Real Test Research Articles

Analysis of real world FRα testing in ovarian, fallopian tube, and primary peritoneal cancers

BackgroundEpithelial ovarian cancer (EOC) remains a significant challenge in gynecologic oncology, particularly in the context of platinum-resistant disease. Mirvetuximab soravtansine (MIRV), was approved after trials revealed favorable response and survival outcomes. MIRV targets folate receptor alpha (FRα), a cell-surface receptor that is overexpressed in EOC and has been associated with aggressive disease phenotypes. MethodsThis retrospective study analyzed 425 patient samples tested for FRα using the VENTANA® FOLR1 RxDx immunohistochemical assay. The patient cohort included cases with high grade serous carcinoma predominantly, tested across various anatomical sites. Statistical analysis examined the correlation between FRα positivity and clinical parameters such as tumor site and histology. ResultsFRα was highly expressed in 36.3 % of the cases, with a significant association between FRα positivity and high grade serous ovarian histology. Tumor samples from the ovary, fallopian tube, adnexa, and dominant pelvic masses showed higher FRα positivity compared to metastatic sites (positive rates of 44.4 % vs 32.5 %, p = 0.02), highlighting the potential influence of tumor origin on expression of FRα. Time between sample collection and testing did not impact FRα expression, with sample testing spread over a median of 19.5 months post-collection. Eight patients had more than one specimen tested, of which 3 (37.5 %) had discordant results when a subsequent specimen was tested. ConclusionOur results highlight a need for standardized protocols for FRα testing to ensure accurate biomarker evaluation across varied clinical settings. The heterogeneity in FRα expression, influenced by tumor histology and anatomical origin, warrant further investigation to optimize therapeutic outcomes. Prior presentationPreliminary findings from this study were previously presented in poster format at the Society of Gynecologic Oncology 2024 Annual Metting. We confirm that the submission complies with the journal requirements.

Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle

This paper considers a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). By tilting its propellers, the aircraft can transition from rotary-wing (RW) multirotor mode to fixed-wing (FW) mode and vice versa. A novel architecture of a neural network-based controller (NNC) is presented. An “imitative learning” approach is employed to train the NNC to mimic the response of an expert but computationally expensive model predictive controller (MPC). The resulting NNC approximates the MPC’s solution while significantly decreasing the computational cost. The NNC is trained on the longitudinal axis. Successful simulations and real flight tests prove that the NNC is suitable for the longitudinal axis control of a complex nonlinear system such as the tilt-rotor VTOL UAV through a sequence of transitions between the RW mode to the FW mode, and vice versa, in a forward flight.

Turkish mobile speech discrimination in real noise 4-word sentence test findings in individuals with hearing loss aged 8–12 years

BackgroundIn daily life, there has been a need to establish verbal communication with sentences instead of single-syllable words and to be tested with sentences that better reflect social life. In this study, it was aimed to evaluate signal noise ratio thresholds and speech comprehension scores in noise in children with severe sensorineural hearing loss by using real ambient noises, four-word spectral balanced sentences, and light, medium, and high input signal levels.ResultsA total of 40 children, including 20 children with severe sensorineural hearing loss, using bilateral hearing aids, (10.23 ± 1.64) and 20 children with normal hearing (9.25 ± 1.48), aged 8–12 years, were included in the study. The newly developed Turkish Mobile Sentence Test for Distinguish Speech in Real Noises was applied to the participants. This test is performed using spectral and coherence balanced 4-word sentences, at − 10 dB, − 5 dB, 0 dB, 5 dB, and 10 dB SNR thresholds and 40, 60, and 80 dB sound levels at shopping mall and amusement park. Children with hearing loss had higher signal noise ratio thresholds at all sound levels in shopping malls, amusement parks, and classroom environments, and their speech comprehension scores in noise were lower at these thresholds (p < 0.05).ConclusionsChildren with hearing loss aged 8–12 have difficulties in verbal communication, in real ambient noises, and in speech discrimination tests, even with 4-word sentences at mild, medium, and high levels of intensity, compared to children with normal hearing. Further studies evaluating hearing aid fitting settings with signal noise ratio thresholds are needed.

