Search Results (2,447)

To address the low trajectory tracking accuracy and limited robustness of conventional reinforcement learning algorithms under complex marine environments involving wind, wave, and current disturbances, this study proposes a proximal policy optimization (PPO) algorithm incorporating an intrinsic curiosity mechanism to solve the unmanned surface vehicle (USV) trajectory tracking control problem. The proposed approach is developed on the basis of a three-degree-of-freedom (3-DOF) USV model and formulated within a Markov decision process (MDP) framework, where a multidimensional state space and a continuous action space are defined, and a multi-objective composite reward function is designed. By incorporating a pure pursuit guidance algorithm, the complexity of engineering implementation is reduced. Furthermore, an improved PPO algorithm integrated with an intrinsic curiosity mechanism is adopted as the trajectory tracking controller, in which the exploration incentives provided by the intrinsic curiosity module (ICM) guide the agent to explore the state space efficiently and converge rapidly to an optimal control policy. The final experimental results indicate that, compared with the conventional PPO algorithm, the improved PPO–ICM controller achieves a reduction of 54.2% in average lateral error and 47.1% in average heading error under simple trajectory conditions. Under the complex trajectory condition, the average lateral error and average heading error are reduced by 91.8% and 41.9%, respectively. These results effectively demonstrate that the proposed PPO–ICM algorithm attains high tracking accuracy and strong generalization capability across different trajectory scenarios, and can provide a valuable reference for the application of intelligent control algorithms in the USV domain. Full article

The present paper analyses the extent to which European Union regulatory frameworks induce the development of circular agriculture within the European Union. In order to evaluate the progress towards circular agriculture within the European Union, a composite Agricultural Circularity Index (ACI) was developed for all EU-27 Member States for the period of 2014–2023. An expert interview and a TOPSIS multi-criteria decision-making technique were employed for the construction of the ACI. Results indicate only marginal improvements in the development of circular agriculture on the aggregate EU level, although a pronounced cross-country divergence towards achieving circularity in agriculture was observed. The following four distinct trajectories in the evolution of the circular economy within the EU were distinguished: structurally advancing promoters, short-term breakthrough cases, high baseline, but eroding systems, and mixed or stagnating countries. Indicator decomposition analysis reveals that durable circularity gains in agriculture arise when increased material recirculation coincides with verifiable bandwidth, whereas intensifying input use frequently negates the progress. The findings underscore that regulatory ambition alone is insufficient: implementation design, uncompromised enforcement, and market integration determine whether initial initiatives towards circular agriculture materialize into sustainable practices or remain transitory. From a policy perspective, the ACI functions as a diagnostic tool to locate structural bottlenecks and to target CAP-style interventions where circular flows can be scaled most effectively. Full article

The rapid development of detection technologies has increased the demand for multispectral camouflage materials capable of broadband concealment and effective thermal management. To address the conflicting optical requirements between infrared camouflage and LiDAR camouflage, we propose a composite design combining a germanium–ytterbium fluoride (Ge/YbF₃) selective emitter with an amorphous silicon (a-Si) two-dimensional periodic microstructure. The multilayer film, optimized using the transfer-matrix method and a particle swarm optimisation algorithm, achieves low emissivity in the 3–5 μm and 8–14 μm infrared atmospheric windows and high emissivity within 5–8 μm for radiative cooling, while introducing a narrowband absorption peak at 1.55 μm. Additionally, the a-Si microstructure provides strong narrowband absorption at 10.6 μm via a grating-resonance mechanism. FDTD simulations confirm low emissivity in the infrared windows, high absorptance at LiDAR wavelengths, and good angular and polarization robustness. This work demonstrates a multifunctional photonic structure capable of integrating infrared camouflage, laser camouflage, and thermal-radiation control. Full article

