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The causal relationships between external driving forces and the ecological degradation of lakes are characterized as complex and multidimensional, with multiple inputs and outputs, nonlinearity, and many interactions. Conventional parametric statistical methods such as correlation analysis and multiple linear regression cannot handle these characteristics simultaneously. Thus, we developed an integrated analytical framework to screen, identify, and predict the factors related to the ecological degradation of lakes based on redundancy analysis (RDA), variance partitioning analysis (VPA), and principal component analysis-based generalized additive models (PCA-based GAM). The RDA and VPA methods were employed to identify and rank the driving factors that explained the decrease in species richness (specifically of key aquatic organisms, including phytoplankton, submerged plants, zooplankton, benthic animals, and fish), which is a critical ecological indicator closely associated with lake ecological degradation. PCA-based GAM was used to explore the patterns associated with driving forces. The driving forces related to the changes in species richness during the 35 years from 1986 to 2020 were investigated in Baiyangdian (BYD) Lake, China. Three categories of driving forces were identified: anthropogenic pollution, climate change, and hydrological conditions. Significant detrimental changes in species richness were detected in the first decade, followed by relative stability in the next decade, and favorable changes since 2015. Anthropogenic pollution, climate change, and hydrological conditions explained 41%, 18%, and 13% of the total variance, respectively. The best predictive model structures included the water level (WL), air temperature (AT), total phosphorus (TP), and (WL*TP) interaction, and they explained 98.4% of the total data variance. The proposed method offers actionable solutions for lake management, including real-time ecological health monitoring, adaptive strategies and indicating ecological degradation.

The six-dimensional (6D) pose object estimation is a key task in robotic manipulation and grasping scenes. Many existing two-stage solutions with a slow inference speed require extra refinement to handle the challenges of variations in lighting, sensor noise, object occlusion, and truncation. To address these challenges, this work proposes a decoupled one-stage network (DON6D) model for 6D pose estimation that improves inference speed on the premise of maintaining accuracy. Particularly, since the RGB images are aligned with the RGB-D images, the proposed DON6D first uses a two-dimensional detection network to locate the interested objects in RGB-D images. Then, a module of feature extraction and fusion is used to extract color and geometric features fully. Further, dual data augmentation is performed to enhance the generalization ability of the proposed model. Finally, the features are fused, and an attention residual encoder–decoder, which can improve the pose estimation performance to obtain an accurate 6D pose, is introduced. The proposed DON6D model is evaluated on the LINEMOD and YCB-Video datasets. The results demonstrate that the proposed DON6D is superior to several state-of-the-art methods regarding the ADD(-S) and ADD(-S) AUC metrics.

In this paper, we study the flag-transitive automorphism groups of 2-designs and prove that if G is a flag-transitive automorphism group of a 2-design D with r>λ(k-3) , then G is a primitive permutation group of affine, almost simple type or product type. Moreover, it generalizes the above result to the case that r>(r,λ)(k-3) .

This paper addresses a new variant of location-routing problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). A hyper-heuristic approach based on iterated local search (ILS-HH) is introduced to automatically optimize the LRPSPD. On basis of the novel proposed framework of hyper-heuristic, four selections mechanisms and five activation strategies are developed to examine the performance of the proposed framework. Three types computational evaluations were carried out and several conclusions can be drawn: (1) the proposed framework performs better than the classical one with performing several heavy-duty combinations of strategies in terms of solution quality and computing time; (2) different activated strategies have slight (not significant) effect on exploiting best solutions; (3) FRR-MAB-TS (fitness ratio rank based on multi-armed bandit with tabu search) works best among all selection methods. Moreover, the proposed approach could provide competitive, even better results compared to fine-tuned bespoke state-of-the-art approaches.

