Search Results (11,687)

Diffusion-based generative models achieve high-fidelity image synthesis; however, controlling internal representations for abstract visual concepts remains challenging due to the ambiguity of textual descriptions. In this work, we propose a prompt-guided concept-vector learning framework for the controllable manipulation of such concepts without requiring external human-annotated image pairs, segmentation masks, identity labels, or manually annotated editing targets. The method introduces a learnable concept vector optimized in the bottleneck (mid-block) feature space of a pretrained Stable Diffusion U-Net, while keeping all pretrained model parameters frozen. A multi-prompt data generation strategy based on paired positive and neutral prompts provides weak semantic guidance for capturing the target concept direction and reducing dependence on a single prompt formulation. The learned vector is further applied in an image-to-image setting through controlled noise injection and concept-guided denoising, enabling the semantic modification of real images while preserving structural content. The concept strength is controlled by a scaling parameter $\gamma$, while the image-to-image noise strength is controlled by $\beta$, allowing for a practical balance between semantic modification and structural fidelity. Experiments are conducted on two main abstract concepts, perfect skin and peaceful lake, with additional qualitative analysis on subjective portrait-level concepts. Quantitative evaluation using SSIM, LPIPS, and CLIP similarity demonstrates that the proposed method improves semantic alignment while maintaining structural preservation compared with Stable Diffusion image-to-image baselines. A human preference study further shows that concept-injected outputs are preferred in 76.0% of responses for perfect skin and 85.7% for peaceful lake. Ablation studies further demonstrate the controllability and robustness of the proposed framework. Overall, the method provides a simple and parameter-efficient approach for interpretable concept-level manipulation in diffusion models. Full article

Wheat leaf rust, caused by Puccinia triticina Erikss. (Pt), is a major foliar disease that poses a significant threat to wheat production in Henan Province, a major wheat-growing region of China. Elucidating the population genetic structure of Pt is critical for predicting pathogen dispersal trends and guiding disease management. In this study, 384 Pt isolates collected from 13 locations in Henan were genotyped using 11 polymorphic simple sequence repeat (SSR) loci. A total of 204 multilocus genotypes (MLGs) were identified. The Sanmenxia (SMX) population exhibited the highest genetic diversity (H = 3.42; G = 30.12; λ = 0.967), while Shangqiu (SQ) showed the lowest (H = 2.09; G = 4.59; λ = 0.782). Analysis of molecular variance (AMOVA) revealed that 87% of the total genetic variation occurred within populations and 13% among populations. Population structure analyses consistently separated the 13 populations into two genetic clusters, with Xinyang (XY) and SQ forming one distinct group and the remaining 11 populations from the western, central, and northern regions constituting the other. The relative migration network further supported this pattern, showing a highly interconnected network among the central and western populations, but with XY and SQ forming an isolated subnetwork. Full article

Reconstructing building models from urban SAR tomography (TomoSAR) point clouds is often constrained by limited resolution, low positioning accuracy in elevation, as well as data incompleteness and artifacts caused by microwave imaging mechanisms. These challenges seriously restrict the extraction of high-accuracy building models with structural details from TomoSAR point clouds. This paper proposes a refined urban building modeling method that effectively utilizes structural priors, including directionality, orthogonality, and potential symmetry. First, a piecewise fitting strategy integrated with density-based segmentation is employed to iteratively estimate the main directions of the buildings and capture finer geometric variations of complex façade footprints than simple-plane approximations. Second, a roof extraction algorithm combining an adaptive Doug-las–Peucker approach with symmetry evaluation and constraints is developed to regularize roof outlines and repair data defects. Crucially, to handle extreme cases where roof data are entirely missing, a novel building width estimation method based on building shadow analysis is proposed. Experiments conducted on the SARMV3D-1.0 and SARMV3D-3.0 point cloud datasets demonstrate that the proposed method significantly enhances reconstruction accuracy and geometric fidelity in urban regions compared to state-of-the-art approaches. Full article

