Search

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

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

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

The investigation into trade-offs among plant functional traits sheds light on how plants strategically balance growth and survival when facing environmental stress. This study sought to evaluate whether trade-offs observed at both community and individual species levels could indicate adaptive fitness across an intensity of flooding intensity. The study was conducted at 25 sampling sites spanning approximately 600 km along the riparian zone in the Three Gorges Reservoir area, China. The findings revealed that, along the flooding gradient, the overall riparian community did not exhibit significant trade-offs between leaf and root traits. Examining three broadly distributed dominant species (Cynodon dactylon, Xanthium strumarium, and Abutilon theophrasti), perennial plants showed pronounced trade-offs under low flooding intensity, while annuals exhibited trade-offs under moderate and low flooding intensity. The trade-offs were evident in traits related to nitrogen-carbon resources, such as specific leaf area, root tissue density, and photosynthetic rate. However, under strong flooding intensity, the relationship between leaf and root traits of the species studied was decoupled. Furthermore, the study identified a significant correlation between soil nitrogen and the trade-off traits under moderate and low flooding intensity. Integrating results from the CSR (Competitors, Stress-tolerators, Ruderals) strategy model, species niche breath analysis, and nitrogen-regulated trade-off, the study revealed that, in the face of high flooding intensity, perennial species (C. dactylon) adopts an S-strategy, demonstrating tolerance through a conservative resource allocation that decouples leaf-root coordination. Annual species (X. strumarium and A. theophrasti), on the other hand, exhibit niche specialization along the flooding gradient, employing distinct strategies (R- and C-strategy). As flooding stress diminishes and soil nitrogen level decreases, plant strategies tend to shift towards an R-strategy with a competition for reduced N resources. In conclusion, the study highlighted the pivotal roles of soil nitrogen and flooding intensity acting as the dual determinants of species growth and tolerance. These dynamics of growth-tolerance balance were evident in the diverse trade-offs between leaf and root traits of individual plant species with different life histories, underscoring the array of adaptive strategies employed by riparian plants across the flooding intensity gradient.

The deterioration of water quality caused by human activities has triggered significant impacts on aquatic ecosystems. Submerged macrophytes play an important role in freshwater ecosystem restoration. Understanding the relative contributions of the sources and environment to the adaptive strategies of submerged macrophytes is crucial for freshwater restoration and protection. In this study, the perennial submerged macrophyte Myriophyllum spicatum was chosen as the experimental material due to its high adaptability to a variable environment. Through conducting reciprocal transplant experiments in two different artificial environments (oligotrophic and eutrophic), combined with trait network and redundancy analysis, the characteristics of the plant functional traits were examined. Furthermore, the adaptive strategies of M. spicatum to the environment were analyzed. The results revealed that the plant source mainly influenced the operational pattern among the traits, and the phenotypic traits were significantly affected by environmental factors. The plants cultured in high-nutrient water exhibited a higher plant height, longer leaves, and more branches and leaves. However, their physiological functions were not significantly affected by the environment. Therefore, the adaptation strategy of M. spicatum to the environment mainly relies on its phenotypic plasticity to ensure the moderate acquisition of resources in the environment, thereby ensuring the stable and efficient operation of plant physiological traits. The results not only offered compelling evidence on the adaptation strategies of M. spicatum in variable environments but also provided theoretical support for the conservation of biodiversity and sustainable development.

Morphology plays a crucial role in understanding the intricacies of biological forms. Traditional morphometric methods, focusing on one- or two-dimensional geometric levels, often fall short of accurately capturing the three-dimensional (3D) structure of organisms. The advent of 3D scanning techniques has revolutionized the study of organismal morphology, enabling comprehensive and accurate measurements. This study employs a 3D structured light scanning system to analyze the morphological variations in the Chinese toad (Bufo gargarizans Cantor, 1842) along the Yellow River Basin. The 3D digital model obtained from the scan was used to calculate various morphological parameters including body surface area, volume, fractal dimensions, and limb size. The research explores geographic variability patterns and identifies environmental drivers affecting the 3D phenotypic variation of B. gargarizans. Results reveal a bimodal pattern of variation in the toad population, with higher elevations exhibiting smaller body sizes, greater appendage proportions, and more complex body structures. Linear regression analyses highlight the influence of elevation and annual mean temperature on the morphological variation of B. gargarizans, with elevation playing a significant role. This study underscores the significance of 3D morphometric analysis in unraveling the intricacies of organismal morphology and understanding the adaptive strategies of species in diverse environments.

