Search Results (16,128)

Whether learned cognition can affect evolutionary outcomes remains a long-standing question. This study addresses a narrower mechanism: whether a model-based planner benefits from learned rules that explicitly condition on the action just taken. The testbed is a spatial artificial ecology with plants, shelters, a predator, reproduction, and a day/night cycle. Five rule-use arms are evaluated on matched simulation seeds. At age 200, agents switch to a weaker learned-lite planner that relies more strongly on learned rule predictions. The pre-specified hypothesis is that access to filtered action-conditioned rules improves outcomes relative to an otherwise identical no-rule-policy baseline, in which rules are still induced and stored but are not used for action selection. In thirty paired replicates under the default reproductive gates, the action-conditioned arm outperforms the no-rule baseline on all four pre-specified primary endpoints. The strongest effect is behavioural: the action arm produces 91.4 additional successful post-switch eating events per run ($d_z=1.56$, 93.3% paired win rate, $p<{10}^{-4}$). It also produces 10 additional crystallized clean-causal rules per replicate ($d_z=0.58$, $p_t=0.0034$). All four primary paired-tp-values remain significant after Bonferroni correction across the four-endpoint family. A diagnostic check shows that omitting reproductive cooldown from the planner’s rollout reverses the arm ordering on the same paired seeds; reinstating cooldown recovers the reported result. Two exploratory checks delimit the claim: broad unfiltered rule access can impair foraging, and a means–ends extension shifts behaviour toward reproduction without producing a robust whole-life fitness gain. Within this simulation, access to action-conditioned rules has a measurable effect on post-switch behaviour that is distinct from passive environmental prediction and from clean-crystallized rules alone. Full article

Early detection of cucumber mosaic virus (CMV) and potato virus X (PVX) infection at the pre-symptomatic stage is essential for timely disease management and for limiting viral spread. Conventional molecular assays are accurate, but they generally require destructive sampling and are time-consuming. To enable rapid and non-destructive identification of these two viruses before visible symptom development, Nicotiana benthamiana seedling leaves were used as experimental materials, and CMV and PVX were selected as target viruses. A hyperspectral dataset of healthy, CMV-infected, and PVX-infected pre-symptomatic samples was constructed and validated by RT-PCR. After spectral preprocessing, eight selected wavelengths were identified and used to develop a 1D-CNN model, a MobileNetV3 model, and a spectral–spatial dual-branch fusion model. The 1D-CNN achieved an accuracy of 0.9074, whereas MobileNetV3 achieved 0.6835. The feature-level fusion model performed best, with an accuracy of 0.944, a precision of 0.945, a recall of 0.944, and an F1-score of 0.944. These results suggest that spectral information provides the primary discriminative basis, while image information offers complementary spatial and textural features for early non-destructive detection of plant viruses. Full article

Background: Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a group of chronic gastrointestinal (GI) diseases with complex and multifactorial pathophysiology. The global prevalence of IBD is increasing, highlighting the need to develop new therapeutic approaches. Plant-derived extracts have recently gained prominence due to their anti-inflammatory properties. Methods: This study investigated: apricot leaves (ALE), peach leaves (PLE), black chokeberry fruit (BCHE), rosehip seeds (RSE), passion fruit seeds (PSE), and linden blossom (LBE) (all at the concentration 10–200 µg/mL) in RAW 264.7 mouse macrophages. Cytotoxicity was assessed using the neutral red uptake (NRU) assay, and anti-inflammatory activity was assessed using Griess assay in the lipopolysaccharide (LPS)-induced inflammation. Additionally, the mRNA expression levels of key inflammatory genes (interferon-γ (Ifn-γ), interleukin-6 (Il-6), nitric oxide synthase (Nos2), and tumor necrosis factor-α (Tnf-α)) were analyzed. Results: ALE and PLE exhibited minimal cytotoxicity and strong anti-inflammatory activity, reducing the expression of all analyzed genes. PSE demonstrated anti-inflammatory activity in the Griess assay, but did not alter mRNA expression. Conclusions: ALE and PLE exhibit promising anti-inflammatory properties and warrant further preclinical investigation. Comprehensive in vitro and in vivo studies are necessary to confirm these results. Full article

