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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]

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]

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]

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]

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]

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]

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

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

Lotus corniculatus L. is a perennial high-quality legume forage species but is vulnerable to drought, and water deficit reduces productivity. To understand the drought response mechanism of L. corniculatus , we investigated physiological responses under drought stress and constructed suppression subtractive hybridisation (SSH) cDNA libraries to isolate drought-inducible genes and quantitatively study the expression levels of candidate drought- responsive genes. Genes encoding calmodulin-like protein, mitogen-activated protein kinase, indole-3-acetic acid-induced protein, ser/thr-protein phosphatase homolog-related proteins, and β -galactosidase-related protein with hydrolysis activity were isolated and considered the main factors that explained the resistance of L. corniculatus to drought. Approximately 632 expressed sequence tags (ESTs) were identified and confirmed in the constructed SSH library. The Gene Ontology (GO) analysis revealed that these genes were involved mainly in transcription processes, protein synthesis, material metabolism, catalytic reactions, sugar metabolism, and photosynthesis. The interaction between the functions of these drought-related genes and the physiological responses preliminarily explains the drought resistance mechanisms of L. corniculatus. Planting forage with improved drought tolerance on hot arid marginal lands will be a potential and sustainable way to resolve the conflict between food crops and pastures competing for limited farmland resources. In this study, for the first time, we isolated drought-related genes by detecting physiological responses and constructing suppression subtractive hybridisation (SSH) cDNA libraries, and preliminarily explained the drought resistance mechanism of Lotus corniculatus L. This adaptive strategy could provide a basis for improving forage stress resistance. [ABSTRACT FROM AUTHOR]

Trait plasticity (variation of a trait under environmental variability or gradients) and trait integration are both crucial for plant adaption to environmental change. Variations in different suite of root traits such as biomass allocation, morphology and physiology underlie diverse phosphorus (P) acquisition strategies among plants. Yet, how the intraspecific plasticity and integration of root traits influence plant adaptation to different P supply remains obscure. To characterize diverse adaptive strategies in relation to plant P acquisition, eight root traits were assessed in 20 wheat Triticum aestivum genotypes grown in a culture room with low and high P supply. High P supply increased shoot P accumulation and biomass of all wheat genotypes. The shoot P accumulation in genotypes with high P sensitivity (PS: calculated as shoot P content at low P/shoot P content at high P supply) was higher with high P supply and lower with low P supply compared with that in the genotypes with low PS. The high‐PS genotypes exhibited larger variation in root length, root/shoot ratio and rhizosphere pH across P supplies than the low‐PS genotypes, suggesting an integrated response at the whole‐plant level. At low P supply, the high‐PS genotypes had greater root length and specific root length, but lower acid phosphatase activity than the low‐PS genotypes, which suggests contrasting P‐acquisition strategies across the genotypes. Strong co‐variation of root traits occurred across low‐PS genotypes regardless of P supply; conversely, the high‐PS genotypes only exhibited strong trait integration at low P supply, whereas high P supply sharply reduced root trait co‐variation. Our findings suggest that P stress may strengthen root trait integration in wheat plants, and that both plasticity and integration of root traits drive plant adaptive strategies and tolerance to P‐deficiency stress. [ABSTRACT FROM AUTHOR]

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

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

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

Aiming at the shortcomings of the basic Gravity search algorithm（GSA）, such as prone to premature maturity, easy to fall into local optimum, and lack an effective acceleration mechanism, this paper proposed a GSA（IABHGSA） based on the improved adaptive black hole mechanism. The algorithm used the improved Tent mapping to initialize the population, which makes the distribution of the initial population more random, uniform and traversal, and it enhanced the global exploration capability of the algorithm; The algorithm introduced an improved adaptive black hole mechanism and selected the position update strategy, according to the evolution of the particles, which makes the position update more reasonable, Effectively reduce the possibility of particles falling into local optima; Through the optimal and worst particle update strategy based on learning ideas, enhanced the algorithm’s ability to escape from local optima and improved the algorithm’s optimization speed; The algorithm introduced group migration to provide effective accelerated convergence mechanism for the algorithm. Finally, this paper selected 8 benchmark functions to test IABHGSA and compared with the experimental results of related algorithms, the results show that IABHGSA has better optimization performance. [ABSTRACT FROM AUTHOR]

