Time Behavior Research Articles

Correction to: Employment Vulnerability and Union Dissolution: Evidence on Intentions and Behaviors in Times of COVID-19 in Five European Countries

Correction to: Employment Vulnerability and Union Dissolution: Evidence on Intentions and Behaviors in Times of COVID-19 in Five European Countries

Anomaly Detection over Streaming Graphs with Finger-Based Higher-Order Graph Sketch

A streaming graph is a constantly growing sequence of edges, which forms a dynamic graph that changes with every edge in the stream. An anomalous behavior in a streaming graph can be modeled as an edge or a subgraph that is unusual compared to the rest of the graph. Identifying anomalous behaviors in real time is essential to the early warning of abnormal or notable events. Due to the complexity of the problem, little work has been reported so far to solve the problem. In this paper, we propose Finger-based Higher-order Graph Sketch (FHGS for short), which is an approximate data structure for streaming graphs with linear memory usage, high update speed, and high accuracy and supports both edge and subgraph anomaly detection. FHGS first maps each edge into a matrix based on hash functions, and then counts its frequency in a time window with unique fingerprints for detecting anomalies. Extensive experiments confirm that our approach generate high-quality results compared to baseline methods.

Characterization of solutions in Besov spaces for fractional Rayleigh–Stokes equations

This paper considers fractional Rayleigh–Stokes equations with a power-type nonlinearity. The linear equation can be simulated a non-Newtonian fluid for a generalized second grade fluid and display a nonlocal behavior in time. Because the coexistence of fractional and classical derivatives leads to the lack of semigroup structure of the solution operator, we need to develop a suitable tool to establish some Lp−Lq estimates in the framework of Lp spaces and Besov spaces, respectively. Further, global existence of solutions is showed in spaces of Besov type.

PDOSPERT: A New Scale to Predict Domain‐Specific Risk‐Taking Behaviors in Times of a Pandemic

ABSTRACTUnderstanding risk tolerance is crucial for predicting and changing behavior across various domains, including health and safety, finance, and ethics. This remains true during a crisis, such as the COVID‐19 pandemic, and leads to a key question: Do current risk measures reliably predict risk‐taking in the drastically different context of a pandemic? The Domain Specific Risk‐Taking (DOSPERT) scale, one of the most widely used risk‐taking measures, assesses self‐reported risk‐taking in response to 30 risky situations across five domains. With the hypothetical risks of the DOSPERT being based on prepandemic circumstances, we estimate that three out of four of its risk‐taking situations were not possible due to preventive measures or did not reflect risk‐taking in times of COVID‐19. In addition, COVID‐19 brought forth new behaviors deemed risky. With an aim to better predict risk‐taking in times of a pandemic, we introduce the Pandemic DOSPERT (PDOSPERT). We summarize three preregistered online studies with 1254 UK participants to validate the scale against the original DOSPERT and three other common risk‐taking measures. We also test its ability to predict pandemic risk‐related behaviors at three points in time over 2 years. Overall, we find that the PDOSPERT scale significantly improves predictions for pandemic‐related risk behavior as compared to the original DOSPERT. In particular, the health/safety subscale is significantly and strongly associated with pandemic‐related risk behavior. We not only validate a pandemic‐specific risk task but also introduce a template for developing context‐ and domain‐sensitive measures for risk‐taking in the future.

Green florescent carbon dots synthesized from various household green wastes for detection of parathion methyl pesticide

The purpose of the current study was to examine the use of green household wastes, such as lemon peel, bottle gourd peel (BG), culinary banana peel, and sugarcane bagasse, as a source for synthesizing carbon dots (C-dots) and to determine how the composition of the material affected the functional characteristics of the C-dots and its interaction behaviour for detection of pesticide. The synthesized C-dots showed green fluorescence with varying particle sizes and red shifting fluorescence with more than 100 nm Stokes shift, indicating time and excitation dependent luminescence behaviour. C-dots produced from bottle gourd (BG) peel had a positive impact on the functional characteristics with a maximum emission of 514 nm and excitation of 410 nm. The SAED and TEM pattern confirmed its amorphous structure. The hydronium ion diameter and the quantum yield were 0.99 nm and 3.1 % respectively. Contributing towards food safety, the synthesized C-dots from BG showed significant behaviour in the detection of parathion methyl (MP) like organophosphorus pesticide (OP) as it conjugated with iron and 1 ppm of parathion methyl, which exhibited “Turn-Off-On” fluorescence behaviour with noticeable changed in intensity and act as a promising qualitative tool for detection of parathion methyl in real sample. The intensity of FL and concentration of OP is directly proportional which is established by the investigated OP in vegetable, fruit and water samples. The significance of iron and MP-OP was also investigated in the presence of other metal ions and pesticides and showed that the C-dots synthesized probe solution was reliable and sensitive towards the detection of MP-OP in real samples.

