Warning Signals Research Articles

Unveiling key adaptation strategies: A systematic review of climate variability’s impact on agriculture

This review aims to comprehensively analyze the variability of climate in agriculture and theadaptive behaviour of farmers in response to climate change, synthesizing existing knowledge andexamining measures for mitigate climate fluctuations. To achieve this, a systematic literaturereview (SLR) was conducted using the Scopus database, covering the past eleven years. A total of177 articles were identified through a methodological application of the Preferred Reporting Itemsfor Systematic Reviews and Meta-analyses (PRISMA) framework. While these studies encompass abroad geographical scope, the selection was deliberately constrained by a small sample size toexclude regions lacking conflicting evidence, ensuring relevance and high-quality information, astandard practice in systematic reviews within this field. This study hypothesizes that adaptivestrategies, such as climate-smart agriculture and early warning systems, significantly reducefarmers’ vulnerability under climate change. Key findings indicates that practices like conservationtillage, agroforestry, and other climate-resilient approaches enhances agricultural sustainability andresilience. The results highlight the urgent need for further investigation and policy incentives toencourage adaptive behaviour and provide food security in the presence of climatic variability.Thus, the conclusion of these findings encourages the widespread uptake of climate-smart practicescoupled with early warning mechanisms to reduce climatic risks effectively.

External Morphology, Defensive Adaptations, Aposematic Coloration, and Sexual Dimorphism of the Fifth Instar Larva of Cricula Silkmoth, Cricula trifenestrata Helfer (Lepidoptera: Saturniidae) from Thailand

External Morphology, Defensive Adaptations, Aposematic Coloration, and Sexual Dimorphism of the Fifth Instar Larva of Cricula Silkmoth, Cricula trifenestrata Helfer (Lepidoptera: Saturniidae) from Thailand

Compounds Involved in the Invasive Characteristics of Lantana camara.

Lantana camara L. is native to tropical America and has naturalized in many other tropical, subtropical, and temperate regions in Asia, Africa, Oceania, North and South America, and Europe. L. camara infests diverse habitats with a wide range of climatic factors, and its population increases aggressively as one of the world's 100 worst invasive alien species. Its infestation reduces species diversity and abundance in the natural ecosystems and reduces agricultural production. The life history characteristics of L. camara, such as its high reproductive ability and high adaptive ability to various environmental conditions, may contribute to its ability to infest and increase its population. Possible evidence of the compounds involved in the defense functions of L. camara against natural enemies, such as herbivore mammals and insects, parasitic nematodes, pathogenic fungi and bacteria, and the allelochemicals involved in its allelopathy against neighboring competitive plant species, have accumulated in the literature over three decades. Lantadenes A and B, oleanonic acid, and icterogenin are highly toxic to herbivore mammals, and β-humulene, isoledene, α-copaene thymol, and hexadecanoic acid have high insecticidal activity. β-Caryophyllene and cis-3-hexen-1-ol may function as herbivore-induced plant volatiles which are involved in sending warning signals to undamaged tissues and the next plants of the same species. Farnesol and farnesal may interrupt insect juvenile hormone biosynthesis and cause abnormal metamorphosis of insects. Several triterpenes, such as lantanolic acid, lantoic acid, pomolic acid, camarin, lantacin, camarinin, ursolic acid, and oleanonic acid, have demonstrated nematocidal activity. Lantadene A, β-caryophyllene, germacrene-D, β-curcumene, eicosapentaenoic acid, and loliolide may possess antimicrobial activity. Allelochemicals, such as caffeic acid, ferulic acid, salicylic acid, α-resorcylic acid, p-hydroxybenzoic acid, vanillic acid, unbelliferone, and quercetin, including lantadenes A and B and β-caryophyllene, suppress the germination and growth of neighboring plant species. These compounds may be involved in the defense functions and allelopathy and may contribute to L. camara's ability to infest and to expand its population as an invasive plant species in new habitats. This is the first review to focus on how compounds enhance the invasive characteristics of L. camara.

Early warning signals of critical transitions in ecosystems: entropy reduction in vegetation spatial patterns

Early warning signals of critical transitions in ecosystems: entropy reduction in vegetation spatial patterns

Coastal eutrophication transforms shallow micro-benthic reef communities.

