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

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

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

The cross-domain aspect detection and categorization is a vital task in natural language processing, enabling the automated identification and categorization of aspects in textual data spanning diverse domains. Traditional methods face several complexities such as scalability, limited contextual understanding of words, data sparsity, and adaptation difficulty. So, a novel method named gated bidirectional recurrent encoder-based adaptive honey badger (GBRE-AHB) algorithm is proposed for cross-domain aspect detection and categorization. In this study, the bidirectional encoder representations for transformers (BERT) is utilized to capture contextual information from text and enable better aspect identification and categorization by understanding the context. The local optimization problems are identified and solved by determining an adaptive strategy. Also, the gated recurrent unit (GRU) is employed to sequence the text data and allow aspect detection by considering the sequence in which aspects appear within a document. The study is validated on the datasets, namely the IMDB dataset of 50 K movie reviews and the cell phone reviews sentiment analysis-body dataset. The efficiency is validated by various metrics that attained the ranges as F1-score (97.85%), specificity (97.83%), recall (97.82%), precision (97.94%), and accuracy (98.67%), respectively. The experimental results revealed that the proposed method for cross-domain aspect detection and categorization as well as improved the reliability as well as longevity of the model and determined the impacts applied in the categorization process. [ABSTRACT FROM AUTHOR]

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

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

The growing need for data publication and the escalating concerns regarding data privacy have led to a surge in interest in Privacy-Preserving Data Publishing (PPDP) across research, industry, and government sectors. Despite its significance, PPDP remains a challenging NP-hard problem, particularly when dealing with complex datasets, often rendering traditional traversal search methods inefficient. Evolutionary Algorithms (EAs) have emerged as a promising approach in response to this challenge, but their effectiveness, efficiency, and robustness in PPDP applications still need to be improved. This paper presents a novel Hierarchical Adaptive Evolution Framework (HAEF) that aims to optimize t-closeness anonymization through attribute generalization and record suppression using Genetic Algorithm (GA) and Differential Evolution (DE). To balance GA and DE, the first hierarchy of HAEF employs a GA-prioritized adaptive strategy enhancing exploration search. This combination aims to strike a balance between exploration and exploitation. The second hierarchy employs a random-prioritized adaptive strategy to select distinct mutation strategies, thus leveraging the advantages of various mutation strategies. Performance bencmark tests demonstrate the effectiveness and efficiency of the proposed technique. In 16 test instances, HAEF significantly outperforms traditional depth-first traversal search and exceeds the performance of previous state-of-the-art EAs on most datasets. In terms of overall performance, under the three privacy constraints tested, HAEF outperforms the conventional DFS search by an average of 47.78%, the state-of-the-art GA-based ID-DGA method by an average of 37.38%, and the hybrid GA-DE method by an average of 8.35% in TLEF. Furthermore, ablation experiments confirm the effectiveness of the various strategies within the framework. These findings enhance the efficiency of the data publishing process, ensuring privacy and security and maximizing data availability. [ABSTRACT FROM AUTHOR]

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]

Fish typically adapt to their environment through evolutionary traits, and this adaptive strategy plays a critical role in promoting species diversity. Onychostoma macrolepis is a rare and endangered wild species that exhibits a life history of overwintering in caves and breeding in mountain streams. We analyzed the morphological characteristics, histological structure, and expression of circadian clock genes in O. macrolepis to elucidate its adaptive strategies to environmental changes in this study. The results showed that the relative values of O. macrolepis eye diameter, body height, and caudal peduncle height enlarged significantly during the breeding period. The outer layer of the heart was dense; the ventricular myocardial wall was thickened; the fat was accumulated in the liver cells; the red and white pulp structures of the spleen, renal tubules, and glomeruli were increased; and the goblet cells of the intestine were decreased in the breeding period. In addition, the spermatogenic cyst contained mature sperm, and the ovaries were filled with eggs at various stages of development. Throughout the overwintering period, the melano-macrophage center is located between the spleen and kidney, and the melano-macrophage center in the cytoplasm has the ability to synthesize melanin, and is arranged in clusters to form cell clusters or white pulp scattered in it. Circadian clock genes were identified in all organs, exhibiting significant differences between the before/after overwintering period and the breeding period. These findings indicate that the environment plays an important role in shaping the behavior of O. macrolepis, helping the animals to build self-defense mechanisms during cyclical habitat changes. Studying the morphological, histological structure and circadian clock gene expression of O. macrolepis during the overwintering and breeding periods is beneficial for understanding its unique hibernation behavior in caves. Additionally, it provides an excellent biological sample for investigating the environmental adaptability of atypical cavefish species. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui 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.)