Background: Type 2 diabetes mellitus (T2DM) is a global epidemic in which gut microbiota dysbiosis contributes to impaired glucose homeostasis and chronic inflammation. Intermittent fasting (IF) and probiotic supplementation have independently demonstrated glycemic benefits in T2DM, largely through microbiota remodeling. This narrative review synthesizes evidence up to October 2025 to clarify the microbiota-dependent mechanisms of IF and probiotics, and to evaluate the biological plausibility and preliminary clinical data for their combined application in T2DM management. Methods: We conducted a comprehensive literature review of preclinical and clinical studies (PubMed, Embase, Web of Science, and Cochrane Library) examining IF regimens (primarily time-restricted feeding and 5:2 protocols) and multi-strain probiotics containing Lactobacillus and Bifidobacterium species in T2DM or relevant models. Mechanistic pathways, microbial compositional shifts, and metabolic outcomes were qualitatively synthesized, with emphasis on overlapping signaling (short-chain fatty acids, bile acids, GLP-1, and barrier function). Results: IF consistently increases Akkermansia muciniphila and, variably, Faecalibacterium prausnitzii abundance, restores microbial circadian rhythmicity, and enhances SCFA and secondary bile acid production. Multi-strain probiotics modestly reduce HbA1c (–0.3% to –0.6%) and fasting glucose, outperforming single-strain preparations. Both interventions converge on reduced endotoxaemia and improved intestinal integrity. Preclinical models indicate potential synergy, whereas the only direct human trial to date showed neutral results. Conclusions: IF and probiotics engage overlapping microbiota-mediated pathways, supporting their combined use as an adjunctive strategy in T2DM. Adequately powered randomized trials incorporating deep metagenomics, metabolomics, and hard clinical endpoints are now required to confirm additive or synergistic efficacy. Full article

The growing accumulation of plastic and electronic waste highlights the urgent need for sustainable and biodegradable polymers. However, developing intrinsically conductive biodegradable polymers remains challenging, particularly for packaging and sensing applications. Poly(lactic acid) (PLA) is intrinsically non-conductive, and enhancing its functionality without compromising structural integrity is a key research goal. In this study, PLA-based filaments were developed using melt extrusion, incorporating cellulose nanocrystals (CNCs), graphene nanoplatelets (GNPs), and carbon nanotubes (CNTs), individually and in hybrid combinations with total filler contents between 1 and 5 wt%. The inclusion of CNC enhanced the dispersion of GNP and CNT, promoting the formation of interconnected conductive networks within the PLA matrix, allowing the percolation threshold to be reached at a lower fillers concentration. Hybrid formulations showed a balance melt strength and processability suitable for fused deposition modelling (FDM) 3D printing and prototypes successfully made. This study also provides the first systematic evaluation of temperature-dependent thermal conductivity of PLA-based composites at multiple temperatures (25, 5, and −20 °C), relevant to typical food and medical supply chains conditions. Full article

We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR activation/interference (A/I) that operates directly on two-snapshot (“pre → post”) measurements and yields parameters with clear mechanistic meaning. The core log–linear–exp residual block exactly represents multiplicative effects, so synergistic multi-locus responses appear as explicit components rather than opaque composites. On a 53k-cell × 18k-gene neuronal Perturb-seq matrix, a three-replica LazyNet ensemble trained under a matched 1 h budget achieved strong threshold-free ranking and competitive error (genome-wide r ≈ 0.67) while running on CPUs. For comparison, we instantiated transformer (scGPT-style) and state-space (RetNet/CellFM-style) architectures from random initialization and trained them from scratch on the same dataset and within the same 1 h cap on a GPU platform, without any large-scale pretraining or external data. Under these strictly controlled, low-data conditions, LazyNet matched or exceeded their predictive performance while using far fewer parameters and resources. A T-cell screen included only for generalization showed the same ranking advantage under the identical evaluation pipeline. Beyond prediction, LazyNet exposes directed, local elasticities; averaging Jacobians across replicas produces a consensus interaction matrix from which compact subgraphs are extracted and evaluated at the module level. The resulting networks show coherent enrichment against authoritative resources (large-scale co-expression and curated functional associations) and concordance with orthogonal GPX4-knockout proteomes, recovering known ferroptosis regulators and nominating testable links in a lysosomal–mitochondrial–immune module. These results position LazyNet as a practical option for from-scratch, low-data CRISPR A/I studies where large-scale pretraining of foundation models is not feasible. Full article

