Allometry of the box crab Calappa convexa De Saussure, 1853 (Brachyura, Calappidae) in the southeastern Gulf of California

The relative growth of Calappa convexa can be used to identify dimorphism or morphological change related to sexual maturation. The carapace width (CW) against width and length of the propodus of the right and left cheliped were tested to obtain a coefficient of allometry. CW against total wet weight WT values were analysed for length-weight relationships (LWR). Relative growth was analysed by a multi-model approach. Five models were tested and the best was selected due to the greater weight of the Akaike criterion (). Significant differences between sexes were found in CW (, ) and WT (, ). The negative or positive allometry varied according to the morphometric relationship and sex. No real winner model () was observed in the LWR analysis under a multi-model approach. The average model estimated a breakpoint at 118 mm CW in males and 132 mm CW in females.

Modelling the growth of rearing cattle

Scientific growth literature often uses the models of Brody, Gompertz, Verhulst, and von Bertalanffy. The versatile five-parameter Bertalanffy-Pütter (BP) model generalizes them. Using the least-squares method, we fitted the BP model to mass-at-age data of 161 calves, cows, bulls, and oxen of cattle breeds that are common in Austria and Southern Germany. We used three measures to assess the goodness of fit: R-squared, normalized root-mean squared error, and the Akaike information criterion together with a correction for sample size. Although the BP model improved the fit of the linear growth model considerably in terms of R-squared, the better fit did not, in general, justify the use of its additional parameters, because most of the data had a non-sigmoidal character. In terms of the Akaike criterion, we could identify only a small core of data (15%) where sigmoidal models were indispensable.

Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

Standardization of landing efficiency for opilio crab in the western Bering Sea by using of generalized additive models

Generalized additive models are applied for standardization of daily landing per unit effort (LPUE) for opilio crab using the data of fishery statistics for the West Bering Sea fishery zone in 2003–2020. A set of 12 models with various combinations of predictors was examined and the best model with the smallest value of Akaike criterion was selected (information criterion Akaike 21743, explained variance 58.6 %). The selected model reflects the effect of depth, distance from the coast, daily effort and tensor product of geographic coordinates and day of the year. LPUE was standardized using the selected model by substituting median values of nominal predictors and modal values of categorical predictors. Then the crab stock was estimated using the state-space form of Deriso-Schnute delay-difference model. The estimates based on both standardized and nominal indices are compared and a significant difference between them is found: the stock is assessed as 23,040 t with nominal indices but as 17,070 t using the standardized indices.

Investigation of the Wastewater Treatment Plant Processes Efficiency Using Statistical Tools

The paper presents modelling of wastewater treatment plant (WWTP) operation work efficiency using a two-stage method based on selected probability distributions and the Monte Carlo method. Calculations were carried out in terms of sewage susceptibility to biodegradability. Pollutant indicators in raw sewage and in sewage after mechanical treatment and biological treatment were analysed: BOD5, COD, total suspended solids (TSS), total nitrogen (TN) and total phosphorus (TP). The compatibility of theoretical and empirical distributions was assessed using the Anderson–Darling test. The best-fitted statistical distributions were selected using Akaike criterion. Performed calculations made it possible to state that out of all proposed methods, the Gaussian mixture model (GMM) for distribution proved to be the best-fitted. Obtained simulation results proved that the statistical tools used in this paper describe the changes of pollutant indicators correctly. The calculations allowed us to state that the proposed calculation method can be an effective tool for predicting the course of subsequent sewage treatment stages. Modelling results can be used to make a reliable assessment of sewage susceptibility to biodegradability expressed by the BOD5/COD, BOD5/TN and BOD5/TP ratios. New data generated this way can be helpful for the assessment of WWTP operation work and for preparing different possible scenarios for their operation.

Antivenom accessibility impacts mortality and severity of Brazilian snake envenomation: a geospatial information systems analysis

