Estimating lags in a kraft mill

In pulp mills, lags obscure the effect of upstream operations on downstream measurements. Here, we estimate lags in a Canadian pulp mill using autoregressive exogenous (ARX) models. First, we show that ARX models can approximate lags in a process simulation that resembles the liquor storage tanks in pulp mills, a major source of lag in the kraft recovery cycle. Then, we use ARX models to estimate the lagged effect of a change in species pulped on as-fired liquor heating value, viscosity, and boiling point rise. Additionally, we compare the predictions of the ARX models to autoregressive (AR) models and a persistence model. The estimated lags between a change in species and heating value (49 h) and boiling point rise (41 h) agree with a detailed simulation of the mill and are close to estimated hydraulic residence times, suggesting that the liquor tanks exhibit imperfect mixing. A lagged effect of species change on viscosity could not be identified. ARX and AR models produce similar predictions that are slightly better than those of a persistence model. Finally, we show that process measurements upstream of units characterized by large residence times will likely provide little benefit to prediction accuracy.

An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM

Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement.

Mortality Associated With Infectious Diseases in Dutch Nursing Homes

BACKGROUND

Although older people in nursing homes have a larger susceptibility to infectious diseases, the extent to which infectious diseases contribute to their mortality is unknown. Therefore, we quantified the associations between seasonal infectious diseases and all-cause mortality in Dutch nursing homes.

METHODS

We analyzed time series (January 2009 to December 2021) of the weekly sentinel surveillance of infectious diseases in Dutch nursing homes. A generalized linear model with binomial distribution and identity link was used to associate the proportion of all-cause mortality with the incidence of infections: COVID-19 (2020-2021), gastroenteritis (GE), influenza-like illness (ILI), and lower respiratory tract infections (LRTIs) at 0- to 4-week time-lags (mortality incidence at 0 to 4 weeks after infections incidence).

RESULTS

Over 13 years, 81 nursing homes participated, with 20 to 35 homes each year (mean: 26). A total of 11,555 all-cause deaths occurred over 1,864,667 resident weeks, averaging a mortality incidence of 6.2 per 1000 resident weeks. All 4 tested infectious diseases exhibited a significant association with all-cause mortality in nursing homes (P ≤ .01). Collectively, these infectious diseases were associated with 18.6% (95% CI, 17.8%-19.3%) of all deaths. The association between mortality and ILI was significant in 7 of 12 ILI seasons. Yearly mortality associated with the specific infectious diseases was as follows: LRTI (10.2%; 95% CI, 9.6%-10.8%), ILI (8.2%; 95% CI, 7.5%-8.9%) (over the 7 of 12 significant seasons), COVID-19 (6.5%; 95% CI, 5.4%-7.7%) (over 2019-2021 as there was no previous SARS-CoV-2 circulation), and GE (2.3%; 95% CI, 2.0%-2.5%).

CONCLUSION AND IMPLICATIONS

In nursing homes, the occurrence of seasonal respiratory and gastrointestinal infections is associated with nearly one-fifth of all-cause deaths. Although infection prevention and surveillance may already be performed in some nursing homes, it is vital to implement, and enhance targeted strategies like (hand) hygiene protocols, environmental cleaning practices, reducing droplet and aerosol transmission, and vaccination to effectively address specific infections.

The associations of supervised consumption services with the rates of opioid-related mortality and morbidity outcomes at the public health unit level in Ontario (Canada): A controlled interrupted time-series analysis

INTRODUCTION

This study aimed to assess the impact of the implementation of legally sanctioned supervised consumption sites (SCS) in the Canadian province of Ontario on opioid-related deaths, emergency department (ED) visits and hospitalisations at the public health unit (PHU) level.

METHODS

Monthly rates per 100,000 population of opioid-related deaths, ED visits and hospitalisations for PHUs in Ontario between December 2013 and March 2022 were collected. Aggregated and individual analyses of PHUs with one or more SCS were conducted, with PHUs that instituted an SCS being matched to control units that did not. Autoregressive integrated moving average models were used to estimate the impact of SCS implementation on opioid-related deaths, ED visits and hospitalisations.

RESULTS

Twenty-one legally sanctioned SCS were implemented across nine PHUs in Ontario during the study period. Interrupted time series analyses showed no statistically significant changes in opioid-related death rates in aggregated analyses of intervention PHUs (increase of 0.02 deaths/100,000 population/month; p = 0.27). Control PHUs saw a significant increase of 0.38 deaths/100,000 population/month; p < 0.001. No statistically significant changes were observed in the rates of opioid-related ED visits in intervention PHUs (decrease of 0.61 visits/100,000 population/month; p = 0.39) or controls (increase of 0.403 visits; p = 0.76). No statistically significant changes to the rates of opioid-related hospitalisations were observed in intervention PHUs (0 hospitalisations/100,000 population/month; p = 0.98) or controls (decrease of 0.05 hospitalisations; p = 0.95).

