Search Results (219)

Log parsing converts raw system logs into structured templates and is a key preprocessing step for Artificial Intelligence for IT Operations (AIOps). Existing parsers face a practical trade-off: rule-based methods offer high throughput but limited adaptability across heterogeneous log sources, whereas Large Language Model (LLM)-based parsers achieve broader semantic coverage at the cost of inference latency, privacy exposure, and cloud dependency. This paper presents FlanBC, a log parsing framework that formulates template extraction as a BIO (Beginning, Inside, Outside) sequence-labeling task and integrates a Flan-T5 semantic encoder, Bidirectional Long Short-Term Memory (BiLSTM) layers for local sequential modeling, and a Conditional Random Field (CRF) decoder for structured label prediction. Log-specific preprocessing and a subword-to-token alignment mechanism adapt the general-purpose encoder to semi-structured log data. A layer-freezing strategy reduces the number of parameters updated during training. The framework supports local inference without external API dependency. Experiments on three benchmark datasets from LogHub (HDFS, BGL, OpenStack) under a supervised random-split setup evaluate parsing accuracy, training efficiency, statistical stability across random seeds, and component contributions. FlanBC achieves a Group Accuracy of 99.32% on HDFS and 98.47% on BGL, with an inference throughput of 700+ logs/s on a consumer-grade GPU. On OpenStack, performance is lower (GA = 92.54%), reflecting the challenge that diverse natural-language-like logs pose for compact encoder-based models. Under a stricter template-disjoint split that prevents template overlap between training and test sets, FlanBC achieves an average Group Accuracy of 91.14%, indicating that the model generalizes to unseen templates beyond in-distribution recognition. Ablation results indicate that the semantic encoder, BiLSTM module, and CRF decoder each contribute to prediction accuracy. These findings suggest that domain-adapted semantic encoders combined with structured decoding offer a practical accuracy–efficiency balance for log parsing in settings where local, cloud-free inference is preferred. Full article

Complex hollow metallic microstructures are essential for microelectromechanical systems (MEMS), lab-on-a-chip microfluidics, and bio-integrated devices, yet their fabrication remains challenging because geometric complexity, microscale precision, and high aspect ratios must be satisfied simultaneously. This study proposes filament-guided electrolyte–column electrodeposition (FG-ECD), which couples a removable filament template with a nozzle-confined electrolyte column to define internal channels in situ during localized metal growth, thereby avoiding the collapse risks associated with conventional template removal routes. A two-dimensional axisymmetric multiphysics model reveals that the embedded filament reorganizes the electrolyte into a stable annular flow and shifts the cathodic current density maximum from the substrate toward the advancing dome front, establishing a self-consistent, quasi-stable localized reaction zone, while a parametric sweep shows that the total current scales the current density magnitude without altering its spatial profile. Experiments demonstrate that a current of 3.6 mA produces smooth dome front growth at approximately 20 μm/min, whereas 5.5 mA triggers sustained hydrogen evolution and a transition to cellular deposition. Under optimized conditions, straight, 540° spiral, and R-shaped dual-channel hollow nickel microstructures were fabricated with continuous, collapse-free internal channels of 50 ± 5 μm, aspect ratios exceeding 10:1, and dimensional accuracy within ±35 μm. FG-ECD provides a low-temperature processing route for complex hollow metallic architectures and offers process regulation principles based on co-regulation of the flow field and current density for electrochemical microfabrication. Full article

Biomimetic structures are now a major topic of research, as natural systems achieve high performance through hierarchical organization, multifunctional interfaces, and scale-bridging design principles. Nanofabrication provides a powerful approach to recapitulate biological architectures from the nanoscale to the macroscale, allowing accurate control of the surface chemistry, geometry, transport, mechanics and function. Recent work demonstrates that this approach is especially critical for bionic devices and systems, including biosensors, drug delivery platforms, tissue-engineered constructs, organ-on-chip systems, soft robots, and biohybrid devices. The aim of this review is to provide a systematic overview on how nanofabrication allows the construction of biomimetic structures, with emphasis on bio-templating and replication of natural structures, applications of nanofabrication in bionic devices and systems, and cross-scale biomimetics. Full article

