Statistical Parameters Research Articles

Evolution and stability of liner shipping networks in Northeast Asia from 2018 to 2022

Ports are severely challenged by the uncertainty of the transportation environment. To evaluate the stability of maritime transportation network in Northeast Asia under port disruption, a network stability analysis model based on node failure rate that considers static and structural, dynamic and functional is proposed in this paper. In this model, the stability of the maritime transportation network is redefined. Based on complex network theory, the statistical topological parameters and evolution of the network are quantitatively analyzed. The active response and passive adaptation process of the maritime transportation network to the failure rate of the port are analyzed. The evaluation results illustrate that the initial stability of the port is proportional to the non-redundant connections and independence of the port. Network location advantages profoundly affect the probability of port failures. The diffusion of port failures in maritime transportation is phased, and the network is adaptive to port disruptions and has high stability. These results contribute to better planning of maritime transportation routes and strategies for port calls by strategic planners of shipping companies.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

DEVELOPMENT AND VERIFICATION OF UV SPECTROPHOTOMETRIC TECHNIQUE FOR DETERMINING N-ACETYLCYSTEINE IN TABLET FORMULATIONS

Objective: This study aimed to determine the concentration of N-Acetyl Cysteine (NAC) in tablet formulations using UV-Visiblespectroscopy. Methods: A precise and accurate UV spectrophotometric method was developed for determining N-acetylcysteine in tablets, using 0.1N NaOH as a diluent. The drug's purity was assessed using UV-visible spectrophotometry, which validated linearity, accuracy, precision, specificity, limit of detection, and quantification. Results: The calibration curve had a high correlation coefficient (r2 = 0.9992) and maximum absorbance at 235 nm. There was no interference in dosage. The results showed 100.17% mean % recovery and 100.27% tablet assay, with a precision of 0.60% and 0.57%, respectively. The method was robust and rugged. Conclusion: The method was evaluated using statistical parameters, including precision, accuracy, linearity, recovery, and robustness. The results showed no significant differences compared to other methods. The method can be used to analyse pharmaceutical formulations quickly, and no significant differences in mean values and standard deviations were found.

Deep learning based x-ray spectrometer for high repetition rate characterization of betatron radiation

Betatron radiation produced from a laser-wakefield accelerator is a broadband, hard x-ray (>1 keV) source that has been used in a variety of applications in medicine, engineering, and fundamental science. Further development and optimization of stable, high repetition rate (HRR) (>1 Hz) betatron sources will provide a means to extend their application base to include single-shot dynamical measurements of ultrafast processes or dense materials. Recent advances in laser technology used in such experiments have enabled increases in shot-rate and system stability, providing improved statistical analysis and detailed parameter scans. However, unique challenges exist at high repetition rate, where data throughput and source optimization are now limited by diagnostic acquisition rates and analysis. Here, we present the development of a machine-learning algorithm for the real-time analysis of betatron radiation. We report on the fielding of this deep learning algorithm for online source characterization at the Institut National de la Recherche Scientifique's Advanced Laser Light Source. By fine-tuning an algorithm originally trained on a fully synthetic dataset using a subset of experimental data, the algorithm can predict the betatron critical energy with a percent error of 7.2 % with a reconstruction time of 1.5 ms, providing a valuable tool for real-time, multi-objective optimization at HRR.

Pattern recognition of drying lysozyme–glucose droplets using machine learning classifiers

Out-of-equilibrium processes, such as sessile droplet drying, often result in distinctive macroscopic residual patterns in systems containing molecules, proteins, and colloids. Protein–glucose mixtures are particularly effective models for studying the behavior of complex fluids containing biomolecules. This study investigates the drying patterns of lysozyme droplets with varying initial glucose concentrations. Without glucose, the crack patterns are chaotic and dispersed throughout the droplet. Interestingly, cracks predominantly form around the droplet edges at intermediate glucose concentrations, while the deposits become uniform and crack-free at high glucose concentrations. To understand and classify the unique patterns related to the initial compositional changes, we developed an automated pattern recognition pipeline. We used two methods for analyzing images captured throughout the drying process. The first method involved extracting statistical textural parameters from the images as quantitative features for machine learning classifiers. The second method utilized a neural network-based classifier to directly classify the images, achieving an accuracy of 97%. The results demonstrate the effectiveness of using images from the entire drying process, not just the final images, for pattern classification. This approach may be useful in gaining a fundamental understanding of unique crack pattern that emerge when glucose is added to a protein solution.

