Continuous Time Research Articles

Associations of Near Work, Time Outdoors, and Sleep Duration With Myopic Regression 5 Years After SMILE and FS-LASIK: A Cross-sectional Study.

To investigate the association of near work, time outdoors, and sleep duration with myopic regression 5 years after small incision lenticule extraction (SMILE) and femtosecond laser-assisted in situ keratomileusis (FS-LASIK) . This cross-sectional study included patients who received SMILE or FS-LASIK at Beijing Tongren Hospital 5 years ago. The patients underwent ophthalmic examinations including visual acuity, intraocular pressure, subjective refraction, slit-lamp examination, keratometry, corneal topography, optical coherence tomography, and fundus examination from January 2020 to March 2023. Fluorescein break-up time was measured and the Ocular Surface Disease Index questionnaire was completed to exclude dry eye. A self-administered questionnaire was used to collect data on near work exposure, physical activities, and sleep duration. A total of 323 eyes were included in the analysis, with a 5-year incidence rate of myopic regression after SMILE or FSLASIK of 16.1%. After adjusted for all confounders, total near work more than 8 hours/day revealed a significant association with myopic regression (odds ratio: 2.461; 95% CI: 1.143 to 5.298, P = .021), particularly in younger adults, women, and patients with high myopia and FS-LASIK treatment. The significant association between sleep duration 8 hours/day or more and myopic regression was restricted to women and patients with FS-LASIK (near significant). However, no significant associations were observed between continuous near work or time outdoors and myopic regression. Excessive near work exposure was associated with greater myopic regression 5 years after SMILE or FS-LASIK. It is crucial to maintain good visual behavior and care for preventing regression after SMILE or FS-LASIK, especially for younger patients and eyes with high myopia. [J Refract Surg. 2024;40(1):e10-e19.].

Effect of Virtual Versus In Person Interpreting on Diabetes Outcomes in Non-English Language Preference Patients: A Pilot Study.

The objective of this pilot study was to explore the impact of interpreter format (virtual vs in person) on clinical outcomes in patients with non-English language preference (NELP) and type 2 diabetes mellitus (T2DM) in a primary care setting. We hypothesized that NELP patients utilizing in person interpreters would have improved HbA1c values, better follow-up rate, and more complex care plans compared to patients utilizing virtual interpreters. We completed a retrospective chart review of 137 NELP patients with T2DM who required a medical interpreter (February to June 2021). We calculated univariate and bivariate statistics to characterize the sample and assess the extent to which measures of continuity (follow-up visit rate and time to follow-up visit), quality (change in HbA1c), and complexity (medication intervention complexity) were associated with interpreter type. There was no statistically significant difference in follow-up rate or average days to follow-up visit for NELP patients with in person as opposed to virtual interpreters. Patients with virtual interpreters demonstrated a non-statistically significant decrease in HbA1c compared to those with in person interpreters. Finally, there was no statistically significant association between interpreter format and intervention complexity. Quality medical interpretation contributes to optimal health outcomes in NELP patients with diabetes. Our study suggests that both in person and virtual interpreters can be effective in providing care for NELP patients, especially for chronic disease management in the context of a primary care relationship. It also highlights the importance of pursuing additional qualitative and mixed method studies to better understand the benefits of various interpreter formats across different visit types.

Layerwise Quantum Deep Reinforcement Learning for Joint Optimization of UAV Trajectory and Resource Allocation

This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: (1) UAV trajectory planning, (2) user grouping, and (3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its non-convexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station while maintaining quality-of-service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV base station in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.

Analysis of the vanishing discount limit for optimal control problems in continuous and discrete time

Analysis of the vanishing discount limit for optimal control problems in continuous and discrete time

Extreme measures in continuous time Conic Finance

Extreme measures in continuous time Conic Finance

On Model of Recurrent Neural Network on a Time Scale: Exponential Convergence and Stability Research.

The majority of the results on modeling recurrent neural networks (RNNs) are obtained using delayed differential equations, which imply continuous time representation. On the other hand, these models must be discrete in time, given their practical implementation in computer systems, requiring their versatile utilization across arbitrary time scales. Hence, the goal of this research is to model and investigate the architecture design of a delayed RNN using delayed differential equations on a time scale. Internal memory can be utilized to describe the calculation of the future states using discrete and distributed delays, which is a representation of the deep learning architecture for artificial RNNs. We focus on qualitative behavior and stability study of the system. Special attention is paid to taking into account the effect of the time-scale parameters on neural network dynamics. Here, we delve into the exploration of exponential stability in RNN models on a time scale that incorporates multiple discrete and distributed delays. Two approaches for constructing exponential estimates, including the Hilger and the usual exponential functions, are considered and compared. The Lyapunov-Krasovskii (L-K) functional method is employed to study stability on a time scale in both cases. The established stability criteria, resulting in an exponential-like estimate, utilizes a tuple of positive definite matrices, decay rate, and graininess of the time scale. The models of RNNs for the two-neuron network with four discrete and distributed delays, as well as the ring lattice delayed network of seven identical neurons, are numerically investigated. The results indicate how the time scale (graininess) and model characteristics (weights) influence the qualitative behavior, leading to a transition from stable focus to quasiperiodic limit cycles.