Molecularly imprinted polymers (MIPs) offer robust, cost-effective, and highly selective alternatives to fragile biological receptors. Specifically, electropolymerization has emerged as a versatile strategy that enables the precise, in situ formation of uniform MIP films directly on electrode surfaces. This review provides a comprehensive overview of electropolymerized MIPs (eMIPs) supported on advanced carbon-based materials for electrochemical (bio)sensing. We emphasize how the synergistic integration of eMIPs with carbonaceous architectures significantly enhances electron transfer, active surface area, and overall analytical sensitivity. Key fabrication aspects are systematically discussed, including monomer selection, electropolymerization parameters, and efficient template removal. A central aspect of this work is the critical categorization of sensing mechanisms into direct and indirect detection strategies. This distinction elucidates how eMIPs can quantify a broad spectrum of electroactive and non-electroactive targets in complex matrices, while strategically avoiding excessively high applied potentials. Finally, alongside outlining the transition of these systems into portable technologies, we address a critical shortcoming in the current literature: the urgent need for analytical standardization through the rigorous reporting of Imprinting and Selectivity Factors using Non-Imprinted Polymer (NIP) controls. Full article

Globalization and international trade have increased the importance of customs authorities in ensuring national security. However, regulatory differences regarding substances such as cannabis derivatives, the emergence of new psychoactive substances (NPSs), and the limitations of detection technology challenge customs in identifying suspicious cross-border goods. Traditional attribute extraction methods struggle with professional terminology and cross-sentence reasoning, making it difficult to regulate unknown or emerging substances. To address this, we propose a generative question answering (QA) framework based on inverse reinforcement learning (IRL) that converts attribute extraction into natural language QA tasks. Our approach, CAESAR (Chemical-Attribute Extraction with Sub-rewArd Reinforcement), uses a customs database to match known profiles and cross-references extracted attributes with benchmarks to enhance detection. It integrates the BioBART model with multi-objective reward optimization, using QA templates to capture implicit attributes. IRL automates the learning of reward weights from expert annotations. Experiments show that CAESAR achieves a competitive F1 score of 77.82 on explicit attributes and obtains the highest BLEU score and the lowest perplexity among the compared generative methods. For implicit attributes, ROUGE-L and BLEU scores are 43.08 and 44.46, respectively, with a perplexity of 11.3. These results are obtained in an open-ended generative QA setting rather than a closed-set classification setting, indicating that the proposed framework can provide practically useful attribute-level evidence for customs-oriented risk pre-screening and expert-assisted prioritization. This study offers an efficient solution for mining implicit knowledge in chemical texts and provides insights into multi-objective generative tasks. Full article

Growing environmental concerns associated with non-renewable and persistent materials have intensified the search for sustainable alternatives, with cellulosic fibers and nanocellulose emerging as promising candidates. This review examines diverse product opportunities where interdigitation plays a critical role, including nanopaper and barrier films, wet wipe technologies, spun cellulose-based yarns, hydrogels, and composite materials. Particular emphasis is placed on the interplay between colloidal stability, fibrillar alignment, hydrogen bonding, and time-dependent network evolution in governing material performance. Additionally, emerging strategies such as hydroentanglement, ice-templating, in situ crosslinking, and post-formation modification are discussed as means to optimize interdigitated structures. The article further explores how conventional papermaking processes may be reimagined to better exploit interdigitation through innovations in fiber dispersion, alignment, and controlled crosslinking. Interdigitation is presented not as a discrete processing tool but as a unifying framework for understanding and engineering hierarchical cellulose networks. By leveraging the inherent fibrillar nature of cellulose and the dynamics of self-assembly, this paradigm offers new pathways towards the development of next-generation, high-performance, bio-based products that contribute to a circular economy. Full article

