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Climate change has led to an increasing frequency of droughts, potentially undermining soil stability. In such a changing environment, the shallow reinforcement effect of plant roots often fails to meet expectations. This study aims to explore whether this is associated with the alteration of plant traits as a response to environmental change. Focusing on Amorpha fruticosa, a species known for its robust root system that plays a crucial role in soil consolidation and slope stabilization, thereby reducing soil and water erosion, we simulated a drought-rewetting event to assess the legacy effects of drought on the soil shear strength and the mechanical and hydrological traits associated with the reinforcement provided by A. fruticosa. The results show that the legacy effect of drought significantly diminishes the soil shear strength. Pretreated with drought, plant roots undergo morphological alterations such as deeper growth, yet the underground root biomass and diameter decline, thereby influencing mechanical reinforcement. Chemical composition analysis indicates that the plant's adaptation to drought modifies the intrinsic properties of the roots, with varying impacts on different root types and overall reinforcement. Concurrently, the stomatal conductance and transpiration rate of leaves decrease, weakening the capacity to augment soil matric suction through transpiration and potentially reducing hydrological reinforcement. Although rewetting treatments aid in recovery, drought legacy effects persist and impact plant functional attributes. This study emphasizes that, beyond soil matric suction, plant adaptive mechanisms in response to environmental changes may also contribute significantly to reduced soil shear strength. Consequently, ecological restoration strategies should consider plant trait adaptations to drought, enhancing root systems for soil conservation and climate resilience. [ABSTRACT FROM AUTHOR]

The Crowned Porcupine Optimization (CPO) algorithm exhibits certain deficiencies in initialization efficiency, convergence speed, and adaptability. To address these issues, this paper proposes an enhanced Crowned Porcupine Optimization algorithm (ICPO) based on multiple improvement strategies. ICPO optimizes the initialization process by introducing Logistic chaotic mapping, thereby expanding the search space. It accelerates convergence through an elite retention strategy and enhances global search capability by integrating stochastic operations, mutation-like operations, and crossover-like operations to increase population diversity. Additionally, adaptive step tuning based on fitness values is employed to comprehensively improve the algorithm's performance. To verify the effectiveness of ICPO, 23 standard functions were used for a comprehensive evaluation, and its practicality was further validated through optimization of actual engineering design problems. The experimental results demonstrate significant improvements in convergence speed, solution quality, and adaptability with ICPO. [ABSTRACT FROM AUTHOR]

Available data on the reproductive ecology of widespread bivalves from the families Margaritiferidae and Unionidae in the most vulnerable period of their reproduction in complex life cycle has been generalized. The features of evolutionarily adaptive strategy of mollusks to various environmental conditions and relationships with fish have been revealed. A detailed review and analysis of the reproductive cycles of mollusks from the genera Margaritifera, Unio, and Anodonta reveal that, from the boreal regions with moderate continental climate to those with subtropical one, temperature is the main factor regulating all stages and timing of the reproductive process. [ABSTRACT FROM AUTHOR]

Multi-manipulator systems face challenges in coordinating their movement in complex environments. To enhance the collaborative control stability of multi-manipulator systems in intelligent manufacturing, this study utilizes event-triggered (ET) mechanisms to reduce the signal transmission burden and frequency, and combines adaptive strategy to solve interference factors in complex environments. In addition, the study combines adaptive strategy with neural network structure using adaptive neural network control methods, and adopts ET mechanism to design auxiliary variables. Then, the neural network approximates the nonlinear uncertain model of the system online to cope with external disturbances and improve the robustness. From the results, the maximum fitting error of the multi-manipulator system based on ET and adaptive strategy was 0.59%, which was 2.05 and 3.99% lower than the errors of the other two advanced control systems, respectively. In summary, the research on multi-manipulator systems in intelligent manufacturing on the basis of ET and adaptive strategy effectively improved its control stability in intelligent manufacturing.

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization.