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

Free living ciliates are exposed to environmental challenges such as starvation, temperature fluctuations, high population density and salinity variations. Ciliates lower their metabolic activity and form cysts, an adaptive strategy evolved in response to environmental stress. In the present study on Pseudourostyla levis, a novel phenomenon of pseudoencystment had been noticed in which cells entered a state of dormancy but did not secrete a cyst wall. The process of pseudoencystment, which physiologically resembled true encystment, was functionally unrelated to it and was thus designated as pseudoencystment. Unlike true cysts, pseudocysts remained dormant for about 12 hours and then reverted to the active trophic state without any change in the environmental conditions. Another unique feature of pseudoencystment, was the synchronized induction of pseudocysts formation, achieved by a brief spell of starvation and the pseudocysts reverted to active trophic state without any change in environmental conditions. The state of dormancy did not provide any long-term protection to the ciliate and appeared to be a short term adaptation to the environment. The present study aimed to analyze the parameters for the induction of pseudocyst formation, cortical topological changes and to study the possible significance of this process. However, occasionally true cysts were also obtained by prolonged starvation, lasting 3-4 days but true cysts required either food or a fresh culture medium for excystation. [ABSTRACT FROM AUTHOR]

The article identifies the nature, patterns, and determinants of seasonal outmigration in Tsagibji District, in the Amhara Region, Ethiopia. In the study on which the article is based, a quantitative research approach was primarily employed, complemented with a qualitative approach to triangulate the findings. Primary data were gathered from rural households using a household survey, key informant interviews, and focus group discussions. Secondary data sources were employed to complement the primary data. Descriptive and inferential statistics were computed. The findings indicate that rural–urban migration is a dominant pattern of seasonal outmigration, as about half of the total sample households migrated to urban areas to avoid the seasonal food shortages and economic shocks caused by environmental factors. The binary logistic model analysis revealed that male-headed households, household size, and participation in productive safety-net programmes were significantly and directly related to seasonal outmigration. Other aspects had a significant and negative association with seasonal outmigration. Additionally, environmental and economic push and pull factors drive households to employ migration as a coping and adaptive livelihood strategy. The authors conclude that addressing rural unemployment, livelihood diversification, and environmental rehabilitation activities is crucial to reduce the volume and scale of seasonal outmigration in Tsagibji District. [ABSTRACT FROM AUTHOR]

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

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

In recent decades agricultural decision-making system has played a vital role in the field monitoring process. For these emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and wireless sensors are utilized for precise data extraction and analysis. However numerous techniques are developed for increasing agricultural production and enhancing operational efficiency. But still, they possess various challenges like lack of accuracy, increased power utilization, and costs. Insects and pathogens cause plant diseases that reduce productivity if not diagnosed at a proper time. Thus this paper develops Deep Q Rapidly-exploring Random tree-based Adaptive Fire hawk (DQRR-AFH) for diagnosing leaf diseases and monitoring them. It comprises various phases including data acquisition; Image processing, segmentation, feature extraction, and classification. Further, the classification is performed by exploring random trees, and the hyperparameters are tuned via the Adaptive Fire Hawk Optimizer algorithm is utilized to enhance the efficiency of the model. The proposed method monitors the soil moisture content and prevents cotton leaf diseases by spraying chemicals on the plants. Multiple cotton leaf images are obtained to verify the performance with various metrics. Compared to conventional methods such as WL-CNN, ECPRC, and DT, the proposed model achieved exceptional performance with an accuracy of 98.88%, precision of 97%, and an F1-score of 99.21%. [ABSTRACT FROM AUTHOR]

