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While mean-field models of cellular operations have identified dominant processes at the macroscopic scale, stochastic models may provide further insight into mechanisms at the molecular scale. In order to identify plausible stochastic models, quantitative comparisons between the models and the experimental data are required. The data for these systems have small sample sizes and time-evolving distributions. The aim of this study is to identify appropriate distance metrics for the quantitative comparison of stochastic model outputs and time-evolving stochastic measurements of a system. We identify distance metrics with features suitable for driving parameter inference, model comparison, and model validation, constrained by data from multiple experimental protocols. In this study, stochastic model outputs are compared to synthetic data across three scales: that of the data at the points the system is sampled during the time course of each type of experiment; a combined distance across the time course of each experiment; and a combined distance across all the experiments. Two broad categories of comparators at each point were considered, based on the empirical cumulative distribution function (ECDF) of the data and of the model outputs: discrete based measures such as the Kolmogorov–Smirnov distance, and integrated measures such as the Wasserstein-1 distance between the ECDFs. It was found that the discrete based measures were highly sensitive to parameter changes near the synthetic data parameters, but were largely insensitive otherwise, whereas the integrated distances had smoother transitions as the parameters approached the true values. The integrated measures were also found to be robust to noise added to the synthetic data, replicating experimental error. The characteristics of the identified distances provides the basis for the design of an algorithm suitable for fitting stochastic models to real world stochastic data. [ABSTRACT FROM AUTHOR]

EpiRiskNet combines time-series data with graph and static information to enhance forecasting accuracy. This model features the SCI-Block for improved feature extraction and interaction learning, leveraging the capabilities of SCINet and Triformer to manage diverse feature scales. The model's standout attribute, scalability, is driven by Triformer's Patch Attention mechanism, ensuring efficient processing of large-scale data. EpiRiskNet was tested across several locations, including Liaoning, Chongqing, Heilongjiang, and Guangxi, where it demonstrated greater accuracy than other methods. This accuracy is crucial for effectively forecasting disease risks. The model's adaptability to various regional conditions underscores its significance in public health and epidemiology. Moreover, its modular and flexible design makes EpiRiskNet suitable for a wide range of applications that require advanced data processing and predictive analytics. [ABSTRACT FROM AUTHOR]

This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning. [ABSTRACT FROM AUTHOR]

During the shale gas extraction process, affected by the internal pressure of the geological layer and other factors, the internal pressure will gradually decrease with time, and the production will also decrease, and it is necessary to rely on artificial pressurization and other ways to keep the production stable. Accordingly, we analyzed the high-frequency data of shale gas production obtained from a block in the southwest shale gas field, and proposed a data-driven prediction model combining Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and Long Short-Term Memory (LSTM), which can determine whether artificial pressurization is needed from the predicted results. The model adopts multi-variate input and uni-variate output, firstly, the PCA algorithm for processing the characteristic parameters (casing pressure, oil pressure, pre-valve temperature) and labeling parameters (instantaneous production), secondly, the PSO algorithm is used to iteratively search for the optimal hyper-parameters of the LSTM to find the most suitable hyper-parameter configuration, and finally, established a data-driven shale gas production prediction model. The combined model is analyzed through an example study, and the accuracy is higher in shale gas production prediction. [ABSTRACT FROM AUTHOR]

Although neural networks have revolutionized various fields, their deployment in mobile environments confronts significant challenges due to limitations in battery and cooling capacity, especially for internet of things devices [1]. A lightweight neural network is urgently needed to address these issues. In this study, we explore the use of the Hadamard product and assess the usefulness of the method to enhance the neural network performance in mobile environments. The method has less computational complexity compared with other matrix multiplication methods [2]. Hadamard product methodologies are applied to the input features to amplify useful data and diminish noise. Our research involved the use of 48 electromyography signals sourced from the calves of three individuals with the signal time frame being iterated from 10 to 990 with a step size of 10. Findings indicate that the utilization of Hadamard products significantly improves the model performance relative to the increase in model size. [ABSTRACT FROM AUTHOR]

The paper "Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions" (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model's root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones. [ABSTRACT FROM AUTHOR]

Fuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization‐based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU‐COA, and GWO+RNN method. [ABSTRACT FROM AUTHOR]

