Sudden Environmental Changes Research Articles

Lost and found: Investigating a bilingual child’s L1 lexical attrition and recovery

Aims and Objectives: We examined the extent of L1 vocabulary attrition in a Korean child following her relocation to the United States at the age of 6 years 10 months, and the subsequent restoration of her L1 vocabulary upon her return to Korea 10 months later. Methodology: A picture-naming task was administered on a regular basis over a period of several months, beginning shortly after the child’s arrival in the United States. Data and Analysis: The data were analyzed to determine the child’s accuracy in producing common nouns in the L1. Findings/Conclusion: The study shows significant L1 vocabulary attrition after just 1 month, with an inability to access half of the test items after just 4 months and a drop in accuracy to 25% after 9 months. Upon return to Korea, the child’s L1 vocabulary improved rapidly, surpassing English usage within 3 weeks and reaching 82% accuracy within 6 weeks. These findings underscore the vulnerability of children to language attrition in a monolingual environment while also highlighting the remarkable resilience of their L1 skills. Originality: The study traced the child’s L1 decline and recovery in accordance with a strict timetable. By beginning measurements within a week of the child’s exposure to English following her subsequent reintroduction to Korean, the study was able to document the earliest manifestations of lexical attrition, its rapid progression, and its subsequent reversal—a pattern of decline and recovery that is rarely studied in this manner. Significance/Implications: This study contributes to the field by systematically documenting the impact on L1 vocabulary of a sudden change in linguistic environment, providing rare insights into the dynamics of lexical attrition in early bilingualism.

Effects of window view and local airflow on human thermal comfort in a sudden change environment

Transitioning from outdoors to indoors involves a sudden environmental change. However, current thermal comfort standards and design guidelines focus on steady-state environments, which do not fully address the urgent need for comfort during dynamic recovery periods. This study uses a climate chamber experiment to investigate the effects of window view and local airflow on thermal recovery under sudden environmental changes. Twenty participants, after exercising in a 34°C environment for 10 minutes, sat for 60 minutes in a 28°C room (with a microenvironment featuring window view/local airflow). Physiological parameters and subjective responses were measured during this period. The results indicate that under sudden environmental changes, window view significantly positively affects thermal recovery through psychological effects, while local airflow mainly accelerates convective heat exchange. The influence on thermal sensation recovery and recovery speed is as follows: window view and local airflow coupling > local airflow > window view. In terms of physiological parameters, both of them only had a significant effect on skin temperature but not on heart rate and blood pressure. The results of the study not only reveal the key role of indoor microenvironment on the effect and speed of thermal sensation recovery under the sudden change environments, which enriches the theory of dynamic thermal comfort, but also provides a scientific basis for the control strategy of indoor environments under sudden change environments.

Human-driven evolution of color in a stonefly mimic.

Rapid adaptation is thought to be critical for the survival of species under global change, but our understanding of human-induced evolution in the wild remains limited. We show that widespread deforestation has underpinned repeated color shifts in wild insect populations. Specifically, loss of forest has led to color changes across lineages that mimic the warning coloration of a toxic forest stonefly. Predation experiments suggest that the relative fitness of color phenotypes varies between forested and deforested habitats. Genomic and coloration analyses of 1200 specimens show repeated selection at the ebony locus controlling color polymorphism across lineages. These findings represent an example of human-driven evolution linked to altered species interactions, highlighting the possibility for populations to adapt rapidly in the wake of sudden environmental change.

Enhancement of the reactivation process of long-term starved anammox granular sludge with gravel balls: Microbial succession and metabolic impact

Anaerobic ammonium oxidation (Anammox) process is an economical and energy-efficient method of wastewater nitrogen removal. However, they are highly susceptible to starvation stress caused by sudden environmental changes. Rapid reactivation of starved anammox sludge is a crucial method to address seed sludge shortages and expand practical applications. This study investigated the impact of gravel balls on the reactivation of long-term starved anammox granular sludge (628 days). The results showed that gravel balls enhanced the recovery of nitrogen removal performance in starved anammox sludge, with nitrogen removal efficiency being 19.88% higher than the control group at the end of the recovery phase. The gravel balls also increased extracellular polymeric substance (EPS) secretion, contributing to the stability of the anammox system. Furthermore, the gravel balls promoted the proliferation of anammox bacteria, with the relative abundance of anammox bacteria reaching 38.25% on the 80th day. The analyses of microbial functions indicated that gravel balls facilitated cross-feeding and co-metabolism among microbes, while enhancing quorum sensing associated with anammox bacteria, forming a multifunctional community network centered on anammox bacteria. This indicates that gravel balls can effectively accelerate the reactivation process of long-term starved anammox sludge, aiding the reutilization of long-term starved anammox sludge.

