The objective of this research was to evaluate the unit rates of MSW generation in Cumba in the years 2016 and 2022. The calculations were based on the weights of the MSW disposed in the dump located 5 km from the city of Cumba since 2012. The GPC, physical composition, density, humidity were determined in the years 2016 and 2022, studied according to the methodology and group classification of Peruvian regulations. The results show that 5.45 Tn/day⁻¹ are generated in 2016, 4.37 Tn/day⁻¹ in 2022; according to its physical composition, 82% RO, 14% MICVC and 4% MISVC in 2016; 77% RO, 16% MICVC, 7% MISVC in 2022; density 137.90 kg/m⁻³ in 2016 and 172.69 kg/m⁻³ in 2022; humidity 67.67% in 2016 and 63.43% in 2022. It was also found that in 100.00% there is no solid waste treatment; Everything generated in homes, businesses and streets is evacuated to the final disposal site, which is a dump. In 2022, Cumba acquired 10 hectares to have adequate sanitary infrastructure and begin the closure and recovery of its current dump. This study will contribute to providing accurate data on MSW generation that allows the local government to promote the optimization of collection routes and schedules, resulting in cost savings and reduction of carbon emissions in the Amazon Region. Therefore, it is necessary to raise awareness at all levels of society through various means of communication and education, so that the risks of spreading health risks can be minimized by improving MSW management.

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.

This research evaluates the regionalization of tourism in Hungary, revealing the breakdown of the national gross domestic product (GDP) of tourism. It also explores the density, spatial variations, and features of these indicators. A multimodal approach is used to evaluate the competitiveness of Hungarian counties, and the distribution of these tourism regions is analyzed using the tourism penetration index. Furthermore, regional GDP is calculated for the whole territory of Hungary. The study identifies significant regional disparities in tourism competitiveness, highlighting Budapest-Central Danube as the most competitive region and Lake Balaton as underperforming despite its potential. The research contributes by providing a detailed regional GDP analysis and emphasizing the need for targeted policy interventions to enhance tourism development across all regions.

This study employs a mixed-methods approach to explore the financial ramifications and perceived hurdles of adopting international accounting guidelines on asset value reduction in small and medium-sized enterprises (SMEs) in Barranquilla, Colombia, over a recent multi-year timeframe. Through scrutiny of fiscal data and thorough dialogues with SME leaders and finance professionals, the investigation unveils significant industry-specific variations in the monetary impact of embracing these global standards. Manufacturing SMEs are found to shoulder a weightier burden compared to their counterparts in the service sector. The research underscores the pivotal role of perceived standard intricacy in molding the financial outcomes for SMEs, even when accounting for factors such as acquaintance with the guidelines and professional tenure. These discoveries augment our comprehension of global accounting standard adoption in emerging economies and accentuate the necessity for bespoke support mechanisms to assist SMEs in traversing the complexities of implementing these international norms. The insights gleaned from this inquiry can guide policymakers and accounting authorities in crafting sector-specific directives and resources. Such targeted assistance can aid SMEs in harmonizing with worldwide accounting practices while curtailing potential adverse effects on their fiscal performance.

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

In order to scientifically evaluate the germplasm resources of Momordica charantia in southern China, the diversity, correlation and cluster analysis were carried out on the main botanical characters of 56 Momordica charantia varieties, such as melon length, melon transverse diameter, single melon weight, internode length, stem diameter, leaf length and leaf width. The results showed that the variation coefficients of 7 agronomic characters of 56 Momordica charantia varieties ranged from 8.81% to 19.44%, the average variation coefficient was 14.21%, the maximum variation coefficient of single melon weight was 19.44%, and the minimum variation coefficient of melon cross diameter was 8.81%. The correlation analysis showed that there were correlations among the agronomic traits. The positive correlation coefficient between leaf length and leaf width was up to 0.978, and the negative correlation coefficient between single melon weight and internode length was up to 0.451. The 56 varieties were divided into 3 groups by cluster analysis, of which 92.86% of the materials were concentrated in the first and second groups, and there were only 4 materials in the third group. The results can provide a reference for the cultivation, utilization and genetic improvement of Momordica charantia resources in southern China.