Warning Signals Research Articles

Analysis of the impact of different background colors in VR environments on risk preferences.

Metaverse virtual reality (VR) technology offers an environment that provides a sense of presence that cannot be achieved through traditional online interactions. Although this technology is used in education and industry, challenges remain to be overcome for further penetration into society. In this study, we conducted an experiment using a risk preference task in blue and red spaces to verify color impressions and their effects in VR environments. Consistent with previous studies, high calmness was observed in the blue space. However, contrary to expectations, the risk preference task results showed risk-averse behavior in red space but not in blue space. Therefore, conventional color psychology may not be applicable to constructing the same space in a virtual environment. Previous studies have shown consistent experimental results regarding emotional responses to color stimuli; however, no consistent outcomes have been reported regarding cognitive performance. This is because the effect of color impressions is thought to depend not only on the color itself but also on what is recalled. The results of this study suggest that red in a VR environment may be interpreted as a warning color, preventing people from losing focus.

Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.

Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk.

Sepsis is a clinical syndrome caused by infection, leading to organ dysfunction due to a dysregulated host response. In recent years, its high mortality rate has made it a significant cause of death and disability worldwide. The pathophysiological process of sepsis is related to the body's dysregulated response to infection, with microcirculatory changes serving as early warning signals that guide clinical treatment. The Peripheral Perfusion Index (PI), as an indicator of peripheral microcirculation, can effectively evaluate patient prognosis. This study aims to develop two new prediction models using PI and other common clinical indicators to assess the mortality risk of sepsis patients during hospitalization and within 28 days post-ICU admission. This retrospective study analyzed data from sepsis patients treated in the Intensive Care Unit of Peking Union Medical College Hospital between December 2019 and June 2023, ultimately including 645 patients. LASSO regression and logistic regression analyses were used to select predictive factors from 35 clinical indicators, and two clinical prediction models were constructed to predict in-hospital mortality and 28-day mortality. The models' performance was then evaluated using ROC curve, calibration curve, and decision curve analyses. The two prediction models performed excellently in distinguishing patient mortality risk. The AUC for the in-hospital mortality prediction model was 0.82 in the training set and 0.73 in the validation set; for the 28-day mortality prediction model, the AUC was 0.79 in the training set and 0.73 in the validation set. The calibration curves closely aligned with the ideal line, indicating consistency between predicted and actual outcomes. Decision curve analysis also demonstrated high net benefits for the clinical utility of both models. The study shows that these two prediction models not only perform excellently statistically but also hold high practical value in clinical applications. The models can help physicians accurately assess the mortality risk of sepsis patients, providing a scientific basis for personalized treatment.

Speaking valve with integrated biomimetic overpressure release and acoustic warning signal

Speaking valves enable tracheostomy patients to speak naturally. However, improper use may cause dangerous overpressure, leading to severe complications or even patient’s death. We address this life-threatening issue by creating a biomimetic speaking valve, which incorporates an integrated overpressure valve that automatically opens when reaching critical pressure levels. To enhance safety, we integrated a whistle module to provide an audible alert for medical staff. Fundamental research on the Utricularia vulgaris trapdoor inspired our abstracted valve form. Through a comprehensive analysis using generalized linear mixed-effect models, we examined various membrane parameter effects on the function of the biomimetic overpressure valve. This enabled us to adjust the valve’s opening pressure to cater to patient’s unique requirements, thus potentially saving lives by applying a solution from nature.

Research on flood warning model based on dimensionality reduction and integrated neural network

Flood disasters have long-term impacts on human societies and ecosystems, and population growth and urbanization lead to changes in surface conditions that increase the frequency and magnitude of floods. Therefore, understanding and predicting flood probability and constructing a flood warning mechanism are urgent problems. In this paper, a flood warning mechanism based on the probability of flood occurrence is proposed to construct a high-performance and highly interpretable model in a step-by-step manner using machine learning and deep learning methods. First, the correlation level is elucidated by the PCA dimensionality reduction method. Secondly, the K-means clustering method is used to replace the qcut binning method, and the classification model is built by combining the multidimensional data point distribution and classified by polynomial logistic regression. Finally, the integrated neural network model is constructed, the visual model performance is used for full feature optimization, and the performance of the classification model is optimized to achieve the flood prediction effect through the mean square error as the combination of the weights of the integrated model.