Cadmium (Cd) and lead (Pb) co-contamination in construction and demolition waste landfill soils presents a significant challenge to ecosystem health, necessitating effective remediation strategies. This study investigated a synergistic approach combining a composite amendment (compost, superphosphate, desulfurized gypsum) with seven plant species to elucidate the interactions driving metal immobilization and phytoextraction. The amendment significantly altered soil properties: it reduced total Cd while increasing its bioavailability, and enhanced soil fertility (e.g., elevated organic matter and total nitrogen). Plant responses varied: Solanum americanum Mill. and Tagetes patula L. exhibited high Cd phytoextraction capacity, whereas Lolium perenne L. sequestered Cd/Pb primarily in roots. The bacterial community shifted from an oligotrophic, stress-tolerant state (e.g., Sphingomonas-dominated) in contaminated soil to a copiotrophic, functionally active state (e.g., Streptomyces-enriched) in amended soil. Community structure was strongly correlated with available Cd, pH, and nutrient levels. Key microbial biomarkers were specifically enriched in different plant rhizospheres. In contrast, the fungal community exhibited minimal responsiveness. These findings demonstrate that remediation efficiency is governed by an integrated “amendment–plant–microbe” framework: amendments regulate metal bioavailability, plants execute extraction or stabilization, and the restructured microbiome supports nutrient cycling and plant health. This integrated remediation strategy directly supports the Sustainable Development Goals of the 2030 Agenda, especially on environmentally sound management of chemicals and wastes and land degradation neutrality. This mechanistic understanding underscores the necessity of combined biological and chemical strategies for sustainable remediation of co-contaminated soils, ultimately enabling ecological reclamation and safe recycling of such urban marginal lands into productive uses. Full article

The management of fine bauxite tailings, rich in clay minerals, represents an environmental and operational challenge for the aluminum industry. This study (Part 1) presents a pilot-scale investigation into the dewatering of these ultrafine tailings using a decanter centrifuge, 0.62 m in diameter, as an alternative to conventional wet storage. Tests were conducted at three bowl speeds, 1600 rpm, 1700 rpm, and 1800 rpm, corresponding to G-forces of 888, 1003, and 1124 G. The feed slurry behaved as a non-Newtonian, yield-pseudoplastic fluid, as confirmed by rheology tests. A comprehensive mass balance and performance analysis were conducted. The results demonstrated a monotonic improvement in key performance metrics with increasing bowl speed. Accordingly, increasing the G-force from 888 G to 1124 G improved the final cake solid content from 66.3% to 71.5% (by weight), together with an increase in the average solid recovery from 40.0% to 56.2%. Partition curve analysis revealed the primary limitation: while recovery of particles coarser than 20 µm was very high (>98%), recovery of particles finer than 20 µm remained low, ranging from 22.0% to 35.1%. Partition curve analysis using the Whiten model identified a mechanical cut size (d_50c) ranging from 9.72 µm to 12.0 µm. Hydraulic bypass increased from 8.35% to 14.9% with increasing bowl speed, indicating a significant non-size-selective component of separation. Rheological analysis further showed that the apparent viscosity at 100 s⁻¹ decreased from 0.332 to 0.111 Pa·s across the tested conditions, confirming enhanced slurry mobility and its contribution to increased ultrafine bypass. While overall solid recovery reached 56.2% at 1124 G, the mechanical capture of the ultrafine fraction (<5 µm) remains the primary bottleneck for industrial viability. It is concluded that while the decanter centrifuge is mechanically viable for producing a high-solid cake, the limited recovery of fines would create an unsustainable circulating load in an industrial plant. These results demonstrate that G-force alone, within the tested range, is insufficient to manage these tailings and provide the basis for the mathematical modeling required to design the process, as described in Part 2 of this investigation. Full article