In this paper, the asymptotic consensus control of multi-agent systems with general linear agent dynamics is investigated. A neighbor-based adaptive event-triggering strategy with a dynamic triggering threshold is proposed, which leads to a fully distributed control of the multi-agent system, depending only on the states of the neighboring agents at triggering moments. By using the Lyapunov method, we prove that the states of the agents converge asymptotically. In addition, the proposed event-triggering strategy is proven to exclude Zeno behavior. The numerical simulation results illustrate that the agent states achieve consensus in sense of asymptotic convergence. Furthermore, the proposed strategy is shown to be scalable in case of variable agent numbers.

This paper proposes the Lock-Position-Based RFID Adaptive Parallel Collision Tree (LAPCT) algorithm to address the issues of excessive time slots required in the identification process of collision tree algorithms for multiple tags and the high communication complexity between the reader and multiple tags. The LAPCT algorithm adopts a single-query multiple-response mechanism and dynamically divides the response sub-cycle numbers in the identification cycle based on an adaptive strategy. It uses Manchester encoding to lock collision positions and generate a common query prefix, effectively reducing the number of reader queries. This reduction in queries decreases the total number of required time slots and transmitted bits during the reader–tag communication process, thereby improving the efficiency of multiple tag recognition. Theoretical and simulation experiments demonstrate that compared to similar algorithms, the LAPCT algorithm achieves a maximum reduction of 37% in total time slots required, a maximum improvement of 30% in recognition efficiency, and a maximum reduction of 90% in communication complexity. Furthermore, with an increase in the number of tags, the performance advantages of the LAPCT algorithm become more pronounced, making it suitable for large-scale tag scenarios.

The adaptive potential of plants is commonly used as an indicator of genotypes with higher breeding program potential. However, the complexity and interaction of plant metabolic parameters pose a challenge to selection strategies. In this context, this study aimed to explore phenotypic plasticity within the germplasm of Hybrid Timor coffee. Additionally, we assessed the utility of the multivariate phenotypic plasticity index (MVPi) as a promising tool to predict genotype performance across diverse climatic conditions. To achieve this, we evaluated the performance of seven accessions from the Hybrid Timor germplasm in comparison to the Rubi and IPR 100 cultivars, known for their susceptibility and resistance to drought, respectively. The experiment took place in a greenhouse under two conditions: one with normal soil moisture levels near maximum capacity, and the other with a water deficit scenario involving a period of no irrigation followed by rehydration. Data on physiological and biochemical factors were collected at three stages: before applying the water deficit, during its imposition, and after rehydration. Growth data were obtained by the difference between the beginning and end of the experimental period Furthermore, field evaluations of the productivity of the same genotypes were carried out over two consecutive seasons. Based on physiological and biochemical assessments, the MVPi was computed, employing Euclidean distance between principal component multivariate analysis scores. Subsequently, this index was correlated with growth and productivity data through linear regressions. Our findings reveal that the plastic genotypes that are capable of significantly altering physiological and biochemical parameters in response to environmental stimuli exhibited reduced biomass loss in both aerial and root parts. As a result, this positively influenced their productivity. Enhanced plasticity was particularly prominent in accessions from the MG Germplasm Collection: MG 311—Hybrid Timor UFV 428-02, MG 270—Hybrid Timor UFV 377-21, and MG 279—Hybrid Timor UFV 376-31, alongside the Rubi MG 1192 cultivar. The MVPi emerged as a valuable instrument to assess genotype adaptability and predict their performance under varying climatic scenarios.