Study on the characteristics of acoustic-thermal precursors of destabilization damage in coal-rock combination bodies with different proportions

The instability and failure processes of coal-rock combinations are accompanied by the release of acoustic emission (AE) and infrared radiation (IR) signals. To investigate the characteristics of AE and IR signals during the failure process in coal-rock combinations with different ratios, and to analyze the effectiveness and applicability of the monitoring methods for these two signals in various specimen ratios. In this paper, the uniaxial compression tests were conducted on seven coal-rock combinations with different ratios by using the rock mechanics loading system and the cooperative monitoring platform of infrared and acoustic emission. The results show that (1) the AE counts for failure precursors in coal-rock combinations are positively correlated with the coal-rock ratio, whereas the AE peak counts are negatively correlated. Moreover, as the coal-rock ratio decreases, the slope of the cumulative AE count curve during the peak failure stage approaches 1. (2) During the elastic and yield stages, the maximum infrared radiation temperature (MIRT) curve fluctuates, and just before peak failure, the curve displays a distinctive “V” shape. This “V” shape becomes more pronounced as the coal-rock ratio decreases. (3) In the monitoring of damage precursors of coal-rock combinations, when the ratio of coal to rock is 1:3 or less, IR monitoring is better than AE monitoring. Conversely, when the ratio of coal to rock is greater than 1:3, AE monitoring is more suitable. Consequently, the combined monitoring of AE and IR signals can increase the reliability and precision of early warning signals for coal-rock assemblage damage precursors.

Impact of Debt Composition and Accounting Conservatism on Financial Distress in Emerging Markets

Aims: This study investigates the impact of debt composition, specifically short-term and long-term debt, on corporate financial distress, focusing on the moderating role of accounting conservatism. It aims to understand how firms in emerging markets, particularly Kenya, can achieve financial stability through prudent debt structuring and conservative accounting practices. Study Design: The research adopted an explanatory research design. Place and Duration of Study: The study utilized financial data from firms listed on the Kenyan Securities Exchange between 2008 and 2021. Methodology: The study analyzed a sample of 45 firms trading at the Nairobi Stock Exchange over 14 years from 2008 to 2021. The study used panel logistic regression, a statistical method for analyzing data with multiple observations over time, to test its hypotheses. Results: The results show that both short-term and long-term debt increase the likelihood of financial distress, with accounting conservatism further intensifying the risk for firms heavily reliant on short-term debt while mitigating it for those with long-term debt. This suggests that conservative accounting practices, which promote early recognition of losses, may exacerbate financial distress for firms heavily reliant on short-term debt. In contrast, for long-term debt, accounting conservatism has a mitigating effect helping firms manage their debt obligations more effectively over time. This highlights a nuanced role of accounting conservatism, where it increases the pressure on firms with short-term debt while providing stability for those with long-term obligations. Conclusion: The findings suggest that corporate managers in emerging markets should carefully balance short- and long-term debt to minimize financial distress risks. Additionally, adopting conservative accounting practices can serve as a safeguard, providing early warning signals for financial trouble and enhancing corporate stability. Managers should therefore carefully balance short-term and long-term debt to reduce financial distress while adopting conservative accounting practices to enhance financial transparency and risk management. Policymakers can support firms by promoting responsible debt management and offering incentives for conservative financial reporting to strengthen overall financial stability.

Defensive Behavior and Integumentary Morphology of the Hairy Dwarf Porcupine Coendou spinosus (Rodentia: Erethizontidae).

New World porcupines (Erethizontidae) exhibit behaviors and possess integumentary structures, including the quills, that are used for self-defense. The North American porcupine (Erethizon dorsatum) has been well studied regarding these features; however, information is lacking for the South American Coendou species. We describe the defensive behavior and integumentary morphology of Coendou spinosus to understand the defensive strategies of this species and to compare with those reported for other species. We assessed the behaviors related to warning, defense, and escape of eight porcupines, as well as the characteristics of their pelage and quills. Furthermore, we microscopically analyzed skin samples of a roadkill adult male specimen. Similar to E. dorsatum, C. spinosus exhibited omnidirectional quill erection, revealing an aposematic color and, with their backs toward the perceived human threat, they performed quick tail and body movements to strike the hands of the human trying to capture them by the tail. Furthermore, C. spinosus presented an integumentary structure similar to that of E. dorsatum, and mechanisms to facilitate quill release when touched, penetration, and fixation in the opponent. The most distinct warning behavior noted was the vibration of the quills, which has not been reported for Erethizon. Our study confirms that, like other erethizontids, C. spinosus does not attack but exhibits warning, defense, and escape mechanisms and behaviors when threatened or touched. The dissemination of such information helps to counter the negative stigma associated with porcupines, as they can be the victims of attacks by dogs and humans, and to promote their conservation.

Building Effective Academic Early Warning Mechanisms: Theoretical Framework and Practical Strategies

The academic early warning mechanism plays an important role in modern educational management, by identifying and intervening in students' academic problems early on to help them complete their studies successfully. This paper explores how to build an effective academic warning mechanism from both theoretical frameworks and practical strategies, with the aim of providing reference and guidance for educational managers and teachers.