Background and Aims: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity.Methods: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity.Results: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity.Conclusions: Roots are of critical importance to plants due to their role in absorbing soil water and nutrients and adapting to ever-changing environmental conditions. Grazing changes plant and soil conditions and can affect root growth and resource utilization strategies. However, it is still unclear how grazing intensity affects plant root traits in desert steppes, especially by altering soil resource availability.Here, we studied the effect of four levels of grazing intensity, including no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG), on the root traits of Stipa breviflora and soil physical, chemical, and microbial properties in a desert steppe dominated by S. breviflora under in Siziwang Banner, Inner Mongolia, China.Compared to the CK treatment, all grazing treatments significantly reduced root diameter, and increased root length density and root-to-shoot ratio, but did not affect root nitrogen concentration and tissue density. The light grazing treatment significantly increased the root fractal dimension, root fractal abundance, and root biomass. The heavy grazing treatment significantly increased specific root length and root fractal abundance. Principal component analysis revealed that grazing influenced the root-mycorrhizal “collaboration” gradient, shifting root resources utilization strategies from “outsourcing” in the CK to “do-it-yourself” in the grazed plots. Structural equation modeling showed that shifts in root traits were mainly associated with changes in soil pH, ammonium nitrogen availability, and microbial diversity under grazing.Under increasing grazing intensity, S. breviflora adapted to higher soil pH and lower nitrogen availability by producing longer, thinner, more branching roots and a “do-it-yourself” strategy. Our results suggest that changes in root traits play a very important role in the adaption of a dominant desert steppe plant to grazing intensity. [ABSTRACT FROM AUTHOR]

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

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

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

The geographic distribution of the common viper, Vipera berus (L. 1758), was believed to be limited in the north and northeast by an assumed lack of hibernacula with temperatures exceeding 0°C, because the species is not freeze-tolerant. To test this hypothesis, we measured in the laboratory the cold resistance of vipers that inhabit Kizhi Island (Lake Onega, Karelia, Russian North), and compared the results obtained with the temperatures in the hibernacula and soils of the region. Freezing of the adults happened at an average temperature of −3.0°C (ranging from −2 to −3.9°C). Vipera berus failed to tolerate complete freezing, but could withstand short-term (1–8 hours) partial freezing. At subzero temperatures (−2 to −2.2°C), animals remained supercooled for 23–121 days. Vipers remained active at temperatures dropping down to −1.5°C, moving in experimental containers; this possibly happens in overwintering places as well. Our study allows us to conclude that the species' ability to withstand long-term supercooling (up to four months) is one of the key determinants of the adaptive strategy of the common viper in cold regions. The critical temperature of the species in overwintering sites must be considered to be not 0°C, but −2°C. This threshold temperature can be a factor limiting the geographic distribution of V. berus. Temperatures in hibernacules on Kizhi Island (heapes of stones) and also possibly in all soils of Karelia are favorable for the species' overwintering, leading to its great abundance levels. [ABSTRACT FROM AUTHOR]

Inverse kinematic solution is a critical procedure for the control of manipulators; however, only a small number of manipulators that meet certain conditions have closed-form solutions. In this paper, the conventional solution was replaced by the optimization process of a proposed fitness function. The fitness function aims to obtain a set of accurate joint angles for the desired position and orientation of the end effector. The function is based on forward kinematics; hence, this approach can avoid singularities and be suitable for solving many manipulators. For solving this optimization problem, particle swarm optimization algorithm was chosen as the basis due to its easy principle and excellent performance. To enhance the performance of the algorithm in searching global optimum, an adaptive inertia weight strategy applied to adjust the velocity of particles was proposed. Besides, for particles, to avoid trapping in local optimum at the boundary, a special boundary treatment was presented. In this study, two serial robotic manipulators were used to test the performance of adaptive particle swarm optimization (APSO). Meanwhile, several powerful PSO variants improved from disparate methods were selected to compare with APSO. The experimental results demonstrate that the proposed fitness function with APSO can solve the inverse kinematic problem of multi-DOF manipulators efficiently and accurately. [ABSTRACT FROM AUTHOR]

