Search Results (24)

Particulate matter is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM₁₀ is a pollutant composed of particles smaller than 10 µm. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM₁₀ data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the available PM₁₀ data and evaluate the quality of time series classical models and neural networks for short-term forecasting. This study demonstrates that classical time series models, particularly ARIMA and SSA, achieve lower average forecast errors than LSTM across stations SMP, CRB, and ATE. This finding suggests that for data with seasonal patterns and relatively short time series, traditional models may be more efficient and robust. Although neural networks have the potential to capture more complex relationships and long-term dependencies, their performance may be limited by hyperparameter settings and intrinsic data characteristics. Full article

Background. Healthy eating habits are essential for preventing chronic diseases and improving quality of life. However, there is a lack of brief and culturally adapted instruments for accurate assessment. Therefore, the aim of this study was to develop and validate the Brief Healthy Eating Habits Scale (BHEHS-6B, Version 1.0), based on Harvard’s Healthy Eating Plate, in a sample of young, middle-aged, and older Peruvian adults. Methods. The study followed a psychometric design. A non-probabilistic sample of 223 participants (both sexes; mean age = 41.6, SD = 15.8) was drawn from Metropolitan Lima, Peru. The BHEHS-6B (Version 1.0) was administered. Results. The bifactor model confirmed the unidimensional structural validity of the BHEHS-6B, showing acceptable global fit indices (CFI = 0.987, TLI = 0.937, SRMR = 0.025, RMSEA = 0.081) and an adequate hierarchical omega for the general factor (G = 0.638), supporting the use of a single total score. Finally, internal consistency was adequate for the total scale (α = 0.769, ω = 0.780). Conclusions. The BHEHS-6B is a valid and reliable instrument for assessing healthy eating habits, demonstrating evidence of strong content validity, internal consistency, and an adequate factor structure. Moreover, as a brief instrument, it is particularly useful for studies aiming to evaluate multiple variables and for the implementation of public health policies focused on improving community health. Full article

Damage assessment methods fall into contact and non-contact approaches. Contact methods, like physical measurements, material sampling, and ultrasonic testing, provide detailed data but are time-consuming and require specialized equipment. In contrast, non-contact methods assess damage remotely, allowing for faster, safer, and large-scale evaluations, especially useful in post-disaster scenarios. However, there are currently no standardized non-contact methods for assessing damage levels in confined masonry walls after damaging seismic events in Peru. On the other hand, an experimental database of cyclic loading tests on confined masonry walls is available, supporting numerical simulations with calibrated mathematical models to estimate damage levels. This research extends the application of this database by analyzing the crack pattern imagery from the tested walls and correlating it with the lateral deformation (drift) to identify the damage levels. A high-accuracy crack measurement technique was developed, combining a convolutional neural network to generate a binary crack mask and a binary search algorithm to extract polylines and convert them into length measurements, achieving a detection accuracy of 78%. The measured crack patterns were normalized into an index, which was then correlated with the amplitude of the lateral deformation in each hysteretic loop. Finally, a relationship was established between drift and the damage level index. These findings contribute to the development of a rapid, non-contact damage assessment method for confined masonry walls in seismic-prone regions. Full article

Urbanization in large cities has altered the urban thermal balance, creating urban heat islands. In this context, green areas are crucial in regulating the urban climate. This study uses remote sensing data to evaluate their performance using the fractional vegetation cover (FVC) and its impact on land surface temperature (LST) in Metropolitan Lima, Peru, between 1986 and 2024. The spatial and temporal relationship between FVC and LST is analyzed, and districts are classified based on their effectiveness in thermal regulation. The Mann–Kendall test was applied to identify trends along with a Spearman correlation analysis and a clustering analysis to group districts according to the cooling effectiveness of their urban green areas. The results show that urban expansion has increased LST by an average of 6.43 °C since 1990, and there is a significant negative correlation (p < 0.001) between FVC and LST, indicating positive impacts of vegetation regulating LST at a spatial level. However, it does not reduce LST at a temporal level. This suggests that, while effective locally, green areas are insufficient to counteract the overall warming of LST over time. Based on FVC and LST characteristics, the districts have been classified into four groups: those with well-preserved green areas, such as La Molina and San Isidro, which have a lower LST, compared to areas where urbanization has replaced vegetation, such as Carabayllo and Lurigancho (Chosica). Finally, this study highlights the importance of integrating green area management into urban planning to mitigate urban warming and promote sustainable development. Full article

This study examines the impact of digital competencies (DCs) and organizational culture (OC) on digital transformation (DT) in small and medium-sized enterprises (SMEs) in metropolitan Lima. Using a non-experimental and cross-sectional design, 307 business owners were surveyed using a previously validated questionnaire. Data were analyzed through partial least squares structural equation modeling (SEM-PLS). The results show that DCs have a direct and significant impact on DT, being the main driver of this process. Additionally, OC acts as a partial mediator between DCs and DT, although its influence is lesser, compared with DCs. The study highlights the importance of DCs in driving digitalization in SMEs, while OC facilitates, although does not solely determine, the success of the digital transformation process. Despite the limitations and the cross-sectional nature of the study, the findings provide valuable insights for SMEs in emerging economies and offer a basis for future research on the factors influencing digital transformation in similar contexts. Full article