BackgroundIn 2017, the World Health Organization declared the snakebite envenomation as a neglected tropical disease. Annually, snakebite envenomation causes approximately 400,000 permanent disabilities and 95,000 deaths worldwide. People with the greatest risk of envenomation lack access to adequate health care, including treatment with antivenom. We developed an analysis of accessibility to antivenom in Brazil in order to verify the impacts on mortality.Methods and FindingsInformation about number of accidents, deaths, antivenom, medical assistance, and species, were retrieved from the Brazilian Health Informatics Department (DATASUS) from 2010 to 2015 and analyzed using geostatistics to evaluate the association between snakebite accidents and mortality. An Spatial analysis using Global Moran’s I was performed in order to verify the presence of spatiality as an independent variable to the distribution of the accidents. In addition, we also tested three different analysis of regression using Ordinary Least Square (OLS), Spatial Error, and Geographically Weighed Regression (GWR), together with the information obtained from the DATASUS and sociodemographic indicators, to verify the spatial-temporal distribution of envenomation cases and time to reach the healthcare centers. The regression presenting the lowest Akaike Criterion Information (AIC), highest adjusted R2, and variables with p < 0.05 was selected to represent our model. Lastly, the accessibility index was performed using 2-step floating catchment area based on the amount of hospital beds and inhabitants. This study revealed 141,039 cases of snakebites, 598 deaths, and mortality rate of 3.13 per 1,000,000 inhabitants. Moreover, GWR presented the best fit (AIC = 55477.56; adjusted R2 = 0.55) and showed that illiteracy, income, percentage of urban population, percentage of antivenom, accessibility index for hospital beds with antivenom, proportion of cases with more than 3 hours to reach healthcare are correlated with the mortality rate by snakebite (p < 0.05).ConclusionThis study identified regions affected by snakebite and how the accessibility to antivenom treatment plays an important role in the mortality in Brazil. Public interventions can located to those most vulnerable regions in order to improve the accident outcome.

Optimal risk classification with statistical evidence in endometrial cancer

Background It is often clinically useful to classify tumor markers into risk groups. This study was aimed to investigate whether beginning with a statistically sound method would find cut points more reasonable than conventional ones. Methods We used data of endometrial cancer including 442 patients. The optimal number of cutoffs was based on the Akaike criterion and statistical algorithms were adapted to find the best locations. Codes were provided as a package. Results Myometrium invasion was an independent risk factor for lymph nodal metastasis when stratified into three groups by 0.41 and 0.89. Tumor size was an independent risk factor for overall survival when stratified into two groups by 4.11 cm. Both had better prediction than conventional choices and clinical relevance. Conclusion A statistically sound algorithm should be used to stratify patients into risk groups.

Factors affecting time until myocardial infarction in hypertension patients using truncated Cox regression

Background and aims: Hypertension is one of the most common and important risk factors of cardiovascular diseases. This phenomenon, if not cured, causes lots of problems in vital organs of the body like kidneys, brain, eyes, and heart. The aim of the current study was to identify factors which affect the time until myocardial infarction (MI) in hypertension patients using truncated Cox regression. Methods: This study was a population retrospective cohort and its statistical population included patients with hypertension symptoms, who had MI for the first time in Sari. A decision was made based on continuing the study as a nested case and control study because the number of MI incidents was low (7%) in the studied sample. In addition, the Cox’s model and truncated Cox were used for determining the factors affecting the patients’ survival. Results: The median and mean survival times in this sample were equal to 57 and 66.44 months, respectively. Considering time truncation, family history (HR=1.70) and the age at diagnosis (HR=0.99) were statistically significant in the Cox’s model (P< 0.05). Conclusion: Overall, using the Akaike criterion for comparing the survival time without the effect of truncation, the Cox’s model demonstrated better fitness by controlling the truncation effect. These results show the importance of controlling the truncation in those diseases in which it is impossible to distinguish the time of the appearance from the symptoms.

Oxytree Pruned Biomass Torrefaction: Mathematical Models of the Influence of Temperature and Residence Time on Fuel Properties Improvement

Biowaste generated in the process of Oxytree cultivation and logging represents a potential source of energy. Torrefaction (a.k.a. low-temperature pyrolysis) is one of the methods proposed for the valorization of woody biomass. Still, energy is required for the torrefaction process during which the raw biomass becomes torrefied biomass with fuel properties similar to those of lignite coal. In this work, models describing the influence of torrefaction temperature and residence time on the resulting fuel properties (mass and energy yields, energy densification ratio, organic matter and ash content, combustible parts, lower and higher heating values, CHONS content, H:C and O:C ratios) were proposed according to the Akaike criterion. The degree of the models’ parameters matching the raw data expressed as the determination coefficient (R2) ranged from 0.52 to 0.92. Each model parameter was statistically significant (p < 0.05). Estimations of the value and quantity of the produced torrefied biomass from 1 Mg of biomass residues were made based on two models and a set of simple assumptions. The value of torrefied biomass (€123.4·Mg−1) was estimated based on the price of commercially available coal fuel and its lower heating value (LHV) for biomass moisture content of 50%, torrefaction for 20 min at 200 °C. This research could be useful to inform techno-economic analyses and decision-making process pertaining to the valorization of pruned biomass residues.