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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]

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]

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]

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]

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 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]

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.

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]

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]

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]

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]

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

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

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

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 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]

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.

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]

By virtue of remarkable tolerance on hypoxia and adaptive specialization in morphology, diverse hexactinellid sponges prospered in an early Cambrian environment characterized by dysoxic bottom waters documented by black shales. New fossils from the black shale of the Niutitang Formation (basal Stage 3 of Cambrian) in Hunan Province of China, reveal for the first time an articulated body of the sponge Hyalosinica archaica Mehl & Reitner in Steiner et al., 1993, which possesses an ovoid main body and an impressive long stalk. The spicular skeleton includes large diactines that are generally organized as fan-shaped clusters, a few small stauractines and hexactines, and twisted bundles of long monaxons that form the stalk/root tuft. This hexactinellid sponge represents the oldest extinct taxon that took advantage of a long stalk to elevate the main body above the sediment surface and thus to adapt to the oxygen-deficient sea-bottom environment. The long root tuft links Hyalosinica to a series of fossil and recent sponge taxa and proves a deep origin of the stalk-bearing morphology, indicating a likely parallel evolution within the Hexactinellida in response to special environmental pressures. Furthermore, the overall skeletal organization indicates that Hyalosinica, as well as related early ‘rossellimorphs’, are basal stem group representatives of Hexactinellida and probably branched before the extinct Reticulosa and before the two extant hexactinellid subclasses. [ABSTRACT FROM AUTHOR]

Background: The selection of adaptive strategies in MR‐guided adaptive radiotherapy (MRgART) usually relies on subjective review of anatomical changes. However, this kind of review may lead to improper selection of adaptive strategy for some fractions. Purpose: The purpose of this study was to develop prediction models based on deformation vector field (DVF) features for automatic and accurate strategy selection, using prostate cancer as an example. Methods: 100 fractions of 20 prostate cancer patients were retrospectively selected in this study. Treatment plans using both adapt to position (ATP) strategy and adapt to shape (ATS) strategy were generated. Optimal adaptive strategy was determined according to dosimetric evaluation. DVFs of the deformable image registration (DIR) of daily MRI and CT simulation scans were extracted. The shape, first order statistics, and spatial features were extracted from the DVFs, subjected to further selection using the minimum redundancy maximum relevance (mRMR) method. The number of features (Fn) was hyper‐tuned using bootstrapping method, and then Fn indicating a peak area under the curve (AUC) value was used to construct three prediction models. Results: According to subjective review, the ATS strategy was adopted for all 100 fractions. However, the evaluation results showed that the ATP strategy could have met the clinical requirements for 23 (23%) fractions. The three prediction models showed high prediction performance, with the best performing model achieving an AUC value of 0.896, corresponding accuracy (ACC), sensitivity (SEN) and specificity (SPC) of 0.9, 0.958, and 0.667, respectively. The features used to construct prediction models included four features extracted from y direction of DVF (DVFy) and mask, one feature from z direction of DVF (DVFz). It indicated that the deformation along the anterior‐posterior direction had a greater impact on determining the adaptive strategy than other directions. Conclusions: DVF‐feature‐based models could accurately predict the adaptive strategy and avoid unnecessary selection of time‐consuming ATS strategy, which consequently improves the efficiency of the MRgART process. [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]

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]

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.

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.

The shifts in adaptive strategies revealed by ecological succession and the mechanisms that facilitate these shifts are fundamental to ecology. These adaptive strategies could be particularly important in communities of arbuscular mycorrhizal fungi (AMF) mutualistic with sorghum, where strong AMF succession replaces initially ruderal species with competitive ones and where the strongest plant response to drought is to manage these AMF. Although most studies of agriculturally important fungi focus on parasites, the mutualistic symbionts, AMF, constitute a research system of human‐associated fungi whose relative simplicity and synchrony are conducive to experimental ecology. First, we hypothesize that, when irrigation is stopped to mimic drought, competitive AMF species should be replaced by AMF species tolerant to drought stress. We then, for the first time, correlate AMF abundance and host plant transcription to test two novel hypotheses about the mechanisms behind the shift from ruderal to competitive AMF. Surprisingly, despite imposing drought stress, we found no stress‐tolerant AMF, probably due to our agricultural system having been irrigated for nearly six decades. Remarkably, we found strong and differential correlation between the successional shift from ruderal to competitive AMF and sorghum genes whose products (i) produce and release strigolactone signals, (ii) perceive mycorrhizal‐lipochitinoligosaccharide (Myc‐LCO) signals, (iii) provide plant lipid and sugar to AMF, and (iv) import minerals and water provided by AMF. These novel insights frame new hypotheses about AMF adaptive evolution and suggest a rationale for selecting AMF to reduce inputs and maximize yields in commercial agriculture. [ABSTRACT FROM AUTHOR]