Curve Parameters Research Articles

A Bayesian hierarchical model for the analysis of visual analogue scaling tasks.

In psychophysics and psychometrics, an integral method to the discipline involves charting how a person's response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analog Scaling tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word "a" or more like word "b" (i.e. each participant is giving a continuous categorization response). By taking all the continuous categorization responses across the speech continuum, a parametric curve model can be fit to the data and used to analyze any individual's response pattern by speech continuum. Standard statistical modeling techniques are not able to accommodate all of the specific requirements needed to analyze these data. Thus, Bayesian hierarchical modeling techniques are employed to accommodate group-level non-linear curves, individual-specific non-linear curves, continuum-level random effects, and a subject-specific variance that is predicted by other model parameters. In this paper, a Bayesian hierarchical model is constructed to model the data from a Visual Analog Scaling task study of mono-lingual and bi-lingual participants. Any nonlinear curve function could be used and we demonstrate the technique using the 4-parameter logistic function. Overall, the model was found to fit particularly well to the data from the study and results suggested that the magnitude of the slope was what most defined the differences in response patterns between continua.

УПРАВЛІННЯ ГЕОМЕТРІЄЮ ПЕРЕХІДНОЇ КРИВОЇ ПРОФІЛЮ ТА ВЕЛИЧИНОЮ ГРАНИЧНОГО ДІАМЕТРА ЦИЛІНДРИЧНОГО ЗУБЧАСТОГО КОЛЕСА

Information is given on the use of cylindrical gears. The influence of the geometry of the transition curve on other parameters is considered. With the development of numerically controlled machines and 3D printing, it becomes possible to manufacture gears with any geometry, in particular, it is possible to manufacture a transition surface with any parameters of the transition curve. The formula for determining the limit diameter is presented. The above formula is valid for gears that do not have undercutting. As can be seen from the obtained formula, the maximum diameter depends on the module, the number of teeth, the angle of the profile, the offset and other parameters of the initial contour. An example of a formed depression of a gear wheel is presented. The results of calculation and modeling of the value of the limit diameter depending on different values of the radius of curvature of the output contour were obtained.

Contrast-enhanced ultrasound combined with elastic imaging for predicting the efficacy of concurrent chemoradiotherapy in cervical cancer: a feasibility study.

Contrast-enhanced ultrasound (CEUS) and elastography are of great value in the diagnosis of cervical cancer (CC). However, there is limited research on the role of contrast-enhanced ultrasound combined with elastography in predicting concurrent chemoradiotherapy and disease progression for cervical cancer. The purpose of this study was to evaluate the feasibility of contrast-enhanced ultrasound combined with elastography and tumor prognosis. MRI was performed on 98 patients with cervical cancer before and after treatment. Before, during, and 1 week after the treatment, contrast-enhanced ultrasound and elastography were conducted, and the alterations of ultrasound-related parameters at each time point of the treatment were compared. The correlation between contrast-enhanced ultrasound combined with elastic imaging and oncological outcome was assessed. There was no notable difference in overall clinical data between the complete remission (CR) group and the partial remission (PR) group (P>0.05). Before treatment, there were no statistically significant differences in elasticity score, time to peak (TTP), and peak intensity (PI) between the CR group and the PR group. However, there were no statistical differences in elastic strain ratio (SR) and area under the curve (AUC) before and after treatment between the CR group and the PR group, and there were also no statistical differences in the elastic strain ratio (SR) and area under the curve (AUC) of contrast-enhanced ultrasound parameters between the CR group and the PR group before and during treatment. There was a statistically significant difference after treatment (P<0.05).At present, the follow-up of patients is about 1 year, 7 patients were excluded due to loss to follow-up, and 91 patients were included in the follow-up study. Through the review of the cases and combined with MRI (version RECIST1.1) and serology and other related examinations, if the patient has a new lesion or the lesion is larger than before, the tumor marker Squamous cell carcinoma antigen (SCC-Ag) is significantly increased twice in a row, and the patient is divided into progressive disease (PD). Those who did not see significant changes were divided into stable disease (SD) group. The relationship between clinical characteristics, ultrasound parameters and disease progression in 91 patients was compared. There was no significant difference in age and clinical stage between the two groups (P>0.05), but there was a significant difference in the elevation of tumor marker squamous cell carcinoma antigen (SCC-Ag) between the two groups (P<0.05).With the growth of tumors, TTP decreased, elasticity score and PI increased, and the difference was statistically significant (P<0.05). The AUC of SCC-Ag was 0.655, the sensitivity was 85.3%, and the specificity was 45.6%.The AUC, sensitivity and specificity of ultrasound parameters combined with SCC-Ag predicted disease progression was 0.959, 91.2% and 94.8%. Using contrast-enhanced ultrasound and elastography to predict the efficacy and disease progression of concurrent chemoradiotherapy is feasible. In addition, the combination of SCC-Ag with contrast-enhanced ultrasound and elastography can further enhance the efficiency of predicting disease progression.

