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In this work, an adaptive controller, formulated as linear feedback controls plus nonlinear parts, is synthesized to achieve bipartite leader-following synchronization of delayed incommensurate fractional-order memristor-based neural networks (FMNNs), in which follower FMNNs are linearly coupled under a signed digraph. The salient features of this research lie in two aspects: (1) the assumption on time-varying delays is very weak, since it neither requires boundedness of delays nor restricts the differentiation of time delay functions; (2) the adaptive controller contains no delay term, and it is feasible for both bounded and unbounded activation functions. As the preparatory work for stability analysis of the controlled synchronization error system, the ready-made results on delayed incommensurate fractional-order linear positive systems, especially stability condition and comparison principle, are perfected by relaxing premises of time-varying delays. Besides, a group of differential inclusion inequalities are established as a powerful aid in scaling the bipartite synchronization error system. More relevantly, an algebraic synchronization criterion, formulated in terms of coupling strength, inner coupling matrix, Laplacian matrix and FMNN system parameters, is proved with the benefit of vector Lyapunov function and positive system theory. With the controller utilized, linear feedback controls can be exerted merely on partial neurons of some selected FMNNs, which is exemplified by numerical simulations. [ABSTRACT FROM AUTHOR]

Metamorphosis is the vital biological and morphologic process in sea urchins with planktonic larval stage. With complex physiological and biochemical mechanisms demanded during metamorphosis, high mortality is always a problem in the purple sea urchin Heliocidaris crassispina. In our study, the transcriptome of the entire individual was assembled de novo and compared between eight-arm larva (LA) and post-larva (PL) of H. crassispina, aimed to explore the differential regulators associated with metamorphosis. Differential expression genes (DEGs) analysis demonstrated 29,542 DEGs in two groups, with 22,507 significant up-regulated genes and 7035 significant down-regulated genes, respectively. These DEGs may make a difference to cellular components, molecular functions, and biological processes correlated with metamorphosis. Afterward, with the threshold among depths of SNPs, qualities of SNPs, Bonferroni Correction for p -value, minor allele frequency (MAF) values, and minor allele frequency imbalance (AFI), 157 potential positive SNPs associated with metamorphosis were screened from a total of 672,329 SNPs in this RNA-seq. Among them, 8 candidate SNPs were screened and genotyped accurately on SNP loci of 264 PL individuals and 288 LA individuals via Genotyping-in-thousands by sequencing technology (GT-seq), respectively. Subsequent correlation analysis indicated that three SNPs might be closely associated with the threshold trait that metamorphoses rapidly or slowly. More importantly, these data suggest that sea urchin H. crassispina could exhibit a biochemical-level and genetic-level adaptive strategy when facing selection pressure. This study lays a solid foundation for further exploring the molecular mechanism of metamorphosis in purple sea urchin H. crassispina and then provides basic data and reasonable methods to screen and revalidate SNPs for metamorphic traits of purple sea urchin H. crassispina. • Transcriptome analyses showed that sea urchin larvae at different development stages could exhibit a biochemical-level adaptation in the face of environmental or selection pressure during metamorphosis. • Genotyping results by GT-seq (Genotyping-in-Thousands by sequencing) reveal that SNP screening methods are relatively strict and reasonable for digging out SNPs, revalidating, and identifying potential SNPs. • SNPs significantly associated with metamorphosis were identified by differential genotype and allele distributions on candidate SNPs of two different development stages and illustrated a genetic-level adaptive strategy during metamorphosis. [ABSTRACT FROM AUTHOR]

In this article, the adaptive finite-time passivity (FTP) and finite-time synchronization (FTS) of coupled fractional-order memristive neural networks with multi-state couplings (CFMNNMCs) are investigated. Firstly, a sufficient condition for guaranteeing the FTP of CFMNNMCs is obtained by developing an edge-based adaptive strategy and a state-feedback controller. Furthermore, a node-based adaptive controller is designed to ensure the FTP of CFMNNMCs. Additionally, with the help of properties of Gamma function and some inequality techniques, the adaptive FTS problem for the considered network model is addressed. Finally, two numerical examples are given to demonstrate the validity of the adaptive FTP and FTS criteria. [ABSTRACT FROM AUTHOR]

An efficient sampling approach 'Adaptive Combined Line Sampling' is proposed for evaluating the 'time-variant failure probability function' (TFPF) of structures. Line Sampling is implemented in an adaptive and iterative way, where each individual Line Sampling run is carried out based on adaptively selected important directions, in order to ensure a sufficiently precise estimation of the TFPF over the whole time interval of analysis. An adaptive strategy and an optimal combination algorithm are developed for the practical implementation of the Line Sampling process. The adaptive strategy allows to determine the optimal important direction which is then used in the next Line Sampling run. The combination strategy allows to collect all these adaptive sampling runs together in an optimal way, which aims at minimising the coefficient of variation (C.o.V.) of the TFPF estimate. Due to these strategies, the proposed approach can estimate the TFPF in a more efficient way than the traditional Line Sampling, while guaranteeing that the C.o.V. of the estimate remains below a prescribed threshold over the whole time of analysis. Thus it can be seen as an extended version of classical Line Sampling specially tailored for time-variant reliability analysis. Examples are given to illustrate the performance of the proposed approach. • Adaptive Combination Approach is proposed for time-variant reliability analysis. • Line Sampling is applied in an iterative and adaptive manner. • An adaptive strategy and an optimal combination algorithm are proposed. • Application to a turbine blade is presented. [ABSTRACT FROM AUTHOR]

Developers typically employ Graphical User Interface (GUI) testing to ensure the expected behavior of applications, but they face the challenge of designing appropriate test cases with functional features. Recently, researchers have proposed several test reuse methods based on semantic matching to alleviate the burden. However, the limited text semantic information and semantic matching rules between events severely limit the existing test reuse methods. In this paper, we propose TREADROID (Test Reuse EnhAncer for anDROID applications), a framework that combines GUI events deduplication with the adaptive semantic matching strategy to enhance the usability of the reused tests. Considering the connection between widget attribute texts, we categorize attributes and measure widget similarity based on the same corresponding attributes as well as across attributes in the same group. In addition, we propose a deduplication strategy for GUI events to reduce the redundancy caused by reusing a test with unique functionality. To further bridge the semantic gap, we design a two-stage adaptive matching strategy to search for the target test with functionality closer to that of the source test. Experimental evaluation against the baseline methods on 25 applications demonstrates that: (i) the adaptive semantic matching strategy overall improves the performance of widget mapping; (ii) GUI events deduplication dramatically increases the precision of events on average, even reaching 100% for multiple tests; (iii) TREADROID can significantly reduce the manual effort of creating tests for similar applications. • Enhancing test reuse with GUI events deduplication and adaptive semantic matching. • Design a novel two-stage adaptive matching strategy to search for the target test. • Employ fitness function for guiding the search direction in the adaptive semantic matching phase. [ABSTRACT FROM AUTHOR]

