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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]

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]

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]

Collecting statistics is a time- and resource-consuming operation in database systems. It is even more challenging to efficiently collect statistics without affecting system performance, meanwhile keeping correctness in distributed database. Traditional strategies usually consider one dimension during collecting statistics, which is lack of adaptiveness. In this paper, we propose an adaptive strategy for statistics collecting(ASC), which well balances collecting efficiency, correctness of statistics and effect to system performance. We formally define the procedure of collecting statistics and abstract the relationships among collecting efficiency, correctness of statistics and effect to system performance, and introduce an elastic structure(ESI) storing necessary information generated during proceeding our strategy. ASC can pick appropriate time to trigger collecting action and filter unnecessary tasks, meanwhile reasonably allocating collecting tasks to appropriate executing locations with right executing models through the information stored at ESI. We implement and evaluate our strategy in a distributed database. Experiments show that our solutions generally improve the efficiency and correctness of collecting statistics, moreover, reduce the negative effect to system performance comparing with other strategies. [ABSTRACT FROM AUTHOR]

This systematic review of research on adaptive learning used a strategic search process to synthesize research on adaptive learning based on publication trends, instructional context, research methodology components, research focus, adaptive strategies, and technologies. A total of 61 articles on adaptive learning were analyzed to describe the current state of research and identify gaps in the literature. Descriptive characteristics were recorded, including publication patterns, instructional context, and research methodology components. The count of adaptive learning articles published fluctuated across the decade and peaked in 2015. During this time, the largest concentration of adaptive learning articles appeared in Computers and Education. The majority of the studies occurred in higher education in Taiwan and the United States, with the highest concentration in the computer science discipline. The research focus, adaptive strategies, and adaptive technologies used in these studies were also reviewed. The research was aligned with various instructional design phases, with more studies examining design and development, and implementation and evaluation. For examining adaptive strategies, the authors examined both adaptive sources based on learner model and adaptive targets based on content and instructional model. Learning style was the most observed learner characteristic, while adaptive feedback and adaptive navigation were the most investigated adaptive targets. This study has implications for adaptive learning designers and future researchers regarding the gaps in adaptive learning research. Future studies might focus on the increasing availability and capacities of adaptive learning as a learning technology to assist individual learning and personalized growth. [ABSTRACT FROM AUTHOR]

Soil contamination by heavy metals and metalloids is a serious problem which needs to be addressed. There are several methods for removal of contaminants, but they are costly, while the method of phytoremediation is eco-friendly and cost-effective. Pteridophytes have been found to remediate heavy metal-contaminated soil. Pteridophytes are non-flowering plant that reproduces by spores. Pteris vittata has been reported as the first fern plant to hyperaccumulate arsenic. The Pteris species belongs to the order Pteridales. Other ferns that are known phytoremediators are, for example, Nephrolepis cordifolia and Hypolepis muelleri (identified as phytostabilisers of Cu, Pb, Zn and Ni); similarly Pteris umbrosa and Pteris cretica accumulate arsenic in leaves. So, pteridophytes have a number of species that accumulate contaminants. Many of them have been identified, while various other are being explored. The present review article describes the phytoremediation potential of pteridophytes plants and suggests as a potential asset for phytoremediation programs. [ABSTRACT FROM AUTHOR]

Adjustments to the allocation of aboveground biomass allocation are an important component of the adaptive strategies of plant growth. Plants can modify their biomass allocation patterns to adapt to various environmental conditions. Sandy areas vary widely in topography, generating a diversity of microenvironments. However, knowledge about biomass allocation patterns of shrubs in different microtopographic areas in sandy habitats remains limited. Moreover, most studies have not considered the effect of age on plant biomass allocation responses to habitat differences. In August 2017, we measured the age, height, ground-level diameter, and leaf and stem matter of Salix psammophila, a species that overwhelmingly dominates a wide range of habitats in China. Biomass allocation patterns (leaf vs. stem biomass, biomass vs. diameter) were compared between two sandy vegetation environments in an area of the Mu Us Sandy Land. The results indicated that the rate of biomass accumulation decreased with age and, as shoots aged, they tended to allocate more biomass to stems, which provide support and transport functions, than to leaves (assimilatory organs). Soil moisture was the main environmental factor influencing the allocation strategy of S. psammophila. Favorable moisture habitats increased the overall biomass accumulation rate by promoting the allocation of biomass to leaf tissue. Habitat and age had an interactive effect on biomass accumulation and allocation, and larger plants were more likely to suffer from resource limitation. This study contributes to a better understanding of the life-history strategies of shrubs under frequently changing sandy conditions, which can contribute to efforts in vegetation recovery management. [ABSTRACT FROM AUTHOR]