Reverse Model for Curved Bridge Measurement Based on 3D Laser Scanning Technology

Receiving inspection plays a crucial role in ensuring construction quality after the completion of engineering projects. Traditional inspection measurement methods, such as manual observation means and optical equipment measurement methods, have limitations in terms of the number of measurement sites and the range of measurements. These traditional methods fail to provide accurate curve parameters and continuous spatial morphology information for large-span curved bridge structures. This paper proposes a reverse model measurement method to address this issue. The reverse model is built based on point cloud data acquired by 3D laser scanning technology. Finally, take the Taizicheng No. 1 Bridge as an example, the validity of the proposed method is verified.

Mitigating adverse impacts of reservoir impoundment on lake ecology: A case study of the Three Gorges Reservoir and Dongting Lake

Upstream reservoir impoundment has brought adverse impacts on the downstream river-connected lakes, including lower water levels, frequent droughts, and reduced biodiversity. Existing research on ecological reservoir operation focused more on the effectiveness of operation redesign for ecology improvement, but less discussion on why they are eco-friendly. In this paper, the reservoir operation mechanisms targeting lake ecology are explored from three perspectives: ecological impoundment strategy, dominant characteristics of ecology and conservation tradeoffs, and critical parameters of the rule curves. Therefore, a reservoir operation framework, which couples the lake hydrological response model and multi-objective optimization model, is developed in this study. This framework is to maximize the water conservation and lake ecology benefits, assessing the latter by ecological reliability and ecological water shortage rate. Taking the Three Gorges Reservoir and Dongting Lake as the case study, operation rule curves are optimized for different impoundment strategies. The results indicate that impoundment with early storage time and slow storage rate can improve the ecological reliability and ecological water shortage by 9.90 % and 16.39 % than conventional operation. The ecological objectives are weakly competitive or synergistic when impounding fast, but strong competitive when impounding slow. Meanwhile, as the storage rate declines, the ecological improvement causes a slight decrease in power generation and a significant decrease in water supply. Finally, the crucial parameters of the rule curves are explored and appropriate values are recommended for different ecological preferences.

The Unusual Brightness Phase Curve of (65803) Didymos

On 2022 September 26, NASA's Double Asteroid Redirection Test (DART) successfully hit Dimorphos, the smaller companion of the binary system formed with the asteroid (65803) Didymos. Both the binary system and the impact event were imaged by the Light Italian Cubesat for Imaging of Asteroids, detached from DART 15 days before the impact. Images from the onboard LUKE red, green, and blue camera together with ground-based observations enabled the reconstruction of Didymos's brightness phase curve, with phase angles ranging from 2.35° to 107.7°. The opposition effect regime was studied using the exponential-linear equation, the “Shevchenko” function and the linear-by-parts model while the IAU-official HG1G2 magnitude system was applied to the full phase curve. The opposition effect indicates an unusual asteroid surface for an S type, with characteristics similar to M-type asteroids. While the HG1G2 parameters from the full phase curve place Didymos well among asteroids of the taxonomic C complex. Didymos’s phase curve parameters when compared to near-Earth asteroids are very close to the Q type (1862) Apollo, indicating possible depletion of fine submicrometric grains through resurfacing. Didymos's geometric albedo (0.15 ± 0.01) is reported to be 30%–45% smaller than the average geometric albedo for near-Earth S types (0.26 ± 0.04). We propose that Didymos might be an LL ordinary chondrite analog containing albedo-suppressing, shock-darkened/impact melt minerals that have undergone resurfacing processes in the past. A comparison with meteorites indicates that, less likely, Didymos could also contain materials analog to carbon-bearing brecciated L3 ordinary chondrites.

Bayesian approach for evaluation of lactation curve in cross bred cattle based on monthly and bimonthly test day milk yield.

