This research aims to estimate the reliability function of the Rayleigh distribution Under Type-II-Censored Data, which represents. The time of the machine's operation until the failure, and three methods were chosen to estimate the reliability function, which are, the maximum likelihood method, the momentary method, and the least squares method. As the values of the reliability function were calculated for real data related to the machines of theof the machines of Baghdad Pepsi Cola Company, and the comparison was made between the methods of estimating reliability by choosing the largest arithmetic mean for the reliability of the machines, and the greatest possibility appeared the best method, and the researcher concluded that the average times working until holidays is (4) months with a reliability of 87%.

Air quality in Western Java is highly dynamic and shaped by environmental changes influenced by intense human activities. Aerosols—tiny particulate matter that affects air quality, weather, and climate—can be quantified using Aerosol Optical Depth (AOD), which measures aerosol concentrations in the atmospheric column. This research uses spatial regression analysis to examine the spatial distribution of AOD from GEE’s platform (Google Earth Engine) and its relationship with rainfall and wind patterns during both the wet and dry seasons. The findings indicate that wind speed does not significantly impact AOD values, but wind direction does affect the distribution of rainfall and AOD, likely due to the monsoon system. During the wet season (December to March), high-intensity and widespread rainfall effectively cleanses the atmosphere of aerosols, leading to no significant effect on AOD (p-value > 0.05). In contrast, during the dry season, rainfall significantly influences AOD spatial patterns (p-value < 0.05). These results highlight the intricate interplay between meteorological factors and aerosol’s behavior, emphasizing the seasonal variability in their interactions.

Analysis of the use of the Boruvka algorithm method in the context of electricity supply is an important topic in the development of electricity systems. In this study, we evaluate the effectiveness and application of the Boruvka algorithm in distribution optimization and power grid management. We study ways in which the Boruvka algorithm can be used to identify optimal electricity distribution paths, improve system efficiency, and minimize the potential for grid damage or failure. This research provides a deeper understanding of the potential and limitations of the Boruvka algorithm in the context of modern electricity infrastructure.

The estimated rice sales at Perum Bulog Sub Divre Medan is crucial information in planning and managing rice supplies. In this study, we apply the Double Exponential Smoothing forecasting method to estimate rice sales at Perum Bulog Sub Divre Medan. This approach allows us to identify complex sales patterns and generate accurate forecasts to aid in informed decision making. We use historical rice sales data to train a forecasting model and evaluate its performance. Experimental results show that the Double Exponential Smoothing method can provide reliable estimates of rice sales, with a satisfactory level of accuracy. The implications of these findings are discussed in the context of inventory management and operational planning of Perum Bulog Sub Divre Medan.

This study aims to analyze students' mathematical connections through the application of the Think Talk Write (TTW) learning style and collaboration in the context of mathematics learning. The Think Talk Write (TTW) method emphasizes developing critical thinking, communication and writing skills in understanding mathematical concepts. Collaboration between students is also integrated to strengthen mathematical understanding through discussion and joint problem solving. This research uses a qualitative approach with case studies as the analytical framework. Data was collected through classroom observations, interviews with students, and analysis of students' writing. Research findings show that implementing the Think Talk Write (TTW) method with collaboration can improve students' mathematical connections, strengthen conceptual understanding, and improve communication and mathematical problem solving skills. Pedagogical implications of these findings are discussed to promote more effective and inclusive mathematics learning practices.

Quantum computing offers promising alternatives to classical approaches for solving complex linear algebra problems. This paper presents a comparative study of the performance of quantum algorithms versus classical algorithms in solving systems of linear equations and matrix operations. Through simulation and analysis, we demonstrate that while quantum computing holds advantages in specific problem sets, classical computing remains efficient for general applications. These findings highlight the current limitations and potential of quantum computing.

Natural resource management involves complex decision-making processes that often result in non-linear optimization problems. This study explores the application of genetic algorithms (GA) and particle swarm optimization (PSO) to manage resources like water and forest reserves more efficiently. We compare the effectiveness of these algorithms in achieving sustainable utilization while minimizing environmental impact. The results show that GA outperforms PSO in forest management scenarios, while PSO is more suitable for water resource distribution.

The application of the STAD type cooperative learning method (Student Teams-Achievement Divisions) is a relevant strategy in efforts to improve students' mathematical abilities. In this research, we examine the effectiveness of using the STAD type cooperative learning style in improving students' mathematical abilities. This approach involves dividing students into small teams to encourage collaboration, joint problem solving, and knowledge exchange. We evaluated the impact of implementing this learning model on students' mathematical abilities and gained insight into effective learning strategies in the context of improving mathematics achievement.

Poverty is a complex problem that affects various aspects of life in various regions. In this study, we use the Geographically Weighted Regression (GWR) method to identify factors that cause poverty in North Sumatra Province. This approach allows a more detailed analysis of the relationship between independent variables and poverty levels in each geographic location. We use socio-economic and spatial data to build GWR models and identify possible hidden patterns in the distribution of poverty. The findings from this research provide valuable insights for stakeholders to formulate more effective policies in overcoming poverty in North Sumatra Province.

Dragon fruit plants originating from Central and South America have not been widely cultivated. This plant has only been cultivated intensively in several countries such as: Israel, Colombia, Nicaragua, Vietnam, Thailand, China and Australia. Macroscopic identification showed that the fungi associated with dragon fruit stems had similarities to the colony form of Colletotrichum gloeosporioides sp. Trichoderma is known to have antagonistic abilities against pathogenic fungi. Trichoderma is easily found in soil ecosystems and plant roots. The method in this study is a survey method. Determination of the sample using purposive sampling or Judgmental sampling method, namely purposive sampling is a sampling method that selects objects (vines) that are considered attacked or have symptoms of pathogen attack. Trichoderma sp is also tolerant to stress and able to inactivate pathogenic microbial enzymes. In addition, Trichoderma sp. Able to produce enzymes that hydrolyze pathogenic cell walls which will inhibit the synthesis of pathogenic cell walls and increase their fungicidal activity, as a result of which pathogenic fungi experience a decrease in growth rate.

To overcome the difficulties of high-dimensional data, Elastic Net Principal Component Regression (ENPCR), a potent statistical technique, combines Elastic Net regularization with Principal Component regression (PCR). When dealing with Multicollinearity among predictors, this method is especially helpful because it enables efficient variable selection while preserving interpretability. PCA is initially used in ENPCR to reduce the dataset's dimensionality by converting correlated variables into a group of uncorrelated principal components. The Elastic Net regression model then uses these elements as inputs and penalizes the regression coefficients using both L1 and L2 penalties. By promoting sparsity, this dual regularization lessens overfitting and helps the model concentrate on its most important components. simulated studies and Real datasets are used to demonstrate the our proposed method .

This study analyzes related to the application of mathematical concepts to the demand and supply function of MSME products in North Pelawi Village, Babalan District, Langkat Regency. The results show that there is an elasticity associated with mathematical concepts for the demand function and the supply function. Usually, because MSME products are hampered by supply chain disruptions and production. However, through recovery strategies and interventions to apply mathematical concepts in the demand and supply functions, a balance curve between the demand and supply functions is formed. Awareness to apply mathematical concepts for business actors has an important role in controlling the rate of supply of goods so that the number of goods demand can be fulfilled according to the number of desires and the prices offered can be adjusted to the prices requested by consumers. This research highlights the importance of awareness and understanding the role of business actors who are aware of the application of mathematical concepts so that regulating demand and supply can run systematically and not damage the market price offered to consumers.