The spherical evolution algorithm (SE) is a unique algorithm proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due to the challenges arising from the multiple choices of operators and the utilization of a spherical search method. In this paper, we introduce an enhancement method that incorporates weights in individuals’ dimensions that are affected by individual fitness during the iteration process, aiming to improve SE by adaptively balancing the tradeoff between exploitation and exploration during convergence. This is achieved by reducing the randomness of dimension selection and enhancing the retention of historical information in the iterative process of the algorithm. This new SE improvement algorithm is named DWSE. To evaluate the effectiveness of DWSE, in this study, we apply it to the CEC2017 standard test set, the CEC2013 large-scale global optimization test set, and 22 real-world problems from CEC2011. The experimental results substantiate the effectiveness of DWSE in achieving improvement.

The drought tolerance of plants is significantly influenced by their root architecture traits and root adaptive strategies, but the key root architecture traits that affect drought tolerance and the differences in drought adaptative strategies of species with varying root architectures are not yet clear. This study aimed to investigate the response of three species’ roots to drought and evaluate the key root architecture traits affecting the drought tolerance of the three species. One-year-old potted seedlings of three species [Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), masson pine (Pinus massoniana (Lamb.)), and moso bamboo (Phyllostachys edulis (Carr.) H. de Lehaie f. edulis)] were planted in a greenhouse under three drought conditions (sufficient water supply, moderate drought, and severe drought) for 90 days. Biomass, root morphology [root surface area (RSA), root length (RL), root diameter (RD)], root architecture [root topological index (TI), fractal dimension (FD), and root branching angle (RBA)] of seedlings were measured monthly. The drought tolerance of species was quantified by studying the response ratio (RR) of root length and biomass in response to drought. We found that: (i) different levels of drought inhibited the biomass accumulation and root growth of the three species, and drought tolerance showed a decreasing order as pine > Chinese fir > bamboo; (ii) drought decreased the RD in bamboo but increased it in pine. Both bamboo and Chinese fir reduced their FD and RBA under drought stress, while pine was relatively stable. All the three species’ roots tended to develop a herringbone branching architecture (increase their TI) under drought stress; (iii) both TI and FD were negatively correlated with the drought tolerance of the seedlings. Our results indicated that plants could adapt to drought by different strategies such as adjusting biomass allocation and root morphology, reducing root branch strength, and branching angles. Roots with narrower branching angles, greater branching complexity, larger TI, and consuming higher cost are more drought-tolerant.

Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow shops. Hence, a lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS) was studied. A mixed integer linear programming (MILP) model was established, and a heuristic-based adaptive iterated greedy algorithm (HAIG) with three modifications was designed to solve the problem. Specifically, a two-layer encoding method was proposed to decouple the sub-lot-based connection. Two heuristics were embedded in the decoding process to reduce the manufacturing cycle. Based on this, a heuristic-based initialization is proposed to improve the performance of the initial solution; an adaptive local search with four specific neighborhoods and an adaptive strategy has been structured to improve the exploration and exploitation ability. Besides, an acceptance criterion of inferior solutions has been improved to promote global optimization ability. The experiment and the non-parametric Kruskal–Wallis test (p = 0) showed the significant advantages of HAIG in effectiveness and robustness compared with five state-of-the-art algorithms. An industrial case study verifies that intermingling sub-lots is an effective technique to enhance the utilization ratio of machines and shorten the manufacturing cycle.

Learning density estimation is important in probabilistic modeling and reasoning with uncertainty. Since B-spline basis functions are piecewise polynomials with local support, density estimation with B-splines shows its advantages when intensive numerical computations are involved in the subsequent applications. To obtain an optimal local density estimation with B-splines, we need to select the bandwidth (i.e., the distance of two adjacent knots) for uniform B-splines. However, the selection of bandwidth is challenging, and the computation is costly. On the other hand, nonuniform B-splines can improve on the approximation capability of uniform B-splines. Based on this observation, we perform density estimation with nonuniform B-splines. By introducing the error indicator attached to each interval, we propose an adaptive strategy to generate the nonuniform knot vector. The error indicator is an approximation of the information entropy locally, which is closely related to the number of kernels when we construct the nonuniform estimator. The numerical experiments show that, compared with the uniform B-spline, the local density estimation with nonuniform B-splines not only achieves better estimation results but also effectively alleviates the overfitting phenomenon caused by the uniform B-splines. The comparison with the existing estimation procedures, including the state-of-the-art kernel estimators, demonstrates the accuracy of our new method.