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization. [ABSTRACT FROM AUTHOR]

Rising coal price and increasingly stringent emission policy highlight the importance of balancing economic benefit and sustainable development of coal-fired power plants. In order to make the power plant operate economically and safely, and reduce the emission of pollutants as much as possible, this paper proposed a coal blending optimization framework for coal-fired power plants. This framework constructs a set of mathematical model based on the minimization objective function, including the economy, safety and environmental protection of coal. And a multi-strategy fusion multi-objective particle swarm optimization (MSF-MOPSO) method is proposed to optimize the coal blending model. The feasibility, effectiveness and superiority of the method are theoretically verified by convergence analysis and several sets of simulation experiments. And the practical application of this framework shows that under the coal blending strategy based on different weight combinations, the algorithm proposed in this paper can produce high-quality Pareto spatial distribution. Comparing with other state-of-the-art coal blending algorithms, this method can reduce the coal purchase cost by 4.42% and ${\rm{S}}{{\rm{O}}_{\rm{2}}}$ S O 2 emission by 5.11% at least under the same condition. It has significant environmental protection and economic benefit. [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. Methods: 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. Results: 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. Conclusions: 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]

Previous studies have shown that nutrients accumulation played important roles in resisting to stress resistance of plants. Our study examined the ecostoichiometric internal stability (EIS) of nutrients accumulation and, concomitantly identified the main resistant regulating substances and their contributions to stress resistance of reed (Gramineae) in arid desert areas. Plants (digging method) and soil samples (quartering method)) obtained from sand dune (SD), desert steppe (DP), interdune lowland (IL), saline meadow (SM) and wetland (W) habitats were brought back to the lab for nutrients analysis. Results indicated that soil nutrients differed obviously, while reed maintained relatively stable ratios of SiO2:N, N:K, and P:K when the eco-environments changed in different habitats. Furthermore, reed exhibits common adaptive characteristics by mainly accumulating large amounts of SiO2 (122.6-174.0 g/kg) and NO3- (166.1-216.6 g/kg), as well as moderate levels of soluble sugar (SS: 24.0-55.0 g/kg), which are mainly stored in leaves for stress resistance. The contribution of ions to stress resistance was 80.03%-91.15% (with SiO2 and NO3- accounting for 54.91%-63.10%), whereas the contribution of solutes was only 8.85%-19.97% (with SS contributing to 5.14%-10.91%) in different habitats. These findings suggest that maintaining relatively high EIS, while still accumulating SiO2 and NO3- as main physiological regulators might be an effective strategy for reed to positively respond to adverse habitats, which provide a strong theoretical basis and technical reference for searching useful methods for restoration and reconstruction of the degraded ecosystems in desert oasis regions. [ABSTRACT FROM AUTHOR]

The adaptability of marine organisms to changes in salinity has been a significant research area under global climate change. However, the underlying mechanisms of this adaptability remain a debated subject. We hypothesize that neglecting salinity fluctuation properties is a key contributing factor to the controversy. The ciliate Euplotes vannus was used as the model organism, with two salinity fluctuation period sets: acute (24 h) and chronic (336 h). We examined its population growth dynamics and energy metabolism parameters following exposure to salinity levels from 15‰ to 50‰. The carrying capacity (K) decreased with increasing salinity under both acute and chronic stresses. The intrinsic growth rate (r) decreased with increasing salinity under acute stress. Under chronic stress, the r initially increased with stress intensity before decreasing when salinity exceeded 40‰. Overall, glycogen and lipid content decreased with stress increasing and were significantly higher in the acute stress set compared to the chronic one. Both hypotonic and hypertonic stresses enhanced the activities of metabolic enzymes. A trade-off between survival and reproduction was observed, prioritizing survival under acute stress. Under chronic stress, the weight on reproduction increased in significance. In conclusion, the tested ciliates adopted an r-strategy in response to salinity stress. The trade-off between reproduction and survival is a significant biological response mechanism varying with salinity fluctuation properties. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive Grey Wolf Optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges. [ABSTRACT FROM AUTHOR]

To tackle the shortcomings of the Dung Beetle Optimization (DBO) Algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization (SFEDBO) Algorithm is proposed. This algorithm utilizes an elite opposition-based learning strategy as the method for generating the initial population, resulting in a more diverse initial population. To address the imbalance between exploration and exploitation in the algorithm, an adaptive strategy is employed to dynamically adjust the number of dung beetles and eggs with each iteration of the population. Inspired by the Manta Ray Foraging Optimization (MRFO) algorithm, we utilize its somersault foraging strategy to perturb the position of the optimal individual, thereby enhancing the algorithm's ability to escape from local optima. To verify the effectiveness of the proposed improvements, the SFEDBO algorithm is utilized to optimize 23 benchmark test functions. The results show that the SFEDBO algorithm achieves better solution accuracy and stability, outperforming the DBO algorithm in terms of optimization results on the test functions. Finally, the SFEDBO algorithm was applied to the practical application problems of pressure vessel design, tension/extension spring design, and 3D unmanned aerial vehicle (UAV) path planning, and better optimization results were obtained. The research shows that the SFEDBO algorithm proposed in this paper is applicable to actual optimization problems and has better performance. [ABSTRACT FROM AUTHOR]

Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slow response times for traditional algorithms. Surrogate model expensive multi-objective evolutionary algorithms (SM-EMOEAs), which are computationally efficient and converge quickly, are effective methods for the solution of MSIMP. However, the recent advances in this field have not been comprehensively summarized; therefore, this work provides a comprehensive overview of this subject. Firstly, the basic classification of MSIMP and its different fields of application are introduced, and the constraints of MSIMP are comprehensively analyzed. Secondly, the MSIMP problem is described to clarify the application scenarios of traditional optimization algorithms in MSIMP and their properties. Thirdly, the process of MSIMP and the classical expensive multi-objective evolutionary algorithms are reviewed to explore the surrogate model and the expensive multi-objective evolutionary algorithms based on MSIMP. Fourthly, improved SM-EMOEAs for MSIMP are analyzed in depth in terms of improved surrogate models, adaptive strategies, and diversity maintenance and quality assessment of the solutions. Finally, SM-EMOEAs and SM-EMOEA-based MSIMP are analyzed in terms of the existing literature, and future trends and directions are summarized. [ABSTRACT FROM AUTHOR]

Fault prediction ensures safe and stable production, and cuts maintenance costs. Due to the changing operating conditions that lead to the changes in the characteristics of industrial processes, there is a need to monitor the fault state of batch processes in real‐time and to accurately predict fault trends. An adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction is proposed. Firstly, SFA is used to extract the time‐varying features of process data and establish the update index of the NPE model. Then, to extract local nearest‐neighbor features and reconstruct them by the NPE model with adaptive update capability, square prediction error (SPE) statistics are constructed as fault state features based on the reconstructed error. Further, the hunter‐prey optimization (HPO) algorithm optimizes the weights and biases in the stochastic configuration network, and the singular value decomposition (SVD) and QR decomposition of column rotation are introduced to solve the ill‐posed problem of SCN and obtain the prediction model of ISCN. Finally, the obtained statistics SPE is formed into a time series, and the ISCN model is used to predict the process state trend. The effectiveness of the proposed algorithm is verified by case studies of industrial‐scale penicillin fermentation processes and the Hot strip mill process. We propose an adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction. The SFA is utilized to extract time‐varying features of process data, which are combined with NPE model reconstruction errors to construct squared prediction error (SPE) statistics as fault state features. Hunter‐prey optimization (HPO) algorithm is used to optimize the weights, and biases of SCN and SVD and QR decomposition are introduced to solve the ill‐posed problem of SCN. [ABSTRACT FROM AUTHOR]

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency. [ABSTRACT FROM AUTHOR]

Microplastic (MP) pollution and alien plant invasions are two important threats to terrestrial ecosystems. Microplastics alter the physical and chemical characteristics of soil, potentially affecting the performance of alien plants. However, previous studies have overlooked the impact of weather on invasive plants in areas polluted by MPs. With the global increase in extreme rainfall events, it is imperative to redefine the correlation between MPs and invasive plants.Here, we conducted an experiment in a climate chamber to examine the effects of MPs on the growth and development of both native and invasive alien plants under constant and fluctuating water regime (FWR). The FWR simulated extreme water pulses during the 2016–2020 growing seasons in Wuhan, China.Our results indicated that biomass accumulation and root development were influenced by water conditions and MP pollution in both invasive and native species. The extent of the effects varied between the two groups of plant species. FWR promoted plant growth and fine root development in invasive plants but reduced the maximum quantum efficiency of photosystem II (Fv/Fm) and nonphotochemical quenching (NPQ) indices of native plants. Moreover, FWR attenuated the negative effects of polybutylene succinate (PBS, degradable MPs) on biomass and root characteristics (length, surface area and tips). FWR compensated the negative impacts of MPs on the total and below‐ground biomass of the invasive species Paspalum dilatatum and Sphagneticola trilobata, but not on the native species. Consequently, invasive species showed better performance than native species in the fine root development of biomass growth and chlorophyll fluorescence under the combined effects of MPs and FWR.Synthesis and applications. Our findings suggest that MP pollution enhances the competitiveness of invasive alien species over the native species when exposed to pronounced dry–wet water cycle conditions, potentially affecting the composition and biodiversity of the ecosystems. Thus, controlling MP pollution should be a part of the management strategy to conserve biodiversity and ecosystems. [ABSTRACT FROM AUTHOR]

