Influence of Storage Temperature and Duration of Tomato Leaf Samples on Proline Content

In arid and semi-arid countries such as Jordan, shortage in water sources might affect agricultural development and reduces the effectiveness of economic benefits of most crops planted in such areas. Tomato is an important agricultural crop and faces severe drought stress due to climate changes, therefore, measurement of proline accumulation in plant tissues is used as an indicator for drought stress tolerance. This research was conducted at Jarash University Campus in northern Jordan. A field experiment was carried out to investigate the impact of different storage temperature (+4ºC, - 20ºC and -80ºC) and different storage durations (0, 3, 6 and 11 weeks) on proline content in five different Jordanian tomato landraces. Results indicated that the average free proline content for samples tested directly after leaves collection was 7.1 µmol/g. Proline content in leaves stored at +4 ºC for 3, 6, and 11 weeks was 4.8, 1.8, and 1.1µmol/g, respectively, while for -20ºC was 11.8, 7.9, and 9.5 µmol/g for samples stored for 3, 6, 11 weeks respectively. In contrast the highest values for these parameters were obtained from samples stored at -80ºC, the average measured values of free proline content were 9.5, 7.8, and 12.9 µmol/g at 3, 6, and 11 weeks of storage, respectively. Based on the results obtained by this research, it is recommended to measure proline content directly after leaves collection. However, for large number of samples, keeping the samples at -20ºC not longer than six weeks could be a solution. Finally, we highly recommend the development of in-field method for measurement of free proline content

Symmetry of gamma distribution data about the mean after processing with EWMA function

Abstract Statistical Process Control (SPC) plays a vital role in maintaining quality and reducing variability in manufacturing processes. Among SPC techniques, the Exponentially Weighted Moving Average (EWMA) stands out for its ability to detect small process shifts quickly, making it a valuable tool in ensuring product consistency and preventing quality issues. EWMA constructs control charts to monitor process mean shifts, tracks product/service quality by identifying variations, and monitors manufacturing process parameters for early detection of deviations and necessary adjustments. EWMA control chart has been proposed as an alternative to the Shewhart control chart. Sequential measurements are processed using the EWMA function before being placed on the control chart. One of the crucial concerns about the EWMA control chart is the asymmetry of the data around the mean. Although processing with the EWMA function reduces data skewness, the problem of asymmetric data may not be solved. The control chart is designed to leave in front of the upper control limit (UCL) α/2 of the data and behind the lower control limit (LCL) another α/2 of the data, and this does not occur in the case of symmetric data. α/2 represents the significance level for each tail in a two-tailed hypothesis test, indicating the probability of incorrectly rejecting the null hypothesis for each side of the distribution. Since many of the distributions in real life can be approximated by the Gamma distribution, the Gamma distribution was adopted in this study. The Monte Carlo simulation methodology was implemented to generate Gamma distributed data, process it with EWMA function and assess the skewness and kurtosis. The purpose of this paper is to evaluate the effect of EWMA parameters on the performance of the EWMA control chart. Moreover, it focuses on skewness and kurtosis reduction after data processing using the EWMA function. The findings help researchers and practitioners to select the best parameters. Further, the research investigates the effect of EWMA parameter on the shape of distribution

Strategical selection of maintenance type under different conditions

Abstract Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration