Partitioning Scheme Research Articles

Quasi-random Fractal Search (QRFS): A dynamic metaheuristic with sigmoid population decrement for global optimization

Global optimization of complex and high-dimensional functions remains a central challenge with broad applications in science and engineering. This study introduces a new optimization approach called quasi-random metaheuristic based on fractal search (QRFS), which harnesses the power of fractal geometry, low discrepancy sequences, and intelligent search space partitioning techniques. The QRFS uses fractals’ inherent self-similarity and intricate structure to guide the solution space exploration. For the proposal, a deterministic but quasi-random element is used in the search process using low discrepancy sequences, such as Sobol, Halton, Hammersley, and Latin Hypercube. This integration allows the algorithm to systematically cover the search space while maintaining the level of diversity necessary for efficient exploration. The QRFS employs a dynamic strategy of partitioning the search space and reducing the population of solutions to optimize the use of function accesses, which causes it to adapt well to the characteristics of the problem. The algorithm intelligently identifies and prioritizes promising regions within the fractal-based representation, allocating computational resources where they are most likely to yield optimal solutions. Experimental evaluations on several benchmark problems demonstrate that QRFS consistently outperforms modern, canonical metaheuristics and variants of algorithms such as differential evolution (DE), particle swarm optimization (PSO), covariance matrix adaptive evolution strategy (CMA-ES), regarding solution quality. Besides, the algorithm shows remarkable scalability, which makes it suitable for high-dimensional optimization tasks. Overall, QRFS offers a robust and efficient approach to solving complex optimization problems in various domains, paving the way for improved decision-making in real-world applications.

CPS-3WS: A critical pattern supported three-way sampling method for classifying class-overlapped imbalanced data

Class-imbalance problem widely exists in real applications ranging from medial diagnosis to economic fraud detection, etc. As one of the mainstream techniques in dealing with imbalanced data, SMOTE (Synthetic Minority Over-sampling TEchnique) and its extensions mainly rebalance the datasets via generation of observations in specific regions with various adapted strategies. Many of them do not consider the cost of role assignment of samples, and the intractable data complexity (overlap, small disjuncts, etc.) poses additional challenges to them. This paper proposes a critical pattern supported three-way sampling method (CPS-3WS) for classifying class-overlapped imbalanced data, introducing the philosophy of thinking in threes to effective classification in imbalanced learning. Specifically, CPS-3WS uses a three-way sample partition strategy with the Bayes posterior probability by dividing majority and minority classes into three disjoint subsets: risky, critical and safe patterns. CPS-3WS conducts a three-way hybrid sampling through (i) evaluating the risky majority pattern to be eliminated and (ii) selecting critical minority pattern to synthesize new samples under local information constraint. Extensive experiments on 42 UCI benchmark datasets demonstrate the superiority of the proposed CPS-3WS compared with 11 data-level methods. The source code of CPS-3WS is available at https://github.com/ytyancp/CPS-3WS.

Determination of the optimal total oxygen concentration and oxygen partitioning ratio based on the heat transfer characteristics of oxy-coal combustion in a swirl burner

Oxy-coal combustion is an attractive option for CO2 capture from coal-fired power plants due to high carbon capture efficiency and favorable economic performance. It is critical for the operation of the power plants to maintain similar heat transfer characteristics by rationalizing the partitioning strategy of the oxidant streams when retrofitting coal-fired power plants with oxy-fuel combustion technology. In the present work, the oxy-coal combustion characteristics under various total oxygen concentrations XO2,total of 21 %–32 % and oxygen partitioning ratio conditions in a 100 kW vertical swirl burner were numerically investigated by using computational fluid dynamics (CFD) models. And the effects of the XO2,total and oxygen partitioning ratio on the heat transfer characteristics of oxy-coal combustion were numerically simulated to quantitatively obtain the optimal oxygen supply partitioning strategy for the retrofitting of coal-fired power plants. Then the flame structure, heat transfer flux, and char burnout rate at different oxygen partitioning ratios between the primary and secondary streams were systematically analyzed to further determine the optimal oxygen partitioning ratio. The results showed that the maximum flue gas temperature Tmax increased by 220 K and the total heat transfer flux on the furnace wall increased by 42 % when the XO2,total was increased from 21 % to 32 %, respectively. Under oxy-coal combustion conditions, the Tmax at the XO2,total of 32.4 % was consistent with that under air combustion condition, while the total heat transfer flux at the XO2,total of 29.4 % matched with that under air combustion condition. Moreover, the total heat transfer flux and the flame length were increased by 17.3 % and 46.3 %, respectively, as the oxygen concentration of the primary stream XO2,Pri was increased from 6.5 % to 45 % at the XO2,total of 29 %. More importantly, the total heat transfer flux at the XO2,Pri of 31.9 % under oxy-coal combustion condition matched with that under air combustion condition. The optimal total oxygen concentration and oxygen partitioning ratio of oxy-coal combustion based on the heat transfer characteristics were quantitatively determined, which gave an important reference for the oxy-coal combustion retrofitting of traditional coal-fired power plants.