This paper used a binary Logistic regression model to analyze the impact of farmers′ drought risk perception on their adaptation intention based on farmer survey data to strengthen farmers′ ability to adapt to climate change and improve the resilience of agroecological systems to climate change. The results showed that farmers′ perception of the severity of drought risk is ranked from high to low, as parttime farmers, multiple occupations farmers, non-farmers, and pure farmers. Their adaptive perception is ranked from high to low, as nonfarmers, pure farmers, multiple occupations farmers, and part-time farmers. Part-time farmers have the strongest perception of the severity of drought risk and the lowest adaptive perception, while pure farmers have the lowest perception of the severity of drought risk and the highest adaptive perception. It can be seen that there is a complex nonlinear correlation between farmers′ perception of drought risk and their intention to adapt. 47.5% of the surveyed farmers were willing to actively respond to the drought risk, and the adaptation intention of part-time farmers and multiple occupations farmers with higher non-agricultural level was stronger. 72.34% and 58.33% of the farmers have positive adaptation intention, 36.19% of the pure farmers have positive adaptation intention, and the proportion of non-farmers is only 23.08%. There is a positive correlation between adaptation perception, adaptation motivation, natural capital, physical capital, social capital, and adaptation intention. Based on this, the paper proposes some countermeasures and suggestions to improve farmers′ adaptability to drought risk in northern China′s agro-pastoral ecotone. [ABSTRACT FROM AUTHOR]

Abstract 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.

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]

Intelligent scheduling plays a crucial role in minimizing transportation expenses and enhancing overall efficiency. However, most of the existing scheduling models fail to comprehensively account for the requirements of urban development, as exemplified by the vehicle routing problem with time windows (VRPTW), which merely specifies the minimization of path length. This paper introduces a new model of the heterogeneous green city vehicle routing problem with time windows (HGCVRPTW), addressing challenges in urban logistics. The HGCVRPTW model considers carriers with diverse attributes, recipients with varying tolerance for delays, and fluctuating road congestion levels impacting carbon emissions. To better deal with the HGCVRPTW, an adaptive multi-objective genetic algorithm based on the greedy initialization strategy (AMoGA-GIS) is proposed, which includes the following three advantages. Firstly, considering the impact of initial information on the search process, a greedy initialization strategy (GIS) is proposed to guide the overall evolution during the initialization phase. Secondly, the adaptive multiple mutation operators (AMMO) are introduced to improve the diversity of the population at different evolutionary stages according to their success rate of mutation. Moreover, we built a more tailored testing dataset that better aligns with the challenges faced by the HGCVRPTW. Our extensive experiments affirm the competitive performance of the AMoGA-GIS by comparing it with other state-of-the-art algorithms and prove that the GIS and AMMO play a pivotal role in advancing algorithmic capabilities tailored to the HGCVRPTW. [ABSTRACT FROM AUTHOR]

