Fluctuation Patterns Research Articles

Seasonal Price Adjustments in Winter Tourism: Impact on the Car Rental Market in Canada (2015-2022)

This research explored seasonal price adjustments within the car rental market, offering a comprehensive analysis contextualized within the sphere of winter tourism in Canada from 2015 to 2022. Through an exploration of historical trends, consumer behavior, and market forecasts, the study uncovers the cyclical pattern of price fluctuations closely tied to the peaks and troughs of winter tourism demand. Noteworthy transformations within the winter tourism industry during this period, coupled with the increased reliance on car rentals for accessing remote winter destinations, underscore the pivotal role of seasonal price adjustments in shaping the accessibility and affordability of winter experiences. The implications extend across stakeholders, prompting car rental companies to balance profitability and competitiveness, while policymakers are urged to craft adaptable regulatory frameworks. Industry stakeholders are guided by insights to develop customer-centric strategies, embracing technology and sustainability for enhanced resilience. The study serves as a stepping stone for understanding the symbiotic relationship between winter tourism and the car rental market in Canada, providing valuable insights for industry adaptation and innovation.

Modeling Of Hyperparameter Tuned RNN-LSTM and Deep Learning For Garlic Price Forecasting In Indonesia

In the Indonesian garlic industry, the unpredictability of garlic prices poses a substantial challenge, impacting the sector's stability and growth. This research aims to address this issue by developing a highly accurate predictive model using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The study employs a dataset spanning 782 days, meticulously divided with 80% dedicated to training and 20% to testing. The model, equipped with 50 LSTM units, undergoes intensive training over 100 epochs, with a batch size of 5. Its effectiveness is evaluated using the Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), revealing exceptional predictive capabilities. The model achieves a low RMSE and MAPE in both training and testing phases, underscoring its accuracy and reliability in forecasting garlic prices. These results indicate not only the success of the RNN-LSTM model in capturing the complex patterns of price fluctuations but also highlight the potential of machine learning in enhancing time series analysis. This breakthrough offers significant implications for stakeholders in the garlic industry, providing a powerful tool for informed decision-making and strategic market planning, thereby contributing to the sector's sustainable development and stability

Demand and Supply: A Case Study of Meat Price Fluctuations at Pasar Baru-Jayapura Ahead of Hari Raya

December marks the end of the month with a vivid array of bustling activities and market events that significantly stand out. This market activity is influenced by the Christmas and New Year's moments, leading to a surge in demand for chicken and pork in Jayapura, Papua. The purpose of this research is to analyze the fluctuation patterns of chicken and pork prices and their contributing factors. The applied method in this study is a qualitative descriptive method using data collection techniques through interviews and document analysis of the ledger books of chicken and pork traders in Pasar Baru, Jayapura, Papua. The interview technique involves in-depth interviews with a sample of 3 chicken traders and 2 pork traders, accompanied by recording techniques. The interview results are then recorded or transcribed by converting oral text into written form. The transcribed results are further processed using the reduction technique, eliminating irrelevant text and grouping selected text for presentation as presentation data to be analyzed using domain analysis. Meanwhile, document analysis is conducted to categorize three income or debit sections based on the time of sale classification, namely early December, approaching Christmas and New Year, and post-New Year. The research results indicate that factors influencing price fluctuations are production factors and the quality of chicken and pork products, local cultural intervention factors, marketing strategy and management factors, consumer purchasing power or consumer behavior, and market nature or demand factors. Document analysis shows a significant increase in demand leading up to Christmas and New Year's Day, adhering to the law of supply and demand in direct proportion.

Effect of local active fluctuations on structure and dynamics of flexible biopolymers.

Active fluctuations play a significant role in the structure and dynamics of biopolymers (e.g. chromatin and cytoskeletal proteins) that are instrumental in the functioning of living cells. For a large range of experimentally accessible length and time scales, these polymers can be represented as flexible chains that are subjected to spatially and temporally varying fluctuating forces. In this work, we introduce a mathematical framework that correlates the spatial and temporal patterns of the fluctuations to different observables that describe the dynamics and conformations of the polymer. We demonstrate the power of this approach by analyzing the case of a point fluctuation on the polymer with an exponential decay of correlation in time with a finite time constant. Specifically, we identify the length and time scale over which the behavior of the polymer exhibits a significant departure from the behavior of a Rouse chain and the range of impact of the fluctuation along the chain. Furthermore, we show that the conformation of the polymer retains the memory of the active fluctuation from earlier times. Altogether, this work sets the basis for understanding and interpreting the role of spatio-temporal patterns of fluctuations in the dynamics, conformation, and functionality of biopolymers in living cells.

