Time-dependent Data Research Articles

Investigating the impact of steroid dependence on gastrointestinal surgical outcomes from UK Biobank

Although corticosteroids are an important treatment for inflammatory bowel disease (IBD) patients, many subjects develop dependence, leading to serious long-term side effects. We applied causal inference analyses to investigate the length of steroid use on reoperations in IBD patients. We identified subjects in the UK Biobank general practice dataset with at least one major GI surgery and followed them for at least 5 years to evaluate subsequent operations. We defined steroid dependence as at least 12 weeks of use (vs. acute steroid use) prior to baseline surgery. Of the 363 subjects included in our analyses, 163 (45%) were prescribed steroids on or before baseline surgery, and of these (N = 125 of 163, 77%) were dependent. Additional analyses for time-dependent data on prescriptions found a link between prescription length and reoperation. Among UC subjects with acute use, the odds of reoperation were significantly lower (OR: 0.32, 95% CI: 0.0–0.73). Steroid dependence resulted in a delay of reoperation (median 1.2 vs. 2.3 years, P = 0.01). Our findings indicate that long-term steroid use tends to increase the need for reoperation, whereas short-term use may reduce it.

Facile Synthesis of Polypyrrole-Decorated RGO-CuS Nanocomposite for Efficient Nickel Removal from Wastewater.

Efficient wastewater treatment, particularly the removal of heavy metal ions, remains a challenging priority in environmental remediation. This study introduces a novel sandwich-structured nanocomposite, RGO-CuS-PPy, composed of reduced graphene oxide (RGO), copper sulfide (CuS), and polypyrrole (PPy), synthesized via a straightforward hydrothermal method. The unique combination of RGO, CuS, and PPy offers enhanced adsorption capacity for Ni(II) ions due to RGO's high surface area and CuS's active binding sites, supported by PPy's structural stability contributions. This study is among the first to explore this specific nanocomposite architecture for Ni(II) removal, achieving an adsorption capacity of 166.67 mg/g and a high removal efficiency of 94.9% within 210 min for 55 mg/L of Ni(II) concentration at pH 6 and adsorbent dose of 3 mg/15 mL. The kinetic analysis shows the best fitted time-dependent experimental data with the pseudo-second-order model, indicating chemisorption. Isotherm studies confirmed the Langmuir model as the best fit, yielding a high monolayer adsorption capacity of 166.67 mg/g. Thermodynamic analysis shows the adsorption process was endothermic (ΔH° = 80.23 kJ/mol) and spontaneous (ΔG° ranging from -6.985 to -14.399 kJ/mol). Additionally, reusability tests using 0.1 M HCl for desorption demonstrated good reusability, emphasizing the RGO-CuS-PPy nanocomposite's potential as a sustainable adsorbent for Ni(II) removal in wastewater treatment applications.

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Transitive reasoning: A high-performance computing model for significant pattern discovery in cognitive IoT sensor network

Current research on the Internet of Things (IoT) has given rise to a new field of study called cognitive IoT (CIoT), which aims to incorporate cognition into the designs of IoT systems. Consequently, the CIoT inherits specific attributes and challenges from IoT. The CIoT applications generate vast, diverse, constantly changing, and time-dependent data due to the billions of devices involved. The efficient operation of these CIoT systems requires the extraction of valuable insights from vast data sources in a computationally efficient manner. Therefore, this study proposes transitive reasoning to glean significant concepts and patterns from a 21.25-year environmental dataset. To reduce the effects of missing entries, the proposed methodology includes a grouping of data using probabilistic clustering and applying total variance regularization in the alternate direction method of multipliers (ADMM) to regularize the sensory data. As a result, noisy entries will be less conspicuous. Afterward, it calculates the transitional plausibility value for each cluster using the transited value and then turns it into binary data to create concept lattices. In addition, each concept that is formed is assigned a weight, and the concept with the largest transitive strength value is chosen, followed by calculating the mean value. Therefore, this pattern is seen as significant. Experimental results on 21.25-year environmental data show an accuracy of over 99.5%, outperforming competing methods, as shown by cross-validation using multiple metrics.

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes. The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant's gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker. The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a "ball of rotations"; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material. With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

Toward Explainable Affective Computing: A Review.

