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Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting process. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards, a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy's prediction accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP. [ABSTRACT FROM AUTHOR]

The cross-domain aspect detection and categorization is a vital task in natural language processing, enabling the automated identification and categorization of aspects in textual data spanning diverse domains. Traditional methods face several complexities such as scalability, limited contextual understanding of words, data sparsity, and adaptation difficulty. So, a novel method named gated bidirectional recurrent encoder-based adaptive honey badger (GBRE-AHB) algorithm is proposed for cross-domain aspect detection and categorization. In this study, the bidirectional encoder representations for transformers (BERT) is utilized to capture contextual information from text and enable better aspect identification and categorization by understanding the context. The local optimization problems are identified and solved by determining an adaptive strategy. Also, the gated recurrent unit (GRU) is employed to sequence the text data and allow aspect detection by considering the sequence in which aspects appear within a document. The study is validated on the datasets, namely the IMDB dataset of 50 K movie reviews and the cell phone reviews sentiment analysis-body dataset. The efficiency is validated by various metrics that attained the ranges as F1-score (97.85%), specificity (97.83%), recall (97.82%), precision (97.94%), and accuracy (98.67%), respectively. The experimental results revealed that the proposed method for cross-domain aspect detection and categorization as well as improved the reliability as well as longevity of the model and determined the impacts applied in the categorization process. [ABSTRACT FROM AUTHOR]

The growing need for data publication and the escalating concerns regarding data privacy have led to a surge in interest in Privacy-Preserving Data Publishing (PPDP) across research, industry, and government sectors. Despite its significance, PPDP remains a challenging NP-hard problem, particularly when dealing with complex datasets, often rendering traditional traversal search methods inefficient. Evolutionary Algorithms (EAs) have emerged as a promising approach in response to this challenge, but their effectiveness, efficiency, and robustness in PPDP applications still need to be improved. This paper presents a novel Hierarchical Adaptive Evolution Framework (HAEF) that aims to optimize t-closeness anonymization through attribute generalization and record suppression using Genetic Algorithm (GA) and Differential Evolution (DE). To balance GA and DE, the first hierarchy of HAEF employs a GA-prioritized adaptive strategy enhancing exploration search. This combination aims to strike a balance between exploration and exploitation. The second hierarchy employs a random-prioritized adaptive strategy to select distinct mutation strategies, thus leveraging the advantages of various mutation strategies. Performance bencmark tests demonstrate the effectiveness and efficiency of the proposed technique. In 16 test instances, HAEF significantly outperforms traditional depth-first traversal search and exceeds the performance of previous state-of-the-art EAs on most datasets. In terms of overall performance, under the three privacy constraints tested, HAEF outperforms the conventional DFS search by an average of 47.78%, the state-of-the-art GA-based ID-DGA method by an average of 37.38%, and the hybrid GA-DE method by an average of 8.35% in TLEF. Furthermore, ablation experiments confirm the effectiveness of the various strategies within the framework. These findings enhance the efficiency of the data publishing process, ensuring privacy and security and maximizing data availability. [ABSTRACT FROM AUTHOR]