As urban areas expand, the sustainable management of municipal water becomes a critical challenge, especially in arid and semi-arid regions facing severe water scarcity. Accurate assessment of urban plant water requirements (PWR) is essential for developing sustainable landscape architecture and resilient green infrastructure. In this study, a new quantitative equation (PWR_q) was developed as a regional proof of concept to adjust reference evapotranspiration estimates for hyper-arid conditions. A Tree Morphology Coefficient (K_tm) is introduced to combine canopy features (form, height) and leaf traits (size, density) with an updated drought-resistance coefficient (K_dr). Field measurements of 277 mature trees, representing 27 native and introduced species in Riyadh and Jeddah, Saudi Arabia, were analyzed. The framework explicitly includes an empirical multiplier to account for extreme urban heat island (UHI) effects and aerodynamic canopy scaling. Instead of direct empirical validation, the PWR_q model was benchmarked against established reference indices: Water Use Classification of Landscape Species (WUCOLS) and Simplified Landscape Irrigation Demand Estimation (SLIDE), showing strong alignment with established categorical indices and structural traits. The results confirm that the morphology-based method effectively makes previously subjective classifications objective. Notably, the quantitative assessment found that the dominant introduced species require about 3.5 times more water than native species. As a proof of concept, future research should empirically validate these findings against direct physical measurements, such as sap flow sensors or lysimeters. The proposed framework presents a practical, objective decision-support tool for municipal policymakers and landscape architects to optimize species selection, implement nature-based solutions (NBS), and achieve long-term sustainability in urban greening. Full article

High-throughput single-cell RNA sequencing (scRNA-seq) has substantially advanced plant transcriptional landscapes. However, decoding cell-type-specific transcriptional regulation in non-model crops like tomato (Solanum lycopersicum) remains challenging. An integrated computational pipeline was applied using high-dimensional weighted gene co-expression (hdWGCNA) and ensemble machine learning to analyze tomato leaf single-cell transcriptomes. Unsupervised clustering identified 19 cell subpopulations mapped to five major cell-types: mesophyll cells (50.6%), guard cells (31.0%), trichomes (8.3%), vascular cells (7.5%), and lamina epidermis (2.6%). hdWGCNA revealed eight cell-type-specific modules, linking mesophyll cells to photosynthesis and guard cells to redox homeostasis. Machine learning classifiers prioritized candidate transcription factors (TFs), with XGBoost achieving the highest accuracy (0.85) to define cell identity. A consensus of 33 core TFs was identified, from which four candidate TFs (SlWRKY-78, SlWRKY-75, SlERF-57, and SlGLK-49) were selected for in silico knockout (KO) analysis. The simulations predicted that these knockouts might dysregulate core functional pathways, such as serine-type endopeptidase inhibitor activity and protein binding. Furthermore, CellOracle simulations suggested that the virtual deletion of the guard-cell-associated SlWRKY-78 and SlWRKY-75 could induce a directional trajectory shift from the terminally differentiated guard cells back to the less differentiated mesophyll territory. These findings provide a promising computational framework for deciphering cell-type-specific regulatory programs in horticultural crops. Full article

Lentils constitute a strategically important crop within sustainable agricultural systems, particularly in the context of rising global demand for plant-based protein sources. In Switzerland, approximately 95% of lentil seeds are imported, underscoring the untapped potential for domestic production. This study systematically evaluated the performance of multiple lentil genotypes, alongside optimal seeding densities and growing seasons, through a series of field experiments conducted over five years. In addition, a sensory evaluation was performed on 12 selected genotypes to assess consumer-relevant quality traits. The findings revealed substantial variability in yield among genotypes, ranging from 0.9 to 1.6 t/ha; however, interannual variation exerted a more pronounced influence, with yields fluctuating between 0.1 and 2.0 t/ha. Notably, autumn-sown lentils achieved yields of up to 2.7 t/ha in three out of four growing seasons, even among genotypes lacking full winter-hardiness, indicating significant production potential under appropriate management conditions. Optimal plant densities were identified within the range of 180–240 plants/m². From an economic standpoint, higher seeding densities appear justifiable, as the increased seed costs are offset by corresponding gains in yield. Since intercropping of lentils with oats did not negatively affect grain yield nor the thousand kernel weight, the benefits of this cropping system are highlighted. Sensory analysis demonstrated statistically significant differences in attributes such as mealiness and juiciness, leading to the classification of genotypes into three distinct sensory clusters. Despite these differences, overall sensory variation was relatively limited, suggesting that genotype selection may be guided primarily by agronomic performance, climatic adaptability, and winter-hardiness, as well as by market preferences for seed colour and size. Collectively, these results highlight the potential of autumn sowing as a viable strategy to enhance lentil production and reduce the risk of crop failure in Swiss agricultural systems. Full article