In recent years, due to the increasing demands for environmental monitoring, disaster warning, urban management, and national defense development, the construction of earth observation platforms has become a strategic requirement for countries in the aerospace sector. Autonomous airships in near space are highly competitive in terms of endurance, power consumption, and coverage area. An important aspect of earth observation is task scheduling, which generates allocation decisions under certain constraints. However, in the current literature, there is insufficient research in this field, which is crucial for human safety. Therefore, it is imperative to establish models to fill the research gap in this area. The novelties and our contributions to the paper are as follows. (i) Based on the consideration of distance, resolution, energy, and time constraints, the multi-airship earth-observation task scheduling (MAEOTS) model is developed to maximize the observation quality and minimize the energy cost. (ii) To solve this model efficiently, a three-stage multi-objective evolutionary algorithm (TSEA) based on sparsity knowledge combined with three indicators (SKTI) is proposed, also called TSEA/SKTI. (iii) Using three types of sparsity knowledge to guide the genetic operator and introducing the fitness evaluation strategy of three indicators for population selection, the competitiveness of populations in diversity and convergence is improved. (iv) A combination of decision variable score and number of tasks is used to initialize the populations. An adaptive strategy is designed to determine the range of operator crossover and mutation. The degree of crowding determined by the sharing function between individuals is used for environmental selection. The experiment was repeated in 4 data sets while comparing with 4 relevant popular evolutionary algorithms. Experimental results show that the proposed algorithm has advantages in terms of effectiveness and diversity of solutions, and demonstrates excellent characteristics in solving the MAEOTS model, exhibiting good time robustness and complexity. An actual case study in Zhuo Zhou City has been conducted to assess the effectiveness and applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Most of the excellent methods for visual object tracking are based on RGB videos. With the popularity of depth cameras, the research on RGB-D(RGB+depth) tracking has gradually gained extensive attention. The depth map provides more available information for dealing with intractable tracking problems. How to make full use of depth maps to construct a better tracker is the foremost problem to be settled. The fully-convolutional siamese network shows excellent performance in 2D tracking, but still cannot achieve satisfying tracking performance in complex scenarios. Therefore, we have proposed the RGB-D tracker integrated with the single-scale siamese network as well as the adaptive bounding boxes, which achieves stable tracking performance under the challenges such as occlusion, scale variation and background clutter. Our proposed adaptive strategy enables the bounding box to adjust automatically when the target appearance changes during the tracking, instead of multi-scale input in the siamese network. We design an effective algorithm to quickly obtain the target depth and construct the 3D local visual field to eliminate the interference from background and similar objects. In addition, the total occlusion handling approach combined with RGB and depth information has achieved more reliable occlusion detection and target recovery. Our presented object tracker, including the strategies of 3D local visual field, adaptive bounding boxes and occlusion handling, has been evaluated on two widely utilized RGB-D tracking benchmarks and achieves suprior performance especially for the situations of occlusion and pedestrian detection. [ABSTRACT FROM AUTHOR]

Human activities have significantly increased the accessibility of nitrogen and manganese, which has had an impact on the evolution of microorganisms. To investigate the effect of increasing manganese accessibility on microbial nitrogen cycle, Mn-HA (Manganese -Humus chelates), a naturally occurring compound in the form of organochelate, was introduced into the conventional nitrification-denitrification process. The findings revealed that the addition of Mn-HA significantly facilitated the removal of ammonium and nitrate potentially by Mnammox (anaerobic ammonium oxidation mediated by Mn (IV)-oxide reduction) and Mn-based autotrophic denitrification. Metagenomic analyses suggested the presence of manganese-redox microorganisms involved in nitrification and denitrification, indicating the occurrence of manganese redox-coupled nitrogen metabolism processes. Additionally, the production and consumption of bio-MnOx (bio-Manganese Oxides) were observed in conjunction with denitrification and nitrification. The production of unequal amounts of bio-MnOx suggested that bio-MnOx may have been deposited in the sediment. This was confirmed by the BCR sequential extraction experiment. Coupling Mn metabolism with N metabolism is an adaptive strategy for nitrogen metabolizing microorganisms. For Mn-sensitive populations, they gain respite from Mn-rich environments by adopting strategies such as manganese oxidation, regulating manganese transport, pumping out intracellular manganese and evolving other manganese resistance genes. [Display omitted] • Mn-HA accelerates ammonium and nitrate removal. • Manganese redox microorganisms perform nitrification and denitrification. • Mn–N cycling is achieved by Mnammox and Mn-based autotrophic denitrification. • Adaptive strategies were employed by Mn-resistance population in nitrogen-culturing sludge. [ABSTRACT FROM AUTHOR]

Deep learning-based classification algorithms are promising in gesture recognition with soft e-skin patches. However, the reported algorithms usually require large amount of training data, resulting in the time-consuming data collection process. In this paper, we present a deep transfer learning-based adaptive strategy for accurate gesture recognition of a soft e-skin patch with reduced training data and time. To this end, we first train a base neural network as the general feature extraction network. Next, we transfer the front layers of the pre-trained base network to target networks of new gesture recognition tasks. Further, we apply the fine-tune technique to refine the copied parameters. Finally, with our custom-built soft e-skin patch, we experimentally verify the developed strategy on two typical transfer cases, termed as the user transfer case (Case I) and the gesture transfer case (Case II). The experimental results show that, to ensure the stable accuracy of 95 %, the training data with and without the adaptive strategy are 1,312 vs 10,912 for Case I, and 8,192 vs 12,032 for Case II, respectively. In this sense, the training time of target networks can be reduced by 62.96 % for Case I and 34.20 % for Case II, respectively. This work shows the potential to promote the widespread application of e-skins in human computer interaction. [Display omitted] • A general gesture recognition strategy of a custom-built soft e-skin patch. • An adaptive strategy to enhance the rapid adjustment of the soft e-skin patch. • Reduced data demand and training time achieved by the adaptive strategy. • Determination of different adaptive strategies for 2 typical transfer cases. [ABSTRACT FROM AUTHOR]

