Search Results (2,004)

The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a Reynolds-equation hydrodynamic solver with temperature-/pressure-dependent viscosity and cavitation; (2) elastic deformation obtained from FEA (finite element analysis)-based compliance matrices; (3) a break-in module that iteratively adjusts surface profiles before steady-state simulation; (4) a three-body heat transfer model resolving heat conduction, convection, and solid–liquid interfacial heat exchange. Applied to a heavy-duty diesel engine, the framework reproduces experimentally observed behaviors, including bottom-edge rounding at the small end and the slow unidirectional drift of the floating pin. By integrating multi-physics modeling with design-level flexibility, this work aims to provide a robust digital twin for the piston-pin system, enabling virtual diagnostics, early-stage failure prediction, and data-driven design optimization for engine development. Full article

This work presents a compact refractometric system based on In-Line Digital Holography (ILDH) for the non-invasive characterization of transparent media, encompassing both liquids and high-refractive-index optical glasses. The core of the system is a cost-effective, lensless setup in which a 532 nm laser source and a microscope objective generate a divergent spherical wavefront that illuminates a 10 μm aluminum particle. The resulting diffraction pattern, modulated by samples in the optical path, is recorded by a CMOS sensor. The refractive index of the sample is determined by numerically locating the axial position of the particle-reconstructed image, which directly corresponds to the optical path difference introduced by the test medium. The optimal reconstruction plane is objectively located using an autofocus algorithm based on the Kurtosis metric, which identifies the sharpest image. The system successfully characterizes media across a broad refractive index range from 1.33 to 1.78, yielding linear calibration curves for both liquid and solid samples. The instrument achieves an axial reconstruction resolution of 30 μm and a refractive index precision of ±0.01 RIU. This ILDH approach offers a highly portable, cost-effective, and non-contact solution for refractive index measurement, demonstrating significant potential for industrial quality control and high-throughput point-of-care applications. Full article

This study presents a field-oriented AI web system for durian orchard management that recognizes leaf health from on-orchard images under variable illumination. Two complementary pipelines are employed: (1) a rule-based module operating in HSV and CIE Lab color spaces that suppresses sun-induced specular highlights via V/L* thresholds and applies interpretable hue–chromaticity rules with spatial constraints; and (2) a Deep Feature (PCA–SVM) pipeline that extracts features from pretrained ResNet50 and DenseNet201 models, performs dimensionality reduction using Principal Component Analysis, and classifies samples into three agronomic classes: healthy, leaf-spot, and leaf-blight. This hybrid architecture enhances transparency for growers while remaining robust to illumination variations and background clutter typical of on-farm imaging. Preliminary on-farm experiments under real-world field conditions achieved approximately 80% classification accuracy, whereas controlled evaluations using curated test sets showed substantially higher performance for the Deep Features and Ensemble model, with accuracy reaching 0.97–0.99. The web interface supports near-real-time image uploads, annotated visual overlays, and Thai-language outputs. Usability testing with thirty participants indicated very high satisfaction (mean 4.83, SD 0.34). The proposed system serves as both an instructional demonstrator for explainable AI-based image analysis and a practical decision-support tool for digital horticultural monitoring. Full article

The pursuit of fine chemicals from natural sources is advancing rapidly, driven by a growing demand for safe, sustainable, and high-performance ingredients in cosmetic and pharmaceutical formulations. Emerging extraction and biotransformation technologies, including enzyme-assisted procedures, precision fermentation, and green solvent systems, are enabling the selective recovery of complex molecules with enhanced purity and stability. Simultaneously, AI-guided approaches to the discovery of bioactive compounds are accelerating the identification of multifunctional molecules exhibiting, for example, anti-inflammatory, antioxidant or microbiome-modulating activities. These developments not only expand the chemical diversity accessible to the cosmetic and pharmaceutical sectors but also promote the adoption of circular bioeconomy frameworks. Together, they define a new generation of natural fine chemicals with strong potential for targeted therapeutic and cosmetic applications. Accordingly, this commentary focuses on emerging trends and key technological advances in the use of renewable, natural sources for the production of fine chemicals relevant to cosmetic and pharmaceutical industries. It further highlights the critical roles of biotechnology, green chemistry, and digital innovation in shaping a more sustainable future for cosmetic and pharmaceutical chemistry. Full article