In field progeny testing program milk recording at monthly or bimonthly intervals and prediction of first lactation 305-day milk yield (FL305DMY) from these test day yields have been adapted as an alternative to daily milk recording. Wood's incomplete gamma function is the one of the commonly used nonlinear lactation curve model. In recent years Bayesian approach of fitting nonlinear biological models is gaining attention among researchers. In this study Wood's incomplete gamma function was fitted using Bayesian approach using monthly (MTDY) and bimonthly test day (BTDY) yields. The lactation curve parameters thus obtained were used for prediction of FL305DMY. Efficiency of prediction based on monthly and bimonthly test day milk yield were compared using error of prediction. It was found to be 5.78% and 7.59% as root mean square error (RMSE) based on MTDY and BTDY respectively.The Breeding values of 97 Karan Fries sires were estimated using BLUP-AM based on actual and predicted FL305DMY thus obtained. The RMSE was calculated as the difference between estimated breeding values based on actual and predicted yield. It was found that RMSE calculated based on MTDY showed only a marginal superiority of 0.79% over BTDY and showed high degree of correlation with actual yield. Therefore, recording at bimonthly intervals could be an economical alternative without compromising the efficiency.

Fractional Approach for Belousov-Zhabotinsky Reactions Model with Unified Technique

The Belousov-Zhabotinsky reaction model represents chemical oscillators that exhibit periodic vibrations as a result of complex physic-chemical phenomena. The non-linear behaviour exhibited by Belousov-Zhabotinsky model is the cause of Turing patterns, birth of spiral waves, rise of limit cycle attractors, and deterministic chaos in many chemical reaction processes. Due to these noteworthy characteristics, in this paper, we have analyzed mathematical Belousov-Zhabotinsky model by a novel numerical approach q-Homotopy analysis transformation method. To interpret new observations, we have incorporated Caputo fractional derivative in the model. The numerical result are presented graphically and concerning the absolute error of solutions. With the help of the homotopy parameter curve, we have projected the convergence region with reference to diverse values of fractional derivative. This work establishes that the projected numerical algorithm is a well-organized tool to analyze the multifaceted coupled partial differential equation representing Belousov-Zhabotinsky type reactions.

An alternative parameterization for the binormal ROC curve, with applications to sizing and simulation studies.

Because the conventional binormal ROC curve parameters are in terms of the underlying normal diseased and nondiseased rating distributions, transformations of these values are required for the user to understand what the corresponding ROC curve looks like in terms of its shape and size. In this paper I propose an alternative parameterization in terms of parameters that explicitly describe the shape and size of the ROC curve. The proposed two parameters are the mean-to-sigma ratio and the familiar area under the ROC curve (AUC), which are easily interpreted in terms of the shape and size of the ROC curve, respectively. In addition, the mean-to-sigma ratio describes the degree of improperness of the ROC curve and the AUC describes the ability of the corresponding diagnostic test to discriminate between diseased and nondiseased cases. The proposed parameterization simplifies the sizing of diagnostic studies when conjectured variance components are used and simplifies choosing the binormal a and b parameter values needed for simulation studies.

A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation

A novel zero-reference low-light image-enhancement approach based on noise estimation (ZLEN) is proposed to mitigate noise interference in image-enhancement processes, while the tenets of zero-reference and lightweight network architecture are maintained. ZLEN improves the high-order curve expression governing the mapping of low-light images to their enhanced counterparts, addressing image noise through a meticulously designed noise-estimation module and a zero-reference noise loss function. First, the higher-order curve expression with a noise term is defined, and then the noise map undergoes feature extraction through the semantic-aware attention module; following this, the resulting features are integrated with the low-light image. Ultimately, a lightweight convolutional neural network is adjusted to estimate higher-order curve parameters that link the low-light image to its enhanced version. Notably, ZLEN achieves luminance enhancement and noise reduction without paired or unpaired training data. Rigorous qualitative and quantitative evaluations were conducted on diverse benchmark datasets, demonstrating that ZLEN attained state-of-the-art (SOAT) status among existing zero-reference and unpaired-reference image-enhancement methodologies, while it exhibited comparable performance to full-reference image-enhancement methods. To confirm the practicality and robustness of ZLEN, the luminance enhancement was applied to mine images, which yielded satisfactory results.

The value of MRI-based radiomics for evaluating early parotid gland injury in primary Sjögren's syndrome.