Precipitation is a primary determinant of plant community structure in drylands. However, the empirical evidence and predictions are lacking for how plant functional diversity in desert and steppe communities respond to altered precipitation regimes.We examined how precipitation changes along the natural and experimental gradients affect different components of functional diversity in desert‐shrub and steppe‐grass communities. We compared the associations of precipitation changes with community‐weighted means (CWMs) of six traits, functional divergence (FDvar) of each single‐trait and multi‐trait functional richness (FRic) and dispersion (FDis) for shrub and grass communities along the natural and experimental gradients. We also disentangle the roles of species turnover and intraspecific variations in affecting the responses of different functional diversity to precipitation changes.We found that in general, the similar responses of functional traits or diversity to both the natural and experimental precipitation gradients were dependent on plant community type. Across both two gradients, precipitation was positively associated with CWM of plant height and negatively associated with the CWM of SLA and leaf thickness in grass community, while positively associated with FDvar of four traits and FDis in shrub communities. Both species turnover and intraspecific variations contributed to the responses of grass community traits to precipitation changes across both two gradients, and to FDvar of traits and FDis in shrub community along the natural gradient. In contrast, species turnover variations contributed to FDvar of traits and FDis in shrub community in experiment.These results suggest that there is better concordance between the effects of naturally and experimentally increased precipitation on functional diversity of plant communities, but different mechanisms behind the relationship of functional diversity–precipitation between shrub and grass communities. Grass communities can adapt to precipitation changes by average trait differences, while shrub communities persist through FDvar of single‐trait and multi‐trait dispersion, thus highlighting the important differences in adaptive strategies between shrub and grass communities. Our findings demonstrate that short‐term responses of plant communities to manipulative precipitation changes can reflect long‐term shifts at spatial scales depending on the specific functional trait and diversity. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

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

Sustainable development goals (SDGs) emphasize a holistic achievement instead of cherry-picking a few. However, no assessment has quantitatively considered the evenness among all 17 goals. Here, we propose a systematic method, which first integrates both the evenness and the overall status of all goals, to distinguish the ideal development pathways from the uneven ones and then revisit the development trajectory in China from 2000 to 2015. Our results suggest that, despite the remarkable progress, a bottleneck has occurred in China since 2013 due to the stagnant developments in some SDGs. However, many far-reaching policies in China have been targeting these deficiencies since then, providing a perspective on how a country approaches sustainable development by promoting evenness among all SDGs. Our results also indicate that regions with the slowest progress are the developed provinces, owing to the persistent uneven status of all goals. Our study demonstrates the importance of adopting evenness in assessing and guiding sustainable development. [ABSTRACT FROM AUTHOR]

In the arc welding process, a reasonable welding path can improve welding efficiency and quality, especially when a large number of welding seams exist. Hence, this article introduces an intelligent path optimization strategy to optimize the sequence of welding seams. The shortest path length, power consumption, and welding deformation are considered optimization objectives, and welding deformation is studied based on the proxy model to improve optimization efficiency. Then, the improved discrete multiobjective decomposition algorithm based on event-triggering strategy (DMOEA/D-ET) is proposed. Here, the grid method and decomposition multiobjective algorithm (MOEA/D) are used to improve search performance. The adaptive neighborhood strategy is used to improve the quality and distribution of noninferior solutions. After the comparison with NSGA-II, NSGA-III, and CGMOPSO algorithms, the DMOEA/D-ET algorithm shows better performance on both convergence and diversity. Finally, the proposed strategy is used for welding robot path planning, and the simulation results show its effectiveness. [ABSTRACT FROM AUTHOR]

Porosity is an important parameter of reservoir physical properties and plays an important role in reservoir evaluation. Porosity can be measured by direct and indirect methods. However, most methods are very time-consuming and expensive. Therefore, it is of great significance to establish a fast, economical and effective method for accurate prediction of porosity. In this paper, an alternative method of porosity prediction, based on the integration between novel adaptive population heuristic intelligent optimization algorithm (ADA) and support vector regression (SVR) is presented. In this study, the simulation results of the new model were compared with the DA-SVR, BP, and ELM methods. The experimental results show that the ADA-SVR prediction model can achieve higher prediction accuracy and the prediction accuracy is 96.3%. Therefore, the proposed ADA-SVR model is feasible and effective for predicting porosity and can be used as an effective tool for predicting other reservoir parameters. [ABSTRACT FROM AUTHOR]

