Search Results (179)

As a major export crop in China, Saccharina japonica cultivation suffers from significant economic losses due to disease outbreaks, with pathogen identification remaining a critical bottleneck for mariculture. In this study, a dominant bacterial strain, LJ53, was isolated from the diseased farmed S. japonica. Artificial challenge assay confirmed that this strain is the direct causative agent of bleaching symptoms on sporophytes. Based on morphological characteristics and 16S rRNA gene-based phylogeny, it was identified as Pseudoalteromonas agarivorans LJ53. Ultrastructural observation revealed that this strain destroyed host cells and caused typical pathological changes such as chloroplast disintegration. Interestingly, metagenomic analysis showed no significant difference in the relative abundance of this pathogen between healthy and diseased S. japonica tissues. However, the co-occurrence network of the disease community exhibited increased connectivity, altered modularity, and features characteristic of microbial dysbiosis. This dysbiosis disrupts the water ecological balance by destabilizing microbial symbiosis and nutrient cycling, which are essential for overall ecosystem resilience. As a result, these imbalances can exacerbate disease transmission and weaken the self-regulating capacity of marine environment, highlighting the need for integrated management strategies to restore equilibrium. These findings provide a theoretical basis for elucidating the mechanisms of bacterial diseases in S. japonica and developing future control strategies. Full article

With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets. Full article

Leishmaniasis, a neglected tropical disease caused by protozoa of the genus Leishmania, presents a wide clinical spectrum from self-healing cutaneous lesions to life-threatening visceral disease. Its epidemiology and severity vary by geography and species (Old vs. New World), vector biology, and host factors. Pathogenesis reflects a tripartite interplay among parasite, host, and sand fly saliva. Parasite virulence determinants—including lipophosphoglycan, GP63, proteophosphoglycans, and GPI-anchored antigens—facilitate complement evasion, macrophage entry, and suppression of microbicidal pathways. Innate defenses (complement, neutrophils, dendritic cells, NK cells) and PRR signaling (TLRs/NLRs) shape early outcomes, while the balance between Th1-mediated macrophage activation and Th2/regulatory responses dictates clearance versus persistence. Clinically, most infections remain cutaneous; a minority disseminate to mucosa, driven by immunopathology and species traits. Management must be individualized by Leishmania species, lesion burden/site, immune status, geographic region and drug availability. Local therapies (intralesional antimonials, cryo-/thermotherapy) are suitable for limited disease, whereas systemic agents (antimonials, amphotericin B, miltefosine, pentamidine, azoles) are reserved for complex, mucosal, disseminated, or immunosuppressed cases. Drug resistance—via altered uptake/efflux, metabolic rewiring, and genomic plasticity—increased toxicity and treatment failure. Targeting parasite virulence and unique metabolic pathways, improving species-specific diagnostics, and integrating host-directed strategies are priorities to shorten therapy and improve clinical outcomes. Full article

Permeability evolution in remolded fault gouge creates critical uncertainties in geotechnical parameterization for dam foundations. However, the underlying multi-scale mechanisms, including mineral migration and pore structure changes, remain insufficiently understood. This study investigates these mechanisms using remolded plastic-thrust fault gouge from the Yulong Kashi hydropower project in China. We developed an innovative sample preparation method that combines in situ mineral self-cementation and directional compaction. The study integrated multidisciplinary tests including field in situ permeability tests; seepage–stress coupling tests; and micro-scale NMR/XRD/SEM-EDS analyses. Results demonstrate that remolded samples exhibit 1–2 orders of magnitude lower permeability (10⁻⁷ cm/s) than in situ samples (10⁻⁵ cm/s). This significant reduction is primarily caused by the loss of cementing agents and the uniform compaction of remolded samples, which leads to degraded pore connectivity. SEM-EDS analysis highlighted the leaching of cementing materials (such as K⁺, Ca²⁺ ions), while XRD revealed changes in mineral composition, with chlorite dissolution being the primary mineral alteration associated with permeability decay. Additionally, artificially enhanced cohesion distorted the mechanical behavior of the samples. These findings provide an explanation for why conventional laboratory tests tend to underestimate in situ permeability and overestimate shear strength in fault zones. This study establishes microstructure-informed correction frameworks for hydraulic and mechanical parameters in fault-crossing hydraulic engineering applications Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare Lévy-flight kicks enable long escapes. A reflection/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems. Full article

