The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.

The wide distribution of the common beech (Fagus sylvatica) in Europe reveals its great adaptation to diverse conditions of temperature and humidity. This interesting aspect explains the context of the main objective of this work: to carry out a dendroclimatic analysis of the species Fagus sylvatica in the Polaciones valley (Cantabria), an area of transition with environmental conditions from a characteristic Atlantic type to more Mediterranean, at the southern limit of its growth. The methodology developed is based on the analysis of 25 local chronologies of growth rings sampled at different altitudes along the valley, generating a reference chronology for the study area. Subsequently, the patterns of growth and response to climatic variations are estimated through the response and correlation function, and the most significant monthly variables in the annual growth of the species are obtained. Finally, these are introduced into a Geographic Information System (GIS) where they are cartographically modeled in the altitudinal gradient through multivariate analysis, taking into account the different geographic and topographic variables that influence the zonal variability of the species response. The results of the analyses and cartographic models show which variables are most determinant in the annual growth of the species and the distribution of its climatic response according to the variables considered.

In order to seek management alternatives for anthracnose caused by the fungus Colletotrichum gloeosporioides in blackberry (Rubus glaucus Benth.), at the Tibaitatá Research Center of the Colombian Agricultural Research Corporation AGROSAVIA (formerly CORPOICA), an experiment was conducted to evaluate the effect of the application of the major elements nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) on infections of the fungus C. gloeosporioides strain-52. For this purpose, a randomized complete block design was used with an arrangement of treatments in an orthogonal central composite design. To evaluate the relationship of fertilization levels and disease severity, an artificial inoculation was made on thorny blackberry stems using 0.5 cm mycelial discs at a concentration of 9.53 × 104 conidia. Observations consisted of: disease severity (S), incubation period (IP) and rate of development (r). Data analysis was done by the cluster method on the severity variable, a Pearson correlation analysis between variables, as well as a regression to estimate the effect of nutrients applied on the severity of C. gloeosporioides strain-52. The treatments were concentrated in four groups with the ranges (in parentheses) S (15.9% and 91.8%), PI (9 and 15.3) and Tr (0.0254 and 0.0468). A positive and significant correlation was observed between S and r (P < 0.001) and a negative correlation between PI with S and r (P < 0.001). By means of regression analysis, a linear model was generated that showed a reduction in disease severity with increasing N dose and an increase with the levels of P and Ca applied.

Aims of this study clarify the intrinsic value of Galileo’s law of inertia, which holds significance in the history of science, and the process through which such law of inertia was formed, for educational purposes, and explores a possible conversion of this intrinsic value into an environmental ethical value. The research methodology is to establish a value schema and, through its application, to explore the changes in the active intrinsic value principle of Galileo’s law of inertia based on the history of science. This study derived the following results: First, Galileo professed the value he assigned and discovered as a complete experience to support heliocentrism. Second, he realized his personal religious ideal, or in other words, the ideal of life as a whole. Third, the overall process is to feel a comprehensive and integral expansion of the self. Above all, it shows that the principle of active intrinsic value based on Galileo’s experimental activities has changed and expanded throughout the history of science. One internalizes one’s faith in accordance with the activity-centered value. Only when combined with aesthetic experience does education make one ethical. As general school education does not necessarily guarantee ethics, we must lead our values education toward ecocentric ethics education, which highlights beauty. It shows that these active intrinsic values also extend to ethical values.

This study employed the theory of planned behavior to examine how green urban spaces influence walking behaviors, with a focus on Chongqing’s Jiefangbei Pedestrian Street. Using structural equation modelling to analyse survey data from 401 respondents, this study assessed the relationships between attitudes, subjective norms, perceived behavioral control, walking intentions, and actions. The results revealed that attitudes toward walking (β = 0.335, p < 0.001) and subjective norms (β = 0.221, p < 0.001) significantly predict walking intentions, which strongly determine actual walking behavior (β = 0.379, p < 0.001). Moreover, perceived behavioral control exerts a direct significant impact on walking actions (β = 0.332, p < 0.001), illustrating that both environmental and social factors are crucial in promoting pedestrian activity. These findings suggest that enhancing the appeal and accessibility of urban green spaces can significantly encourage walking, providing valuable insights for urban planning and public health policy. This study can guide city planners and health professionals in creating more walkable and health-conducive urban environments.

The artificial intelligence (AI)-based architect's profile's selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains' integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager's or key team members' polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain' integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises' HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF's original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.