Variety Of Environments Research Articles

Seafloor sediment characterization improves estimates of organic carbon standing stocks: an example from the Eastern Shore Islands, Nova Scotia, Canada

Abstract. Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how the ocean carbon cycle will be altered by climate change and possibly human activities. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolutions, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in dynamic and spatially variable shelf environments. This lack of knowledge reduces our ability to understand and predict how much and for how long the ocean sequesters CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive four different spatial estimates of organic carbon stock: (i) OC density estimates scaled to the entire study region assuming a homogenous seafloor, (ii) interpolation of OC density estimates using empirical Bayesian kriging, (iii) OC density estimates scaled to areas of soft substrate estimated using a high-resolution classified substrate map, and (iv) empirical Bayesian regression kriging of OC density within areas of estimated soft sediment only. These four distinct spatial models yielded dramatically different estimates of standing stock of OC in our study area of 223 km2: 80 901, 58 406, 16 437 and 6475 t of OC, respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves and for the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies.

High-Resolution Infrared Reflectance Distribution Measurement Under Variable Temperature Conditions

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

Z-Scheme Ag2S-Ag-In2O3 Heterostructure with Efficient Antibiotics Removal under Natural Sunlight.

The widespread distribution of antibiotics in natural waters is a great threat to human health. Photocatalytic degradation is an environmentally friendly technology to remediate antibiotic-polluted waters, driven by endless solar energy. Herein, a Z-scheme Ag2S-Ag-In2O3 heterostructure photocatalyst is prepared to remove antibiotics under environmental conditions. Under natural sunlight (light intensity: ∼78 mW/cm2) irradiation, the optimal Ag2S-Ag-In2O3 (10-ASAIO) exhibits considerable performance for decomposing diverse antibiotics, including norfloxacin (NOR), tetracycline hydrochloride, sulfisoxazole, ciprofloxacin, chlortetracycline hydrochloride, and ofloxacin. The NOR photodegradation rate constant of 10-ASAIO reaches 0.025 min-1, which is 12.50, 5.00, and 6.25 times higher than that of In2O3 (0.002 min-1), Ag-In2O3 (0.005 min-1), and Ag2S-In2O3 (0.004 min-1), respectively. This performance of the 10-ASAIO photocatalyst for decomposing NOR under natural sunlight exceeds most of the previously reported photocatalysts under a xenon lamp. Particularly, due to the intermittency of natural sunlight, a light-emitting diode (LED) lamp (light intensity: 5.1 mW/cm2) is also used as a light source, and 72.20% of NOR can be degraded with irradiation for 12 h. The effects of many water characteristics (water bodies, coexisting inorganic anions, pH, and humic acid) on the degradation performance of 10-ASAIO have been investigated, which exhibits stable degradation efficiency in variable aquatic environments. A 10-ASAIO catalyst-coated frosted glass sheet is fabricated to settle the problem of recovery of powder photocatalysts, and the immobilized catalyst shows outstanding activity and stability to decompose NOR. The photocatalytic mechanism and pathway of degrading NOR over 10-ASAIO have also been systemically investigated and proposed. The ecotoxicity (phytotoxicity and biotoxicity) of the 10-ASAIO photocatalyst and treated NOR solution have been tested by their toxic effects on cabbage seeds and Staphylococcus aureus (S. aureus). This work provides a feasible photocatalytic system for environmental pollutant remediation under natural sunlight or an LED lamp.

Four MYB transcription factors regulate suberization and non-localized lignification at the root endodermis in rice.

In response to variable environments, rice (Oryza sativa) roots have developed lignified and suberized diffusion barriers at the endodermis to permit selective nutrient uptake for optimal growth. Here, we demonstrate that endodermal suberization and non-localized lignification are redundantly regulated by four MYB transcription factors: OsMYB39a, OsMYB41, OsMYB92a, and OsMYB92b. These transcription factors function downstream of the OsMYB36a/b/c, CASPARIAN STRIP INTEGRITY FACTOR (OsCIF)-SCHENGEN3 (OsSGN3), and stress-inducible signaling pathways in rice. Knockout of all four MYB genes resulted in the complete absence of endodermal suberin lamellae (SL) and almost no lignin deposition between the Casparian strip and the cortex-facing lignified band at cell corners under all conditions examined. In contrast, endodermis-specific overexpression of any of these MYB genes was sufficient to induce strong endodermal suberization and non-localized lignification near the root tip. Furthermore, OsMYB92a-overexpressing lines showed an altered ionomic profile and enhanced salinity tolerance. Transcriptome analysis identified 152 downstream genes regulated by OsMYB39a/41/92a/92b, including the key SL formation gene OsCYP86A1 and other genes involved in endodermal lignification and suberization under normal and stress conditions. Our results provide important insights into the molecular mechanisms underlying suberization and non-localized lignification at the root endodermis and their physiological significance in ion homeostasis and acclimation to environmental stress.