Background: Lychee fruits are sweet and juicy, yet mitochondrial genomic data for this species remains scarce, limiting in-depth studies of its genetic and evolutionary characteristics. To address this gap, in this study, the abortive-seeded cultivar ‘Xianjinfeng’ (XJF) and the large-seeded cultivar ‘Xinqiumili’ (XQML) were selected for analysis. Using third-generation sequencing technology, we sequenced, assembled, and annotated their mitochondrial genomes, and compared their structural characteristics and evolutionary relationships. Results: Assembly revealed mitochondrial genome sizes of 579,270 bp for XJF and 579,261 bp for XQML, both with 45.41% GC content. The mitogenomes contain 396 repetitive sequences, including 47 tandem repeats and 165 dispersed repeats, with SSR loci primarily 10–14 bp in length. Each genome encoded 62 genes, comprising 22 tRNAs, 3 rRNAs, and 35 protein-coding genes. Further analysis revealed 15 homologous sequences originating from chloroplasts in both mitochondrial genomes, totaling 12,194 bp (2.11% of the mitochondrial genome). These included 9 tRNA genes, 4 rRNA genes, and partial protein-coding sequences. Additionally, 184 simple sequence repeats (SSRs) were identified in both cultivars, whereas 564 and 563 potential RNA editing sites were predicted by computational tools in XJF and XQML, respectively, indicating subtle genetic differences between the cultivars. This study also analyzed codon usage preferences, nucleotide diversity, and chloroplast-to-mitochondria gene transfer events. Collinearity and comparative genomics results indicate that lychee is closely related to Nephelium lappaceum L. and Xanthoceras sorbifolium Bunge within the Sapindaceae family. Conclusions: In this study, two high-quality lychee mitochondrial genomes were successfully assembled and annotated, enriching the mitochondrial genome resources of Sapindaceae plants and laying a foundation for future lychee phylogenetic and evolutionary studies of closely related species. Full article

Background: Strychnos madagascariensis Poir (Loganiaceae) is a drought-tolerant indigenous fruit tree of East and southern Africa, valued for its food, medicinal, and socio-economic contributions to rural communities. Despite its importance as a candidate food crop, intraspecific morphological and genetic diversity had not previously been characterized, and no simple sequence repeat (SSR) markers had been developed for this species, leaving breeders and conservation planners without the basic diversity baseline needed to prioritize material for domestication. Methods: This study assessed vegetative and reproductive trait variation, variance components, and broad-sense heritability, and SSR-based genetic diversity among 27 morphologically defined S. madagascariensis morphotypes at Bonamanzi Game Reserve, KwaZulu-Natal, South Africa. Three trees were measured per morphotype (81 trees total), over two growing seasons. Genetic diversity was characterized in one representative tree per morphotype using seventeen newly developed SSR loci, the first such markers reported for this species, and analyzed with population structure (STRUCTURE version 2.3.4), PCA, and Nei’s genetic distance. Results: Twenty-seven morphotypes were identified based on leaf colour, shape, hairiness and size, dominated by grey (41%), elongated (59%), less hairy (48%), and medium-sized (>50–90 mm) leaves. Fruit diameter and mass showed the highest inter-morphotype variation (r = 0.949) and also the highest broad-sense heritability (H² = 55.3% and 47.8%, respectively), indicating strong genetic control of these traits and their suitability as targets for selective breeding. Environmental variance exceeded genotypic variance for most traits. A total of 144 alleles were identified across 17 SSR loci (mean 4.24 alleles/locus; mean PIC = 0.31). Population structure gave a preliminary, tentative signal of two genetic clusters (K = 2) with substantial admixture, which we interpret cautiously, given the limited sampling depth. Conclusions: This is the first study to characterize intraspecific morphological variation in S. madagascariensis and the first to develop SSR markers for the species. The results provide a preliminary, single-site framework for conservation genetics and crop improvement that should be validated with larger, multi-site samples. Grey morphotypes GyEvH1, GyEvH2, GyEvH3, GyRlH1 and GyEH2 combined consistent fruiting performance with favourable fruit-trait values and are proposed as priority candidates for further evaluation in domestication and breeding programmes. Full article