Chronic pregabalin treatment reduced anxiety, and acute pregabalin treatment increased depression-like behaviors in rats

BackgroundPregabalin is an antiepileptic drug that binds to the alpha-2/delta unit at presynaptic voltage-dependent calcium channels. We aimed to investigate the effect of acute and chronic pregabalin administration on anxiety and depression-like behaviors.MethodsFifty-six male Wistar albino rats were divided into seven groups: control, vehicle, and five different dose groups (5, 10, 30, 60, and 100 mg/kg). Pregabalin was administered for two weeks. Depression-like behaviors were evaluated by Forced swimming test. Anxiety-like behavior (ALB) was evaluated by Open field test (OFT), Elevated Plus Maze (EPM), and light-dark box. Subjects underwent the forced swimming test (FST) after the first dose, while the open field test (OFT), elevated plus maze (EPM), and light-dark box (LDB) were performed after two weeks of treatment. Further sucrose preference test was conducted to evaluate anhedonia until the end of the experiment.ResultsIn the forced swimming test, depression-like behaviors increased after acute single-dose administration of 10, 30, 60, 100 mg/kg pregabalin. According to OFT results, chronic 100 mg/kg pregabalin showed anxiolytic effects by decreasing grooming, and freezing behaviors. In addition, 100 mg/kg chronic pregabalin administration significantly increased the time spent in the central region, the number of entries to the center, and the unsupported rearing number without causing any change in locomotor activity. According to EPM results, both chronic 60 and 100 mg/kg pregabalin treatments showed anxiolytic effects by increasing open arm time and head dipping behavior. In addition, 60 and 100 mg/kg chronic pregabalin administration significantly decreased stretch attend posture. All pregabalin administrations between 5 and 100 mg/kg displayed anxiolytic effects in the LDB. Sucrose preference was above 65% for the duration of all experiments and subjects did not show anhedonia.ConclusionAcute pregabalin treatment triggered depression-like behaviors. Anhedonia, which may be associated with depression, was not observed during chronic treatment. Moreover, chronic treatment with pregabalin revealed potent anxiolytic effects in different behavior patterns and doses for all tests of unconditional anxiety. In particular, 100 mg/kg chronic pregabalin administration decreased anxiety-like behaviors in all experiment setups. Although the anxiolytic effect was demonstrated in chronic treatment, acute treatment of pregabalin induced depression-like behaviors, and thus in clinical practice should be done with caution, especially in patients with anxiety-depression comorbidity.

Can existing corporate finance theories explain security offerings during the COVID-19 pandemic?

We document substantial increases in corporate security offerings during the COVID pandemic. While the increase in seasoned equity offerings (SEOs) can be attributed to shifts in macroeconomic conditions, increases in convertible and straight bond offerings cannot be explained by standard security choice determinants. We furthermore find that COVID-period SEO announcements are often contaminated with Research and Development (R&D)-related news, with the SEO proceeds more likely to be hoarded as cash. Overall, COVID-period SEOs align with market timing behavior, but the increase in COVID-period convertibles and straight bonds cannot be reconciled with pre-pandemic corporate financing rationales or government interventions. We furthermore demonstrate that the COVID pandemic differs substantially from the Global Financial Crisis (GFC) in terms of security offering choices and announcement returns.