The relationship between insect diversity and crop production has been of continuous scientific interest. Understanding insect community dynamics using various sampling and monitoring methods at different crop phenology stages is crucial for enhancing pest management and ecosystem service functioning. The present study assessed the influence of four trap types (Blue, Yellow, White, and Malaise) applied at four tomato developmental stages (start of planting, flowering, flowering fruit development and harvest) on insect diversity in northeastern Tunisian open-field conditions. A total of 1771 insect individuals belonging to seven orders and 31 families were trapped, with the order Hymenoptera being the most common in the sampled plots, which was represented by 25 families. Trap type exerted a strong effect on both abundance and alpha diversity parameters. Yellow pan traps showed the highest diversity, with family richness (S) ranging from 1 to 16, Shannon diversity (H) reaching 2.54, Simpson (Is) diversity ranging from 0.72 to 0.90 and Pielou’s evenness (J) ranging from 0.83 to 0.98. Blue and white traps displayed intermediate diversity (Blue: S = 6 and H = 1.7; White: S = 7 and H = 1.6), while Malaise traps captured the least diverse assemblages (S = 4, H = 1.2 and Is = 0.65). These differences were highly significant (p < 0.05). Phenological stage significantly structured Hymenoptera diversity. Richness peaked at the start of planting (S = 1–16 and H up to 2.54) and declined sharply at harvest (S = 1–6). Pollinator families (Apidae, Halictidae, Megachilidae) were the most abundant during flowering, whereas parasitoid families (Braconidae, Eulophidae) dominated during the fruit development stage. Beta diversity analyses (NMDS, stress = 0.25) and PERMANOVA showed that trap type and phenological stage jointly explained 15.5% of the variation in community composition (R² = 0.155, p = 0.014). Although a strong taxonomic overlap among traps was observed, Indicator Value analysis revealed significant trap-specific associations, including the family Andrenidae with Blue traps and the family Scoliidae with White and Yellow traps. Overall, the results of the present study demonstrate that both trap type and crop phenology significantly influence insect population diversity. A multi-trap sampling strategy combining colored pan traps and Malaise traps could be recommended to accurately characterize insect communities and associated ecosystem services in Mediterranean open-field tomato systems. Full article

Reducing the consumption of energy-intensive cement and promoting the resource utilization of industrial waste are two critical challenges that should be urgently addressed to achieve the goals of carbon neutrality and green sustainable development in the building materials field. Among these, the massive stockpiling of industrial waste such as fly ash and silica fume poses serious threats to the environment and human health, making their efficient utilization an urgent need to alleviate environmental pressure. This study employs the ice-template method to incorporate fly ash and silica fume as functional components into a cement-based system, fabricating a novel composite material. This material features a layered porous structure, which not only reduces cement usage but also results in a lighter weight. The introduction of the ice-templating method successfully constructed an anisotropic lamellar structure, leading to significant enhancements in flexural strength and toughness—by approximately 26.6% and 30%, respectively, vertical to the lamellae compared to conventional dense cement. Meanwhile, the hybrid blend of silica fume and fly ash effectively improved the deformability of the material, as evidenced by a notable increase in compressive failure strain. These excellent behaviors of mechanical properties are attributed to the formation of a multi-scale microstructure characterized by “macroscopically continuous and microscopically dense” features. Moreover, this specific microstructure offers greater advantages in sound absorption performance. The acoustic impedance tube tests demonstrate that the noise reduction coefficient of the novel cement-based material incorporating fly ash and silica fume is improved by 82%, holding promising applications in noise reduction for the construction and transportation fields. This research provides a feasible pathway for the high-value application of industrial solid waste in low-carbon materials. Full article

This study investigated the molecular and microbial factors behind the higher disease resistance of hybrid taimen by combining gut microbiome profiling with host transcriptomic analysis of intestinal and liver tissues. Both hybrid taimen and H. taimen were raised under the same recirculating aquaculture system (RAS) conditions. After recording survival rates following three enteritis outbreaks, samples of intestinal contents and tissues were collected from both groups. The gut microbiota was analyzed using full-length 16S rRNA sequencing in PacBio, and host gene expression was assessed with Illumina RNA-seq. Functional predictions were made using PICRUSt2 and Gene Set Enrichment Analysis (GSEA). Results showed that hybrids had significantly higher survival rates after enteritis (p < 0.05). Although microbial alpha diversity was similar, beta diversity revealed slight compositional differences. Hybrids showed higher levels of Hapalosiphon and Tepidimicrobium, microbes associated with antimicrobial compounds and the metabolism of short-chain fatty acids (SCFAs). Functional predictions indicated enrichment in selenocompound metabolism and ansamycin biosynthesis in hybrids. Transcriptomic analysis identified 4233 differentially expressed genes (DEGs) in the intestine and 3980 in the liver. In hybrids, intestinal tissues exhibited increased expression of immune pathways, including complement activation, lysosomal activity, and the transforming growth factor-beta (TGF-β) signaling pathway. Liver tissues demonstrated higher expression of genes related to cholesterol synthesis, fatty acid degradation, and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. qRT-PCR validated the expression patterns of 20 selected DEGs. These findings tentatively suggest that the elevated disease resistance of hybrid taimen may be linked, at least in part, to a combination of microbial taxa inferred to produce antimicrobial metabolites and short-chain fatty acids, as well as an apparent intensification of intestinal immune and barrier-related gene expression, and hepatic pathways that possibly support energy supply and steroid-based immunity. However, this multi-omics data set is only correlational. We still do not know whether a single strain or a few host genes are enough to produce the resistant phenotype. Gnotobiotic trials, microbiota transplants, and targeted metabolomics will be necessary to turn these interesting associations into solid evidence. Full article