Offshore wind farms are increasingly and rapidly expanding due to their ability to harness strong and consistent wind energy resources. Large offshore wind farms are connected to mainland grids through High-Voltage Direct Current (HVDC) technology. However, offshore wind farms can often experience disturbances related to sudden wind changes, voltage drops/dips, faults related to converter switching, and unbalanced grid conditions which affect both the HVDC operation and wind turbine output. As a result, there is a growing need for more advanced and reliable modeling and monitoring tools. Moreover, traditional proportional-integral (PI) controllers are widely applied in wind turbines and HVDC systems due to their simple structure, easy implementation, and reliability. However, PI controllers perform poorly under non-linear and abnormal/fast-changing conditions, especially during sudden drops in wind power and grid faults. With this background, this paper first develops a digital twin model of an offshore wind farm that enables remote operation and monitoring of individual wind turbines. Also, an artificial intelligence (AI)-based controller, namely a fuzzy logic controller (FLC), is proposed to maintain transient stability of a full digital twin-based offshore wind farm connected to the HVDC grid under fault conditions. The effectiveness of the proposed FLC is demonstrated by considering a digital twin-enabled 700 MW offshore wind farm. The performance of the proposed FLC has been compared with that of the PI controller. Simulations performed by the MATLAB/Simulink software show that during the moderate voltage dip at 15 s, the PI controller experienced a 29.8% power reduction with a recovery time of approximately 9 s, whereas the FLC reduced the power drop to 23.1% and recovered within 6 s. During the severe converter disturbance at 15 s, the PI controller recorded a 36.9% power reduction compared to 23.4% for the FLC. Similarly, during the short-duration turbulence at 15 s, the PI controller exhibited a 36.73% power drop and recovered in approximately 7 s, while the FLC limited the power reduction to 19.17% and recovered within 5s. Overall, the FLC provided improved voltage stability, faster recovery, reduced oscillations, and superior fault ride-through capability compared with the conventional PI controller, demonstrating its effectiveness for digital twin-enabled offshore wind farm application. Full article

Perovskite manganites (AMnO₃) and perovskite-like manganites (A’_1−xA_xMnO₃) are complex oxide materials that have attracted significant attention from the scientific community in recent years due to their structural flexibility, mixed-valence state, tunable electronic configuration, and multifunctional properties. This review systematically analyzes the synthesis methods, structural classification, and physicochemical characterization of perovskite manganites, as well as their magnetic, optical, electrical, dielectric, and catalytic properties. The influence of solid-state reactions, sol–gel, Pechini, hydrothermal, co-precipitation, microwave, and other mild chemical approaches on phase purity, morphology, particle size, and oxygen stoichiometry was examined. The structural diversity of perovskite and perovskite-like manganites, including simple ABO₃, double perovskites, multilayer, and low-dimensional systems, was characterized in relation to their functional properties. The review discussed the capabilities of methods for synthesizing and analyzing morphological properties, demonstrating the role of doping, cation substitution, oxygen vacancies, and Jahn–Teller distortions in controlling material properties. Prospects for the application of perovskite manganites in spintronics, magnetocaloric cooling, photocatalysis, gas-sensing devices, and energy conversion and storage systems were analyzed. This review highlights the structure–property–application relationship in perovskite manganites. Full article