The mountainous grassland ecosystem in Golden Gate National Park (South Africa) has post-fire ecological resilience. However, vegetation species composition and structure can alter when the ecosystem continually has uncontrolled fires. This study developed a vegetation response-ability model by integrating environmental factors (elevation, aspect, rainfall, land surface temperature, soil, and fire severity), adaptive strategies (flowering months, water requirements, resprouter/seeders) and ecological status (increaser or decreaser) for the park. Vegetation recovery index derived from pre- and post-fire normalised difference vegetation index (NDVI) was used with correlation and regression analysis to validate the model. The results showed that amongst the environmental variables, elevation and fire were the most important factors influencing vegetation response-ability followed by soil, aspect, rainfall, and land surface temperature. Almost half (48%) of the park had a high vegetation response-ability, 43% medium, and 9% low. On the other hand, the vegetation recovery index showed 34% of the park fully recovered to pre-fire conditions, while 61% and 5% largely and slightly recovered, respectively. There was a strong correlation between vegetation response-ability and vegetation recovery index. The regression analysis showed a good relationship between vegetation response-ability and vegetation recovery index (R = 0.91) with 83.34% coefficient of determination. [ABSTRACT FROM AUTHOR]

In a combinatorial search problem we wish to identify an unknown element by binary tests, where the edges of a hypergraph specify the available tests. We show that, for rather general cases of this problem, the worst-case minimum number of tests, even if adaptive testing is permitted, can already be achieved in a small number of rounds of parallel tests. In particular, the maximum number of necessary rounds grows only as the square root of the number of elements, and two rounds are enough if, e.g., the test number is close to the number of elements, or the hypergraph is a graph. We also provide polynomial-time, hardness, and parameterized results on the computational complexity of finding optimal strategies for some cases, including graphs and tree hypergraphs. • We study multi-round combinatorial search on test hypergraphs in full generality. • We show a tight bound on the number of rounds needed for the optimal test number. • In several natural cases, strategies with optimal test number need only two rounds. • Related computational problems are NP-hard but fixed-parameter tractable. • The case of graphs can be completely solved in polynomial time. [ABSTRACT FROM AUTHOR]

• An adaptive phase-field method is proposed based on trilinear multi-node elements. • The length scale insensitive phase-field model is combined with the adaptive method. • A robust adaptive criterion associated with the internal length scale is outlined. • Substantial computational memory and time required in phase-field modelling are reduced. • The method is successfully used to simulate simple and mixed-mode fracture in three-dimensional conditions. As a diffused fracture theory, phase-field models can seamlessly simulate complex crack patterns such as extending, branching, and merging. Despite the success of phase-field models, there are two issues in previous methods of three-dimensional (3-D) fracture. Firstly, the nonlinear governing equations lead to the huge computational costs, which hinder the application of phase-field models in 3-D problems. Secondly, these models, which are mostly developed based on a simple quadratic degradation function, provide numerical solutions that are sensitive to a length scale. Hence, this work addresses an efficient adaptive phase-field model with the aid of trilinear multi-node elements. The order of the elements remains constant with the increase of the number of nodes. As the mesh size and length scale significantly influence the numerical precision, a robust adaptive criterion is established in which the element refinement is controlled by both internal length scale and phase-field. According to the criterion, an expected mesh density in the failure domain can be obtained even for nonuniform initial mesh. Besides, being able to extend the phase-field regularized cohesive zone model, the adaptive model provides length scale insensitive responses for both crack path and peak load. The failure of brittle and quasi-brittle materials in three-dimensional conditions, including simple and mixed-mode fracture, can be simulated by the proposed model. Several benchmark examples are analyzed to show the efficiency and accuracy of the trilinear element-based adaptive phase-field model (TAPFM), and the results are compared with the standard phase-field model as well as experimental data. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new adaptive strategy to efficiently exploit velocity discontinuities in 3D finite-element-based upper bound limit analysis formulations. Based on an initial upper bound result, obtained with a conventional approach without velocity discontinuities, possible planes of plastic flow localisation are determined at each strain-rate evaluation node and, subsequently, this information is used to sequentially introduce discontinuities into the considered discretised structure. During a few iterations, by means of introducing new and adjusting existing discontinuities, an optimal velocity discontinuity layout is obtained. For the general 3D case, the geometry of this layout is defined by the well-known level set method, standardly used to define the geometry of cracks in the extended finite element method. To make this method also applicable for orthotropic strength behaviours, traction-based yield functions defining the plastic flow across discontinuities are derived from their stress-based counterparts. This procedure is outlined in detail and the obtained traction-based yield functions are verified numerically, to guarantee a consistent strength behaviour throughout the whole discretised structure. By means of three different examples, including isotropic as well as orthotropic yield functions, the performance of the proposed strategy is investigated and upper bound results as well as failure modes are compared to reference solutions. The proposed approach delivers reliable upper bounds for each example and the majority of plastic flow takes place across the sensibly-arranged discontinuities. For this reason, very good upper bounds can be obtained with a quite coarse finite element mesh and only few introduced velocity discontinuities. This represents an attractive alternative to commonly-used adaptive mesh refinement strategies, where often a huge number of degrees of freedom need to be added to capture localised failure. Highlights • Implementing velocity discontinuities into numerical upper bound formulations. • Developing an adaptive strategy to implement discontinuities in the optimal layout. • Using traction-based yield functions for strength behaviours across discontinuities. • Deriving a projection algorithm to guarantee consistent strength behaviours. [ABSTRACT FROM AUTHOR]

Abstract A plastic-dissipation-based mesh adaptive algorithm is first introduced into the upper-bound finite-element method to determine the stability of axisymmetric problems using an equivalent plane strain model. The failure loads for two geotechnical examples were presented in the form of dimensionless stability numbers. The refined meshes and power dissipations were also plotted to highlight evolutional characteristics of failure mechanisms. Comparisons are given to verify the effectiveness of the mesh adaptive strategy. The advantage of the present method lies in the improved accuracy of the upper-bound solution and the ability to provide failure patterns with greater clarity. [ABSTRACT FROM AUTHOR]