This study aimed to evaluate the value of machine learning models (ML) based on MRI radiomics in diagnosing early parotid gland injury in primary Sjögren's syndrome (pSS). A total of 164 patients (114 in the training cohort and 50 in the testing cohort) with pSS (n=82) or healthy controls (HC) (n=82) were enrolled. Itksnap software was used to perform two-dimensional segmentation of the bilateral parotid glands on T1-weighted (T1WI) and fat-suppressed T2-weighted imaging (fs-T2WI) images. A total of 1548 texture features of the parotid glands were extracted using radiomics software. A radiomics score (Radscore) was constructed and calculated. A t-test was used to compare the Radscore between the two groups. Finally, five machine learning models were trained and tested to identify early pSS parotid injury, and the performance of the machine learning models was evaluated by calculating the acceptance operating curve (ROC) and other parameters. The Radscores between the pSS and HC groups showed significant statistical differences (p<0.001). Among the five machine learning models, the Extra Trees Classifier (ETC) model performed high predictive efficacy in identifying early pSS parotid injury, with an AUC of 0.87 in the testing set. MRI radiomics-based machine learning models can effectively diagnose early parotid gland injury in primary Sjögren's syndrome.

A de-noising method of magnetotelluric signals based on the generalized S-transform

The generalized S-transform has excellent time-frequency resolution capability. It can effectively demonstrate the non-stationary characteristics of signals and the dynamic changes in energy. This provides the conditions for denoising in the time-frequency domain. The magnetotelluric (MT) survey method is often affected by various artificial noises, and these noises are very prominent in certain frequency bands. To address this problem, a method based on the generalized S transform is proposed to de-noise MT data. The generalized S transform is used to improve the time-frequency localization of the different signal components, meanwhile the electromagnetic noise can be conveniently suppressed in the time-frequency domain. Simulated signals and measured MT signals contaminated by common sinusoidal and triangular wave interference are processed. The results were compared with those of the Wavelet threshold method. Results show that the presented method is more conducive to the identification and suppression of electromagnetic noise and shows better de-noising results than the Wavelet threshold method. After de-noising, the useful information of the MT signal affected by strong interference is highlighted, which provides favorable conditions for power spectrum and impedance estimation in the future. Further, the response parameter curve calculated from the de-noised data becomes stable. The proposed method provides a new alternative for de-noising of MT signals, especially those with low signal-to-noise ratios.

Assessment of the Baking Properties of Rye Flour Based on the Polysaccharide Content and Properties

This study aimed to determine the baking quality of rye flour in terms of the content and properties of its polysaccharides, i.e., starch and pentosans. The study materials were low- and high-extract rye flours produced in industrial mills from the rye grain of two growing seasons (2019 and 2020). The results of the starch content, falling number, amylograph properties, DSC test, content of pentosans, swelling curve test, and laboratory baking test were determined. It was found that the type of flour had a greater impact on the baking quality of rye flour than the year of its production. Research has shown that the most frequently used parameters, such as the falling number and the maximum viscosity of starch paste, are not good indicators for assessing the baking value of currently produced rye flours. From the parameters used for evaluating the properties of the starch–amylolytic complex, the initial and onset temperatures for starch gelatinization were the best indicators for evaluating the baking quality of rye flour. This study revealed a significant correlation between the pentosan content (total, water soluble, and insoluble), swelling curve parameters and quality parameters of rye bread, such as the specific bread volume, bread crumb moisture, and bread crumb hardness. Assessment of the baking value of rye flour based only on the evaluated properties of the starch–amylolytic complex is currently not sufficient to determine the baking quality of rye flour and predict the quality of rye bread. This study on the baking quality of rye flour should be extended to include the assessment of the dough properties related to the pentosan content and the enzymes that degrade these components. It was shown that the properties of rye dough related to the content of pentosan can be characterized based on the swelling curve test as a method that, together with the initial and onset starch gelatinization temperatures, allows better assessment of the baking quality of the commercial rye flour and its suitability for the good-quality rye bread production.

Bézier Cubics and Neural Network Agreement along a Moderate Geomagnetic Storm

The discussion models the IRI-2012 TEC map over a moderate geomagnetic storm period (5 days) in February 2015 and compares the yield of the models. The models are constructed with the help of cubic Bézier curves and machine learning. In a sense, the comparison of a classical and mechanical approach with a modern and computer-based one is a considerable experience for the paper. The parametric curve approach governs models of piecewise continuous Bézier cubics, while the models employ only the TEC map. The design is separated into curve components at every five-hour curvature point, and each component is handled independently. Instead of the traditional least squares method for finding control points of cubics, it utilizes the mean of every five-hour of the piecewise curves of the TEC data. Accordingly, the prediction error can be controlled at a rate that can compete with the modern network approach. In the network model, 120 hours of the solar wind parameters and the TEC map of the storm are processed. The reliability of the network model is assessed by the (R) correlation coefficient and mean square error. In modeling the TEC map with the classical approach, the mean absolute error is 0.0901% and the correlation coefficient (R) score is 99.9%. The R score of the network model is 99.6%, and the mean square error is 0.71958 (TECU) (at epoch 47). The results agree with the literature.