In evolutionary computation, balancing the diversity and convergence of the population for multiobjective evolutionary algorithms (MOEAs) is one of the most challenging topics. Decomposition-based MOEAs are efficient for population diversity, especially when the branch partitions the objective space of multiobjective optimization problem (MOP) into a series of subspaces, and each subspace retains a set of solutions. However, a persisting challenge is how to strengthen the population convergence while maintaining diversity for decomposition-based MOEAs. To address this issue, we first define a novel metric to measure the contributions of subspaces to the population convergence. Then, we develop an adaptive strategy that allocates computational resources to each subspace according to their contributions to the population. Based on the above two strategies, we design an objective space partition-based adaptive MOEA, called OPE-MOEA, to improve population convergence, while maintaining population diversity. Finally, 41 widely used MOP benchmarks are used to compare the performance of the proposed OPE-MOEA with other five representative algorithms. For the 41 MOP benchmarks, the OPE-MOEA significantly outperforms the five algorithms on 28 MOP benchmarks in terms of the metric hypervolume. [ABSTRACT FROM AUTHOR]

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

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

Purpose: This paper aims to propose a new adaptive strategy for two-dimensional (2D) nonlinear finite element (FE) analysis of the minimal surface problem (MSP) based on the element energy projection (EEP) technique. Design/methodology/approach: By linearizing nonlinear problems into a series of linear problems via the Newton method, the EEP technique, which is an effective and reliable point-wise super-convergent displacement recovery strategy for linear FE analysis, can be directly incorporated into the solution procedure. Accordingly, a posteriori error estimate in maximum norm was established and an adaptive 2D nonlinear FE strategy of h-version mesh refinement was developed. Findings: Three classical known surfaces, including a singularity problem, were analysed. Moreover, an example whose analytic solution is unavailable was considered and a comparison was made between present results and those computed by the MATLAB PDE toolbox. The results show that the adaptively-generated meshes reflect the difficulties inherent in the problems and the proposed adaptive analysis can produce FE solutions satisfying the user-preset error tolerance in maximum norm with a fair adaptive convergence rate. Originality/value: The EEP technique for linear FE analysis was extended to the nonlinear procedure of MSP and can be expected to apply to other 2D nonlinear problems. The employment of the maximum norm makes point-wisely error control on the sought surfaces possible and makes the proposed method distinguished from other adaptive FE analyses. [ABSTRACT FROM AUTHOR]

This study aims to explore the psychological burden of leprosy women and their adaptive strategies. Leprosy has a destructive character and is opportunistic in nature, so that it becomes a burden for women because of the defects in the body and the resulting beauty crown. The study was conducted at the Center for Public Health in Eastern Indonesia with a purposive sampling method on 17 leprosy women. The interview became the most dominant instrument, and the results were analyzed by Phenomenological Analytical Interpretation. The result: mental stress is part of everyday life; physical conditions that are far from aesthetic value; guarantee of happiness that is difficult to obtain by sufferers; it is necessary to find a method to surrender to the trials received. The final conclusion shows that the psycho-emotional suffering caused by leprosy can be reduced through efforts to surrender to the trials received, to obey God's commands in any situation and to His servants to always try and pray and remain steadfast in maintaining the role, function and duty as a woman even though constrained by leprosy that accompanies. [ABSTRACT FROM AUTHOR]

The sharp rise in anthropogenic activities and climate change has caused the extensive degradation of grasslands worldwide, jeopardizing ecosystem function, and threatening human well‐being. Toxic weeds have been constantly spreading in recent decades; indeed, their occurrence is considered to provide an early sign of land degeneration. Policymakers and scientific researchers often focus on the negative effects of toxic weeds, such as how they inhibit forage growth, kill livestock, and cause economic losses. However, toxic weeds can have several potentially positive ecological impacts on grasslands, such as promoting soil and water conservation, improving nutrient cycling and biodiversity conservation, and protecting pastures from excessive damage by livestock. We reviewed the literature to detail the adaptive mechanisms underlying toxic weeds and to provide new insight into their roles in degraded grassland ecosystems. The findings highlight that the establishment of toxic weeds may provide a self‐protective strategy of degenerated pastures that do not require special interventions. Consequently, policymakers, managers, and other personnel responsible for managing grasslands need to take appropriate actions to assess the long‐term trade‐offs between the development of animal husbandry and the maintenance of ecological services provided by grasslands. [ABSTRACT FROM AUTHOR]