Search

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

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

This article delves into the role of adaptive environmental strategies, absorptive capacity, and eco-innovation in enhancing environmental performance, particularly in the context of market turbulence. The study, conducted among 568 companies in Colombia, employs structural equation modeling to evaluate relationships between the studied variables. The findings suggest that adaptive strategies alone do not directly impact environmental performance, emphasizing the need for integration with eco-innovation initiatives. Moreover, organizations with high absorptive capacity can leverage market turbulence to drive eco-innovative initiatives, highlighting the indirect yet significant impact of market turbulence on environmental performance through absorptive capacity. The study underscores the critical role of eco-innovation in directly influencing environmental outcomes, suggesting that the effectiveness of adaptive strategies and absorptive capacities hinges on successful eco-innovation initiatives. These insights offer practical guidance for organizations seeking to enhance their environmental performance in turbulent markets, providing a roadmap for sustainable business practices. [ABSTRACT FROM AUTHOR]

The study aimed to explore the similarities and differences in gut microorganisms and their functions in regulating body mass in Eothenomys miletus across different altitudes in the Hengduan Mountains when exposed to a high-fat diet. Eothenomys miletus specimens were gathered from Dali (DL) and Xianggelila (XGLL) in Yunnan Province, China, and categorized into control, high-fat (1 week of high-fat diet), and re-feeding groups (1 week of high-fat diet followed by 2 weeks of standard food). The analysis utilized 16S rRNA sequencing to assess the diversity and structure of intestinal microbial communities in E. miletus. The investigation focused on the impact of high-fat diet consumption and different altitudes on gut microbial diversity, structure, and physiological markers. Results revealed that a high-fat diet influenced the beta diversity of gut microorganisms in E. miletus, leading to variations in microbial community structure between the two regions with different altitudes. High-fat food significantly affected body mass, white adipose tissue mass, triglycerides, and leptin levels, but not food intake. Specific intestinal microorganisms were observed in the high-fat groups, aiding in food digestion and being enriched in particular flora. In particular, microbial genera like Lactobacillus and Hylemonella were enriched in the highfat group of DL. The enriched microbiota in the control group was associated with plant polysaccharide and cellulose decomposition. Following a high-fat diet, gut microbiota adapted to support lipid metabolism and energy supply, while upon re-feeding, the focus shifted back to cellulose digestion. These findings suggested that alterations in gut microbial composition, alongside physiological markers, play a vital role in adaptation of E. miletus to the diverse habitats of the Hengduan Mountains at varying altitudes. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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. [ABSTRACT FROM AUTHOR]

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

The aim of this research is to devise and assess a holistic approach for optimizing the performance of the Internet of Things Sensor Network (IoTSN), thereby enabling it to better adapt to diverse and unpredictable environments. Initially, this study delves into the self-organizing network, achieving intelligent nodal cooperation and allowing the network to autonomously reshape its topology. Subsequently, an adaptive approach is incorporated to intelligently modulate the operational modes and task allocations of nodes by continuously monitoring network load, energy usage, and data transfer efficiency. To validate the efficacy of our method, we utilized the SensorScope Dataset, composed of real-time data from various IoT devices, encompassing diverse environmental metrics such as temperature, humidity, and lighting. Experimental findings reveal that, in contrast to conventional strategies, our integrated self-organization and adaptive approach offer substantial benefits in terms of load balancing, energy consumption, and data transfer latency. More specifically, we recorded a 30% drop in average load, a 25% decrease in energy use, and a 20% reduction in average transmission delay. These outcomes strongly support the effectiveness of our comprehensive strategy in boosting IoTSN performance when juxtaposed with alternative methods. Additionally, we offer an in-depth exploration of the experimental data and identify potential areas for refinement and future research avenues, opening the door to fresh perspectives and advancements in IoTSN intelligence and efficiency. This investigation is pivotal in advancing IoT technology and bolstering the adaptability and performance of IoTSN in a wide range of practical scenarios.