• Enhanced method named ELADE is proposed for parameter extraction of PV models. • ELADE combines four improved strategies to boost differential evolution. • ELADE performs better in five PV models compared with other fifteen competitors. • The effect of maximum population size on ELADE is empirically discussed. • The impact of different strategies on ELADE is investigated. Photovoltaic power generation is becoming increasingly vital as the global call for environmental protection rises. Establishing an equivalent model for a photovoltaic cell and extracting accurate parameters of the model have a crucial role in supporting the fault diagnosis, performance analysis, and maximum power point tracking of the photovoltaic system. To better tackle this problem, an improved algorithm, i.e., Elite Learning Adaptive Differential Evolution (ELADE) is proposed. Four strategies, including the parameters adaptive strategy, elite learning strategy, chaotic last-place elimination strategy, and population size reduction strategy are combined to boost the exploitation process of differential evolution to effectively balance the ability to avoid local optimum and accelerate convergence speed. The suggested ELADE is applied to five photovoltaic cell models. Experimental results show that the maximum population size affects the performance of ELADE, and a recommended value 50 can promote it to achieve the most accurate and reliable parameters in comparison with other peer algorithms. Its superiority is further confirmed by two statistical test methods, including the Friedman for mean root mean square error (RMSE) values and Wilcoxon's rank-sum of RMSE values for each independent run. Besides, the influence of different strategies on ELADE is also empirically investigated, showing that the parameters adaptive strategy contributes the most. [ABSTRACT FROM AUTHOR]

Climate-related extremes such as droughts have led to significant impacts on some watersheds. To assess watershed health and develop effective management plans, information about the function and structure of the watersheds in the context of their climatic response, especially to take into account rainfall anomalies and climate change adaptation, is needed. Integration of climatic variables with reliability, resilience and vulnerability (RRV) indicators, is a novel approach for generating this information. This study investigated the behavior of RRV indicators with respect to rainfall variability and drought patterns for three watersheds governed by different climates. Reliability was defined as the probability of a watershed to be in the range of satisfactory Standardized Precipitation Index (SPI) values. Resilience was indicated as the speed of recovery from an unsatisfactory condition. Vulnerability was defined as a function of the exposure of a watershed to climate change and variation using the SPI. The study areas were the Foyle Watershed in Northern Ireland (temperate oceanic, Cfb), the Xarrama Watershed in Portugal (Mediterranean hot summer, Csa) and the Shazand Watershed in Iran (moderate to cold semi-arid (Bsk). Based on the SPI pattern of each watershed, the SPI of −0.1 for the Foyle and Xarrama watersheds and +0.1 for the Shazand Watershed was selected as the drought threshold. The drought based RRV index was subsequently calculated from long-term (1981–2012) RRV indicators, resulting in means of 0.52 ± 0.25, 0.53 ± 0.21 and 0.30 ± 0.18 for the three watersheds, respectively. These means reflect the status of the watersheds in terms of climatic conditions, which was moderate dry (0.41–0.60) for the Foyle and Xarrama watersheds and dry (0.21–0.40) for the Shazand Watershed. The temporal trend of the drought based RRV index was found to be non-significantly increasing (P-value >0.52) for the Foyle and Xarrama watersheds and non-significantly decreasing for the Shazand Watershed (P-value >0.48). The vulnerability indicator and drought based RRV index were significantly (p-value = 0.00) affected by the climatological gradient. The results of the conceptual framework linked to statistical trends can provide researchers, policy makers, and land managers a more comprehensive base to assess variability of watershed health and design drought management plans. [ABSTRACT FROM AUTHOR]

Sinking experiments using living nassellarian radiolarians with a conical shell form were performed to examine the spatial behaviour of nassellarians and a related feeding model. A small water tank and surface-reflecting mirror were used to observe the vertical movement of each specimen, and the sinking posture and speed were analysed. The experimental results showed that the sinking posture of nassellarians was stable with a cephalis-down orientation, and the sinking speed was considerably slower than that of Dictyocoryne spp. as a control experiment. The stable posture was maintained by a streamlining shell form and the relationship between the centres of gravity and buoyancy. Moreover, a lift force in moving fluids would likely change the posture of the organism to the cephalis facing upstream, as in a weathercock effect. As a result, nassellarians were able to capture organic matter around the rear side of the aperture where prey-capturing pseudopodia are extended. Because the pseudopodia may be attributed to the lifelong performance of the surface of cell membranes, the drastic change in flow intensity around the pseudopodia leads to an easier collapse of the prey-capturing apparatus. A slower sinking speed as the size increases is a beneficial feature for avoiding the feeding risk, resulting in effective predation as a form of homeostatic spatial behaviour. [ABSTRACT FROM AUTHOR]

In this paper, an explicit analytical method called the variational iteration method is presented for solving the second-order singular initial value problems of the Lane–Emden type. In addition, the local convergence of the method is discussed. It is often useful to have an approximate analytical solution to describe the Lane–Emden type equations, especially in the case that the closed-form solutions do not exist at all. This convince us that an effective improvement of the method will be useful to obtain a better approximate analytical solution. The improved method is then treated as a local algorithm in a sequence of intervals. Besides, an adaptive version is suggested for finding accurate approximate solutions of the nonlinear Lane–Emden type equations. Some examples are given to demonstrate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables of a reliability problem. Knowledge on this FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. However, its calculation is usually a challenging task. Therefore, this paper presents an efficient approach for estimating the FPF based on an adaptive strategy and a combination algorithm. The proposed approach involves three basic elements: (1) a Weighted Importance Sampling approach, which allows determining local FPF estimates; (2) an adaptive strategy for determining at which realisations of the distribution parameters it is necessary to perform local FPF estimation; and (3) an optimal combination algorithm, which allows to aggregate local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach. • A global approach for failure probability function estimation is proposed. • It utilises weighted importance sampling to efficiently obtain local estimators. • It includes an adaptive active strategy to guide the local estimations. • It adopts an optimal combination algorithm to combine the local FPF estimators. [ABSTRACT FROM AUTHOR]