Dynamic microbiome diversity shaping the adaptation of sponge holobionts in coastal waters.

During long-term evolution, sponge holobionts, among the oldest symbiotic relationships between microbes and metazoans, developed two distinct phenotypes with high- and low-microbial abundance (HMA and LMA). Despite sporadic studies indicating that the characteristic microbial assemblages present in HMA and LMA sponges, the adaptation strategies of symbionts responding to environments are still unclear. This deficiency limits our understanding of the selection of symbionts and the ecological functions during the evolutionary history and the adaptative assessment of HMA and LMA sponges in variable environments. Here, we explored symbiotic communities with two distinct phenotypes in a 1-year dynamic environment and combined with the meta-analysis of 13 sponges. The different strategies of symbionts in adapting to the environment were basically drawn: microbes with LMA were more acclimated to environmental changes, forming relatively loose-connected communities, while HMA developed relatively tight-connected and more similar communities beyond the divergence of species and geographical location.

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

Calibration modeling of drilling fluid rheological parameters in variable temperature environment based on support vector machine.

Traditional measurement of drilling fluid rheological parameters suffers from significant lag due to the inability of the instruments to promptly capture real-time parameters of the drilling fluid. These measurement models are typically constructed based on fixed temperature conditions and empirical formulas, rendering them inadequate for complex temperature gradient environments. Consequently, this limitation results in increased prediction errors, severely compromising the precise monitoring of drilling fluid performance. Aiming at the problems of low accuracy and poor stability of drilling fluid measurements under variable temperature conditions, a support vector machine-based calibration model for drilling fluid rheological parameters in a variable temperature environment is proposed in this paper. First, the measurement principle of the double-tube differential pressure rheology real-time measurement device is analyzed. The relationship between shear stress and shear rate is then established using differential pressure sensor and flow rate data. Utilizing the gray wolf optimization algorithm to optimize the kernel function weights and parameters, an SVM-based calibration model for predicting drilling fluid rheology correction parameters is constructed. Finally, a real-time monitoring platform for drilling fluid is developed. Experimental results show that the maximum relative errors for the predictions of apparent viscosity, plastic viscosity, and yield point are within ±5%, with coefficients of determination (R2) all greater than 0.95. These results validate the effectiveness of the proposed method in accurately monitoring the rheological performance of drilling fluids.

Photon number adaptive single-photon detection with time-walk passive compensation.

In long-distance laser time transfer, such as satellite-to-satellite and satellite-to-ground, the accuracy of flight time measurement for photon pulses is significantly compromised by the time walk phenomenon, stemming from variations of the incident photon numbers. In this paper, we propose a single-photon detection method, which is adaptive to the photon number, and passively compensates for the time walk effect. This method utilizes a fiber ring to divide an incident photon pulse into a series of photon pulse trains, with each pulse in the train separated by equal time intervals and subject to equal attenuation. The last detected photon pulse in the pulse train can always be attenuated to a single-photon level sufficient to render the time walk effect negligible. The experimental results demonstrate that the proposed method effectively mitigates the time walk effect across a range of average photon numbers, spanning from 1 to 100. It provides high-precision single-photon detection for laser time transfer in complex and variable environments.