Основанный на генетическом подходе алгоритм внутрикодирования для H.266/VVC

This paper presents a genetic approach for optimizing intra coding in H.266/VVC. The proposed algorithm efficiently selects coding tools and Multi-Type Tree (MTT) partitions to achieve a balance between encoding time and video quality. The fitness evaluation function, which combines perceptual metrics and coding efficiency metrics, is used to assess the quality of each candidate solution. The results demonstrate a significant reduction in encoding time without compromising video quality. The proposed algorithm selects coding tools from a set of available tools in H.266/VVC. These tools include intra prediction modes, transform units, quantization parameters, and entropy coding modes. The MTT partitioning scheme includes four types of partitions: quadtree, binary tree, ternary tree, and quad-binary tree. Perceptual metrics are used to evaluate the visual quality of the encoded video. Coding efficiency metrics are used to evaluate the coding efficiency of the encoded video. The fitness evaluation function combines perceptual metrics and coding efficiency metrics to assess the quality of each candidate solution.

SPMSD: An Partitioning-Strategy for Parallel General Sparse Matrix-Matrix Multiplication on GPU

SpGEMM (General Sparse Matrix-Matrix Multiplication) is one of the kernels of an algebraic multi-grid method, graph algorithm, and solving linear equations. Due to the non-uniformity of some sparse matrices, the existing parallel SpGEMM algorithms suffer from load imbalance, lead to a decrease in computational efficiency. This paper proposes a new algorithm, SPMSD (SpGEMM Based on Minimum Standard Deviation). The algorithm is developed based on a hash table and partition strategy. First, the number of intermediate results in the matrix is divided into multiple blocks based on a new partition strategy to ensure the minimum standard deviation among blocks. Second, the input matrix is transformed according to the result of the partition strategy. Finally, SPMSD performs the parallel computing of SpGEMM based on the advantages of fast insertion and also fast access storage of the hash table and the calculation process controls the insertion and merging of intermediate results according to the offset to avoid the shortage of atomic operations. These experiments indicate the execution of SPMSD is faster than the existing cuSPARSE libraries by 7.4x. Compared with the Out of Core method, SPMSD improves the computational performance by 1.2x, SPMSD memory utilization is decreased by 0.19x.

Infrared Small Target Detection Based on Density Peak Search and Local Features

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. Second, a density-peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Dense-sparse quantum Monte Carlo algebraic diagrammatic construction and importance ranking.

Quantum Monte Carlo Algebraic Diagrammatic Construction (QMCADC) has been proposed as a reformulation of the second-order ADC scheme for the polarization propagator within the projection quantum Monte Carlo formalism. Dense-sparse partitioning and importance ranking filtering strategies are now exploited to accelerate its convergence and to alleviate the signproblem inherent in such calculations. By splitting the configuration space into dense and sparse subsets, the corresponding projection operator is decomposed into four distinct blocks. Deterministic calculations handle the dense-to-dense and sparse-to-dense blocks, while the remaining blocks, dense-to-sparse and sparse-to-sparse, are stochastically evaluated. The dense set is efficiently stored in a fixed-size array, and the sparse set is represented through conventional floating random Monte Carlo walks. The stochastic projection is further refined through importance ranking criteria, enabling a reduction in the required number of walkers with a controllable bias. Our results demonstrate the integration of dense-sparse partitioning with importance ranking filtering to significantly enhance the efficiency of QMCADC, enabling large-scale molecular excited-state calculations. Furthermore, this novel approach maximizes the utilization of the sparsity of ADC(2), transforming QMCADC into a tailored framework for ADC calculations.