A study of the ecological morphology of plants is a necessary condition in solving a number of fundamental and applied problems of ecology, botany, geography, and physiology of plants when monitoring global and regional environments, as well as the problems of selecting and introducing plants. This approach is successfully used to study flowering plants of different botanical and geographical zones. At the same time, ferns usually fall outside of the field of interest of botanists. The aim of this study is to investigate a photosynthetic apparatus of sporophytes of proto-epiphyte fern Lemmaphyllum microphyllum at different cell organization levels and determine the ways it adapts to environmental conditions. The morphology and structure of a photosynthetic apparatus of plants are analyzed using a Siams Mesoplant laboratory complex. This equipment makes it possible to analyze L. microphyllum at the level of fronds, mesophyll cells, and plastid apparatus, as well as obtain parameters such as the specific surface density of fronds, the number of cells per frond unit area, the number of chloroplasts per cell, the frond thickness, the surface area and volume of chloroplasts, the cell area and volume, etc. We have found that the fern uses different organs and functions for adapting to habitats on tree trunks and branches and on humus-free flat rock surfaces. Adaptations to the light and humidity conditions are characterized by the maximum manifestations in its morphology and anatomy. Such adaptations include the maximum light interception and light excess protection, as well as both the minimization of water losses and the ability to excrete water. They are manifested at different organizational levels and include combinations of xero-, meso-, helio-, and sciomorphic traits, allowing the species to form stable populations in coenoses. In the case of frond-area reduction, the optimum compensatory strategy includes the development of a water-retaining capacity of cells and tissues (succulentization) based on the polyfunctionality of a chlorenchyma, as well as the improvement of conductive xylem elements due to the appearance of vessels. These characteristics do not solve the problem of long-term dehydration, since they provide only a tolerance to short-term water deficiency. For this reason, such ferns can be called cryptic succulents or subsucculents. [ABSTRACT FROM AUTHOR]

Craniometric features (measurements, indexes) in several species of the genus Sylvaemus (S. sylvaticus, S. flavicollis, and S. uralensis from Eastern Europe and some adjacent territories, as well as S. ponticus and S. witherbyi from the Caucasus) have been studied using multivariate statistical analysis. For the first time, craniometric characters of the genus Sylvaemus are considered in relation to the feeding ecology of species. A total of 614 skulls were studied. Based on original material, discriminant keys have been developed using skull measurements. The keys appear to be suitable for the diagnostics of the studied Sylvaemus species, including sibling ones. The largest skulls are shown to be characteristic of S.flavicollis, and the smallest ones, of S. uralensis. Cluster analysis of the absolute measurements of the skull cumulative selection has revealed the greatest isolation of S. flavicollis and the similarity of coexisting but phylogenetically remote S. ponticus and S. witherbyi. Factor analysis showed that, within the genus Sylvaemus, the general sizes are less essential than the characters related to food smashing (the length of the lower jaw and rostrum, the width of the occipital part of the skull), and possibly also to the thickness of the incisors. Comparative analyses of the relative skull measurements (the indexes related to the food preferences of the animals, granivory or folivory) allow us to conclude that the food adaptations to granivory in the structure of the skull and teeth increase in the series S. uralensis–S. sylvaticus–S. witherbyi–S. flavicollis–S. ponticus. The cranial evolution in species within the genus Sylvaemus is suggested to have been directed to increased adaptations in the mechanisms in seed peeling and processing. [ABSTRACT FROM AUTHOR]

We consider the moving element search problem with the minimum total cardinality of tests. As a search space, we consider the set of integer points of a segment of length n. We prove that the total test cardinality of an asymptotically optimal adaptive strategy is n + 2 n . [ABSTRACT FROM AUTHOR]