Self-assembly Pt hollow nanospheres for highly selective and ultrasensitive detection of dopamine

Abnormal concentration of dopamine, one critical neurotransmitter in central nervous system, causes several neurological diseases associated with behavioral responses and brain functions in human beings. Highly sensitive detection of dopamine is of great significance yet challengeable. In this work, Pt hollow nanospheres (Pt HNSs) with a uniform diameter of 30 nm were successfully synthesized in water by self-assembly method under room temperature. The fabricated Pt hollow/Nafion nanospheres modified glassy carbon electrode (Pt HNSS/Nafion/GCE) exhibited superior electrocatalytic performance, with peak currents 2.24 and 4.54 times higher than those of Nafion-modified glassy carbon electrode (Nafion/GCE) and bare glassy carbon electrode (GCE), respectively. Theoretical calculations indicate that dopamine has an adsorption energy of −3.18 eV on the Pt(111) surface, with an electron transfer of + 1.84 |e|, aligning with the 2-electron transfer mechanism. Furthermore, the low energy barrier observed during dopamine oxidation process suggests that the Pt(111) surface is highly favorable for dopamine oxidation. Meanwhile, Pt HNSS/Nafion/GCE electrochemical sensor exhibits highly sensitive and selective response in determination of dopamine ranging from 0.01 to 250 μM with low detection limits of 0.804 nM (S/N = 3). Moreover, the electrochemical sensor shows reliable signals in real test with great anti-interference and stability during the experiments, making it a promising candidate for applications in dopamine sensing.

Improved convergence speed using hybrid AI for TD EM modeling in power electronics

This paper presents a time-domain (TD) approach based on hybrid artificial intelligence (AI) to speed up convergence of radiating sources characterization in power electronics. To obtain a representative equivalent model of device under test, a dedicated optimization framework has been developed in TD using a particle swarm optimization (PSO) toolbox. In addition, for elementary feature extraction, a pseudo-Zernike moment invariant (PZMI) descriptor has been defined. Finally, with the aim of identifying remaining dipole parameters and classification problems, artificial neural networks (ANN) have been implemented. A coupling of TD electromagnetic (EM) inverse method based on a PSO algorithm along with PZMI and ANN application has been investigated and applied to a real test case. Experimental measurements have been conducted using the near-field scanning technique above an alternating current (AC)/direct current (DC) converter. Obtained results are discussed based on a comparison between measured and estimated EM field distributions using both the hybrid AI method and a conventional TD inverse method based on genetic algorithms (GA) only. This study confirms that, compared with those given by non-hybrid method, the proposed algorithm further improves the convergence speed while maintaining high accuracy. Hence, the present work offers an impressive perspective for radiated emissions characterization using hybrid AI algorithms.

Optimization and greenness assessments of the supramolecular solvent-assisted cloud point extraction method for copper spectrophotometric determination in water and food samples

For the first time, the green supramolecular solvents (SUPRASs)-based cloud point extraction (CPE) approach was created for copper spectrophotometric determination in food and water samples. SUPRASs were synthesized in six different forms and used as synergistic reagents. SUPRAS (water, salicylic acid, and THF) mixed with TritonX-114 (TX-114) to be the most efficient extraction solvent. The validation investigation found that the linearity range, limit of detection (LOD), limit of quantification (LOQ), enhancement factor (EF), and preconcentration factor (PF) are 0.2-700.0 μg L-1, 0.1 μg L-1, 0.33 μg L-1, 85.0, and 50.0, respectively. The technique's accuracy was calculated using standard reference materials (SRMs) (GBW07605 Tea Leaves and GBW10019 Apple) and ranged between 96.0 and 98.9%. Lastly, a novel SUPRAS-CPE approach was utilized to determine copper concentrations in real water, food tests, and SRMs. Furthermore, the SUPRAS-CPE conforms to green chemistry principles by using less both time and energy than previous studies. Also, the greenness values were determined by three recent tools. The Analytical Greenness Metric for Sample Preparation (AGREEprep), Complex Modified GAPI (ComplexMoGAPI), and Sample Preparation Metric of Sustainability (SPMS) programs produced results of 0.79, 86.0, and 8.42, respectively. These results are strongly aligned with both green ideals and environmentally friendly practices. The finding increases the possibilities for using SUPRAS-CPE to extract additional mineral ions.