Coral reefs are impacted worldwide by coastal eutrophication, which is often translated by a decrease in coral cover and an increase in potentially harmful invertebrates and algal blooms. Additionally to corals and other macro-benthos, micro-benthic communities are affected tremendously, however few studies reported the specific effect of eutrophication on those communities. This study addresses how micro-benthic communities are impacted by turbidity and associated eutrophication. To answer this question, we compared three groups that have been previously suggested as bioindicators of reef environmental conditions: foraminifera, diatoms and bacteria, from 12 islands (and 600 samples) in the Spermonde Archipelago (Indonesia) along a near- to offshore turbidity gradient. Insights from environmental DNA metabarcoding and satellite images highlighted differences between the reef slope (deep) and the reef flat (shallow) communities, a distinction that has been omitted in most previous studies. The reef flat communities were 1.5- to 2-fold more affected by turbidity parameters compared to the reef slope, which we argue is related to eutrophication. Based on previous work, we expected that prokaryotes would be the most impacted group by water quality. However, we found that large benthic foraminifera and diatom communities were highly impacted by turbidity, whereas the reef flat prokaryotic communities were primarily shaped by the substrate type. Additionally, a total of 112 exact sequence variants (ESVs) were identified as indicators of different turbidity levels, of which 87 foraminifera and diatom ESVs were associated with high-moderate turbid waters. Our study revealed fundamental knowledge and provides key information of the specific effect of turbidity and associated eutrophication and habitat variables (substrate type and reef area) on foraminifera, diatoms and prokaryotic communities. We argue that local and regional eutrophication participates in shaping reef flat micro-benthic communities, which may therefore play an important role as early warning signals for local degradation in coral reefs.

Power spectrum based early warning signal of neuronal firing

Brain diseases often occur simultaneously with critical changes in neural system and abnormal neuronal firing. Studying the early warning signals (EWSs) of critical changes can provide a promising approach for predicting neuronal firing behaviors, which is conducible to the early diagnosis and prevention of brain diseases. Traditional EWSs, such as autocorrelation and variance, have been widely used to detect the critical transitions in various dynamical systems. However, these methods have limitations in distinguishing different types of bifurcations. In contrast, the EWSs with power spectrum have shown a significant advantage in not only predicting bifurcation points but also distinguishing the types of bifurcations involved. Previous studies have demonstrated its predictive capability in climate and ecological models. Based on this, this study applies the EWS with power spectrum to neuronal systems in order to predict the neuronal firing behaviors and distinguish different classes of neuronal excitability. Specifically, we compute the EWSs before the occurrence of saddle-node bifurcation on the invariant circle and subcritical Hopf bifurcation in the Morris-Lecar neuron model. Additionally, we extend the analysis to the Hindmarsh-Rose model, calculating the EWSs before both saddle-node bifurcation and supercritical Hopf bifurcation. This study contains the four types of codimension-1 bifurcations corresponding to the neuronal firing. For comparison, we also calculate two types of conventional EWSs: lag-1 autocorrelation and variance. In numerical simulations, the stochastic differential equations are simulated by the Euler-Maruyama method. Then, the simulated responses are detrended by the Lowess filter. Finally, the EWSs are calculated by using the rolling window method to ensure the detection of EWS before bifurcation points. Our results show that the EWS with power spectrum can effectively predict the bifurcation points, which means that it can predict neuronal firing activities. Compared with the lag-1 autocorrelation and the variance, the EWSs with power spectrum not only accurately predict the neuronal firing, but also distinguish the classes of excitability in neurons. That is, according to the different characteristics of the power spectrum frequencies, the EWS with power spectrum can effectively distinguish between saddle-node bifurcations and Hopf bifurcations during neuronal firing. This work provides a novel approach for predicting the critical transitions in neural system, with potential applications in diagnosing and treating brain diseases.

Fluctuation-variable correlation as early warning signals of non-equilibrium critical transitions

Fluctuation-variable correlation as early warning signals of non-equilibrium critical transitions

Abnormalities of resting-state EEG microstates in older adults with cognitive frailty.

