Labor movement is considered important in Britain because it is an international phenomenon of the feudal system and one of the flowerings of the era of the capitalist machine. At the beginning of the 18th century, the world witnessed a change and progress in many aspects of the industrial fields such as the different means of production .this era is called the industrial revolution which especially appeared in Britain and extended to other areas of the world .it leads to many changes in Europe and participated in different economic, political and cultural and social fields. and it has a very important role in progress and development but its disadvantage is that it is exploited by the capitalists to achieve their special rights .in other words ,it enables them to have power over the labors who have suffered poverty ,destitution and unemployment .as a result ,many thinkers ,intellectuals and labor leaders appear to form unions and associations that help to call for the labors right and deliver their voices to the highest levels under the name of " a trade union " .these unions ,associations and trade union are able to work as "a committee to represent workers " .it continues to do political work till it is able to reach the parliament and constitutes an independent party called "workers party " for the labor class in order to continue work for the sake of improving the conditions of the labors.
To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip
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The using of recycled aggregates from construction and demolition waste (CDW) can preserve natural aggregate resources, reduce the demand for landfill, and contribute to a sustainable built environment. Concrete demolition waste has been proven to be an excellent source of aggregates for new concrete production. At a technical, economic, and environmental level, roller compacted concrete (RCC) applications benefit various civil construction projects. Roller Compacted Concrete (RCC) is a homogenous mixture that is best described as a zero-slump concrete placed with compacting equipment, uses in storage areas, dams, and most often as a basis for rigid pavements. The mix must be sufficiently dry to support
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The sustainable development according to the United Nation, listed firms throughout globally now routinely provide sustainability data. However, there is not enough information on Sustainability Performance Quality (SPQ) in the majority of emerging economies, including Malaysia. This study looks at how the SPQ of the top 100 Malaysian-listed businesses is affected by factors as connected with corporate governance (e.g., board meeting, board size, and board ethnic diversity). Utilizing 500 firm-year data, a longitudinal sample of 500 nonfinancial firms on the Bursa Malaysia for 2015-2019 is employed in this study. The findings from the analysis using the panel regression demonstrated that: ethnic diversity and board siz
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The study of Lake Hamrin is of enormous importance in understanding changes in water levels and their impact on the local environment. It helps to clarify the role of climatic and human factors in the decline of its water resources from 2013 to 2023 in order to ensure sustainable solutions. Satellite imagery from Landsat 8 is used, and processing is performed using ArcGIS Pro and ENVI. The normalized vegetation index (NDVI) is applied to show the changes in the vegetation cover and reveal the amount of change in the surface water area of the lake. It is noted that the water area changes in one year as it increases in the rainy season and decreases in the summer, and it also varies from one year to another with the difference in the amount o
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A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification
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