Constrained optimization problems (COPs) are most commonly encountered problems in science and engineering design field. To solve such kind of problem effectively, in this paper, we put forward a new approach named CAMDE which integrates adaptive differential evolution (DE) with new multi-population-based mutation operators. In CAMDE, some inferior solutions with low objective values are maintained in an external population. During mutation process, this external population is combined with main population to generate promising progress directions toward optimal region. Furthermore, DE's control parameters F and CR are adaptively adjusted according to the statistical information learnt from the previous searches in generating improved solutions. The advantageous performance of CAMDE is validated by comparisons with some representatives of state of the art in constrained optimization over 24 test instances. Moreover, four widely used constrained mechanical engineering problems are selected to validate the search ability of CAMDE for real-world problems. The performance results show that CAMDE is an effective approach to solving COPs, which is basically enabled by the integration of multi-population-based mutation operators and adaptive control strategy for DE's control parameters. [ABSTRACT FROM AUTHOR]

In this paper, natural neighbor Galerkin meshless method is employed for adaptive analysis of plates and laminates. The displacement field and strain field of plate are based on Reissner-Mindlin plate theory. The interpolation functions employed here were developed by Sibson and based on natural neighbor coordinates. An adaptive refinement strategy based on recovery energy norm which is in turn based on natural neighbors is employed for analysis of plates. The present adaptive procedure is applied to classical plate problems subjected to in-plane loads. In addition to that the laminated composite plates with cutouts subjected to transverse loads are investigated. Influence of the location of the cutout and the boundary conditions of the plate on the results have been studied. The results obtained with present adaptive analysis are accurate at lower computational effort when compare to that of no adaptivity. Further, the adaptive analysis provided accurate magnitude of maximum stresses and their locations in the laminate plates with and without cutout subjected to transverse loads. Additionally, failure prone areas in the geometry of the plates subjected to loads are revealed with the adaptive analysis. [ABSTRACT FROM AUTHOR]

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of exponential lag synchronization for a class of neural networks with mixed delays including discrete and distributed delays is investigated via adaptive intermittent control. Based on piecewise analytic method, some sufficient conditions for globally exponential lag synchronization are established through constructing a piecewise continuous auxiliary function. It is noted that both the control periods and the control widths in our adaptive intermittent control strategy are allowed to be nonidentical, which extends the scope of application of periodically intermittent control with fixed both control period and control width employed widely in previous works. Moreover, it is shown that the derived globally exponential lag synchronization criteria are related to the control rates rather than the control periods, which facilitates the choice of the control periods for practical problems. Finally, a numerical example is given to illustrate the correctness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Trust region method is a robust method for optimization problems. In this paper, we propose a novel adaptive nonmonotone technique based on trust region methods for solving unconstrained optimization. In order to accelerate the convergence of trust region methods, an adaptive trust region is generated according to the Hessian of the iterate point. Both the nonmonotone techniques and this adaptive strategies can improve the trust region methods in the sense of convergence. We prove that the proposed method is locally superlinear convergence under some standard assumptions. Numerical results show that the new method is effective and has a high speed of convergence in practice. [ABSTRACT FROM AUTHOR]

With the popularity of social networks, people can easily generate rich content with multiple modalities. How to effectively and simply estimate the similarity of multi-modal content is becoming more and more important for providing better information searching service of rich media. This work attempts to enhance the similarity estimation so as to improve the accuracy of multi-modal data searching. Toward this end, a novel multi-modal feature extraction approach, which involves the neighborhood reversibility verifying of information objects with different modalities, is proposed to build reliable similarity estimation among multimedia documents. By verifying the neighborhood reversibility in both single- and multi-modal instances, the reliability of multi-modal subspace can be remarkably improved. In addition, a new adaptive strategy, which fully employs the distance distribution of returned searching instances, is proposed to handle the neighbor selection problem. To further address the out-of-sample problem, a new prediction scheme is proposed to predict the multi-modal features for new coming instances, which is essentially to construct an over-complete set of bases. Extensive experiments demonstrate that introducing the neighborhood reversibility verifying can significantly improve the searching accuracy of multi-modal documents. [ABSTRACT FROM AUTHOR]