This study aims to analyze the characteristics of EEG microstates across different cognitive frailty (CF) subtypes, providing insights for the prevention and early diagnosis of CF. This study included 60 eligible older adults. Their resting-state EEG microstates were analyzed using agglomerative adaptive hierarchical clustering. Microstate temporal parameters were extracted through global field power peak-based backfitting. Spearman's partial correlation analysis and linear mixed-effects models were employed to investigate the relationship between microstate temporal parameters and CF. Statistical differences were observed in transition probabilities (TPs) from microstate B to A between healthy controls (HCs) and reversible cognitive frailty (RCF) group (t = -2.076, P = 0.042). Potentially reversible cognitive frailty (PRCF) and RCF group also exhibited statistical differences in the TPs from microstate B to A (t = 3.122, P = 0.003). In the RCF group, the occurrence of microstates A and B differed significantly from microstate C (tAC = 3.455, PAC = 0.002; tBC = 3.108, PBC = 0.004). In the PRCF group, the occurrence of microstates A, B, and C differed significantly from microstate D (tAD = -3.688, PAD = 0.001; tBD = -3.334, PBD = 0.002; tCD = -4.188, PCD < 0.001). The neural networks and processing modes engaged by microstate D during executive memory tasks differ between RCF and PRCF. A decreased occurrence of microstate C and higher TPs of microstates A and B may serve as early warning signals for RCF. Conversely, an increased occurrence of microstate D and decreased TPs of microstates C and D indicate the onset of PRCF.

The impact of Helicobacter pylori on gastric cancer formation and early warning signal identification.

Gastric cancer is highly prevalent in Asia and is characterized by poor prognosis post-surgery and a high recurrence rate within five years. Research has highlighted the role of Helicobacter pylori in initiating or accelerating gastric cancer development. However, quantitative analysis of its impact on gastric cancer carcinogenesis is still lacking. This study employs gene regulatory networks and landscape and flux theory, integrating genetic and epigenetic factors, to quantitatively elucidate how Helicobacter pylori influences gastric cancer progression. Varied Helicobacter pylori infection concentrations lead to significant shifts in system thermodynamic and dynamic driving forces, altering gene expression levels. Quantitative analysis of entropy production rate and mean-flux in the gastric cancer system reveals the global changes in thermodynamic and dynamic driving forces. Coupled with autocorrelation, cross correlation, and variance analysis, we pinpoint critical stages of Helicobacter pylori infection, serving as early warning signals for gastric cancer. This approach bridges theoretical and clinical realms, dynamically assessing Helicobacter pylori's impact on gastric cancer and identifying crucial early warning signals, with significant clinical and translational implications.

Intensive transmission in wild, migratory birds drove rapid geographic dissemination and repeated spillovers of H5N1 into agriculture in North America.

Since late 2021, a panzootic of highly pathogenic H5N1 avian influenza virus has driven significant morbidity and mortality in wild birds, domestic poultry, and mammals. In North America, infections in novel avian and mammalian species suggest the potential for changing ecology and establishment of new animal reservoirs. Outbreaks among domestic birds have persisted despite aggressive culling, necessitating a re-examination of how these outbreaks were sparked and maintained. To recover how these viruses were introduced and disseminated in North America, we analyzed 1,818 Hemagglutinin (HA) gene sequences sampled from North American wild birds, domestic birds and mammals from November 2021-September 2023 using Bayesian phylodynamic approaches. Using HA, we infer that the North American panzootic was driven by ∼8 independent introductions into North America via the Atlantic and Pacific Flyways, followed by rapid dissemination westward via wild, migratory birds. Transmission was primarily driven by Anseriformes, shorebirds, and Galliformes, while species such as songbirds, raptors, and owls mostly acted as dead-end hosts. Unlike the epizootic of 2015, outbreaks in domestic birds were driven by ∼46-113 independent introductions from wild birds, with some onward transmission. Backyard birds were infected ∼10 days earlier on average than birds in commercial poultry production settings, suggesting that they could act as "early warning signals" for transmission upticks in a given area. Our findings support wild birds as an emerging reservoir for HPAI transmission in North America and suggest continuous surveillance of wild Anseriformes and shorebirds as crucial for outbreak inference. Future prevention of agricultural outbreaks may require investment in strategies that reduce transmission at the wild bird/agriculture interface, and investigation of backyard birds as putative early warning signs.