The importance of monitoring in assessing structural safety and durability continues to grow. With recent technological advancements, Internet of Things (IoT) sensors have garnered attention for their complex scalability and varied detection capabilities, becoming essential devices for monitoring. However, during the data collection process of IoT sensors, anomalies arise due to network instability, sensor noise, and malfunctions, degrading data quality and compromising monitoring system reliability. In this study, Interquartile Range (IQR), Long Short-Term Memory Autoencoder (LSTM-AE), and time-series decomposition were employed for anomaly detection in Structural Health Monitoring (SHM) processes. IQR and LSTM-AE produce irregular patterns; however, time-series decomposition effectively detects such anomalies. In road monitoring influenced by weather and traffic, the time-series decomposition approach is expected to play a crucial role in enhancing monitoring accuracy. [ABSTRACT FROM AUTHOR]

Given the importance of climate in shaping species' geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data--such that individuals at the cool edge of a species' distribution should benefit from warming (the "leading edge"), whereas individuals at the warm edge should suffer (the "trailing edge"). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species' distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming--the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species' distribution with respect to temperature and the majority of the species' distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances. [ABSTRACT FROM AUTHOR]

We conducted this study to investigate the relationship between environmental stressors and mesozooplankton fluxes in inner Kongsfjorden, Svalbard. The ongoing Arctic amplification, characterized by phenomena such as increased temperatures, glacial and watershed runoff, and diminishing ice cover, poses significant challenges to marine ecosystems. Our multi-year time-series analysis (2010–2018) of mesozooplankton, collected from a moored automatic sediment trap at approximately 87 m depth, aims to elucidate seasonal and interannual variations in fluxes within this Arctic fjord. We integrate meteorological, hydrological, and chemical datasets to assess their influence on zooplankton populations. Principal component analysis reveals the impact of seawater characteristics on mesozooplankton fluxes and composition, while two-way ANOVA highlights the role of seasonality in driving variations in our dataset. We observe a decrease in swimmer fluxes following the maxima mass flux event (from 2013 onwards), coupled with an increase in community diversity, possibly attributed to copepod decline and functional diversity. Notably, sub-Arctic boreal species such as Limacina retroversa have been detected in the sediment trap since 2016. Our continuous multi-year dataset captures the physical, chemical, and biological dynamics in this extreme environment. With Arctic amplification in Kongsfjorden and increasing submarine and watershed runoff, we anticipate significant shifts in mesozooplankton communities in the medium to long-term. This underscores the urgency for further research on their adaptation to changing environmental conditions and the potential introduction of alien species. [ABSTRACT FROM AUTHOR]

Disengagement of students during online learning significantly impacts the effectiveness of online education. Thus, accurately estimating when students are not engaged is a critical aspect of online-learning research. However, the inherent characteristics of public datasets often lead to issues of class imbalances and data insufficiency. Moreover, the instability of video time-series data further complicates data processing in related research. Our research aims to tackle class imbalances and instability of video time-series data in estimating learner engagement, particularly in scenarios with limited data. In the present paper, we introduce "Skipped Moving Average", an innovative oversampling technique designed to augment video time-series data representing disengaged students. Furthermore, we employ long short-term memory (LSTM) and long short-term memory fully convolutional network (LSTM-FCN) models to evaluate the effectiveness of our method and compare it to the synthetic minority over-sampling technique (SMOTE). This approach ensures a thorough evaluation of our method's effectiveness in addressing video time-series data imbalances and in enhancing the accuracy of engagement estimation. The results demonstrate that our proposed method outperforms others in terms of both performance and stability across sequence deep learning models. [ABSTRACT FROM AUTHOR]

The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies. [ABSTRACT FROM AUTHOR]

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies. [ABSTRACT FROM AUTHOR]

Gyroscopes and camera sensors, widely utilized for motion recognition across industries, are celebrated for their low power consumption and user-friendly interaction. Nevertheless, challenges such as accuracy fluctuations due to calibration methods and computational delays in real-time processing persist. In this innovative study, we introduce ConvIMage, a groundbreaking approach that transforms Inertial Measurement Unit (IMU) sensor time-series data into image data through Convolutional Neural Network (CNN) algorithms, enhancing motion estimation capabilities. ConvIMage ingeniously assembles pattern images by converting IMU-derived time-series data into distinctive color channels, empowering a CNN model to discern intricate movement patterns. Experimental assessments underscore ConvIMage's commendable performance, showcasing accuracies ranging from 83.42% to 85.97%, contingent upon the specific type of motion. This auspicious outcome positions ConvIMage as a robust solution for future IoT service development, leveraging motion data with enhanced accuracy and real-time processing capabilities for a multitude of applications. [ABSTRACT FROM AUTHOR]

