Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

The objective is to determine the impact of economic growth on the externalities of infrastructure investments for the Peruvian case for the periods from 2000 to 2022. The methodologies used are descriptive, explanatory and correlational, analyzing qualitative and mainly quantitative methods. Econometric software was used, and correlations of variables were created for each proposed hypothesis. The estimated model shows that all the independent variables have a significant t-statistic greater than 2 and a probability of less than 5%, which indicates that they are significant and explains the model. The R² is 98.02% which indicates that there is a high level of explanation by the independent variables to the LOG(RGDP). The results of the estimated models demonstrate the existence of a positive and significant relationship of investments in infrastructure and externalities on the growth of the non-deterministic component of real GDP, therefore, in a practical way, private and public investment has a positive effect on the non-deterministic growth of real GDP.

Introduction: The growing global focus on Environmental, Social, and Governance (ESG) standards necessitates that companies optimize their corporate governance to balance economic, social, and ecological responsibilities. This study examines how the synergistic effects of Corporate Social Responsibility (CSR) and Environmental Responsibility (ER) can promote sustainable corporate development. Objective: The objective of this study is to analyze the critical elements of corporate governance structure optimization and to explore how companies can enhance their governance to achieve sustainable development through strengthened social and environmental management practices. Methods: The study uses case analysis and literature review to assess high-performing enterprises in CSR and ER integration, examining their governance, policy, and environmental strategies to uncover the factors behind their success in economic, social, and environmental spheres. Results: The research shows that optimizing governance structures markedly improves operational effectiveness. Companies need to create strong internal controls for equitable and transparent decisions, embedding CSR and ER into their strategies. CSR fulfillment builds public trust and environmental support, whereas ER improves brand reputation and competitiveness, driving sustainable and mutually advantageous development. Conclusion: The key to sustainable development in ESG practice lies in optimizing corporate governance and strengthening the synergy between social and environmental responsibilities. It is imperative for companies to build a governance structure that complies with ESG standards and to incorporate social and environmental considerations into their corporate strategies to effectively manage the triple bottom line of economic, social, and environmental performance.

Family violence is the act that causes harm, suffering, or death to members of the family group, especially if they are in a situation of vulnerability due to characteristics associated to age or physical condition. Objective: The social characteristics of aggressors were associate in the risk level of victims of family violence in the city of Arequipa, Peru. Method: The study was descriptive, quantitative, and non-experimental. A total of 205 randomly selected judicial files of aggressors reported for domestic violence were evaluated. The data were secondary, and the chi-square test (association of categorical variables) was used for statistical analysis. Results: A moderate risk level (31.2%) was found, with a tendency to be severe and very severe (49.5%). Likewise, the most observed types of violence are physical and psychological violence (89.3%) and sexual abuse (10.7%). The female aggressor exerts mild violence, while the male aggressor exerts moderate to extreme severe violence, causing more harm to the victim. The profile of the aggressor with low or high education, with high or low incomes, and who occupies a house or only one room can be associated the level of violence that occurs. Conclusion: Men are more likely to attack women, and similarly, female aggressors tend to target men more frequently. Moreover, men exhibit a higher tendency to attack their partners, including wives, cohabitants, and ex-partners, whereas women tend to target a broader range of family members, including parents, children, grandparents, nephews, cousins, as well as in-laws such, brothers-in-law and other relatives.

The target date for achieving the 2030 UN Agenda [Sustainable Development Goals (SDGs)] is fast approaching. The construction sector is critical to achieving many SDGs, including Goal 5. Studies regarding achieving Goal 5 (Gender Equality) in the construction industry, especially women’s consultancy participation in developing countries, are scarce and complexly interrelated. Societal problems and divergence may have contributed to this. Therefore, this study explores issues hindering gender equality and suggests measures to promote more women construction consultants through policy to improve achieving Goal 5 in Nigeria. The research employed face-to-face data collection via a qualitative mechanism to achieve this. The study covered Abuja and Lagos. It accomplished saturation at the 20th participant. The research utilised a thematic method to analyse the collected data from knowledgeable participants. The perceived hindrances facing Nigerian construction consultants’ gender equality were clustered into culture/religion-related, profession-related, and government-related encumbrances. Achieving Goal 5 will be a mirage if these issues are not addressed. Thus, the study recommended measures to motivate women to study construction-related programmes and employment opportunities, including consultancy services slots through programmes and policy mechanisms to achieve Goal 5. As part of the implications, the study suggests that Nigerian construction consultants and other stakeholders need to make feasible improvements to achieve gender equality (Goal 5).