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Abstract Background/Introduction Accurately predicting and managing the risk of sudden cardiac death (SCD) in the general population is a formidable challenge. Purpose This study aimed to develop and validate a machine learning (ML)-based prediction model for SCD using data from the Community Cardiovascular Cohort (CCCC) in Taiwan. Methods A community-based cohort study was used for model construction . Four ML algorithms, including extreme gradient boosting (XGBoost), random forests (RF), logistic regression, and deep neural network (DNN), underwent meticulous evaluation. Performance assessment encompassed receiver operating characteristic (ROC) curves, area under the curves (AUC), and accuracy metrics. Significant risk factors were identified through feature importance analysis. Results We employed a random partitioning strategy, allocating 1744 participants (55%) to the training dataset, 952 participants (30%) to the test dataset, and 476 participants (15%) to the validation dataset. In the data training stage, the XGBoost algorithm demonstrated outstanding prediction performance for SCD (AUC: 1.000); In the validation stage, the XGBoost algorithm demonstrated good prediction performance for SCD prediction (AUC: 0.791, 95%CI: 0.617-0.964; Accuracy: 0.971). Using XGBoost-generated predicted probabilities (P(XGB)), we identified individuals with P(XGB) ≥ 0.5 (Group 1) who displayed significantly higher SCD risk (HR 32.0; 95% CI: 9.29-109.9) compared to those with P(XGB) < 0.5 (Group 0). Conclusions Our study demonstrates the effectiveness of ML algorithms, particularly XGBoost, in predicting event of SCD in the general population. The identification of these pivotal risk factors holds profound implications, empowering the identification of high-risk individuals and facilitating the implementation of precisely tailored preventive strategies.Figure 1:KM sruvival curveFigure 2:Feature importance analysis

Electrostatic-induced ion-confined partitioning in graphene nanolaminate membrane for breaking anion–cation co-transport to enhance desalination

Constructing nanolaminate membranes made of two-dimensional graphene oxide nanosheets has gained enormous interest in recent decades. However, a key challenge facing current graphene-based membranes is their poor rejection for monovalent salts due to the swelling-induced weak nanoconfinement and the transmembrane co-transport of anions and cations. Herein, we propose a strategy of electrostatic-induced ion-confined partitioning in a reduced graphene oxide membrane for breaking the correlation of anions and cations to suppress anion-cation co-transport, substantially improving the desalination performance. The membrane demonstrates a rejection of 95.5% for NaCl with a water permeance of 48.6 L m−2 h−1 bar−1 in pressure-driven process, and it also exhibits a salt rejection of 99.7% and a water flux of 47.0 L m−2 h−1 under osmosis-driven condition, outperforming the performance of reported graphene-based membranes. The simulation and calculation results unveil that the strong electrostatic attraction of membrane forces the hydrated Na+ to undergo dehydration and be exclusively confined in the nanochannels, strengthening the intra-nanochannel anion/cation partitioning, which refrains from the dynamical anion-cation correlations and thereby prevents anions and cations from co-transporting through the membrane. This study provides guidance for designing advanced desalination membranes and inspires the future development of membrane-based separation technologies.

Optimal service caching, pricing and task partitioning in mobile edge computing federation

Mobile Edge Computing (MEC) federations aim to establish a joint edge service model between Edge Infrastructure Providers (EIPs) and clouds, facilitating the sharing and utilization of MEC services and resources. However, in such a hierarchical multi-EIP MEC federation environment, optimizing service caching, task partitioning, and pricing strategies to maximize the profits gained by EIPs and minimize the cost of mobile devices (MDs) is challenging. To address this challenge, This paper first formulates a two-stage multi-leader, multi-follower Stackelberg game between EIPs and MDs. Then, a new method combining a Stackelberg-based Multi-Agent Deep Deterministic Policy Gradient (STMADDPG) algorithm and a Transformer-based Popularity Prediction (TPP) model is devised to learn the optimal strategies. Extensive experiments with real-world datasets are conducted to demonstrate the excellent performance of the proposed STMADDPG-TPP method.

Bayesian cross-validation by parallel Markov chain Monte Carlo

Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or ‘brute force’ CV approaches are often too computationally costly for interactive modeling workflows, especially when inference relies on Markov chain Monte Carlo (MCMC). We propose overcoming this limitation using massively parallel MCMC. Using accelerator hardware such as graphics processor units, our approach can be about as fast (in wall clock time) as a single full-data model fit. Parallel CV is flexible because it can easily exploit a wide range data partitioning schemes, such as those designed for non-exchangeable data. It can also accommodate a range of scoring rules. We propose MCMC diagnostics, including a summary of MCMC mixing based on the popular potential scale reduction factor (R^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widehat{\ extrm{R}}$$\\end{document}) and MCMC effective sample size (ESS^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widehat{\ extrm{ESS}}$$\\end{document}) measures. We also describe a method for determining whether an R^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widehat{\ extrm{R}}$$\\end{document} diagnostic indicates approximate stationarity of the chains, that may be of more general interest for applications beyond parallel CV. Finally, we show that parallel CV and its diagnostics can be implemented with online algorithms, allowing parallel CV to scale up to very large blocking designs on memory-constrained computing accelerators.