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

This study explores the impacts of global warming on the reproductive efforts of Posidonia oceanica, a key seagrass species endemic to the Mediterranean Sea. By analyzing data from five areas in Sardinia in February 2023, we have investigated the relationship between thermal descriptors and P. oceanica flowering and fruiting variables. In fact, whether climate change has been affected the distribution of seagrasses by changing their reproductive efforts is still controversial and our findings contribute to this ongoing debate, highlighting a noticeable increase in reproductive events potentially affected by rising temperature. Overall, mean summer sea surface temperature (SST) was positively associated to inflorescence density, presence of living fruits and fruit size; living fruits and fruit size were also influenced positively by the maximum SST within 3 months before sampling (November–February). Both flowering and fruiting were negatively affected by the maximum SST within 1 month before inflorescence observation and mean winter SST, respectively, suggesting that P. oceanica reproductive effort depends on the duration and the temporal context in which the anomaly occurs. However, the high variability between meadows of the same area highlights the need of future studies to focus on the uncertainties related to local factors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department 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.)

Halophilic and halotolerant microorganisms have a high biotechnological potential. They are producers of biologically active substances, stress-protective agents, hydrolytic enzymes, and are used for environmental bioremediation. At the same time, the characterization of novel halotolerant bacteria and determination of their salt tolerance strategies are important basic problems. The present work reports the isolation of a new strain, MX2, from the salt well brine of the Yakshinskoe potassium−magnesium salt deposit. The isolate was an aerobic, gram-positive, nonmotile bacterium that did not form spores. The cell morphology varied from cocci to short rods capable of producing V-shaped forms. The colonies on agar were circular, with an entire edge and raised center, glistening and orange. Bacteria of strain MX2 were halotolerant microorganisms capable of growing at NaCl concentrations up to 9%. The genome of strain MX2 was sequenced. Its estimated size was 3 747 717 bp, and the number of protein-coding genes was 3562. Strain MX2 was identified as belonging to the species Dietzia maris based on analysis of the 16S rRNA, gyrB, rpoB, recA, and ppk gene sequences and using time-of-flight mass spectrometry (MALDI-TOF-MS). D. maris MX2 had complete metabolic pathways for the synthesis of ectoine, hydroxyectoine, and trehalose, as well as the transport systems for ectoine, hydroxyectoine, trehalose, glycerol, glycerol-3-phosphate, L-proline, and glycine-betaine. Thus, to ensure the osmotic balance, D. maris MX2 used the strategy of accumulating compatible organic solutes. [ABSTRACT FROM AUTHOR]

Despite intensive studies on plant functional traits, the intraspecific variation and their co-variation at the multi-scale remains poorly studied, which holds the potential to unveil plant responses to changing environmental conditions. In this study, intraspecific variations of 16 leaf functional traits of a common fig species, Ficus tinctoria G. Frost. , were investigated in relation to different scales: habitat types (hemiepiphytic and terrestrial), growth stages (small, medium and large) and tree crown positions (upper, middle and lower) in Xishuangbanna, Southwest China. Remarkable intraspecific variation was observed in leaf functional traits, which was mainly influenced by tree crown position, growth stage and their interaction. Stable nitrogen isotope (δ15N) and leaf area (LA) showed large variations, while stable carbon isotope (δ13C), stomata width and leaf water content showed relatively small variations, suggesting that light- and nitrogen-use strategies of F. tinctoria were plastic, while the water-use strategies have relatively low plasticity. The crown layers are formed with the growth of figs, and leaves in the lower crown increase their chlorophyll concentration and LA to improve the light energy conversion efficiency and the ability to capture weak light. Meanwhile, leaves in the upper crown increase the water-use efficiency to maintain their carbon assimilation. Moreover, hemiepiphytic medium (transitional stage) and large (free-standing stage) figs exhibited more significant trait differentiation (chlorophyll concentration, δ13C, stomata density, etc.) within the crown positions, and stronger trait co-variation compared with their terrestrial counterparts. This pattern demonstrates their acclimation to the changing microhabitats formed by their hemiepiphytic life history. Our study emphasizes the importance of multi-scaled intraspecific variation and co-variation in trait-based strategies of hemiepiphyte and terrestrial F. tinctoria , which facilitate them to cope with different environmental conditions. [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.

Rising coal price and increasingly stringent emission policy highlight the importance of balancing economic benefit and sustainable development of coal-fired power plants. In order to make the power plant operate economically and safely, and reduce the emission of pollutants as much as possible, this paper proposed a coal blending optimization framework for coal-fired power plants. This framework constructs a set of mathematical model based on the minimization objective function, including the economy, safety and environmental protection of coal. And a multi-strategy fusion multi-objective particle swarm optimization (MSF-MOPSO) method is proposed to optimize the coal blending model. The feasibility, effectiveness and superiority of the method are theoretically verified by convergence analysis and several sets of simulation experiments. And the practical application of this framework shows that under the coal blending strategy based on different weight combinations, the algorithm proposed in this paper can produce high-quality Pareto spatial distribution. Comparing with other state-of-the-art coal blending algorithms, this method can reduce the coal purchase cost by 4.42% and ${\rm{S}}{{\rm{O}}_{\rm{2}}}$SO2 emission by 5.11% at least under the same condition. It has significant environmental protection and economic benefit. [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. [ABSTRACT FROM AUTHOR]