Many studies have been conducted on climate change vulnerability assessments to develop adaptive strategies for climate change on a national or global scale. The development of an assessment tool for climate change on a process-level is necessary for evaluating vulnerability and to suggest an effective adaptive strategy in wastewater treatment plants (WWTP). Therefore, we proposed a vulnerability assessment index at the process-level in a WWTP to evaluate adaptive strategies for climate change in this study. The suggested process-level vulnerability assessment index is based on three performance WWTP indices: the effluent quality index (EQI), global warming potential (GWP), and operational cost index (OCI). Four different advanced WWTP processes were evaluated using the suggested vulnerability assessment index based on the A2 scenario, which is one of the carbon emission scenarios making predictions out to 2100 developed by the intergovernmental panel on climate change (IPCC). The adaptive strategies were evaluated at four conventional treatment processes to see the improvement of vulnerability of their processes, where the changes of their vulnerabilities are compared together. Suggested adaptive strategies in case studies showed that the process-level vulnerabilities were significantly decreased in the anaerobic/anoxic/aerobic (AO) and Virginia initiative project (VIP) processes, especially during the flood and winter seasons. Therefore, it is expected that the proposed vulnerability assessment index can be useful as a decision-supporting tool for selecting the appropriate adaptive strategy for each process. [ABSTRACT FROM AUTHOR]

Habitat use can be complex, as tradeoffs among physiology, resource abundance, and predator avoidance affect the suitability of different environments for different species. Green sturgeon ( Acipenser medirostris), an imperiled species along the west coast of North America, undertake extensive coastal migrations and occupy estuaries during the summer and early fall. Warm water and abundant prey in estuaries may afford a growth opportunity. We applied a bioenergetics model to investigate how variation in estuarine temperature, spawning frequency, and duration of estuarine residence affect consumption and growth potential for individual green sturgeon. We assumed that green sturgeon achieve observed annual growth by feeding solely in conditions represented by Willapa Bay, Washington, an estuary annually frequented by green sturgeon and containing extensive tidal flats that harbor a major prey source (burrowing shrimp, Neotrypaea californiensis). Modeled consumption rates increased little with reproductive investment (<0.4%), but responded strongly (10-50%) to water temperature and duration of residence, as higher temperatures and longer residence required greater consumption to achieve equivalent growth. Accordingly, although green sturgeon occupy Willapa Bay from May through September, acoustically-tagged individuals are observed over much shorter durations (34 d + 41 d SD, N = 89). Simulations of <34 d estuarine residence required unrealistically high consumption rates to achieve observed growth, whereas longer durations required sustained feeding, and therefore higher total intake, to compensate for prolonged exposure to warm temperatures. Model results provide a range of per capita consumption rates by green sturgeon feeding in estuaries to inform management decisions regarding resource and habitat protection for this protected species. [ABSTRACT FROM AUTHOR]

Female birds can potentially utilise various mechanisms, such as intraclutch egg size variation (ICESV), to modulate offspring quality. If this represents an 'adaptive' strategy allowing females to adjust reproductive effort after laying of the first egg, as information on resources becomes more predictable later in the laying sequence, then we would predict that ICESV would vary with ecological context, e.g. in 'good' versus 'bad' years. Here, in European Starlings ( Sturnus vulgaris), we tested specific predictions that (1) females would lay relatively small last laid eggs in 'poor' years (with early laying and below-average breeding productivity); and (2) that ICESV would be lower in good years (late laying, above-average productivity) so that late-hatched chicks would not be disadvantaged. At the individual level we predicted (3) that repeatability of ICESV would be low if females adjusted this in relation to the environment in different years; and (4) that within-individual variation in ICESV should correlate with the number, and timing, of loss of chicks. Mean ICESV averaged −1.9% (measured as last egg mass relative to mean egg mass), typical of other hole-nesting passerines. However, our results did not support any of our hypotheses: (1) although there was annual variation in ICESV, in all years egg mass decreased through the laying sequence; (2) annual variation in ICESV was not consistent with variation in phenology or breeding productivity among years; (3) marked individual variation in ICESV (last laid egg varied from −20 to +10% relative to mean egg mass within clutches) was repeatable, i.e. individual females did not adjust ICESV in subsequent breeding attempts; and (4) variation in the magnitude of ICESV, alone and combined with hatching asynchrony, did not predict early brood loss. Our study suggests that ICESV is a relatively inflexible component of an individual female's phenotype that might contribute to variation in individual quality. [ABSTRACT FROM AUTHOR]