• Effects of ramet connection within clonal plants in homogeneous habitats are inconsistent. • Stolon severing acted as disturbance to enhance the plant performance in this research. • Apical dominance seems more important than root competition for clonal growth. • We observed weak root competition in homogeneous environments. There is little consistency on effects of ramet physical connection in homogeneous environments. To revisit this issue, we chose Cynodon dactylon (Linn.) Pers. (Bermuda grass) as a model plant to establish three experiments. In the water availability experiment (I), stolon severing had no significant effects on plant biomass; however, it significantly affected plant morphology by increasing main stolon length of C. dactylon grass under the homogeneous high water (i.e., less stress) condition, possibly due to an escape strategy from disturbance (stolon severing). Apical dominance and root competition are common factors shaping these plant performance measures. We followed these results with two additional experiments to test if such results could be explained by the partial release of apical dominance or root competition. In the apical dominance release experiment (II), biomass and number of branches of distal sections were significantly increased. However, at the whole clonal fragment level, main stolon and inter-node length were both significantly decreased after apical ends were disconnected. In the root competition experiment (III), no significant effect was found indicating weak root competition in homogeneous environments. We concluded that stolon connection had no significant effects on the biomass of C. dactylon while it affected plant morphology in homogeneous environments. Secondly, clonal architecture is partially determined by the presence of ramet connection and apical dominance. Removal of the apical end may have enhanced development of the distal sections, but the loss of stolon connection with distal sections inhibited the performance of apical sections leading to negative effects to the whole clonal fragment. Apical dominance seems more important than root competition for plant performance of C. dactylon under homogeneous environments, but this is only one clonal species in one environmental condition. [ABSTRACT FROM AUTHOR]

Group testing aims at identifying the defective elements of a set by testing selected subsets called pools. A test gives a positive response if the tested pool contains some defective elements. Adaptive strategies test the pools one by one. Assuming that only a tiny minority of elements are defective, the main objective of group testing strategies is to minimize the number of tests. De Bonis introduced in COCOA 2014 a problem variant where one also wants to limit the number of positive tests, as they have undesirable side effects in some applications. A strategy was given with asymptotically optimal test complexity, subject to a constant factor. In the present paper we reduce the test complexity, making also the constant factor optimal in the limit. This is accomplished by a routine that searches for a single defective element and uses pools of decreasing sizes even after negative responses. An additional observation is that randomization saves a further considerable fraction of tests compared to the deterministic worst case, if the number of permitted positive responses per defective element is small. [ABSTRACT FROM AUTHOR]

There are four hypotheses on the origin of microblade technology in North China: i) that it originated from Siberia before the Last Glacial Maximum; ii) that it emerged in Siberia after the Last Glacial Maximum; iii) that it developed from the long-narrow flakes produced by the long-established knapping tradition in North China; and iv) that it had multi-regional origins, among which, a microblade industry may have emerged from the bipolar technology in northern China. Based on archaeological data from Siberia, Mongolia, the Japanese archipelago, Korean Peninsula, and North China, it is reasonable to say that the technological groundwork of Siberia was more substantial, and the initial microblade technology appeared in Siberia earlier than in the other areas. Microblade technology was involved in core preparation, systematic knapping, soft hammer and indirect/pressure flaking techniques, which were all present in the blade technology in Siberia but hardly ever evidenced in the traditional flaking technology systems in North China. It is well accepted that the microblade technology was closely related the high mobility of foragers to live in harsh environments. The climatic fluctuations of the last glacial in Siberia were more severe than in low latitudes, and placed greater subsistence pressure on hunter–gatherers in Siberia. The advantages of the initial microblade technology in dealing with these hostile circumstances encouraged foragers to employ and develop it, and finally generated its widespread use during the Upper Paleolithic through human migration and cultural transmission in Northeast Asia and North America. [ABSTRACT FROM AUTHOR]

Water resources play a principal role in supporting environment. Climate change will challenge the current water resources management practices and threaten water security through acceleration of the hydrological cycle. Historical variation of recorded runoff on main streams and influence of climate change on water resources in the Huang-huai-hai area (including the Yellow River, Hai River, and the Huai River) were analyzed with statistical methods and a hydrological simulation approach. Results indicate that the recorded runoff showed a significant decreasing trend during past 50 years for the Hai River and the middle and lower reaches of the Yellow River, with insignificant change occurring in the Huai River basin and the upstream of the Yellow River basin. Temperature was projected to steadily rise in the next 30–50 years while precipitation was expected to increase slightly. Water resources in 2021–2050 under the three scenarios of RCP2.6, RCP4.5, and RCP8.5 were estimated to change −1.3%, 1.0% and −2.3% relative to the reference period of 1961–1990, respectively. The water shortage for the Huang-huai-hai area might be aggravated due to climate change. It is therefore of significance to enhance water saving construction, make full use of non-traditional water sources, and speed up water conservancy project planning and implementation for adaptation to climate change. [ABSTRACT FROM AUTHOR]

There is an increasing recognition that microplastics contamination in soils has become an important threat for terrestrial ecosystems, and can interact with drought. In addition, due to the increasingly serious environmental pollution and the destruction of the ozone layer, the UV-B radiation to the earth's surface has gradually increased. However, we currently have no information about potential effects of microplastics, UV-B, and drought on plant communities. In order to make up for the vacancy, we conducted an experiment with grassland plant communities. Polyester fiber microplastics (absent, present), UV-B (fully transparent polythene film, attenuating UV-B radiation), and soil water conditions (well-watered, drought) were applied in a fully factorial design. A plant community consisting of four indigenous species and one invasive species, co-occurring in the terrestrial ecosystem of the northern temperate zone was established, and we investigated the effects of microplastics, UV-B, drought and their interactions on plant functional traits and plant community structure. We found that shoot and root biomass decreased with drought but increased with microfibers, and drought significantly decreased specific leaf area at the community level. Physiological and biochemical indexes of individual species and plant community were affected by microfibers, UV-B, drought and their interaction to a varying degree. More importantly, five species were divided into three clusters along PC1 corresponding to individuals from G. longituba and P. depressa , B. bipinnata and M. sativa , plus G. parviflora, which indicated that at the same conditions, G. parviflora would occupy unique ecological niches that affect the growth of native species. Our research offers insights into the mechanisms of the coexistence of native and invasive plants, as well as the ecological consequences of microplastics and other environment factors on plant communities. [Display omitted] • Polyester fiber-microplastics increased height and shoot biomass for all plant species, especially invasive species G. parviflora. • Drought negatively affected height and shoot biomass for all plant species. • At the same conditions, G. parviflora would occupy a unique ecological niche and affect the growth of native species. [ABSTRACT FROM AUTHOR]