In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru. Full article

The objective of this research is to investigate the characteristics of remote work in Peru during the COVID-19 pandemic. In addition, the study will allow exploring the advantages, disadvantages, challenges and opportunities that Peruvian remote workers face during this crisis scenario. This was a basic-type, descriptive-level study employing a quantitative approach and a non-experimental design. The sample consisted of 275 workers from two companies located in Metropolitan Lima, and the data were collected in the year 2021. A questionnaire with 30 questions was proposed for data collection; it was validated by three experts, and its reliability was α = 0.85. The findings of the remote work variable place it at a medium level with 40.73%; in the flexibility dimension, the medium level prevailed with 42.55%; the autonomy dimension exhibited a high level with 41.09%; and the productivity dimension exhibited a medium level with 43.64%. In the technology dimension, the low level prevailed with 36.36%, while the psychosocial risks dimension exhibited a medium level with 33.18%. In conclusion, the characterization of remote work in Peru during the COVID-19 pandemic allowed us to establish the most relevant aspects that affected workers who migrated to this form of work. Full article

This study aims to provide evidence that managers’ commitment towards preventing gender violence against women is affected by implicit resistance from the patriarchal culture. A structured questionnaire was given to 673 managers of 243 small, medium, and large private companies in Metropolitan Lima, Peru. We design and test a conceptual model using covariance-based structural equation modeling. Even though 90.3% of managers report being committed to and in favor of preventing gender violence in companies, 48.6% have intense implicit resistance against it. In general, 3 out of 4 managers do not believe in violence against women because they consider it “biased”, and think that policies should only talk about family or partner violence. In addition, 2 out of 4 believe that equality policies have “hidden interests” that generate mistrust. The structural equations show that implicit resistance, directly and indirectly, decreases managers’ commitment and actions towards preventing gender violence in organizations. Gender biases, irrational beliefs about sexual violence, and a lack of appreciation of gender equality strongly predict these resistances. Business involvement in the prevention of gender violence is a more complex process than expected, requiring a reinforced strategy aimed at overcoming managers’ implicit resistance. Full article

Lima is the second-largest capital of the world located in a desert and already faces water scarcity. Here, more than 30% of the population is supplied by only 2.2% of the national water resources. The urbanization process has an informal nature and occurs at a very accelerated rate. These new settlements lack water infrastructure and access to other services. The objectives of this study are to quantify changes in the urban landscape of Lima Metropolitan Area from 1990 to 2021 to propose a water-sensitive urban plan by detecting changes, urbanization trends and identifying alternative water sources. The trend suggests a future constant increment of the urban areas, diversification of the landscape and more equally distributed land cover. Lima has more disconnected settlements and more complex shapes of urban patches nowadays. The landscape is also more mingled, but cracked. Overall, the trend is to become more disaggregated, demanding small and scattered water solutions. The WSUP includes the implementation of treatment plants in new multi-family buildings, hybrid desalination plants at the coast and parks with fog collectors on the hills. Additionally, these solutions will require the beneficiary community and the local authorities to work together in the planning and maintenance. Full article

Automobile emissions in urban cities, such as Peru, are significant; however, there are no published studies of the effects of these emissions on PM_2.5 (fine particulate matter) formation. This study aims to analyze the contributions of vehicle aerosol emissions to the surface mass concentration of PM_2.5 in the Metropolitan Area of Lima and Callao (MALC), one of the most polluted cities in Latin America and the Caribbean (LAC) known to have high concentrations of PM_2.5. In February 2018, we performed two numerical simulations (control and sensitivity) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). We considered both trace gasses and aerosol emissions from on-road traffic for the baseline simulation (hereinafter referred to as “control”); gasses without particulate emissions from vehicles were considered for the sensitivity simulation (hereinafter referred to as WithoutAerosol). For control, the model’s performance was evaluated using in situ on-ground PM_2.5 observations. The results of the predicted PM_2.5 concentration, temperature, and relative humidity at 2 m, with wind velocity at 10 m, indicated the accuracy of the model for the control scenario. The results for the WithoutAerosol scenario indicated that the contributions of vehicular trace gasses to secondary aerosols PM_2.5 concentrations was 12.7%; aerosol emissions from road traffic contributed to the direct emissions of fine aerosol (31.7 ± 22.6 µg/m³). Full article