Affective computing has an unprecedented potential to change the way humans interact with technology. While the last decades have witnessed vast progress in the field, multimodal affective computing systems are generally black box by design. As affective systems start to be deployed in real-world scenarios, such as education or healthcare, a shift of focus toward improved transparency and interpretability is needed. In this context, how do we explain the output of affective computing models? and how to do so without limiting predictive performance? In this article, we review affective computing work from an explainable AI (XAI) perspective, collecting and synthesizing relevant papers into three major XAI approaches: premodel (applied before training), in-model (applied during training), and postmodel (applied after training). We present and discuss the most fundamental challenges in the field, namely, how to relate explanations back to multimodal and time-dependent data, how to integrate context and inductive biases into explanations using mechanisms such as attention, generative modeling, or graph-based methods, and how to capture intramodal and cross-modal interactions in post hoc explanations. While explainable affective computing is still nascent, existing methods are promising, contributing not only toward improved transparency but, in many cases, surpassing state-of-the-art results. Based on these findings, we explore directions for future research and discuss the importance of data-driven XAI and explanation goals, and explainee needs definition, as well as causability or the extent to which a given method leads to human understanding.

Continuous Biosensor Based on Particle Motion: How Does the Concentration Measurement Precision Depend on Time Scale?

Continuous biosensors measure concentration-time profiles of biomolecular substances in order to allow for comparisons of measurement data over long periods of time. To make meaningful comparisons of time-dependent data, it is essential to understand how measurement imprecision depends on the time interval between two evaluation points, as the applicable imprecision determines the significance of measured concentration differences. Here, we define a set of measurement imprecisions that relate to different sources of variation and different time scales, ranging from minutes to weeks, and study these using statistical analyses of measurement data. The methodology is exemplified for Biosensing by Particle Motion (BPM), a continuous, affinity-based sensing technology with single-particle and single-molecule resolution. The studied BPM sensor measures specific small molecules (glycoalkaloids) in an industrial food matrix (potato fruit juice). Measurements were performed over several months at two different locations, on nearly 50 sensor cartridges with in total more than 1000 fluid injections. Statistical analyses of the measured signals and concentrations show that the relative residuals are normally distributed, allowing extraction and comparisons of the proposed imprecision parameters. The results indicate that sensor noise is the most important source of variation followed by sample pretreatment. Variations caused by fluidic transport, changes of the sensor during use (drift), and variations due to different sensor cartridges and cartridge replacements appear to be small. The imprecision due to sensor noise is recorded over few-minute time scales and is attributed to stochastic fluctuations of the single-molecule measurement principle, false-positive signals in the signal processing, and nonspecific interactions. The developed methodology elucidates both time-dependent and time-independent factors in the measurement imprecision, providing essential knowledge for interpreting concentration-time profiles as well as for further development of continuous biosensing technologies.

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation.

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Optimal Sensor Placement and Multimodal Fusion for Human Activity Recognition in Agricultural Tasks

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications.

Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.

Polymer Free Volume-Controlled Molecular Clock and Emitter for Multicolored Transient Data Display in Advanced Photonic Cryptography.

The booming demand on data security has aroused great interest for developing smart materials with temporal display feature and dynamic multicolor fluorescence. However, it remains challenging to implement both features on most responsive molecules. Herein, we construct a polymer free volume-controlled "molecular clock and emitter" via covalently embedding a multi-stimuli responsive molecular switch (i.e., spiropyran) into a polymer network (i.e., poly(pentafluorophenyl acrylate)) with programmable crosslink density and free volume. By the aminolysis of pentafluorophenyl ester with different amount of diamine crosslinkers, pPFPA-co-SP networks with controllable crosslink densities are generated, which have different confinement effects on the rate constant of SP/MC isomerization, thus leading to time-dependent photochromism. In addition, PTF1, a fluorescent probe that is sensitive to polymer rigidity, is introduced to further endow pPFPA-co-SP system with phototunable dynamic full-color emission. Therefore, relying on their synergistical responses to the rigidity of the polymer network, we have successfully developed a versatile molecular clock and emitter via an "one stone two birds" manner, which shows time-dependent data display along with dynamic multicolor fluorescence switching, providing great potential for advanced encryption and anticounterfeiting with a high security level.

