Sudden Events Research Articles

Research Progress on the Pathogenesis of Primary Enuresis in Children

Nocturnal enuresis (NE) is a common disease in children, commonly known as bedwetting. The International Children's Continence Society (ICCS) defines intermittent urinary incontinence during nighttime sleep in children aged 5 and above as enuresis. Enuresis can be divided into two types: primary nocturnal enuresis (PNE) and secondary nocturnal enuresis (SNE). PNE refers to nocturnal urinary control ability that has never been achieved for more than 6 months in children aged 5 and above with enuresis; SNE refers to the recurrence of enuresis symptoms after a period of more than 6 months of continuous nocturnal urinary control ability. SNE is relatively rare in clinical practice and is often caused by sudden stress events or other diseases such as snoring. Its pathogenesis and treatment are different from PNE. PNE is a multifactorial disease related to genetics, and its pathogenesis is not fully understood. There are not many research and review articles on its pathogenesis. In recent years, there has been significant progress in the epidemiology and pathogenesis of PNE. The following is a summary.

A nose for trouble: ecotoxicological implications for climate change and disease in Saiga antelope (S. t. tatarica)

In recent decades, Saiga antelope (Saiga t. tatarica) mass die-offs have become more common. The mass die-off of 2015 in central Kazakhstan, recorded 140,000 individual deaths across multiple herds. Previously, research has shown atmospheric humidity, the bacterium Pasteurella multocida serotype B, and resultant haemorrhagic septicaemia, were the primary cause. However, other synergistic factors may have impacted this process. Here we use a multivariate compositional data analysis (CoDA) approach to assess what other factors may have been involved. We show a pollutant linkage mechanism where relative humidity and dewpoint temperature combine with environmental pollutants, potentially toxic elements (e.g., Hg, As), complex carbon compounds (e.g., Acetone, Toluene), and inorganic compounds (e.g., CHx, SO2) which affected the Saiga during the calving season (start and peak) and at the onset of the mass die-off. We suggest a mechanism for this process. Upon arrival at their carving grounds, the Saiga experienced a sudden precipitation event, a spike in temperatures, and resultant high humidity occurs. The infectious bacterium P. multocida serotype B then spreads. Further, environmental pollutants contained within steppe soils are released to the air, forming localised smog events, these synergistically combine, and mass die-off occurs.

Cryptocurrency Turmoil: Unraveling the Collapse of a Unified Stablecoin (USTC) through Twitter as a Passive Sensor.

This study sought to explore whether Twitter, as a passive sensor, could have foreseen the collapse of the Unified Stablecoin (USTC). In May 2022, in just a few days, the cryptocurrency went to near-zero valuation. Analyzing 244,312 tweets from 89,449 distinct accounts between April and June 2022, this study delved into the correlation between personal sentiments in tweets and the USTC market value, revealing a moderate correlation with polarity. While sentiment analysis has often been used to predict market prices, the results suggest the challenge of foreseeing sudden catastrophic events like the USTC collapse solely through sentiment analysis. The analysis uncovered unexpected global interest and noted positive sentiments during the collapse. Additionally, it identified events such as the launch of the new Terra blockchain (referred to as "Terra 2.0") that triggered positive surges. Leveraging machine learning clustering techniques, this study also identified distinct user behaviors, providing valuable insights into influential figures in the cryptocurrency space. This comprehensive analysis marks an initial step toward understanding sudden and catastrophic phenomena in the cryptocurrency market.

