases. At high doses, the exchange bias effect is suppressed due to the disordered antiferromagnetic moment caused by heat accumulation during cascade collision, which significantly decreases the thermal stability of the sample by 5–6 times. In addition, the non-monotonic evolution of high-frequency magnetic properties is observed with increasing irradiation doses. This work provides important foundational data for designing future spintronic/memory devices to enhance radiation tolerance and stability.

USING MACHINE LEARNING ALGORITHMS TO DETECT ANOMALOUS TRAFFIC BEHAVIOR

The article describes a method of using machine learning for detecting anomalous traffic behavior. For this purpose, a data set containing a significant amount of traffic collected at the time of the attack on the Web application is used. The set contains three attack options: Brute Force, XSS, SQL injection. A traffic dump containing an Infiltration attack is considered separately. A comparative analysis of machine learning models was carried out with the selection of the most optimal one. The article also provides a description of the data preprocessing procedure, which is carried out in order to eliminate anomalies and voids in array records, which can lead to incorrect operation of the trained model. Models were trained on selected data in order to identify anomalous traffic behavior indicating a specific type of attack. In addition, a study was conducted on a data set that does not contain information about attacks.

Expanding the Lognormal Response Time Model Using Profile Similarity Metrics to Improve the Detection of Anomalous Testing Behavior

AbstractThe Lognormal Response Time (LNRT) model measures the speed of test‐takers relative to the normative time demands of items on a test. The resulting speed parameters and model residuals are often analyzed for evidence of anomalous test‐taking behavior associated with fast and poorly fitting response time patterns. Extending this model, we demonstrate the connection between the existing LNRT model parameters and the “level” component of profile similarity, and we define two new parameters for the LNRT model representing profile “dispersion” and “shape.” We show that while the LNRT model measures level (speed), profile dispersion and shape are conflated in model residuals, and that distinguishing them provides meaningful and useful parameters for identifying anomalous testing behavior. Results from data in a situation where many test‐takers gained preknowledge of test items revealed that profile shape, not currently measured in the LNRT model, was the most sensitive response time index to the abnormal test‐taking behavior patterns. Results strongly support expanding the LNRT model to measure not only each test‐taker's level of speed, but also the dispersion and shape of their response time profiles.

Diffusion in Porous Rock Is Anomalous.

Molecular diffusion of chemical species in subsurface environments─rock formations, soil sediments, marine, river, and lake sediments─plays a critical role in a variety of dynamic processes, many of which affect water chemistry. We investigate and demonstrate the occurrence of anomalous (non-Fickian) diffusion behavior, distinct from classically assumed Fickian diffusion. We measured molecular diffusion through a series of five chalk and dolomite rock samples over a period of about two months. We demonstrate that in all cases, diffusion behavior is significantly different than Fickian. We then analyze the results using a continuous time random walk framework that can describe anomalous diffusion in heterogeneous porous materials such as rock. This methodology shows extreme long-time tailing of tracer advance as compared to conventional Fickian diffusion processes. The finding that distinct anomalous diffusion occurs ubiquitously implies that diffusion-driven processes in subsurface zones should be analyzed using tools that account for non-Fickian diffusion.

Lagrangian statistics of concentrated emulsions

The dynamics of stabilised concentrated emulsions presents a rich phenomenology including chaotic emulsification, non-Newtonian rheology and ageing dynamics at rest. Macroscopic rheology results from the complex droplet microdynamics and, in turn, droplet dynamics is influenced by macroscopic flows via the competing action of hydrodynamic and interfacial stresses, giving rise to a complex tangle of elastoplastic effects, diffusion, breakups and coalescence events. This tight multiscale coupling, together with the daunting challenge of experimentally investigating droplets under flow, has hindered the understanding of concentrated emulsions dynamics. We present results from three-dimensional numerical simulations of emulsions that resolve the shape and dynamics of individual droplets, along with the macroscopic flows. We investigate droplet dispersion statistics, measuring probability density functions (p.d.f.s) of droplet displacements and velocities, changing the concentration, in the stirred and ageing regimes. We provide the first measurements, in concentrated emulsions, of the relative droplet–droplet separations p.d.f. and of the droplet acceleration p.d.f., which becomes strongly non-Gaussian as the volume fraction is increased above the jamming point. Cooperative effects, arising when droplets are in contact, are argued to be responsible of the anomalous superdiffusive behaviour of the mean square displacement and of the pair separation at long times, in both the stirred and in the ageing regimes. This superdiffusive behaviour is reflected in a non-Gaussian pair separation p.d.f., whose analytical form is investigated, in the ageing regime, by means of theoretical arguments. This work paves the way to developing a connection between Lagrangian dynamics and rheology in concentrated emulsions.

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics.

Government agencies are now encouraging industries to enhance their security systems to detect and respond proactively to cybersecurity incidents. Consequently, equipping with a security operation center that combines the analytical capabilities of human experts with systems based on Machine Learning (ML) plays a critical role. In this setting, Security Information and Event Management (SIEM) platforms can effectively handle network-related events to trigger cybersecurity alerts. Furthermore, a SIEM may include a User and Entity Behavior Analytics (UEBA) engine that examines the behavior of both users and devices, or entities, within a corporate network. In recent literature, several contributions have employed ML algorithms for UEBA, especially those based on the unsupervised learning paradigm, because anomalous behaviors are usually not known in advance. However, to shorten the gap between research advances and practice, it is necessary to comprehensively analyze the effectiveness of these methodologies. This paper proposes a thorough investigation of traditional and emerging clustering algorithms for UEBA, considering multiple application contexts, i.e., different user-entity interaction scenarios. Our study involves three datasets sourced from the existing literature and fifteen clustering algorithms. Among the compared techniques, HDBSCAN and DenMune showed promising performance on the state-of-the-art CERT behavior-related dataset, producing groups with a density very close to the number of users.

C-V characterization of the trap-rich layer in a novel Double-BOX structure

A new Double-BOX structure is introduced to explore the electrical properties of the trap-rich layer used to enhance the performance of radio frequency Silicon-on-Insulator substrates. Capacitance-voltage (C-V) measurements reveal anomalous behavior with a “shoulder” emerging in the electron accumulation region and a shift towards negative gate voltage in hysteresis. TCAD simulation shows that these features are related to trap states at the grain boundary in the trap-rich polycrystalline silicon (polysilicon) layer. These traps form a potential barrier and affect the C-V curves. To determine the traps density in polysilicon, a three-element circuit model is used. The effective density of fast traps is evaluated from the corrected C-V curve, while the hysteresis of double-sweep C-V measurement yields the slow traps density at the grain boundary in polysilicon.