Tutoring Systems Construction of Dynamic Early Warning Mechanism of Ideological and Political Education in Universities Based on Intelligent CAD

Tutoring Systems Construction of Dynamic Early Warning Mechanism of Ideological and Political Education in Universities Based on Intelligent CAD

Influence of Contaminants Mercury and PAHs on Somatic Indexes of the European Hake (Merluccius merluccius, L. 1758).

This research investigates the dynamics of contaminant exposure in European hake (Merluccius merluccius, L. 1758) from the Adriatic Sea (Central Mediterranean Sea) by examining the levels of total mercury (THg) and polycyclic aromatic hydrocarbons (PAHs) in the muscle fish tissues. The study explores the correlations between these pollutants and somatic indexes to identify the early warning signals of pollution and ecological effects. The levels of pollutants are influenced by season and sex. Lipids appear to have a minimal effect on the PAH levels, whereas they exhibit a positive correlation with mercury levels in the muscle. No significant relationships between the pollutants and condition indexes were observed, except for a positive correlation between THg and the gonadosomatic index, indicating a potential impact on the reproductive health of fish. In contrast, PAHs showed no meaningful correlation with condition indexes. Differences in contaminant accumulations and lipid levels between sexes reflect variations in metabolic activity, reproductive costs, and adaptive strategies to seasonal changes and energy demands. This study highlights the importance of long-term monitoring to improve pollution management, environmental conservation, and the protection of marine organisms' health.

Evaluating the impact of demographic characteristics on residential mortgage default risk: Evidence from Lebanon

Purpose: This study aims to examine the relationship between borrower's demographic characteristics and default risk in mortgage loans to help financial institutions develop more effective lending policies. Design/Methodology/Approach: Cross-sectional data were elicited from randomly selected 6743 individual accounts from Lebanese housing banks. This study applied the binary logistic and stepwise regression models to analyze the dataset using the Stata statistical software. Model diagnosis is performed using the Hosmer-Lemeshow goodness of fit test, likelihood ratio test, model accuracy classification table and statistically significant test-ROC curve. Findings: The findings revealed that there is a significant relationship between residential loan default risk and borrower’s marital status, nature of job occupation, job economic sector, job location and loan purpose. The performance of the binary logistic regression analysis demonstrates the overall percentage who is correctly classified is 91.61%. Conclusion: The log odds of default risk for widowed borrowers are about 90 percent higher than those of divorced borrowers and that of self-employed borrowers is about 54 percent higher than that of employed borrowers. Borrowers working in the banking and real estate sectors have lower default rates than borrowers working in other economic sectors. In addition, loans granted for renovation purposes have the lowest default rates compared to loans provided for purchase, under-construction and construction purposes. Practical Implications: The empirical results help financial institutions to have early warning signals in detecting financial distress and to differentiate between a high- and low-risk group of borrowers, helping in the development of tailored risk mitigation strategies and adjusting the lending criteria.

Slow down and be critical before using early warning signals in psychopathology

Slow down and be critical before using early warning signals in psychopathology

Immune Responses of Club Cells in Fish: A Review

The primary line of defense against pathogens from the environment is often fish epidermis tissue. Nevertheless, little is understood about the physiological mechanisms that underlie the non-specific and/or specific protection that these cells can offer. The exact nature of the relationship between the evolution of ostariophysan fish club cells and chemical warning signals is still unknown and controversial. Fish epidermis layer cells comprise mucus cells, lymphocytes, macrophage cells, cuboidal and squamous epithelial cells, and cells specific to certain fish species. Club cells, also called "alarm cells," are chemical alarms that sound in the event of a potentially hazardous scenario. These cells will burst in the presence of a predator, releasing pheromones that, if the skin is physically damaged, trigger an avoidance and terror reaction. In sturgeon larvae, mucus cells were visible in week 1, but club cells did not appear until week 4. Due to their later development during ontogenesis and after wounding, club cells may not have as much of a protective effect during wound healing as filament or mucus cells. Club cells are mostly found in the epidermis of the skin, and it is thought that when they work in tandem with mucus and goblet cells to fight infections, they serve as the body's first line of defense.