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Morphology can give us a better understanding of amphibian adaptations to the environment, which are not well responded to by traditional morphological studies. Therefore, we used 3D scanning technology, combined with the analysis of environmental factors, to reveal the 3D morphological characteristics, spatial variation patterns, and driving factors of Bufo gargarizans in the Yellow River Basin. The results showed that the overall body size of B. gargarizans tended to become smaller with increasing altitude, confirming that the change in body size did not follow Bergman's law. We also found larger appendages and more complex bodies in high-elevation populations compared to low-elevation populations. In addition, altitude also had a strong influence on the morphogeometric patterns of B. gargarizans, and latitude and temperature was also closely related to their morphogeometric patterns. 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. [ABSTRACT FROM AUTHOR]

Trade-offs in growth and reproduction are essential parts of the adaptive strategies of clonal plants. How rhizomatous psammophytes respond to aeolian processes (sand burial and wind erosion) by means of trade-offs is supposed to be especially important for their colonization on the active sand dune. Despite partial documentation of the responses of rhizomatous species to aeolian processes, how these clonal species respond to aeolian processes by means of potential trade-offs in growth and reproduction still remains unclear. In this study, we employed field investigation and biomass modeling to evaluate the trade-offs between vegetative and reproductive growth as well as between the number and size of ramets of Phragmites communis in response to sand burial and wind erosion. Sand burial enhanced the accumulation of seed biomass and reproductive effort. Wind erosion reduced reproductive effort but had no significant influence on seed biomass. Sand burial increased the biomass of ramets, while wind erosion increased ramet population density and accelerated ramet maturation. Our results demonstrate that rhizomatous psammophytes adjust their growth strategies in response to aeolian processes, i.e. reproductive growth and ramet size increase in response to sand burial, while vegetative growth and ramet numbers increase in response to wind erosion. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

The rapid emergence of Internet+, artificial intelligence, virtual reality, and other innovative technologies has led to the gradual penetration of online learning into traditional teaching, and face-to-face teaching and online teaching have moved towards deep integration. This paper explores the Internet+ teaching mode of online and offline integration of community-based senior education in the context of a smart city and designs a personalized learning platform for community-based geriatric education. Subsequently, a learning path recommendation model based on two-dimensional features of learners and learning resources is constructed, and then the SASBPSO algorithm is used to realize model optimization and improve the online learning effect of community-based senior education, and the effect of the model application is analyzed. The SASBPSO algorithm is more reliable than other algorithms when solving optimal solutions and converges at less than 0.1, which is significantly superior to other algorithms in terms of convergence. The time used by elderly learners under the use of this paper method is significantly shorter than the traditional method, and the average learning time gap is smaller. The difference between the average scores of the pre-test and post-test of the two groups is −16.247 and −8.146, and the significance value of the difference is 0.003 and 0.016, respectively, 0.003

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 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. [ABSTRACT FROM AUTHOR]

To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA. [ABSTRACT FROM AUTHOR]

This paper defines the nature of collective irrationality that flourished during the COVID-19 pandemic and lays out specific individual and shared traits and dispositions that facilitate it. Drawing on the example of globally experienced phenomenon of panicked toilet paper buying and hoarding during the COVID-19 pandemic and resources from philosophy, psychology, sociology, and economics this paper identifies four essential features of collective irrationality: weak shared mentality; non-cognitive and immediate mimicry; affective contagion; and psychosocial adaptivity. After (I) initially pointing out conceptual problems around benchmarking collectivity and irrationality, this paper (II) identifies weak mentality as serving the goals of “group” recognition internally and externally. It is argued that (III) the non-cognitive and immediate mimicry and emotional contagion are shared and individual dispositional conditions that facilitate collective irrationality in environments affected by uncertainty (IV). The human mimetic faculty and susceptibility to emotional contagion are presented as enabling and augmenting conditions under which collective irrationality flourishes. Finally, (IV) presenting collective irrationality in the context of psychosocial adaptivity, the paper provides evolutionary reasons for engaging in irrational behaviors, rendering collective irrationality as an adaptive strategy. [ABSTRACT FROM AUTHOR]

This study examines the business continuity plans of selected travel companies in the Philippines. Growth determinants, as influenced by awareness to external and internal environments, include product, price, customer, cost of goods sold, sales volume, operating expenses, profit and loss with research and repositioning. Responses were collected from the travel companies or 80% (218) of the targeted respondents. A stratified sampling technique was used with the Pearson correlation coefficient which proved the link between BCPs or business continuity plans and growth determinants. It is recommended that periodic adjustments on operations or standard procedures be made as travel related businesses are market and customer driven. Changes in tourism policies, government regulations, preferences of clients, even the current inflation prod strategies to manage sales, income and company expenses. Adapting current business continuity plans ensure growth for travel companies in the now normal. Future research can deal with tour operators, accommodation, other travel and tourism providers using BCPs and discerning if their growth indicators are similar or not. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic as a deadly epidemic that has spread since 2020 has affected various aspects of human life. Various policies to prevent the spread of this epidemic have affected the activities of tourism business. The biggest impact is the decline in the income of businessmen, due to the drastic drop of tourist visits. Tourism businessmen have to find ways to cope and adapt themselves during this pandemic, and this method is called as adaptive strategy. This study aims to find out about businessmen’s strategies in Sidamanik Tea Garden tourist area to survive during the Pandemic. The method used is descriptive research wtih qualitative approach. The results of this study indicate that tourism businessmen apply several adaptive strategy, namely active strategy is done by working part-times, and involve the family members to increase the income. The passive strategy is done by implementing a frugal lifestyle, reducing the expenditures. The network strategy is done by lending money from relatives, and utilizing assistance programs by the government.