Hot and cold exposure triggers distinct transcriptional and behavioral responses in laboratory-inbred pond snails

Animals exhibit remarkable behavioral and molecular adaptations to cope with thermal stressors, which are crucial for survival in variable environments that are exacerbated by climate change. Aquatic poikilotherms like our model organism—the pond snail Lymnaea stagnalis—face significant challenges due to their dependence on external temperatures. Our study provides valuable insights into the different behavioral and molecular responses of lab-inbred snails to cold and heat shock stressors (i.e., 4 °C and 30 °C), particularly in the context of learning and memory formation. We found that while short-term (1 h) cold exposure transiently upregulated the expression levels of HSP70 and HSP40 in the snail’s central ring ganglia, prolonged cold exposure (24 h) resulted in a significant downregulation of LymMIPII and an upregulation of LymMIPR. These data suggest, albeit at the transcriptional level, the existence of a negative feedback loop necessary for sustaining cellular functions when metabolic demands might shift towards conserving energy during prolonged cold exposure. At the behavioral level, we found that, compared to heat shock, cold exposure did not result in a Garcia effect (i.e., a “special form” of conditioned taste aversion). The difference in memory outcomes was associated with changes in the expression levels of selected targets involved in neuronal plasticity and the stress response. While both cold and heat shock upregulated the HSP levels in the snail's central ring ganglia, cold exposure did not affect the expression levels of the neuroplasticity genes LymGRIN1 and LymCREB1, contrasting with heat shock’s neurogenic effects. Overall, this study provides insights into L. stagnalis's adaptive responses to thermal stressors, emphasizing different molecular strategies for coping with heat versus cold challenges in aquatic environments. These findings contribute to our understanding of thermal biology and stress physiology in aquatic organisms, underscoring the importance of molecular mechanisms in shaping species' resilience in dynamic environments.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Design of a turbo-based deep semantic autoencoder for marine Internet of Things

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

Dune Hares: Population Indices, Home Range Size, and Habitat Selection of the European Hare on a Danish Island.

Population indices, such as transect counts of animals, can provide important information concerning population changes over time. Moreover, data concerning the home range size and habitat selection of individuals can provide valuable insight into spatial requirements of animals and how they can adapt to variable environments. Here, we describe the population development of European hares (Lepus europaeus) and investigated home range sizes and habitat selection of six radio-tagged individuals on the small (80 ha) Danish Wadden Sea island Langli. The average minimum hare population density from 1983 to 1997 was 64 ± 36 (mean ± SD) hares/km2, with hare numbers varying among years and seasons. The average home range size was 23.3 (CI: 18.9-28.7) ha, which is comparable to agricultural areas of high structural diversity. Moreover, hare habitat selection was generally bimodal, with hares moving over larger areas and selecting marsh habitat for foraging during nighttime, and dune and grassland habitat for resting during daytime, especially during winter. Combined, our results indicate that hare abundance and space use in the dunal landscape of Langli Island were similar to agricultural areas of comparatively high habitat quality. Thus, dunal marsh landscapes offer high-quality habitat for hares and might be of importance as population strongholds at a time when hare populations are declining in many agricultural areas across Europe.

Grape clusters detection based on multi-scale feature fusion and augmentation

This paper addresses the challenge of low detection accuracy of grape clusters caused by scale differences, illumination changes, and occlusion in realistic and complex scenes. We propose a multi-scale feature fusion and augmentation YOLOv7 network to enhance the detection accuracy of grape clusters across variable environments. First, we design a Multi-Scale Feature Extraction Module (MSFEM) to enhance feature extraction for small-scale targets. Second, we propose the Receptive Field Augmentation Module (RFAM), which uses dilated convolution to expand the receptive field and enhance the detection accuracy for objects of various scales. Third, we present the Spatial Pyramid Pooling Cross Stage Partial Concatenation Faster (SPPCSPCF) module to fuse multi-scale features, improving accuracy and speeding up model training. Finally, we integrate the Residual Global Attention Mechanism (ResGAM) into the network to better focus on crucial regions and features. Experimental results show that our proposed method achieves a mAP0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{0.5}$$\\end{document} of 93.29% on the GrappoliV2 dataset, an improvement of 5.39% over YOLOv7. Additionally, our method increases Precision, Recall, and F1 score by 2.83%, 3.49%, and 0.07, respectively. Compared to state-of-the-art detection methods, our approach demonstrates superior detection performance and adaptability to various environments for detecting grape clusters.

Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach.