Beyond volatility: Systemic resilience and risk mitigation in interconnected commodity markets

In today's interconnected commodity markets, understanding volatility spillover dynamics is crucial. This research builds on Diebold & Yilmaz (2012, 2014) to estimate System Resilience, a vital measure for investors and policymakers. By using connectedness-based estimates, a unique stability score for each system component is constructed and aggregated to estimate overall System Resilience. This concept is applied to three key commodity segments: Energy, Agriculture, and Metals. Results show that most commodities, especially bullion, maintain moderate stability, offering investment alternatives even during periods of heightened systemic risk. The study also examines rolling estimates of System Resilience to identify early warning signals through increased standard deviation in successive resilience series. Tipping points indicate critical slowdowns where recovery to a stable state is hindered. Findings suggest that apart from the Global Financial Crisis (GFC), commodity portfolios remain relatively stable. The implications are significant for energy policymakers, traders, and financial investors. Early detection of warning signals supports strategic risk management. This research contributes to academic discourse and provides actionable insights, highlighting the importance of early warning indicators in enhancing financial resilience in an ever-evolving market landscape. It underscores the value of foresight in navigating global financial stability.

Adapting Altman Z-score models for early warning signals: Evidence from delisted mining stocks on the Johannesburg Stock Exchange

ABSTRACT The aim of the study is to measure the predictive prowess of adapting the Altman’s Z-score models when used to predict financial health and subsequent delisting of mining companies listed on the Johannesburg Stock Exchange between 1994 and 2021. The study applied Altman’s Z-score models to the financials of listed mining companies to measure the adaptability of the model for predicting the financial health status of mining companies and subsequent delisting in 1-, 2- and 3-years’ time against the actual listing status of each company in the sample over corresponding time horizons. Key findings indicate that both models reliably predict delisting when the horizon is only a year in the future.

Evaluation of the Adriatic Sea pollution using mesozooplankton as an environmental indicator

The Adriatic Sea is an enclosed basin threatened by marine pollution due to its hydrographic features and anthropogenic pressure. Although zooplankton has been worldwide regarded as an immediate warning signal of contamination, limited information is available on the contamination of these organisms at the Adriatic level. Hence, this study provides comprehensive data on the presence and levels of multiple pollutants in zooplankton collected from 46 locations. With regards to legacy contaminants, both PCB and DDT levels have declined since the 1980s. Specifically, most samples were characterized by low DDT contamination (average of 3 ± 2.7 ng g− 1 dry weight) and only few of these accumulated levels of concern for what concerns PCB, pointing out possible hotspots of contamination in the central-eastern Adriatic Sea. As regards metal(loid)s, the Metal Pollution Index identified areas of concern in the north Adriatic Sea (Gulf of Venice) with high levels of Co, Cu, Hg, Cr and Pb; in the Central Adriatic Sea (Tremiti islands) with high levels of Co, Ni, Hg, Cr and Pb; in the Southern Adriatic Sea (Taranto and offshore Corfu), with high levels of most metal(loid)s, especially Cr, Ni and Zn. Certain metal(loid)s (e.g. Cd, Pb and Hg) have declined over time and most of them are lower than well-known contaminated worldwide marine ecosystems. Only Cu appears to be particularly high in the Mediterranean zooplankton. Overall, this work suggests a general improvement of the status of contamination of the Adriatic Sea.

Isotopic seasonality of fluvial-derived greenhouse gases implies active layer deepening

Abstract Climate change in the northern circumpolar regions is rapidly thawing organic-rich permafrost soils, releasing substantial dissolved CO2 and CH4 into fluvial systems. This mobilization impacts local ecosystems and global climate feedback loops, playing a crucial role in the Arctic carbon cycle. Here, we analyze the stable carbon (δ13C) and radiocarbon (F14C) isotopic composition of dissolved CO2 and CH4 in the Sagavanirktok and Kuparuk River watersheds on the North Slope, Alaska. By examining spatial and seasonal variations in these isotopic signatures, we identify patterns of carbon release and transport along the river continuum. We find consistent CO2 isotopic signatures across the fluvial continuum, reflecting a mixture of geogenic and biogenic sources integrated throughout the watershed. Bayesian mixing models further demonstrate a systematic depletion in 13C and 14C signatures of dissolved CO2 sources from spring to fall, indicating increasing contributions of aged carbon as the active layer deepens seasonally. This seasonal deepening allows percolating groundwater to access deeper, older soil horizons, transporting CO2 produced by aerobic and anaerobic soil respiration to streams and rivers. In contrast, we observe no clear relationships between the 13C and 14C compositions of dissolved CH4 and landscape properties. Given the reduced solubility of CH4, which facilitates outgassing and limits its transport in aquatic systems, the isotopic signatures are likely indicative of localized contributions from streambeds, adjacent water saturated soils, and lake outflows. Our study demonstrates that dissolved greenhouse gases are sensitive indicators of the release of old carbon from thawing permafrost and serve as early warning signals for permafrost carbon feedback. It establishes a crucial baseline for understanding their role in regional carbon cycling and Arctic environmental change.

Leveraging crop yield forecasts using satellite information for early warning in Senegal

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Recent achievements in nonlinear dynamics, synchronization, and networks.

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.