Next-Generation MPPT: neural network-driven optimization for superior solar performance

This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV) systems and raised broadband oscillations. To address these issues, we propose an intelligent MPPT strategy utilizing NNC. The NNC inputs are the error and derived error (E&ΔE) between the reference and actual inputs, with the duty cycle (D) as the target output. The proposed algorithm demonstrates an efficiency of approximately 0.75% compared to 0.63% for the P&O method. Simulation and experimental validation results confirm the NNC strategy's effectiveness, robustness, and efficiency, providing a thorough system analysis.

Adaptive threshold based outlier detection on IoT sensor data: A node-level perspective

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.

Impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels of college students in China

BackgroundHigher education students exhibit heightened sensitivity to environmental changes as they navigate the critical transition from adolescence to adulthood. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to universities worldwide, exemplifying a crisis that profoundly affects the learning outcomes and psychological status of college students. Although it is known that campus lockdown has triggered dramatic changes in lifestyles, learning outcomes, and psychological statuses, in-depth knowledge of the causal relationships among these changes remains largely unclear.MethodsHere, we conducted a cross-sectional survey designed to assess the impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels among college students. We surveyed over 3,500 junior college, undergraduate and graduate participants from 94 colleges/universities across 30 provinces, municipalities, and autonomous regions in China.We employed structural equation modeling (SEM) to explore the relationships between changes in lifestyle, educational performance, and levels of anxiety associated with campus open or closure regulations during the COVID-19 pandemic.ResultsOur results discovered that sleep duration, physical activity involvement, and social connections were crucial for sustaining students' learning outcomes and mental health. The shift to online learning and campus lockdown exacerbated stressors, contributing to heightened anxiety (β = 0.066), disrupted sleep patterns, and enhanced physical activity (β = 0.070) and reduced learning effect (β = -0.059). Sleep patterns were disrupted by the campus lockdown, an effect mediated by the degradation of relationships among classmates. Nonetheless, the best-fitting SEM uncovered the intricate relationships among lifestyle changes, learning outcomes, and psychological status in response to sudden environmental changes (Fisher’s C = 80.949, P = 0.328). These results highlight the critical role of adaptable, supportive campus policies tailored to meet the diverse needs and interests of students during and beyond crises (Fisher’s C = 59.568, P = 0.809).ConclusionsOur study advocates for a holistic approach that addresses the multifaceted aspects of student life to cultivate a resilient academic community. This approach contributes to a deeper understanding of the effects of sudden environmental changes on students' psychological well-being and academic performance in the post-pandemic era.

Shared and redundant proteins coordinate signal cross-talk between MAPK pathways in yeast.

All cells must detect, interpret, and adapt to multiple and concurrent stimuli. While signaling pathways are highly specialized, different pathways often share components or have components with overlapping functions. In the yeast Saccharomyces cerevisiae, the high osmolarity glycerol (HOG) pathway has two seemingly redundant branches, mediated by Sln1 and Sho1. Both branches are activated by osmotic pressure, leading to phosphorylation of the MAPKs Hog1 and Kss1. The mating pathway is activated by pheromone, leading to phosphorylation of the MAPKs Fus3 and Kss1. Given that Kss1 is shared by the two pathways, we investigated its role in signal coordination. We activated both pathways with a combination of salt and pheromone, in cells lacking the shared MAPK and in cells lacking either of the redundant branches of the HOG pathway. By systematically evaluating MAPK activation, translocation, and transcription programs, we determined that Sho1 mediates cross talk between the HOG and mating pathways and does so through Kss1. Further, we show that Kss1 initiates a transcriptional program that is distinct from that induced by Hog1 and Fus3. Our findings reveal how redundant and shared components coordinate concurrent signals and thereby adapt to sudden environmental changes.