Among a broad range of promising applications, the use of cellular solids as lightweight structural components is an important field of research that requires reliable predictions of their stiffness and strength. Predictive and general models should not depend on extensive parameter-fitting experiments and should not rely on computationally intensive numerical calculations for each new set of geometric parameters and loading conditions. An overview of models for 2D, 2.5D, and three-dimensional structures will be presented. Most 2D and 2.5D models neglect out-of-plane behaviour and the face sheets used in sandwich panels. 3D studies, mainly by finite element models (FEMs), are often limited to a narrow set of geometries and simple loading conditions. Elastic anisotropy is well covered, but calculating yield surfaces remains a challenge. Simplified models based on structural mechanics are rare and often limited in scope. They offer a flexible, computationally efficient approach for simulating truss-based materials. For more advanced designs, parameter-based FEMs must be developed for any loading condition to facilitate the generalised incorporation of 3D cellular solids in mechanical design. Artificial intelligence and machine learning are promising approaches for making optimal use of experimental and FEM results across multidimensional parameter spaces. Full article

Stilbocrea banihashemiana Bolboli, Tavakolian & Mostowf. is an emerging pathogen causing canker and dieback in a broad range of fruit and ornamental trees in Iran, and its distribution is expanding across the country. Extensive surveys conducted over five consecutive years (2019–2023) yielded 88 isolates of S. banihashemiana from multiple hosts, including different fig (Ficus caricae L.) cultivars, as well as loquat (Eryobotria japonica (Thunb.) Lindl.), pomegranate (Punica granatum L.), and walnut (Juglans regia L.) trees, across eight distinct regions of southern Iran. Species identification was performed morphologically and molecularly by employing the S. banihashemiana-specific primer pair TEF-Sb1 and TEF-Sb3. The genetic diversity of the S. banihashemiana population of isolates was assessed using eight inter-simple sequence repeats (ISSRs) markers. The UPGMA dendrogram demonstrated broad genetic variability among the isolates, with similarity coefficient values spanning from 0.46 to 1.00. This wide range indicates the presence of multiple divergent genotypes within the population, rather than a single dominant lineage. Principal coordinate analysis (PCoA) grouped the 88 isolates into three distinct genetic clusters that partially corresponded to geographic origin and host species. Pathogenicity assessment of 53 selected isolates from various hosts and geographic origins on detached fig shoots demonstrated highly significant variability in aggressiveness among isolates originating from different host species and geographically distinct regions. Multivariate analysis using principal component analysis (PCA) combined with heatmap-based clustering of the aggressiveness dataset clearly separated the isolates into four distinct groups, ranging from highly to less aggressive. A susceptibility assessment of 10 fig cultivars using the ex-type-isolate of S. banihashemiana revealed that the pathogen caused internal lesions and wood discoloration in all cultivars. Based on statistical analysis, the cultivars were classified into three groups: susceptible (cv. ‘Siah’), moderately susceptible (‘Brown Turkey’, ‘C8-M’, ‘C8-F’, ‘Dehdez’, ‘Gilasi’, ‘Payves’, ‘Shah-Anjeer’ and ‘Sabz’), and less susceptible (‘Matti’). High genetic variability, multiple-host association, and partial geographic structure indicate that in Fars Province S. banihashemiana’s population structure and epidemiology are complex, with high adaptive potential. This complexity may influence disease spread, management strategies, and long-term evolutionary trajectories. Full article

With the deep integration of artificial intelligence and big data, intelligent optimization algorithms have become key tools for solving many complex problems. However, as problem scale and complexity grow rapidly, the performance of traditional algorithms often faces significant challenges. The Teaching Learning Based Optimization algorithm has attracted widespread attention for its simple structure, few parameters, and high solution efficiency, and has been successfully applied across various engineering and scientific fields. Nevertheless, when dealing with high-dimensional, multimodal global optimization problems and real-world applications, the standard Teaching Learning Based Optimization still exhibits certain limitations, such as reduced accuracy of the optimal solution due to insufficient initial population diversity, and difficulty in escaping local optima caused by premature convergence. To address these issues, this paper proposes an Improved Teaching Learning Based Optimization algorithm. The improved ITLBO upgrades original TLBO from three perspectives: first, a population interaction strategy combining chaotic disturbance and Gaussian mutation is designed to enrich initial population diversity; second, bipolar cooperative search utilizing dynamic weighting of optimal and worst individuals balances global exploration and local exploitation to avoid premature convergence; third, oscillatory random mapping learning with sinusoidal oscillation factor periodically perturbs individuals to continuously replenish population diversity in iterations. Numerical results show that the proposed method exhibits superior convergence performance and stability on classical global optimization benchmarks. Furthermore, the algorithm is applied to practical cloud resource scheduling problems, and experimental outcomes verify that ITLBO improves solution accuracy by approximately one order of magnitude over original TLBO and reduces small-scale cloud scheduling cost by 12% while achieving preferable robustness. Full article