Climate change is a global environmental issue, which is challenging water resources management and practices. This study investigates the impact of climate change on water resources of the Yellow River basin, a major grain-producing area in China, and provides recommendations on strategies to increase adaptive capacity and resilience in the basin region. Results show that the recorded stream flows of the Yellow River declined from 1951 to 2010 and have decreased significantly in the middle and lower reaches. The variable infiltration capacity (VIC) model performs well as a tool to simulate monthly discharge of both the tributary catchments and the whole Yellow River basin. Temperature across the Yellow River basin over 2021-2050 is expected to continue to rise with an average rates of approximately 0.039-0.056 °C/annum. The average annual precipitation in the basin is projected to increase by 1.28-3.29 % compared with the 1991-2010 baseline. Runoff during 2021-2050 is projected to decrease by 0.53-9.67 % relative to 1991-2010 with high decadal and spatial variability. This is likely due to the model's projections of a significant rise in temperature and changes in precipitation patterns. Climate change will likely aggravate the severity and frequency of both water shortages and flooding in the basin region. It is therefore essential to devote sufficient attention on structural and non-structural measures for the Yellow River basin to cope with climate change. At the global level, strategies to increase adaptive capacity and build resilience to climate change focus on public education to improve awareness of climate risks, implementing the integrated water resources management and planning based on impact assessments. [ABSTRACT FROM AUTHOR]

A wide range of applications in engineering and scientific computing are based on the sparse matrix computation. There exist a variety of data representations to keep the non-zero elements in sparse matrices, and each representation favors some matrices while not working well for some others. The existing studies tend to process all types of applications, e.g., the most popular application which is matrix-vector multiplication, with different sparse matrix structures using a fixed representation. While Graphics Processing Units (GPUs) have evolved into a very attractive platform for general purpose computations, most of the existing works on sparse matrix-vector multiplication (SpMV, for short) consider CPUs. In this work, we design and implement an adaptive GPU-based SpMV scheme that selects the best format for the input matrix having the configuration and characteristics of GPUs in mind. We study the effect of various parameters and different settings on the performance of SpMV applications when employing different data representations. We then employ an adaptive scheme to execute different sparse matrix applications using proper sparse matrix representation formats. Evaluation results show that our run-time adaptive scheme properly adapts to different applications by selecting an appropriate representation for each input sparse matrix. The preliminary results show that our adaptive scheme improves the performance of sparse matrix multiplications by 2.1 $$\times $$ for single-precision and 1.6 $$\times $$ for double-precision formats, on average. [ABSTRACT FROM AUTHOR]

Symbiosis with genetically diverse Symbiodinium has been shown to affect host coral physiological responses to environmental stresses. Hong Kong, located in a subtropical region, is a marginal environment for coral growth largely due to its wide annual temperature fluctuation with low mean winter sea water temperature (~16 °C) and variable salinity conditions. The symbiont diversity in Hong Kong corals is therefore worth investigating to enrich our understanding on symbioses in marginal and fluctuating environments. Examination of 56 scleractinian coral species and five soft coral species using denaturing gel gradient electrophoresis of the internal transcribed spacer region 2 found only five distinct subclades of Symbiodinium with C1 the dominant type occurring in all but one scleractinian coral and all soft coral species investigated. C15 and C21 Symbiodinium were found in Porites spp. and Montipora peltiformis, respectively, both of which are vertical transmitters. D8-12 was found in Oulastrea crispata, a stress-tolerant species, and D1 in a single sample of Goniastrea aspera. No spatial differences in Symbiodinium composition were found among different regions of Hong Kong. Seasonal monitoring of tagged Platygyra acuta and Porites spp. colonies also revealed no changes in their symbiont types despite wide ranges of in situ temperature fluctuation. Hong Kong scleractinian corals hosted a remarkably low symbiont diversity compared with corals in the surrounding regions. The predominance of a single subclade, C1 Symbiodinium, suggests that this subclade is best acclimatized to local fluctuating conditions and/or low winter temperature. Forming symbiosis with the best acclimatized symbiont, instead of with a diverse group of symbionts with different physiological performances, either sequentially or simultaneously, may be a strategy used by Hong Kong corals to cope with stressful conditions. [ABSTRACT FROM AUTHOR]

This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newton formulas helps to handle large-scale problems. Theoretical analysis indicates that the new approach preserves the global convergence to a fi rst-order stationary point under classical assumptions. Moreover, the superlinear and the quadratic convergence rates are also established under suitable conditions. Preliminary numerical experiments show the effectiveness of the proposed approach for solving large-scale unconstrained optimization problems. [ABSTRACT FROM AUTHOR]