Gravitational effects on optical lens fabrication: First insides on Earth- and microgravity experiments

Abstract Understanding the effects of gravity on manufacturing processes is a pioneering extension of the process parameter space used to date. Until now, the improvement of manufacturing technologies has mainly focused on process parameters such as temperature, pressure, and material composition, as access to variable gravity environments is limited. The Einstein-Elevator opens up new possibilities for the variation of these process parameters and the development of in-space manufacturing technologies. Together with the research of innovative production processes for optical components within the PhoenixD Cluster of Excellence, this creates an entirely new field of research. The research presented here focuses on investigating gravity's effects on dispensed optical lens production. Using a jet dispenser, sessile droplets are produced during a flight phase in the Einstein-Elevator and cured directly by UV polymerization. As part of this study, optical lenses were produced and compared under microgravity and Earth's gravitational conditions. Geometric properties such as height and contact angle of the lenses produced were analyzed. It was found that lenses fabricated under microgravity have a larger contact angle than those fabricated under Earth gravity. Similarly, the height increases with decreasing gravity. These results are consistent with the theoretical assumptions described, although generalized theories to describe the morphology of a sessile droplet are not yet available. The case study findings on the influence of gravity as a process parameter on drop morphology represent a fundamental improvement for additive manufacturing technologies, especially for in-space manufacturing.

Genome-wide identification of clock-associated genes and circadian rhythms in Fragaria × ananassa seedlings

Flowering time in plants is regulated by a photoperiod-responsive mechanism. Some plant species use a circadian clock-based control mechanism to adapt to variable environments. Strawberry is a horticultural crop that responds to certain photoperiods and temperatures to induce flowering. However, clock-associated genes in octoploid cultivated strawberry (Fragaria × ananassa) have not been defined, and their regulatory mechanism for responding to photoperiods is unclear. We herein targeted 12 clock-associated genes reported in other plant species and performed a genome-wide analysis and expression comparison in F. × ananassa seedlings. Seventy-eight sequences were selected from the F. × ananassa genome. The major domains and cis-acting elements were conserved in each sequence. Transcripts were clearly expressed under continuous light conditions in F. × ananassa seedlings (‘Yotsuboshi’) acclimated to long days. Among them, 9 genes maintained their unique autonomous circadian rhythms and may function as clock genes. LHY (LATE ELONGATED HYPOCOTYL) had the Myb domain and LHY expression peaked in the dawn. PRR (PSEUDO-RESPONSE REGULATOR) family members (PRR9, PRR7, PRR5, and TOC1 (TIMING OF CAB EXPRESSION 1)) had a pseudo-receiver domain and CCT domain, and peak expression times began sequentially from the afternoon for PRR9 to the evening for TOC1. LUX (LUXARRHYTHMO) had a Myb domain, and LUX expression peaked in evening with ELF3 (EARLY FLOWERING 3). FKF1 (FLAVIN-BINDING KELCH REPEAT F BOX 1) had PAS and F-box domains, and FKF1 expression peaked in the afternoon. GI (GIGANTEA) expression also peaked in the afternoon. F. × ananassa (‘Yotsuboshi’) appears to have multiple feedback loops comprising clock-associated genes. Although the rhythmic expression of CHE (CCA1 HIKING EXPEDITION) and ZTL (ZEITLUPE) was not observed, they had conserved domains, CHE with the TCP domain and ZTL with the PAS and F-box domains. The present results provide basic information on the circadian clock for the control of F. × ananassa flowering.

Acclimation to constant and fluctuating temperatures promote distinct metabolic responses in Arctic char (Salvelinus alpinus).

The Arctic is warming three times faster than the global average, imposing challenges to cold-adapted fish, like Arctic char (Salvelinus alpinus). We evaluated stress and metabolic responses of Arctic char to different thermal acclimation scenarios to determine if responses to thermal variation differed from those to stable exposures. Fish were exposed for 7 days to one of 4 treatments: (1) control (12 °C), (2) mean (16 °C), corresponding to the mean temperature of the diel thermal cycle, (3) constant high temperature (20 °C), and (4) diel thermal cycling (12 to 20 °C every 24 h). Exposure to 20 °C causes increases plasma lactate and glucose, an imbalance in antioxidant systems, and oxidative stress in the liver. The 20 °C treatment also elevated fractional rates of protein synthesis and caused oxidative stress in the heart. Stress responses were more pronounced in diel thermal cycling than in mean (16 °C) fish, indicating that peak exposure temperatures or variation are physiologically important. Cortisol was highest in diel thermal cycling fish and oxidative stress was noted in the liver. Gill Na+/K+-ATPase activity was also significantly reduced in diel thermal cycling fish, suggesting gill remodeling in response to an osmoregulatory stress. Exposure to a constant 20 °C, was more challenging than a diel thermal cycle, demonstrating the importance of daily cooling to recovery. Arctic char inhabit a thermally variable environment and understanding how this impacts their physiology will be critical for informing conservation strategies in the context of a rapidly warming Arctic.