Objective: To assess the factors associated with adequate breastfeeding (ABF) among Peruvian mothers during 2019. Materials and Methods: We performed a secondary analysis of the 2019 Demographic and Family Health Survey (ENDES) database of Peru. ABF was defined based on the recommendations of the World Health Organization, which defined it as starting breastfeeding within the first hour of life and continuing with exclusive breastfeeding for up to 6 months. To determine the factors associated with ABF, a Poisson generalized linear models with log-link function was used. Adjusted prevalence ratios (aPR) with their respective 95% confidence intervals (95% CI) were calculated. Results: A prevalence of ABF of 48.1% was identified among 11,157 women who reported at least one child in the last five years. Most of them were young (68.6%) and lived in urban areas (65.5%). Furthermore, being unemployed (aPR:1.02; 95% CI:1.00–1.04); residing on the coast, except for Metropolitan Lima (aPR:1.08; 95% CI:1.04–1.11), the highlands (aPR:1.14; 95% CI:1.11–1.18), and the jungle (aPR:1.20; 95% CI: 1.16–1.24); having had a vaginal delivery (aPR:1.30; 95% CI:1.27–1.05); and having two children (aPR:1.03; 95% CI:1.01–1.05) or three or more children (aPR:1.03; 95% CI:1.01–1.05) were associated with a higher frequency of ABF. Conclusions: One out of two women between 18–59 with children performed ABF. The factors associated with ABF were the current occupation, region of residence, type of delivery, and parity. Health policies and strategies should be implemented, considering our results, to promote maternal counseling by health personnel in order to increase the prevalence of ABF in the Peruvian population. Full article

The most extreme precipitation event in Metropolitan Lima (ML) occurred on 15 January 1970 (16 mm), this event caused serious damage, and the real vulnerability of this city was evidenced; the population is still not prepared to resist events of this nature. This research describes the local climate variability and extreme climate indices of temperature and precipitation. In addition, the most extreme precipitation event in ML is analyzed. Extreme climate indices were identified based on the methodology proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Some extreme temperature indices highlight an initial trend toward warm conditions (1965–1998); this trend has changed towards cold conditions since 1999, consistent with the thermal cooling during the last two decades in ML (−0.5 °C/decade) and other coastal areas of Peru. The variations of extreme temperature indices are mainly modulated by sea-surface temperature (SST) alterations in the Niño 1 + 2 region (moderate to strong correlations were found). Extreme precipitation indices show trends toward wet conditions after the 1980s, the influence of the Pacific Ocean SST on the extreme precipitation indices in ML is weak and variable in sign. The most extreme precipitation event in ML is associated with a convergence process between moisture fluxes from the east (Amazon region) at high and mid levels and moisture fluxes from the west (Pacific Ocean) at low levels, and near the surface. Full article

There is evidence in the literature that anonymity when investigating individual variables could increase the objectivity of the measurement of some psychosocial constructs. However, there is a significant gap in the literature on the theoretical and methodological usefulness of simultaneously assessing the same measurement instrument across two groups, with one group remaining anonymous and a second group revealing identities using names. Therefore, the aim of this study was to compare the psychometric characteristics of a measure of depressive symptoms in two groups of adolescents as a consequence of identification or anonymity at the time of answering the measuring instrument. The participants were 189 adolescents from Metropolitan Lima; classrooms were randomly assigned to the identified group (n = 89; application requesting to write one’s own name) or to the anonymous group (n = 100; application under usual conditions), who responded to the Childhood Depression Inventory, short version (CDI-S). Univariate characteristics (mean, dispersion, distribution), dimensionality, reliability, and measurement invariance were analyzed. Specific results in each of the statistical and psychometric aspects evaluated indicated strong psychometric similarity. The practical and ethical implications of the present results for professional and research activity are discussed. Full article

In the face of the psychological crisis of fear caused by the COVID-19 pandemic, it is relevant to know the positive impact of hope and resilience during this context. The purpose of this study was to determine the correlation between hope and resilience with fear of COVID-19 in young people. The design was non-experimental, cross-sectional, and correlational. The sample consisted of 192 young people living in Metropolitan Lima, Peru. We used the Hope-Despair Questionnaire, the Resilience Scale, and the COVID-19 Fear Questionnaire. The results show that there is a significant correlation between hope, resilience, and fear of COVID-19 in young people. On the other hand, a significant difference was found in resilience according to gender. Likewise, it was found that the variables hope and resilience explain 81% (R² adjusted) of the fear of COVID-19 (F test = 21.53; p < 0.01). Hope and resilience are protective factors that have a positive impact when facing the fear of COVID-19. Thus, policies, programs, and public health strategies related to positive mental health should be promoted, with emphasis on hope and resilience. Full article

The objective was to determine the validity of the UWES-3, an ultrashort measure of work engagement lacking evidence in Hispanic populations. In total, 200 workers with heterogeneous positions and careers from Metropolitan Lima were enrolled via nonprobabilistic sampling. The UWES-3 and measures of external variables (work accidents, stress overload, and others) were used. Data were collected through a web platform. Items were analysed, nonparametric response theory methods (Mokken scale analysis and Ramsay curves) were applied to the items, and ordinal and linear regression were used to determine the relationships with external variables. The items had statistically similar distributional properties and monotonic associations with external variables but with fewer functional response options. The UWES-3 complied with the monotonic homogeneity model and invariant ordering of items; the scaling of the items, score (greater than 0.80), and reliability (0.94) were high. With the effects of age and sex controlled, the UWES-3 significantly predicted minor accidents at work and job satisfaction and revealed effects of stress overload and perceived efficacy. The theoretical implications of the UWES-3 as a brief unidimensional measure integrating the three original dimensions of the instrument and the practical implications of its use for research and professional practice are discussed. Full article