Perovskite solar cells (PSCs) have emerged as a rising next-generational photovoltaic technology due to low fabrication costs through solution processing as compared to traditional silicon solar cells and high-power conversion efficiency. However, the poor long-term operational stability due to environmental and mechanical degradation remains a hindrance to commercialization. Herein, self-healing polymer additives are utilized by researchers to enhance the photovoltaic performance of PSCs by enabling self-restorative behavior from physical damage or chemical degradation. This review explores the design and application of self-healing polymers in both flexible and rigid PSCs, contrasting the two main reversible bonding mechanisms: physical bonds, such as hydrogen bonds, and chemical bonds, such as dynamic covalent disulfide bonds. Physical bonds provide passive healing at ambient conditions; meanwhile, chemical bonds offer a stronger restoration under external stimuli such as heat or light. These polymers are exceptionally effective at mitigating mechanical stress and cracks in flexible PSCs and combating moisture-induced degradation in rigid PSCs. The applications of self-healing polymers are categorized based on substrate type, healing mechanism, and perovskite composition, with the benefits and limitations of each approach highlighted. Additionally, the review explores the potential of multifunctional self-healing polymers to passivate defects at the grain boundaries and on surface of perovskite films, thereby enhancing the overall photovoltaic performance. Full article

Background/Objectives: The vaginal microbiota constitutes a highly dynamic microbial ecosystem shaped by the distinct mucosal, hormonal, and immunological environment of the female genital tract. Accumulating evidence suggests that shifts in cervical microbial composition and function may influence host–microbe interactions and contribute to gynecological disease risk. Within this framework, the present study aimed to perform an in-depth genomic characterization of the cervical microbiota in a well-defined cohort of Greek women. The primary objective was to explore the functional microbial landscape by identifying dominant bacterial taxa, taxon-specific signatures, and potential microbial pathways implicated in cervical epithelial homeostasis, immune modulation, and disease susceptibility. Methods: Microbial genomic DNA was isolated from 60 cervical samples using the Magcore Bacterial Automated Kit and analyzed through full-length 16S rRNA gene sequencing using the Nanopore MinION™ platform, allowing high-resolution taxonomic assignment and enhanced functional inference. In parallel, cervical samples were screened for 14 HPV genotypes using a real-time PCR-based assay. Results: The cervical microbial communities were dominated by Lactobacillus iners, Lactobacillus crispatus, and Aerococcus christensenii, collectively representing over 75% of total microbial abundance and suggesting a functionally protective microbiota profile. A diverse set of low-abundance taxa—including Stenotrophomonas maltophilia, Stenotrophomonas pavanii, Acinetobacter septicus, Rhizobium spp. (Rhizobium rhizogenes, Rhizobium tropici, Rhizobium jaguaris), Prevotella amnii, Prevotella disiens, Brevibacterium casei, Fannyhessea vaginae, and Gemelliphila asaccharolytica—was also detected, potentially reflecting niche-specific metabolic functions or environmental microbial inputs. No HPV genotypes were detected in any of the cervical samples. Conclusions: This genomic profiling study underscores the functional dominance of Lactobacillus spp. within the cervical microbiota and highlights the contribution of low-abundance taxa that may participate in metabolic cross-feeding, immune signaling, or epithelial barrier modulation. Future large-scale, multi-omics studies integrating metagenomics and host transcriptomic data are warranted to validate microbial functional signatures as biomarkers or therapeutic targets for cervical health optimization. Full article

The mucus layer covering the gastrointestinal tract forms a specialised interface where mucins, microbes, and extracellular vesicles create a dynamic, self-regulating ecosystem. Here, we introduce the concept of the muco-microbiotic layer as an integrated eco-physiological system that maintains mucosal homeostasis through coordinated structural, metabolic, and immune functions. The MuMi layer varies regionally in its biochemical composition, microbial inhabitants, and environmental parameters—from the acidic stomach to the anaerobic colon—thereby generating distinct niches for microbial colonisation and metabolite production. We summarise current evidence on how mucin glycans, mucus-associated microbiota, and vesicle-mediated signalling sustain barrier integrity, nutrient flux, and immune tolerance. Perturbations in any of these components lead to barrier failure, microbial encroachment, and inflammation, contributing to a broad spectrum of disorders, including gastritis, inflammatory bowel disease, colorectal cancer, and metabolic syndrome. Methodological advances such as organoid and mucus-on-chip models, spatial multi-omics, and vesiculomics are now enabling site-specific analyses of this complex system. Conceptually, defining the mucus, microbiota, and vesicular compartments as a single MuMi layer provides a new framework for understanding mucosal physiology and pathophysiology, emphasising the interdependence between structure and function. Integrating this perspective into experimental and clinical research may open new avenues for diagnostics and therapies targeting mucosal health. Full article