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Global Bounded Solutions and Large Time Behavior of a Chemotaxis System with Flux Limitation

Global Bounded Solutions and Large Time Behavior of a Chemotaxis System with Flux Limitation

Asymptotic bound on slow-roll parameter in stringy quintessence model

We study the late time behavior of the scalar part of the volume modulus and the dilaton in stringy quintessence model, focusing on their contributions to the Hubble slow-roll parameter ϵ which directly measures the deviation of the spacetime geometry from de Sitter space. When only one of the moduli is allowed to move, ϵ converges to the stable fixed point at late time. The fixed point value is larger than 1, thus the slow-roll cannot be realized. Moreover, if the decay rate of the quintessence potential is larger than some critical value, the positivity of the potential imposes that the stable fixed point value is just given by 3, independent of the details of the moduli dynamics. Otherwise, the fixed point value coincides with the potential slow-roll parameter. When both the volume modulus and the dilaton roll down the potential simultaneously, we can find the relation between the contributions of two moduli to ϵ satisfied at the fixed point. In this case, the fixed point value is not in general the simple sum of fixed point values in the single field case and cannot be larger than 3.

Time evolutions of information entropies in a one-dimensional Vlasov–Poisson system

A one-dimensional Vlasov–Poisson system is considered to elucidate how the information entropies of the probability distribution functions of the electron position and velocity variables evolve in the Landau damping process. Considering the initial condition given by the Maxwellian velocity distribution with the spatial density perturbation in the form of the cosine function of the position, we derive linear and quasilinear analytical solutions that accurately describe both early and late time behaviors of the distribution function and the electric field. The validity of these solutions is confirmed by comparison with numerical simulations based on contour dynamics. Using the quasilinear analytical solution, the time evolutions of the velocity distribution function and its kurtosis indicating deviation from the Gaussian distribution are evaluated with the accuracy of the squared perturbation amplitude. We also determine the time evolutions of the information entropies of the electron position and velocity variables and their mutual information. We further consider Coulomb collisions that relax the state in the late-time limit in the collisionless process to the thermal equilibrium state. In this collisional relaxation process, the mutual information of the position and velocity variables decreases to zero, while the total information entropy of the phase-space distribution function increases by the decrease in the mutual information and demonstrates the validity of Boltzmann's H-theorem.

Hybrid finite element and laplace transform method for efficient numerical solutions of fractional PDEs on graphics processing units

Fractional Partial Differential equations (FPDEs) are essential for modeling complex systems across various scientific and engineering areas. However, efficiently solving FPDEs presents significant computational challenges due to their inherent memory effects, often leading to increased execution times for numerical solutions. This study proposes a highly parallelizable hybrid computational approach that combines the Finite Element Method (FEM) for spatial discretization with Numerical Inversion of the Laplace Transform (NILT) for time-domain solutions, optimized for execution on Graphics Processing Units (GPUs). The NILT method’s high parallelizability, stemming from the independence of its series terms, combined with the robust spatial discretization provided by FEM, enables the efficient and accurate solution of FPDEs on GPUs, demonstrating substantial performance improvements over traditional CPU-based implementations. We observe a generalized pattern in execution time behavior that accounts for both the number of nodes and the number of NILT terms. Specifically, execution time scales quadratically with the number of nodes, while showing only a logarithmic marginal increase with the number of NILT terms These behaviors not only enables consistent performance assessment but also highlights potential areas for algorithm optimization. Validation against exact solutions of fractional diffusion and wave equations, employing Caputo, modified Caputo-Fabrizio, and modified Atangana-Baleanu derivatives, demonstrates the accuracy and convergence of the hybrid FEM-NILT method. Notably, the exact solutions of wave equation are novel in literature. The results highlight the method’s potential for enabling high-precision, large-scale simulations in fractional calculus applications, thereby advancing computational capabilities and efficiency in the field.

Large time behavior for the Oldroyd-B model

Large time behavior for the Oldroyd-B model

Eating frequency, timing of meals, and sleep duration before and after a randomized controlled weight loss trial for breast cancer survivors.

To examine eating frequency, timing of meals, and sleep duration before and after a weight loss intervention for breast cancer survivors. Female breast cancer survivors (n = 159; 55 ± 9years; 31.4 ± 5.0 kg/m2; stage I-III, median [IQR] 9.5 [5.5] months post-diagnosis) participated in a randomized controlled trial of a 12-month weight loss intervention versus usual care. Eating frequency, proportion of daily calories consumed after 5 PM, eating after 8 PM, nightly fasting duration, and sleep duration were estimated and categorized based on existing associations with factors influencing breast cancer prognosis and breast cancer outcomes. These behaviors at baseline were compared to women from an Australian national survey with similar age and BMI range. Mixed-effects linear regression models were used to examine the changes in health behaviors from baseline to 18months between intervention and usual care groups. Before the trial, eating after 8 PM (67%) was higher, and short nightly fasting duration (< 13h, 83%) and long sleep duration (> 9h/day, 26%) were marginally higher, in breast cancer survivors than women in the national survey (52%, 75%, and 17%, respectively). "Less optimal" eating behaviors and sleep duration tended to co-occur. Behaviors remained unchanged over the 18-month follow-up, irrespective of the study group (p > 0.05; Cohen's effect sizes < 0.3). Later timing of eating and long sleep duration were prevalent in breast cancer survivors and continued following a weight loss intervention. Future multi-behavior interventions in breast cancer survivors should consider specific messages to target eating timing behaviors and sleep.