Correlations of gray matter volume with peripheral cytokines in Parkinson's disease

IntroductionPeripheral cytokine levels may affect specific brain volumes. Few studies have examined this possible relationship. ObjectiveIn a case–control study, we used magnetic resonance imaging (MRI) voxel-based morphological analysis techniques to examine the relationship between gray matter volume changes and cognitive, motor and emotional dysfunction as well as between gray matter volume changes and peripheral blood cytokine levels. MethodA total of 134 subjects, comprising 66 PD patients and 68 healthy controls, were recruited. Peripheral venous blood was collected to measure the concentrations of 12 cytokines, including IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, IFN-α, IFN-γ, and TNF-α. All the subjects also underwent MRI, where 3D-T1-weighted MR images were used for the analysis. In addition, the Montreal Cognitive Assessment (MoCA), Mini-Mental Status Examination (MMSE), Unified Parkinson's disease Rating Scale (UPDRS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) scores were assessed in PD patients. Statistical parameter mapping 12 software was used for the statistical analysis of the images. ResultCompared with control patients, PD patients presented decreased gray matter volume (GMV) in the bilateral frontal lobe, temporal lobe, parietal lobe, occipital lobe, insula, and right cerebellar lobule VIII. Regional GMV in the temporal lobe, parietal lobe, and cerebellum was correlated with MoCA, MMSE, UPDRS, HAMA, and HAMD scores in PDs. In addition, the regional GMV in PDs was correlated with the concentrations of cytokines, including IL-4, IL-6, IFN-γ, and TNF-α. The IL-6 concentration was negatively correlated with the UPDRS-IV score. ConclusionPD patients exhibit gray matter atrophy in a wide range of brain regions, which are symmetrically distributed and mainly concentrated in the frontal and temporal lobes, and these changes may be linked to motor disorders and neuropsychiatric manifestations. Cytokine concentrations in peripheral blood are correlated with regional gray matter volume in PDs, and the IL-6 level affects gray matter volume in the left precentral gyrus and the manifestation of motor complications.

Probabilistic back analysis of reservoir landslide considering hydro-mechanical coupled observations

Precisely assessing statistical parameters to characterize spatial soil variability presents a significant challenge in probabilistic slope stability analysis, primarily due to inherent soil uncertainties and limited field-specific data. Probabilistic back analysis, recognized as an effective and reliable technique, offers a rational method for utilizing observational data to invert parameters. Nevertheless, previous investigations into parameter inversion for slope stability analysis have seldom considered the coupling of hydro-mechanical characteristics in reservoir landslides. This study proposes a novel integrated Bayesian framework to perform back analysis of reservoir landslides, incorporating the spatial variability of hydro-mechanical parameters. Within this framework, a hypoplastic constitutive model is developed to characterize the step-like deformation of the reservoir slope. The posterior knowledge of the saturated permeability coefficient and shear strength parameters is obtained by collecting field monitoring data of the underground water level and ground displacement. The Maliulin landslide, located in the Three Gorges Reservoir area of China, is used as an illustrative example to validate the proposed framework through back analysis of spatially variable soil parameters and probabilistic stability analysis. The results demonstrate that the proposed Bayesian updating framework significantly reduces the uncertainties associated with statistical values of soil parameters, providing more accurate and reasonable updated soil parameters for probabilistic slope stability analysis.