Model Order Reduction (MOR) methods are employed in many fields of Engineering in order to reduce the processing time of complex computational simulations. A usual approach to achieve this is the application of Galerkin projection to generate representative subspaces (reduced spaces). However, when strong nonlinearities in a dynamical system are present and this technique is employed several times along the simulation, it can be very inefficient. This work proposes a new adaptive strategy, which ensures low computational cost and small error to deal with this problem. This work also presents a new method to select snapshots named Proper Snapshot Selection (PSS). The objective of the PSS is to obtain a good balance between accuracy and computational cost by improving the adaptive strategy through a better snapshot selection in real time (online analysis). With this method, it is possible a substantial reduction of the subspace, keeping the quality of the model without the use of the Proper Orthogonal Decomposition (POD). [ABSTRACT FROM AUTHOR]

Nitrogen (N) and phosphorus (P) are the most important nutrients involved in plant reproduction and typically the most limiting in terrestrial ecosystems. The natural soil fertility gradient of the Hawaiian archipelago, in which younger islands are N limited and older islands are P limited, provides a model system to examine questions regarding allocation of nutrients. Using fertilized plots (+N or +P) at the extreme sites of the Hawaiian archipelago, vegetative productivity (e.g., net primary productivity, growth, and litterfall) and foliar nutrient responses have previously been studied for the dominant canopy tree, Metrosideros polymorpha. Here, we investigated whether the reproductive response of M. polymorpha mirrors the previously found vegetative productivity and foliar nutrient responses, by quantifying: (1) inflorescence and seed productivity, and (2) nutrient concentration of reproductive structures. Fertilization with N and P did not significantly affect the productivity of inflorescences or seeds, or seed viability at either site. However, nutrient concentrations increased after fertilization; %P increased in inflorescences in the +P treatment at the P-limited site. Seeds and inflorescences generally contained higher nutrient concentrations than leaves at both sites. Unlike foliar data, reproductive strategies of M. polymorpha differed depending on soil nutrient limitation with emphasis on quality (higher seed viability/greater nutrient concentrations) at the P-limited site. We suggest that in response to P additions M. polymorpha employs a nutrient conservation strategy for its inflorescences and an investment strategy for its seeds. Examining N and P simultaneously challenges a basic assumption that reproductive allocation follows a similar pattern to the often measured aboveground productivity. [ABSTRACT FROM AUTHOR]

A distributed leader-follower flocking problem of multiple robotic fish governed by extended second-order unicycles is studied in this paper. The multi-agent system consists of only one leader with pre-appointed and bounded speeds. A distributed flocking algorithm on the basis of the combination of consensus and attractive/repulsive functions is investigated, in which adaptive strategy is adopted to compute the weight of the velocity coupling strengths. The proposed control algorithm enables followers to asymptotically track the leader's varying velocities and approach the equilibrium distances with their neighbors. Furthermore, the arbitrarily-shaped formation flocking problem of the system can also be solved by adding the information of a desired formation topology to the potential function term. Finally, simulations are carried out to verify the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

Urbanization has resulted in obvious changes in plant species diversity. We analyzed the dynamics of ruderal species diversity in Harbin over the past half century using historical data collected in 1955 and data of the present spatial distribution in 2010-2011. The results show that, the number of ruderal species decreased from 611 to 175 with remarkable tendency of decreasing in perennial species and increasing in winter annual species in the past half century, which caused the shift of life form spectrum from perennial mono-dominant type to summer annual and perennial co-dominant type. Meanwhile, the proportion of tropical originated species increased and the proportion of temperate originated species decreased which were considered to relate with the increase of temperature in urban area during the past half century. Moreover, there was a distinct decrease of the proportion of aquatic and hygrophytic ruderal species while an increase of mesic and xeric ruderal species which suggested a drought trend in urban habitats that consistent with the change of land use characterized as decrease of natural water bodies and wetlands and increase of urban land. Comparison of ruderal species along urbanization gradient also got the similar results with the above results from analysis on temporal scales and confirmed the effect of urbanization on decreasing plant richness. Our results suggested that land use change combined with its effect on temperature and disturbance regimes in urban habitats preferred species with short life span, high drought tolerance, fast growth rates and high seed yields. [ABSTRACT FROM AUTHOR]