Research on Intelligent Translation System of Spoken English Based on Cyclic Neural Network Model

This paper explores the development of an intelligent translation system for spoken English using Recurrent Neural Network (RNN) models. The fundamental principles of RNNs and their advantages in processing sequential data, particularly in handling time-dependent natural language data, are discussed. The methodology for constructing the translation system is outlined, covering key steps such as data preprocessing, model architecture design, and training optimization. The system's performance is evaluated in terms of translation accuracy, fluency, and real-time processing capabilities. The study identifies limitations of the current system and proposes future research directions, including the integration of attention mechanisms, refinement of model architectures, and enhancement of multilingual translation capabilities. Ultimately, this research contributes theoretical insights and practical guidance to the ongoing development of intelligent translation systems for spoken English.

A novel naphthalimide-derived “turn-off” chemosensor for the detection of Cu2+, F- and CN- ions

The development of selective chemosensor for the detection of detrimental analytes in the environment is crucial due to their catastrophic effects. In this study, a novel Schiff base derived from naphthalimide and salicylaldehyde was designed and prepared for the detection of Cu2+, F-, and CN- ions in CH3CN as medium. The chemosensor exhibits a red shift in the presence of Cu2+, F-, and CN- ions accompanied by naked eye color change in the presence of F- and CN- ions. On the other hand, a “turn-off” response was observed in the presence of ion Cu2+, F-, and CN- ions in the fluorescent spectrum. The good anti-interference characteristics, strong binding, and low limit of detection (LOD) of the chemosensor revealed its sensitivity against Cu2+, F-, and CN- ions i.e. 1.25 × 10-5 M, 1.07 × 10-6 M, and 0.98 × 10-6 M, respectively. The binding ratio (1:1) was determined by Job's plot between the chemosensor and ions. In addition, the binding of ions with the chemosensor was proposed which was further supported by time-dependent density functional theory (TD-DFT) and IR spectrum data.

Effect of Energy Utilization on Pakistan’s Economic Development: A Time Series Analysis

The present research has been conducted to study the causal relation among GDP, electricity utilization, exports, real capital and labor force for Pakistan. Time dependent data for the mentioned parameters have been used for the time period of 1980 to 2022. Inter-relations among the above-mentioned variables have been studied in this work by the method of cointegration using bounds test. The results illustrate that there is an existence of long run relation among the parameters where GDP has been taken into consideration as the dependent variable. Granger causation analysis has also been performed for the variables. Results show that Granger causality between GDP and electricity utilization runs in both directions. Moreover, the study discloses that Electricity utilization granger cause exports and per capita real capital. Exports granger cause per capita real capital. Per capita real capital granger cause GDP. Labor force granger cause GDP and exports. The long run relation equation of GDP, Electricity utilization, exports, real capital and labor force has also been examined for parameter stability. The parameters are found to be stable with the significance level of 5%. The research also suggests some significant strategy recommendations.

Investigation of coverage dependence of the stretching frequency of CO adsorbed on Pd surfaces at low coverage limits

The stretching frequency of the CO bond is a sensitive probe of the local environment of a surface-bound CO molecule, including the adorption site and density, i.e. surface coverage. In this work, we extend our analysis beyond the frequency shift due to differences in adsorption configurations. Using density functional theory (DFT) calculations, we directly explore the correlations between surface coverage and the stretching frequency of adsorbed CO on Pd surfaces. We also perform constant pressure infrared reflection absorption measurements of CO on Pd(111) and use existing relations between pressure and coverage to derive coverage dependency. Both results are compared to previously reported experimental data. Our derived correlations of peak frequency and area with surface coverage can help interpret experimental IR spectra in real time and extract time-dependent concentration data from transient kinetic experiments.

Exploring physical and digital architectures in magnetic nanoring array reservoir computers

Physical reservoir computing (RC) is a machine learning technique that is ideal for processing of time dependent data series. It is also uniquely well-aligned to in materio computing realisations that allow the inherent memory and non-linear responses of functional materials to be directly exploited for computation. We have previously shown that square arrays of interconnected magnetic nanorings are attractive candidates for in materio RC, and experimentally demonstrated their strong performance in a range of benchmark tasks (Dawidek et al 2021 Adv. Funct. Mater. 31 2008389, Vidamour et al 2022 Nanotechnology 33 485203, Vidamour et al 2023 Commun. Phys. 6 230). Here, we extend these studies to other lattice arrangements of rings, including trigonal and Kagome grids, to explore how these affect both the magnetic behaviours of the arrays, and their computational properties. We show that while lattice geometry substantially affects the microstate behaviour of the arrays, these differences manifest less profoundly when averaging magnetic behaviour across the arrays. Consequently the computational properties (as measured using task agnostic metrics) of devices with a single electrical readout are found to be only subtly different, with the approach used to time-multiplex data into and out of the arrays having a stronger effect on properties than the lattice geometry. However, we also find that hybrid reservoirs that combine the outputs from arrays with different lattice geometries show enhanced computational properties compared to any single array.