Acute pancreatitis (AP) is an inflammatory disorder of the pancreas that can lead to serious systemic complications. Its clinical presentation varies widely, ranging from mild, self-limiting symptoms to severe, life-threatening illness. Currently, there are no specific therapies approved for the treatment of AP, and management primarily relies on supportive care. However, a growing number of clinical trials have evaluated the translational potential of effective therapies derived from experimental models and have identified promising pharmacological agents that may help ameliorate disease severity. Alongside pharmacological approaches, nutritional management of AP has been gaining increasing attention. Evidence supports the use of enteral nutrition over parenteral feeding, as it is associated with a lower risk of necrotic infections, multiple organ dysfunction, mortality, and other associated complications of AP. In this review, we summarize the therapeutic potential of pharmacological and dietary/nutritional interventions, including naturally occurring bioactive compounds, for AP in the context of its molecular pathology, with the aim of supporting improved clinical decision-making, enhancing patient outcomes, and informing future research directions. Full article

Diabetic nephropathy (DN) remains the leading cause of end-stage renal disease worldwide, with proximal tubular epithelial cells (PTECs) playing a central role in its pathogenesis. Under hyperglycemic conditions, PTECs drive a pathological triad of inflammation, apoptosis, and fibrosis. Recent advances reveal that these processes interact synergistically to form a self-perpetuating vicious cycle, rather than operating in isolation. This review systematically elucidates the molecular mechanisms underlying this crosstalk in PTECs. Hyperglycemia induces reactive oxygen species (ROS) overproduction, advanced glycation end products (AGEs) accumulation, and endoplasmic reticulum stress (ERS), which collectively activate key inflammatory pathways (NF-κB, NLRP3, cGAS-STING). The resulting inflammatory milieu triggers apoptosis via death receptor and mitochondrial pathways, while apoptotic cells release damage-associated molecular patterns (DAMPs) that further amplify inflammation. Concurrently, fibrogenic signaling (TGF-β1/Smad, Hippo-YAP/TAZ) promotes epithelial–mesenchymal transition (EMT) and extracellular matrix (ECM) deposition. Crucially, the resulting fibrotic microenvironment reciprocally exacerbates inflammation and apoptosis through mechanical stress and hypoxia. Quantitative data from preclinical and clinical studies are integrated to underscore the magnitude of these effects. Current therapeutic strategies are evolving toward multi-target interventions against this pathological network. We contrast the paradigm of monotargeted agents (e.g., Finerenone, SGLT2 inhibitors), which offer high specificity, with that of multi-targeted natural product-based formulations (e.g., Huangkui capsule, Astragaloside IV), which provide synergistic multi-pathway modulation. Emerging approaches (metabolic reprogramming, epigenetic regulation, mechanobiological signaling) hold promise for reversing fibrosis. Future directions include leveraging single-cell technologies to decipher PTEC heterogeneity and developing kidney-targeted drug delivery systems. We conclude that disrupting the inflammation–apoptosis–fibrosis vicious cycle in PTECs is central to developing next-generation therapies for DN. Full article