The gravitational search algorithm is a global optimization algorithm that has the advantages of a swarm intelligence algorithm. Compared with traditional algorithms, the performance in terms of global search and convergence is relatively good, but the solution is not always accurate, and the algorithm has difficulty jumping out of locally optimal solutions. In view of these shortcomings, an improved gravitational search algorithm based on an adaptive strategy is proposed. The algorithm uses the adaptive strategy to improve the updating methods for the distance between particles, gravitational constant, and position in the gravitational search model. This strengthens the information interaction between particles in the group and improves the exploration and exploitation capacity of the algorithm. In this paper, 13 classical single-peak and multi-peak test functions were selected for simulation performance tests, and the CEC2017 benchmark function was used for a comparison test. The test results show that the improved gravitational search algorithm can address the tendency of the original algorithm to fall into local extrema and significantly improve both the solution accuracy and the ability to find the globally optimal solution.

This article covers a set of models and methods of portfolio investment which help adapt modern economic and mathematical instruments of portfolio investment to the current financial market situation. The main hypotheses serve as a basis for the adaptive dynamic investment portfolio. The experimental analysis shows that the adaptive dynamic investment strategy is more beneficial than classical approaches. The advantage of the adaptive strategy is that it is based on forecast data, whereas classical strategies focus only on historical data.

Rhizome fragmentation and sand burial are common phenomena in rhizomatous clonal plants. These traits serve as an adaptive strategy for survival in stressful environments. Thus far, some studies have been carried out on the effects of rhizome fragmentation and sand burial, but how the interaction between rhizome fragmentation and sand burial affects the growth and reproduction of rhizomatous clonal plants is unclear. We investigated the effect of the burial depth and rhizome fragment size on the survival and growth of the rhizomatous herb Phragmites communis using 288 clonal fragments (6 burial depths × 8 clonal fragment sizes × 6 replicates) in a field rhizome severing experiment. The ramet survival of the rhizomatous species significantly increased with the sand burial depth and clonal fragment size (p < 0.01), and the effects of the clonal fragment size on ramet survival depended on the sand burial depth. Sand burial enhanced both the vertical and horizontal biomass (p < 0.05), while the clonal fragment size affected the vertical biomass rather than the horizontal biomass. Sand burial facilitated the vertical growth of ramets (p < 0.05) while the number of newly produced ramets firstly increased and then decreased with the increasing clonal fragment size, and the maximal value appeared in four clonal fragments under a heavy sand burial depth. There is an interaction between the burial depth and rhizome fragment size in the growth of rhizome herbaceous plants. The population growth increases in the increase of sand burial depth, and reaches the maximum under severe sand burial and moderate rhizome fragmentation.

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image’s layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC’2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.

The red palm weevil (RPW), Rhynchophorus ferrugineus, is one of the worst palm pests worldwide. In this work, we studied the physiological basis underlying its adaptive strategy against low temperatures. Specifically, we analyzed the main low-molecular-weight biochemical substances acting as possible endogenous cryoprotectants, as well as their efficiency in reducing cold injury by preserving K+/Na+ homeostasis. Wild pre-pupae were cold-treated (5.0 ± 0.5 °C) or non-treated (23 ± 1 °C) for 7 days. We then determined the levels of: (a) glucose, trehalose and glycerol, spectrophotometrically, (b) amino acids, by liquid chromatography and (c) potassium and sodium, by inductively coupled plasma mass-spectrometry. Cold-treated larvae increased their potassium level, suggesting some degree of chill injury. However, part of the cold-exposed animals was able to develop an efficient overall cryoprotective response which primarily includes glucose, as well as glycerol and several amino acids (mainly alanine). Our study shows for the first time that RPW is capable of deploying effective physiological mechanisms for a rapid response to cold, which could be relevant to improving predictive models of geographic distribution, especially in a context of climate change. The knowledge of the specific molecules involved would allow future studies to try to prevent its adaptive strategy, either by natural or chemical methods.

