Fire, a phenomenon occurs in most parts of the world and causes severe financial losses, even, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, remote sensing and the reviewed papers that predicted the fire risk in the field of remote sensing and geographic information system were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy analytic hierarchy process (AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings indicate that RS and GIS are effective tools in the study of fire risk prediction.

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.

To deal with problems of traditional geographic information collection, such as low real-time, poor authenticity of the data, and unclear description of detailed areas, a design scheme of remote sensing-based geographic information system is proposed. The system mainly consists of information collection, imaging processing, data storage management, scene control and data transmission module. By use of remote sensing technology, the reflected and radiated electromagnetic waves of the target area are collected from a long distance to form an image, and the hue–intensity–saturation (HIS) transformation method is used to enhance the image definition. Weighted fusion algorithm is adopted to process the details of the image. The spatial database stores and manages the text and image data respectively, and establishes the attribute self-correlation mechanism to render the ground objects in the picture with SketchUp software. Finally, using RS422 protocol to transmit information can achieve the effect of multi-purpose, and enhance the anti-interference of the system. The experimental results show that the practical experience of the proposed system is excellent, the geographic information image presented is clear, and the edge details are clearly visible, which can provide users with effective geographic information data.

Integrated Resource Management plays a crucial role in sustainable development by ensuring efficient allocation and utilization of natural resources. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as powerful tools for collecting, analyzing, and managing spatial data, enabling comprehensive and integrated decision-making processes. This review article uniquely focuses on Integrated Resource Management (IRM) and its role in sustainable development. It specifically examines the application of RS and GIS in IRM across various resource management domains. The article stands out for its comprehensive coverage of the benefits, challenges, and future directions of this integrated approach.

The rapid advancement of information and communication technology has greatly facilitated access to information across various sectors, including healthcare services. This digital transformation demands enhanced knowledge and skills among healthcare providers, particularly in comprehensive midwifery care. However, midwives in rural areas face numerous challenges such as limited resources, cultural factors, knowledge disparities, geographic conditions, and technological adoption. This research aims to evaluate the impact of AI utilization on midwives’ knowledge and behavior to optimize the implementation of healthcare services in accordance with Delima Midwife Service standards in rural settings. The analysis encompasses competencies, characteristics, information systems, learning processes, and health examinations conducted by midwives in adopting AI. The research methodology employs a cross-sectional approach involving 413 rural midwives selected proportionally. Results from Partial Least Squares Structural Equation Modeling indicate that all reflective evaluation variables meet the required criteria. Fornell-Larcker criterion demonstrates that the square root of AVE is greater than other variables. The primary findings reveal that information systems (0.029) and midwives’ competencies (0.033) significantly influence AI utilization. Furthermore, midwives’ competencies (0.002), characteristics (0.031), and AI utilization (0.011) also significantly impact midwives’ knowledge and behavior. Midwives’ characteristics also significantly affect their competencies (0.000), while midwives’ learning influences health examinations (0.000). Midwives’ knowledge and behavior affect the transformation of healthcare services in rural midwifery (0.022). The model fit results in a value of 0.097, empirically supporting the explanation of relationships among variables in the model and meeting the established linearity test.

Context: Noise in the work environment, in all types of productive activities, represents a hazard and has not really been valued in its real dimension. Little has been seen that stakeholders have determined the urgency of managing noise control programs. Therefore, losses resulting from medical treatment and absenteeism, represented in health care and social services, result in hidden work-related costs that directly affect the gross domestic product in any country.

Method: This article compiles different case studies from around the world. The studies were divided for review into general studies on the effects of workforce noise and then particularized according to the effects of industrial noise on workers’ health. At a control level, the assessment and measurement of noise is defined through the use of tools such as noise maps and their respective derivations, in addition to spatial databases.

Results: According to the collection of information and its analysis, we observe that in the medium term, the economies will be diminished in an important percentage due to the consequences generated by the exposure to noise. Specific information can be found in the development of the article.

Conclusions: The data provided by the case studies point to the need for Colombia, a country that is no stranger to this phenomenon, and which additionally has the great disadvantage of not having significant studies in the field of noise analysis, should strengthen studies based on spatial data as a mechanism for measurement and control.

Financing: Fundación universitaria Los Libertadores.