There is an increasing recognition that microplastics contamination in soils has become an important threat for terrestrial ecosystems, and can interact with drought. In addition, due to the increasingly serious environmental pollution and the destruction of the ozone layer, the UV-B radiation to the earth's surface has gradually increased. However, we currently have no information about potential effects of microplastics, UV-B, and drought on plant communities. In order to make up for the vacancy, we conducted an experiment with grassland plant communities. Polyester fiber microplastics (absent, present), UV-B (fully transparent polythene film, attenuating UV-B radiation), and soil water conditions (well-watered, drought) were applied in a fully factorial design. A plant community consisting of four indigenous species and one invasive species, co-occurring in the terrestrial ecosystem of the northern temperate zone was established, and we investigated the effects of microplastics, UV-B, drought and their interactions on plant functional traits and plant community structure. We found that shoot and root biomass decreased with drought but increased with microfibers, and drought significantly decreased specific leaf area at the community level. Physiological and biochemical indexes of individual species and plant community were affected by microfibers, UV-B, drought and their interaction to a varying degree. More importantly, five species were divided into three clusters along PC1 corresponding to individuals from G. longituba and P. depressa , B. bipinnata and M. sativa , plus G. parviflora, which indicated that at the same conditions, G. parviflora would occupy unique ecological niches that affect the growth of native species. Our research offers insights into the mechanisms of the coexistence of native and invasive plants, as well as the ecological consequences of microplastics and other environment factors on plant communities. [Display omitted] • Polyester fiber-microplastics increased height and shoot biomass for all plant species, especially invasive species G. parviflora. • Drought negatively affected height and shoot biomass for all plant species. • At the same conditions, G. parviflora would occupy a unique ecological niche and affect the growth of native species. [ABSTRACT FROM AUTHOR]

Abstract: This work describes how Adaptive Body Biasing can be applied on the Clock Distribution Networks (CDNs) to dynamically compensate the thermally induced skews. By selectively changing the bulk polarizations of the clock buffers, i.e., Forward Body Bias (FBB) to speed-up, or Reverse Body Bias (RBB) to slow down, it is possible to recover the timing phase shifts that accumulate along the clock tree paths due to on-chip thermal gradients. The design constraints that are imposed by the semi-custom layout rules make the physical implementation of the proposed technique a non-trivial task. In fact, such rules require that the biasing should be applied with a relatively coarse granularity, e.g., a row of the layout rather than a single cell. In this paper we propose a row-based Integer Linear Programming (ILP) formulation for identifying a physically constrained optimal body-biasing assignment for thermal clock skew compensation. We also describe a faster linear heuristic that accounts the timing penalties imposed on the logic circuits by the application of the row-based body-biasing. In order to assess the applicability and the effectiveness of the proposed strategies, we benchmarked both the ILP and the heuristic against a cell-based approach using as test cases a set of circuits mapped onto an industrial 40nm technology provided by STMicroelectronics. Experimental results show how a sensible reduction of the thermally induced skew can be achieved, while maintaining compliance with the imposed physical design rules and while keeping timing penalties under control. [Copyright &y& Elsevier]

Abstract: Tuning compilations is the process of adjusting the values of a compiler options to improve some features of the final application. In this paper, a strategy based on the use of a genetic algorithm and a multi-objective scheme is proposed to deal with this task. Unlike previous works, we try to take advantage of the knowledge of this domain to provide a problem-specific genetic operation that improves both the speed of convergence and the quality of the results. The evaluation of the strategy is carried out by means of a case of study aimed to improve the performance of the well-known web server Apache. Experimental results show that a 7.5% of overall improvement can be achieved. Furthermore, the adaptive approach has shown an ability to markedly speed-up the convergence of the original strategy. [Copyright &y& Elsevier]

Abstract: A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physics-based models. Typically, models are calibrated and model parameters are estimated by minimization of the discrepancy between model simulations characterizing the system and existing observations requiring a substantial number of model evaluations. Squads is designed to be computationally efficient and robust in identification of the global optimum (i.e. maximum or minimum value of an objective function). It integrates global and local optimization using Adaptive Particle Swarm Optimization (APSO) and Levenberg–Marquardt (LM) optimization using adaptive rules based on runtime performance. The global strategy (APSO) optimizes the location of a set of solutions (particles) in the parameter space. The local strategy (LM) is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to APSO. Therefore, squads is a global strategy that utilizes a local optimization speedup. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg–Marquardt (LM), Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO; i.e. TRIBES), and an existing hybrid optimization strategy (hPSO). All the strategies are tested on 2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a synthetic hydrogeologic application to identify the source of a contaminant plume in an aquifer. Tests are performed using a series of runs with random initial guesses for the estimated parameters. The performance of the strategies are compared based on their robustness, defined as the percentage of runs that identify the global optimum, and their efficiency, quantified by a statistical representation of the number of function evaluations performed prior to identification of the global optimum. Squads is observed to have better performance than the other strategies for the test functions and the hydrogeologic application when both robustness and efficiency are taken into consideration. [Copyright &y& Elsevier]

Abstract: Relational Tri-training (R-Tri-training for short), as a relational semi-supervised learning system, can effectively exploit unlabeled examples to improve the generalization ability. However, the R-Tri-training may also suffer from the common problem in traditional semi-supervised learning, i.e., the performance is usually not stable for the unlabeled examples often be wrongly labeled and accumulated during the iterative learning process. In this paper, a new Relational Tri-training system named ADE-R-Tri-training (R-Tri-training with Adaptive Data Editing) is proposed. Not only does it employ a specific data editing technique to identify and correct the examples possibly mislabeled throughout the co-labeling iterations, but it also takes an adaptive strategy to decide whether to trigger the editing operation according to different cases. The adaptive strategy consists of five pre-conditional theorems, all of which ensure the iterative reduction of classification error under PAC (Probably Approximately Correct) learning theory. Experiments on well-known benchmarks show that ADE-R-Tri-training can more effectively enhance the performance of the hypothesis learned than R-Tri-training. [Copyright &y& Elsevier]