Incidence of serious maternal Morbidity at the RUASHI military hospital (DRC)

Maternal morbidity is a public health problem in developing countries. The aim of our study was to determine the frequency of serious maternal morbidity at the Ruashi military hospital (DRC) with a view to ensuring good care and monitoring of pregnant women. A longitudinal descriptive study was conducted among 154 pregnant women in a prospective manner, data entry and analysis of results were carried out using Microsoft office 2016 and statistical test parameters were made using of Epi Info 7.2. The incidence of maternal morbidity was 24.2%, of which 32.4% had anemia followed by hemorrhage with 27% and finally sepsis and uterine subinvolution with 5.4% each. However, 94.6% had followed the prenatal consultation against 5.4% and 29.7% had a frequency of 3 participations in the prenatal consultation and finally 8.1% had a frequency of 6 participations in the CPN. The analysis showed that the frequency of prenatal consultation and postnatal visits, literacy and socio-economic level are statistically associated with maternal morbidity. Improving the socio-economic level of households, combating illiteracy and monitoring prenatal consultation are among the factors that will determine maternal morbidity.

Forecasting the design maxima water discharges of floods on the Latorica river according to the data of the Mukachevo gauging station using plotting position formulas

This article presents the results of forecasting design maxima discharges on the Latorica River within Mukachevo town based on hydrological observation data at the “Mukachevo” gauging station using plotting position formulas. While solving the task, a novel non-parametric method of forecasting using observation data is applied. The method includes extrapolating the discrepancy (divergence, disagreement) between the estimates of the statistical annual probabilities of exceedance obtained by different plotting position formulas. The task is considered in the frame of the stationarity hypothesis of the maximum river flow employing a time series of maximal discharges of the Latorica River observed at the “Mukachevo” gauging station from 1947 to 1999.We involved the thirteen plotting position formulas. There was no specific criterion for choosing them to solve the task. All applied formulas were considered admissible options, and results obtained after using them – expert judgments reflecting decision-makers’ predisposition to more cautious or less expensive decision options in flood management strategies.The epistemic uncertainty of the different plotting positions was reduced by employing the Fishburn rule. According to this rule, the significance of various plotting positions was given by arranging their estimates in descending order of importance of their values under decision-making. Depending on the selected significance option assignment of the different plotting position formulas, such rank-weighted estimates of the design peak discharges (each of them for annual exceedance probability 1%, 0.5%, and 0.2%) were computed: (1) the rank-weighted upper bound estimate (sup-estimate) corresponding to the predisposition to more cautious decision options; (2) the rank-weighted lower bound estimate (inf-estimate) corresponding to the predisposition to less expensive decision options. As possible control theoretical alternatives for forecasting design maximal discharges considered were five parametric probability distributions: 1) the Kritskyi-Menkel three-parameter gamma distribution; 2) Pearson’s type III distribution; 3) the Extreme value type I distribution (Gumbell’s type I distribution); 4) the Logarithmic Pearson type III distribution; and 5) the Two-parameters logarithmic-normal distribution. The population statistical parameters for these parametric probability distributions were estimated from the sample statistics by the method of moments.

РЕЗУЛЬТАТЫ ПРИМЕНЕНИЯ СТАТИСТИЧЕСКОГО МЕТОДА ДЛЯ ПРОГНОЗА НЕФТЕГАЗОНОСНОСТИ ТЕКТОНО-КОНЦЕНТРИЧЕСКИХ СТРУКТУР ДАЛЬНЕГО ВОСТОКА РОССИИ С ИСПОЛЬЗОВАНИЕМ ЭТАЛОННЫХ ТЕРРИТОРИЙ

he paper presents the results of using the statistical method to analyze geological and geophysical data measured within several tectonic-concentric structures in oil and gas-bearing regions in the Far East of the Russian Federation (depth of the Mohorovichi surface, depth of the the lithosphere bottom, values of geothermal energy). The distribution and values of some statistical parameters (histograms, distribution densities, probabilities, median and mean values) used for forecasting zones of hydrocarbon deposits location have been calculated. It was found that the most informative initial data used for statistical prediction of areas of location of oil and gas deposits are the presence of concentric zones at the depth of the Mohorovichich surface, and concentric inhomogeneites at the lower boundary of the lithosphere associated with the «root» structures of paleo-mantle plumes. Based on the calculated statistical parameters, four local tectonic-concentric structures promising for prospecting new oil and gas fields were identified.