Artificial intelligence has advanced from merely predicting static protein structures to modeling equilibrium conformational ensembles. It now concurrently forecasts structure and binding affinity and actively participates in candidate selection during the initial stages of drug discovery. Foundation models introduced between 2024 and 2026, including BioEmu, AlphaFlow, DiG, Boltz-2, Chai-1, NeuralPLexer, and explicit-solvent prediction systems such as SuperWater, have begun to address issues previously identified as fundamental concerns in earlier critiques of AI in drug discovery. Nevertheless, many of these models are presently accessible only as preprints and require validation through independent peer review. Evidence indicates a shift in the primary bottleneck from representation challenges to validation difficulties. However, this transition remains incomplete and heavily dependent on context. The risks associated with AI-enabled drug discovery are increasingly not solely about the models’ capacity to accurately represent ensembles, but also about whether the evidentiary standards used to validate AI-derived predictions keep pace with the rapidity with which these predictions are generated and employed. This article introduces a four-tier validation framework designed to align the extent of computational and experimental evidence with the translational and regulatory risks associated with various artificial intelligence (AI) applications within the molecular sciences. These applications include machine learning (ML) models that analyze sequences, structures, conformational ensembles, protein–ligand complexes, and molecular dynamics trajectories. Tier 1 addresses the internal reproducibility of ML inference when applied to molecular inputs; Tier 2 pertains to the robustness of molecular-science benchmarks such as CASP, CASF-2016, PoseBusters, and OpenFE; Tier 3 involves prospective experimental validation against biophysical and biochemical measurements; and Tier 4 encompasses clinical and translational calibration within physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) frameworks. This validation hierarchy functions as an explicit conceptual guide, serving as a framework rather than a regulatory requirement. It is firmly grounded in established principles derived from ICH Q8/Q9/Q10, the FDA model-informed drug development (MIDD) approach, the EMA reflection paper on AI in the medicinal product lifecycle, and the EU AI Act. The manuscript further incorporates recent evidence from ensemble-aware AI, prospective docking, free-energy campaigns, and clinical-stage AI-derived candidates. It concludes with specific recommendations pertaining to lifecycle governance, uncertainty reporting, and the adoption of harmonized evidentiary templates for AI/ML applications in the molecular sciences. Full article

The transition of coal regions under the European Green Deal and Just Transition Fund creates a need for quantitative, transparent monitoring of ecological recovery on post-mining land. This study presents an autonomous UAV-based methodology for high-resolution monitoring of vegetation dynamics on a reclaimed coal waste heap in Upper Silesia, Poland. A DJI Mavic 3 Multispectral platform with RTK positioning conducted approximately biweekly flights from August 2024 to October 2025 over three study plots acquiring RGB and multispectral imagery at approximately 4 cm/pixel. Photogrammetric processing in DJI Terra produced radiometrically corrected orthomosaics and NDVI maps, which were analyzed using an automated QGIS workflow for reprojection, clipping, NDVI-based classification, and quantification of vegetation area across three different reclamation variants. The results indicate that intensive soil conditioning through the application of compost derived from bio-waste achieved a maximum vegetation cover of 94.4%. This treatment consistently maintained the highest level of cover during periods of environmental stress and significantly surpassed both seeding-only treatments and those combining seeding with irrigation. Baseline vegetation cover below 6% confirmed the necessity of active reclamation. This workflow provides rapid and reproducible metrics that are suitable for adaptive management and regulatory reporting. It also offers a scalable template for monitoring coal waste heaps across Europe undergoing SDG-aligned reclamation. Full article

Biotemplated strategies inspired by natural architecture have emerged as an effective strategy to improve the performance of photocatalytic materials. In this work, TiO₂-based photocatalysts were synthesized using olive leaves as a biological template to reproduce their hierarchical microstructure and enhance photocatalytic hydrogen production. The artificial olive leaf (AOL) support was obtained through a biotemplated ion-exchange process followed by hydrolysis and calcination. It was then modified by photodeposition of Au or Pt nanoparticles. The materials were characterized by SEM, XRD, N₂ adsorption–desorption, UV–Vis spectroscopy, and XPS to evaluate their structural and optical properties. SEM confirmed the successful replication of both the external morphology and internal architecture of the olive leaf, while XRD revealed low crystallinity with anatase as the only TiO₂ phase. Optical characterization showed a reduced band gap (~2.97 eV), and extended absorption toward the visible region, with Au nanoparticles exhibiting a plasmonic band at ~550 nm, whereas Pt enhanced light-harvesting efficiency. XPS indicated the presence of oxygen vacancies and Ti³⁺ species that promote metal–support interactions. Photocatalytic glycerol photoreforming showed a strong enhancement in hydrogen production after noble metal incorporation, reaching up to 14-fold under UV irradiation and 23-fold under simulated solar light for the Pt-modified catalyst, highlighting the synergy between biotemplated structuring and noble metal deposition. Full article