Dose-Response Associations of Internet Use Time and Internet Addiction With Depressive Symptoms Among Chinese Children and Adolescents: Cross-Sectional Study.

Children's lives are increasingly mediated by digital technologies, yet evidence regarding the associations between internet use and depression is far from comprehensive and remains unclear. This study aimed to investigate the dose-response association between internet use, including use time and addiction behaviors, and depressive symptoms among children and adolescents in Zhejiang Province. Data were collected from a school-based health survey China Common Disease and Risk Factor Surveillance Among Students, encompassing 21,336 students in Zhejiang Province. The daily internet use time, internet addiction (IA) behaviors, and depressive symptoms were assessed with questionnaires. Logistic regression models were used to explore the associations of internet use time and IA behaviors with depressive symptoms among children and adolescents. Restricted cubic spline curves were used to determine the dose-response associations. A total of 6225 (29.2%) students had depressive symptoms. Compared to those reporting no internet use, boys using the internet for >2 hours/day (odds ratio [OR] 1.53, 95% CI 1.34-1.74) and girls using internet for 1.1-2 hours/day (OR 1.22, 95% CI 1.06-1.39) and >2 hours/day (OR 1.70, 95% CI 1.50-1.93) were at higher risks of depressive symptoms. A significant J-shaped association was identified between internet use time and depressive symptoms among children and adolescents, especially in boys and primary school students (nonlinear P values were .006, .003, and <.001, respectively). Increased IA behaviors were associated with a higher odd of depressive symptoms (1 IA behavior: OR 2.01, 95% CI 1.83-2.21; 2 IA behaviors: 2.91, 95% CI 2.57-3.29; and ≥3 IA behaviors: 4.72, 95% CI 4.26-5.22). A positive nonlinear association between the number of IA behaviors and depressive symptoms was found in total population, girls, and primary school students (nonlinear P values were .02, .002, .007, respectively). Findings suggested that excessive internet use time and IA behaviors were significantly associated with an increased risk of depressive symptoms, highlighting the importance of interventions to regulate and educate about adequate internet use during childhood and adolescence.

SBD-Net: Incorporating Multi-Level Features for an Efficient Detection Network of Student Behavior in Smart Classrooms

Detecting student behavior in smart classrooms is a critical area of research in educational technology that significantly enhances teaching quality and student engagement. This paper introduces an innovative approach using advanced computer vision and artificial intelligence technologies to monitor and analyze student behavior in real time. Such monitoring assists educators in adjusting their teaching strategies effectively, thereby optimizing classroom instruction. However, the application of this technology faces substantial challenges, including the variability in student sizes, the diversity of behaviors, and occlusions among students in complex classroom settings. Additionally, the uneven distribution of student behaviors presents a significant hurdle. To overcome these challenges, we propose Student Behavior Detection Network (SBD-Net), a lightweight target detection model enhanced by the Focal Modulation module for robust multi-level feature fusion, which augments feature extraction capabilities. Furthermore, the model incorporates the ESLoss function to address the imbalance in behavior sample detection effectively. The innovation continues with the Dyhead detection head, which integrates three-dimensional attention mechanisms, enhancing behavioral representation without escalating computational demands. This balance achieves both a high detection accuracy and manageable computational complexity. Empirical results from our bespoke student behavior dataset, Student Classroom Behavior (SCBehavior), demonstrate that SBD-Net achieves a mean Average Precision (mAP) of 0.824 with a low computational complexity of just 9.8 G. These figures represent a 4.3% improvement in accuracy and a 3.8% increase in recall compared to the baseline model. These advancements underscore the capability of SBD-Net to handle the skewed distribution of student behaviors and to perform high-precision detection in dynamically challenging classroom environments.