Recurring seasonality exposes dominant species and niche partitioning strategies of open ocean picoeukaryotic algae

Ocean spring phytoplankton blooms are dynamic periods important to global primary production. We document vertical patterns of a diverse suite of eukaryotic algae, the prasinophytes, in the North Atlantic Subtropical Gyre with monthly sampling over four years at the Bermuda Atlantic Time-series Study site. Water column structure was used to delineate seasonal stability periods more ecologically relevant than seasons defined by calendar dates. During winter mixing, tiny prasinophytes dominated by Class II comprise 46 ± 24% of eukaryotic algal (plastid-derived) 16S rRNA V1-V2 amplicons, specifically Ostreococcus Clade OII, Micromonas commoda, and Bathycoccus calidus. In contrast, Class VII are rare and Classes I and VI peak during warm stratified periods when surface eukaryotic phytoplankton abundances are low. Seasonality underpins a reservoir of genetic diversity from multiple prasinophyte classes during warm periods that harbor ephemeral taxa. Persistent Class II sub-species dominating the winter/spring bloom period retreat to the deep chlorophyll maximum in summer, poised to seed the mixed layer upon winter convection, exposing a mechanism for initiating high abundances at bloom onset. Comparisons to tropical oceans reveal broad distributions of the dominant sub-species herein. This unparalleled window into temporal and spatial niche partitioning of picoeukaryotic primary producers demonstrates how key prasinophytes prevail in warm oceans.

Bi-directional ensemble differential evolution for global optimization

Hybridizing multiple mutation strategies has shown much effectiveness in helping differential evolution (DE) algorithms achieve good optimization performance. Though abundant adaptive ensemble strategies have been developed to adaptively employ multiple mutation strategies to evolve the population, most of them ignore to make full use of the properties and characteristics of the multiple mutation strategies. To fill this gap, this paper devises a bi-directional ensemble scheme for DE to adaptively assemble totally 8 mutation strategies with different properties and characteristics. As a result, a novel DE, which we call bi-directional ensemble DE (BDEDE), is developed. Specifically, this paper sorts the 8 mutation strategies roughly from two opposite perspectives, namely the convergence and the diversity. Then, we assign each mutation strategy with two different non-linear probabilities, which are calculated on the basis of its two rankings obtained from the two perspectives. Subsequently, to make full use of these mutation schemes, we first partition the whole population into two separate parts, namely elite individuals and non-elite individuals. Then, for each elite individual, we randomly select a mutation strategy from the 8 candidates based on the probabilities calculated by the convergence rankings, while for each non-elite individual, we stochastically choose a mutation scheme from the same 8 candidates but based on the probabilities computed by the diversity rankings. In this manner, the elite individuals prefer to exploit the located optimal areas, while the non-elite individuals tend to explore the solution space. Therefore, it is likely that BDEDE expectedly maintains a good balance between search diversity and search convergence. To further help BDEDE achieve such a purpose, this paper devises an adaptive partition strategy to dynamically separate the whole population into the two categories. With the above two techniques, BDEDE anticipatedly obtains good optimization performance. To verify its effectiveness and efficiency, we conduct experiments on the CEC2014 and the CEC2017 benchmark sets by comparing BDEDE with totally 14 well-known and state-of-the-art DE variants. Experimental results have shown that BDEDE performs competitively with or even significantly better than the 14 compared DE variants. The source code of BDEDE can be downloaded from https://gitee.com/mmmyq/BDEDE.

Successive model-agnostic meta-learning for few-shot fault time series prognosis

Meta learning is a promising technique for solving few-shot fault prediction problems, which have attracted the attention of many researchers in recent years. Existing meta-learning methods for time series prediction, which predominantly rely on random and similarity matching-based task partitioning, facing three major limitations: (1) feature exploitation inefficiency; (2) suboptimal task data allocation; and (3) limited robustness with small samples. To this end, we introduce a novel‘meta-task’ partitioning scheme, underpinned by a differential autoregressive algorithm, that treats a continuous time period of a time series as a meta-task, composed of multiple successive short time periods. This approach allows us to extract more comprehensive features and relationships from the data, resulting in more accurate predictions. Furthermore, our findings indicate that the differential autoregressive approach significantly bolsters robustness across diverse datasets. Extensive experiments on several fault and time series prediction datasets demonstrate that our approach substantially enhances prediction performance and generalization capability under both few-shot and general conditions.