This article delves into the role of adaptive environmental strategies, absorptive capacity, and eco-innovation in enhancing environmental performance, particularly in the context of market turbulence. The study, conducted among 568 companies in Colombia, employs structural equation modeling to evaluate relationships between the studied variables. The findings suggest that adaptive strategies alone do not directly impact environmental performance, emphasizing the need for integration with eco-innovation initiatives. Moreover, organizations with high absorptive capacity can leverage market turbulence to drive eco-innovative initiatives, highlighting the indirect yet significant impact of market turbulence on environmental performance through absorptive capacity. The study underscores the critical role of eco-innovation in directly influencing environmental outcomes, suggesting that the effectiveness of adaptive strategies and absorptive capacities hinges on successful eco-innovation initiatives. These insights offer practical guidance for organizations seeking to enhance their environmental performance in turbulent markets, providing a roadmap for sustainable business practices. [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 study aimed to explore the similarities and differences in gut microorganisms and their functions in regulating body mass in Eothenomys miletus across different altitudes in the Hengduan Mountains when exposed to a high-fat diet. Eothenomys miletus specimens were gathered from Dali (DL) and Xianggelila (XGLL) in Yunnan Province, China, and categorized into control, high-fat (1 week of high-fat diet), and re-feeding groups (1 week of high-fat diet followed by 2 weeks of standard food). The analysis utilized 16S rRNA sequencing to assess the diversity and structure of intestinal microbial communities in E. miletus. The investigation focused on the impact of high-fat diet consumption and different altitudes on gut microbial diversity, structure, and physiological markers. Results revealed that a high-fat diet influenced the beta diversity of gut microorganisms in E. miletus, leading to variations in microbial community structure between the two regions with different altitudes. High-fat food significantly affected body mass, white adipose tissue mass, triglycerides, and leptin levels, but not food intake. Specific intestinal microorganisms were observed in the high-fat groups, aiding in food digestion and being enriched in particular flora. In particular, microbial genera like Lactobacillus and Hylemonella were enriched in the highfat group of DL. The enriched microbiota in the control group was associated with plant polysaccharide and cellulose decomposition. Following a high-fat diet, gut microbiota adapted to support lipid metabolism and energy supply, while upon re-feeding, the focus shifted back to cellulose digestion. These findings suggested that alterations in gut microbial composition, alongside physiological markers, play a vital role in adaptation of E. miletus to the diverse habitats of the Hengduan Mountains at varying altitudes. [ABSTRACT FROM AUTHOR]

The stochastic heavy ball momentum (SHBM) method has gained considerable popularity as a scalable approach for solving large-scale optimization problems. However, one limitation of this method is its reliance on prior knowledge of certain problem parameters, such as singular values of a matrix. In this paper, we propose an adaptive variant of the SHBM method for solving stochastic problems that are reformulated from linear systems using user-defined distributions. Our adaptive SHBM (ASHBM) method utilizes iterative information to update the parameters, addressing an open problem in the literature regarding the adaptive learning of momentum parameters. We prove that our method converges linearly in expectation, with a better convergence bound compared to the basic method. Notably, we demonstrate that the deterministic version of our ASHBM algorithm can be reformulated as a variant of the conjugate gradient (CG) method, inheriting many of its appealing properties, such as finite-time convergence. Consequently, the ASHBM method can be further generalized to develop a brand-new framework of the stochastic CG method for solving linear systems. Our theoretical results are supported by numerical experiments. [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]

The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times. [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]

Grime's competitive, stress‐tolerant, ruderal (CSR) theory predicts a shift in plant communities from ruderal to stress‐tolerant strategies during secondary succession. However, this fundamental tenet lacks empirical validation using long‐term continuous successional data. Utilizing a 60‐year longitudinal data of old‐field succession, we investigated the community‐level dynamics of plant strategies over time. Our findings reveal that while plant communities generally transitioned from ruderal to stress‐tolerant strategies during succession, initial abandonment conditions crucially shaped early successional strategies, leading to varied strategy trajectories across different fields. Furthermore, we found a notable divergence in the CSR strategies of alien and native species over succession. Initially, alien and native species exhibited similar ruderal strategies, but in later stages, alien species exhibited higher ruderal and lower stress tolerance compared to native species. Overall, our findings underscore the applicability of Grime's predictions regarding temporal shifts in CSR strategies depending on both initial community conditions and species origin. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the problem of premature convergence inherent in standard PSO algorithms is mitigated. The algorithm further refines and controls the search space of the particle swarm through time-varying inertia coefficients, symmetric cooperative swarms concepts, and adaptive strategies, balancing global search and local exploitation. The performance of VASPSO was validated on 29 standard functions from Cec2017, comparing it against five PSO variants and seven swarm intelligence algorithms. Experimental results demonstrate that VASPSO exhibits considerable competitiveness when compared with 12 algorithms. The relevant code can be found on our project homepage. [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]