Applicability of Utilizing Remote Sensing Rainfall Products Data in Arid and Semi-Arid Poorly Gauged Catchments: Study of Wadi Ghoweiba Watershed, Egypt

AbstractDue to the scarcity of established rain gauge stations, obtaining continuous time series daily rainfall observation data is a major challenge. The availability of these data is very essential to conduct hydrological studies and predicting flood events. Nowadays, several satellites could be used to provide such data. This study aims to investigate the appropriate remote sensing (RS) rainfall product and use its data to develop frequency analysis and intensity–duration–frequency curve (IDF curve) at 5-, 10-, 25-, 50-, 100-, and 200-year return periods in the study area. The evaluation of the PERSIANN family products (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now) with NASA-POWER datasets compared to the available annual maximum of daily observation rainfall in Suez Rain Gauge (SRG) station as a reference was conducted. The results show that the PERSIANN-CDR product is the appropriate satellite-based rainfall dataset product based on the outcomes of the comparison criteria (RMSE, Bias, CC, and R2 error measures) of rainfall characteristics analysis and Chi-squared test of distributions. The frequency analysis results confirmed that the values of about 30.3–38.6 and 40–53.3 mm/day corresponding to the biggest flood reported in 1965 and 2020 in SRG station and PERSIANN-CDR product have a return period of 50–100 years, classifying it as an extreme event in Wadi Ghoweiba. These findings would considerably benefit decision-makers in estimating flood risks and planning the appropriate structure protections in the Wadi Ghoweiba Watershed.

Reliability analysis of three state linear consecutive 2-out-of-4:F system by continuous time Markov model

Reliability analysis of three state linear consecutive 2-out-of-4:F system by continuous time Markov model

Signal Propagation: The Framework for Learning and Inference in a Forward Pass.

We propose a new learning framework, signal propagation (sigprop), for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation (BP). In sigprop, there is only the forward path for inference and learning. So, there are no structural or computational constraints necessary for learning to take place, beyond the inference model itself, such as feedback connectivity, weight transport, or a backward pass, which exist under BP-based approaches. That is, sigprop enables global supervised learning with only a forward path. This is ideal for parallel training of layers or modules. In biology, this explains how neurons without feedback connections can still receive a global learning signal. In hardware, this provides an approach for global supervised learning without backward connectivity. Sigprop by construction has compatibility with models of learning in the brain and in hardware than BP, including alternative approaches relaxing learning constraints. We also demonstrate that sigprop is more efficient in time and memory than they are. To further explain the behavior of sigprop, we provide evidence that sigprop provides useful learning signals in context to BP. To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with the Hebbian updates and train spiking neural networks (SNNs) with only the voltage or with biologically and hardware-compatible surrogate functions.

A continuous time projection algorithm for solving elastic-net regularization and application to signal recovery

A continuous time projection algorithm for solving elastic-net regularization and application to signal recovery

Persistence in a dynamic moral hazard game

This paper explores how the persistence of past choices creates incentives in a continuous time stochastic game involving a large player (e.g., a firm) and a sequence of small players (e.g., customers). The large player faces moral hazard and her actions are distorted by a Brownian motion. Persistence refers to how actions impact a payoff‐relevant state variable (e.g., product quality depends on past investment). I characterize actions and payoffs in Markov perfect equilibria (MPE) for a fixed discount rate, show that the perfect public equilibrium (PPE) payoff set is the convex hull of the MPE payoff set, and derive sufficient conditions for a MPE to be the unique PPE. Persistence can serve as an effective channel for intertemporal incentives in a setting where traditional channels fail. Applications to persistent product quality and policy targeting demonstrate the impact of persistence on equilibrium behavior.

Resolving degeneracies in Google search via quantum stochastic walks

The internet is one of the most valuable technologies invented to date. Among them, Google is the most widely used search engine. The PageRank algorithm is the backbone of Google search, ranking web pages according to relevance and recency. We employ quantum stochastic walks (QSWs) with the hope of bettering the classical PageRank (CPR) algorithm, which is based on classical continuous time random walks. We implement QSW via two schemes: only incoherence and dephasing with incoherence. PageRank using QSW with only incoherence or QSW with dephasing and incoherence best resolves degeneracies that are unresolvable via CPR and with a convergence time comparable to that for CPR, which is generally the minimum. For some networks, the two QSW schemes obtain a convergence time lower than CPR and an almost degeneracy-free ranking compared to CPR.