DISCUSSION AND CONCLUSIONS

This study did not find significant mortality or morbidity effects associated with SCS availability at the population level in Ontario. In the context of a highly toxic drug supply, additional interventions will be required to reduce opioid-related harms.

How much rainwater contributes to a spring discharge in the Guarani Aquifer System: insights from stable isotopes and a mass balance model

Outcrops play an important role in groundwater recharge. Understanding groundwater origins, dynamics and its correlation with different water sources is essential for effective water resources management and planning in terms of quantity and quality. In the case of the Guarani Aquifer System (GAS) outcrop areas are particularly vulnerable to groundwater pollution due to direct recharge processes. This study focuses on the Alto Jacaré-Pepira sub-basin, a watershed near Brotas, a city in the central region of the state of São Paulo, Brazil, where groundwater is vital for supporting tourism, agriculture, urban water supply, creeks, river and wetlands. The area has a humid tropical climate with periods of both intense rainfall and drought, and the rivers remain perennial throughout the year. Therefore, the aim of this study is to investigate the interconnections between a spring and its potential sources of contribution, namely rain and groundwater, in order to elucidate the relationships between the different water sources. To achieve this, on-site monitoring of groundwater depth, rainfall amount, and stable isotope ratios (deuterium (2H) and oxygen-18 (18O)) from rain, spring discharge, and a monitoring well was carried out from 2013 to 2021. The results indicate that the mean and standard deviations for δ18O in rainwater exhibit higher variability, resulting in -4.49 ± 3.18 ‰ VSMOW, while δ18O values from the well show minor variations, similar to those of the spring, recording -7.25 ± 0.32 ‰ and -6.94 ± 0.28 ‰ VSMOW, respectively. The mixing model's outcomes reveal seasonal variations in water sources contribution and indicate that groundwater accounts for approximately 80 % of spring discharge throughout the year. Incorporating stable isotopes into hydrological monitoring provides valuable data for complementing watershed analysis. The values obtained support the significance of the aquifer as a primary source, thereby offering critical insights into stream dynamics of the region.

Exploring the exponential sensitivity of risk perception in the COVID-19 pandemic

Individual's risk perception regarding specific hazards is a dynamic process that evolves over time. This study analyzed the relationship between the number of COVID-19 cases and the South Korean public's risk perceptions from the outset of the pandemic to the recent past. More than 70 repeated cross-sectional surveys were conducted biweekly to measure individuals' risk perception. An autoregressive integrated moving average with explanatory variable time series analysis was used to characterize the relationship between the number of COVID-19 cases and level of risk perceptions. It revealed that individuals' risk perception and the number of COVID-19 cases were not linearly related but were logarithmically correlated. This finding can be understood as a psychic numbing effect, suggesting that people's perception of risk is not linear but rather exponentially sensitive to changes. The findings also revealed a significant influence of individuals' trust in local governments on their risk perceptions, highlighting the substantial role played by local governments in direct risk management during the COVID-19 pandemic.

Looking beyond vaccines: Cultural tightness-looseness moderates the relationship between immunization coverage and disease prevention vigilance

Advancements in vaccination technologies mitigate disease transmission risks but may inadvertently suppress the behavioral immune system, an evolved disease avoidance mechanism. Applying behavioral immune system theory and utilizing robust big data analytics, we examined associations between rising vaccination coverage and government policies, public mobility, and online information seeking regarding disease precautions. We tested whether cultural tightness-looseness moderates the relationship between mass immunization and disease prevention vigilance. Comprehensive time series analyses were conducted using American data (Study 1) and international data (Study 2), employing transfer function modeling, cross-correlation function analysis, and meta-regression analysis. Across both the US and global analyses, as vaccination rates rose over time, government COVID-19 restrictions significantly relaxed, community mobility increased, and online searches for prevention information declined. The relationship between higher vaccination rates and lower disease prevention vigilance was stronger in culturally looser contexts. Results provide initial evidence that mass immunization may be associated with attenuated sensitivity and enhanced flexibility of disease avoidance psychology and actions. However, cultural tightness-looseness significantly moderates this relationship, with tighter cultures displaying sustained vigilance amidst immunization upticks. These findings offer valuable perspectives to inform nuanced policymaking and public health strategies that balance prudent precautions against undue alarm when expanding vaccine coverage worldwide.

RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI

Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments.

Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals

Topological data analysis (TDA) is increasingly recognized as a promising tool in the field of neuroscience, unveiling the underlying topological patterns within brain signals. However, most TDA related methods treat brain signals as if they were static, i.e., they ignore potential non-stationarities and irregularities in the statistical properties of the signals. In this study, we develop a novel fractal dimension-based testing approach that takes into account the dynamic topological properties of brain signals. By representing EEG brain signals as a sequence of Vietoris-Rips filtrations, our approach accommodates the inherent non-stationarities and irregularities of the signals. The application of our novel fractal dimension-based testing approach in analyzing dynamic topological patterns in EEG signals during an epileptic seizure episode exposes noteworthy alterations in total persistence across 0, 1, and 2-dimensional homology. These findings imply a more intricate influence of seizures on brain signals, extending beyond mere amplitude changes.

Suggestions and Comparisons of Two Algorithms for the Simplification of Bluetooth Sensor Data in Traffic Cordons

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin-destination (i-j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements.

Associations of Suicide Referents With Different Moral Connotation With Actual Suicides

Background: Different words used for suicide (so-called suicide referents) have different moral connotations, and neutral referents are recommended in media reporting guidelines. Aims: To assess how different referents in media reports are related to actual suicides. Method: Austrian news articles for each month between 2000 and 2021 (n = 276 months) were obtained from the Austrian Press Agency. Time series were modeled for media items referring to suicide as a crime [Selbstmord], an act of freedom [Freitod], or neutral connotation [Suizid]. Temporal associations with suicides in the month before, during, and after the reporting were examined. Results: Terminology referring to suicide as an act of free will [Freitod] was weakly associated with increases in total, male, and female suicides and with suicides in up to 64-year-olds in the same month. No other statistically significant associations were found. Limitations: No detailed content analysis of media reports was done. Conclusion: During times of prevalent use of referents suggesting suicide to be an act of freedom, there are small-sized increases in suicides. The simultaneous occurrence of this referent and suicides might reflect effects of a societal framing present in both the media and the community rather than a sheer media effect.

Birth Patterns in the Aftermath of the 1918 Influenza Pandemic in India: The Case of Madras City

This paper examines the timing of one-time fluctuations in births subsequent to the 1918 influenza pandemic in Madras (now Chennai), India. After seasonally decomposing key demographic aggregates, we identified abrupt one-time fluctuations in excess births, deaths, and infant deaths. We found a contemporaneous spike in excess deaths and infant deaths and a 40-week lag between the spike in deaths and a subsequent deficit in births. The results suggest that India experienced the same kind of short-term postpandemic "baby bust" that was observed in the United States and other countries. Identifying the mechanisms underlying this widespread phenomenon remains an open question and an important topic for future research.

Erratum: Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

[This corrects the article DOI: 10.3389/fpubh.2024.1393677.].

Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.

Evaluation of a regional tobacco control programme (Greater Manchester's Making Smoking History) on quitting and smoking in England 2014 to 2022: a time-series analysis

INTRODUCTION

This study aimed to assess the impact of Greater Manchester's Making Smoking History programme - a region-wide smoking cessation programme launched in January 2018 - on key smoking and quitting outcomes.

METHODS

Data were from a nationally-representative monthly survey, 2014-2022 (n=171,281). We used interrupted time-series analyses (Autoregressive Integrated Moving Average [ARIMA] and generalised additive models [GAM]) to examine regional differences between Greater Manchester and the rest of England, before and during the programme's first five years. Outcomes were rates of quit attempts and overall quits among smokers, quit success rates among smokers who tried to quit (pre-registered outcomes), and current smoking prevalence among adults (unregistered outcome).

RESULTS

Results showed mixed effects of the programme on quitting. Primary ARIMA models showed comparative reductions in quit success rates (change in quarterly difference between regions = -11.03%; 95%CI -18.96;-3.11) and overall quit rates in Greater Manchester compared with the rest of England (-2.56%; 95%CI -4.95;-0.18), and no significant change in the difference in the quit attempt rate (+2.95%; 95%CI -11.64;17.54). These results were not consistently observed across sensitivity analyses or GAM analyses. Exploratory ARIMA models consistently showed smoking prevalence in Greater Manchester declined more quickly than in the rest of England following initiation of the programme (-2.14%; 95%CI -4.02;-0.27).