Using Artificial Intelligence to Forecast Continuous Glucose Monitor(CGM) readings for Type One Diabetes

Type One diabetes (T1D) is an incurable condition in which a person produces little to no insulin, leading to high glucose levels in the bloodstream. Advancements in technology have led to the development of Continuous Glucose Monitors (CGM), involving inserting a sensor under the skin that continuously measures glucose levels in the bloodstream. CGMs provide real-time data on glucose levels and patterns of fluctuation and have evolved as an essential part of diabetes management. Current CGMs cannot forecast glucose levels. In this study, we hypothesized that machine learning algorithms can be utilized to forecast glucose values using data provided by the CGM device. This could help T1D patients optimize medication timing, dietary adjustments, and physical activity to improve metabolic control and prevent T1D complications. Our study used publicly available data from a study that compared CGMs with and without routine blood glucose monitoring in adults with T1D. We used different AI models to provide 30-minute forecasts of blood glucose levels to show how model structure and parameters affect Root Mean Squared Error (RMSE), and whether the error can be reduced to 5% as per medical standards. Our research used RandomForest as a baseline algorithm and LSTM, a recurrent neural network. Our results demonstrated that the LSTM model achieved an error of under 5% in predicting future blood glucose levels. Hence, researchers can use LSTM models to enhance CGMs to aid T1D patients. The results of our study supported our hypothesis that AI can be used to predict glucose levels.

Evaluation of blood minerals and oxidative stress changing pattern in prepartum and postpartum Achai and Holstein Friesian dairy cows.

This research focused on evaluating blood mineral levels and malondialdehyde (MDA) as an indicator of oxidative stress before and after giving birth in Achai and Holstein Friesian (HF) dairy cows. Blood samples were obtained from a total of 50 cows representing both breeds on the third, second, and first week prior to calving, as well as on the day of calving (day 0). Subsequently, samples were collected on the 1-3 weeks postpartum to allow a comprehensive evaluation of blood parameters throughout the peripartum period. Results showed a significant (p < .01) decrease in serum zinc (Zn) levels on the day of parturition in both breeds. Additionally, HF cows exhibited higher (p < .01) Zn levels on week 2 before giving birth. Interestingly, blood selenium (Se) concentration increased (p < .01) in HF cows on weeks 2 and 3 after calving. In contrast, Achai cows showed a rise (p < .01) in blood Se on week 3 of parturition. Blood copper (Cu) levels were higher (p < .01) on weeks 2 and 3 after parturition in HF cows and on third week before parturition compared to Achai cows, where serum Cu remained high on week 1 of parturition. The findings indicated that blood magnesium (Mg) was higher (p < .01) on third week before parturition in Achai cows and on weeks 2 and 3 after parturition. Serum calcium (Ca) was higher (p < .01) in both HF and Achai cows on weeks 2 and 3 after parturition, and lower (p < .01) on third week before giving birth in both breeds. The mean blood MDA levels in Achai cows were lower (p < .01) on weeks 1 to 3 postpartum or 3 weeks before giving birth. In HF cows, serum MDA increased (p < .01) just before 2 weeks of parturition and remained elevated until 3 weeks of parturition. Thus, both breeds exhibited a similar pattern of mineral fluctuations; yet, Achai cows demonstrated greater resilience to oxidative stress compared to HF cows during the transition period.