Performance Modeling of Hyperledger Fabric 2.0: A Queuing Theory-Based Approach

Hyperledger Fabric (shortened to Fabric) is an open-source, enterprise-level, permissioned distributed ledger technology platform with a highly modular, configurable architecture. It supports writing smart contracts in general-purpose programing languages and has become the preferred choice for enterprise-level blockchain applications. However, the transaction throughput of the Fabric system remains a critical factor that restricts the further application of this technology in various fields. Therefore, it is necessary to evaluate and optimize the performance of the Fabric blockchain platform. Existing performance modeling methods need to be improved in terms of compatibility and effectiveness. To address this, we propose a performance-compatible modeling method for Fabric using queuing theory, which considers the limited transaction pool and the situation where node groups are attacked. Using the Fabric 2.0 version as an example, we have established a model of the transaction process in the Fabric network. By analyzing the model’s continuous 3D time Markov process, we solved the system stationary equation and obtained analytical expressions for performance indicators such as system throughput, system steady-state queue length, and system average response time. We conducted extensive analyses and simulations to verify the models’ and formulations’ accuracy and validity. We believe this approach can be extended to various scenarios in other blockchain systems.

Best practice: quality assessment outcomes of the Practice Enhancement Program among family physicians in Saskatchewan, Canada.

Increased family physician workloads have strained primary care. The objective of this study was to describe the frequency and types of quality concerns identified among Saskatchewan's family physicians, changes in these concerns over time, associated physician characteristics, and recommendations made for improvement. In this repeated cross-sectional study (1997-2020), we examined family physician assessment reports from the Saskatchewan Practice Enhancement Program, a mandatory practice review strategy, for quality concerns on three outcomes: care, medical record, and facility. We recorded demographic and practice characteristics, the presence or absence of quality concerns, and the type of recommendations made. Concern incidence was calculated both overall and across subperiods, and three outcome-specific multiple logistic regression models were developed. Recommendations made were quantified, and their nature was evaluated using thematic analysis. Among 824 assessments, 20.8% identified concerns, with a statistically significant increase in 2015-20 over earlier years (14.2% versus 43.4%, P < .001). Corresponding proportions also significantly increased within each quality outcome (6.0%-37.1%, P < .001 for care concerns; 12.7%-19.6%, P = .03 for medical record concerns; 3.9%-21.0%, P < .001 for facility concerns). We found statistically significant adjusted associations between care concerns and both urban location [odds ratio (OR): 2.2; 95% confidence interval (CI): 1.30, 3.8] and international medical training (OR: 2.4; 95% CI: 1.34, 4.2); facility concerns and solo practice (OR: 2.5 95% CI: 1.10, 5.7); and medical record concerns and male gender (OR: 1.88; 95% CI: 1.09, 3.3), solo practice (OR: 1.67; 95% CI: 1.01, 2.7), and increased age. Reflecting a statistically significant interaction found between age as a continuous covariate and time period, older physicians were more likely to have a medical record concern in later years (OR: 1.072; 95% CI: 1.026, 1.120) compared to earlier ones (OR: 1.021; 95% CI: 1.001, 1.043). Among physicians where a concern was identified, recommendations most frequently pertained to documentation (91.2%), chronic disease management (78.2%), cumulative patient profiles (62.9%), laboratory investigations (53.5%), medications (51.8%), and emergency preparedness (51.2%). A concerning and increasing proportion of family physicians have quality gaps, with identifiable factors and recurring recommendations. These findings provide direction for strategic support development.

Coupled continuous time random walk with Lévy distribution jump length signifies anomalous diffusion?

The continuous time random walk (CTRW) models are highly valued for studying anomalous diffusion, a ubiquitous phenomenon in complex media whose mean squared displacement (MSD) exhibits power-law behavior. It is widely accepted that the Lévy distribution jump length of random particles with infinite second moment could be responsible for the superdiffusion phenomenon. In this paper, we study the coupled CTRW with exponentially truncated Lévy distribution jump length, deduce the corresponding integrodifferential diffusion equation, and give the propagator expression. It is interesting that we find the MSD for random particles is linearly related to time in the symmetric Lévy distribution case, and it reduces to a simple expression. Additionally, for a non-zero truncated exponent, the MSD also has a linear relationship with time, which reflects the special dependence between waiting time and jump length in this coupled CTRW model.