Water, a critical resource, suffers from significant losses due to leakages in Water Distribution Networks (WDN). These losses present financial, environmental, and public health challenges. With water utility companies amassing vast amounts of data from enterprise information systems, the opportunity for data-driven leakage detection arises. With the increased use of enterprise information systems, water utility companies are generating vast amounts of data that can be valuable for predicting or detecting water leaks early and also for the development of new, automatic, and effective data-driven leak detection techniques. The presented study utilizes this data, by applying anomaly detection methods to heterogeneous time-series data from various components of the WDN in Slovakia. Our results demonstrate that components of the network, such as measured power consumption, water source temperature, and water source levels show significant positive associations with faults in the Water Distribution Network. [ABSTRACT FROM AUTHOR]

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios. [ABSTRACT FROM AUTHOR]

In the face of escalating cyber threats that have contributed significantly to global economic losses, this study presents a comprehensive dataset capturing the multifaceted nature of cyber-attacks across 225 countries over a 14-month period from October 2022 to December 2023. The dataset, comprising 77,623 rows and 18 fields, provides a detailed chronology of cyber-attacks, categorized into eight critical dimensions: spam, ransomware, local infection, exploit, malicious mail, network attack, on-demand scan, and web threat. The dataset also includes ranking data, offering a comparative view of countries' susceptibility to different cyber threats. The results reveal significant variations in the frequency and intensity of cyber-attacks across different countries and attack types. The data were meticulously compiled using modern AI-based data acquisition techniques, ensuring a high degree of accuracy and comprehensiveness. Correlation tests against the eight types of cyber-attacks resulted in the determination that on-demand scan and local infection are highly correlated, with a correlation coefficient of 0.93. Lastly, neural-network-based forecasting of these highly correlated factors (i.e., on-demand scan and local infection) reveals a similar pattern of prediction, with an MSE and an MAPE of 1.616 and 80.13, respectively. The study's conclusions provide critical insights into the global landscape of cyber threats, highlighting the urgent need for robust cybersecurity measures. [ABSTRACT FROM AUTHOR]

Introduction: Carbon neutrality has become a key strategy to combat global climate change. However, current methods for predicting carbon emissions are limited and require the development of more effective strategies to meet this challenge. This is especially true in the field of sports and competitions, where the energy intensity of major events and activities means that time series data is crucial for predicting related carbon emissions, as it can detail the emission patterns over a period of time. Method: In this study, we introduce an artificial intelligence-based method aimed at improving the accuracy and reliability of carbon emission predictions. Specifically, our model integrates an Improved Mahjong Search Algorithm (ISSA) and GRU-Transformer technology, designed to efficiently process and analyze the complex time series data generated by sporting events. These technological components help to capture and parse carbon emission data more accurately. Results: Experimental results have demonstrated the efficiency of our model, which underwent a comprehensive evaluation involving multiple datasets and was benchmarked against competing models. Our model outperformed others across various performance metrics, including lower RMSE and MAE values and higher R2 scores. This underscores the significant potential of our model in enhancing the accuracy of carbon emission predictions. Discussion: By introducing this new AI-based method for predicting carbon emissions, this study not only provides more accurate data support for optimizing and implementing carbon neutrality measures in the sports fi eld but also improves the accuracy of time series data predictions. This enables a deeper understanding of carbon emission trends associated with sports activities. It contributes to the development of more effective mitigation strategies, making a significant contribution to global efforts to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