Providing adaptive strategies to offset functional limitation is common therapeutic practice with frail older adults in home care or rehabilitation. This study examines modifiable factors that influence the use of two types of adaptive strategies, environmental (assistive technology, home modifications) and behavioral (energy conservation, performance techniques). The sample included 154 functionally vulnerable elders, aged 70 years or older, who participated in a randomized trial that was implemented to improve daily function. Strategy use scores reflected the percentage of strategies utilized of those introduced in intervention. Hierarchical regression analyses revealed that a person’s enhanced readiness to change predicted environmental strategy use, whereas living alone and a person’s desire to learn new mobility skills predicted behavioral strategy use. Higher education was associated with behavioral strategy use for Whites but not for non-Whites, most of whom were African American. Other participant demographic and health related characteristics were not predictive of either strategy type. Findings show that predictors differ for each type of adaptive strategy, suggesting that training for use be tailored to strategy type and individual characteristics. [ABSTRACT FROM AUTHOR]

This paper presents adaptive hybrid protocols based on the declarative network and mainly discusses the principle and realization of the Bayesian-estimation based adaptive hybrid protocol in the declarative network, which is well adapted to the Mobile Ad hoc NETwork (MANET). The adaptive hybrid protocol is designed for ad hoc networks which have characteristics like self-organizing, no trusted party, flexibility, etc. The nodes that run the hybrid protocol can automatically select one routing protocol that is suitable for different network environment. The Bayesian-estimation based adaptive strategy, that improves the adaptability and stability of the protocol, succeeds in the Rapidnet, a declarative network engine. The result in the Rapidnet proves that the hybrid protocol and the adaptive strategy are feasible. The experiment on the ns-3 simulator, an emerging discrete-event network simulator, validates that this protocol performs well and reduces communication overheads. [ABSTRACT FROM AUTHOR]

This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all individuals in the population are grouped into a superior group and an inferior group based on their fitness values. The two groups perform different mutation operations. The local mutation model is applied to individuals with better fitness values, i.e., in the superior group, to search for better solutions near the current best position. The global mutation model is applied to the inferior group, which is composed of individuals with lower fitness values, to search for potential solutions. Subsequently, the GDE algorithm employs crossover and selection operations to produce offspring for the next generation. In this paper, an adaptive tuning strategy based on the well-known 1/5th rule is used to dynamically reassign the group size. It is thus helpful to trade off between the exploration ability and the exploitation ability. To validate the performance of the GDE algorithm, 13 numerical benchmark functions are tested. The simulation results indicate that the approach is effective and efficient. [ABSTRACT FROM AUTHOR]

This paper introduces a new evolutionary optimization algorithm named hybrid adaptive differential evolution (HADE) and applies it to the mobile robot localization problem. The behaviour of evolutionary algorithms is highly dependent on the parameter selection. This algorithm utilizes an adaptive method to tune the mutation parameter to enhance the rate of convergence and eliminate the need for manual tuning. A hybrid method for mutation is also introduced to give more diversity to the population. This method which constantly switches between two mutation schemes guarantees a sufficient level of diversity to avoid local optima. We use a well-known test set in continuous domain to evaluate HADE's performance against the standard version of differential evolution (DE) and a self-adaptive version of the algorithm. The results show that HADE outperforms DE and self-adaptive DE in three of four benchmarks. Moreover, we investigate the performance of HADE in the well-known localization problem of mobile robots. Results show that HADE is capable of estimating the robot's pose accurately with a decreased number of individuals needed for convergence compared with DE and particle swarm optimization methods. Comparative study exposes HADE algorithm as a competitive method for mobile robot localization. [ABSTRACT FROM AUTHOR]