Optimize &amp; Reduce: A Top-Down Approach for Image Vectorization

Vector image representation is a popular choice when editability and flexibility in resolution are desired. However, most images are only available in raster form, making raster-to-vector image conversion (vectorization) an important task. Classical methods for vectorization are either domain-specific or yield an abundance of shapes which limits editability and interpretability. Learning-based methods, that use differentiable rendering, have revolutionized vectorization, at the cost of poor generalization to out-of-training distribution domains, and optimization-based counterparts are either slow or produce non-editable and redundant shapes. In this work, we propose Optimize &amp; Reduce (O&amp;R), a top-down approach to vectorization that is both fast and domain-agnostic. O&amp;R aims to attain a compact representation of input images by iteratively optimizing Bezier curve parameters and significantly reducing the number of shapes, using a devised importance measure. We contribute a benchmark of five datasets comprising images from a broad spectrum of image complexities - from emojis to natural-like images. Through extensive experiments on hundreds of images, we demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes. Moreover, we show that our algorithm is x10 faster than the state-of-the-art optimization-based method. Our code is publicly available: https://github.com/ajevnisek/optimize-and-reduce

Enhanced laser-induced fluorescence and Raman spectroscopy with gold nanoparticles for the diagnosis of oral squamous cell carcinoma

BackgroundThe incidence, mortality, and recurrence rates of oral cancer are high worldwide. It is a common and aggressive type of tumor. Owing to the challenges associated with early illness diagnosis, squamous cell carcinoma, a kind that is prevalent of oral cancer, has an unacceptably high fatality rate. The management of the condition and the prevention of cancer, on the other hand, depend greatly on early detection. Therefore, alternative methods for the treatment and early diagnosis are essential for oral cancer. The detection of tongue squamous cell carcinoma is aided by coupled surface plasmon resonance, which can occur in gold nanoparticles (AuNPs). Compared to the currently utilized imaging contrast chemicals, AuNPs are more biocompatible and capable of targeting specific surface molecules. In the current study, AuNPs were synthesized in one step via citrate reduction and applied to tongue samples of a Caucasian man's Homo sapiens (Squamous cell carcinoma from ATCC cell-lines) in order to improve early detection using and laser-induced fluorescence and Raman spectroscopy.ResultsUV–visible spectroscopy, Zeta potential, TEM, and FTIR spectroscopic technique were used to characterize the synthesized nanoparticles. The synthesized AuNPs measured 13 ± 3 nm with uniform size distribution and high stability. Results demonstrate the significance of AuNPs in improving the identification of tongue squamous cell carcinoma.ConclusionObtained results revealed that the use of AuNPs modifies the emitted spectra in the two employed spectroscopic techniques and provides more significant receiver operating characteristic curve parameters, hence a higher detection rate of cancer.

Comparison of ASL and DSC perfusion methods in the evaluation of response to treatment in patients with a history of treatment for malignant brain tumor