Ensemble filter-transfer learning algorithm

Transfer learning algorithms are capable to apply previously learned knowledge in source domain, which alleviates much expensive efforts of knowledge recollection in target domain. But the knowledge in source domain is always imperfect due to redundant or contaminated information. To solve this problem, an ensemble filter-transfer learning (EFTL) algorithm based on the source knowledge reconstruction is proposed in this paper. First, a knowledge partition strategy based on model is developed to classify the source knowledge into different types. Then, the positive knowledge can be identified, which contributes to target domain with a rejection of the negative transfer. Second, a knowledge filter algorithm is introduced to filter out the redundant information in non-positive knowledge. Then, the non-positive knowledge can be reconstructed by this algorithm to prevent the loss of available information. Third, an ensemble transfer mechanism is established to realize the synchronous transfer of omnidirectional knowledge for the target domain. Finally, comparative experiments on model prediction in practical applications are provided to illustrate the dependability of EFTL.

Privacy-Enhanced Cooperative Storage Scheme for Contact-Free Sensory Data in AIoT with Efficient Synchronization

The growing popularity of contact-free smart sensing has contributed to the development of the Artificial Intelligence of Things (AIoT). The contact-free sensory data has great potential to mine and analyze the hidden information for AIoT-enabled applications. However, due to the limited storage resource of contact-free smart sensing devices, data is naturally stored in the cloud, which is at risk of privacy leakage. Cloud storage is generally considered insecure. On one hand, the openness of the cloud environment makes the data easy to be attacked, and the complex AIoT environment also makes the data transmission process vulnerable to the third party. On the other hand, the Cloud Service Provider (CSP) is untrusted. In this article, to ensure the security of data from contact-free smart sensing devices, a Cloud-Edge-End cooperative storage scheme is proposed, which takes full advantage of the differences in the cloud, edge, and end. Firstly, the processed sensory data is stored separately in the three layers by utilizing well-designed data partitioning strategy. This scheme can increase the difficulty of privacy leakage in the transmission process and avoid internal and external attacks. Besides, the contact-free sensory data is highly time-dependent. Therefore, combined with the Cloud-Edge-End cooperation model, this article proposes a delta-based data update method and extends it into a hybrid update mode to improve the synchronization efficiency. Theoretical analysis and experimental results show that the proposed cooperative storage method can resist various security threats in bad situations and outperform other update methods in synchronization efficiency, significantly reducing the synchronization overhead in AIoT.

Ab initio dispersion potentials based on physics-based functional forms with machine learning.

In this study, we introduce SAPT10K, a comprehensive dataset comprising 9982 noncovalent interaction energies and their binding energy components (electrostatics, exchange, induction, and dispersion) for diverse intermolecular complexes of 944 unique dimers. These complexes cover significant portions of the intermolecular potential energy surface and were computed using higher-order symmetry-adapted perturbation theory, SAPT2+(3)(CCD), with a large aug-cc-pVTZ basis set. The dispersion energy values in SAPT10K serve as crucial inputs for refining the abinitio dispersion potentials based on Grimme's D3 and many-body dispersion (MBD) models. Additionally, Δ machine learning (ML) models based on newly developed intermolecular features, which are derived from intermolecular histograms of distances for element/substructure pairs to simultaneously account for local environments as well as long-range correlations, are also developed to address deficiencies of the D3/MBD models, including the inflexibility of their functional forms, the absence of MBD contributions in D3, and the standard Hirshfeld partitioning scheme used in MBD. The developed dispersion models can be applied to complexes involving a wide range of elements and charged monomers, surpassing other popular ML models, which are limited to systems with only neutral monomers and specific elements. The efficient D3-ML model, with Cartesian coordinates as the sole input, demonstrates promising results on a testing set comprising 6714 dimers, outperforming another popular ML model, component-based machine-learned intermolecular force field (CLIFF), by 1.5 times. These refined D3/MBD-ML models have the capability to replace the time-consuming dispersion components in symmetry-adapted perturbation theory-based calculations and can promptly illustrate the dispersion contribution in noncovalent complexes for supramolecular assembly and chemical reactions.