Comprehensive analysis of soil electrical conductivity: an experimental and machine learning approach

Experimental test is conducted to find a relation between soil electrical conductivity with some soil properties. Five input parameters namely water content (w), dry density of soil (γd), degree of saturation (Sr), porosity (η) and voltage (V) are considered to compute electrical conductivity of soil (SEC). The effect of w, γd, Sr and η on SEC are analyzed. These datasets are used in soft computing technique to predict SEC. Five hybrid models namely ANN-GA, ANN-PSO, ANN-FA, ANN-GWO and ANN-MFO are used to predict SEC. To check the performance of these hybrid model, numerous statistical performance parameters (R2, a-10 index, VAF, LMI, RMSE, MAE, MAD and U95) are used. On the basis of these performance parameters, ANN-GA performs better (Due to higher value of R2, VAF, a-10 index and LMI and lower value of RMSE, MAE, MAD and U95) as compare to the other proposed model. The model's performance is also examined using rank analysis, regression curve, Williams plot, error matrix, objective function (OBJ) criterion, and Akaike information criterion (AIC). By applying all the above criteria, it has been observed that ANN-GA model outperforms than the other model to predict SEC. This was recognized to its maximum R2 = 0.9998 and the lowest RMSE = 0.0041 during the training phase, as well as R2 = 0.9975 and RMSE = 0.0112 during the testing phase. The model's reliability index (β) is calculated using the first-order second moment (FOSM) approach, and the result is compared to its actual condition. Additionally, a sensitivity analysis is carried out to ascertain the impact of each input parameter on the output (SEC).Based on the results, the hybrid model of ANN i.e., ANN-GA is potential to assist engineers to predict SEC in the design phase of high voltage buried power cables, ground modification techniques or in the field of agriculture.

New model for evaluating the impact response of steel fiber reinforced concrete subjected to the repeated drop-weight

The investigation of available experimental results obtained in the non-instrumented drop weight test recommended by American Concrete Institute on the steel-fiber-reinforced concrete reveals that the data distribution does not match a normal distribution. For this reason, logarithmic scales of the first-crack (number of blows to cause the first visible crack) and failure strengths (number of blows to spread the cracks sufficiently) are adopted to increase the fitness of the normal curve to the frequency histogram. The authors propose a novel model to predict the failure strength and evaluate the steel fiber effect on the post-cracking behavior of steel-fiber-reinforced concrete. The new model improves the prediction of the experimental results obtained in literature review for the various ranges of volume fraction of steel fiber analyzed. The proposed model is compared with other models using statistical parameters and the comparison shows a significant improvement considering different failure states. To verify the performance of the proposed model, in addition to results from another research, an experimental impact test was conducted. Besides, a reliable regression model is developed to validate experimental results. The findings underscore the potential of the proposed model to significantly enhance predictive accuracy, thereby proposing valuable insights for optimizing steel-fiber-reinforced concrete.

Multifractal structure and Gutenberg–Richter parameter associated with volcanic emissions of high energy in Colima, Mexico (years 2013–2015)

Abstract. The evolution of multifractal structures in various physical processes, such as climatology, seismology, or volcanology, serves as a crucial tool for detecting changes in corresponding phenomena. In this study, we explore the evolution of the multifractal structure of volcanic emissions with varying energy levels (observed at Colima, Mexico, during the years 2013–2015) to identify clear indicators of imminent high-energy emissions nearing 8.0×108 J. These indicators manifest through the evolution of six multifractal parameters: the central Hölder exponent (α0); the maximum and minimum Hölder exponents (αmax, αmin); the multifractal amplitude (W=αmax-αmin); the multifractal asymmetry (γ=[αmax-α0]/[α0-αmin]); and the complexity index (CI), calculated as the sum of the normalized values of α0, W, and γ. Additionally, the results obtained from adapting the Gutenberg–Richter seismic law to volcanic energy emissions, along with the corresponding skewness and standard deviation of the volcanic emission data, further support the findings obtained through multifractal analysis. These results, derived from multifractal structure analysis, adaptation of the Gutenberg–Richter law to volcanic emissions, and basic statistical parameters, hold significant relevance in anticipating potential volcanic episodes of high energy. Such anticipation can be further quantified using an appropriate forecasting algorithm.

Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.