CONCLUSIONS

The first five years of Greater Manchester's Making Smoking History programme did not appear to be associated with substantial increases in quitting activity. However, exploratory analyses showed a significant reduction in the regional smoking rate, over and above changes in the rest of England over the same period.

IMPLICATIONS

Taken together, these results show a relative decline in smoking prevalence in Greater Manchester but equivocal data on quitting, introducing some uncertainty. It is possible the programme has reduced smoking prevalence in the absence of any substantial change in quitting activity by changing norms around smoking and reducing uptake, or by reducing the rate of late relapse. It is also possible that an undetected effect on quitting outcomes has still contributed to the programme's impact on reducing prevalence to some degree. It will be important to evaluate the overall impact of the programme over a longer timeframe.

Unveiling the underlying drivers of Phanerozoic marine diversification

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture-mark-recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities.

Reporting delays: A widely neglected impact factor in COVID-19 forecasts

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean, and devoid of noise? The complexity and variability inherent in data collection and reporting suggest otherwise. While we cannot evaluate the integrity of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Using our framework, we expose and analyze reporting delays in eight regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data by using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50%. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.

Evaluating Population-Level Interventions and Exposures for Suicide Prevention

Evaluations of interventions targeting the population level are an essential component of the policy development cycle. Pre-post designs are widespread in suicide prevention research but have several significant limitations. To inform future evaluations, our aim is to explore the three most frequently used approaches for assessing the association between population-level interventions or exposures and suicide - the pre-post design, the difference-in-difference design, and Poisson regression approaches. The pre-post design and the difference-in-difference design will only produce unbiased estimates of an association if there are no underlying time trends in the data and there is no additional confounding from other sources. Poisson regression approaches with covariates for time can control for underlying time trends as well as the effects of other confounding factors. Our recommendation is that the default position should be to model the effects of population-level interventions or exposures using regression methods that account for time effects. The other designs should be seen as fall-back positions when insufficient data are available to use methods that control for time effects.

RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

Radiological complexity of nuclear facilities: an information complexity approach to workplace monitoring

Nuclear energy is crucial for achieving net-zero carbon emissions. A big challenge in the nuclear sector is ensuring the safety of radiation workers and the environment, while being cost-effective. Workplace monitoring is key to protecting workers from risks of ionising radiation. Traditional monitoring involves radiological surveillance via installed radiation monitors, continuously recording measurements like radiation fields and airborne particulate radioactivity concentrations, especially where sudden radiation changes could significantly impact workers. However, this approach struggles to detect incremental changes over a long period of time in the radiological measurements of the facility. To address this limitation, we propose abstracting a nuclear facility as a complex system. We then quantify the information complexity of the facility's radiological measurements using an entropic metric. Our findings indicate that the inferences and interpretations from our abstraction have a firm basis for interpretation and can enhance current workplace monitoring systems. We suggest the implementation of a radiological complexity-based alarm system to complement existing radiation level-based systems. The abstraction synthesized here is independent of the type of nuclear facility, and hence is a general approach to workplace monitoring at a nuclear facility.

Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.

Application of Recurrence Plot Analysis to Examine Dynamics of Biological Molecules on the Example of Aggregation of Seed Mucilage Components

The goal of the research is to describe the aggregation process inside the mucilage produced by plant seeds using molecular dynamics (MD) combined with time series algorithmic analysis based on the recurrence plots. The studied biological molecules model is seed mucilage composed of three main polysaccharides, i.e. pectins, hemicellulose, and cellulose. The modeling of biological molecules is based on the assumption that a classical-quantum passage underlies the aggregation process in the mucilage, resulting from non-covalent interactions, as they affect the macroscopic properties of the system. The applied recurrence plot approach is an important tool for time series analysis and data mining dedicated to analyzing time series data originating from complex, chaotic systems. In the current research, we demonstrated that advanced algorithmic analysis of seed mucilage data can reveal some features of the dynamics of the system, namely temperature-dependent regions with different dynamics of increments of a number of hydrogen bonds and regions of stable oscillation of increments of a number of hydrophobic-polar interactions. Henceforth, we pave the path for automatic data-mining methods for the analysis of biological molecules with the intermediate step of the application of recurrence plot analysis, as the generalization of recurrence plot applications to other (biological molecules) datasets is straightforward.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Background

The use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs.

Methods

This is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends.

Results

Overall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for "Respiratory Diseases," whereas the "Mental Disorders" category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08-0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30-0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98-1.69).

Conclusion

Our findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.