To overcome the challenges of conventional low-carbon retrofits for existing buildings—such as high construction volume, cost, and implementation difficulty—this study proposes a minimally invasive design and optimization method for Photovoltaic–Energy Storage–Direct Current–Flexible (PEDF) systems. The goal is to maximize energy savings and economic benefits while minimizing physical intervention. First, the minimally invasive retrofit challenge is decomposed into two coupled problems: (1) collaborative PV-ESS layout optimization and (2) flexible energy management optimization. A co-optimization framework is then developed to address them. For the layout problem, a model with multiple constraints is established to minimize retrofitting workload and maximize initial system performance. A co-evolutionary algorithm is employed to handle the synergistic effects of electrical pathways on equipment placement, efficiently obtaining an optimal solution set that satisfies the minimally invasive requirements. For the operation problem, an energy management model is developed to maximize operational economy and optimize grid interactivity. A deep reinforcement learning (DRL) agent is trained to adaptively make optimal charging/discharging decisions. Case simulations of a typical office building show that the proposed method performs robustly across various scenarios (e.g., office, commercial, and public buildings). It achieves an energy saving rate exceeding 20% and reduces operational costs by 10–15%. Moreover, it significantly improves building–grid interaction: peak demand is reduced by 33%, power fluctuations are cut by 75%, and voltage deviation remains below 5%. The DRL-based policy outperforms both rule-based strategies and the DDPG algorithm in smoothing grid power fluctuations and increasing the PV self-consumption rate. Full article

Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically grounded surveys, this paper introduces a novel method-oriented taxonomy of hallucination mitigation strategies in text-based LLMs. The taxonomy organizes over 300 studies into six principled categories: Training and Learning Approaches, Architectural Modifications, Input/Prompt Optimization, Post-Generation Quality Control, Interpretability and Diagnostic Methods, and Agent-Based Orchestration. Beyond mapping the field, we identify persistent challenges such as the absence of standardized evaluation benchmarks, attribution difficulties in multi-method systems, and the fragility of retrieval-based methods when sources are noisy or outdated. We also highlight emerging directions, including knowledge-grounded fine-tuning and hybrid retrieval–generation pipelines integrated with self-reflective reasoning agents. This taxonomy provides a methodological framework for advancing reliable, context-sensitive LLM deployment in high-stakes domains such as healthcare, law, and defense. Full article

Pulmonary fibrosis (PF) is a progressive and fatal lung disease characterized by irreversible alveolar destruction and pathological extracellular matrix (ECM) deposition. Currently approved agents (pirfenidone and nintedanib) slow functional decline but do not reverse established fibrosis or restore functional alveoli. Multifunctional bioscaffolds present a promising therapeutic strategy through targeted modulation of critical cellular processes, including proliferation, migration, and differentiation. This review synthesizes recent advances in scaffold-based interventions for PF, with a focus on their dual mechano-epigenetic regulatory functions. We delineate how scaffold properties (elastic modulus, stiffness gradients, dynamic mechanical cues) direct cell fate decisions via mechanotransduction pathways, exemplified by focal adhesion–cytoskeleton coupling. Critically, we highlight how pathological mechanical inputs establish and perpetuate self-reinforcing epigenetic barriers to regeneration through aberrant chromatin states. Furthermore, we examine scaffolds as platforms for precision epigenetic drug delivery, particularly controlled release of inhibitors targeting DNA methyltransferases (DNMTi) and histone deacetylases (HDACi) to disrupt this mechano-reinforced barrier. Evidence from PF murine models and ex vivo lung slice cultures demonstrate scaffold-mediated remodeling of the fibrotic niche, with key studies reporting substantial reductions in collagen deposition and significant increases in alveolar epithelial cell markers following intervention. These quantitative outcomes highlight enhanced alveolar epithelial plasticity and upregulating antifibrotic gene networks. Emerging integration of stimuli-responsive biomaterials, CRISPR/dCas9-based epigenetic editors, and AI-driven design to enhance scaffold functionality is discussed. Collectively, multifunctional bioscaffolds hold significant potential for clinical translation by uniquely co-targeting mechanotransduction and epigenetic reprogramming. Future work will need to resolve persistent challenges, including the erasure of pathological mechanical memory and precise spatiotemporal control of epigenetic modifiers in vivo, to unlock their full therapeutic potential. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