ObjectivePerfusion MRI is of great benefit in the post-treatment evaluation of brain tumors. Interestingly, dynamic susceptibility contrast-enhanced (DSC) perfusion has taken its place in routine examination for this purpose. The use of arterial spin labeling (ASL), a perfusion technique that does not require exogenous contrast material injection, has gained popularity in recent years. The aim of the study was to compare two different perfusion techniques, ASL and DSC, using qualitative and quantitative measurements and to investigate the diagnostic effectiveness of both. The fact that the number of patients is higher than in studies conducted with 3D pseudo-continious ASL (pCASL), the study group is heterogeneous as it consists of patients with both metastases and glial tumors, the use of 3D Turbo Gradient Spin Echo (TGSE), and the inclusion of visual (qualitative) assessment make our study unique.MethodsNinety patients, who were treated for malignant brain tumor, were enrolled in the retrospective study. DSC Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF) and ASL CBF maps of each case were obtained. In qualitative analysis, the lesions of the cases were visually classified as treatment-related changes (TRC) and relapse/residual mass (RRT). In the quantitative analysis, three regions of interest (ROI) measurements were taken from each case. The average of these measurements was compared with the ROI taken from the contralateral white matter and normalized values (n) were obtained. These normalized values were compared across events.ResultsUncorrected DSC normalized CBV (nCBV), DSC normalized CBF (nCBF) and ASL nCBF values of RRT cases were higher than those of TRC cases (p < 0.001). DSC nCBV values were correlated with DSC nCBF (r: 0.94, p < 0.001) and correlated with ASL nCBF (r: 0.75, p < 0.001). Similarly, ASL nCBF was positively correlated with DSC nCBF (r: 0.79 p < 0.01). When the ROC curve parameters were evaluated, the cut-off values were determined as 1.211 for DSC nCBV (AUC: 0.95, 93% sensitivity, 82% specificity), 0.896 for DSC nCBF (AUC; 0.95, 93% sensitivity, 82% specificity), and 0.829 for ASL nCBF (AUC: 0.84, 78% sensitivity, 75% specificity). For qualitative evaluation (visual evaluation), inter-observer agreement was found to be good for ASL CBF (0.714), good for DSC CBF (0.790), and excellent for DSC CBV (0.822). Intra-observer agreement was also evaluated. For the first observer, good agreement was found in ASL CBF (0.626, 70% sensitive, 93% specific), in DSC CBF (0.713, 76% sensitive, 95% specific), and in DSC CBV (0.755, 87% sensitive - 88% specific). In the second observer, moderate agreement was found in ASL CBF (0.584, 61% sensitive, 97% specific) and DSC CBF (0.649, 65% sensitive, 100% specific), and excellent agreement in DSC CBV (0.800, 89% sensitive, 90% specific).ConclusionIt was observed that uncorrected DSC nCBV, DSC nCBF and ASL nCBF values were well correlated with each other. In qualitative evaluation, inter-observer and intra-observer agreement was higher in DSC CBV than DSC CBF and ASL CBF. In addition, DSC CBV is found more sensitive, ASL CBF and DSC CBF are found more specific for both observers. From a diagnostic perspective, all three parameters DSC CBV, DSC CBF and ASL CBF can be used, but it was observed that the highest rate belonged to DSC CBV.

Assessment of post-earthquake fire capacity of RC frames without seismic design

To study the importance of post-earthquake fire events, 500 reinforced concrete (RC) frames were designed without seismic considerations. These consist in three-bays frames with a variation in the number of floors from two to six (100 frames each). Numerical analyses were performed in SAFIR to investigate the influence of different types of damage, damage location, fire location and fire curves on the post-earthquake fire resistance of the structures. It was observed that the seismic damage can significantly decrease the fire resistance of the frames. In an earthquake scenario it can be expected an increase in the response times of the firefighters as well as malfunctioning and damage of active and passive firefighting measures, which means that the use of parametric fire curves without active firefighting measures can be a better approach instead of the use of the fire curve ISO 834. It was observed, for the analysed cases, that parametric fire curves without active firefighting measures led to lower collapse times when compared to the fire curve ISO 834. On the other hand, when the active firefighting measures are considered, there is a very significant number of structures that do not collapse, which indicates the importance that these firefighting measures can have in a fire after an earthquake. The development of these analysis emphasizes the impacts of applying different fire curves on the damaged structure which consequently brings attention to an adequate definition of the fire curves.

Performance Differences of a Touch-Based Serial Reaction Time Task in Healthy Older Participants and Older Participants With Cognitive Impairment on a Tablet: Experimental Study.

Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT). This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups. A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning. Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=-0.34; SE 0.12; t=-2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%). Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice.

Analysing postprandial amino acid responses in crossover studies with the aaresponse package for R

Nowadays, a healthier and more sustainable lifestyle is the subject of much research. One example is the use of crossover trials to investigate the uptake of proteins, usually from alternatives to animal-based sources, by healthy volunteers. The data analysis is complex and requires many decisions on the part of the scientists involved. Such a process can be streamlined and made more objective and reproducible through bespoke software. This paper describes such a software package, aaresponse\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{aaresponse}$$\\end{document}, for the R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{R}$$\\end{document} environment, available as open source. It features ample visualization functions, supports consistent curation strategies, and compares amino acid uptake of different protein meals (interventions) through the use of mixed models analysing parameters of interest like the area under the curve (AUC). The defining feature is the use of parametric curves to fit the amino acid levels over time, increasing the robustness of the approach and allowing for more strict quality control strategies.