Early response of understory vegetation to the mass dieback of Norway spruce in the European lowland temperate forest

Spruce-dominated forests are commonly exposed to disturbances associated with mass occurrences of bark beetles. The dieback of trees triggers many physical and chemical processes in the ecosystem resulting in rapid changes in the vegetation of the lower forest layers. We aimed to determine the response of non-tree understory vegetation to the mass dieback of Norway spruce (Picea abies) in the first years after the disturbance caused by the European spruce bark beetle (Ips typographus) outbreak. Our study area was the Białowieża Biosphere Reserve covering the Polish part of the emblematic Białowieża Forest, in total 597 ​km2. The main data source comprised 3,900 phytosociological relevés (combined spring and summer campaigns) collected from 1,300 systematically distributed forest sites in 2016–2018 – the peak years of the bark beetle outbreak. We found that the understory responded immediately to mass spruce dieback, with the most pronounced changes observed in the year of the disturbance and the subsequent year. Shade-tolerant forest species declined in the initial years following the mass spruce dieback, while hemicryptophytes, therophytes, light-demanding species associated with non-forest semi-natural communities, as well as water-demanding forest species, expanded. Oxalis acetosella, the most common understory species in the Białowieża Forest, showed a distinct fluctuation pattern, with strong short-term expansion right after spruce dieback, followed by a gradual decline over the next 3–4 years to a cover level 5 percentage points lower than before the disturbance. Thus, our study revealed that mass spruce dieback selectively affects individual herb species, and their responses can be directional and non-directional (fluctuation). Furthermore, we demonstrated that the mass dieback of spruce temporarily increases plant species diversity (α-diversity).

Design and Analysis of a Bionic Skate Based on MPF Movement Patterns

This paper uses pectoral fins fluctuation pattern class triangular skate as the object of study. The skate generates waveforms through its broad pectoral fins, which are transmitted backwards to push the water backwards, and the reaction force of the water obtained is the forward thrust i.e. the MPF pattern. The fins are designed to meet the fluctuation pattern of the sinusoidal function. A prototype pectoral fin of the ray is analysed to establish the kinematic model of the fluttering wing, the envelope coefficients are designed to decouple and simplify the kinematic equations, and a three-dimensional mesh is drawn using MATLAB; fluid simulation is carried out by Fluent to obtain the vortex cloud; the bionic ray is tested and tested to meet the actual requirements.

Patterns of runoff fluctuations of the Dniester River (Ukraine) under conditions of climate change at the beginning of the 21st century

The relevance of the chosen topic is associated with the impact of climate change on fluctuations of water content in Ukrainian rivers. The consequences of warming depend on physical and geographical conditions and vary significantly across different natural zones. The behaviour of catchment areas affected by climate change varies depending on landscape (mountainous or flat terrain) and latitude. This results in instability of boundaries of catchment areas with annual runoff synchronous and out-of-phase fluctuations. The study aims at establishing the patterns of annual runoff fluctuations in the Dniester basin with relation to fluctuations in the maximum runoff of rainfalls and spring floods. The spring floods are the ones that determine a significant part of annual runoff and depend on the impact of warming during the winter season. The study's main method is the method of constructing difference integral curves of annual and maximum rainfalls and maximum spring runoff and their analysis. There are 7 groups (zones) within the Dniester River catchment area identified by the type of difference integral curves of annual runoff. Fluctuations in annual, maximum rain and spring runoff were compared using the curves averaged within each group. The degree of synchronicity of fluctuations was estimated via correlation coefficients between the ordinates of difference integral curves. It was found that the main river oscillations retain their cyclicity along its entire length, even below the Dniester HPP. The last transition to a low-water phase occurred in 2010-2011. It was discovered that the main river's cyclical nature mostly results from fluctuations in the runoff of mountainous Carpathian tributaries that form the Dniester's runoff formation zone. This part of the catchment area is characterized by a high amount of snow and rainfalls feeding the river with the role of rainfalls having a predominant character. In the upper reaches of Carpathian rivers, where the impact of warming is not yet sufficiently pronounced at high altitudes, spring floods play a significant role in runoff formation. In the upper reaches of Carpathian rivers there is an additional zone where fluctuations in annual runoff are caused by both rainfalls and spring floods. The boundaries of this zone (2nd district) may change as the effects of warming increase high in the mountains. On the left-bank tributaries, the runoff fluctuations in Verkhnepodilskyi and Serednepodilskyi districts are similar to those of the main river, however, there is a shift in the dates of the last transition to a low-water phase. The study indicates the likelihood of karst and artificial reservoirs influence. It also indicates a significant difference in the nature of runoff fluctuations of the upper left-bank tributaries (Strvyazh, Vereshchytsia, Shcherek) and lower left-bank tributaries. They are affected by other atmospheric processes and climatic factors associated with such processes.