A preliminary survey of the cellular responses of the black fungus Cryomyces antarcticus to long and short-term dehydration.

The McMurdo Dry Valleys in Southern Victoria Land, Antarctica, are known for their extreme aridity, cold, and nutrient-poor conditions. These valleys provide a valuable comparison to environments on Mars. The survival of microorganisms in these areas hinges on their ability to withstand dehydration due to the limited availability of liquid water. Some microorganisms have adapted to survive extended periods of metabolic inactivity and dehydration, a physiological response to the harsh conditions in which they exist. This adaptation is significant for astrobiology studies as it allows for testing the resilience of microorganisms under extraterrestrial conditions, exploring the boundaries and potential for life beyond Earth. In this study, we examined the survivability, metabolic activity, cellular membrane integrity, and ultrastructural damage of Cryomyces antarcticus, a eukaryotic organism used for astrobiological studies, following two dehydration processes. We conducted a fast dehydration process, simulating what happens on the surface of Antarctic rocks under typical environmental conditions, and a slow dehydration process, which is commonly used in astrobiological experiments. Our findings revealed a higher percentage of damaged cells following slow dehydration treatments, confirming that rapid dehydration reflects the adaptability of microorganisms to respond to sudden and drastic changes in the Antarctic environment.

Risk factors for cognitive biases when making management decisions

Decision-making in case of sudden changes in the external environment of the enterprise can occur in a situation of time interval restrictions. Cognitive biases inherent in a manager can influence such decisions when there is not enough time. The purpose of this work is to clarify the influence of cognitive biases on the subjective perception of the probability of events occurring when making decisions. Such cognitive biases can also affect risk analysis and assessment, consumer behavior etc. The development of anti-risk control impacts will be implemented with this in mind. The research is based on the methodological tools of the operational theory of risk management, the fundamentals of behavioral economics developed in the works of D. Kahneman and A. Tversky, and the so-called «theory of two systems (Systems 1 and Systems 2)». Individual examples of cognitive biases in decision-making under the influence of System 1 are considered. The results of an experiment to identify quantitative estimates of the perception of the probability of events occurring based on a visual series of images presented to subjects with System 1 turned on and System 2 suppressed are described. The solutions to predictive problems are assessed. It seems reasonable to take into account all the problems identified during the implementation of the experiment. It is planned to conduct a new series of experiments with specified conditions, since expanding and clarifying the understanding of the subjective perception of probability by economic agents can help revise many theories in the field of preparing managerial decision-making and consumer behavior. Recommendations for the introduction of anti-risk control impacts with the inclusion of System 2 to minimize the impact of cognitive distortions on decisions are proposed

Cretaceous Limestone Dolomitization and Argument of Its Mg2+ Sources: Exampling the Bekhme and Qamchuqa Formations in the Zagros Collision Belt

This study unveils new considerations regarding the dolomitization of the Bekhme and Qamchuqa formations (BQFs), two reefal Cretaceous units in the Iraqi Zagros collisional belt. Previous studies proposed the dolomitization of these formations due to the ascent of Mg-rich hydrothermal solutions through deep faults but lacked substantial supporting evidence. This paper present new considerations suggesting that the dolomitization of the BQFs resulted from the Cretaceous environmental conditions and basin paleogeographic setting of the platform on which these formations were deposited. To justify this consideration, seven proofs presented. First is the occurrence of dolomite as vast layers extending tens of kilometers laterally and bounded between limestone beds, which resemble a strata-bound occurrence. Second is the absence of vertical selective dolomitization such as, hydrothermal mineralization, vertical dolomite dykes, hydrothermal vents, tubes, or zones. Third is the barrenness of most of the fault surfaces and their surrounding rocks with selective dolomitization. Fourth is the sharp boundary between the dolomite and limestone layers, indicating a sudden environmental change from lagoon to reef or forereef. Fifth is the record of dolomitization in the sediments reworked by bioturbation, evident in thalassinoides burrows. Sixth is the succession of BQFs underlain by limestone and green marl devoid of dolomitization and hydrothermal upward movement. Seventh is the contradictory notion of hydrothermal dolomitization among the most recent study published in the Science journal which atributed dolomitization to normal temperate on earth surface. In the present study, the source of the Mg is argued and its presence is attributed to the warmth of the Arabian platform (plate) near the equator during the Cretaceous period. Another source is the connection of the Arabian platform with the Neo-Tethys Sea, which was the locus of Mg-rich mafic volcanism in the form of arcs and mid-oceanic ridge topography, which supplied a voluminous amount of Mg to the Arabian carbonate platform through ocean currents.