In order to improve the load-carrying capacity of the hydrodynamic bearing, and reduce the calorific value and friction coefficient at the same time, a multi-objective optimization model of the hydrodynamic bearing was established. Aiming at the shortcomings of the traditional optimization algorithm that the convergence speed is slow and it is easy to fall into the local optimal solution, an adaptive strategy assisted the golden jackal optimization algorithm is proposed to improve the exploration and exploration ability of the golden jackal algorithm. The performance of the adaptive strategy assisted the golden jackal optimization algorithm is verified by an example with constraints. The results show that the adaptive strategy assisted the golden jackal optimization algorithm has good convergence performance. The algorithm is used to solve the multi-objective optimization problem of hydrodynamic bearings. The optimization results show that the optimized structure has a great improvement in performance compared with the initial structure, the load capacity is increased by 12.257%, and the calorific value and friction coefficient are reduced respectively by 15.610% and 33.333%.

Biomarkers plays an important role in the prediction and diagnosis of cancers. Therefore, it is urgent to design effective methods to extract biomarkers. The corresponding pathway information of the microarray gene expression data can be obtained from public database, which makes possible to identify biomarkers based on pathway information and has been attracted extensive attention. In the most existing methods, all the member genes in the same pathway are regarded as equally important for inferring pathway activity. However, the contribution of each gene should be different in the process of inferring pathway activity. In this research, an improved multi-objective particle swarm optimization algorithm with penalty boundary intersection decomposition mechanism (IMOPSO-PBI) has been proposed to quantify the relevance of each gene in pathway activity inference. In the proposed algorithm, two optimization objectives namely t-score and z-score respectively has been introduced. In addition, in order to solve the problem that optimal set with poor diversity in the most multi-objective optimization algorithms, an adaptive mechanism for adjusting penalty parameters based on PBI decomposition has been introduced. The performance of the proposed IMOPSO-PBI approach compared with some existing methods on six gene expression datasets has been given. To verify the effectiveness of the proposed IMOPSO-PBI algorithm, experiments were carried out on six gene datasets and the results has been compared with the existing methods. The comparative experiment results show that the proposed IMOPSO-PBI method has a higher classification accuracy and the extracted feature genes are verified possess biological significance.

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.

Increasing evidence has shown that light exists in a diverse range of deep‐sea environments. We unexpectedly found that blue light is necessary to produce excess zero‐valent sulfur (ZVS) in Erythrobacter flavus 21‐3, a bacterium that has been recently isolated from a deep‐sea cold seep. E. flavus 21‐3 is able to convert thiosulfate to ZVS using a novel thiosulfate oxidation pathway comprising a thiosulfate dehydrogenase (TsdA) and a thiosulfohydrolase (SoxB). Using proteomic, bacterial two‐hybrid and heterologous expression assays, we found that the light–oxygen–voltage histidine kinase LOV‐1477 responds to blue light and activates the diguanylate cyclase DGC‐2902 to produce c‐di‐GMP. Subsequently, the PilZ domain‐containing protein mPilZ‐1753 binds to c‐di‐GMP and activates TsdA through direct interaction. Finally, Raman spectroscopy and gene knockout results verified that TsdA and two SoxB homologs cooperate to regulate ZVS production. As ZVS is an energy source for E. flavus 21‐3, we propose that deep‐sea blue light provides E. flavus 21‐3 with a selective advantage in the cold seep, suggesting a previously unappreciated relationship between light‐sensing pathways and sulfur metabolism in a deep‐sea microorganism. Synopsis: Blue light has been shown to affect metabolism in certain deep‐sea bacteria. Here, we show that blue light promotes the formation of zero‐valent sulfur (ZVS) in the deep‐sea cold‐seep bacterium Erythrobacter flavus 21‐3 via a pathway involving a light‐sensing LOV histidine kinase and downstream c‐di‐GMP production. Blue light stimulates the production of zero‐valent sulfur (ZVS) in E. flavus 21‐3.The light–oxygen–voltage histidine kinase LOV‐1477 phosphorylates the diguanylate cyclase DGC‐2902 and promotes the production of c‐di‐GMP.mPilZ‐1753 binds c‐di‐GMP, activating thiosulfate dehydrogenase (TsdA) via a direct interaction.Two thiosulfohydrolase (SoxB) homologs collaborate with TsdA to generate ZVS. [ABSTRACT FROM AUTHOR]