The use of Brushless-DC (BLDC) motor drives in many industrial settings has grown in recent years. In an ideal situation, the torque generated via BLDC motors with a trapezoidal back electromotive force (BEMF) remains unchanged. But, in actuality, the generated torque is distorted by torque ripples (TRs). These TRs make it essential that variable-speed drives operate smoothly. Power electronic switches are used by BLDC motors during commutation, which causes harmonics within the armature current. Sliding-mode control (SMC) is parameter-resistant and dynamic. SMC design involves reaching and sliding. SMC flaws include chattering. A reduced discontinuous gain control policy may strengthen this problem. The controller's dynamic responsiveness will degrade. The standard SMC strategy's indefinite converging rate for error to zero lowers dynamic reaction time. For the SMC to converge indefinitely, an adaptive strategy in fuzzy-based SMC (hybrid control technique) is developed to control the TRs in BLDC motor drives. In a BLDC motor, the recommended controller is used to regulate the harmonics as well as the speed. Based on modeling with MATLAB and observations on an operational 3 kW BLDC motor, the suggested approach is beneficial in the lowering of torque waves and current harmonics. The findings reported here support the efficiency of the suggested approach. [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]

The Pearl River Delta is a complex ecological‐artificial system. The low‐elevation landform and land subsidence make the Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA) more vulnerable to heavy rainstorms with sea‐level rise in the future. Therefore, this study discussed how to optimize the delta‐related built environment based on locality via cooperation between urban designers and hydrology researchers so that urban development in the GBA can adapt to water disasters. First, this study proposed three adaptive spatial strategies for urban design to improve flood resilience: (a) adding more sunken space and rearranging the locations of manholes; (b) adding more retention space to the waterway system; and (c) improving foundation elevation for suitable development intensity. With the prediction of sea‐level rise and land subsidence, four different scenarios were defined. Second, a coupled 1D/2D hydrodynamic model was built for scenario‐based urban inundation simulations. Third, a Python workflow was developed to reveal the water volume exchange, and an ArcMap workflow to realize the impact of inundation. The simulation results show that (a) more sunken space can lower the peak runoff discharges and decrease the runoff coefficient; (b) more retention space can extend the capacity of the urban drainage system and adapt to the rise of sea level; and (c) improving foundation elevation and more sunken green space can direct the surface water to submerge urban land in reasonable order, which can protect the significant infrastructures and development lands. This paper suggests that these adaptive spatial strategies can serve as solutions for resilience development. [ABSTRACT FROM AUTHOR]

The supply chain disruptions caused primarily by the COVID-19 pandemic catastrophe are expected to prolong the global chip shortage for several more years, making it a pressing concern across industries worldwide. Nonetheless, the influence of supply chain disruptions on firm performance within the semiconductor industry, especially in developing countries like Malaysia, has received limited attention in previous studies. This study aims to determine how supply chain adaptive techniques that combine robustness, agility, and resilience during the COVID-19 pandemic affected multinational firms' performance in the semiconductor industry in Malaysia. The nature of the study is quantitative, using survey methodology. The data were collected from 82 multinational corporations of semiconductor firms through an online platform. The findings show that resilience in supply chain adaptive strategies significantly impacted multinational corporations' semiconductor industries during the COVID-19 pandemic. However, the success of semiconductor firms during the COVID-19 pandemic was not greatly affected by the resilience and agility of supply chain adaptive strategies. The findings offer valuable insights into how firms implemented supply chain adaptive strategies during the challenging times of the COVID-19 pandemic. The knowledge can enable them to lessen the effect of the COVID-19 pandemic supply chain disruption and eventually enhance the firm's performance. Future studies should examine the new aspect of supply chain strategies, such as demand-driven planning, which uses technological tools for climate adaptation in times of crisis. [ABSTRACT FROM AUTHOR]

Universal Adaptive Strategy Theory aims to predict how taxa and assemblages respond to disturbances on the basis of adaptive strategy group (ASG) membership. Here, we test such predictions using the adaptive strategy scheme for reef-building corals developed by Darling et al. (Ecol Lett 15:1378–1386, 2012) and a long-term dataset of coral assemblage structure from inshore reefs on the central Great Barrier Reef. Several disturbances including mass bleaching and tropical storms were recorded in this 15-year interval from 1998 to 2013. ASG membership did not predict how a given taxon responded to disturbance. In fact, all ASGs were on average equally affected by bleaching and a period of multiple disturbances. Furthermore, there were no consistent winners at these sites in response to the 1998 bleaching in contrast to previous work suggesting clear hierarchies in susceptibility to bleaching. In conclusion, while further efforts to re-evaluate the utility of ASGs for reef corals should be encouraged our results and a re-examination of the literature suggests that direct trait-based approaches might prove more useful when exploring how corals respond to disturbance. [ABSTRACT FROM AUTHOR]

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]

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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]