Zootechnical Additives for Optimum Production in Livestocks in Stallfed Condition

This is the point of interest to use zootechnical additives in ruminants to control the accumulation of lactate in the rumen. The primary and main cause of acidosis is due to consumption of rapidly digestible carbohydrate in high amount. In normal animal husbandry (stall fed) practices generally livestock are feed with the left out or waste products rich in carbohydrates. This excessive carbohydrate consumption leads to sudden change in ruminal environment, causing sub-acute or chronic lactic acidosis. The ruminal lactic acidosis problem can be averted by either zootechnic additives such as Saccharomyces cereviseae and Megasphaera elsdenii or adding sodium bicarbonate/enzymes in feed or ration.

Studija o otpornosti na krizu Poljskih kotiranih kompanija nakon pandemije

Businesses’ response to black swans is crucial to developing a country’s economy. Resilience can be defined as the company’s ability to adapt and survive despite sudden shocks and environmental changes. Crisis resilience allows companies to overcome emerging shocks, remain competitive, and maintain a sound financial position. Theoretical considerations focus on the essence of crises and the concept of resilience in management sciences. The article aims to assess Polish companies’ resilience from the construction sector to the shock caused by the Covid-19 pandemic. The empirical study of Polish listed companies was conducted based on financial statements for 2017-2021, divided into stable and unstable periods. The indicators of liquidity, operational efficiency, and profitability were evaluated. In addition, competitors’ market share was considered, using the Herfindahl- Hirschman index to measure industry concentration.

Prototype of pH and Water Temperature Control System in Discus Fish Farming Using IoT-based Sugeno Fuzzy

Challenges in cultivating discus fish often arise from abrupt pH and temperature fluctuations attributed to manual and sluggish intervention. An IoT-based prototype for automatic pH and water temperature regulation was developed to address this. The study aimed to evaluate the efficacy of the prototype in controlling pH levels and water temperature and to explore the application of IoT-based fuzzy logic in discus fish cultivation. Test data from the implemented tools and sensors revealed an error comparison value of 0.0132% and an accuracy level of 99.986% for pH measurement. In comparison, temperature sensing yielded an error value of 0% with 100% accuracy. The IoT-based fuzzy Sugeno system demonstrated regular and effective operation in regulating pH and water temperature in discus fish cultivation, showcasing superiority over manual handling systems in mitigating sudden environmental changes.

Astyanax mexicanus surface and cavefish chromosome-scale assemblies for trait variation discovery.

The ability of organisms to adapt to sudden extreme environmental changes produces some of the most drastic examples of rapid phenotypic evolution. The Mexican Tetra, Astyanax mexicanus, is abundant in the surface waters of northeastern Mexico, but repeated colonizations of cave environments have resulted in the independent evolution of troglomorphic phenotypes in several populations. Here, we present three chromosome-scale assemblies of this species, for one surface and two cave populations, enabling the first whole-genome comparisons between independently evolved cave populations to evaluate the genetic basis for the evolution of adaptation to the cave environment. Our assemblies represent the highest quality of sequence completeness with predicted protein-coding and noncoding gene metrics far surpassing prior resources and, to our knowledge, all long-read assembled teleost genomes, including zebrafish. Whole-genome synteny alignments show highly conserved gene order among cave forms in contrast to a higher number of chromosomal rearrangements when compared with other phylogenetically close or distant teleost species. By phylogenetically assessing gene orthology across distant branches of amniotes, we discover gene orthogroups unique to A. mexicanus. When compared with a representative surface fish genome, we find a rich amount of structural sequence diversity, defined here as the number and size of insertions and deletions as well as expanding and contracting repeats across cave forms. These new more complete genomic resources ensure higher trait resolution for comparative, functional, developmental, and genetic studies of drastic trait differences within a species.