Abstract: A better understanding of plant water relations is needed for evaluating the suitability of plant species to site-specific reforestation programs in the heterogeneous karst landscapes in southwestern China that are characterized by temporary water deficit. During both wet and dry periods, leaf water potentials of 65 plant species from five different growth forms were studied at three representative sites (forest, shrubland and grassland), to compare their adaptive strategies against water stress and assess their suitability for reforestation programs. Herbs showed the highest predawn and midday water potentials and smallest diurnal ranges of water potential values at all the three sites, indicating that they follow water stress avoidance strategies. During the dry period, evergreen shrubs showed low water potentials, the largest diurnal ranges and highest soluble sugar contents. This indicates that they have a tolerance strategy responding to water stress. Deciduous shrubs and trees still showed relatively large diurnal ranges of water potential values and high soluble sugar contents, and did not shed leaves when experiencing the lowest midday water potentials during the dry period. They shed leaves only later in the dry winter period when even more serious drought was experienced. Their strategies seem to include both tolerance and avoidance mechanisms. Evergreen trees revealed relatively low water potentials with smallest diurnal range water potentials at the shrubland site, especially during the dry period, which indicated their weak ability to tolerate severe water stress. Increasing degradation of the vegetation clearly impacts negatively plant water relations. Using the ranges of leaf water potentials, the relative suitability of the plants for reforestation could be evaluated. [Copyright &y& Elsevier]

Abstract: This Letter investigates cluster synchronization in the adaptive complex dynamical networks with nonidentical nodes by a local control method and a novel adaptive strategy for the coupling strengths of the networks. In this approach, the coupling strength of each node adjusts adaptively only based on the state information of its neighborhood. By means of the proposed scheme, the sufficient conditions for achieving cluster synchronization are derived analytically by utilizing Lyapunov stability theory. It is demonstrated that the synchronization performance is sensitively affected by the control gain, the inner-coupling matrix and the network topological structure. The numerical simulations are performed to verify the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: The seasonality and unpredictability of environmental conditions at high altitudes and latitudes govern the life cycle patterns of organisms, giving rise to stresses that cause death or development of specific adaptations. Ice formation is a major variable affecting the survival of both freshwater fauna and fauna inhabiting lichens, mosses and leaf litter. Tardigrades occupy a wide range of niches in marine, freshwater and terrestrial environments. The highest number of species is found in terrestrial habitats thanks to their ability to enter anhydrobiosis and cryobiosis. The cryobiotic ability of tardigrade species from polar regions is well known. Consequently, we focused our research on the ability to survive freezing in the active hydrated state using seven tardigrade species differing in phylogenetic position and collected at various altitudes and from different habitats in a temperate area. Specimens were cooled at different cooling rates (from 0.31°Cmin−1 to 3.26°Cmin−1). Even though the final survival and the time required by animals to recover to active life were both inversely related to the cooling rate, highly significant interspecific differences were found. Species survival ability ranged from excellent to none. Species living in xeric habitats withstood freezing better than those living in hygrophilous habitats, while true limnic species did not exhibit any cryobiotic ability. The ability to withstand freezing seems linked to the anhydrobiotic ability. The differences in cryptobiotic performance among tardigrade species seem more influenced by selective pressures linked to local adaptation to habitat characteristics than by phylogenetic relationships. [Copyright &y& Elsevier]

Abstract: In this paper, an adaptive trust region filter algorithm for unconstrained optimization is presented. The global convergence results of the algorithm are established and the numerical results show that the new algorithm is efficient in practical computation. [Copyright &y& Elsevier]

The conduct of trials in children is beset with special difficulties associated with the dearth of treatment data, the considerable heterogeneity of pediatric patient populations connected with (for example) developmental stage, and the strong desire of parents to see that children are provided with the best possible care at all times. To address these issues, we propose the adaptive treatment strategy (ATS) study in which medication changes are adaptively determined according to the evolving treatment response of the individual child. To formalize this methodological approach, we parameterize an ATS as a “threshold” decision rule that monitors whether the patient’s response trajectory crosses some threshold of failure. In this formulation, the threshold represents a priori judgments about when to give up on response to medication, and the goal is to find the right response thresholds for continuation, augmentation, or switching. Our exposition is developed in the specific clinical context of childhood mania to maximize accessibility of the ideas but applies more generally to other chronic mental and physical health disorders that are difficult to treat. [Copyright &y& Elsevier]

Abstract: As computer simulation increasingly supports engineering design, the requirement for a computer software environment providing an integration platform for computational engineering software increases. A key component of an integrated environment is the use of computational engineering to assist and support solutions for complex design. In the present paper, an integrated software environment is demonstrated for multi-disciplinary computational modeling of structural and geotechnical problems. The SUT-DAM is designed in both popularity and functionality with the development of user-friendly pre- and post-processing software. Pre-processing software is used to create the model, generate an appropriate finite element grid, apply the appropriate boundary conditions, and view the total model. Post-processing provides visualization of the computed results. In SUT-DAM, a numerical model is developed based on a Lagrangian finite element formulation for large deformation dynamic analysis of saturated and unsaturated soils. An adaptive FEM strategy is used into the large displacement finite element formulation by employing an error estimator, adaptive mesh refinement, and data transfer operator. This consists in defining new appropriate finite element mesh within the updated, deformed geometry and interpolating (mapping) the pertinent variables from one mesh to another in order to continue the analysis. The SUT-DAM supports different yield criteria, including classical and advanced constitutive models, such as the Pastor–Zienkiewicz and cap plasticity models. The paper presents details of the environment and includes several examples of the integration of application software. [Copyright &y& Elsevier]

We give a survey of common strategies for numerical integration (adaptive, Monte-Carlo, Quasi-Monte Carlo), and attempt to delineate their realm of applicability. The inherent accuracy and error bounds for basic integration methods are given via such measures as the degree of precision of cubature rules, the index of a family of lattice rules, and the discrepancy of uniformly distributed point sets. Strategies incorporating these basic methods often use paradigms to reduce the error by, e.g., increasing the number of points in the domain or decreasing the mesh size, locally or uniformly. For these processes the order of convergence of the strategy is determined by the asymptotic behavior of the error, and may be too slow in practice for the type of problem at hand. For certain problem classes we may be able to improve the effectiveness of the method or strategy by such techniques as transformations, absorbing a difficult part of the integrand into a weight function, suitable partitioning of the domain, transformations and extrapolation or convergence acceleration. Situations warranting the use of these techniques (possibly in an “automated” way) are described and illustrated by sample applications. [Copyright &y& Elsevier]