The genus Aegilops comprises important wild relatives of cultivated wheat and represents a valuable genetic resource for wheat improvement. In this study, the complete chloroplast genomes of five Aegilops species (Ae. crassa, Ae. cylindrica, Ae. juvenalis, Ae. tauschii, and Ae. triuncialis) collected from Kazakhstan and Uzbekistan were sequenced, assembled, and comparatively analyzed. The chloroplast genomes exhibited a conserved quadripartite structure consisting of a large single-copy (LSC), a small single-copy (SSC), and two inverted repeat (IR) regions. Genome sizes ranged from 135,612 to 136,840 bp, with an identical GC content of 38% across all species. Comparative analyses revealed high structural conservation among chloroplast genomes, particularly within IR regions, whereas greater sequence divergence was observed in the non-coding regions of the LSC and SSC. Sliding-window analysis identified several highly polymorphic regions, including rpl32-trnL(UAG), ndhF-rpl32, trnC(GCA)-rpoA, psbA, and ndhD, which may serve as potential DNA barcodes and informative markers for phylogenetic studies. A total of 850 chloroplast simple sequence repeats (SSRs) were detected, predominantly A/T-rich mononucleotide repeats. Codon usage analysis demonstrated a conserved preference for A/U-ending codons across all species. Ka/Ks analysis indicated that most chloroplast protein-coding genes are under strong purifying selection, although relatively elevated evolutionary rates were detected in rpoA and ycf4. Phylogenetic analyses based on complete chloroplast genomes strongly supported sectional relationships within Aegilops and confirmed close maternal relationships among several species. Overall, this study provides chloroplast genome resources for Aegilops and contributes to understanding chloroplast genome evolution, phylogeny, and molecular marker development. Full article

This work presents the kinematic analysis of a redundant planar parallel manipulator within the framework of screw theory. The main contribution of this work is the introduction and kinematic modeling of a novel redundant planar parallel manipulator topology composed exclusively of revolute joints. The proposed architecture is motivated by the search for structurally simple mechanisms with favorable analytical properties for screw-theoretic formulation and potential applications in robotic systems requiring compact and efficient planar motion. For completeness, the displacement analysis is included. Thanks to the simple topology of the otherwise complex mechanism, the inverse–forward displacement problem is resolved through straightforward quadratic equations. The velocity input–output relationship is derived without reliance on passive joint rate velocities, and the acceleration input–output equation is obtained independently of passive joint rate accelerations. These simplifications are achieved by exploiting reciprocal line properties. Numerical examples are provided to illustrate the robustness and effectiveness of the proposed kinematic analysis method across the main topics addressed in this contribution. Full article