Wastewater membrane bioreactors (MBRs) have become an important advanced treatment technology due to their ability to produce high-quality effluent suitable for discharge and water reuse. However, their broader and more sustainable application remains constrained by membrane fouling, elevated energy demand, and the operational complexity of coupled biological and membrane separation processes. This comprehensive review critically evaluates the growing application of machine learning (ML), explainable artificial intelligence (XAI), and digital twin (DT) technologies in MBR systems. Published studies on fouling prediction, energy optimization, effluent quality estimation, and intelligent operational support are critically evaluated, with explicit attention to model performance, dataset limitations, and generalizability. The reviewed literature shows that ML models, particularly ensemble methods, support vector machines, and deep learning approaches, have demonstrated strong potential for predicting major MBR performance indicators, including transmembrane pressure, permeate flux, fouling resistance, and selected effluent-quality variables. In parallel, XAI methods such as SHAP, LIME, and Anchors are increasingly being used to enhance model transparency and to reveal the dominant factors controlling process performance. Digital twin frameworks further extend this potential by enabling the integration of mechanistic understanding, online sensor data, data-driven prediction, and interpretable decision support within real-time operational platforms. Nevertheless, several barriers continue to hinder practical implementation, including the limited number of full-scale studies, the scarcity of openly accessible and standardized datasets, insufficient consideration of uncertainty and model drift, and the early-stage maturity of DT deployment in operational plants. The evidence reviewed suggests that integrating ML, XAI, and DT can substantially improve the reliability, interpretability, and operational efficiency of MBR systems. Future research should therefore focus on full-scale validation, the development of benchmark datasets, uncertainty-aware modeling, and practical deployment strategies for interpretable intelligent MBR management. Full article

Plant extracts and microbiological supernatants were subjected to qualitative and compositional analyses to characterize their bioactive profiles and assess their potential agricultural applications. The garlic (Allium sativum) extract was rich in allicin and selected free amino acids, contained betulin as the dominant triterpene, and displayed a favorable elemental profile with high levels of potassium, phosphorus, sulfur, calcium, and magnesium, with no detectable heavy metals. Detectable amounts of B-group vitamins and vitamin E isoforms were also identified. Qualitative phytochemical screening confirmed the presence of saponins and flavonoids in the garlic extract. The Jerusalem artichoke (Helianthus tuberosus) extract exhibited a significantly higher total phenolic content compared to the garlic extract, with qualitative analysis confirming the presence of saponins, tannins, and flavonoids, suggesting a broader spectrum of bioactive compounds. The two bacterial supernatants were characterized by HPLC analysis and differed in their metabolic profiles: the Enterobacter sp. fermentation broth contained glycerol, 2,3-butanediol, and acetic acid, while the Paenibacillus sp. supernatant additionally contained lactic acid, ethanol, and succinic acid, reflecting distinct fermentation pathways. The in vitro and greenhouse studies aimed to evaluate biological preparations for controlling wheat diseases caused by fungi of the Fusarium genus as well as diseases affecting the stem base. Plant extracts (garlic—Allium sativum, Jerusalem artichoke—Helianthus tuberosus) and supernatants (fermentation broths) obtained with the Paenibacillus and Enterobacter bacteria were tested at three concentrations. In laboratory experiments, the degree of inhibition of the growth of the mycelium of the tested fungal species was determined, while in greenhouse studies, the effectiveness in limiting the development of stem base diseases and the impact of the applied biopreparations on plant growth were evaluated. Among the plant extracts, H. tuberosus demonstrated superior antifungal activity, achieving up to 100% inhibition of R. cerealis mycelial growth at 10% concentration and reducing disease severity by 34.3% compared to the untreated control under greenhouse conditions. Paenibacillus sp. supernatant demonstrated strong in vitro antifungal activity. The results indicate that H. tuberosus extract represents a promising candidate for further field evaluation as a component of sustainable wheat protection programs. Full article