The investigation into trade-offs among plant functional traits sheds light on how plants strategically balance growth and survival when facing environmental stress. This study sought to evaluate whether trade-offs observed at both community and individual species levels could indicate adaptive fitness across an intensity of flooding intensity. The study was conducted at 25 sampling sites spanning approximately 600 km along the riparian zone in the Three Gorges Reservoir area, China. The findings revealed that, along the flooding gradient, the overall riparian community did not exhibit significant trade-offs between leaf and root traits. Examining three broadly distributed dominant species ( Cynodon dactylon , Xanthium strumarium , and Abutilon theophrasti ), perennial plants showed pronounced trade-offs under low flooding intensity, while annuals exhibited trade-offs under moderate and low flooding intensity. The trade-offs were evident in traits related to nitrogen-carbon resources, such as specific leaf area, root tissue density, and photosynthetic rate. However, under strong flooding intensity, the relationship between leaf and root traits of the species studied was decoupled. Furthermore, the study identified a significant correlation between soil nitrogen and the trade-off traits under moderate and low flooding intensity. Integrating results from the CSR (Competitors, Stress-tolerators, Ruderals) strategy model, species niche breath analysis, and nitrogen-regulated trade-off, the study revealed that, in the face of high flooding intensity, perennial species ( C. dactylon ) adopts an S-strategy, demonstrating tolerance through a conservative resource allocation that decouples leaf-root coordination. Annual species ( X. strumarium and A. theophrasti ), on the other hand, exhibit niche specialization along the flooding gradient, employing distinct strategies (R- and C-strategy). As flooding stress diminishes and soil nitrogen level decreases, plant strategies tend to shift towards an R-strategy with a competition for reduced N resources. In conclusion, the study highlighted the pivotal roles of soil nitrogen and flooding intensity acting as the dual determinants of species growth and tolerance. These dynamics of growth-tolerance balance were evident in the diverse trade-offs between leaf and root traits of individual plant species with different life histories, underscoring the array of adaptive strategies employed by riparian plants across the flooding intensity gradient.

The deterioration of water quality caused by human activities has triggered significant impacts on aquatic ecosystems. Submerged macrophytes play an important role in freshwater ecosystem restoration. Understanding the relative contributions of the sources and environment to the adaptive strategies of submerged macrophytes is crucial for freshwater restoration and protection. In this study, the perennial submerged macrophyte Myriophyllum spicatum was chosen as the experimental material due to its high adaptability to a variable environment. Through conducting reciprocal transplant experiments in two different artificial environments (oligotrophic and eutrophic), combined with trait network and redundancy analysis, the characteristics of the plant functional traits were examined. Furthermore, the adaptive strategies of M. spicatum to the environment were analyzed. The results revealed that the plant source mainly influenced the operational pattern among the traits, and the phenotypic traits were significantly affected by environmental factors. The plants cultured in high-nutrient water exhibited a higher plant height, longer leaves, and more branches and leaves. However, their physiological functions were not significantly affected by the environment. Therefore, the adaptation strategy of M. spicatum to the environment mainly relies on its phenotypic plasticity to ensure the moderate acquisition of resources in the environment, thereby ensuring the stable and efficient operation of plant physiological traits. The results not only offered compelling evidence on the adaptation strategies of M. spicatum in variable environments but also provided theoretical support for the conservation of biodiversity and sustainable development.