Communication Disorders Essentials for Parents

Between September and December 2023, Babylonia collected questions from parents regarding their children’s language development. This article aims to answer the following questions: What are some red flags for potential communication disorder? How can I foster strong communication skills in children with communication disorders? How can I differentiate between language difficulties and learning difficulties? What resources or therapies are there for children with language delays? What are signs of language delay or disorder? [Summary generated by Claude-3-Haiku-200k - we refer the reader to the full article in PDF format for a complete response] This article provides essential information to parents about communication disorders in children. It answers key questions, such as the revealing signs of potential disorders, the differentiation between language difficulties and learning disorders, and the available resources/therapies. Communication disorders can take various forms and affect up to one-quarter of children under 5 years old. They may manifest as language delays or articulation disorders and are often linked to risk factors such as prematurity, low birth weight, or genetic disorders. Known developmental milestones help identify early signs of difficulties, such as lack of reactivity or babbling before 12 months. Language delays, grammatical errors, reduced intelligibility, or limited social interactions in young children can also be warning signals. Differentiating between language difficulties and learning disorders relies on specific characteristics. Speech sound disorders, language delays, and developmental language disorder are distinct categories that may coexist with specific learning disorders like dyslexia or dyscalculia. Parents play a crucial role in supporting their child's communication development. Strategies like social object play, following interests, or narrating actions can enhance language skills in a positive and stimulating environment. Professional resources are also available for children with confirmed delays or disorders. In conclusion, this article provides parents with important guidelines to identify early signs of difficulties and implement appropriate support, in collaboration with qualified professionals.

Critical Fluctuations as an Early Warning Signal of Sports Injuries? A Proof of Concept Using Football Monitoring Data.

There has been an increasing interest in the development and prevention of sports injuries from a complex dynamic systems perspective. From this perspective, injuries may occur following critical fluctuations in the psychophysiological state of an athlete. Our objective was to quantify these so-called Early Warning Signals (EWS) as a proof of concept to determine their explanatory performance for injuries. The sample consisted of 23 professional youth football (soccer) players. Self-reports of psychological and physiological factors as well as data from heart rate and GPS sensors were gathered on every training and match day over two competitive seasons, which resulted in an average of 339 observations per player (range = 155-430). We calculated the Dynamic Complexity (DC) index of these data, representing a metric of critical fluctuations. Next, we used this EWS to predict injuries (traumatic and overuse). Results showed a significant peak of DC in 30% of the incurred injuries, in the six data points (roughly one and a half weeks) before the injury. The warning signal exhibited a specificity of 95%, that is, correctly classifying non-injury instances. We followed up on this promising result with additional calculations to account for the naturally imbalanced data (fewer injuries than non-injuries). The relatively low F1 we obtained (0.08) suggests that the model's overall ability to discriminate between injuries and non-injuries is rather poor, due to the high false positive rate. By detecting critical fluctuations preceding one-third of the injuries, this study provided support for the complex systems theory of injuries. Furthermore, it suggests that increasing critical fluctuations may be seen as an EWS on which practitioners can intervene. Yet, the relatively high false positive rate on the entire data set, including periods without injuries, suggests critical fluctuations may also precede transitions to other (e.g., stronger) states. Future research should therefore dig deeper into the meaning of critical fluctuations in the psychophysiological states of athletes. Complex Systems Theory suggests that sports injuries may be preceded by a warning signal characterized by a short window of increased critical fluctuations. Results of the current study showed such increased critical fluctuations before 30% of the injuries. Across the entire data set, we also found a considerable number of critical fluctuations that were not followed by an injury, suggesting that the warning signal may also precede transitions to other (e.g., healthier) states. Increased critical fluctuations may be interpreted as a window of opportunity for the practitioner to launch timely and targeted interventions, and researchers should dig deeper into the meaning of such fluctuations.

The Importance of Prevention in School Safety Planning and Response.

School violence is a significant public health concern. Most perpetrators exhibit psychosocial and behavioral warning signals which are often inadequately addressed. While policy and funding typically reinforce politically popular target hardening and threat assessment strategies, these are not most supported by research or requested by education faculty and administration. The most common threats to school safety are ubiquitous events causing students to feel disconnected, alienated, isolated, and unsafe in school. Theoretical and empirical research indicates that focusing on rare but extreme school violence is less productive than preventative approaches including universal socio-emotional programs, mental health supports, and climate and culture building to produce effective strategies for safe school environments. Comprehensive school safety plans should include three essential preventative components: (1) targeted, student-centered approaches addressing social, emotional, and behavioral wellbeing; (2) equity-focused institutional culture and climate approaches aligned with appropriate disciplinary strategies; and (3) well-defined target hardening, threat assessment and reporting measures. Increasing evidence suggests that keeping schools safe requires coordinated approaches to school safety including student-centered psycho-social behavioral initiatives, systematic attention to equitable culture and climate, and meaningful, racially sensitive target hardening coordinated among and between service professionals.

