Search Results (250)

With the industrialization and standardization of Chinese cuisine, accurate discrete element simulation parameters are essential for analyzing the flow and conveying behavior of dishes. This study focused on standardized Kung Pao Chicken and employed the Hertz–Mindlin (JKR) model to develop a discrete element model suitable for cohesive, multi-component Chinese cuisine. The triaxial dimensions of diced chicken, peanuts, and scallions were measured to construct the model. Physical experiments were conducted to obtain basic parameters. The main parameters of the constitutive model were determined using a stepwise regression fitting method. For inter-material contact parameters that are difficult to measure directly, key model parameters were calibrated by fitting simulated repose angle results to experimental measurements. The calibrated parameters enabled high simulation accuracy, with repose angle errors below 0.05%, confirming the model’s reliability. This study provides a theoretical foundation for the simulation and design of automated conveying systems tailored to Chinese cuisine. Full article

With the rise of vehicle automation, accurate estimation of driving dynamics has become crucial for ensuring safe and efficient operation. Vehicle dynamics control systems rely on these estimates to provide necessary control variables for stabilizing vehicles in various scenarios. Traditional model-based methods use wheel-related measurements, such as steering angle or wheel speed, as inputs. However, under low-traction conditions, e.g., on icy surfaces, these measurements often fail to deliver trustworthy information about the vehicle states. In such critical situations, precise estimation is essential for effective system intervention. This work introduces an AI-based approach that leverages perception sensor data, specifically camera images and lidar point clouds. By using relative kinematic relationships, it bypasses the complexities of vehicle and tire dynamics and enables robust estimation across all scenarios. Optical and scene flow are extracted from the sensor data and processed by a recurrent neural network to infer vehicle states. The proposed method is vehicle-agnostic, allowing trained models to be deployed across different platforms without additional calibration. Experimental results based on real-world data demonstrate that the AI-based estimator presented in this work achieves accurate and robust results under various conditions. Particularly in low-friction scenarios, it significantly outperforms conventional model-based approaches. Full article

Automated document processing and circulation systems face critical challenges in achieving reliable retrieval accuracy and robust classification performance, particularly in security-critical organizational environments. Traditional approaches suffer from fundamental limitations, including fixed fusion strategies in hybrid retrieval systems, inability to model inter-document relationships in classification tasks, and lack of confidence estimation for result reliability. This paper introduces AttenFlow, a novel context-aware architecture that revolutionizes document management through two core technical innovations. First, we propose the retriever consensus confidence fusion (RCCF) method, which addresses the limitations of conventional hybrid retrieval approaches by introducing consensus-based fusion strategies that dynamically adapt to retriever agreement levels while providing confidence estimates for results. RCCF measures the consensus between different retrievers through sophisticated ranking and scoring consistency metrics, enabling adaptive weight assignment that amplifies high-consensus results while adopting conservative approaches for uncertain cases. Second, we develop adversarial mutual-attention hybrid-dimensional graph attention network (AM-HDGAT) for text, which transforms document classification by modeling inter-document relationships through graph structures while integrating high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms. The approach incorporates adversarial training to enhance robustness against potential security threats, making it particularly suitable for critical document processing applications. Comprehensive experimental evaluation across multiple benchmark datasets demonstrates the substantial effectiveness of our innovations. RCCF achieves improvements of up to 16.9% in retrieval performance metrics compared to traditional fusion methods while providing reliable confidence estimates. AM-HDGAT for text demonstrates superior classification performance with an average F1-score improvement of 2.23% compared to state-of-the-art methods, maintaining 82.4% performance retention under adversarial attack scenarios. Real-world deployment validation shows a 34.5% reduction in manual processing time and 95.7% user satisfaction scores, establishing AttenFlow as a significant advancement in intelligent document management technology. Full article

Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R² = 0.77), while RF demonstrated superior performance for dry biomass estimation (R² = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation. Full article

Micromixing is a crucial process in microfluidic systems. In biochemical and chemical analysis, the sample is usually tested with reagents. These solutions must be well mixed for the reaction to be possible, generally using micromixers manufactured with sophisticated and expensive technology. The present work shows the design and evaluation of micromixers fabricated with LTCC (low-temperature co-fired ceramics) and FDM (fused deposition modeling) technologies for the development of functional and complex geometries. Two-dimensional planar serpentine and 3D chaotic convection serpentine micromixers were manufactured and implemented in an automated microanalytical system using photometric methods. To evaluate the performance of the micromixers, flow, mixing and absorbance measurements were carried out. Green tape and PP materials were used and showed good resistance to the acidic chemical solutions. The devices presented achieved mixing times in seconds, a reduced dispersion due to their aspect ratio, high sensitivity, and precision in photometric measurement. The optical sensing cells stored sample volumes in a range of 10 to 600 µL, which allowed the reduction of reagent consumption and waste generation. These are ideal characteristics for in situ measurement, portable, and low-cost applications focused on green chemistry and biochemistry. Full article

The growing need to reduce methane emissions from ruminants while enhancing feed utilization has driven the development of innovative in vitro measurement techniques. This review examines the Gas Endeavour (GES), an automated volumetric apparatus that quantifies both total gas and methane production in real time during rumen fermentation. Utilizing the principles of liquid displacement and buoyancy, the GES integrates a thermostatically controlled water bath, specialized gas flow cells, and an alkaline CO₂ absorption unit to deliver precise kinetic data on fermentation. Compared to conventional methods—which often rely on manual measurements and post-incubation gas chromatography—the GES provides continuous monitoring and immediate data acquisition, reducing labour and potential errors. This review discusses the system’s design, operational challenges such as controlling headspace pressure and ensuring consistent inoculum preparation, and its applications in both animal nutrition and biomethane potential assessments. The findings suggest that, with further standardization and protocol refinement, the GES could significantly advance research aimed at optimizing feed digestibility and mitigating methane emissions in ruminant production systems. Full article

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors. Full article

Background/Objectives: Tears are a promising source of biomarkers reflecting both ocular and systemic conditions. However, small sample volumes and low cell yields pose technical challenges in analytical workflows. This study aimed to evaluate the feasibility of quantifying total cell counts and characterizing HLA-DR and CD3 expression in tear-derived cells using an automated cell counter with fluorescence detection (Countess 3 FL). Methods: Tears were collected from 31 patients, centrifuged and the resulting pellet was incubated with HLA-DR and CD3 antibodies, markers of inflammation and T lymphocytes, respectively. Data obtained from Countess 3 FL were compared with conventional flow cytometry and immunofluorescence. For technical performance analysis, precision and reproducibility of cell count and staining were measured. For method validation, an in vitro model of hyperosmolar stress was assessed by culturing conjunctival epithelial cells (CCL20.2) with 350 or 450 mOsm NaCl. Results: The total cell yield in each tear sample correlated with the tear surnatant volume, in a range of 1–40μL (mean total cell number: 1.3 ± 1.1 × 10⁴, correlation analysis with tear volume: r = 0.47, p < 0.05). HLA-DR and CD3 were detected in all samples, with a mean value, respectively, of 43.6% (±21.0) and 25.0% (±15.0) intensity. Data were comparable to those obtained from standard flow cytometry analysis.HLA-DR increase in CCL20.2 exposed to hyperosmolar stress was recorded using Countess 3FL reading, confirming the detection capacity of the proposed method. Conclusions: The automated cell counter can provide HLA-DR and CD3 quantification in tear cell samples, despite the high variability and the low volume availability of tear samples. Method standardization and technical improvements are necessary to strengthen this application in the clinical setting. Full article

Electrical capacitance tomography holds significant potential for multiphase flow parameter measurements, but its application has been limited by the challenge of reconstructing high-quality images, especially under complex and uncertain conditions. We propose an innovative multi-objective robust optimization model to alleviate this limitation. This model integrates advanced optimization methods, multimodal learning, and measurement physics, structured as a nested upper-level optimization problem and lower-level optimization problem to tackle the challenges of complex image reconstruction. By integrating supervised learning methodologies with optimization principles, our framework synchronously achieves parameter tuning and performance enhancement. Utilizing the regularization theory, the multimodal learning prior image, sparsity prior, and measurement physics are incorporated into a novel lower-level optimization problem. To enhance the inference accuracy of the prior image, a new multimodal neural network leveraging multimodal data is developed. An innovative nested algorithm that mitigates computational difficulties arising from the interactions between the upper- and lower-level optimization problems is proposed to solve the proposed multi-objective robust optimization model. Qualitative and quantitative evaluation results demonstrate that the proposed method surpasses mainstream imaging algorithms, enhancing the automation level of the reconstruction process and image quality while exhibiting exceptional robustness. This study pioneers a novel imaging framework for enhancing overall reconstruction performance. Full article

Background and Objectives: Metabolic-flow (<0.35 L/min) anesthesia is practiced more often as manufacturers provide newer technologies, yet the benefits of metabolic-flow anesthesia have not been fully investigated. This study aimed to investigate the feasibility and safety of automated gas control (AGC^®) mode, which provides metabolic-flow anesthesia, in a pediatric population. Materials and Methods: Pediatric surgery patients between 1 and 10 years of age were included in this prospective observational trial. After intravenous induction and safe orotracheal intubation, AGC^® was initiated, and total sevoflurane consumption (mL) and wash-in speed-based sevoflurane consumption data were collected to measure feasibility. For safety, inspired (F_iO₂), alveolar (F_AO₂), and expired (F_EO₂) oxygen concentration data, and inspired and alveolar sevoflurane (F_iSevo and F_ASevo, respectively) concentration data, were recorded. Changes in fresh gas flow (FGF) throughout the procedure and postoperative recovery data were also compared. Results: A total of 130 patients were eligible for this study, and 121 patients were included in the analyses; 30 patients had a wash-in speed of 4 (WI4) and 91 patients had a wash-in speed of 8 (WI8) at follow-up. The total mean sevoflurane consumption was 9.35 ± 4.93 mL for a median surgery duration of 100 min. WI8 patients consumed more sevoflurane (9.92 ± 5.08 mL vs. 7.79 ± 4.19 mL, p = 0.04). At the 15th and 30th minutes, the FGF dropped under minimal flow and metabolic flow limits, respectively (p < 0.001). The times to extubation and obeying commands were shorter in WI8 patients (8 (5–10) vs. 11 (5–15) p = 0.03, and 9.5 (5–10.5) vs. 13 (9–17) p < 0.01). Conclusions: Maintenance with AGC^® may offer up to 40 h of anesthesia, considering that the volume of a sevoflurane bottle is 250 mL, reflecting exceptional savings compared to conventional anesthesia management. Metabolic flow anesthesia driven by AGC^® is feasible and safe in pediatric anesthesia practice. Full article

This article deals with the removal of manganese from water via ultrafiltration and the oxidation of manganese with chlorine dioxide or potassium permanganate before ultrafiltration. The dose of oxidizing agents, time of contact with water, and manganese concentration in raw and treated water were monitored. A fully automated ultrafiltration device with membrane module UA-640 (Microdyn-Nadir) was used. A tubular reactor with a static mixer was used to reach a sufficient contact time for water with an oxidizing agent, enabling the oxidation of manganese in water. The concentration of Mn in the water source ranged from 0.150 to 0.250 mg/L Mn. The results of the experiments showed that in the case of chlorine dioxide, the efficiency of removing Mn from water of 74.31% was achieved at a flow rate of 60 L/h, a dose of 0.4 mg/L ClO₂ and a retention time of 30.5 min; the concentration of Mn in the treated water was 0.037 mg/L, while in the case of KMnO₄ the efficiency was up to 100% at a flow rate of 650 L/h, a dose of 0.3 mg/L Mn (determined after adding KMnO₄) and a retention time of 2.8 min; the concentration of Mn in the treated water was below the detection limit of 0.005 mg/L of the measuring device. Pilot plant experiments confirmed the efficiency of ultrafiltration, demonstrating the possibility of decreasing the manganese concentration below the limit for drinking water using the considered method. Full article

The colonization of pressurized irrigation networks by zebra mussels (Dreissena polymorpha) poses a serious risk to water delivery, reducing pipeline capacity and potentially causing complete blockages. Despite the critical need for early detection and effective management, existing methods often rely on costly, time-consuming field inspections or indirect indicators with limited accuracy. To address this gap, we present SIMZEBRA, a cloud-based toolkit that assesses invasions using hydraulic monitoring and simulation. The tool employs the Normalized Pressure Method, comparing real-time pressure data from transducers with EPANET simulations of a mussel-free network. An optimization process adjusts friction coefficients in network segments until simulated and measured pressures align, enabling the generation of infestation maps over user-defined time periods. Compared to conventional approaches, SIMZEBRA enhances detection accuracy, reduces the reliance on physical inspections, and provides a scalable, automated solution for continuous monitoring. The tool also integrates experimental data to establish relationships between mussel density, pipeline diameter, and roughness. In the presented case study, roughness increases of up to 10 mm were detected in affected pipes, while local head losses at hydrants ranged between 9 and 11 m, depending on flow conditions. Developed in R with CPU parallelization, the toolkit operates remotely on a cloud server, ensuring fast, efficient, and cost-effective detection and management of zebra mussel infestations. This approach improves early warning capabilities and supports proactive invasive species management in pressurized irrigation networks. Full article

Rice is irrigated by flooding, maintaining constant water levels and achieving high water requirements. At the outlet of the plot is a drainage canal whose monitoring using a long-throated flume to determine the flow rate leaving the plot allows for the establishment of practices to reduce highwater consumption. Since the drainage channel has a trapezoidal cross-section and is built on land, the throat of the flume is also trapezoidal to ease the transition between the two sections and to reduce head losses. Herein, a new accurate procedure is developed that provides a quick and automated design of a long-throated flume. This method allows direct calculation of the dimensions of the narrowed section, side slope, and bottom width by choosing the modular limit, the sill height, and the length of the throat based on the characteristics of the channel where the flume is to be installed. The new process is applied to the design of a long-throated flume that allows us to measure the entire range of flow rates required. The design developed based on our methodology is evaluated using the WinFlume Version 2.1 software, and the results obtained demonstrate its strength and suitability. The modular limit values considered (between 0.5 and 0.8) ensure a significant reduction in head losses as water passes through. Full article

Background: Therapeutic drug monitoring (TDM) of infliximab (IFX) and adalimumab (ADL) mainly relies on the use of enzyme-linked immunosorbent assays (ELISA). More recently, rapid assays have been developed and validated to reduce turnaround time (TAT). Here, we compared IFX and ADL concentrations measured with both ELISA and a new fluorescence-based lateral flow immunoassay (AFIAS). Methods: In serum samples from pediatric patients, IFX and ADL drug levels, and total anti-IFX antibodies were measured using clinically validated ELISA kits (Immundiagnostik AG). Samples were further analyzed using a new rapid assay (AFIAS, Boditech Med Inc.) to measure drug levels and total anti-IFX antibodies. Results: Spearman’s correlation coefficients (rho) were 0.98 [95% confidence interval (CI) 0.97 to 0.99] for IFX (p < 0.001) and 0.83 (95% CI 0.72 to 0.90) for ADL (p < 0.001). Calculated % bias was −14.09 (95% Limits of agreement, LoA, −52.83 to 24.66) for IFX and 15.79 (LoA −37.14 to 68.73) for ADL. For the evaluation of total anti-IFX antibodies, we did not collect sufficient data to establish a statistically significant correlation between AFIAS and ELISA. The inter-rater agreement showed a “substantial” and a “moderate” agreement for IFX and ADL, respectively. Conclusions: Our results show that the AFIAS assay has an accuracy and analytical performance comparable to that of the ELISA method used for TDM of IFX and ADL. Therefore, the introduction of this device into routine clinical practice could provide results more quickly and with similar accuracy as ELISA, allowing clinicians to rapidly formulate clinical decisions. Full article

The accurate, robust, and efficient prediction of transition in viscous flows is a significant challenge in computational fluid dynamics. We present a coupled high-fidelity iterative approach that leverages the FUN3D flow solver and the LASTRAC stability code to predict transition in low-disturbance environments, initiated by the linear growth of boundary-layer instability modes. Our method integrates the ability of FUN3D to compute mixed laminar–transitional–turbulent mean flows via transition-sensitized Reynolds-Averaged Navier–Stokes equations with the ability of LASTRAC to perform linear stability analysis, all within an automated framework that requires no intermediate user involvement. Unlike conventional frameworks that rely on classical stability theory or reduced-order metamodels, our approach employs parabolized stability equations to provide more accurate and reliable estimates of disturbance growth for multiple instability mechanisms, including Tollmien–Schlichting, Kelvin–Helmholtz, and crossflow modes. By accounting for the effects of mean-flow nonparallelism as well as the surface curvature, this approach lays the foundation for improved N-factor correlations for transition onset prediction in a broad class of flows. We apply this method to three distinct flow configurations: (1) flow over a zero-pressure-gradient flat plate, (2) the NLF-0416 airfoil with both natural and separation-induced transition, and (3) a 6:1 prolate spheroid, where transition is primarily driven by crossflow instability. For two-dimensional cases, a formulated intermittency distribution is used to model the transition zone between the laminar and fully turbulent flows. The results include comparisons with experimental measurements, similar numerical approaches, and transport-equation-based models, demonstrating good agreement in surface pressure coefficients, transition onset locations, and skin-friction coefficients for all three configurations. In addition to contributing a couple of new insights into boundary-layer transition in these canonical cases, this study presents a powerful tool for transition modeling in both research and design applications in aerodynamics. Full article