Modeling and Improvement Strategies for Safety Resilience in Maritime Hazardous Chemical Transportation System Based on Dissipative Structure Theory and System Dynamics

Maritime hazardous chemical transportation accidents have the characteristics of strong suddenness, wide influence, and great harm. To analyze the ability of a maritime hazardous chemical transportation system (MHCTS) to cope with sudden disturbance events, “resilience” is introduced into MHCTS safety research. The key to studying resilience is modeling its evolutionary process. Based on the dissipative structure theory, this study analyzes the entropy flow mechanism of MHCTS safety resilience evolution. Through a statistical analysis of 197 investigation reports on maritime hazardous chemical transportation accidents, the factors influencing the safety resilience of the MHCTS were determined. The entropy value and weight of each influencing factor were calculated using the entropy method and entropy weight method, respectively. Based on this, an entropy model of the safety resilience evolution of the MHCTS was established. The evolution process falls under four categories of disturbance strengths, which were simulated using the system dynamics method. The degree of contribution of absorptive, adaptive, and restorative capacities to the improvement of system safety resilience under four disturbance conditions and the sensitivity of each influencing factor to the absorptive, adaptive, and restorative capacities were analyzed. Based on the analyses, targeted resilience improvement strategies are proposed. The research results provide a theoretical reference for the study of safety resilience mechanisms and resilience management in the MHCTS.

Research on electric vehicle load forecasting considering regional special event characteristics

With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic.

Construction of Knowledge Graph for Emergency Resources

Knowledge graphs can effectively organize and represent information related to emergency resources for unforeseen sudden events. In this study, we construct a model layer for the knowledge graph of emergency resources, focused on sudden events, through the classification and analysis of unforeseen disaster measures. This study defines eight interconnected entity types, each characterised by a set of attributes and engaging in one or more relationships with other entity types. Utilizing 121 incident investigation reports from the emergency management departments of various provinces and cities over the past five years, we select five entities with the highest frequency of occurrence along with their corresponding four relationships. We then design an extraction plan for these entities and relationships. Based on the completed knowledge graph data, we formulate 14 questions related to emergency resources for sudden events and construct 19 corresponding question-and-answer templates using a template-based question-answering (QA) approach. We retrieve the corresponding Cypher statement templates through template mapping and obtain the question answers through querying. Finally, we design a knowledge graph question-and-answer system using the Django web framework, which includes entity queries and knowledge QA functions, specifically for emergency resources related to sudden events.

Research on Spatiotemporal Heterogeneity of the Impact of Earthquakes on Global Copper Ore Supply Based on Geographically Weighted Regression

The large and super large copper deposits worldwide are mainly distributed in the Pacific Rim and Gondwana metallogenic domains, and they are highly coupled with the spatial location of seismically active zones. Major copper-producing countries such as Chile are located in areas with high seismic activity. Earthquakes often cause copper mines to shut down, copper prices to soar, and even mining disasters, affecting the stable supply of global copper. In order to study the impact of earthquakes on the global copper ore supply, information on resource endowments, production, and earthquake data from the past 30 years are collected. This article mainly analyzes (1) the spatial correlation between earthquakes and copper mine projects, and the spatial characteristics of earthquakes’ impact on copper mine production, using correlation analysis and geographically weighted regression (GWR); (2) the impact of sudden earthquake events on the export volume and price of copper mines from the perspective of time, using the earthquake index constructed based on the magnitude of the earthquake, the depth of the epicenter, and the distance from the copper mine. The results indicate that the regions with high spatial correlation between copper mine projects and earthquakes are mainly located along the Pacific coast of South and North Americas. Earthquakes can lead to an increase in copper prices, but they will fall significantly in the short term. The impact of earthquakes on export volume generally has a delay period of 1–2 months, resulting in a short-term decrease in export volume. This article quantitatively evaluates the impact of earthquake risk on the supply chain from both spatial and temporal dimensions, providing reference for supply chain risk monitoring, risk impact assessment, and prediction. On the basis of the research results of this article, mineral resource management departments can quantitatively evaluate the spatiotemporal impact of natural risks such as earthquakes on the supply chain, adjust management policies in a timely manner, and improve the level of refined management of supply chain risks.

Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic

BackgroundConcept drift and covariate shift lead to a degradation of machine learning (ML) models. The objective of our study was to characterize sudden data drift as caused by the COVID pandemic. Furthermore, we investigated the suitability of certain methods in model training to prevent model degradation caused by data drift.MethodsWe trained different ML models with the H2O AutoML method on a dataset comprising 102,666 cases of surgical patients collected in the years 2014–2019 to predict postoperative mortality using preoperatively available data. Models applied were Generalized Linear Model with regularization, Default Random Forest, Gradient Boosting Machine, eXtreme Gradient Boosting, Deep Learning and Stacked Ensembles comprising all base models. Further, we modified the original models by applying three different methods when training on the original pre-pandemic dataset: (1) we weighted older data weaker, (2) used only the most recent data for model training and (3) performed a z-transformation of the numerical input parameters. Afterwards, we tested model performance on a pre-pandemic and an in-pandemic data set not used in the training process, and analysed common features.ResultsThe models produced showed excellent areas under receiver-operating characteristic and acceptable precision-recall curves when tested on a dataset from January-March 2020, but significant degradation when tested on a dataset collected in the first wave of the COVID pandemic from April-May 2020. When comparing the probability distributions of the input parameters, significant differences between pre-pandemic and in-pandemic data were found. The endpoint of our models, in-hospital mortality after surgery, did not differ significantly between pre- and in-pandemic data and was about 1% in each case. However, the models varied considerably in the composition of their input parameters. None of our applied modifications prevented a loss of performance, although very different models emerged from it, using a large variety of parameters.ConclusionsOur results show that none of our tested easy-to-implement measures in model training can prevent deterioration in the case of sudden external events. Therefore, we conclude that, in the presence of concept drift and covariate shift, close monitoring and critical review of model predictions are necessary.

Community-based education: An effective policy for earthquake preparedness in Iran: Empirical study.

As natural or man-made shocks, disasters are sudden events that require emergency and fundamental measures. The need to be prepared for disasters has become clearer than ever worldwide. This study aimed to investigate the effect of earthquake preparedness training using the face-to-face or cascade method in rural households living in Juybar County (Iran) in 2018. Following an experimental design with a pretest and posttest, the study was performed on married women aged 10-49 living in Kurdkolay village of Juybar county (n = 372). Participants were randomly categorized into two groups of training (n = 95) and face-to-face cascade method (n = 95). Data were collected using the Disaster Preparedness Assessment Index, comprising demographic and education sections. Data were collected using the disaster preparedness assessment index and analyzed using SPSS version 24. The findings indicated improved preparedness in all five dimensions in both groups. There was a significant difference between the study groups immediately and one week after the intervention (P < 0.05); i.e. a higher impact for the face-to-face cascade method. Meanwhile, both groups presented a similar level of preparedness 90 days after receiving the intervention (P > 0.05). The face-to-face cascade method was more effective in the short term, while no difference was found in the long term. Crisis managers and policymakers should choose their preferred method based on available facilities, human resources, and context to increase preparedness against earthquakes. Mothers play a vital role in educating and nurturing their children. It is suggested to conduct educational programs at the community level using the most appropriate methods that are chosen based on the best evidence.

Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration

&lt;span&gt;This paper explores using cameras aimed at the accelerator and brake pedals during sudden unintended acceleration in cars, removing noise from captured images to determine driver incompetence. A car model was constructed using Raspberry Pi to simulate brake malfunction using random functions, increasing the revolutions per minute (RPM) to simulate sudden acceleration. By employing a DC encoder motor to measure the motor's rotational speed through waveform counts, the RPM was calculated. The study recognized sudden acceleration when the brake malfunctioned through the DC encoder motor, causing an abnormal RPM increase, allowing camera capture toward the accelerator and brake during sudden acceleration events. Precautions were taken for problems arising from noise in captured images. The Unix operating system was utilized to apply Gaussian filter image processing techniques for noise removal. While using an average value filter led to abrupt changes by replacing with the average of surrounding signals, resulting in an unsmooth image, a Gaussian filter was used in this study to decrease weights as distance from the center increased, mitigating these issues.&lt;/span&gt;