Economic, environmental, and social effects are the most dominating issues in cold chain logistics. The goal of this paper is to propose a cost-saving, energy-saving, and emission-reducing bi-objective model for the cold chain-based low-carbon location-routing problem. In the proposed model, the first objective (economic and environmental effects) is to minimize the total logistics costs consisting of costs of depots to open, renting vehicles, fuel consumption, and carbon emission, and the second one (social effect) is to reduce the damage of cargos, which could improve the client satisfaction. In the proposed model, a strategy is developed to meet the requirements of clients as to the demands on the types of cargos, that is, general cargos, refrigerated cargos, and frozen cargos. Since the proposed problem is NP-hard, we proposed a simple and efficient framework combining seven well-known multiobjective evolutionary algorithms (MOEAs). Furthermore, in the experiments, we first examined the effectiveness of the proposed framework by assessing the performance of seven MOEAs, and also verified the efficiency of the proposed model. Extensive experiments were carried out to investigate the effects of the proposed strategy and variants on depot capacity, hard time windows, and fleet composition on the performance indicators of Pareto fronts and cold chain logistics networks, such as fuel consumption, carbon emission, travel distance, travel time, and the total waiting time of vehicles.

Interactions between species and detritus in aquatic ecosystems involve unassimilated food, non‐predator mortality, and complex trophic relationships, making it challenging to quantify interaction strengths. This study utilized classic and revised Lotka–Volterra equations, combined with the food web of Baiyangdian Lake, to develop methods for measuring interaction strengths in a phytoplankton‐based and a detritus food web. The analysis relied on three types of species–detritus interactions and outputs from an Ecopath model (1958–2019). Loop weight and Diagonal strength (S) were employed to assess stability. Lighter loops weight and lower S value indicate higher stability. From 1958 to 2009, the stability of Baiyangdian Lake was limited by a three‐link omnivorous loop: Detritus > zooplankton > filter‐feeding fish. As the predator–prey biomass ratio (filter‐feeding fish/detritus) increased, instability increased, and vice versa. However, the new loop (detritus > zooplankton > phytoplankton) and corresponding new predator–prey biomass ratios (zooplankton/detritus) resulted in stability from 2009 to 2019. It inferred dominant top‐down trophic cascade effects changed to dominant bottom‐up trophic cascade effects. Besides focusing on the heaviest loop weight, it was necessary to examine the heavier loops that may have a chance of evolving into the heaviest ones following catastrophic or long‐term perturbations to the food web. To facilitate management, a geometric mean ratio of predator‐to‐ prey biomass BCBPt $$ {\\left(\\frac{B_C}{B_P}\\right)}_t $$was proposed as a simplified indicator. This metric correlates with diagonal strength (R2 = 0.6645) and offers a practical tool for early‐warning assessments of food web stability, despite its moderate precision. This study highlights the importance of integrating detritus dynamics into stability analyses and using loop weight analysis to identify critical trophic interactions. The proposed empirical indicators provide a bridge between theoretical models and ecosystem management practices. A method for calculating the interaction strength of a phytoplankton‐based and a detritus food web. A geometric mean ratio of predator‐to‐prey biomass BCBPt $$ {\\left(\\frac{B_C}{B_P}\\right)}_t $$as an alternative indicator for food web stability. The changes in the stability of the BYD Lake from the 1950s to the 2010s indicate that two steady‐state transformations have occurred.

Background Gray mold caused by Botrytis cinerea is a major threat to the production of strawberry. An increasing number of studies have reported that wall-associated kinase/wall-associated kinase-like (WAK/WAKL) played an important role in the recognition of oligogalacturonic acids (OGs) and the induction of plant defense, but there have been no systematic studies of FaWAK/FaWAKL in strawberry. Results In this study, we identified 167 FaWAK/FaWAKL gene family members within the strawberry ( Fragaria×ananassa ) genome. The phylogenetic analysis showed the FaWAK/FaWAKL gene family has been divided into five groups, and they were unevenly distributed on 46 chromosomes. An analysis of the cis -regulatory elements suggested the FaWAK/FaWAKL gene family was more sensitive to abscisic acid and methyl jasmonate. A total of 36 FaWAK/FaWAKL genes were activated by B. cinerea according to an RNA-seq analysis, and 8 of them strongly responded to B. cinerea and exogenous treatment with OGs, particularly FaWAK35 . Transient overexpression of FaWAK35 increased the strawberry resistance to B. cinerea. Conclusion This study conducted a comprehensive analysis of FaWAK/FaWAKL and provides foundational insights for further exploration of FaWAK/FaWAKL genes in strawberry resistance to B. cinerea .