Among the existing prediction methods for strata behaviors law in working faces, the methods based on numerical simulation and statistical regression cannot achieve real-time and precise prediction of strata behaviors law in working faces. Deep learning methods have problems such as a large number of hyperparameters that are difficult to set and slow model training speed. In order to solve the above problems, based on the time-series data of internal stress changes in the coal body monitored during the mining process of the 402102 working face in Hujiahe Coal Mine, the particle swarm optimization based gate recurrent unit (PSO-GRU) is applied to predict the strata behaviors law in the working face. The PSO algorithm is used to optimize GRU. The PSO-GRU model is constructed to achieve automatic optimization of hyperparameters, thereby improving the training speed and prediction precision of GRU. Based on the prediction results, the analytic hierarchy process (AHP) is used to establish the evaluation index system of the main control factors of the strata behaviors of the 402102 mining face. The roof conditions, mining technology, coal seam occurrence, and geological structure are identified as the first level indicators affecting the strata behaviors of the working face. The 14 representative second level indicators are further subdivided. The test results show the following points. ① Compared with the unoptimized GRU model, the mean square error (MSE) of the PSO-GRU model is reduced by 83.9%, the root mean square error (RMSE) is reduced by 59.8%, the mean absolute error (MAE) is reduced by 59.0%, and the coefficient of determination R2 is increased by 28.9%. ② The PSO-GRU model has a fitting degree of over 0.980 for predicting strata behaviors data, demonstrating good nonlinear fitting and generalization capabilities. ③ The occurrence factors of coal seams in geological conditions have the greatest impact on the strata behaviors of the mining face, with a weight of 0.47. Among the factors that can be intervened by humans, the impact of advancing speed on the strata behaviors of the working face is the greatest, with a weight of 0.13.

Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. Methods and procedures: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. Results: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ( ${p} < 0.01$ ) compared to prior GLP processing. Conclusion: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. Clinical impact: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.

Summary: Due to the energy crisis and environmental concerns, wind power has seen a considerable increase in use over the past 10 years as a source of renewable energy. Since wind is intermittent and variable, it is obvious that as penetration levels rise, the influence of wind power generation on the electric power system must be considered. Wind power forecasting is essential because large‐scale wind power integration will make it more difficult to plan, operate, and control the power system. An accurate forecast is an efficient way to deal with the operational problems brought on by wind variability. To better utilize wind energy resources, it is essential to increase prediction accuracy. Frequently, the noise in the dataset causes the ramp events to be misclassified or overestimated. The main emphasis of this study is the pre‐processing of wind power data that produces precise time‐series data while reducing noise or artifact content and maintaining the swing feature of the original data. For this task, the data augmentation approach is proposed, where the augmented data (synthetic data) will be added up with the training data, in such a way as to make the strategy useful for real‐time applications. As the next step, the significant features, such as higher‐order statistical features and lower‐order statistical features are extracted. The extracted features act as the input to the recurrent neural network (RNN) classifier, the weights of which are tuned using the proposed honey badger crow (HBCro) optimization algorithm. The proposed HBCro optimization algorithm acts as the major contribution of the proposed model, and it is modeled with the integration of the concepts of the crow search optimization (CSO) algorithm and the honey badger optimization algorithm (HBA). The proposed system is simulated in MATLAB and the effectiveness of the proposed method is validated by comparison with other conventional methods in terms of Error measures. Furthermore, the developed HBCro‐based RNN obtained efficient performance in terms of MSE, MAE, RMSE, RMPSE, MAPE, MARE, MSRE, and RMSRE of 0.1578, 0.0442, 0.2102, 123.238, 124.72, 0.9944, 1.1799, and 1.0732, respectively. [ABSTRACT FROM AUTHOR]

Neural networks inspired by differential equations have proliferated for the past several years, of which neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples. In theory, NCDEs exhibit better representation learning capability for time-series data than NODEs. In particular, it is known that NCDEs are suitable for processing irregular time-series data. Whereas NODEs have been successfully extended to adopt attention, methods to integrate attention into NCDEs have not yet been studied. To this end, we present attentive neural controlled differential equations (ANCDEs) for time-series classification and forecasting, where dual NCDEs are used: one for generating attention values and the other for evolving hidden vectors for a downstream machine learning task. We conduct experiments on 5 real-world time-series datasets and 11 baselines. After dropping some values, we also conduct experiments on irregular time-series. Our method consistently shows the best accuracy in all cases by non-trivial margins. Our visualizations also show that the presented attention mechanism works as intended by focusing on crucial information. [ABSTRACT FROM AUTHOR]

Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model's recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%. [ABSTRACT FROM AUTHOR]

(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling. [ABSTRACT FROM AUTHOR]