Migratory birds modulate niche tradeoffs in rhythm with seasons and life history

Movement is a key means by which animals cope with variable environments. As they move, animals construct individual niches composed of the environmental conditions they experience. Niche axes may vary over time and covary with one another as animals make tradeoffs between competing needs. Seasonal migration is expected to produce substantial niche variation as animals move to keep pace with major life history phases and fluctuations in environmental conditions. Here, we apply a time-ordered principal component analysis to examine dynamic niche variance and covariance across the annual cycle for four species of migratory crane: common crane (Grus grus, n = 20), demoiselle crane (Anthropoides virgo, n = 66), black-necked crane (Grus nigricollis, n = 9), and white-naped crane (Grus vipio, n = 9). We consider four key niche components known to be important to aspects of crane natural history: enhanced vegetation index (resources availability), temperature (thermoregulation), crop proportion (preferred foraging habitat), and proximity to water (predator avoidance). All species showed a primary seasonal niche "rhythm" that dominated variance in niche components across the annual cycle. Secondary rhythms were linked to major species-specific life history phases (migration, breeding, and nonbreeding) as well as seasonal environmental patterns. Furthermore, we found that cranes' experiences of the environment emerge from time-dynamic tradeoffs among niche components. We suggest that our approach to estimating the environmental niche as a multidimensional and time-dynamical system of tradeoffs improves mechanistic understanding of organism-environment interactions.

Sustainable Entrepreneurship: Interval Analysis in Risk Management and Uncertain Economies

Sustainable management in high-tech enterprises is a key aspect of successfully operating modern companies, especially under conditions of risk and uncertainty. This study reviews the field of sustainable management and interval analysis and identifies the main trends and challenges facing high-tech enterprises in the modern world. This study emphasizes the importance of applying interval analysis in making strategic decisions and developing sustainable business models that can adapt to variable environments. This paper presents empirical data, illustrating the practical application of interval analysis tools in the management in high-tech enterprises. It analyzes the effectiveness and potential of this approach to increase the levels of sustainability and competitiveness of organizations in constantly changing business environments. In general, this article is a valuable contribution to the development of sustainable management theory and practice for high-tech enterprises, enriching the existing knowledge in this area and offering new perspectives for research and practical application. Our research has been validated and is presented in the results section. The purpose of this study is to present current developments in methodologies and tools for risk measurement within the probabilistic paradigm of uncertainty, which are supposed to be used in relation to the economic evaluation of real investment projects. The methodological directions or approaches to risk measurement formed in this context are (1) based on quantile measures, within which the quantitative aspect of risk is modeled using quantile quantiles of the distribution of a random variable describing the possible (predicted) results of economic activity; (2) the Monte Carlo method, which is a tool for evaluating the indicators of economic efficiency and risk in justifying real investments, taking into account different distribution laws and mutual relations for the financial and economic parameters of the investment project, as well as its computational and instrumental elaboration.

Dynamic Active Sites in Electrocatalysis

In‐depth understanding of the real‐time behaviors of active sites during electrocatalysis is essential for the advancement of sustainable energy conversion. Recently, the concept of dynamic active sites has been recognized as a potent approach for creating self‐adaptive electrocatalysts that can address a variety of electrocatalytic reactions, outperforming traditional electrocatalysts with static active sites. Nonetheless, the comprehension of the underlying principles that guide the engineering of dynamic active sites is presently insufficient. In this review, we systematically analyze the fundamentals of dynamic active sites for electrocatalysis and consider important future directions for this emerging field. We reveal that dynamic behaviors and reversibility are two crucial factors that influence electrocatalytic performance. By reviewing recent advances in dynamic active sites, we conclude that implementing dynamic electrocatalysis through variable reaction environments, correlating the model of dynamic evolution with catalytic properties, and developing localized and ultrafast in‐situ/operando techniques are keys to designing high‐performance dynamic electrocatalysts. This review paves the way to the development of the next‐generation electrocatalyst and the universal theory for both dynamic and static active sites.