Structured radiology reporting can improve clinical decision support by standardizing clinical findings into hierarchical formats. However, thousands of questions in structured report templates about clinical findings are prohibitively time-consuming, which can limit clinical adoption. Furthermore, early medical VQA datasets primarily focused on free-text and independent question–answer pairs while a recent dataset, Rad-ReStruct, introduced a hierarchical VQA, but the accompanying model still relies heavily on flattened embedding representations and single-path text–image fusion mechanisms that inadequately handle complex hierarchical dependencies in responses. In this paper, we propose DPA-HiVQA (Dual-Path Cross-Attention for Hierarchical VQA), addressing these limitations through two key contributions: (1) multi-scale image embedding representing global semantic embeddings with patch-level spatial features from domain-specific BioViL encoder; (2) dual-path cross-attention mechanism enabling simultaneous holistic semantic understanding and fine-grained spatial reasoning. Evaluated on the Rad-ReStruct benchmark, the model substantially outperforms the established benchmark baseline with an overall F1-score and Level 3 F1-score improvement by 21.2% and 31.9%, respectively. The proposed model demonstrates that dual-path cross-attention architectures can effectively connect holistic semantic understanding and fine-grained spatial detail, paving the way for practical AI-assisted structured reporting systems that reduce radiologist burden while maintaining diagnostic accuracy. Full article

Chronic wounds resulting from bacterial infection remain one of the main challenges in clinical practice. There is a pressing need to develop an injectable hydrogel sealant with multifunctional properties, including remodeling capabilities, self-healing, painless removal, and antibacterial activity, to promote tissue remodeling. In this work, aldehyde carboxymethylated agarose (ACMA) is employed for the first time as a bio-template. Dopamine (DA) is introduced onto the ACMA template via a reversible Schiff-base reaction, endowing it with biomineralization properties to synthesize DA-modified ACMA-Ag nanoparticles (ACMA-DA-Ag). Further, the prepared ACMA-DA-Ag, which possesses both antibacterial activity and injectable behavior, is incorporated into a guar gum hydrogel through the formation of borate/diol bonds, thereby forming a multiple-dynamic-bond crosslinked network. This hydrogel demonstrates adequate mechanical strength, injectability, remodeling capabilities, and self-healing performance. It can reassemble into a new hydrogel within 4 ± 0.6 min upon simple physical contact, and supports tissue adhesion. Furthermore, the hydrogel effectively covers irregular-shaped wound and can be removed without causing secondary injury. More importantly, this multifunctional hydrogel is cost-effective, easy to synthesize, and simple to use, significantly accelerating skin regeneration and promoting the formation of skin appendages, such as hair follicles. The outcome of this research not only serves a tissue sealant for wound healing, but also presents a new strategy for creating novel polysaccharide-based biomaterials. Full article

This work presents the synthesis, characterization, and application of zirconium oxide (ZrO₂)-based catalysts, modified with macro (silica nanospheres, NSP-SiO₂) and mesopore templates (Pluronic 123), impregnated with tungstophosphoric acid (TPA), in the catalytic pyrolysis of tomato agro-industrial residues. The NSP-SiO₂ (SXX) and P123 (PYY) amount mainly influences the ZrO₂SXXPYY-specific surface area (S_BET) and average pore diameter (D_p). ³¹P MAS NMR and FT-IR characterization results show that TPA (H₃PW₁₂O₄₀) was partially transformed into [P₂W₂₁O₇₁]⁶⁻ and [PW₁₁O₃₉]⁷⁻ during the synthesis steps. The acidic properties of ZrO₂SXXPYY samples containing 25 and 50 wt% of TPA (ZrO₂SXXPYYT25 and ZrO₂SXXPYYT50, respectively) are dependent on both the TPA content and the support nature. Bio-oil composition and product selectivity were strongly influenced by the textural and acid-based properties of the catalysts. Notably, non-catalytic pyrolysis favored pathways leading to C2 compounds, with a high content of acetic acid and hydroxyacetone. In contrast, the use of catalysts promoted the formation of higher molecular weight oxygenated compounds (C5–C6), specifically furans, aldehydes, and ketones. Full article

Digital photoelasticity enables non-contact, full-field stress analysis through optical fringe patterns, yet its practical deployment is often constrained by experimental complexity and the limited availability of open, standardized datasets. The emergence of multi-polarizer array cameras provides polarization-resolved measurements with high information content, enabling advanced analysis strategies beyond conventional single-image approaches. This work presents a public experimental dataset composed of synchronized image sequences acquired using a polarizer array camera and a conventional RGB camera under incremental mechanical loading. The dataset comprises nine experiments, including four benchmark specimens and five bio-inspired geometries, each recorded over 720 load steps. In total, the dataset releases 25,920 polarization-resolved images and 6480 RGB images, all provided in lossless format and accompanied by experiment-specific segmentation templates. Although classical and hybrid load-stepping methods are used to demonstrate the utility of the dataset, its scope is not limited to this application. The dataset is intended as a flexible platform for exploring a wide range of photoelastic analysis techniques that leverage polarization information, while enabling direct comparison with conventional color demodulation techniques. Full article

The transition to sustainable thermosetting resins is frequently hindered by the trade-off between high bio-based content and processability. This study reports a novel strategy in developing a highly bio-based, styrene-free unsaturated polyester resin (UPR) by leveraging maltodextrin-derived mixed esters dissolved in dimethyl itaconate (DMI). Unlike conventional polysaccharide-based systems that suffer from extreme viscosity, our functionalized prepolymer–DMI system achieves a low-viscosity curing solution without requiring petroleum-derived diluents such as styrene. Fourier-transform infrared spectroscopy confirmed the formation of a robust crosslinked network via the complete consumption of C=C bonds. Consequently, the cured resin exhibits exceptional thermal and mechanical performance, outperforming many existing bio-based analogs: a glass transition temperature (T_g) reaching 141 °C, a decomposition onset near 250 °C, and superior dimensional stability with a linear thermal expansion coefficient as low as 77 ppm/°C. Demonstrating a fully renewable, easy-to-process formulation with a flexural strength of 44 MPa, this work provides a design template for the next generation of high-performance, eco-friendly industrial thermosets. Full article

The use of large language models (LLMs) to automate the generation of medical case-based multiple-choice questions (MCQs) is increasing, but their accuracy, reliability, and educational validity are still not well understood. This study in a comparative framework examined nine LLMs with four different prompting methods to evaluate LLM-produced MCQs for clinical coherence and readiness for assessment. A uniform evaluation pipeline was constructed to examine automatic text-similarity measures using automated metrics (BLEU, ROUGE, and METEOR), structural and parsability measures, and operational effectiveness (latency, cost, quality-efficiency ratios). Human validation was performed on the best-performing model and prompt combination (OpenBioLLM-70B with Chain-of-Thought) focusing on the model prompt that demonstrated the best linguistic fidelity and clinically aligned reasoning. Two clinical experts independently reviewed 88 items using a five-domain rubric covering appropriateness, clarity, relevance, distractor quality, and cognitive level. Results indicated significant variation across models and prompting strategies, with Chain-of-Thought yielding the best overall performance in comparison to other strategies. The OpenBioLLM-70B model demonstrated the best overall balance of quality, parsability, and efficiency, achieving a prompt template quality score of 90.4, a consistency score of 88.8, and a response time of 3.28 s, with a quality-per-dollar value of 134.11. The expert rating confirmed clinical alignment, but there was consensus that distractor quality needed further improvements. These results provide evidence that LLMs under optimal prompting conditions can reliably support MCQ generation and provide large-scale, cost-effective support for medical assessment production. Full article