Disutility of climate change damages may warrant much stricter climate targets

Cost-benefit integrated assessment models (IAMs) inform the policy deliberation process by determining cost-optimal greenhouse gas emission reduction pathways based on economic considerations. These models seek to maximise economic utility and treat estimates of climate impacts (damages) and mitigation costs at par as GDP losses, having the same impact on utility reduction. However, prospect theory suggests that a certain level of climate damages could be valued higher by society than the same level of mitigation costs, as climate damages often occur as sudden unexpected events. In this paper, we show how this concept could be taken into account in cost-benefit IAMs and explore possible consequences on optimal mitigation pathways. Our results suggest that compared to the standard utility approach, capturing explicit aversion to climate impact incidence shows optimal pathways with earlier and deeper emission reduction, lowering both net-negative emissions and mid-century temperature peaks in line with stringent Paris Agreement targets.

Structural links from trait emotional intelligence to life satisfaction and depressive symptoms in women with breast cancer: post-traumatic responses as mediators

PurposeThe diagnosis of a disease such as breast cancer (BC) can be experienced as a sudden, unexpected, and life-threatening event accompanied by considerable uncertainty. This experience can precipitate the development of post-traumatic symptoms and depression. Conversely, certain individuals exhibit the capacity to reframe this traumatic event and transform it into an opportunity for personal growth. Existing research shows that individuals with high trait emotional intelligence (trait EI) tend to experience fewer post-traumatic stress symptoms (PTS), and greater post-traumatic growth (PTG). The aim of this study was to investigate the interrelationship among these variables and specifically examine whether PTS and PTG play a mediating role between trait EI, depression, and life satisfaction.MethodsQuestionnaires were administered to 338 women with BC to assess trait EI, PTS, PTG, depression, and life satisfaction.ResultsResults highlighted that trait EI was negatively related to PTS and depression and positively related to PTG and life satisfaction. In addition, both PTS and PTG showed a mediating role in the relationship between trait EI, depression, and life satisfaction. This study highlights the close link between depressive symptoms and post-traumatic cognitions in women with BC.ConclusionCurrent findings highlight links between trait EI, PTS, PTG, depressive symptoms, and life satisfaction. Clinicians could use these findings when developing interventions aimed at alleviating PTS, such as low mood and worry, and facilitating PTG. This study demonstrated that trait EI can reduce PTS and increase PTG, therefore it is important to include programs aimed at fostering trait EI.

The impact of volcanism on Scandinavian climate and human societies during the Holocene: Insights into the Fimbulwinter eruptions (536/540 AD)

Recent paleoclimatic research has revealed that volcanic events around 536–540 AD caused severe, short-term global cooling. For this same period, archeological research from various regions evidences significant cultural transformation. However, there is still a lack of understanding of how human societies responded and adapted to extreme climate variability and new circumstances. This study focuses on the effects of the 536/540 AD volcanic event in four Scandinavian regions by exploring the shift in demographic and land use intensity before, during, and after this abrupt climate cooling. To achieve this, we performed climate simulations with and without volcanic eruptions using a dynamically downscaled climate model (iLOVECLIM) at a high resolution (0.25° or ~25 km). We integrated the findings with a comprehensive collection of radiocarbon dates from excavated archeological sites across various Scandinavian regions. Our Earth System Model simulates pronounced cooling (maximum ensemble mean −1.1°C), an abrupt reduction in precipitation, and a particularly acute drop in growing degree days (GDD0) after the volcanic event, which can be used to infer likely impacts on agricultural productivity. When compared to the archeological record, we see considerable regional diversity in the societal response to this sudden environmental event. As a result, this study provides a more comprehensive insight into the demographic chronology of Scandinavia and a deeper understanding of the land-use practices its societies depended on during the 536/540 AD event. Our results suggest that this abrupt climate anomaly amplified a social change already in progress.

