Arabic Text Summarization Challenges using Deep Learning Techniques: A Review

Text summarization is a challenging field in Natural Language Processing due to language modelisation and used techniques to give concise summaries.  Dealing with Arabic language does increase the challenge while taking into consideration the many features of the Arabic language, the lack of tools and resources for Arabic, and the Algorithms adaptation and modelisation. In this paper, we present several researches dealing with Arabic Text summarization applying different Algorithms on several Datasets. We then compare all these researches and we give a conclusion to guide researchers on their further work

Integrating Building Information Modeling and Health and Safety for Onsite Construction

Background: Health and safety (H&S) on a construction site can either make or break a contractor, if not properly managed. The usage of Building Information Modeling (BIM) for H&S on construction execution has the potential to augment practitioner understanding of their sites, and by so doing reduce the probability of accidents. This research explores BIM usage within the construction industry in relation to H&S communication. Methods: In addition to an extensive literature review, a questionnaire survey was conducted to gather information on the embedment of H&S planning with the BIM environment for site practitioners. Results: The analysis of responses indicated that BIM will enhance the current approach of H&S planning for construction site personnel. Conclusion: From the survey, toolbox talk will have to be integrated with the BIM environment, because it is the predominantly used procedure for enhancing H&S issues within construction sites. The advantage is that personnel can visually understand H&S issues as work progresses during the toolbox talk onsite

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 — sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics

Dipole Moments & Structure of Some Hydrazonomalononitriles

254-25

Dielectric Polarization Measurements on Some β-Diketones & β-Ketoesters in Solution: Part I-Ethyl Acetoacetate

639-64

Solvent Effect on the Dipole Moments of Some N-Substituted Maleimides

56-5

Assessment of performance of the regional climate model (RegCM4.6) to simulate winter rainfall in the north of Morocco: The case of Tangier-Tétouan-Al-Hociema Region

We examine the performance of the regional climate model RegCM v4.6 to simulate spatial variability of precipitation in the northwestern region of Morocco during the winter of 2009–2010. Simulations cover 24 months from 2009 to 2010 with 30 km as a horizontal grid. We use NCEP reanalysis as forcing data and for better comparison of results, observed precipitations derived from CRU, CHIRPS, and CMORPH data. Results indicate that, on the whole, the RegCM4 model represents appropriate regional aspects of rainfall over the study area but underestimates precipitations over mountainous and Mediterranean regions of the study area (Case of Tangier-Tétouan-Al-Hociema Region) which is probably due to poor representation of orography in the Model and some aspects of local Mediterranean climate. Projected precipitations are also examined in this work in comparison with the reference period of 1970–2005, with simulations performed by RegCM 4.6 regional model for the period 2023–2099 under scenarios RCP4.5 and RCP8.5, forced by HadGEM2-ES General Circulation Model. Results show a decrease in precipitations mean for (2023–2099) for both RCP4.5 and RCP8.5 scenarios over the study area in comparison with the historical period (1970–2005), with a significant decrease under RCP8.5 scenarios. This work proves that the RegCM v4.6 model can be used for regional climate prediction, particularly for the spatial distribution of precipitation, but for sectorial applications and impact studies, the Model outputs should be bias corrected

An Algebraic Method for Analysing Control Flow of BPMN Models

This paper introduces an approach for formal verification of BPMN models. The incompatible constructs of the BPMN patterns can lead to wrong or incomplete semantics which resulting the behavioral errors such as deadlock and multiple termination. This research is motivated by the need to create a correct business process and in order to generate a more complete formalization of BPMN semantics than existing formalizations. We first introduce the chosen patterns which are the most used in the modelisation of the service-based business processes. Then, we illustrate a definition of the execution semantics of these patterns by using the rules of Max+ Algebra formulas, which have important benefits

Polarity and steric effect of di-lateral substitution on the mesophase behaviour of some azo/ester compounds

<p>Eight homologous series of 2-(or 3-)substituted phenyl 4ʹ-(4″-alkoxy (2ʹ-, or 3″-substituted phenylazo) benzoates (<b>I</b><i>n</i>_XY) were prepared in which the suffix ‘X’ refers to the lateral substituent X attached to the terminal benzene ring that carries the alkoxy group, and the suffix ‘Y’ refers to the substituent attached to the other terminal phenyl group. Within each homologous series, the length of the terminal alkoxy group varies from 8 to 16 carbons, while the lateral polar substituents, X and Y, alternatively varies between CH₃ and F. The mesophase behaviour was investigated by differential scanning calorimetry and identified by polarised optical microscopy. The results were discussed in terms of the polarity and steric effects of the two lateral substituents. Comparative correlations were made to investigate the effect of the second lateral substituent on the mesophase behaviour of the previously investigated mono-laterally substituted analogues. UV–vis spectroscopic study revealed that the compounds <b>I</b><i>8</i>_XY exhibited two absorption bands: low intense bands at 254–263 and a broad band at 364–376 nm. These bands are attributed to the π–π∗ transition of the phenyl rings and the whole mesogenic portion.</p