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This article analyzes the socioeconomic conditions between inhabitants of the Cuxtal Ecological Reserve (REC), which suffers the effects of urban sprawl on forest cover. It was applied randomly 400 surveys in several communities located within REC to meet socioeconomic aspects and describe some productive activities that cause damages to the reserve. The results of the study indicates that the inhabitants of the reserve have not basic knowledge of the protection area. Similarly a PROBIT model analyzes how people are motivated to perform work outside the reserve due to the low level of weekly income obtained. [ABSTRACT FROM AUTHOR]

This work analyzes the electrical impedance (EI) measurement of cervical mucus (CM) using a device to determine the fertile window. In this prospective and longitudinal study, fourteen healthy women aged 18 to 44 were enrolled to evaluate three menstrual cycles. EI was measured through a medical device inserted into the vagina for two minutes daily. Patients were monitored by urine luteinizing hormone (LH) strip, blood collection, and vaginal ultrasound to visualize the dominant follicle. Finally, the predictive EI capacity was validated by the receiver operating characteristic (ROC) of anovulatory vs. ovulatory impedances. The peak of LH was 35.7 (±4.5) mUI/ml and the dominant follicle size was 15.45 mm (±0.559). There were statistical differences in EI measurements between the follicular and luteal phases vs. the ovulation phase (p<0.0361 and p<0.0160). After data normalization, an area under the ROC curve (AUC) of 0.713 (P value= 0.0253), a Youden J index of 0.4545O, a sensitivity of 63.6%, and a specificity of 81.8% were found. Low EI in the ovulatory period belongs to the LH ovulatory peak and follicular release. EI can be used for ovulation monitoring, birth control, or promoting pregnancy as a safe and innocuous method. [ABSTRACT FROM AUTHOR]

• In this paper, an improved clustering method based on biological principles is presented. • The approach incorporates two new mechanism for improving the performance of the original Cellular Clustering Algorithm. • The first mechanism is a pre-processing approach for reducing the dataset dimensionality. • The second process includes an optimization method to automatically find the best data resolution. A clustering methodology based on biological visual models that imitates how humans visually cluster data by spatially associating patterns has been recently proposed. The method is based on Cellular Neural Networks and some resolution adjustments. The Cellular Neural Network rebuilds low-density areas while different resolutions find the best clustering option. The algorithm has demonstrated good performance compared to other clustering techniques. However, its main drawbacks correspond to its inability to operate with more than two-dimensional data sets and the computational time required for the resolution adjustment mechanism. This paper proposes a new version of this clustering methodology to solve such flaws. In the new approach, a pre-processing stage is incorporated featuring a Self-Organization Map that maps complex high-dimensional relations into a reduced lattice yet preserving the topological organization of the initial data set. This reduced representation is employed as the two-dimensional data set for further processing. In the new version, the resolution adjustment process is also accelerated through the use of an optimization method that combines the Hill-Climbing and the Random Search techniques. By incorporating such mechanisms rather than evaluating all possible resolutions, the optimization strategy finds the best resolution for a clustering problem by using a limited number of iterations. The proposed approach has been evaluated, considering several two-dimensional and high-dimensional datasets. Experimental evidence exhibits that the proposed algorithm performs the clustering task over complex problems delivering a 46% faster on average than the original method. The approach is also compared to other popular clustering techniques reported in the literature. Computational experiments demonstrate competitive results in comparison to other algorithms in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]