The Impact of the Tropical Sea Surface Temperature Variability on the Dynamical Processes and Ozone Layer in the Arctic Atmosphere

Tropical sea surface temperature (SST) variability, mainly driven by the El Niño–Southern Oscillation (ENSO), influences the atmospheric circulation and hence the transport of heat and chemical species in both the troposphere and stratosphere. This paper uses Met Office, ERA5 and MERRA2 reanalysis data to examine the impact of SST variability on the dynamics of the polar stratosphere and ozone layer over the period from 1980 to 2020. Particular attention is paid to studying the differences in the influence of different types of ENSO (East Pacific (EP) and Central Pacific (CP)) for the El Niño and La Niña phases. It is shown that during the CP El Niño, the zonal wind weakens more strongly and changes direction more often than during the EP El Niño, and the CP El Niño leads to a more rapid decay of the polar vortex (PV), an increase in stratospheric air temperature and an increase in the concentration and total column ozone than during EP El Niño. For the CP La Niña, the PV is more stable, which often leads to a significant decrease in Arctic ozone. During EP La Niña, powerful sudden stratospheric warming events are often observed, which lead to the destruction of PV and an increase in column ozone.

Sinkhole stability chart for geotechnical investigation

Sinkholes are a common geohazard in karst areas where soluble carbonate bedrock is present. Active groundwater flow causes internal soil erosion that creates cavities in the overburden soil layer. The subsurface cavity propagates upward due to the continuous erosion process, and ultimately, they reach to the ground surface, resulting in a sudden collapse. This cover-collapse type is generally a sudden event, particularly in the area where a thick cohesive soil layer is embedded. In this study, a sinkhole stability chart was developed to quantitatively evaluate the safety against the collapse-type sinkhole. Finite-element (FE) modeling was used to simulate the collapse of sinkhole. Field testing data collected from multiple sinkhole sites were used as input to the FE model. To simulate the progress of sinkhole activity, a number of different scenarios with varied size of cavity, strength of overburden soil were simulated, and the corresponding safety factors were determined. Even though each site has varied soil profile, a general trend of the safety factor was observed. The envelopes of safety factors (or equal-safety lines) were statistically determined by using a multivariate regression method. The chart that quantitatively evaluate the stability of sinkhole is used as a decision-making tool for further engineering investigation or mitigation.

La medicina cardiometabolica nell’obesità

The rising prevalence of obesity worldwide has become a significant health issue, particularly as a key risk factor for cardiovascular disease (CVD). Obesity is linked to various cardiac complications, such as coronary artery disease, heart failure, and an increased risk of sudden cardiac events. It also contributes to other cardiovascular risk factors like dyslipidemia, type 2 diabetes, hypertension, and sleep disorders, heightening the risk of CVD. Understanding the pathophysiology connecting obesity and CVD is essential for effective treatment in obese patients. This includes considering adipose tissue dysfunction, chronic inflammation, immune dysregulation, and changes in the gut microbiome. Recent European Society of Cardiology (ESC) guidelines highlight the critical need to diagnose and treat obesity to prevent CVD. The complex interplay between obesity and CVD presents challenges in clinical practice: obesity can hinder precise CVD diagnosis and necessitates careful tailoring of pharmacological treatments and cardiac procedures. It’s imperative for diabetologists to consider the impact of excess weight in improving cardiometabolic outcomes for those with obesity. This article aims to clarify controversial aspects of managing heart disease in patients with overweight or obesity and reviews evidence on cardiometabolic outcomes from current weight management interventions.

Forecasting the number of customers visiting restaurants in COVID-19

We are studying forecasting the number of customers visiting restaurants methods that make integrated use of internal data such as purchase data accumulated by POS systems, external environmental data such as weather and day of the week, and external data such as events. Since the beginning of 2020, the number of customers visiting restaurants has decreased significantly due to COVID-19. It is difficult to forecast the number of customers visiting the restaurant that has changed rapidly due to a sudden event using conventional methods. In this paper, we propose a forecasting method for the number of customers visiting under the conditions of COVID-19 and discuss the results of the analysis, aiming to establish a forecasting method that can cope with changes caused by sudden events.