Background/Objectives: The Naxi people in Northwest Yunnan of China have used alcohol-soaked Pleione Pseudobulbus, which is the Pseudobulbus of Pleione bulbocodioides Rolfe (PBR), for lung diseases and tumors for a long period of time. This study aims to explore underlying mechanisms of bioactive ingredients in PBR, as well as to underscore the synergy between traditional medicine and modern pharmacological research. Methods: We verified the anti-tumor effects of the PBR extract through in vitro cell experiments, and explored its underlying mechanisms by combining untargeted metabolomics with network pharmacology to predict the related targets. Results: The anti-tumor potential of PBR extracts was systematically evaluated by integrating chemical profiling with in vitro cell-based assays. Untargeted metabolomics tentatively annotated metabolites spanning 12 major chemical classes, several of which have been previously reported to possess anti-tumor activity. To validate these annotations, prioritized candidates were further examined by LC-MS/MS against authentic reference standards at the nanogram scale, which confirmed the presence of sclareol—a naturally occurring diterpene with documented anti-tumor properties—as a constituent of PBR. Consistent with this chemical evidence, the PBR extract exerted multi-faceted anti-tumor effects in A549 lung cancer cells: it significantly suppressed proliferation, migration, and invasion; induced G0/G1-phase cell-cycle arrest; disrupted mitochondrial membrane potential; and modulated the expression of apoptosis-related proteins. Conclusions: By highlighting the pharmacological properties of cultivated PBR, we identified 118 overlapping targets between PBR compounds and lung disease-related targets, and we further selected 25 core lung cancer targets with high topological importance. This study suggests that the primary active compounds of Pleione bulbocodioides (Franch.) Rolfe may exert anti-lung cancer activity, potentially through targeting the EGFR tyrosine kinase inhibitor resistance pathway and the PI3K-Akt signaling pathway. Furthermore, in silico molecular docking suggested that the two major active compounds exhibited favorable predicted binding affinities with four core targets, particularly EGFR and AKT1, providing a basis for further experimental validation. These results support the potential value of Naxi traditional medicine and the need to further research onthese medicinal plants, thereby promoting Chinese herb medicine conservation efforts in the Naxi region. Full article

Vermicomposting is an environmentally sustainable, economically viable, and agronomically valuable method for converting organic waste into nutrient-rich soil amendments, thereby supporting sustainable development. However, the fertilization efficiency of vermicompost can vary significantly depending on the physicochemical properties of the feedstock used. This study aims to compare different feedstocks on vermicompost and evaluate their performance on soil fertility and plant nutritional status. Organic matter (OM), pH, salinity (EC), total Kjeldahl nitrogen (TKN), total phosphorus (TP) and total potassium (TK) of various vermicompost samples were taken into consideration to evaluate their fertilization efficiency as performance determinants in terms of plant growth, plant nutritional status, yield, crop quality and cost with the aim of determining the weights of the specific parameters in the total performance using multi-criteria decision-making (MCDM) methods. The integrated ENTROPY-TOPSIS method was used. Twenty-one different vermicompost feedstock analyses were collected from the literature and compared in order to create an agronomic performance ranking based on the selected criteria. The ENTROPY method revealed that the TP was the most influential factor (21.6%), followed by the EC (20.7%) and the TK (18.5%), while the OM had the lowest impact (11.3%). Based on the TOPSIS ranking, vermicompost from brewer’s spent grain achieved the highest performance, followed by cow manure plus rice straw and olive pruning waste, whereas paper waste ranked at the bottom. A comparative analysis with other objective MCDM weighting methods proved strong correlations, particularly with WENSLO, MPSI and LODECI methods, confirming the robustness of the ENTROPY method. Full article

Fog-dependent Tillandsia landbeckii in the hyper-arid Atacama Desert lacks the red-edge reflectance pattern that supports vegetation monitoring, motivating shortwave infrared (SWIR) approaches. We evaluated a newly developed UAV-borne multispectral SWIR camera system for estimating plant water status and additional plant functional traits (fresh and dry biomass, and N uptake) from four spectral bands (1100, 1200, 1510, and 1650 nm) across 20 destructively sampled plots. Of five traits tested, only canopy water content (CWC) retained statistically robust spectral associations after multiple-testing correction, with most significant predictors concentrated in the 1200–1510 nm wavelength region. A physically interpretable predictor, the mean spectral slope between 1200 and 1510 nm, yielded conditional cross-validated $R_{\mathrm{cv}}^2=0.51$ (${\mathrm{RMSE}}_{\mathrm{cv}}\approx 170$ g m${}^{-2}$), though fully selection-corrected estimates were substantially lower ($R_{\mathrm{cv}}^2=0.10$–$0.20$), reflecting feature-selection instability at the given sample size. The absence of robust biomass- and nitrogen-related signals is physically interpretable given the species’ atypical surface optics. While expanded sampling and independent validation remain necessary to establish transferable performance estimates, these results demonstrate that SWIR-based water-status retrieval is feasible for this spectrally challenging species, opening a pathway toward functional monitoring of fog-dependent desert ecosystems. Full article