Glacial fluctuations in the southern Tibetan Plateau during the past 1000 years

Understanding the pattern and timing of glacial fluctuations in the latest Holocene (<1000 years), along with their cause, is necessary for deconvolving natural drivers of climate variability from anthropogenic impacts. Yet, debates remain on the importance of a global or more regional forcing mechanism. Here, we use 10Be surface exposure dating, in conjunction with geomorphological mapping, to develop a moraine chronology with decades-to-century-scale resolution in the Xianza Range, southern Tibetan Plateau (TP). Five moraine records east of Mt. Jiare are dated to ∼991 Common Era (CE), ∼1415 CE, ∼1567 CE, ∼1711 CE, and ∼1875 CE. The well-established moraine chronologies demonstrate that glaciers achieved the latest Holocene glacial maximum during the Medieval Warm Period (MWP). The less extensive Little Ice Age (LIA) glacial events release an unprecedented finding, which disproves the traditional viewpoint presuming that the LIA in the TP was represented by 2–3 glacial advances. Our results, paired with other climate records, point to, at least, a regional climate cooling signal during the LIA. In the MWP, glacial advance likely reflects the positive North Atlantic Oscillation-related cooling signal as recorded in the Northwestern Atlantic and Mediterranean regions. The strengthened northern mid-latitude westerlies at that time may play a key role in transferring cooling signal to the southern TP.

Medium-Term Forecast of Metro based on Big Traffic Data

With the rise of industrialization and automation, the demand for transportation has been continuously increasing. The demands for quick and effective travel can no longer be met by conventional transportation techniques. In the field of transportation, urban train travel is significant. At the same time, passenger flow prediction has become increasingly important. In addition to short-term forecasts, medium-term forecasts of daily intervals of passenger flow are also essential. This research employs the ARIMA model to forecast medium-term passenger flow, utilizing subway passenger flow data collected in Chengdu City from April 29th to September 28th, 2019. Based on the data and methodology, this study investigates the feasibility of using the ARIMA model for medium-term subway passenger flow prediction. The research findings indicate that the model exhibits strong seasonality, with an ADF unit root test p-value less than 0.05 and an LB statistic test p-value less than 0.05. The Mean Percentage Error is only -1.03%. Furthermore, there is a strong concordance between the fluctuation patterns observed in the actual and predicted values of the model, which closely align with the trends observed in the training dataset. Through this research, it is concluded that the ARIMA model performs well in medium-term subway passenger flow prediction.

The Impact of The Russia-Ukraine War on The Dynamics of The Asean-5 Stock Market

The capital market can be defined as activities related to public offerings, securities trading, public companies, institutions, and certain professions. One of the factors considered in stock investment is the Efficient Market Hypothesis. The Efficient Market Hypothesis is a financial theory that discusses how asset prices in the market already reflect all available information. This study employs an approach to detect market reaction and market efficiency based on the event of the world oil price decline due to the Russian invasion of Ukraine on March 10, 2022, through excess or abnormal return, price impact, and fluctuation patterns observed using a multifractal model. The research results on the abnormal return of the ASEAN-5 stock index showed a significant negative reaction at t-5 and t-3 during the event of the world oil price decline due to the Ukraine and Russia war in 2022 and a significant positive reaction at t-0 during the event. The average abnormal return before and after the announcement of the world oil price decline due to the Ukraine and Russia war in 2022 showed a significant negative reaction, but there was no significant difference in price impact between before and after the event. The efficiency level of the stock index in ASEAN-5 did not show a significant difference after the announcement of the world oil price decline due to the Ukraine and Russia war in 2022.

Blood Pressure Fluctuation Pattern Associated with 90-Day Ischemic Stroke and Transient Ischemic Attack After Total Knee Arthroplasty-A Retrospective Study.