Recently, cognitive training programs using digital content with visuoperceptual stimulation have been developed and commercialized. In particular, digital intervention content for children with attention deficit hyperactivity disorder (ADHD) has been developed as games, enhancing motivation and accessibility for the target population. Active stimulation is required to elicit positive effects on self-regulation training, including attention control and impulse inhibition, through task-based content. Common forms of stimulation include emotional stimuli, such as praise and encouragement, and economic stimuli based on a self-directed token economy system. Economic stimulation can serve as active reinforcement because the child directly engages as the primary agent within the task content. This study applied and validated a token economy intervention using digital therapeutic content in children with ADHD. Behavioral assessments were conducted using the Comprehensive Attention Test (CAT) and the Korean version of the Child Behavior Checklist (K-CBCL). The developed digital intervention content implemented a user-centered token economy based on points within the program. In the CAT Flanker Task, the experimental group (0.84 ± 0.40) showed significantly higher sensitivity factor scores than the control group (0.72 ± 0.59) after 4 weeks, with a large effect size (F = 4.76, p = 0.038, partial η² = 0.150). Additionally, the rate of change in externalizing behavior scores on the K-CBCL showed a significant difference between the two groups (t = 2.35, p = 0.026, Cohen’s d = 0.860), demonstrating greater improvement in externalizing symptoms in the experimental group than in the control group. Therefore, this study suggests that the participant-centered implementation model using token economy mechanisms in digital intervention content may serve as a novel and effective therapeutic approach for children with ADHD. Full article

We report a systematic investigation on the physicochemical properties of polymer systems consisting of polyvinyl alcohol (PVA) and hyaluronic acid (HA) mixed in various volume ratios (1/4, 2/3, 1/1, 3/2, and 4/1). At PVA/HA ratios above 1/1, in the presence of glutaraldehyde and divinyl sulfone as crosslinking agents, hydrogels are formed. Their swelling behavior is dependent on the polymer ratio, with the highest water uptake determined for PVA/HA 4/1. The in situ generation of reactive oxygen species (HO^• radicals) under UV-A irradiation, in the presence of riboflavin as a photoinitiator, is evidenced by electron paramagnetic resonance (EPR) spectroscopy. The diffusion of small paramagnetic molecules across the interface of two PVA/HA 4/1 gel pieces placed in direct contact reveals the occurrence of molecular exchange, which could indicate some degree of self-repair of the hydrogel network. When the paramagnetic moiety is attached to the HA polymer by spin labeling, the absence of diffusion demonstrates the stability of the crosslinked HA chains within the PVA/HA network. The structural modifications induced by crosslinking, by the presence of riboflavin, and by exposure to UV-A light, and the resulting alterations in the mechanical behavior of the hydrogels are monitored by infrared spectroscopy and rheology. Only a slight decrease in the viscoelastic moduli values is noted, indicating that the formation of HO^• radicals has minimal impact on the macroscopic properties of the hydrogels. Full article

Generative AI is reshaping k–12 learning as a multi-agent system in which goals, activities, and roles co-evolve across formal and informal environments. Following PRISMA and appraising quality with MMAT, we synthesize 84 peer-reviewed empirical studies (2020–2025) involving learners aged 3–18. Using Biggs’s 3P model as a systems lens and embedding CIMO logic, we code learning objectives, activity designs, AI role paradigms, and outcomes. Seven recurring objectives emerge (language/literacy; STEM; creativity; socioemotional skills; feedback literacy and self-regulation; motivation; AI literacy). Five dominant activity patterns are identified: dialogic tutoring and formative feedback, generative iterative co-creation, project-based problem-solving, simulation/game-based learning, and assessment support. Across studies, AI roles shift from AI-directed to AI-supported/empowered, re-allocating agency among students, teachers, and caregivers via feedback loops. Reported outcomes span three categories—epistemic, practice, and affective/identity—with opportunities of deeper knowledge, improved practice, and stronger engagement, and risks of hallucinations, reduced originality, over-reliance, motivational loss, and ethical concerns. We propose a goal–activity–role alignment heuristic for instructional design, plus safeguards around teacher professional development, feedback literacy, and ethics. We call for longitudinal and cross-cultural research to evaluate the impacts of GenAI in k–12. Full article