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities. Full article

Crowdfunding platforms like Kickstarter have reshaped early-stage financing by allowing entrepreneurs to connect directly with potential supporters. As a fast-expanding part of digital commerce, crowdfunding offers significant opportunities but also substantial risks for both entrepreneurs and platform operators, making predictive analytics an essential capability. Although crowdfunding shares some operational features with traditional e-commerce, its mix of financial uncertainty, emotionally charged storytelling, and fast-evolving social interactions makes it a distinct and more challenging forecasting problem. Accurately predicting campaign outcomes is especially difficult because of the high-dimensionality and diversity of the underlying textual and behavioral data. These factors highlight the need for scalable, intelligent data science methods that can jointly exploit structured and unstructured information. To address these issues, this study proposes a novel AI-based predictive framework that integrates a Convolutional Block Attention Module (CBAM)-enhanced symmetric autoencoder for compressing high-dimensional Generative AI (GenAI) BERT embeddings with meta-heuristic feature selection and advanced classification models. The framework systematically couples attention-driven feature compression with optimization techniques—Genetic Algorithm (GA), Jaya, and Artificial Rabbit Optimization (ARO)—and then applies Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) classifiers. Experiments on a large-scale Kickstarter dataset demonstrate that the proposed approach attains 77.8% accuracy while reducing feature dimensionality by more than 95%, surpassing standard baseline methods. In addition to its technical merits, the study yields practical insights for platform managers and campaign creators, enabling more informed choices in campaign design, promotional tactics, and backer targeting. Overall, this work illustrates how advanced AI methodologies can strengthen predictive analytics in digital commerce, thereby enhancing the strategic impact and long-term sustainability of crowdfunding ecosystems. Full article

The traditional control strategies for bidirectional power supply full-bridge push-pull DC-DC topologies still face limitations in efficiency, dynamic response, and output stability. To address this, this paper proposes an integrated modulation strategy combining neural network optimization and closed-loop control, which adjusts the phase-shift angle and switching timing through online learning to significantly improve dynamic and steady-state performance. Simulations show that the current peak value was reduced from 16A to 15.2A, the output voltage ripple was significantly suppressed from 90% to 30%, and the system efficiency, calculated through multiple iterations, gradually increased. This paper first analyzes the problems of traditional control strategies, then presents a new control framework, modeling, and simulation. Finally, simulation verification was performed under typical operating conditions. The results show that this strategy is suitable for high-efficiency energy storage systems. Full article

Dry Eye Disease (DED) is a highly characterised multifactorial disease resulting in the loss of tear film homeostasis and associated with a major impact on patient quality of life. DED affects up to half of the global population, with modern lifestyle factors playing a critical role in disease development, particularly excessive use of digital devices. The ultimate treatment goal is restoration of tear film homeostasis and breaking the ‘vicious circle’ of DED. Today, the use of tear substitutes represents the main option for the treatment of DED. These topical formulations aim to provide lubrication, reduce osmolarity, and improve tear clearance. However, they do not interact with the ocular surface epithelium nor modulate ocular inflammation, and do not fully restore natural tear function. T-LysYal is the first supramolecular ocular surface modulator for DED. Studies demonstrate that T-LysYal promotes tissue repair, improves tear breakup time, restores corneal epithelial cell damage, and modulates inflammation processes, significantly reducing the severity of DED symptoms in patients. In addition, T-LysYal provides stability that prolongs activity and favours cell adhesion. Through its 3D nanotube structure, movement of water in the eye is retained and improved, enhancing ocular hydrodynamics. This narrative review introduces T-LysYal for DED whilst highlighting both its in vitro activity and clinical profile against hyaluronic acid, a mainstay of disease management. Full article

This paper presents a hybrid architecture for automatic sentiment analysis of Kazakh-language political discourse. The Kazakh language is characterized by an agglutinative structure, a complex word-formation system, and the limited availability of digital resources, which significantly complicates the application of standard neural network approaches. To account for these characteristics, a multi-level system was developed that combines morphological and syntactic analysis rules, ontological relationships between political concepts, and multilingual representations of the XLM-R model, used in zero-shot mode. A corpus of 12,000 sentences was annotated for sentiment polarity and used for training and evaluation, while Universal Dependencies annotation was applied for morpho-syntactic analysis. Rule-based components compensate for errors related to affixation variability, modality, and directive constructions. An ontology comprising over 300 domain concepts ensures the correct interpretation of set expressions, terms, and political actors. Experimental results show that the proposed hybrid architecture outperforms both neural network baseline models and purely rule-based solutions, achieving Macro-F1 = 0.81. Ablation revealed that the contribution of modules is unevenly distributed: the ontology provides +0.04 to Macro-F1, the UD syntax +0.08, and the rule-based module +0.11. The developed system forms an interpretable and robust assessment of tonality, emotions, and discursive strategies in political discourse, and also creates a basis for further expansion of the corpus, additional training of models, and the application of hybrid methods to other tasks of analyzing low-resource languages. Full article

The advancement of autonomous cargo ships requires dependable anchoring operations, which present significant challenges stemming from reduced maneuverability at low speeds and vulnerability to anchorage disturbances. This study systematically investigates these operational constraints by developing anchoring decision-making methodologies. Safety anchorage areas were quantitatively defined through integration of ship specifications and environmental parameters. An available anchor position identification method based on grid theory, integrated with an anchorage allocation mechanism to determine optimal anchorage selection, was employed. A multi-level guided anchoring trajectory planning algorithm was developed through practical anchoring. This algorithm was designed to facilitate the scientific calculation of turning and stopping guidance points, with the objective of guiding a cargo ship to navigate towards the designated anchorage while maintaining specified orientation. An integrated autonomous anchoring system was established, encompassing perception, decision-making, planning, and control modules. System validation through digital simulations demonstrated robust performance under complex sea conditions. This study establishes theoretical foundations and technical frameworks for enhancing autonomous decision-making and safety control capabilities of intelligent ships during anchoring operations. Full article

In image sensing, measurements such as an object’s position or contour are typically obtained by analyzing digitized images. This method is widely used due to its simplicity. However, relative motion or inaccurate focus can cause motion and defocus blur, reducing measurement accuracy. Thus, video deblurring is essential. However, existing deep learning-based video deblurring methods struggle to balance high-quality deblurring, fast inference, and wide applicability. First, we propose a Current-Aware Temporal Fusion (CATF) framework, which focuses on the current frame in terms of both network architecture and modules. This reduces interference from unrelated features of neighboring frames and fully exploits current frame information, improving deblurring quality. Second, we introduce a Mixture-of-Experts module based on NAFBlocks (MoNAF), which adaptively selects expert structures according to the input features, reducing inference time. Third, we design a training strategy to support both sequential and temporally parallel inference. In sequential deblurring, we conduct experiments on the DVD, GoPro, and BSD datasets. Qualitative results show that our method effectively preserves image structures and fine details. Quantitative results further demonstrate that our method achieves clear advantages in terms of PSNR and SSIM. In particular, under the exposure setting of 3 ms–24 ms on the BSD dataset, our method achieves 33.09 dB PSNR and 0.9453 SSIM, indicating its effectiveness even in severely blurred scenarios. Meanwhile, our method achieves a good balance between deblurring quality and runtime efficiency. Moreover, the framework exhibits minimal error accumulation and performs effectively in temporal parallel computation. These results demonstrate that effective video deblurring serves as an important supporting technology for accurate image sensing. Full article

With smart buildings being widely adopted in urban digital transformation, interactive semantic question answering (QA) systems serve as a crucial bridge between user intent and environmental response. However, they still face substantial challenges in semantic understanding and dynamic reasoning. Most existing systems rely on static frameworks built upon Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which suffer from rigid prompt design, breakdowns in multi-step reasoning, and inaccurate generation. To tackle these issues, we propose LoopRAG, a multi-agent RAG architecture that incorporates a Plan–Do–Check–Act (PDCA) closed-loop optimization mechanism. The architecture formulates a dynamic QA pipeline across four stages: task parsing, knowledge extraction, quality evaluation, and policy feedback, and further introduces a semantics-driven prompt reconfiguration algorithm and a heterogeneous knowledge fusion module. These components strengthen multi-source information handling and adaptive reasoning. Experiments on HotpotQA, MultiHop-RAG, and an in-house building QA dataset demonstrate that LoopRAG significantly outperforms conventional RAG systems in key metrics, including context recall of 90%, response relevance of 72%, and answer accuracy of 88%. The results indicate strong robustness and cross-task generalization. This work offers both theoretical foundations and an engineering pathway for constructing trustworthy and scalable semantic QA interaction systems in smart building settings. Full article

The increasing complexity and digitization of scientific research require Electronic Laboratory Notebooks (ELNs) that are adaptable, sustainable, and compliant across heterogeneous laboratory environments. In response to the limitations of proprietary, inflexible, and cost-intensive ELN solutions, this study systematically derives comprehensive requirements and proposes a modular solution concept for self-configurable ELNs that is explicitly platform-agnostic and broadly accessible. The methodological approach combines a structured requirements analysis with a modular architectural design, followed by theoretical validation through stepwise implementation walkthroughs on Microsoft SharePoint and Google Workspace. These walkthroughs demonstrate the feasibility of deploying self-configurable ELN modules using widely available low-code/no-code tools and native platform extensibility mechanisms. Based on a rigorous literature-driven analysis, key requirements, including modularity, usability, regulatory compliance, interoperability, scalability, auditability, and cost efficiency, are explicitly mapped to concrete architectural features within the proposed framework. The results show that essential ELN functionalities can, in principle, be realized across diverse digital platforms, enabling researchers and local administrators to independently assemble, configure, and adapt ELNs to their specific operational and regulatory contexts. Beyond technical feasibility, the proposed approach fundamentally democratizes ELN deployment and substantially mitigates vendor lock-in by leveraging existing digital infrastructures. Identified limitations, particularly with respect to advanced workflow orchestration and real-time data integration, delineate clear directions for future development. Overall, this work provides a systematic theoretical validation of a modular, self-configurable ELN concept, establishing it as a robust, scalable, and future-ready foundation for digital laboratory infrastructures. Full article

Data-driven quality control (QC) systems for the hot forming of steel parts increasingly rely on deep learning models deployed at the network edge, making multivariate sensor time series a critical asset for both local decisions and management information system (MIS) reporting. However, these models are vulnerable to adversarial perturbations and realistic signal disturbances, which can induce misclassification and distort key performance indicators (KPIs) such as first-pass yield (FPY), scrap-related losses, and latency service-level objectives (SLOs). To address this risk, this study introduces a Digital-Twin-Conditioned Diffusion Purification (DTCDP) framework that constrains latent diffusion-based denoising using process states from a lightweight digital twin of the hot-forming line. At each reverse-denoising step, the twin provides physics residuals that are converted into a scalar penalty, and the diffusion latent is updated with a guidance term. This directly bends the sampling trajectory toward reconstructions that adhere to process constraints while removing adversarial perturbations. DTCDP operates as an edge-side preprocessing module that purifies sensor sequences before they are consumed by existing long short-term memory (LSTM)-based QC models, while exposing purification metadata and physics-guidance diagnostics to the plant MIS. In a four-week production dataset comprising more than 40,000 bars, with white-box ℓ∞ attacks crafted on multivariate sensor time series using Fast Gradient Sign Method and Projected Gradient Descent at perturbation budgets of 1–3% of the physical range, combined with additional realistic disturbances, DTCDP improves the robust classification performance of an LSTM-based QC model from 61.0% to 81.5% robust accuracy, while keeping clean accuracy (≈93%) and FPY on clean data (≈97%) essentially unchanged. These results indicate that physics-aware, digital-twin-guided diffusion purification can enhance the adversarial robustness of edge QC in hot forming without compromising operational KPIs. Full article

A unified digital toolbox is introduced for kinematics analysis and dimension synthesis of parallel robots, addressing challenges in configuration diversity and computational complexity. By integrating hierarchical kinematic modeling with screw theory, the toolbox establishes standardized analytical frameworks for mobility, inverse kinematics and dexterity evaluation. A modular toolbox architecture—comprising interactive, data, external module, database and functional layers—enables systematic design, workspace estimation and dexterity-driven optimization. A hybrid MATLAB-C++ interface ensures computational efficiency and scalability. The efficacy of the toolbox is demonstrated through a case study on a novel 2UPR-2RPS parallel mechanism, achieving optimized dimensional parameters (k₁ = 0.85, k₂ = 1.3, k₃ = 0.85, k₄ = 1.3) with a mean dexterity index of 0.637 and validated workspace symmetry. Results confirm that the toolbox streamlines the design process, ensures computational accuracy and enables rapid adaptation to new robotic configurations. This work provides a robust foundation for advanced parallel robot design, offering significant potential for industrial and research applications requiring high-precision motion control. Full article