Robot swarm manufacturing is a promising direction in smart manufacturing that aggre-gates multiple robots to collaboratively complete production jobs; however, achieving conflict-free scheduling remains a significant challenge. Traditional methods struggle to address this issue since robot swarms are inherently prone to conflicts. This article puts forward a non-sorted metaheuristic method to solve it. First, the conflict-free robot swarm manufacturing problem—integrating a multi-objective optimization problem (MOP), a flexible job-shop scheduling problem (FJSP) for job processing, and a flow-shop schedul-ing problem (FSP) for robot travel—is formulated as a multi-objective job-flow-shop scheduling problem (MJFSP). The robot swarm must accomplish all manufacturing jobs while achieving high manufacturing performance, energy efficiency, and conflict-free op-erations. Second, a non-sorted metaheuristic algorithm based on an artificial plant com-munity (APC) is proposed. It employs a sequential-pairwise single-elimination tourna-ment system (SSTS) to select elites with a time complexity of 𝑂(𝑛), which scales linearly with the population size (𝑛). This surpasses the sorting-based elite selection with poly-nomial time complexity employed in most metaheuristic methods, such as the 𝑂(𝑛²) of the non-dominated sorting genetic algorithm-III (NSGA-III). Third, an MJFSP benchmark dataset is built, and the experimental results uncover the complex dependencies between the FJSP for job processing and the FSP for robot traveling. The proposed method im-proves the makespan by up to 13.10% and reduces non-loaded energy consumption by up to 13.49%, achieving zero collision time and an average solution time 11.18% faster than NSGA-III. Full article

Damage caused by Frankliniella intonsa to sunflower seeds results in the emergence of rusty speckling on the seedcoat, severely compromising seed quality in recent years. Although chemical control has remained the primary management strategy, its application during the flowering period—when F. intonsa is the most active—poses significant risks to pollinating insects and natural enemies, highlighting the urgent need for effective and environmentally sustainable control alternatives. Previous studies have shown that F. intonsa is attracted by buckwheat and that it could be a promising trap crop for F. intonsa. Thus, the attractiveness of Fagopyrum esculentum and F. tataricum to F. intonsa was compared, and the preference of F. intonsa between two buckwheat varieties was examined. Furthermore, the behavioral responses of F. intonsa to volatiles emitted by these plants in different developmental stages were assessed. The study results indicated that F. intonsa had a clear preference for F. tataricum over F. esculentum. In cage trials, the selection rates of 2nd instar nymphs and adults of F. intonsa for F. tataricum were 61.63% and 60.19% at the seedling stage, and 60.74% and 62.50% at the full-bloom stage, all significantly surpassing those of F. esculentum. Olfactory bioassays further confirmed that flowers of F. tataricum were notably more appealing to both 2nd instar nymphs and adults of F. intonsa, with selection rates of 64.17% and 61.67%, respectively. Twenty distinct floral volatiles of two buckwheat varieties were detected through the phytochemical analysis. Orthogonal partial least squares-discriminant analysis (OPLS-DA) identified seven key compounds that accounted for the observed behavioral differences. Both 2nd instar nymphs and adults of F. intonsa demonstrated a significant selection for Δ-Cadinene, with the highest selection rates of 75.00% and 76.67% recorded at a concentration of 0.1 μg/μL. Furthermore, F. intonsa exhibited a marked attraction to higher concentrations of Verbenone, which was unique to F. tataricum, and (S)-2-Methyl-1-butanol, which was unique to F. esculentum. Field intercropping experiments confirmed that F. tataricum outperformed F. esculentum in trapping F. intonsa within sunflower plots. In conclusion, the results indicated that F. tataricum possessed considerable potential as a trap crop for the integrated management of F. intonsa in sunflower cultivation systems. Full article