Productivity and Costs of Mechanized Skidding operations at Sao Hill Forest Plantation, Tanzania

Due to global advancement of technology in forest operations, utilization of advanced machineries such as grapple skidder (GS) in timber harvesting has been increasing in the last decades. However, in order to understand their contribution in sustainable harvesting operations, it is important to understand their performance under different operating environment. Therefore, this study aimed to quantify productivity and cost of mechanised skidding operations at Sao Hill Forest plantation (SHFP). Six variables; diameter at a breast height (dbh), tree height, skidding distance, slope, costs, and cycle time (determined using detailed continuous time study) were collected in 120 GS observations.GS productivity and costs were estimated using productive machine hour (PMH) and delays inclusion approach. Regression models were developed using a generalized linear model (GLM) approach. GS productivity under PMH was 2.6% higher than the one including delay time, while skidding costs was 2.1% higher in the approach including delays. This study revealed significant variations (p-value <0.05) in productivity and cost on various terrain classes. At 0 m – 50 m distance, an average delays free GS productivity was 85.5 m3/h, with costs amounting to 1.7 USD/m3. On the distance exceeding 150 m, productivity dropped to 20.1 m3/h, and costs increased to 12.7 USD/m3. Likewise, in 0.0% - 10.0% slope range, average delays free GS productivity and costs was 100 m3/h and 1.5 USD/m3 respectively, while at 20.1% - 30.0% slope range, productivity dropped to 32.6 m3/h and costs raised to 3.9 USD/m3. Skidding distance, slope, and volume per trip were robust predictors of the GS productivity and costs, yielding pseudo-R2 values of 58.1% and 64.3%, respectively. Therefore, this study developed statistical models useful for predicting GS productivity and costs, however, their applications are recommended to be within the ranges of the variables used to develop the models.

Almost strong equilibria for time‐inconsistent stopping problems under finite horizon in continuous time

AbstractWe consider time‐inconsistent stopping problems for a continuous‐time Markov chain under finite time horizon with non‐exponential discounting. We provide an example indicating that strong equilibria may not exist in general. As a result, we propose a notion of equilibrium called almost strong equilibrium (ASE), which is a weak equilibrium and satisfies the condition of strong equilibria except at the boundary points of the associated stopping region. We provide an iteration procedure and show that this procedure leads to an ASE. Moreover, we prove that this ASE is the unique ASE among all regular stopping policies under finite horizon . In contrast, we show that strong equilibria (and thus ASE) exist and may not be unique for the infinite horizon case . Furthermore, we show that the limit of the finite‐horizon ASE as is a weak equilibrium for the infinite‐horizon problem, and may not be a strong equilibrium or ASE.

A characteristic extraction method for VoicePrint slice statistics base on joint time-frequency processing

Mel-Frequency Cepstral Coefficients (MFCC) and its differential coefficients are widely used as a typical non-linear spectral envelope feature in passive sonar target recognition. MFCC emphasizes the frequency domain information at the low-frequency end of the target, while the differential coefficients add dynamic characteristics of MFCC in the time domain. However, the utilization of time information is not sufficient. In response to this issue, we propose two solutions: one is the characteristic fusion method of Weighted Mel-Frequency Cepstral Coefficient based on Bhattacharyya Distance (BD-WMFCC); the other is the characteristic extraction method of VoicePrint Slice Statistics (VPSS) based on time–frequency joint processing. BD-WMFCC combines weighted differential coefficients with MFCC using Bhattacharyya distance, maximizing the utilization of time information. VPSS is a linear spectral feature that utilizes the stability of the spectral intensity within a certain bandwidth over continuous time. It extracts the steady-state characteristics of the target's spectrum with respect to time changes at equidistant bandwidths. Simulation and real data analysis have shown that VPSS demonstrates outstanding separability and classification performance in both non-linear and linear spectral features, with strong stability. In practical data analysis, the accuracy of VPSS features under the AlexNet algorithm is 3.8 % and 5.7 % higher than the spectral (Fre) and Mel features, respectively.

A CNN-LSTM model for road surface recognition of electric balance vehicles

In recent years, artificial intelligence has developed rapidly, and artificial intelligence equipment has greatly facilitated people's lives. During the operation of intelligent devices, accurate environmental perception is of great importance. To provide more accurate electric balance vehicle instructions, we collect the vibration signal data in different driving environments, and machine learning (ML) methods are adopted to extract and analyse the characteristics of the signal. Then, the classification of the road type is realized. In the study, a Bluetooth wireless acceleration-attitude sensor is used to collect acceleration, angular velocity and angle signal data of the balance vehicle in the X, Y and Z directions. Through analysis, the classification accuracy of using continuous time series data as learning samples is higher than that of using a single time step signal. In the classification based on continuous time series samples, three different models XGBoost, LSTM and 1D-ResNet, are used. XGBoost is applied on artificial feature extraction data and the other models are applied on original sequence data. The F1 scores of the three models are 0.80, 0.77 and 0.79, respectively. To mitigate overfitting, we propose a CNN-LSTM model, whose F1 score is 0.83, outperforming the previous three models.

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.