Tropical forests are characterized by vast biomass, complex structures and mega-biodiversity. However, the adaptation processes of these forests to seasonal water availability are less understood, especially those located in the monsoonal and mountainous regions of tropical Southeast Asia. This study used four representative tropical forests spanning from 2° N to 22° N in continental Southeast Asia to address dry-condition photosynthesis at the seasonal scale. We first provided novel and reliable estimations of ecosystem photosynthesis (gross primary production; GPP) seasonality at all four sites. As expected, both evergreen and deciduous seasonal forests exhibited higher GPPs during the rainy season than during the dry season. A bimodal pattern corresponding to solar radiation occurred in the GPP of the perhumid forest. The surface conductance (G s) was consistently lower both in the dry season and during dry spells (DSPs) than during the wet season and non-dry spells. However, this did not prevent GPP from increasing alongside increasing irradiance in the perhumid forest, suggesting that other ecosystem physiological properties, for example, the light-saturated photosynthetic rate, must have increased, thus surpassing the effect of G s reduction. Thus, perhumid forests could be defined as light-demanding ecosystems with regard to their seasonal dynamics. Seasonal forests are water-stressed ecosystems in the dry season, as shown by the reductions in GPP, G s and related ecosystem physiological properties. At all four forest sites, we observed a lack of consistent adaptive strategy to fit the water seasonality due to the diversity in leaf phenology, soil nutrient availability, root depth and other potential factors. [ABSTRACT FROM AUTHOR]

Green sulfur bacteria (GSB) are a distinct group of anoxygenic phototrophic bacteria that are found in many ecological niches. Prosthecochloris, a marine representative genus of GSB, was found to be dominant in some coral skeletons. However, how coral-associated Prosthecochloris (CAP) adapts to diurnal changing microenvironments in coral skeletons is still poorly understood. In this study, three Prosthecochloris genomes were obtained through enrichment culture from the skeleton of the stony coral Galaxea fascicularis. These divergent three genomes belonged to Prosthecochloris marina and two genomes were circular. Comparative genomic analysis showed that between the CAP and non-CAP clades, CAP genomes possess specialized metabolic capacities (CO oxidation, CO2 hydration and sulfur oxidation), gas vesicles (vertical migration in coral skeletons), and cbb3-type cytochrome c oxidases (oxygen tolerance and gene regulation) to adapt to the microenvironments of coral skeletons. Within the CAP clade, variable polysaccharide synthesis gene clusters and phage defense systems may endow bacteria with differential cell surface structures and phage susceptibility, driving strain-level evolution. Furthermore, mobile genetic elements (MGEs) or evidence of horizontal gene transfer (HGT) were found in most of the genomic loci containing the above genes, suggesting that MGEs play an important role in the evolutionary diversification between CAP and non-CAP strains and within CAP clade strains. Our results provide insight into the adaptive strategy and population evolution of endolithic Prosthecochloris strains in coral skeletons. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Plant communities in green stormwater infrastructure (GSI) such as biofilters play an integral role in ecosystem services provisioning, such that many design manuals now feature plant lists that guide vegetation selection. This study looks at the implications of those lists for biofilter plant communities and their services, focusing on (1) how plants are selected across US climate zones, (2) whether selected plants exhibit adaptive strategy biases (i.e. towards competitive, stress tolerant or ruderal strategies that might impact ecosystem services provisioning) and (3) whether human‐induced selection or natural climatic processes underly any biases revealed. Our results suggest that biofilter plant strategies are significantly biased towards stress tolerance or competitiveness (depending on the climate zone) and away from ruderalness relative to the broader pool of native and wetland‐adapted native species. Competitive bias was evident in humid‐continental climates and stress‐tolerant bias in hot coastal/arid climates, with some degree of anti‐ruderal bias present across all zones. These biases are correlated with human concerns related to water availability and climate (water conservation; p