ENHANCED NETWORK SECURITY: A DATA MINING APPROACH TO INTRUSION DETECTION

In the ever-evolving landscape of network security, the role of Intrusion Detection Systems (IDSs) is critical. These systems serve as the guardians of digital networks, defending against a relentless tide of cyber threats. However, the efficacy of IDSs in accurately detecting and preventing these threats remains a critical concern. The emergence of new attack vectors and the growing sophistication of cyberattacks underscore the need for innovative approaches to intrusion detection. Nonetheless, there is an information vacuum about the efficacy of different data mining approaches in intrusion detection. This study investigates the deployment of seven data mining models, which include Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, and two types of Recurrent Neural Networks (RNN) on five (5) IDS benchmark datasets. Based on the undertaken experiments, it is evident that the RNN is the best classifier as it obtained 100% accuracy for all datasets. Due to its strength in modelling time-dependent and sequential data problems, RNN has become powerful in predicting future attacks. As our digital world evolves, so must our cyber defences; hence, this study strives to equip network security professionals with the knowledge and tools needed to fortify their networks, ensuring resilience against the ever-adaptive landscape of cyber threats.

Time series analysis and applications in data analysis, forecasting and prediction

Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations. This can include data such as daily stock prices, annual national income, quarterly company revenue, and more. The advantage of time series data is that it can capture the state of a variable over time. In contrast, the world is constantly changing, and phenomena rarely remain static they typically exhibit variations over time. Therefore, time series data has highly practical applications and is used in various fields, including statistics, econometrics, financial mathematics, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, telecommunications, and signal processing. ARIMA, which stands for Auto Regressive Integrated Moving Average, is a widely used time series forecasting method in data science. It is a popular model for analyzing and predicting time-dependent data points. ARIMA combines autoregression, differencing, and moving averages to capture different aspects of time series data. In this paper, we study ARIMA, which is a significant model for analyzing and predicting time series data.

Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali

Article Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali Boesjes Freek 1, Jahani Alireza 2, Ooink Bas 3, and Derek Groen 2,* 1 Utrecht University, Heidelberglaan 8, Utrecht, Utrecht 3584 CS, the Netherlands 2 Computer Science Department, Brunel University London, Wilfred Brown Building, Kingston Lane, Uxbridge UB8 3PH, UK 3 Blue Team Intelligence, Jan Aertshof 3, Hoevelaken 3871 WH, the Netherlands * Correspondence: Derek.Groen@brunel.ac.uk Received: 7 September 2023 Accepted: 30 January 2024 Published: 26 March 2024 Abstract: As of 2023, the world has approximately 100 million refugees, many of whom have been displaced by violent conflicts. Accurately predicting where these people may go can help non-government organisations (NGOs) and other support organisations to more effectively help these refugees. In this paper, we extend the existing flee migration forecasting model which models migration using intelligent agents with a dynamic network that represents the physical environment. In doing so, we integrate time-dependent data into four different characteristics from three public data sources. We obtain data from aspects such as the slope, drainage, soil and infrastructure, and use these aspects to systematically modify the movement preferences of forcibly displaced agents in the flee model. We showcase our approach by applying it to the 2012 northern Mali conflict. We find that numerous routes previously deemed traversable are actually inaccessible for prolonged periods according to sensor data, and a range of off-road routes are instead traversable for vehicles. We also perform a validation comparison with the original modelling approach, and find that our revised representation of travel routes leads to a reduction of 4.5% in the averaged relative difference. Our approach can be reused in other flee conflict contexts, of which five are present in the EU-funded ITFLOWS project alone. Our work provides the ability to represent a dynamic physical environment and potentially improves the simulation accuracy in a range of flee conflict situations.

Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence

Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named Temporal MultiPersistence (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.