As a significant branch of production scheduling problem, the Flexibility Job Shop Scheduling Problem (FJSP) is a typical NP-hard problem. Most conventional flexible workshop scheduling primarily focuses on performance aspects involving production efficiency such as time and quality. In recent years, due to increased energy costs and environmental pollution, 'low-carbon scheduling' has garnered attention as a new scheduling paradigm among scholars and engineers. This paper investigates a low-carbon flexible Job shop scheduling problem, proposing a Grey Wolf Optimization algorithm (SC-GWO), aiming to minimize the sum of carbon emission costs and makespan costs. This algorithm employs the Grey Wolf Algorithm (GWO) as the fundamental optimization method, adaptively choosing between global and local searches based on the dispersion degree of individuals. Firstly, integrating the Sine Cosine Algorithm (SCA), the sinusoidal cosine search mechanism is applied to GWO to enhance its local search capability. Secondly, a new leader selection mechanism is introduced to prevent leaders from falling into local optima, thus improving the algorithm's global exploration capability. Utilizing a nonlinear convergence factor strategy controls the global exploration and local exploitation capabilities in different algorithm stages, enhancing optimization accuracy and accelerating convergence, achieving a dynamic balance between the two. Finally, validation of the SC-GWO algorithm's ability to solve low-carbon scheduling problems in flexible job shop scheduling instances is conducted. Experimental results demonstrate the superior performance of SC-GWO in solving low-carbon flexible workshop scheduling instances. Comparative experiments against four other advanced algorithms on 22 classic benchmark test functions confirm SC-GWO's better convergence. Through standard test functions like Bandimarte instances applied to solve FJSP, experimental results showcase the excellent optimization performance of SC-GWO. Compared to HGWO and GWO, the makespan time is reduced by 22.25% and 39.27%, respectively. The proposed SC-GWO algorithm demonstrates favorable solving effects on flexible job shop scheduling instances, meeting actual production scheduling needs. [ABSTRACT FROM AUTHOR]

Fish typically adapt to their environment through evolutionary traits, and this adaptive strategy plays a critical role in promoting species diversity. Onychostoma macrolepis is a rare and endangered wild species that exhibits a life history of overwintering in caves and breeding in mountain streams. We analyzed the morphological characteristics, histological structure, and expression of circadian clock genes in O. macrolepis to elucidate its adaptive strategies to environmental changes in this study. The results showed that the relative values of O. macrolepis eye diameter, body height, and caudal peduncle height enlarged significantly during the breeding period. The outer layer of the heart was dense; the ventricular myocardial wall was thickened; the fat was accumulated in the liver cells; the red and white pulp structures of the spleen, renal tubules, and glomeruli were increased; and the goblet cells of the intestine were decreased in the breeding period. In addition, the spermatogenic cyst contained mature sperm, and the ovaries were filled with eggs at various stages of development. Throughout the overwintering period, the melano-macrophage center is located between the spleen and kidney, and the melano-macrophage center in the cytoplasm has the ability to synthesize melanin, and is arranged in clusters to form cell clusters or white pulp scattered in it. Circadian clock genes were identified in all organs, exhibiting significant differences between the before/after overwintering period and the breeding period. These findings indicate that the environment plays an important role in shaping the behavior of O. macrolepis, helping the animals to build self-defense mechanisms during cyclical habitat changes. Studying the morphological, histological structure and circadian clock gene expression of O. macrolepis during the overwintering and breeding periods is beneficial for understanding its unique hibernation behavior in caves. Additionally, it provides an excellent biological sample for investigating the environmental adaptability of atypical cavefish species. [ABSTRACT FROM AUTHOR]

This study explores the impacts of global warming on the reproductive efforts of Posidonia oceanica, a key seagrass species endemic to the Mediterranean Sea. By analyzing data from five areas in Sardinia in February 2023, we have investigated the relationship between thermal descriptors and P. oceanica flowering and fruiting variables. In fact, whether climate change has been affected the distribution of seagrasses by changing their reproductive efforts is still controversial and our findings contribute to this ongoing debate, highlighting a noticeable increase in reproductive events potentially affected by rising temperature. Overall, mean summer sea surface temperature (SST) was positively associated to inflorescence density, presence of living fruits and fruit size; living fruits and fruit size were also influenced positively by the maximum SST within 3 months before sampling (November–February). Both flowering and fruiting were negatively affected by the maximum SST within 1 month before inflorescence observation and mean winter SST, respectively, suggesting that P. oceanica reproductive effort depends on the duration and the temporal context in which the anomaly occurs. However, the high variability between meadows of the same area highlights the need of future studies to focus on the uncertainties related to local factors. [ABSTRACT FROM AUTHOR]

