Survival analysis is one of the types of data analysis that describes the time period until the occurrence of an event of interest such as death or other events of importance in determining what will happen to the phenomenon studied. There may be more than one endpoint for the event, in which case it is called Competing risks. The purpose of this research is to apply the dynamic approach in the analysis of discrete survival time in order to estimate the effect of covariates over time, as well as modeling the nonlinear relationship between the covariates and the discrete hazard function through the use of the multinomial logistic model and the multivariate Cox model. For the purpose of conducting the estimation process for both the discrete

Low temperature and high relative humidity in the spring season led to decrease of field emergence ratio and growth in maize. Planting dates and seeds stimulation can be appropriate fix. Field experiment was conducted in the two spring seasons of 2022 and 2023. Randomize complete block design with split-plot arrangement and four replications was used. Planting date treatments (February 15th, March 1st and 15th and April 1st, 15th) were placed in main plots. Seeds stimulation treatments (potassium nitrate 6 mg L-1 + licorice extract 6 g L-1 as well as treatment of soaking with distilled water only) were placed in subplots. Seeds stimulation (potassium nitrate+licorice extract) or planting date of February 15th were superior at traits of fiel

          In the present research, a crane frame has been investigated by using finite element method. The damage is simulated by reducing the stiffness of assumed elements with ratios (10% and 20 %) in mid- span of the vertical column in crane frame. The cracked beam with a one-edge and non-propagating crack has been used. Six cases of damage are modeled for crane frame and by introducing cracked elements at different locations with ratio of depth of crack to the height of the beam (a/h) 0.1, 0.20. A FEM program coded in Matlab 6.5 was used to model the numerical simulation of the damage scenarios. The results showed a decreasing in the five natural frequencies from undamaged beam which means

The e-news is one of the most important journalistic arts in new media (the Internet). The process of telling the story by the journalist is an important aspect of the communicative process between the users of the internet and the reporter. The electronic news is characterized by having text, fixed images, animations, videos and sound. All these give greater vitality to the communicative process and increase the semiotic dimensions. Also, it makes the narrative process more distinctive and embodied of the elements of the event. This research studies all these aspects and tries to show the distinction between the semiotics of narration and the electronic news.

This study was conducted in the Department of Employment and Loans at the Ministry of Labor and Social Affairs to indicate the importance and impact of both the empowerment and the functional flexibility in evaluating the performance of the employees. To achieve the objectives of the study, the data was collected through a questionnaire form designed for this purpose based on previous studies. Data obtained for a significant evaluation of the relationship between the components of both the empowerment and the functional flexibility with the components of the evaluation and determining the degree of importance of each component of both the empowerment and functional flexibility for the components of the evaluation by the extractio

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we