An AmpliSeq™ HD approach for specific and sensitive identification of GMO by Next Generation Sequencing

With new genetically modified crops being constantly developed, many new targets need to be analysed for regulatory compliance. Classical PCR-based approaches currently used are however reaching their limits in terms of target throughput and new approaches are needed to adapt to the evolving situation. The Ion AmpliSeq™ HD technology was here adapted for the simultaneous detection of 108 GMO targets by Next Generation Sequencing (NGS) on the Ion GeneStudio S5 platform, using PCR primers mostly retrieved from the official European GMO methods. Tested on a wide array of reference materials, the method delivered specific detection of the expected GMO targets and reached a sensitivity down to 0.01 % (w/w) as required by GMO analytical guidelines. Numerous proficiency test samples were successfully analysed which confirmed the method performance and its applicability on real test samples. With its straightforward library preparation and ready-to-use bioinformatics analysis, the developed method is well tailored for routine GMO testing laboratories.

KeyGAN: Synthetic keystroke data generation in the context of digital phenotyping

Objective:This paper aims to introduce and assess KeyGAN, a generative modeling-based keystroke data synthesizer. The synthesizer is designed to generate realistic synthetic keystroke data capturing the nuances of fine motor control and cognitive processes that govern finger-keyboard kinematics, thereby paving the way to support biomarker development for psychomotor impairment due to neurodegeneration. Methods:KeyGAN is designed with two primary objectives: (i) to ensure high realism in the synthetic distributions of the keystroke features and (ii) to analyze its ability to replicate the subtleties of natural typing for enhancing biomarker development. The quality of synthetic keystroke data produced by KeyGAN is evaluated against two keystroke-based applications, TypeNet and nQiMechPD, employed as’referee’ controls. The performance of KeyGAN is compared with a reference random Gaussian generator, testing its ability to fool the biometric authentication method TypeNet, and its ability to characterize fine motor impairment in Parkinson’s Disease using nQiMechPD. Results:KeyGAN outperformed the reference comparator in fooling the biometric authentication method TypeNet. It also exhibited a superior approximation to real data than the reference comparator when using nQiMechPD, showcasing its adaptability and versatility in mimicking early signs of Parkinson’s Disease in natural typing. KeyGAN’s synthetic data demonstrated that almost 20% of real PD samples could be replaced in the training set without a decline in classification performance on the real test set. Low Fréchet Distance (<0.03) and Kullback–Leibler Divergence (<700) between KeyGAN outputs and real data distributions underline the high performance of KeyGAN. Conclusion:KeyGAN presents strong potential as a realistic keystroke data synthesizer, displaying impressive capability to reproduce complex typing patterns relevant to biomarkers for neurological disorders, like Parkinson’s Disease. The ability of its synthetic data to effectively supplement real data for training algorithms without affecting performance implies significant promise for advancing research in digital biomarkers for neurodegenerative and psychomotor disorders.

Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching Off Completely Idle Machines

Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach considering the idle energy consumption by the HPC units and the possibility of turning off unused units entirely, driven by a quantitative objective function. We found that even when turning off unused machines, the objectives of makespan and energy consumption still conflict and, therefore, their multi-objective optimization nature. This work presents empirical results for AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. The best-performing algorithm is MOCell for the 400 real scheduling problem tests. In contrast, the best-performing algorithm is GWASFGA for a small-instance synthetic testbed.