Large time behavior of solution to a quasilinear chemotaxis model describing tumor angiogenesis with/without logistic source

In this paper, we deal with the following Neumann-initial boundary value problem for a quasilinear chemotaxis model describing tumor angiogenesis: ut=∇⋅(D(u)∇u)−χ∇⋅(u∇v)+ξ∇⋅(u∇w)+μu−μu2,x∈Ω,t>0,vt=Δv+∇⋅(v∇w)−v+u,x∈Ω,t>0,0=Δw−w+u,x∈Ω,t>0,∂u∂ν=∂v∂ν=∂w∂ν=0,x∈∂Ω,t>0,u(x,0)=u0(x),v(x,0)=v0(x),x∈Ω,in a bounded smooth domain Ω⊂Rn(n≤3), where the parameter χ,ξ>0,μ≥0, D(u) is supposed to satisfy the behind property D(u)≥(u+1)αwithα>0.Assume that either μ≥0,α>1 or μ=0,ξ≥λ1∗χ2, where the parameter λ1∗=λ1∗(u0,v0,Ω)>0, then the system admits a global classical solution (u,v,w) by subtle energy estimates. Moreover, when μ=0, it is asserted that the corresponding solution exponentially converges to the constant stationary solution (u0¯,u0¯,u0¯) provided the initial data u0 is sufficiently small, where u0¯=∫Ωu0|Ω|. Finally, when μ>0, it can be proved that the corresponding solution of the system decays to (1,1,1) exponentially for suitable large μ.

Loneliness in times of global polycrises

Global polycrises comprise the synchronous or consecutive occurrence of various crises, with no intervening phases of stability, e.g., coronavirus disease 2019 (COVID-19) pandemic, inflation, war and climate crisis. This poses challenges for both society as awhole and its individual members. Polycrises are associated with enormous psychological stress, especially for vulnerable groups. Polycrises also impair social interaction, which can trigger or intensify subjectively stressful loneliness. The aim of this narrative review is to show how loneliness affects social behavior in times of global polycrises and what the consequences are. Loneliness is associated with both mental and physical morbidity and mortality as well as being asignificant barrier to recovery. Studies have shown that there is amutual detrimental influence between mental illness and loneliness. The social behavior of chronically lonely people is characterized by anegative cognitive bias, hypervigilance in the social context and adysregulated oxytocin system. Furthermore, the quality and quantity of these relationships are significantly impaired. The social consequences of loneliness are, for example, adecrease in social engagement and low voting participation. The phenomenon of loneliness clearly shows that crises can exacerbate latent problems and bring them to the surface; however, crises should also be seen as an opportunity to openly and constructively address shame-laden topics such as loneliness in public discourse and in psychotherapeutic settings.

Behavior in time of solutions to a degenerate chemotaxis system with flux limitation

We study a new class of Keller–Segel models, which presents a limited flux and an optimal transport of cells density according to chemical signal density. As a prototype of this class we study radially symmetric solutions to the parabolic–elliptic system ut=∇⋅(u∇uu2+|∇u|2)−χkf∇⋅(u∇v(1+|∇v|2)α),x∈Ω,t>0,0=Δv−μ+u,x∈Ω,t>0under no flux boundary conditions in a ball B=Ω⊂RN and initial condition u(x,0)=u0(x)>0,χ>0,α>0,kf>0 and μ=1|Ω|∫Ωu0dx. Under suitable conditions on α and u0 it is shown that the solution blows up in L∞-norm at a finite time Tmax and for some p>1 it blows up also in Lp-norm. The proofs are mainly based on an helpful change of variables, on comparison arguments and some suitable estimates.

Dynamics for a nonlocal diffusive SIR epidemic model with double free boundaries

In this paper, we study an SIR epidemic model with nonlocal diffusion and double free boundaries, which can be used to describe a class of biological phenomena: the depletion of native resources by all individuals, the infected individuals do not lose their fertility completely, the recovered individuals are immune and no longer infected, the infected and recovered individuals spread along the same free boundary. We first investigate the existence and uniqueness of global solution, long time behaviors and some sufficient conditions for spreading and vanishing. Then we estimate the spreading speed and derive that accelerated spreading could happen when the kernel function does not satisfy a threshold condition.