• We propose a two-scale iterative scheme to solve a phase-field model for precipitation and dissolution in porous media. • We combine the two-scale iterative scheme with a robust non-linear solver at the micro scale. • We prove the boundedness of the micro-scale and macro-scale solution under certain choice of parameters. • We prove the convergence of the two-scale iterative scheme on a simplified setting. • We enrich the two-scale iterative scheme with an adaptive strategy to make it more efficient at both scales. Mineral precipitation and dissolution processes in a porous medium can alter the structure of the medium at the scale of pores. Such changes make numerical simulations a challenging task as the geometry of the pores changes in time in an apriori unknown manner. To deal with such aspects, we here adopt a two-scale phase-field model, and propose a robust scheme for the numerical approximation of the solution. The scheme takes into account both the scale separation in the model, as well as the non-linear character of the model. After proving the convergence of the scheme, an adaptive two-scale strategy is incorporated, which improves the efficiency of the simulations. Numerical tests are presented, showing the efficiency and accuracy of the scheme in the presence of anisotropies and heterogeneities. [ABSTRACT FROM AUTHOR]

Response surface methods are widely used in slope reliability analysis due to the high efficiency. However, they are often criticized of the curse of high dimensionality, problem-dependent accuracy, and mechanism-free interpretation. To address these issues, this study proposes a response surface-guided adaptive slope reliability analysis method for slopes in spatially varying soils. A sparse quadratic polynomial is initialized using a two-fold dimensionality reduction technique. The response surface-based preliminary slope reliability analysis is successfully corrected to an unbiased, finite element-based target slope reliability analysis, during which the response surface updates iteratively with an increasing high accuracy from special emphasis nearby the failure domain. Two spatially varying soil slopes are investigated. Results indicate that the proposed method can provide an efficient and unbiased estimation of finite element-based target slope failure probability with a low variability, develop a more accurate and problem-oriented response surface model for slope reliability analysis, and explore failure mechanisms of slope stability in spatially varying soils. Its performance is insensitive to the accuracy of initial response surface. Additionally, it prominently outperforms both response surface and finite element-based subset simulations, regardless of high-dimensional or low-failure-probability problems. These advantages significantly enhance the application of response surface on practical slope reliability analysis. [ABSTRACT FROM AUTHOR]

A lower bound (LB) axisymmetric limit analysis formulation is presented for Mohr-Coulomb materials using finite elements and semidefinite programming (SDP). The details of the application of SDP to the axisymmetric LB finite element method for Mohr-Coulomb materials are described in this paper. Through the use of the SDP, the axisymmetric Mohr-Coulomb yield criterion can be directly applied in its native form in terms of principal stresses without any smoothing of the parent yield surface, and no assumption needs to be made about σ θ. A node-stress-based adaptive mesh algorithm is also introduced into the proposed method to obtain an accurate high-accuracy LB solution. The proposed method is then applied to estimate the collapse load of circular footings and the stability of an unlined vertical circular excavation. The results are also compared with those reported in the literature; the results obtained from the proposed method are accurate and can provide failure patterns with greater clarity. [ABSTRACT FROM AUTHOR]

• We determine tunnel stability in soil with a soft upper layer and hard lower layer. • The effects of multiple soil layers and different soft soil parameters on an underground tunnel in China are studied. • We present the upper-bound finite element method with a mesh adaptive strategy. • Two main reasons for tunnel instability and large surface settlement are presented. • Two construction proposals are presented to ensure normal working conditions and safety of engineering. This paper presents a limit analysis of a tunnel in non-homogeneous soil using the upper-bound limit analysis in combination with finite element technology and a mesh adaptive strategy by investigating the critical loads and failure mechanisms of a tunnel cross-section and tunnel face. A case study of an underground tunnel in China is conducted, with a focus on the effects of multiple soil layers and different soft soil parameters. This work demonstrates that the variation in the soil layering plays an important role in the initiation of tunnel collapse, possibly shifting the failure region and even changing the failure mechanism. When the soil changes from a strongly weathered conglomerate to a soft soil, not increasing the low tunnel pressure quickly enough and using the wrong excavation mode are the two main reasons for tunnel instability and large surface settlement. [ABSTRACT FROM AUTHOR]

• A chaos-based non-linear inertia weight is used to balance capacities better. • Stochastic and mainstream learning strategies are devised to enhance diversity. • An adaptive position updating strategy further adjusts balance. • A terminal replacement mechanism is adopted to enhance convergence precise. In expert systems, complex optimization problems are usually nonlinear, nonconvex, multimodal and discontinuous. As an efficient and simple optimization algorithm, particle swarm optimization(PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue. To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems, a modified PSO using adaptive strategy called MPSO is proposed. In MPSO, in order to well balance the global exploration and local exploitation capabilities of the PSO, a chaos-based non-linear inertia weight is proposed. Meanwhile, to avoid the premature convergence, stochastic and mainstream learning strategies are adopted. Finally, an adaptive position updating strategy and terminal replacement mechanism are employed to enhance PSO's ability to solve complex optimization problems in expert systems. 30 complex CEC2017 benchmark functions are utilized to verify the promising performance of MPSO, experimental results and statistical analysis indicate that MPSO has competitive performance compared with 16 state-of-the-art algorithms. The source code of MPSO is provided at https://github.com/lhustl/MPSO. [ABSTRACT FROM AUTHOR]

We propose a high-order adaptive spectral element method based on Gegenbauer polynomial basis for solving singularly perturbed differential equations with Dirichlet boundary conditions and a boundary layer at one end. The method reduces the singularly perturbed boundary value problem into a single system of linear algebraic equations when the solution is smooth on the solution domain, or two systems of linear algebraic equations when the solution has a thin boundary layer. The core of the new adaptive strategy lies in constructing a mathematical model of the last few Gegenbauer coefficients in the Gegenbauer truncated series approximating the second-order derivative of the solution, which can be readily converted into a minimization problem in a least-squares sense and solved using fast optimization techniques. An error and convergence analysis of the method for sufficiently smooth solutions is presented. A numerical test example with various parameter settings is presented to verify the accuracy, effectiveness, and applicability of the proposed method. Numerical comparisons with other rival methods in the literature are included to demonstrate further the power of the proposed method. [ABSTRACT FROM AUTHOR]