Dysregulation of lung tissue collagen level plays a vital role in understanding how lung diseases progress. However, traditional scoring methods rely on manual histopathological examination introducing subjectivity and inconsistency into the assessment process. These methods are further hampered by inter-observer variability, lack of quantification, and their time-consuming nature. To mitigate these drawbacks, we propose a machine learning-driven framework for automated scoring of lung collagen content. Our study begins with the collection of a lung slide image dataset from adult female mice using second harmonic generation (SHG) microscopy. In our proposed approach, first, we manually extracted features based on the 46 statistical parameters of fibrillar collagen. Subsequently, we pre-processed the images and utilized a pre-trained VGG16 model to uncover hidden features from pre-processed images. We then combined both image and statistical features to train various machine learning and deep neural network models for classification tasks. We employed advanced unsupervised techniques like K-means, principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to conduct thorough image analysis for lung collagen content. Also, the evaluation of the trained models using the collagen data includes both binary and multi-label classification to predict lung cancer in a urethane-induced mouse model. Experimental validation of our proposed approach demonstrates promising results. We obtained an average accuracy of 83% and an area under the receiver operating characteristic curve (ROC AUC) values of 0.96 through the use of a support vector machine (SVM) model for binary categorization tasks. For multi-label classification tasks, to quantify the structural alteration of collagen, we attained an average accuracy of 73% and ROC AUC values of 1.0, 0.38, 0.95, and 0.86 for control, baseline, treatment_1, and treatment_2 groups, respectively. Our findings provide significant potential for enhancing diagnostic accuracy, understanding disease mechanisms, and improving clinical practice using machine learning and deep learning models.

Population data for 12 X-STRs loci in Malaysian Malay and Chinese populations

The utilization of X-chromosome short tandem repeats (X-STRs) for human identification particularly in resolving complex kinship cases has been advocated. Since, forensic statistical parameters vary among different populations, and because the X-STRs population data representing the diverse population of Peninsular Malaysia remain unavailable, the specific attempt reported here for the Malays (n = 224) and Chinese (n = 201) populations appears forensically relevant to support the evidential value of the 12 X-STRs markers for human identification in Malaysia. Results derived from the Qiagen Investigator® Argus X-12 kit revealed that DXS10135 as the most polymorphic locus with high genetic diversity, polymorphic information content, heterozygosity as well as power of exclusion. Based on allele frequencies, the combined power of discrimination as well as the mean exclusion chance (MECKrüger, MECKishida, MECDesmarais and MECDesmaraisDuo) values for the Malays and Chinese were individually ≥0.999995532964908. As for the combined power of discrimination as well as the mean exclusion chance (MECKrüger, MECKishida, MECDesmarais and MECDesmaraisDuo) calculated based on haplotype frequencies, the values were ≥0.9999986410567 for the Malays and Chinese populations. In addition, results from the genetic distance, neighbor-joining phylogenetic tree and principal component analysis revealed close biogeographical distributions of the studied populations with other South East Asian populations. Hence, the utilization of the X-STRs data for identifying individuals among the Malays and Chinese populations in Peninsular Malaysia for forensic application appears as highly supported.

Assessment of the Lipophilicity of Indole Derivatives of Betulin and Their Toxicity in a Zebrafish Model.

There are scientific studies indicating that the attachment of an indole moiety to the triterpene scaffold can lead to increased anticancer potential. Lipophilicity is one of the factors that may influence biological properties and is therefore an important parameter to determine for newly obtained compounds as drug candidates. In the present study, previously synthesized 3 and/or 28-indole-betulin derivatives were evaluated for lipophilicity by reversed-phase thin-layer chromatography. The experimental values of lipophilicity (logPTLC) were then subjected to correlation analysis with theoretical values of logP, as well as for selected physicochemical and pharmacokinetic parameters and anticancer activity. A toxicity test using zebrafish embryos and larvae was also conducted. High correlation was observed between the experimental and theoretical values of lipophilicity. We presented correlation equations and statistical parameters describing the relationships between logPTLC and several physicochemical and ADME parameters. We also revealed the lack of correlation between the experimental values of lipophilicity and anticancer activity. Moreover, experiments on zebrafish have confirmed no toxicity of the tested compounds, which was consistent with the results of the in silico toxicity analysis. The results demonstrated, using the example of indole derivatives of betulin, the utility of lipophilicity values in the context of predicting the biological activity of new compounds.