Halophilic and halotolerant microorganisms have a high biotechnological potential. They are producers of biologically active substances, stress-protective agents, hydrolytic enzymes, and are used for environmental bioremediation. At the same time, the characterization of novel halotolerant bacteria and determination of their salt tolerance strategies are important basic problems. The present work reports the isolation of a new strain, MX2, from the salt well brine of the Yakshinskoe potassium−magnesium salt deposit. The isolate was an aerobic, gram-positive, nonmotile bacterium that did not form spores. The cell morphology varied from cocci to short rods capable of producing V-shaped forms. The colonies on agar were circular, with an entire edge and raised center, glistening and orange. Bacteria of strain MX2 were halotolerant microorganisms capable of growing at NaCl concentrations up to 9%. The genome of strain MX2 was sequenced. Its estimated size was 3 747 717 bp, and the number of protein-coding genes was 3562. Strain MX2 was identified as belonging to the species Dietzia maris based on analysis of the 16S rRNA, gyrB, rpoB, recA, and ppk gene sequences and using time-of-flight mass spectrometry (MALDI-TOF-MS). D. maris MX2 had complete metabolic pathways for the synthesis of ectoine, hydroxyectoine, and trehalose, as well as the transport systems for ectoine, hydroxyectoine, trehalose, glycerol, glycerol-3-phosphate, L-proline, and glycine-betaine. Thus, to ensure the osmotic balance, D. maris MX2 used the strategy of accumulating compatible organic solutes. [ABSTRACT FROM AUTHOR]

The supply chain disruptions caused primarily by the COVID-19 pandemic catastrophe are expected to prolong the global chip shortage for several more years, making it a pressing concern across industries worldwide. Nonetheless, the influence of supply chain disruptions on firm performance within the semiconductor industry, especially in developing countries like Malaysia, has received limited attention in previous studies. This study aims to determine how supply chain adaptive techniques that combine robustness, agility, and resilience during the COVID-19 pandemic affected multinational firms' performance in the semiconductor industry in Malaysia. The nature of the study is quantitative, using survey methodology. The data were collected from 82 multinational corporations of semiconductor firms through an online platform. The findings show that resilience in supply chain adaptive strategies significantly impacted multinational corporations' semiconductor industries during the COVID-19 pandemic. However, the success of semiconductor firms during the COVID-19 pandemic was not greatly affected by the resilience and agility of supply chain adaptive strategies. The findings offer valuable insights into how firms implemented supply chain adaptive strategies during the challenging times of the COVID-19 pandemic. The knowledge can enable them to lessen the effect of the COVID-19 pandemic supply chain disruption and eventually enhance the firm's performance. Future studies should examine the new aspect of supply chain strategies, such as demand-driven planning, which uses technological tools for climate adaptation in times of crisis. [ABSTRACT FROM AUTHOR]

Universal Adaptive Strategy Theory aims to predict how taxa and assemblages respond to disturbances on the basis of adaptive strategy group (ASG) membership. Here, we test such predictions using the adaptive strategy scheme for reef-building corals developed by Darling et al. (Ecol Lett 15:1378–1386, 2012) and a long-term dataset of coral assemblage structure from inshore reefs on the central Great Barrier Reef. Several disturbances including mass bleaching and tropical storms were recorded in this 15-year interval from 1998 to 2013. ASG membership did not predict how a given taxon responded to disturbance. In fact, all ASGs were on average equally affected by bleaching and a period of multiple disturbances. Furthermore, there were no consistent winners at these sites in response to the 1998 bleaching in contrast to previous work suggesting clear hierarchies in susceptibility to bleaching. In conclusion, while further efforts to re-evaluate the utility of ASGs for reef corals should be encouraged our results and a re-examination of the literature suggests that direct trait-based approaches might prove more useful when exploring how corals respond to disturbance. [ABSTRACT FROM AUTHOR]

Based on the momentum principle and adaptive learning mechanism, we design online portfolio selection strategies, which are suitable for nonstationary financial market. Firstly, we propose a Moving-window-based Adaptive Exponential Gradient (MAEG) strategy, which updates the learning rate of the EG algorithm by maximizing the recent cumulative return using the price data in a fixed length moving window. Secondly, we consider a special case where all-historical price data is used to design another strategy named Adaptive Exponential Gradient (AEG). Finally, we conduct an extensive numerical analysis using real price data and the empirical results show that the performance of the proposed strategies is steadily superior to some online strategies. In addition, MAEG and AEG are able to withstand certain transaction costs, which further supports their practical applicability in trading applications. [ABSTRACT FROM AUTHOR]