Abstract: Improving Organizational Agility in Order to Push Company Growth Purpose: This study aims to the imperative of fostering organizational agility as a catalyst for driving growth within corporate startups operating in the dynamic Indonesian market. Method: The method used is integrating the VRIO framework and applying Porter's Five Forces and PEST analysis and the SWOT Matrix Diagram. Result: Data shows that Sprinthink has good internal conditions where strengths are more dominant than weaknesses and opportunity factors are more dominant than threats. The results place Sprintthink in quadrant 1. Novelty: The application of this research is in a specific context focusing on Sprintthink and the integration of several strategic analysis frameworks to identify the most appropriate approach. Contribution: This research not only addresses specific challenges but also the broader discourse on strategic management of emerging markets. Abstrak: Meningkatkan Ketangkasan Organisasi Dalam Rangka Mendorong Pertumbuhan Perusahaan Tujuan: Studi ini menggali pentingnya menumbuhkan kelincahan organisasi sebagai katalisator untuk mendorong pertumbuhan di dalam startup korporat yang beroperasi di pasar Indonesia yang dinamis. Metode: Metode yang digunakan adalah mengintegrasikan kerangka kerja VRIO dan menerapkan Porter’s Five Forces serta analisis PEST untuk memahami seluk-beluk lingkungan eksternal dan Diagram Matriks SWOT. Hasil: Data menunjukkan bahwa Sprinthink memiliki kondisi internal yang baik dimana kekuatan lebih dominan daripada kelemahan dan faktor peluang lebih dominan daripada ancaman. Hasilnya menempatkan Sprinthink pada kuadran 1. Kebaruan: Aplikasi penelitian ini berada dalam konteks spesifik yang berfokus pada Sprinthink dan integrasi beberapa kerangka kerja analisis strategis untuk mengidentifikasi pendekatan yang paling sesuai. Kontribusi: Penelitian ini tidak hanya menjawab tantangan spesifik namun juga wacana yang lebih luas tentang manajemen strategis pasar negara berkembang.

Climate change has led to an increasing frequency of droughts, potentially undermining soil stability. In such a changing environment, the shallow reinforcement effect of plant roots often fails to meet expectations. This study aims to explore whether this is associated with the alteration of plant traits as a response to environmental change. Focusing on Amorpha fruticosa, a species known for its robust root system that plays a crucial role in soil consolidation and slope stabilization, thereby reducing soil and water erosion, we simulated a drought-rewetting event to assess the legacy effects of drought on the soil shear strength and the mechanical and hydrological traits associated with the reinforcement provided by A. fruticosa. The results show that the legacy effect of drought significantly diminishes the soil shear strength. Pretreated with drought, plant roots undergo morphological alterations such as deeper growth, yet the underground root biomass and diameter decline, thereby influencing mechanical reinforcement. Chemical composition analysis indicates that the plant’s adaptation to drought modifies the intrinsic properties of the roots, with varying impacts on different root types and overall reinforcement. Concurrently, the stomatal conductance and transpiration rate of leaves decrease, weakening the capacity to augment soil matric suction through transpiration and potentially reducing hydrological reinforcement. Although rewetting treatments aid in recovery, drought legacy effects persist and impact plant functional attributes. This study emphasizes that, beyond soil matric suction, plant adaptive mechanisms in response to environmental changes may also contribute significantly to reduced soil shear strength. Consequently, ecological restoration strategies should consider plant trait adaptations to drought, enhancing root systems for soil conservation and climate resilience.

The Crowned Porcupine Optimization (CPO) algorithm exhibits certain deficiencies in initialization efficiency, convergence speed, and adaptability. To address these issues, this paper proposes an enhanced Crowned Porcupine Optimization algorithm (ICPO) based on multiple improvement strategies. ICPO optimizes the initialization process by introducing Logistic chaotic mapping, thereby expanding the search space. It accelerates convergence through an elite retention strategy and enhances global search capability by integrating stochastic operations, mutation-like operations, and crossover-like operations to increase population diversity. Additionally, adaptive step tuning based on fitness values is employed to comprehensively improve the algorithm’s performance. To verify the effectiveness of ICPO, 23 standard functions were used for a comprehensive evaluation, and its practicality was further validated through optimization of actual engineering design problems. The experimental results demonstrate significant improvements in convergence speed, solution quality, and adaptability with ICPO.

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The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table. [ABSTRACT FROM AUTHOR]

In this work, we proposed a new trust region method for solving large-scale unconstrained optimization problems. The trust region subproblem with a simple form was constructed based on new weak secant equations, which utilized both gradient and function values and available information from the three most recent points. A modified Metropolis criterion was used to determine whether to accept the trial step, and an adaptive strategy was used to update the trust region radius. The global convergence and locally superlinearly convergence of the new algorithm were established under appropriate conditions. Numerical experiments showed that the proposed algorithm was effective. [ABSTRACT FROM AUTHOR]