The Uniqueverse, Natural selection as the unique model of evolutionary development of the Universe

The cosmological theories proposed so far do not postulate a common way of creation that can guide the entire line of growth of the universe, from elementary particles to living beings. But everything consists of the same basic components, combined and regulated differently: elementary particles, atoms and molecules make up celestial bodies, solar systems, single and pluricellular organisms, plants and animals. Common constituents are compatible with a common origin and with an incremental and selective development method. One possible common mode is that the Universe was formed according to an evolutionary method based on natural selection, the same process which then allowed the differentiation of biological species. This unique process may have guided from the beginning the development of the fundamental constituents of matter in the primitive universe and then created the other cosmic structures through the evolution of those elementary particles, up to organic molecules and biological beings. A process of this kind is a slow and gradual process, not based on a sudden event but on the progressive increase in the complexity of the structures present in the Universe, regulated by the selection of the most suitable and stable forms. For the relativistic relationship between energy, mass and velocity, we can think that the Universe was formed at the same time as the formation of elementary structures, slowly in a spacetime that was created in parallel: elementary particles created an initial infinitesimal spacetime system that then grew as more matter formed. The incremental creation of the cosmos would also concern forces and physical laws, which may have undergone the same evolutionary process from the most primordial and simple forms to the present, in proportion to the development of the whole system. This modality could justify the current simultaneous presence of quantum and relativistic laws at different levels (atomic and gravitational), as well as the coexistence of different species in different environments. This evolutionary modality is reflected in the fact that atoms, celestial bodies, chemical compounds, the general structure of the universe, physical laws and biological beings show the presence of stable entities selected among the many theoretically possible or unstable.

Doświadczenie kryzysu jako składnik zbiorowej pamięci przedsiębiorców

Objective – To identify and explain the reasons leading the company to a micro‑scale crisis, which is independent of the market crisis. Research method – The use of in‑depth qualitative interviews with entrepreneurs. They covered entrepreneurs working in the profession for at least twenty years and representing the sector of micro, small and medium‑sized enterprises. Result – The experience of a company crisis on a microscale is an individual experience for each entrepreneur, independent of the market situation. It is the building block of the collective memory of entrepreneurs. Originality / value / implications / recommendations – Based on in‑depth qualitative research, using original statements of entrepreneurs, it has been proven that the company’s crisis, perceived as a sudden event, has its genesis in informal institutions, including ethical and cultural principles in interpersonal relations. A large role in mitigating the effects of the crisis is played by the word, the language of everyday communication of entrepreneurs with clients, contractors, as well as those in power with this professional group.

New-onset autoantibodies to selenoprotein P following severe burn injury.

The liver-derived selenium (Se) transporter selenoprotein P (SELENOP) declines in critical illness as a negative acute phase reactant and has recently been identified as an autoantigen. Hepatic selenoprotein biosynthesis and cotranslational selenocysteine insertion are sensitive to inflammation, therapeutic drugs, Se deficiency, and other modifiers. As severe burn injury induces a heavy inflammatory burden with concomitant Se depletion, we hypothesized an impairment of selenoprotein biosynthesis in the acute post-burn phase, potentially triggering the development of autoantibodies to SELENOP (SELENOP-aAb). To test this hypothesis, longitudinal serum samples from severely burned patients were analyzed over a period of six months. Newly occurring SELENOP-aAb were detected in 8.4% (7/83) of the burn patients, with onset not earlier than two weeks after injury. Prevalence of SELENOP-aAb was associated with injury severity, as aAb-positive patients have suffered more severe burns than their aAb-negative counterparts (median [IQR] ABSI: 11 [7-12] vs. 7 [5.8-8], p = 0.023). Autoimmunity to SELENOP was not associated with differences in total serum Se or SELENOP concentrations. A positive correlation of kidney-derived glutathione peroxidase (GPx3) with serum SELENOP was not present in the patients with SELENOP-aAb, who showed delayed normalization of GPx3 activity post-burn. Overall, the data suggest that SELENOP-aAb emerge after severe injury in a subset of patients and have antagonistic effects on Se transport. The nature of burn injury as a sudden event allowed a time-resolved analysis of a direct trigger for new-onset SELENOP-aAb, which may be relevant for severely affected patients requiring intensified acute and long-term care.