Load-and-go molecular diagnostics for human papilloma virus detection

The present study describes analytical and clinical performance of the HPV 16 &amp; 18 assay for the detection of HPV specific DNA with near point-of-care utility. A room temperature compatible real time PCR test was developed for detection and discrimination of HPV 16/18 in a single tube. The test was integrated and validated on Compact series automated platform for analytical performance (sensitivity, specificity, accuracy). The clinical evaluation was performed on cervico-vaginal specimens. The analytical sensitivity data showed dynamic range from 50 IU/mL to 1´106 IU/mL using NIBSC international standard (19/224 for HPV16 and HPV18). The test was found to be highly specific for both the HPV targets with no cross reactivity against other HPV genotypes (high and low risk) and similar pathogens. The clinical performance on cervical sampling indicated up to 100% agreement with a comparator test method. The HPV 16 and 18 detection assay employing an integrated automated system offers low to high-throughput analysis with a cost-effective, pipette-free approach, eliminating the need for molecular infrastructure and technical expertise. This test named “HPV 16 and 18 detection kit” has been approved by Central Drugs Standard Control Organisation (India) for commercial utility, and would have significant implications for mass screening of cervical cancer.

Detection and analysis of corner case scenarios at a signalized urban intersection

One of the major challenges in automated driving is ensuring that the system can handle all possible driving scenarios, including rare and critical ones, also referred to as corner case scenarios. For the validation of automated driving functions, it is necessary to test the corner cases in simulation environments. However, the effectiveness of simulation-based testing depends on the availability of realistic test data that accurately reflect real-world scenarios. This work aims to detect, cluster, and analyze rare and critical traffic scenarios based on real-world traffic data from an urban intersection and prepare the data for usage in simulation environments. The scenarios are detected by filtering hard braking maneuvers, red light violations, and near misses under adverse weather conditions. A long-term analysis of trajectory, weather, and traffic light data was conducted to find these rare scenarios. Our results show that 24 hard braking maneuvers are included in our dataset with a duration of half a year. They occur due to failure to yield, emergency vehicle operations, and a red light violation. Some of the scenarios include crashes, lateral evasive maneuvers, or are under adverse weather conditions like fog. Altogether, we provide methods to extract corner case scenarios based on multiple data sources and reveal diverse types of corner case scenarios at an urban intersection. In addition, we analyze the behavior of road users in critical scenarios and show influencing factors to avoid crashes. By combining and converting the data to an industry standard for simulation we provide realistic test cases for the validation of automated vehicles. Therefore, the results are relevant for both, traffic safety researchers to learn from road user behavior in these rare scenarios and developers of automated driving systems to test their functions.

A 2.4 GHz IEEE 802.15.4 Multi-Hop Network for Mountainous Forest and Watercourse Environments: Sensor Node Deployment and Performance Evaluation

In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was introduced for practical testing. The proposed system’s communication reliability was tested in two different scenarios: a mountainous forest with sloping areas and trees and a watercourse, which referred to environmental and flooding monitoring applications. Wireless network performances were evaluated through the received signal strength indicator (RSSI) level of each wireless link, a packet delivery ratio (PDR), as the successful rate of packet transmission, and the end-to-end delay (ETED) of all data packets from the transmitter to the receiver. The experimental results demonstrate the success of the multi-hop WSN deployment and communication in both scenarios, where the RSSI of each link was kept at the accepted level and the PDR achieved the highest result. Furthermore, as a real-time response, the data from the source could be sent to the sink with a small ETED.

Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.

Dopamine transporter imaging is routinely used in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [11C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [123I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [11C]CFT PET images to [123I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements. A CycleGAN was trained on [11C]CFT PET (n = 602, 72%PD) and [123I]FP-CIT SPECT (n = 1152, 85%PD) images from PD and non-parkinsonian control (NC) subjects. The model generated synthetic SPECT images from a real PET test set (n = 67, 75%PD). Synthetic images were quantitatively and visually evaluated. Fréchet Inception Distance indicated higher similarity between synthetic and real SPECT than between synthetic SPECT and real PET. A deep learning classification model trained on synthetic SPECT achieved sensitivity of 97.2% and specificity of 90.0% on real SPECT images. Striatal specific binding ratios of synthetic SPECT were not significantly different from real SPECT. The striatal left-right differences and putamen binding ratio were significantly different only in the PD cohort. Real PET and real SPECT had higher contrast-to-noise ratio compared to synthetic SPECT. Visual grading analysis scores showed no significant differences between real and synthetic SPECT, although reduced diagnostic performance on synthetic images was observed. CycleGAN generated synthetic SPECT images visually indistinguishable from real ones and retained disease-specific information, demonstrating the feasibility of translating [11C]CFT PET to [123I]FP-CIT SPECT. This cross-modality synthesis could enhance further AI classification accuracy, supporting the diagnosis of PD and APS.

Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations.

Influence of the use of an adhesive connection on the joint strength of modular hip endoprostheses.

Modular hip implants enables a more precise adaptation of the prosthesis to the patient's anatomy. However, they also carry the risk of increased revision rates due to micromotion at the taper junction. In order to minimize this risk, one potential solution is to establish an adhesive bond between the metal taper junctions. Load-stable bonding techniques, already successfully employed in dentistry for connecting materials such as metals and ceramics or different alloys, offer a promising approach. Nevertheless, the bond strength of tapered adhesive bonds in modular hip implants has not been investigated to date. Twenty-eight tapered junctions, consisting of a taper (female taper) and a trunnion (male taper) were turned using TiAl6V4 ELI (n = 16) and CoCr28Mo6 (n = 12). The process parameters cutting speed (vc = 50 m/min or 100 m/min) and feed (f = 0.1 mm, 0.05 mm or 0.2 mm) were varied for the trunnions. For each set of process parameters, one trunnion and one taper were additionally subjected to sandblasting. To investigate the effect of geometry, angular mismatch in the samples were measured. The taper pairs were bonded with a biocompatible adhesive, and push-out tests were subsequently performed. The push-out forces generated from the taper connections where both tapers were sandblasted showed a mean push-out force of 5.70 kN. For the samples with only the trunnion sandblasted, the mean force was 0.58 kN, while for the samples with only taper sandblasted the mean push-out force was 1.32 kN. When neither of the tapers was sandblasted the mean push-out force was 0.91 kN. No significant effect of the process parameters on the push-out force was observed. Only the reduced valley depth Svk showed a slight correlation for the CoCr28Mo6 samples (R2 = 0.54). The taper pairs with taper mismatch (between trunnion and taper) greater than |0.1°| did not show lower push-out forces than the specimens with lower taper mismatch. Sandblasted and adhesive-bonded tapered connections represent a viable suitable alternative for modular hip implant connections. Slight differences in taper geometry do not result in reduced push-out forces and are compensated by the adhesive. In mechanically joined tapers these differences can lead to higher wear rates. Further investigation under realistic test conditions is necessary to assess long-term suitability.

Efficient generation of HPLC and FTIR data for quality assessment using time series generation model: a case study on Tibetan medicine Shilajit.

The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000m, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality. To address these challenges, this study employed a deep learning model, time vector quantization variational auto- encoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data. Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model's classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability. This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

Evaluation of the performance of different internal insulation systems in real-life conditions ‐ A case study

Thermal retrofitting of historic buildings is essential to reducing heat loss in all buildings. Although exterior insulation often is hygrothermally a better solution, internal insulation is the only option in facades worthy of preservation (e.g., solid masonry external walls). However, mold growth has frequently been discovered in buildings with internal insulation at the interface between the insulation and the original wall, while simulations and lab tests show minimum risk, and vice versa. Therefore, real-life testing is required so that the building owners are more likely to accept these measures.This paper presents a case study of an 1837-built typical historic building. The study focuses on the building's top floor, a residential area (commune). Eight different rooms have four different types of internal insulation installed. The performance of the insulation systems was determined by monitoring the interior and exterior climate and the temperature and relative humidity at the intersection of the insulation and the existing wall for 20 months. The risk of mold growth was estimated based on the measurements. In the wall interfaces, the Mold Index indicated that the risk for mold growth is not severe. Hygrothermal simulations for the measured period and ten years were also performed. To increase simulation accuracy, the original exterior masonry bricks underwent laboratory testing to determine their precise material properties. The main outcome from the simulations was that the three vapor-open systems were more susceptible to indoor moisture load changes and had an elevated risk of mold growth, whereas the vapor-tight system was more robust to variation in insulation thickness and internal moisture level changes, and in this case, demonstrated the best performance in respect to moisture safety.