This work focuses on the implementation of innovative adaptive strategies and a closed-loop chain in a piston-damage-based combustion controller. In the previous paper (Part 1), implemented models and the open loop algorithm are described and validated by reproducing some vehicle maneuvers at the engine test cell. Such controller is further improved by implementing self-learning algorithms based on the analytical formulations of knock and the combustion model, to update the fuel Research Octane Number (RON) and the relationship between the combustion phase and the spark timing in real-time. These strategies are based on the availability of an on-board indicating system for the estimation of both the knock intensity and the combustion phase index. The equations used to develop the adaptive strategies are described in detail. A closed-loop chain is then added, and the complete controller is finally implemented in a Rapid Control Prototyping (RCP) device. The controller is validated with specific tests defined to verify the robustness and the accuracy of the adaptive strategies. Results of the online validation process are presented in the last part of the paper and the accuracy of the complete controller is finally demonstrated. Indeed, error between the cyclic and the target combustion phase index is within the range ±0.5 Crank Angle degrees (°CA), while the error between the measured and the calculated maximum in-cylinder pressure is included in the range ±5 bar, even when fuel RON or spark advance map is changing.

The adaptive potential of plants is commonly used as an indicator of genotypes with higher breeding program potential. However, the complexity and interaction of plant metabolic parameters pose a challenge to selection strategies. In this context, this study aimed to explore phenotypic plasticity within the germplasm of Hybrid Timor coffee. Additionally, we assessed the utility of the multivariate phenotypic plasticity index (MVPi) as a promising tool to predict genotype performance across diverse climatic conditions. To achieve this, we evaluated the performance of seven accessions from the Hybrid Timor germplasm in comparison to the Rubi and IPR 100 cultivars, known for their susceptibility and resistance to drought, respectively. The experiment took place in a greenhouse under two conditions: one with normal soil moisture levels near maximum capacity, and the other with a water deficit scenario involving a period of no irrigation followed by rehydration. Data on physiological and biochemical factors were collected at three stages: before applying the water deficit, during its imposition, and after rehydration. Growth data were obtained by the difference between the beginning and end of the experimental period Furthermore, field evaluations of the productivity of the same genotypes were carried out over two consecutive seasons. Based on physiological and biochemical assessments, the MVPi was computed, employing Euclidean distance between principal component multivariate analysis scores. Subsequently, this index was correlated with growth and productivity data through linear regressions. Our findings reveal that the plastic genotypes that are capable of significantly altering physiological and biochemical parameters in response to environmental stimuli exhibited reduced biomass loss in both aerial and root parts. As a result, this positively influenced their productivity. Enhanced plasticity was particularly prominent in accessions from the MG Germplasm Collection: MG 311-Hybrid Timor UFV 428-02, MG 270-Hybrid Timor UFV 377-21, and MG 279-Hybrid Timor UFV 376-31, alongside the Rubi MG 1192 cultivar. The MVPi emerged as a valuable instrument to assess genotype adaptability and predict their performance under varying climatic scenarios.

Early Neolithic lifeways in North China, which are marked by a low-level food production economy, population aggregation, and sedentism, thrived just after the period of a climatic cooling event at 8.2 ka. Instead of simply regarding this climate fluctuation as a cause for the significant socio-economic transition, this paper attempts to explore the interplay between people’s choices of coping strategies with climate change as a perspective to learn how people respond to this climate fluctuation and how such responses generated the interlocked socio-economic transitions. This analysis indicates that pre-existing changes in human adaptive behaviors prior to the cooling events were sufficient to enable people in certain areas to apply the intensification of food procurement in circumscribed territories as a strategy to cope with the climate fluctuations of the 8.2 ka BP cooling event. The application of such a coping strategy facilitated the economic and sociopolitical transition into Neolithic lifeways and led to the flourishing development of Neolithic cultures after 8 ka BP in North China.

The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a similarity matrix. The algorithm is first presented for the case of a prescribed number k of clusters, then it is extended to the case of a not a priori given k. A heuristic approach improving the standard multistart strategy is proposed and validated by the experimentation.