Blood pressure (BP) fluctuation has been demonstrated to be closely associated with stroke. However, at present, no attention is paid to the BP fluctuation following total knee arthroplasty (TKA). The aim of this study is to investigate what is BP fluctuation patterns in stroke and transient ischemic attack (TIA) patients after primary TKA, compare clinical characteristics among different BP fluctuation patterns and determine whether it could predict the occurrence of a 90-day stroke. A retrospective study was conducted from February 1, 2011 to February 15, 2021. Stroke or TIA patients within 90 days after TKA were included. Data from a hospitalization management system were analyzed, including demographics, smoking status, comorbidities, medications, surgical variables, and 90-day stroke, mortality, and readmission rates. BP fluctuation patterns were defined based on the average BP in the first 3 days after TKA. A logistic regression analysis model was used to identify risk factors for 90-day stroke or TIA following TKA. Of the 1687 patients who underwent primary TKA, 4.0% (68/1687) experienced a 90-day stroke or TIA. A total of 63 patients met the inclusion criteria, with an average age of 67.5 years and 55.6% (35/63) female. The number of stroke or TIA patients in each BP fluctuation pattern (T1-T5) was 6 (9.5%), 5 (7.9%), 4 (6.3%), 13 (20.6%), and 35 (55.6%) respectively. Patients with a T5 pattern tended to be older and had a greater reduction in average systolic blood pressure (SBP) 3 days after surgery compared to other patterns. The T5 pattern was associated with a higher occurrence of stroke and a lower occurrence of TIA. Patients with a T5 pattern also had a longer length of stay within 90 days following TKA. After adjusting for confounding factors, average SBP fluctuation ≥40 mmHg and the T5 pattern of BP fluctuation remained risk factors for 90-day stroke or TIA after TKA. BP fluctuation patterns in the early postoperative period are associated with 90-day stroke and TIA following primary TKA.

The myth of aortic valve annulus changes in aortic valve disease.

The characteristics of aortic annulus changes in aortic regurgitation (AR) patients are poorly understood, and predictive factors among aortic valve disease are yet to be established. This study seeks to elucidate the pattern of annular size fluctuations across different cardiac phases in AR patients and to identify predictors for annular enlargement during either systole or diastole in aortic valve diseases. A retrospective analysis was conducted on 55 patients with severe aortic valve diseases, including 26 patients with aortic stenosis (AS) and 29 with AR, to discern the two groups' contrasting and analogous patterns of annular changes. The patient sample was expanded to 107 to investigate the factors influencing the size of the annulus during different cardiac phases. Based on our findings, patients were then divided into two groups: those with an annulus that is larger during systole (83 patients) and those where the annulus is larger during diastole (24 patients). Typically, AR patients exhibit a dynamic annulus, with both perimeter and area being largest during mid-systole. These dimensions diminish progressively and then increase again in early diastole, a pattern consistent with observations in AS patients. Among 107 patients, 21% had diastolic enlargement. Systolic measurements would lead to prosthesis undersizing in 17% of these. Male gender and lower systolic annulus minimum relative to body surface area (AnMin index) were predictors of diastolic enlargement, with ROC curve areas of 0.70 and 0.87 for AR and AS, respectively. Systolic measurements are recommended for AR patients. Gender and the AnMin index are significant predictors, particularly potent in AS patients.

Reconstructing the phylodynamic history and geographic spread of the CRF01_AE-predominant HIV-1 epidemic in the Philippines from PR/RT sequences sampled from 2008 to 2018.

The Philippines has had a rapidly growing human immunodeficiency virus (HIV) epidemic with a shift in the prevalent subtype from B to CRF01_AE. However, the phylodynamic history of CRF01_AE in the Philippines has yet to be reconstructed. We conducted a descriptive retrospective study reconstructing the history of HIV-1 CRF01_AE transmissions in the Philippines through molecular epidemiology. Partial polymerase sequences (n = 1144) collected between 2008 and 2018 from three island groups were collated from the Research Institute for Tropical Medicine drug resistance genotyping database. Estimation of the time to the most recent common ancestor (tMRCA), effective reproductive number (Re), effective viral population size (Ne), relative migration rates, and geographic spread of CRF01_AE was performed with BEAST. Re and Ne were compared between CRF01_AE and B. Most CRF01_AE sequences formed a single clade with a tMRCA of June 1996 [95 per cent highest posterior density (HPD): December 1991, October 1999]. An increasing CRF01_AE Ne was observed from the tMRCA to 2013. The CRF01_AE Re reached peaks of 2.46 [95 per cent HPD: 1.76, 3.27] in 2007 and 2.52 [95 per cent HPD: 1.83, 3.34] in 2015. A decrease of CRF01_AE Re occurred in the intervening years of 2007 to 2011, reaching as low as 1.43 [95 per cent HPD: 1.06, 1.90] in 2011, followed by a rebound. The CRF01_AE epidemic most likely started in Luzon and then spread to the other island groups of the country. Both CRF01_AE and Subtype B exhibited similar patterns of Re fluctuation over time. These results characterize the subtype-specific phylodynamic history of the largest CRF01_AE cluster in the Philippines, which contextualizes and may inform past, present, and future public health measures toward controlling the HIV epidemic in the Philippines.