The terrestrial ecosystem of Antarctica are among the most extreme on earth, challenging the communities and making their existence difficult by rapidly increasing annual summer influx of solar ultraviolet radiations (UV-R), extremely cold conditions and lesser availability of nutrients. Spring time ozone depletion is due to release of chlorofluorocarbons in the earth atmosphere and is a serious cause of concern among environmentalists. Antarctic continent is mostly dominated by cryptogamic plants with limited distribution in different parts of the icy continent however; their distribution is mostly confined to Sub-Antarctic region. By the virtue of light requirement, cryptogams are exposed to extreme seasonal fluctuation in photosynthetically active radiation (PAR), and ultraviolet (UV) radiation which are closely associated with photosynthetic pigments in photoautotrophic organisms. Antarctic cryptogams cope up the stress imposed by UV radiation by the development of efficient systems for repairing damage by synthesis of screening compounds such as UV-B absorbing pigments and anthocyanin compounds. A major part of the UV absorbing compounds are appeared to be constitutive in lichens which are usnic acid, perlatolic acid and fumarphotocetraric acid which is particularly induced by UV-B. Secondary metabolites such as phenolics, atranorin, parietin and melanin also enhance the plant defense, by different molecular targets in specific solar irradiance and potential for increased antioxidative protection to UV induced vulnerability. [ABSTRACT FROM AUTHOR]

Four cases of coprophagy and two cases of fecal inspection were identified during the 1142 h of observing wild bonobos at Wamba in the Luo Scientific Reserve in the Democratic Republic of the Congo. At least 5 females in the study group practiced coprophagy and/or fecal inspection. According to our daily behavioral observations, boredom and stress, insufficient roughage, and the search for essential nutrients could not explain the coprophagy. Several episodes observed in this study indicated that bonobos might have sought and ingested certain valuable food items, such as hard Dialium seeds, in feces during relatively lean seasons. Although coprophagy occurred only rarely among wild bonobos, this practice appeared to represent a possibly adaptive feeding strategy during periods of food scarcity rather than a behavioral abnormality. [ABSTRACT FROM AUTHOR]

Adolescent idiopathic scoliosis (AIS) is characterized by morphological trunk modifications acting on body mass distribution. Some specific biomechanical strategies during postural regulation have been reported. Given that spinal deformity is three-dimensional, some strategy analysis resulting from different stepping directions should lead to a better understanding of the dynamic adaptation of behaviour. The aim of this study is to identify dynamic strategies of AIS patients stepping in lateral and forward directions. Ten AIS patients with a right thoracic curve and 15 controlled volunteers have been tested. Ground reaction forces (GRF) have been recorded for right-limb stepping and for left-limb stepping associated to forward and lateral directions. Force amplitudes, corresponding occurrences, impulses of stepping phases and an asymmetry index have been computed. Asymmetry and variability increased in the AIS group, compared to the control group, whatever the stepping direction is. Asymmetry for AIS patients systematically provides an increased left initiation GRF compared to a right initiation. Nevertheless, for both groups, lateral initiation shows the largest asymmetry index reported for a forward initiation. More precisely, adaptive dynamic strategies for the AIS group have been characterized by an asymmetry between right and left limbs for lateral and forward initiation. These results can be explained by the influence of scoliosis pathology on dynamic movements due to spinal deformity. A right thoracic curve leads to an extra weight on the limb, which needs to be moved; consequently, stepping initiation with the right limb was more challenging for patients than stepping with the left limb. For the AIS group, the observed variability can also depend on the ontogenesis of adaptive strategies. Lateral step initiation has to be considered as the most relevant paradigm to study scoliosis and may also serve as a clinical basis for treatment to analyse the dynamic postural control and asymmetry strategies of the scoliosis patient. [ABSTRACT FROM AUTHOR]

In Part I, a finite element model of surface tension has been discussed and used to solve some quasi-static problems. The quasi-static analysis is often required to find not only the initial shape of the liquid but also the static equilibrium state of a liquid body before a dynamic analysis can be carried out. In general, natural and industrial processes in which surface tension force is dominant are of dynamic nature. In this second part of this work, the dynamic effects will be included in the finite element model described in Part I. A fully Lagrangian finite element method is used to solve the free surface flow problem and Newtonian constitutive equations describing the fluid behaviour are approximated over a finite time interval. As a result the momentum equations are function of nodal position instead of velocities. The resulting ordinary differential equation is integrated using Newmark algorithm. To avoid overly distorted elements an adaptive remeshing strategy is adopted. The adaptive strategy employs a remeshing indicator based on viscous dissipation functional and incorporates an appropriate transfer operator. The validation of the model is performed by comparing the finite element solutions to available analytical solutions of a droplet oscillations and experimental results pertaining to stretching of a liquid bridge. [ABSTRACT FROM AUTHOR]