As the largest inter-basin water diversion project in eastern China, the Eastern Route of China’s South-to-North Water Diversion Project (ER-SNWDP) plays a crucial role in alleviating water shortages and ensuring regional ecological security. However, large-scale water diversion that uses natural lakes as impounded lakes across different basins has impacted on the structure and function of the original ecosystems. To explore the changes in the food web and ecosystem structure of the impounded lakes during different operation periods of the ER-SNWDP, we constructed Ecopath models for Hongze Lake in 2010–2011 (pre-operation), 2017–2018 (initial operation), and 2023–2024 (operational period). Our results showed that the trophic energy flow in Hongze Lake was dominated by the detrital food chain, with the highest trophic level ranging from 3.06 to 3.50. Energy flows at trophic levels I and II accounted for a high proportion of the total throughput, and the interactions between trophic levels were relatively simple, indicating that Hongze Lake is approaching a mature ecosystem. Compared with the pre-operation period, the average trophic level, food chain length, and energy conversion efficiency of Hongze Lake declined during the initial operation period, but rebounded during the operational period, though still remaining lower than the pre-operation period. Ecosystem stability followed a similar trajectory: the total primary production/total respiration (TPP/TR) and the system omnivory index (SOI) indicated that ecosystem maturity decreased during the initial operation and increased during the operational period. Fishing activities had negative effects on most functional groups during the pre-operation and initial operation periods, whereas the negative effects from zooplankton and non-native species groups increased during the operational period. Based on changes in the food web structure and ecosystem of Hongze Lake across different water diversion periods, we suggest that the management of Hongze Lake should establish precautionary fishing management measures targeting the effects of filter-feeding functional groups and non-native species, optimize the species and quantities of restocking initiatives, prioritize the protection of critical habitat integrity, and implement long-term ecological monitoring. Full article

Digital policy has become an important instrument for promoting sustainable agricultural modernization. However, its effectiveness depends on the strategic responses of the government, agricultural operators, and farmers. This study develops a theoretical framework to examine how digital policy affects sustainable agricultural modernization through multi-agent interaction. Specifically, it constructs a three-party evolutionary game model and a Stackelberg game model to analyze strategy evolution under different implementation costs, subsidies, and penalties, as well as the government’s first-mover role in subsidy design. The results show that digital policy does not promote sustainable agricultural modernization through a simple linear pathway. Instead, it operates by reshaping the incentive structures of agricultural operators and farmers. Lower government implementation costs increase the likelihood of active policy implementation, while subsidies for agricultural operators and farmers strengthen their willingness to adopt digital tools, engage in standardized production, and participate in digital agricultural activities. However, the marginal effect of subsidies weakens as participation and digitalization increase, indicating that unlimited subsidy expansion may reduce policy efficiency and increase fiscal pressure. This study contributes to the literature by linking digital policy design, multi-agent strategic interaction, and sustainable agricultural modernization within a unified theoretical framework. It highlights that effective digital agricultural policy requires incentive compatibility, fiscal sustainability, inclusive participation, and adaptive governance, rather than reliance solely on digital technology investment or subsidy expansion. Full article

Across all kingdoms of life, ribosomes are indispensable molecular machines that translate genetic information into the proteome of living cells. The fundamental catalytic centers of the ribosome, constructed primarily from ribosomal RNA (rRNA), exhibit remarkable conservation between the major domains of life. The ribosome’s A-site deciphers the mRNA’s triplet code, while the P-site synthesizes the growing protein chain and the E-site provides exit for deacylated tRNA; a distinct tunnel facilitates nascent polypeptide export. While the conservation of ribosomal proteins is less pronounced between bacteria and eukaryotes, striking homology exists from simple eukaryotes to humans. Ribosomal proteins were traditionally viewed mainly as scaffolding agents, steering rRNA folding during ribosome biogenesis and maintaining structural stability during translation. However, since the early 2000s, advances in structural and functional ribosome analysis have ushered in a more nuanced paradigm: ribosomes are no longer considered uniform machines. Instead, an array of rRNA and ribosomal protein modifications generates a spectrum of ribosome populations capable of specialized translation. RiboScreen^TM technology leverages this regulatory potential of individual ribosomal proteins, enabling deliberate modulation of target protein output and representing a promising tool for correcting dysregulated protein expression involved in rare and common diseases. This review will first introduce relevant aspects of ribosome biology and then showcase the tools of this new technology. Finally, we report examples for the delivery of small molecules to target ribosomal proteins for tailored restoration of protein production levels in rare and prevalent diseases. Full article