The current analysis analyzed the odds of casualties by road accidents. Hence, data were classified into tertiles for better research, and accident types were classified into five following the authority methodology: rollovers, crash, roadway departure, special accident, and car capsizing. Multi-logistic regression was employed for the data analysis. This research found that rollover was the most deadly accident, and the crash was the most probable to cause injuries.

Polluted air creates health problems for people, plants and animals today due to many factors in industrial cities and power generation projects, transportation and chemical industry and others. It is for this reason that this research in bibliographic review allows us to know the different solutions to produce hydrogen through the analysis of the Scopus database and the VOSviewer tool that allows us to analyze the data, considering the variables that are artificial leaf, hydrogen, production , clean energy through seawater, graphs and tables were obtained which provide us with an analysis of the number of publications, the countries that carry out these investigations and the bibliometric maps worldwide for a global analysis. The results allow us to analyze and learn about the different solutions and materials that are used to carry out artificial photosynthesis that develops the production of hydrogen by separating water molecules with the aim of emitting zero emissions and being able to use it in different applications such as fuel, energy electrical, industrial uses and others. The purpose of this research is to allow us to make better decisions to apply this methodology according to the materials that we have in greater scope and that is a promising future for a generation of the new industry for the following years, also considering the objectives of sustainable development and finally, motivate readers to continue with these investigations and be able to apply it with institutions in charge of combating this problem.

Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.

It is not a secret that road accidents cause significant suffering. Those accidents can last from wounded to dead people, negatively impacting a country. A bunch of recent investigations tried to tie road accidents with the quality of roads. Therefore, in a country with a significant infrastructure gap, it is necessary to analyze the relationship between the different kinds of roads and the severity of car accidents. The current research examined such a relationship by employing the Multi logit regression. It was found that the significance of different car accidents will vary among the road types. Moreover, with the help of probability analysis, it was discovered that speeding, emergency services availability, and road security seemed to have a crucial impact on road accidents.

The objective of this research was to evaluate the effects of public debt sustainability on economic growth in the period 2000-2021 and establish a new optimal debt level that does not affect Peru's economic growth. The general method used to determine this effect was the hypothetical deductive method with a non-experimental and longitudinal trend design, because the data to be analyzed are variations that have occurred over time; the VAR (vector autoregressive) model was used as a specific method, because the evidence was insufficient to consider the simultaneity between the reactions of the variables to propose an SVAR model. Data were collected from economic portals such as the Ministry of Economy and Finance (MEF), as well as the Central Reserve Bank of Peru (BCRP). The estimated sample size was 88 observations representing all quarters from 2000 to 2021. As a result of the econometric regression, the impact of the level of public debt on economic growth is positive, since a one-unit increase in the percentage of public debt will increase the variation of GDP by almost 1.1%. Regarding the debt level forecast and according to the projection made, it was determined that the new debt level that does not affect the sustainability of public finances or the long-term economic growth of the Peruvian economy should be 38% of GDP.

The motivation of this study is to provide empirical evidence of service companies’ performance regarding nine dimensions in a total quality management model during the COVID-19 pandemic. The nine dimensions highlight strategic company activities, and it allows a comparative analysis of the overall effect of having a QMS such as ISO 9001:2015 on Peruvian service companies. A total of 630 Peruvian service companies were surveyed for this study. The questionnaire included 35 Likert-scale items that were further classified into nine (9) dimensions. The Mann-Whitney U test was used to estimate any significant differences between the ISO 9001 certified and non-certified companies. Our findings showed that the performance of ISO 9001:2015 certified companies was significantly higher than that of non-certified companies in all dimensions. Moreover, our findings showed that managers in ISO 9001:2015 certified companies effectively implemented the nine dimensions of the model. The originality of this study lies in proving the positive effect of having a QMS in service companies in a context of slow economic growth and decline of consumer demand such as the COVID-19 pandemic. The findings might encourage service companies, especially those in developing countries, to allocate the necessary resources to obtain a QMS such as the ISO 9001:2015.

The purpose of this research was to look into the work-life interface of professionals in a de-manding and high-risk occupation in an organization in the security sector. Specifically, it focuses on the interaction between supervisor behaviors and culture with turnover intention and work-family conflict. The present study centers on data from a public security organization (3861) in a Latin American country. The validity of the measuring instruments was evaluated through Confirmatory Factor Analysis (CFA). Structural Equation Modeling (SEM) was then applied to evaluate the relationship between variables. Results found show a statistically significant negative impact of the supervisor behaviors in organizational (turnover intention) and individual outcomes (work-family conflict). At the same time, organizational culture negatively affects turnover intentions and work-family conflict. While confirming results coming from the private sector in other countries, the importance of supervisor behaviors and culture provides implications for work and family practices in this type of organization.

The aim of this study was to determine the effectiveness of psychometric tests in the selection of personnel in retail sector jobs. The study used the scientific deductive method of explanatory level, with non-experimental design on companies in the retail sector in the region of Junín, Peru. The most relevant psychometric tests in the study was the interview whose intention was to go into the life of the interviewee ensuring, suggestions, opinions and behavioral attitudes, knowledge tests to assess the capabilities and skills of the candidate and finally personality tests that allow to know the working relationship, performance, satisfaction and staff turnover. The results generated through structural equations, show that the interview, positively influences the selection of personnel (p≤0.05). In relation to knowledge tests based on IQ, the results reveal that it had a positive impact on personnel selection (p≤0.05). Finally, personality tests based on psychological traits, significantly influence in personnel selection (p≤0.05). The conclusion of the study indicates that the interview, knowledge tests and personality tests in the selection of personnel contribute to the efficiency of the human resources area, thus optimizing the resources of the organization.

From 2000 to 2019, trade between the People's Republic of China and the Republic of Peru grew at an average annual rate of 22%, however, income and wealth inequality in Peru remained the same. The aim of this study is to understand the effect of trade between Peru and China on the inclusive economic growth of Peru from 2000 to 2019. The method used was the correlation of variables, and a linear regression between Peru and China trade and several indicators of inclusive economic growth in the Peruvian economy was performed using the Ordinary Least Squares model. The results suggest that there is sufficient statistical evidence to support that inclusive economic growth may depend on increased trade between Peru and China; the study show that if trade growth between Peru and China fluctuates by $1 million per year, labor income will increase by $10.3 per capita in the Economically Active Population (EAP). Moreover, for every 1% increase in trade between Peru and China, GDP per capita increases by 0.1057% and labor productivity increases by 0.0681740%. The variables poverty, vulnerable employment, GINI index and life expectancy at birth were not significant factors.

In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.