Dynamic driving risk in highway tunnel groups based on pupillary oscillations

This study aims to understand the dynamic changes in driving risks in highway tunnel groups. Real-world driving experiments were conducted, collecting pupil area data to measure pupil size oscillations using the Percentage of Pupil Area Variable (PPAV) metric. The analysis focused on investigating relative pupil size fluctuations to explore trends in driving risk fluctuations within tunnel groups. The objective was to identify accident-prone areas and key factors influencing driving risks, providing insights for safety improvements. The findings revealed an overall “whipping effect” phenomenon in driving risk changes within tunnel groups. Differences were observed between interior tunnel areas and open sections, including adjacent, approach, and departure zones. Higher driving risks were associated with locations closer to the tail end of the tunnel group and shorter exit departure sections. Targeted safety improvement designs should consider fluctuation patterns in different directions, with attention to tunnels at the tail end. In open sections, increased travel distance and lengths of upstream and downstream tunnels raised driving risks, while longer open zones improved driving risks. Driving direction and sequence had minimal impact on risks. By integrating driver vision, tunnel characteristics, and the environment, this study identified high-risk areas and critical factors, providing guidance for monitoring and improving driving risks in tunnel groups. The findings have practical implications for the operation and safety management of tunnel groups.

Research on Risk Measurement of China’s Carbon Trading Market

In today’s environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China’s carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China’s carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China’s carbon market.

Response time fluctuations in the sustained attention to response task predict performance accuracy and meta-awareness of attentional states.

Previous research suggests that response time (RT) patterns in the Sustained Attention to Response Task(SART) differentially predict different features of mind wandering but it is unknown how they relate to meta-awareness of attentional states. We applied principal component analysis to blocks of non-target (go) trials prior to target(no-go) trials and attentional state and meta-awareness probes in the SART and identified three distinct patterns that replicated those observed in previous research. A stable response rate was associated with superior target performance, whereas RT acceleration prior to targets was associated with poorer target performance. Self-reported attentional state was not significantly predicted by any of the pattern components. By contrast, meta-awareness was independently associated with two distinct RT fluctuation patterns with evidence that each pattern was specifically related to either meta-awareness of off-task or on-task states. These results suggest that mind wandering and meta-awareness of attentional states have distinct and overlapping imprints on RT patterns in the SART. We conclude by highlighting implications of these results for introspective methods and the measurement of mind wandering.

Twenty-four-hour rhythmicities in disorders of consciousness are associated with a favourable outcome

Fluctuations of consciousness and their rhythmicities have been rarely studied in patients with a disorder of consciousness after acute brain injuries. 24-h assessment of brain (EEG), behaviour (eye-opening), and circadian (clock-controlled hormones secretion from urine) functions was performed in acute brain-injured patients. The distribution, long-term predictability, and rhythmicity (circadian/ultradian) of various EEG features were compared with the initial clinical status, the functional outcome, and the circadian rhythmicities of behaviour and clock-controlled hormones. Here we show that more physiological and favourable patterns of fluctuations are associated with a higher 24 h predictability and sharp up-and-down shape of EEG switches, reminiscent of the Flip-Flop model of sleep. Multimodal rhythmic analysis shows that patients with simultaneous circadian rhythmicity for brain, behaviour, and hormones had a favourable outcome. Finally, both re-emerging EEG fluctuations and homogeneous 24-h cycles for EEG, eye-opening, and hormones appeared as surrogates for preserved functionality in brainstem and basal forebrain, which are key prognostic factors for later improvement. While the recovery of consciousness has previously been related to a high short-term complexity, we suggest in this exploratory study the importance of the high predictability of the 24 h long-term generation of brain rhythms and highlight the importance of circadian body-brain rhythms in awakening.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.