IT-based health event surveillance in mass gathering - Kumbh Mela 2021, Haridwar, Uttarakhand

The Kumbh Mela is a large religious mass gathering event organised in India. During April 01 to April 30, 2021, Kumbh Mela was organized in Haridwar, Uttarakhand, India in the amid of COVID-19 pandemic. To detect and respond to unusual health events, a special surveillance model was established during Kumbh Mela-2021, Haridwar. The Kumbh module of Integrated Health Information Platform (IHIP) is utilized for real time data capturing. An Emergency Operations Centre (EOC) established to monitor COVID-19 and other disease situations through IHIP. Capacity building of Rapid Response Teams (RRT) was conducted. The special surveillance monitored 24 diseases/ syndromes. Kumbh mela area was divided into 4 zones with total 75 health facilities. A total of 31021 cases were reported on IHIP, including 86% communicable diseases (CDs), 8% non-communicable diseases (NCDs) and 6% unusual syndrome. Among total CDs, 27% acute febrile illness (AFI), 25.3% acute respiratory illness (ARI), 19% water borne diseases (WBDs), 15% COVID-19 and 14% others encountered. Out of total NCDs, 67% trauma, 25% dog bite, 8% others. A total of 36 early warning signals generated and responded, of which 22% WBDs, 22% ARI, 17% COVID-19, 14% AFI, 11% unusual syndrome and 14% others. The special disease surveillance helped monitoring health event alerts and health condition alerts. EOC monitored early warning signals and facilitated early detection and quick response to public health threats. Acute febrile illnesses emerged as a leading cause of morbidity. Future Mass Gathering events like Kumbh Mela should include disease surveillance as part of planning and augment capacity to diagnose and management of acute febrile illness.

Research on the innovation of early warning mechanisms of major public health emergencies for poverty alleviation and marginal populations: a case study of Fujian Province.

Poverty alleviation is critical for sustainable development. Establishing a major public health emergency warning and prevention mechanism for poverty alleviation and marginal populations can effectively determine the overall risk situation and primary risk components in diverse regions. It is conducive to formulate specific policies for risk prevention and control of public health emergencies to prevent the occurrence of poverty relapses. Expert evaluation method is used to grade the risk impact and risk probability. Combined with the Borda ordinal value method to rank the importance of risk factors, a judgment matrix is constructed. The Analytic Hierarchy Process method is used to determine the weight of the risk module. And the impact risk of major health emergencies on poverty alleviation and marginal populations is comprehensively evaluated based on the quantified value of the risk impact level. The results indicate that the production and operation risks and unemployment risks in the case area have a relatively significant impact on poverty alleviation and marginal populations. Different regional government forces and regional economic development characteristics may also form different risk weights. Therefore, risk warning models should match government development and regional development goals and be established based on the characteristics of different regions. Effective risk control mechanisms and policies can only be developed based on the characteristics and behavioral traits of different populations in the region.

Early Warning Signals of the Tipping Point in Strongly Interacting Rydberg Atoms

Early Warning Signals of the Tipping Point in Strongly Interacting Rydberg Atoms

An Examination of the Influence of Soviet Educational Policies on the Evolution of Nationalism in the USSR

The role nationalism played in the collapse of the USSR always attracted much heated debate among historians and its importance ranged from irrelevant to contributory. This essay includes not only the importance of nationalism in the fall of the USSR but also the influence of Soviet education on the evolution of nationalism. The essay systematically scrutinizes the educational policies, institutions, and major reforms from 1917 to 1991 and analyzes the effectiveness of education, including history (the major social science subject) and the use of language, in controlling the development of nationalism. The Soviet education was to a large degree failing in controlling the development of nationalist sentiment, because of that the effort to build a transnational proletarian union was upset by the rhetoric of Russian chauvinism and nationalism and volatile policies about the language used in education from Koreniztsiya to Russification sent a warning signal to national minorities in the Soviet Union from Baltic states to Central Asian republics. The nationalism contributed to significant separatism in Georgia and the Baltic states, but its role was insignificant in the deterioration of allegiance to the Union in key republics, such as Ukraine and Russia.