• Introduces in-depth key operating parameters and evaluates technical efficiency. • Dosage of urea, calcium hydrate and lye have relatively high improvement ratio. • Suggestions to promote the disposal facilities. Hazardous waste rotary kiln incineration, as the most effective and comprehensive technology to reduce and detoxify waste, generally faces problems such as low load rate and short continuous operating periods. However, there are few studies on the actual operation of such facilities and evaluation of their technical efficiency. Based on the 77-week time-series data of the case company, this study introduces in-depth key operating parameters and evaluates long-term technical efficiency through the data envelopment analysis (DEA) method. The results show that the continuous operating period of the rotary kiln incineration facility can reach more than half a year, with an average load rate of 91.7%. In the analysis of 9 input indicators, the amount of injected activated carbon could not be effectively evaluated due to the lack of relevant standards and online real-time monitoring of dioxins, which might become a weak link in the control of flue gas pollution. The average comprehensive technical efficiency of rotary kiln incineration facilities was 0.939, of which the average pure technical efficiency was 0.949 while the average scale efficiency was 0.989. With 33 of the 77 decision-making units being invalid, there is scope for improvement. The amount of incineration could be increased by 5.34%, and among the input variables, dosage of urea, calcium hydroxide and lye with a relatively high improvement ratio. Based on the results, targeted suggestions were proposed to advance the scientific and precise compatibility of hazardous waste, strengthen the control of dioxin emissions, and promote the intelligent control of the entire process. [ABSTRACT FROM AUTHOR]

Background: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self‐reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in‐depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time‐consuming and costly but also require systematic and cost‐effective approaches. Objective: The main objective of this study was to investigate the feasibility of training an end‐to‐end deep learning (DL) model by directly inputting time‐series activity tracking and sleep data obtained from consumer‐grade wrist‐worn activity trackers to identify comorbid depression and anxiety. Methods: To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non‐time‐series data. The basic structure of the DL model was designed to process mixed‐input data and perform multi‐label‐based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short‐term memory (LSTM), were applied to process the time‐series data, and model selection was conducted by comparing the performances of the hyperparameters. Results: This study achieved significant results in the multi‐label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed‐input DL model based on activity tracking data. The comparison of hyper‐parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results. Conclusions: This study can be considered as the first to develop a mixed‐input DL model based on activity tracking data for the multi‐label identification of late‐life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer‐grade wrist‐worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi‐label classification framework for identifying the complex symptoms of depression and anxiety. Key points: Deep learning (DL) models, notably ResNet, significantly enhance depression and anxiety classification using activity tracking data, outperforming traditional models in accuracy.Mixed‐input models that combine activity tracking with minimal assessment data demonstrate superior performance, underscoring the advantages of integrating diverse data types for mental health diagnosis.This study confirms the practicality of employing consumer‐grade wearable devices for the multi‐label classification of geriatric mood disorders, presenting an affordable and effective tool for healthcare [ABSTRACT FROM AUTHOR]

Floods are some of the most devastating crop disasters in Southeast Asia. The Pampanga River Basin in the Philippines is a representative flood-prone area, where cultivation patterns vary according to the flood risk. However, quantitative analyses of the effects of flooding on cultivation patterns remain quite limited. Accordingly, this study analyzed MODIS LAI data (MCD15A2H) from 2007 to 2022 to evaluate annual and geographical differences in cultivation patterns in the Candaba municipality of the basin. The analysis consisted of two stages of hierarchical clustering: a first stage for area classification and a second stage for the classification of annual LAI dynamics. As a result, Candaba was divided into four areas, which were found to be partly consistent with the observed flood risk. Subsequently, annual LAI dynamics for each area were divided into two or three clusters. Obvious differences among clusters were caused by flooding in the late rainy season, which delayed the start of planting in the dry season. Clusters also indicated that cultivation patterns slightly changed over the 16 years of the study period. The results of this study suggest that the two-stage clustering approach provided an effective tool for the analysis of MODIS LAI data when considering cultivation patterns characterized by annual and geographical differences. [ABSTRACT FROM AUTHOR]