40S Ribosomal protein S6 kinase integrates daylength perception and growth regulation in Arabidopsis thaliana.

Plant growth occurs via the interconnection of cell growth and proliferation in each organ following specific developmental and environmental cues. Therefore, different photoperiods result in distinct growth patterns due to the integration of light and circadian perception with specific Carbon (C) partitioning strategies. In addition, the TARGET OF RAPAMYCIN (TOR) kinase pathway is an ancestral signaling pathway that integrates nutrient information with translational control and growth regulation. Recent findings in Arabidopsis (Arabidopsis thaliana) have shown a mutual connection between the TOR pathway and the circadian clock. However, the mechanistical network underlying this interaction is mostly unknown. Here, we show that the conserved TOR target, the 40S ribosomal protein S6 kinase (S6K) is under circadian and photoperiod regulation both at the transcriptional and post-translational level. Total S6K (S6K1 and S6K2) and TOR-dependent phosphorylated-S6K protein levels were higher during the light period and decreased at dusk especially under short day conditions. Using chemical and genetic approaches, we found that the diel pattern of S6K accumulation results from 26S proteasome-dependent degradation and is altered in mutants lacking the circadian F-box protein ZEITLUPE (ZTL), further strengthening our hypothesis that S6K could incorporate metabolic signals via TOR, which are also under circadian regulation. Moreover, under short days when C/energy levels are limiting, changes in S6K1 protein levels affected starch, sucrose and glucose accumulation and consequently impacted root and rosette growth responses. In summary, we propose that S6K1 constitutes a missing molecular link where day-length perception, nutrient availability and TOR pathway activity converge to coordinate growth responses with environmental conditions.

Lion lords and sharing hyaenas: Carnivore guild dynamics around elephant carcasses.

Megaherbivore carcasses represent sporadic but energetically rewarding resources for carnivores, offering a unique opportunity to study coexistence dynamics between facultative scavengers. South African fenced protected areas, such as Madikwe Game Reserve (Madikwe hereafter), host viable populations of large carnivores and high densities of elephants, Loxodonta africana. However, high carnivore densities can lead to potentially fatal interspecific encounters and increased competition, particularly around high-quality trophic resources. This study explores the temporal partitioning and co-detection strategies of carnivores at six elephant carcasses in Madikwe, aiming to understand how the increased carrion biomass available at elephant carcasses influences coexistence dynamics. Camera traps were deployed to monitor carcasses during two periods (2019 and 2020), revealing occurrences of six carnivore species. Carnivores, particularly black-backed jackals, Lupulella mesomelas, (hereafter jackal), lions, Panthera leo, and spotted hyaenas, Crocuta crocuta, comprised 56.7% of carcass observations, highlighting their pivotal roles in scavenging dynamics. Pairwise co-detection analysis demonstrated consistent association and shared peak activity periods between jackals and spotted hyaenas, indicating potential resource sharing. However, the minimal co-detection rates between lions and other carnivores highlight their resource domination. There was some evidence of temporal partitioning between carnivores, with most species exhibiting earlier peaks in nocturnal activity to avoid temporal overlap with lions. This study emphasises the importance of elephant carcasses in the diet of multiple species and coexistence techniques utilised to exploit this ephemeral resource. As fenced protected areas become crucial for conserving intact carnivore guilds globally, further research into carnivore behavioural adaptations at carcasses is recommended to shed light on their coexistence strategies.

Adaptive Sampling Offspring Generation Strategy for Multi-objective Optimization

A covariance adaptive sampling offspring generation strategy (CASS) based on fuzzy clustering is proposed, and a multi-objective distribution estimation algorithm (MEDCA) based on this strategy is introduced. The GK-FCM clustering partitioning strategy is designed to build a Gaussian model for each individual, collectively approximating the manifold of the Pareto solution set and generating offspring through sampling. The introduction of an individual’s survival generation adapts the individual’s preference for exploration and exploitation. This is achieved by incorporating it as a scaling factor of the covariance matrix in the sampling model, in order to satisfy the individual’s preferences for development and exploration in different evolutionary stages. This method significantly improves the performance of MEDCA in solving complex multi-objective optimization problems through covariance matrix adaptation sampling strategy and scaling factor adaptation strategy. The experimental results demonstrate the advantages of MEDCA in the application of offspring generation strategies during model sampling.