Comparing frequentist and Bayesian uncertainty quantification in two‐step constitutive model calibration

AbstractThe calibration of constitutive models using experimental observations is a well‐established task in computational solid mechanics. Especially for complex constitutive models, multistep calibrations are regularly applied. While this approach is advantageous for reducing the complexity of the calibration problem, the quantification of parameter uncertainties becomes more challenging. In this study, we perform two‐step statistical parameter inference for an elasto‐plastic constitutive model utilizing a frequentist approach and Bayesian inference. The uncertainties are quantified by different methods, in particular, we compare an asymptotic normality analysis and Gaussian error propagation in the frequentist setting with sampling‐based Bayesian inference. Although frequentist and Bayesian parameter inference differ significantly in the way parameters are calibrated, both methods yield similar estimates. Additionally, the uncertainties quantified by asymptotic normality considerations and Gaussian error propagation are quite similar. However, the uncertainties of the elasticity parameters in the frequentist approach are found to be significantly smaller compared to Bayesian inference, due to the underlying approximations in the frequentist setting. Thus, uncertainties can be estimated in both settings, but it is crucial to be aware of the inherent assumptions and simplifications when interpreting the results.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Cortisol and insulin behaviors during an ultramarathon event: are they real markers of extreme exertion?

The current work aimed to describe and compare the cortisol and insulin concentrations behavior and rate of perceived exertion (RPE) during a 115 km ultramarathon race. Nine ultrarunners (eight males) were evaluated six times (0, 37, 60, 76, 89 and 115 km). At each moment, saliva samples (for cortisol and insulin assessment) and RPE (CR10 scale) were collected. Statistical analysis included correlation, one-way repeated measure ANOVA, and Statistical Parametric Mapping to define discrete and continues changes and compare cortisol, insulin and RPE profiles. Our main findings revealed an early peak in cortisol and RPE, accompanied by a decline in insulin responses (402±49 min of the race, P<0.05). Cortisol and insulin only showed magnitude differences with inverse behaviors until ~6% (7 km) of the ultramarathon duration. Cortisol and RPE presented similar behaviors, rising from the beginning of the race and remaining elevated throughout the race (η2=0.91 and η2=1.0, P<0.001). Insulin levels decreased when the race started, remaining below 60% of baseline values from the midpoint to the end of the race (P=0.04). The study showed an imbalance in the catabolic/anabolic hormone profile during an ultramarathon race, with a prominence in catabolic state. It should be considered in the ultramarathon races preparation and participation due to its possible detrimental effect on the athlete's health.

Tough Cortical Bone-Inspired Tubular Architected Cement-Based Material with Disorder.

Cortical bone is a tough biological material composed of tube-like osteons embedded in the organic matrix surrounded by weak interfaces known as cement lines. The cement lines provide a microstructurally preferable crack path, hence triggering in-plane crack deflection around osteons due to cement line-crack interaction. Inspired by this toughening mechanism and facilitated by a hybrid (3D-printing/casting) process, the study engineers architected tubular cement-based materials with the stepwise cracking toughening mechanism, that enables a non-brittle fracture. Using experimental and theoretical approaches, the study demonstrates the competition between tube size and shape on stress intensity factor from which engineering stepwise cracking can emerge. Two competing mechanisms, both positively and negatively affected by the growing tube size, arise to significantly enhance the overall fracture toughness by up to 5.6-fold compared to the monolithic brittle counterpart without sacrificing the specific strength. This is enabled by crack-tube interaction and engineering the tube size, shape, and orientation, which promotes rising resistance-curves (R-curve). "Disorder" curves and statistical mechanics parameters are proposed for the first time to quantitatively characterize the degree of disorder for describing the representation of the architected arrangement of materials in lieu of otherwise inadequate "periodicity" classification and misperceiveddisorder parameters (perturbation and Voronoi tessellation methods).