Abstract Background Drought is one of the main causes of losses in forage crop yield and animal production. Medicago ruthenica (L.) cv. Zhilixing is a high-yielding alfalfa cultivar also known for its high tolerance to drought. We analyzed the transcriptome profile of this cultivar throughout drought stress and recovery and we were able to describe its phased response through the expression profiles of overlapping gene networks and drought-specific genes. Results The ABA and auxin signal transduction pathways are overlapping pathways in response to drought and drought recovery in forage crops. Medicago ruthenica (L.) cv. Zhilixing adopts different strategies at different degrees of drought stress. On the 9th day of drought, transcriptional regulations related to osmoregulation are enhanced mainly through increased activities of carbohydrate and amino acid metabolism, while photosynthetic activities were reduced to slow down growth. With drought prolonging, on the 12th day of drought, the synthesis of proline and other stored organic substances was suppressed in general. After recovery, Medicago ruthenica synthesizes flavonoids through the flavonoid biosynthesis pathway to remove accumulated ROS and repair the oxidative damage from water stress. In addition, the regulation of circadian rhythm seems to accelerate the drought recovery process. Conclusions Medicago ruthenica adapts to drought by regulating the osmoregulatory system and photosynthesis, which appears to involve the ABA and auxin signaling pathways as key regulators. Furthermore, the synthesis of flavonoids and the regulation of the circadian rhythm can accelerate the recovery process. These results enriched our knowledge of molecular responses to drought and drought recovery in Medicago ruthenica and provide useful information for the development of new legume forage grass varieties with improved adaptability to drought stress.

Various measures have been implemented around the world to prevent the spread of SARS-CoV-2. A potential tool to reduce disease transmission is regular mass testing of a high percentage of the population, possibly with pooling (testing a compound of several samples with one single test). We develop a compartmental model to study the applicability of this method and compare different pooling strategies: regular and Dorfman pooling. The model includes isolated compartments as well, from where individuals rejoin the active population after some time delay. We develop a method to optimize Dorfman pooling depending on disease prevalence and establish an adaptive strategy to select variable pool sizes during the course of the epidemic. It is shown that optimizing the pool size can avert a significant number of infections. The adaptive strategy is much more efficient, and may prevent an epidemic outbreak even in situations when a fixed pool size strategy can not.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 yak (Bos grunniens), an indigenous herbivore raised at altitudes between 3,000 and 5,000 m above sea level, is closely linked to more than 40 ethnic communities and plays a vital role in the ecological stability, livelihood security, socio-economic development, and ethnic cultural traditions in the Asian highlands. They provide the highlanders with meat, milk, fibres, leather and dung (fuel). They are also used as pack animals to transport goods, for travel and ploughing, and are important in many religious and traditional ceremonies. The Asian highlands are known for an extremely, harsh environment, namely low air temperature and oxygen content and high ultraviolet light and winds. Pasture availability fluctuates greatly, with sparse pasture of poor quality over the long seven-month cold winter. After long-term natural and artificial selections, yaks have adapted excellently to the harsh conditions: 1) by genomics, with positively selected genes involved in hypoxia response and energy metabolism; 2) anatomically, including a short tongue with a weak sense of taste, and large lung and heart; 3) physiologically, by insensitivity to hypoxic pulmonary vasoconstriction, maintaining foetal haemoglobin throughout life, and low heart rate and heat production in the cold season; 4) behaviourlly, by efficient grazing and selecting forbs with high nutritional contents; 5) by low nitrogen and energy requirements for maintenance and low methane emission and nitrogen excretion, namely, ‘Low-Carbon’ and ‘Nitrogen-Saving’ traits; 6) by harboring unique rumen microbiota with a distinct maturation pattern, that has co-evolved with host metabolism. This review aims to provide an overview of the comprehensive adaptive strategies of the yak to the severe conditions of the highlands. A better understanding of these strategies that yaks employ to adapt to the harsh environment could be used in improving their production, breeding and management, and gaining benefits in ecosystem service and a more resilient livelihood to climate change in the Asian highlands.

Alongside growing awareness of the significance of environmental change for glacier regions, and their tourism-dependent communities, is the realization of the need to adapt to changing conditions. Such adaptation is necessary for tourism operators, managers, and planners as well as the visitors themselves, and is part of building resilient tourism systems. This paper presents a conceptual framework for understanding the possible stages of adaptation in glacier tourism destinations. The Tourism Adaptation Classification (TAC) framework aligns three stages of adaptation (resilience, transition, and transformation) against adaptation strategies implemented by tourism stakeholders and identifies specific characteristics. Using a desk-based case study approach, the framework is illustrated with reference to Glacier Country in New Zealand's Westland/Tai Poutini National Park in relation to three core dimensions of the tourism system: tourism planning and governance; tourism business and operations; and visitor experience.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]