Theoretical Model and Experimental Application for Solute Diffusion in Leaching Test With Time-Dependent Boundary Conditions

Abstract Measuring the diffusion coefficient of clay-based liner materials is important in estimating and predicting long-term barrier performance in waste containment facilities. Various theoretical models, including the finite cylindrical model, have been commonly used to determine the diffusion properties of clay-based liner materials in leaching tests. However, the assumption of zero-concentration boundary conditions of the traditional finite cylindrical model contradicts the measured variation of concentration in real leaching tests, likely resulting in (1) underestimated and unconservative diffusion coefficient, or (2) requirement of a relatively large liquid-to-soil ratio and frequent leachate replacement in the experiment to maintain the zero-concentration boundary condition. In this study, a theoretical model was developed to evaluate the solute diffusion process within a soil specimen under arbitrary, time-dependent concentration boundary conditions. The proposed model, incorporating the time-dependent boundary conditions, provides efficient calculations of the concentration distribution and the cumulative fraction leached of solute across the soil specimen. The example application of the proposed model to experimental data demonstrates the capability of the proposed model to determine apparent diffusion coefficients of clay-based liner materials without introducing errors associated with the assumption of a zero concentration boundary condition. The proposed model provides a comprehensive method to investigate the dynamic transport behaviors of solutes through clay-based liner materials in future studies.

Fuzzy logic as a method of analysis of hot forging process of 80MnSi8-6 steel

A novel approach was proposed, based on the application of the fuzzy logic (FL) method for the fast analysis of the hot deformation process of 80MnSi8-6 steel. In the first stage, the curves developed from plastometric tests and the results of studies of the microstructure of the deformed samples were used as input data for the analysis. Input and output variables were adopted and a set of rules based on cause-and-effect relationships was defined, defining the interactions between the variables. A fast FL-controller was designed, and the correctness of its operation was verified by comparison with experimental results and the results of finite element method (FEM) analysis, carried out taking into account the evolution of the microstructure. The process of hot compression under isothermal conditions of 80MnSi8-6 steel specimens was simulated on the Warmumformsimulator (WUMSI), assuming such parameters and other conditions as were used in real tests. It was confirmed that the proposed method, based on the analysis of flow curves and prior austenite grain size using a fuzzy controller, gave satisfactory results. Subsequently, a novel FL-controller was developed to analyze the kinetics of dynamic recrystallization (DRX), using data obtained from the author’s model of this phenomenon for its construction and calibration. The correctness of the controller was confirmed by comparing the results of its DRX volume fraction calculations with the distributions of this value determined by the model and the model-based FEM analysis method, respectively. It was shown that FL is applicable also when a model of the analyzed phenomenon is available. Unlike model-based calculations, a properly designed controller allows the indication of deviations from general trends that can be pointed out and interpreted by a human expert, but significantly faster. It can also serve as a component of a system analyzing complex processes, such as hot multi-stage forging. Fuzzy controller can be used in parallel with modeling or replace models in calculations.

A Rapid Colorimetric and Fluorescent Assay of Aspergillus Fumigatus in Food by Loop-Mediated Isothermal Amplification.

Aspergillus fumigatus is a foodborne mycete that can induce recurrent pneumonia, but the current detection methods have insufficient sensitivity and rapidity. Here, we aim to develop an efficient and sensitive loop-mediated isothermal amplification (LAMP) primer set for A. fumigatus detection. First, we designed a novel set of LAMP primers by targeting the Beta-tubulin (β-tub) gene. The LAMP reaction system was optimized by screening reaction temperature and betaine concentration. And then, the specificity of the proposed primers was verified by using 10 interferent microorganism species. The sensitivity of the designed method was compared with that of polymerase chain reaction (PCR) on pure cultures and complex matrix. The accuracy and response time of the method were examined by simulated samples. Our proposed primer set could accurately detect A. fumigatus from different food matrices with no response to other microorganisms. More intriguingly, this method possessed a low limit of detection (2 copies/reaction, 10-fold less than PCR), a short measuring time (<30 min), and a naked-eye readability. A real sample test demonstrates the good recovery rate and accuracy in apple, corn, milk, and other food matrix. Our proposed β-tub primer set provides great potential for rapid assessment of A. fumigatus contamination in food by integrating portable equipment and microscale reaction system.