An adaptive biomolecular condensation response is conserved across environmentally divergent species

Cells must sense and respond to sudden maladaptive environmental changes—stresses—to survive and thrive. Across eukaryotes, stresses such as heat shock trigger conserved responses: growth arrest, a specific transcriptional response, and biomolecular condensation of protein and mRNA into structures known as stress granules under severe stress. The composition, formation mechanism, adaptive significance, and even evolutionary conservation of these condensed structures remain enigmatic. Here we provide a remarkable view into stress-triggered condensation, its evolutionary conservation and tuning, and its integration into other well-studied aspects of the stress response. Using three morphologically near-identical budding yeast species adapted to different thermal environments and diverged by up to 100 million years, we show that proteome-scale biomolecular condensation is tuned to species-specific thermal niches, closely tracking corresponding growth and transcriptional responses. In each species, poly(A)-binding protein—a core marker of stress granules—condenses in isolation at species-specific temperatures, with conserved molecular features and conformational changes modulating condensation. From the ecological to the molecular scale, our results reveal previously unappreciated levels of evolutionary selection in the eukaryotic stress response, while establishing a rich, tractable system for further inquiry.

Can cyclone exposure explain behavioural and demographic variation among lemur species?

Madagascar has a harsh and stochastic climate because of regular natural disturbances. This history of regular cyclones has been hypothesised to have directed evolutionary changes to lemur behaviour and morphology that make them more resilient to sudden environmental change. These adaptations may include: small group sizes, high degrees of energy-conserving behaviours, generalist habitat use, small home ranges, small body size, and a limited number of frugivorous species. To date, however, no one has tested how variation in cyclone exposure across Madagascar is associated with variation in these resilience traits. In this study, we created a detailed cyclone impact map for Madagascar using Koppen-Geiger climate class, historical cyclone tracks, the Saffir Class of cyclone and hurricane intensity, and precipitation data. We also used existing literature to calculate a resilience score for 26 lemur species for which data existed on resilience traits. Our cyclone impact map was then overlaid on known geographic ranges of these species and compared to resilience score while controlling for phylogenetic non-independence and spatial autocorrelation. We found no association between cyclone impact in a lemur range and their resilience score. When assessing traits individually, however, we found that cyclone impact was positively associated with body size, suggesting that the more impacted a species is by cyclones the smaller they are. We also found cyclone impact to be negatively associated with frugivory, with species in higher impact zones eating more fruit. While unexpected, this could reflect an increased production in fruit in tree fall gaps following cyclones. While we did not find a pattern between cyclone impact and behavioural resilience in lemurs, we suggest a similar study at a global scale across all primates would allow for more taxonomic variation and reveal larger patterns key to understanding past and future vulnerability to natural disturbances in primates.

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments.

In complex and dynamic environments, traditional pursuit-evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit-evasion game from a neurodynamic perspective instead of formulating the problem as a differential game. The BINN is topologically organized to represent the environment with only local connections. The dynamics of neural activity, characterized by the neurodynamic shunting model, enable the generation of real-time evasive trajectories with moving or sudden-change obstacles. Several simulation and experimental results indicate that the proposed approach is effective and efficient in complex and dynamic environments.

Helicopter short-term collision avoidance with sampling-based model predictive control

AbstractAutomatic helicopter flight in uncertain surroundings remains a challenging task due to sudden changes in environment, requiring fast response to guarantee safe and collision-free guidance. Increasing numbers of small unmanned aerial vehicles, which are not covered by air traffic control, pose a potential threat to rotorcraft operating in lower airspace. To provide collision avoidance in this scenario, the capability of reacting immediately to appearing obstacles and guiding the rotorcraft along feasible evasive trajectories is required. This paper presents an approach to short-term collision avoidance based on model predictive techniques. The proposed method, originally developed for automotive applications, finds optimal control inputs by predicting a set of trajectories utilizing a model resembling the helicopter dynamics. Compared to model predictive control, no iterative optimization is adopted, resulting in deterministic execution time. The proposed method is evaluated by closed-loop simulations with a non-linear helicopter model. Additional hardware-in-the-loop simulations are conducted to examine the real-time capability of the approach.

Research on Indoor Environment Prediction of Pig House Based on OTDBO-TCN-GRU Algorithm.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO-TCN-GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO-TCN-GRU model, OTDBO-TCN-GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO-TCN-GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO-TCN-GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO-TCN-GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.