Time Behavior Research Articles

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

Detection model for network access data tampering attacks with blockchain technology

This paper studies the detection model of network access data tampering attack based on blockchain technology to solve the problem of over-dependence on central server and easy data tampering in traditional network environment. The model uses decentralization and encryption technology to monitor user behavior in real time through smart contracts, enhances data protection with SHA-256 hash algorithm, and combines consensus algorithm to ensure data consistency and security. The experimental results show that the model performs well in detecting multiple attack types with an accuracy of 99.51% and an F1 score of 0.98, far exceeding traditional methods and other deep learning techniques. The model shows good robustness under multi-node attacks, even with 200 attack nodes, the recognition accuracy is still close to 90%, and the response time is less than 3 seconds. Cross-platform testing showed that the model quickly and consistently detected tampering on both Ethereum and Hyperledger, with an average detection time between 0.33 and 0.47 seconds.The hardware acceleration test further shows that the processing speed and hardware utilization of TPU and GPU have been improved, with TPU processing speed reaching 135 MB/s and GPU 122 MB/s. This study will provide a theoretical basis for improving the security, effectiveness and reliability of current network systems, and also lay a solid theoretical and technical foundation for network applications in future network environments.

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.

Development of a method for refining recommendations using temporal knowledge in individual insurance tasks

The subject of the research is the process of constructing recommendations in individual insurance tasks. The aim is to develop an approach for building recommendations in personalized insurance projects using temporal knowledge to adapt proposed insurance plans according to client behavior on insurance company websites. Objectives: to structure temporal rules considering the peculiarities of the online insurance recommendation process; to develop a method for constructing recommendations for insurance product selection using temporal knowledge. Conclusions: temporal rules have been structured for the task of building recommendations in personalized insurance projects. These rules generalize behavior in relative time for multiple users, enabling the generalization of user action sequences. A method for constructing online recommendations using temporal knowledge has been developed. The method consists of two phases: temporal knowledge formation and recommendation construction. The first phase is performed offline and is designed to form a base of temporal rules that generalize knowledge about user behavior. The second phase is executed online and involves refining and further utilizing temporal rules to adapt recommendations according to current user behavior. The method enables prompt adaptation of recommendations considering the user's current actions, creating conditions for increasing user trust in the recommender system's proposals.

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.

Evidence for universal flow and characteristics of early time thermalization in a scalar field model for heavy ion collisions

We study numerically the evolution of an expanding strongly self-coupled real scalar field. We use a conformally invariant action that gives a traceless energy-momentum tensor and is better suited to model the early time behavior of a system such as QCD, whose action is also conformally invariant. We consider asymmetric initial conditions and observe that when the system is initialized with nonzero spatial eccentricity, the eccentricity decreases and the elliptic flow coefficient increases. This behavior is characteristic of a hydrodynamic system in which pressure gradients are converted into fluid velocities, and therefore spatial anisotropy decreases while momentum anisotropy is generated. We look at a measure of transverse pressure asymmetry that has been shown to behave similarly to the elliptic flow coefficient in hydrodynamic systems and show that in our system their behavior is strikingly similar. We show that the derivative of the transverse velocity is proportional to the gradient of the energy in Milne coordinates and argue that this result means that transverse velocity initially develops in the same way that it does in hydrodynamic systems. We conclude that some aspects of the early onset of hydrodynamic behavior that has been observed in quark-gluon plasmas are seen in our numerical simulation of strongly coupled scalar fields. Published by the American Physical Society 2024

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.

Enhancing mental well-being of undergraduates: establishing cut-off values and analyzing substitutive effects of physical activity on depression regulation.

This study aimed to analyze the effects of physical activity (PA), sleep quality, and sedentary behavior on subthreshold depression (StD) among undergraduates. This study included 834 undergraduates and assessed the impact of PA time, sleep quality, and sedentary behavior on depression. The receiver operating characteristic (ROC) analysis was performed to determine cut-off values for StD risk, while the isochronous substitution analysis was performed to evaluate the effects of different activities on depression regulation. Gender, age, and academic grade had no significant influence on depression levels among undergraduates (p > 0.05). However, students engaging in sedentary behavior for more than 12.1 h per day or with a Pittsburgh Sleep Quality Index score above 3.5 were at an increased risk of subclinical depression. Additionally, the isochronous substitution of light-intensity physical activity for other activities (sleep, sedentary behavior, moderate and vigorous intensity physical activity) showed statistically significant effects (p < 0.05) in both 5-min and 10-min substitution models, demonstrating a positive effect on alleviating depression. The findings indicate that specific lifestyle factors, particularly high levels of sedentary behavior and poor sleep quality, are crucial determinants of subclinical depression among undergraduates, independent of demographic variables such as gender, age, and academic grade. Notably, light-intensity PA plays a key role in StD regulation, as substituting it with more intense physical activities or improving sleep quality substantially reduces depression scores. Furthermore, the benefits such substitution became more pronounced with the increase in duration of the activity.

Tracking and automatic behavioral analysis of group-housed pigs based on YOLOX+BoT-SORT-slim

Automatic analysis of pig behavior is crucial for assessing their health and welfare status in group-housed pig farms. Currently, computer vision technology has been widely applied to recognize and track pig behavior. However, these applications lack the capability for automatic behavior analysis. Additionally, execution speed is crucial for long-term tracking, as the tracking target can easily be lost due to the complex background of pig farms, uneven lighting, and the similar appearance of pigs. To tackle these challenges, we propose a lightweight multi-object tracking (MOT) and behavioral analysis method: YOLOX+BoT-SORT-Slim. Firstly, the YOLOX detector detects pig targets and recognizes the pigs’ four behaviors including “stand”, “lie”, “eat”, and “other”. Secondly, the BoT-SORT-Slim algorithm tracks the detected pigs and their behaviors at high speed. Finally, we devise a behavior automatic analysis algorithm that integrates the behavioral information and the tracking results to complete pigs’ behavior analysis. To evaluate the proposed method, we conducted experiments on 1-minute, 10-minute, and 1-hour datasets. The experimental results show that on the 1-minute public and private test sets, the proposed method achieves 6.4 and 3.6 times the frame rate (FPS) of the original algorithm, with 68.05 FPS and 71.27 FPS, respectively. It also achieves a higher-order tracking accuracy (HOTA) of 84.7 % and 78.7 %, multi-object tracking accuracy (MOTA) of 98.3 % and 97.0 %, and an identification F1 score (IDF1) of 98.4 % and 93.4 %. The proposed method obtains the best results on all test sets compared to five other MOT algorithms including DeepSORT, StrongSORT, ByteTrack, OC-SORT, and BoT-SORT. In the 10-minute and 1-hour test sets, the proposed method achieves 70.28 and 72.24, 63.3 % and 76.4 %, 94.1 % and 99.6 %, and 72.5 % and 90.2 % for FPS, HOTA, MOTA, and IDF1, respectively. In terms of behavior analysis, three types of pigs’ behavior analysis are plotted for each test video to assess their health status. The experimental results indicate that the proposed method performs excellently in behavior analysis and tracking, which can reliably monitor and manage pig behavior in real time, providing automated management technical support.

Mathematical analysis of the motion of a piston in a fluid with density dependent viscosity

We study a free boundary value problem modelling the motion of a piston in a viscous compressible fluid. The fluid is modelled by 1D compressible Navier–Stokes equations with possibly degenerate viscosity coefficient, and the motion of the piston is described by Newton’s second law. We show that the initial boundary value problem has a unique global in time solution, and we also determine the large time behaviour of the system. Finally, we show how our methodology may be adapted to the motion of several pistons.