Leveraging Transactional Data for Analytics: Building Economic Indicators from Wire Transfers, Credit Card, and ACH Transactions

Traditional financial metrics for startup companies are often scarce due to the limited availability of public financial data. This paper proposes a novel approach to building leading economic indicators for startups by leveraging transactional data from wire transfers, credit card transactions, and Automated Clearing House (ACH) payments. These data sources provide rich, granular insights into the financial activities of startups, offering a proxy for their operational health and growth trajectories. The methodology involves clustering transactional data based on sender and receiver information to identify spending patterns. Outgoing transactions are categorized into key expenditure categories such as payroll, marketing, legal, computing, and operational costs. By analyzing the distribution and frequency of these expenses, this framework provides a comprehensive view of a startup's resource allocation and financial priorities. In addition, incoming transactions from various channels are used as a proxy for sales or revenue, enabling the development of dynamic indicators of financial performance. This approach bridges the gap in financial visibility for startups, providing timely and actionable insights into their economic activities. These leading indicators can serve as early warning signals of financial distress or as predictors of growth, offering immense value to investors, analysts, and policymakers. The study highlights the potential of transactional data analytics in enhancing transparency and decision-making in the startup ecosystem, paving the way for more robust analytical frameworks in a traditionally opaque domain. Index Terms: Transactional Data Analysis, Wire Transfers, Credit Card Transactions, ACH Payments, Startup Financial Health, Spending Patterns, Revenue Proxy, Data Clustering, Expense Categorization, Payroll Analysis, Marketing Expenses, Legal Costs, Computing and Operational Costs, Startup Growth Metrics, Financial Visibility, Early Warning Signals, Startup Ecosystem Analytics

An Early Micro Internal Short Circuit Fault Diagnosis Method Based on Accumulated Correlation Coefficient for Lithium-Ion Battery Pack

Early micro internal short circuit (ISC) fault diagnosis is crucial for the safe and reliable operation of lithium-ion batteries. In order to solve the problem that the early micro ISC fault is difficult to identify due to its weak fault characteristics, this paper proposes a fault diagnosis method based on the accumulated correlation coefficient. Specifically, the method uses the accumulated voltage value within the time window as the input feature, constructs an adjustment factor based on the distance difference of the accumulated voltage value to amplify the difference between the fault voltage correlation coefficient and the normal voltage correlation coefficient, and finally achieves the purpose of highlighting the faulty cell. The effectiveness and diagnostic capability of the proposed method are verified in experiments of short circuit faults of different severity. The results show that the proposed method can effectively identify and locate early micro ISC faults within 200 s, and improve the diagnostic capability up to 0.02 C short-circuit severity. In addition, a multi-level diagnostic warning mechanism can be established according to the decrease of the fault voltage correlation coefficient, so as to measure the severity of the fault and track the fault evolution process.

Muscle twitch thresholds depending on the direction of current stimulation

Abstract Acoustic or visual warning signals for workers in hazardous situations might fail under loud and/or lowvisibility work situations. A warning system that uses electrocutaneous stimulation can overcome this problem. This study aimed to compare vertical, diagonal, and horizontal current stimulation directions at the upper arm to select the one with the lowest amount of muscle twitching. Fourteen electrodes were attached in two rows to the upper right arm of 15 participants. The stimulation was conducted with bi-phasic rectangular pulses of 150 μs and amplitudes of up to 25 mA. Muscle twitch thresholds have been determined and a circumferential stimulation signal was presented as warning pattern for the three current stimulation directions and evaluated regarding alertness, discomfort, and urgency. For single stimulation pulses, muscle twitches occurred slightly less often at the horizontal stimulation direction compared to the other two and muscle twitch thresholds showed no systematic differences. For the warning patterns, no considerable differences were found regarding the evaluation of alertness, discomfort, and urgency and no differences were found for muscle twitching. In conclusion, all orientations seem suitable for warning pattern presentation and none of the directions has a clear advantage in reducing muscle twitch.