The goal of the multi-objective optimization algorithm is to quickly and accurately find a set of trade-off solutions. This paper develops a clustering-based competitive multi-objective particle swarm optimizer using the enhanced grid for solving multi-objective optimization problems, named EGC-CMOPSO. The enhanced grid mechanism involved in EGC-CMOPSO is designed to locate superior Pareto optimal solutions. Subsequently, a hierarchical-based clustering is established on the grid for improving the accuracy rate of the grid selection. Due to the adaptive division of clustering centers, EGC-CMOPSO is applicable for solving MOPs with various Pareto front (PF) shapes. Particularly, the inferior solutions are discarded and the leading particles are identified by the comprehensive ranking of particles in each cluster. Finally, the selected leading particles compete against each other, and the winner guides the update of the current particle. The proposed EGC-CMOPSO and the eight latest multi-objective optimization algorithms are performed on 21 test problems. The experimental results validate that the proposed EGC-CMOPSO is capable of handling multi-objective optimization problems (MOPs) and obtaining superior performance on both convergence and diversity.

Unpaired image translation with feature-level constraints presents significant challenges, including unstable network training and low diversity in generated tasks. This limitation is typically attributed to the following situations: 1. The generated images are overly simplistic, which fails to stimulate the network’s capacity for generating diverse and imaginative outputs. 2. The images produced are distorted, a direct consequence of unstable training conditions. To address this limitation, the unpaired image-to-image translation with diffusion adversarial network (UNDAN) is proposed. Specifically, our model consists of two modules: (1) Feature fusion module: In this module, one-dimensional SVD features are transformed into two-dimensional SVD features using the convolutional two-dimensionalization method, enhancing the diversity of the images generated by the network. (2) Network convergence module: In this module, the generator transitions from the U-net model to a superior diffusion model. This shift leverages the stability of the diffusion model to mitigate the mode collapse issues commonly associated with adversarial network training. In summary, the CycleGAN framework is utilized to achieve unpaired image translation through the application of cycle-consistent loss. Finally, the proposed network was verified from both qualitative and quantitative aspects. The experiments show that the method proposed can generate more realistic converted images.

Esophageal squamous cell carcinoma (ESCC) is among the most frequently diagnosed cancer types, and affected patients frequently experience poor prognostic outcomes and high mortality rates. Many genomic studies of ESCC have been performed in recent years, yet the mutational mechanisms driving ESCC and their clinical implications remain incompletely understood. In this study, paired tumor and normal tissue samples from 22 patients with ESCC were used for whole genome sequencing-based analyses of genome-wide mutational events. These comprehensive analyses enabled the detection and characterization of various mutation subtypes in ESCC including somatic single-nucleotide variants, small insertions and deletions, copy number variations, structural variations, and circular extrachromosomal DNA. Of identified genes harboring non-silent mutations, TP53 , NOTCH1 , CSMD3 , EP300 , and FAM135B were the most frequently mutated genes in this study and they were annotated in the COSMIC Cancer Gene Census. With the exception of aging-related signatures, an APOBEC-associated mutational signature was the dominant mutational feature detected in ESCC samples, suggesting that APOBEC-mediated cytidine deamination is likely a major driver of mutations in this cancer type. Notably, our study also detected circular extrachromosomal DNA (ecDNA) events in these ESCC patient samples. The oncogenes COX6C , PVT1 , and MMP12 as well as the oncogenic long non-coding RNA AZIN1-AS1 which were detected in ecDNA regions in these analyses may be associated with worse disease-free survival in ESCC patients.