This review synthesizes research on nuclear factor erythroid 2-related factor 2 (Nrf2) in intestinal health across human, livestock, and mouse models. The Nrf2 signaling pathway serves as a master regulator of cellular antioxidant defenses and a key therapeutic target for intestinal inflammatory disorders, including ulcerative colitis and Crohn’s disease. The interplay between oxidative stress, Nrf2 signaling, and NF-κB inflammatory cascades represents a critical axis in the pathogenesis and resolution of intestinal inflammation. Under normal physiological conditions, Nrf2 remains sequestered in the cytoplasm by Kelch-like ECH-associated protein 1 (Keap1), which facilitates its ubiquitination and proteasomal degradation. However, during oxidative stress, reactive oxygen species (ROS) and electrophilic compounds modify critical cysteine residues on Keap1, disrupting the Keap1-Nrf2 interaction and enabling Nrf2 nuclear translocation. Once in the nucleus, Nrf2 binds to antioxidant response elements (ARE) in the promoter regions of genes encoding phase II detoxifying enzymes and antioxidant proteins, including heme oxygenase-1 (HO-1), NAD(P)H quinone oxidoreductase 1 (NQO1), and glutamate-cysteine ligase. This comprehensive review synthesizes current evidence demonstrating that activation of Nrf2 signaling confers protection against intestinal inflammation through multiple interconnected mechanisms: suppression of NF-κB-mediated pro-inflammatory cascades, enhancement of cellular antioxidant capacity, restoration of intestinal barrier integrity, modulation of immune cell function, and favorable alteration of gut microbiota composition. We systematically examine a diverse array of therapeutic agents targeting Nrf2 signaling, including bioactive peptides, natural polyphenols, flavonoids, terpenoids, alkaloids, polysaccharides, probiotics, and synthetic compounds. The mechanistic insights and therapeutic evidence presented underscore the translational potential of Nrf2 pathway modulation as a multi-targeted strategy for managing intestinal inflammatory conditions and restoring mucosal homeostasis. Full article

In this study, we optimize a class of composite function problems whose objective function is a sum of three terms: a smooth function, a simple convex function, and a composite function composed of a simple convex function and a linear function. This kind of problem is challenging to solve, as the last two functions are both non-smooth and non-separable. Existing algorithms to solve this type of problem have high computational complexity and cannot deal with large-size problems. To efficiently solve these problems, we first reformulate them as multi-block separable convex minimization problems with linear constraints. Then, we solve the reformulated problem and its dual problem using the alternating direction method of multipliers (ADMM). Finally, we also consider a specific application problem to validate the efficacy of our algorithms. Considering the limitations of existing algorithms, the proposed algorithms are expressly designed to avoid the explosive growth of auxiliary variables and constraints. Full article

This study formulates predictive maintenance of complex technical systems as a constrained multi-layer probabilistic optimization problem that unifies four interdependent analytical paradigms. The mathematical framework integrates: (i) Weibull reliability modelling with parametric lifetime estimation; (ii) Bayesian posterior updating for dynamic adaptation under uncertainty; (iii) nonlinear machine-learning inference for data-driven pattern recognition; and (iv) ontology-based semantic reasoning governed by logical axioms and domain-specific constraints. The four layers are synthesized through a formal orchestration operator, defined as a sequential composition, where each sub-operator is governed by explicit mathematical constraints: Weibull cumulative distribution functions, Bayesian likelihood-posterior relationships, gradient-based loss minimization, and description logic entailment. The system operates within a cognitive digital twin architecture, with orchestration convergence formalized through iterative parameter refinement until consistency between numerical predictions and semantic validation is achieved. The framework is validated through a case study on aircraft wheel-hub crack prediction. The mathematical formulation establishes a rigorous analytical foundation for cognitive predictive maintenance systems applicable to safety-critical technical systems including aerospace, energy infrastructure, transportation networks, and industrial machinery. Full article