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.

Identifying anomalies in multi-dimensional sequential data is crucial for ensuring optimal performance across various domains and in large-scale systems. Traditional contrastive methods utilize feature similarity between different features extracted from multidimensional raw inputs as an indicator of anomaly severity. However, the complex objective functions and meticulously designed modules of these methods often lead to efficiency issues and a lack of interpretability. Our study introduces a structural framework called SimDetector, which is a Local-Global Multi-Scale Similarity Contrast network. Specifically, the restructured and enhanced GRU module extracts more generalized local features, including long-term cyclical trends. The multi-scale sparse attention module efficiently extracts multi-scale global features with pattern information. Additionally, we modified the KL divergence to suit the characteristics of time series anomaly detection, proposing a symmetric absolute KL divergence that focuses more on overall distribution differences. The proposed method achieves results that surpass or approach the State-of-the-Art (SOTA) on multiple real-world datasets and synthetic datasets, while also significantly reducing Multiply-Accumulate Operations (MACs) and memory usage.

The increasing use of time-series analyses in exploring the relationship between daily ambient temperature and mortality has expanded our understanding of the potential health impacts of climate change. However, it raises significant concerns about the risk of overinterpretation and misattribution of statistical findings. This review examines the methodological assumptions and interpretation pitfalls prevalent in current research on ambient temperature-mortality associations. Extremely elevated ambient temperatures are well-known to elicit physiological stress and increase mortality risk; however, there is no physiological evidence for lethality risk within normal ambient temperature ranges. Despite this, many studies attribute mortality risks across the entire ambient temperature-mortality curve, including normal range ambient temperatures, thus oversimplifying complex underlying physiological processes. Overinterpretation may lead to inaccurate assessments and misguided public health policies. We caution against the tendency to extrapolate results from extreme heat conditions to milder, more typical summer ambient temperature ranges. We advocate for an interdisciplinary approach that combines physiological, clinical, and epidemiological perspectives, with a strong emphasis on the role of behavioral thermoregulation and socio-economic factors to link normal range ambient temperatures with mortality. We recommend analyses centered on excess mortality during defined heatwave periods, and to incorporate heat stress biomarkers to substantiate causal claims for temperatures below heatwaves threshold. A careful approach to interpreting ambient temperature-mortality associations is crucial for formulating evidence-based public health policies.

This study investigates the forecasting of cardiovascular mortality trends in Greece\'s elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook\'s Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

BACKGROUND: Wastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated.
OBJECTIVE: We assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data.
METHODS: Smooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021-May 2023 at 25 sites in North Carolina, USA.
RESULTS: All four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site.
CONCLUSIONS: Our results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.

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%).
CONCLUSIONS: 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.

This paper examines the relationship between different dimensions of economic uncertainty and suicide rates in England from 1985 to 2020, both in the short and long term. The study employs a non-linear autoregressive distributed lag framework for cointegration estimation. This approach allows testing for the existence of possible asymmetries in the response of suicide mortality to increases in economic uncertainty. Uncertainty is gauged by different proxies that allow computing financial uncertainty and labour market uncertainty indicators. The analysis is replicated by gender and across regions, controlling for unemployment and economic growth. Overall, the analysis suggests that uncertainty intensified during the first year of the COVID-19 pandemic. This is in line with the stylized facts of economic uncertainty and its pronounced role in recessions. When replicating the experiment by gender, we find that women seem to be more sensitive to changes in uncertainty. Regarding the existence of asymmetries, we found that decreases in economic uncertainty have a greater impact on suicide mortality than increases.

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.

BACKGROUND: 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).
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.

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.