In recent decades agricultural decision-making system has played a vital role in the field monitoring process. For these emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and wireless sensors are utilized for precise data extraction and analysis. However numerous techniques are developed for increasing agricultural production and enhancing operational efficiency. But still, they possess various challenges like lack of accuracy, increased power utilization, and costs. Insects and pathogens cause plant diseases that reduce productivity if not diagnosed at a proper time. Thus this paper develops Deep Q Rapidly-exploring Random tree-based Adaptive Fire hawk (DQRR-AFH) for diagnosing leaf diseases and monitoring them. It comprises various phases including data acquisition; Image processing, segmentation, feature extraction, and classification. Further, the classification is performed by exploring random trees, and the hyperparameters are tuned via the Adaptive Fire Hawk Optimizer algorithm is utilized to enhance the efficiency of the model. The proposed method monitors the soil moisture content and prevents cotton leaf diseases by spraying chemicals on the plants. Multiple cotton leaf images are obtained to verify the performance with various metrics. Compared to conventional methods such as WL-CNN, ECPRC, and DT, the proposed model achieved exceptional performance with an accuracy of 98.88%, precision of 97%, and an F1-score of 99.21%. [ABSTRACT FROM AUTHOR]

The geographic distribution of the common viper, Vipera berus (L. 1758), was believed to be limited in the north and northeast by an assumed lack of hibernacula with temperatures exceeding 0°C, because the species is not freeze-tolerant. To test this hypothesis, we measured in the laboratory the cold resistance of vipers that inhabit Kizhi Island (Lake Onega, Karelia, Russian North), and compared the results obtained with the temperatures in the hibernacula and soils of the region. Freezing of the adults happened at an average temperature of −3.0°C (ranging from −2 to −3.9°C). Vipera berus failed to tolerate complete freezing, but could withstand short-term (1–8 hours) partial freezing. At subzero temperatures (−2 to −2.2°C), animals remained supercooled for 23–121 days. Vipers remained active at temperatures dropping down to −1.5°C, moving in experimental containers; this possibly happens in overwintering places as well. Our study allows us to conclude that the species' ability to withstand long-term supercooling (up to four months) is one of the key determinants of the adaptive strategy of the common viper in cold regions. The critical temperature of the species in overwintering sites must be considered to be not 0°C, but −2°C. This threshold temperature can be a factor limiting the geographic distribution of V. berus. Temperatures in hibernacules on Kizhi Island (heapes of stones) and also possibly in all soils of Karelia are favorable for the species' overwintering, leading to its great abundance levels. [ABSTRACT FROM AUTHOR]

Inverse kinematic solution is a critical procedure for the control of manipulators; however, only a small number of manipulators that meet certain conditions have closed-form solutions. In this paper, the conventional solution was replaced by the optimization process of a proposed fitness function. The fitness function aims to obtain a set of accurate joint angles for the desired position and orientation of the end effector. The function is based on forward kinematics; hence, this approach can avoid singularities and be suitable for solving many manipulators. For solving this optimization problem, particle swarm optimization algorithm was chosen as the basis due to its easy principle and excellent performance. To enhance the performance of the algorithm in searching global optimum, an adaptive inertia weight strategy applied to adjust the velocity of particles was proposed. Besides, for particles, to avoid trapping in local optimum at the boundary, a special boundary treatment was presented. In this study, two serial robotic manipulators were used to test the performance of adaptive particle swarm optimization (APSO). Meanwhile, several powerful PSO variants improved from disparate methods were selected to compare with APSO. The experimental results demonstrate that the proposed fitness function with APSO can solve the inverse kinematic problem of multi-DOF manipulators efficiently and accurately. [ABSTRACT FROM AUTHOR]

Multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world. [ABSTRACT FROM AUTHOR]

Background and Aims: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity.Methods: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity.Results: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity.Conclusions: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity. [ABSTRACT FROM AUTHOR]