The application of data analysis tools and procedures to perceive value from vast volume of data created by connected IoT devices is known as IoT data analytics. While predictive analytics on IoT dealing with the prediction involved with the setting of IoT appliances, Prescriptive analytics is the next stage of IoT data analytics involves deriving actionable insights from predictions made in previous stages. The incorporation of time-dependent parameters in prescriptive models provides a more accurate depiction of a complex environment and the decision-making process that goes along with it. The scope of our work is to recommend prescriptive analytical models that make better decisions through the analysis of dynamic IoT data stream in real-time and prescribe an optimal solution. We carry out an analysis of time-series data to identify the patterns of data and learn how they change. In this direction, we attempt to represent time-series data by reducing its length, forecast change points, map change points to prescribed actions, and propose optimal decisions ahead of time events. In this paper, an overview of IoT data analytics, survey of prescriptive analytical models, applications, issues, challenges and platforms for IoT analytics are discussed. [ABSTRACT FROM AUTHOR]

Time-series data are widely used in industrial manufacturing, meteorology, ships, electric power, vehicles, finance, and other fields, which promote the booming development of time-series database management systems. Faced with larger data scales and more diverse data modalities, efficiently storing and managing the data is very critical, and data encoding and compression become more and more important and are worth studying. Existing data encoding methods and systems fail to consider the characteristics of data in different modalities thoroughly, and some methods of time-series data analysis have not been applied to the problem of data encoding. We comprehensively introduce the multimodal data encoding methods and their system implementation in the Apache IoTDB time-series database system, especially for the industrial Internet of Things application scenarios. Our encoding method comprehensively considers data in multiple models including timestamp data, numerical data, Boolean data, frequency domain data, and text data, and fully explores and utilizes the characteristics of the corresponding modal of data, especially the characteristics of timestamp intervals approximation in timestamp modality, to carry out targeted data encoding design. At the same time, the data quality issue that may occur in practical applications has been taken into consideration in the encoding algorithm. Experimental evaluation and analysis at the encoding algorithm level and the system level over multiple datasets validate the effectiveness of our encoding method and its system implementation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Acute kidney injury (AKI) is common among hospitalized patients with sickle cell disease (SCD) and contributes to increased morbidity and mortality. Early identification and management of AKI is essential to preventing poor outcomes. We aimed to predict AKI earlier in patients with SCD using a machine-learning model that utilized continuous minute-by-minute physiological data. Methods: A total of6,278 adult SCD patient encounters were admitted to inpatient units across five regional hospitals in Memphis, TN, over 3 years, from July 2017 to December 2020. From these, 1,178 patients were selected after filtering for data availability. AKI was identified in 82 (7%) patient encounters, using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The remaining 1,096 encounters served as controls. Features derived from five physiological data streams, heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from bedside monitors were used. An XGBoost classifier was used for classification. Results: Our model accurately predicted AKI up to 12 h before onset with an area under the receiver operator curve (AUROC) of 0.91 (95% CI [0.89–0.93]) and up to 48 h before AKI with an AUROC of 0.82 (95% CI [0.80–0.83]). Patients with AKI were more likely to be female (64.6%) and have history of hypertension, pulmonary hypertension, chronic kidney disease, and pneumonia than the control group. Conclusion: XGBoost accurately predicted AKI as early as 12 h before onset in hospitalized SCD patients and may enable the development of innovative prevention strategies. [ABSTRACT FROM AUTHOR]

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys—at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies. [ABSTRACT FROM AUTHOR]

Constructing the reliable dynamic sensitivity profile for the output variable using the machine learning model is a challenging task; however, the dynamic sensitivity trends are helpful to understand the impact of the input variables on the system's performance. In this paper, we have derived the partial-derivative approach-based sensitivity analysis expression for the non-linear auto regressive with exogenous (NARX) model for the first time. The engineering systems-based case studies, i.e., two distillation columns with five and ten stages, respectively are taken which are commonly found in the chemical processing plants. Two output variables, i.e., liquid composition in tray 2 and tray 4 (Y2 and Y4) of a five-stage distillation column, and liquid composition in tray 7 (Y7) of a ten-stage (higher) distillation column are modelled by NARX with respect to time, feed concentration (Xf) and feed flow rate (Lf). The dynamic sensitivity profiles of the output variables with respect to Xf and Lf for the two distillation columns are plotted by the derived partial derivative-based sensitivity expression on the NARX model. Furthermore, the forward difference method of sensitivity analysis (first principle method) is also applied on the ordinary differential equations of the distillation columns to compute the sensitivity values of the output variables. A good agreement in the dynamic sensitivity values of the output variables with respect to the input variables is found for the two sensitivity analysis techniques thereby demonstrating the effectiveness of the partial-derivative approach for the improved NARX's interpretability performance. This research presents the explicit partial-derivative based sensitivity analysis expression for the NARX model which can be utilised for time-series applications and can provide the insights about the model's interpretation performance.

Purpose: The recognition of sepsis as a heterogeneous syndrome necessitates identifying distinct subphenotypes to select targeted treatment. Methods: Patients with sepsis from the MIMIC-IV database (2008–2019) were randomly divided into a development cohort (80%) and an internal validation cohort (20%). Patients with sepsis from the ICU database of Peking University People's Hospital (2008–2022) were included in the external validation cohort. Time-series k-means clustering analysis and dynamic time warping was performed to develop and validate sepsis subphenotypes by analyzing the trends of 21 vital signs and laboratory indicators within 24 h after sepsis onset. Inflammatory biomarkers were compared in the ICU database of Peking University People's Hospital, whereas treatment heterogeneity was compared in the MIMIC-IV database. Findings: Three sub-phenotypes were identified in the development cohort. Type A patients (N = 2525, 47%) exhibited stable vital signs and fair organ function, type B (N = 1552, 29%) was exhibited an obvious inflammatory response and stable organ function, and type C (N = 1251, 24%) exhibited severely impaired organ function with a deteriorating tendency. Type C demonstrated the highest mortality rate (33%) and levels of inflammatory biomarkers, followed by type B (24%), whereas type A exhibited the lowest mortality rate (11%) and levels of inflammatory biomarkers. These subphenotypes were confirmed in both the internal and external cohorts, demonstrating similar features and comparable mortality rates. In type C patients, survivors had significantly lower fluid intake within 24 h after sepsis onset (median 2891 mL, interquartile range (IQR) 1530–5470 mL) than that in non-survivors (median 4342 mL, IQR 2189–7305 mL). For types B and C, survivors showed a higher proportion of indwelling central venous catheters (p

The importance of monitoring in assessing structural safety and durability continues to grow. With recent technological advancements, Internet of Things (IoT) sensors have garnered attention for their complex scalability and varied detection capabilities, becoming essential devices for monitoring. However, during the data collection process of IoT sensors, anomalies arise due to network instability, sensor noise, and malfunctions, degrading data quality and compromising monitoring system reliability. In this study, Interquartile Range (IQR), Long Short-Term Memory Autoencoder (LSTM-AE), and time-series decomposition were employed for anomaly detection in Structural Health Monitoring (SHM) processes. IQR and LSTM-AE produce irregular patterns; however, time-series decomposition effectively detects such anomalies. In road monitoring influenced by weather and traffic, the time-series decomposition approach is expected to play a crucial role in enhancing monitoring accuracy.

Water quality monitoring and analysis in fish farms are of paramount importance for the aquaculture sector; however, traditional methods can pose difficulties. To address this challenge, this study proposes an IoT-based deep learning model using a time-series convolution neural network (TMS-CNN) for monitoring and analyzing water quality in fish farms. The proposed TMS-CNN model can handle spatial–temporal data effectively by considering temporal and spatial dependencies between data points, which allows it to capture patterns and trends that would not be possible with traditional models. The model calculates the water quality index (WQI) using correlation analysis and assigns class labels to the data based on the WQI. Then, the TMS-CNN model analyzed the time-series data. It produces high accuracy of 96.2% in analysis of water quality parameters for fish growth and mortality conditions. The proposed model accuracy is higher than the current best model MANN, which has only had an accuracy of 91%. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online discussion forums are crucial educational tools that facilitate interaction between students and teachers. We created an online discussion forum, with the teacher serving as the moderator. 58 posts and 1,955 comments were collected. We analysed these data through Latent Dirichlet Allocation to determine word cooccurrence and extract key topics from the discussions. We examined how the topics and depth of the discussion changed over time and the roles that the students and teacher played in the forum by using time-series data. The results indicate that the teacher played a crucial role in the forum by initially serving as a moderator and then guiding or prompting students to discuss certain topics. Those who participated more actively achieved higher grades, and those that were passive had lower grades at the end Overall, the online discussion forum enhanced the course by helping students understand the core materials and topics in the course. [ABSTRACT FROM AUTHOR]