The paper deals with the problem of global minimization of a polynomial function expressed through the Frobenius norm of two-dimensional or three-dimensional matrices. An adaptive procedure is proposed which applies a Multistart algorithm according to a heuristic approach. The basic step of the procedure consists of splitting the runs of different initial points in segments of fixed length and to interlace the processing order of the various segments, discarding those which appear less promising. A priority queue is suggested to implement this strategy. Various parameters contribute to the handling of the queue, whose length shrinks during the computation, allowing a considerable saving of the computational time with respect to classical procedures. To verify the validity of the approach, a large experimentation has been performed on both nonnegatively constrained and unconstrained problems.

Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems in wireless sensor networks. These applications require dealing with high volume and continuous data streams with fast processing time on distributed complex event processing (CEP) systems. Therefore, a well-managed parallel processing technique is needed for improving the performance of the system. However, the specific properties of pattern operators in the CEP systems increase the difficulties of the parallel processing problem. To address these issues, a parallelization model and an adaptive parallel processing strategy are proposed for the complex event processing by introducing a histogram and utilizing the probability and queue theory. The proposed strategy can estimate the optimal event splitting policy, which can suit the most recent workload conditions such that the selected policy has the least expected waiting time for further processing of the arriving events. The proposed strategy can keep the CEP system running fast under the variation of the time window sizes of operators and the input rates of streams. Finally, the utility of our work is demonstrated through the experiments on the StreamBase system.

Rhizome fragmentation and sand burial are common phenomena in rhizomatous clonal plants. These traits serve as an adaptive strategy for survival in stressful environments. Thus far, some studies have been carried out on the effects of rhizome fragmentation and sand burial, but how the interaction between rhizome fragmentation and sand burial affects the growth and reproduction of rhizomatous clonal plants is unclear. We investigated the effect of the burial depth and rhizome fragment size on the survival and growth of the rhizomatous herb Phragmites communis using 288 clonal fragments (6 burial depths × 8 clonal fragment sizes × 6 replicates) in a field rhizome severing experiment. The ramet survival of the rhizomatous species significantly increased with the sand burial depth and clonal fragment size (p < 0.01), and the effects of the clonal fragment size on ramet survival depended on the sand burial depth. Sand burial enhanced both the vertical and horizontal biomass (p < 0.05), while the clonal fragment size affected the vertical biomass rather than the horizontal biomass. Sand burial facilitated the vertical growth of ramets (p < 0.05) while the number of newly produced ramets firstly increased and then decreased with the increasing clonal fragment size, and the maximal value appeared in four clonal fragments under a heavy sand burial depth. There is an interaction between the burial depth and rhizome fragment size in the growth of rhizome herbaceous plants. The population growth increases in the increase of sand burial depth, and reaches the maximum under severe sand burial and moderate rhizome fragmentation.

The red palm weevil (RPW), Rhynchophorus ferrugineus , is one of the worst palm pests worldwide. In this work, we studied the physiological basis underlying its adaptive strategy against low temperatures. Specifically, we analyzed the main low-molecular-weight biochemical substances acting as possible endogenous cryoprotectants, as well as their efficiency in reducing cold injury by preserving K + /Na + homeostasis. Wild pre-pupae were cold-treated (5.0 ± 0.5 °C) or non-treated (23 ± 1 °C) for 7 days. We then determined the levels of: (a) glucose, trehalose and glycerol, spectrophotometrically, (b) amino acids, by liquid chromatography and (c) potassium and sodium, by inductively coupled plasma mass-spectrometry. Cold-treated larvae increased their potassium level, suggesting some degree of chill injury. However, part of the cold-exposed animals was able to develop an efficient overall cryoprotective response which primarily includes glucose, as well as glycerol and several amino acids (mainly alanine). Our study shows for the first time that RPW is capable of deploying effective physiological mechanisms for a rapid response to cold, which could be relevant to improving predictive models of geographic distribution, especially in a context of climate change. The knowledge of the specific molecules involved would allow future studies to try to prevent its adaptive strategy, either by natural or chemical methods.