This paper relies on 2 prerequisite mathematical and architectural foundations.
Background: Investigating human health during work importantly affect the safety and efficiency of the society. Different types of activity, including industrial, constructional, heavy duty ones, etc., affect the health of workers during their activities. In this study, the Multi-Objective Decision-Making (MODM) method was combined with fuzzy TOPSIS to investigate and optimize the air pollution risk affecting the health and safety of construction workers of Lar City, Fars Province, Iran. Methods: The comparison matrices (binary) and the Phillips–Perron test of different criteria and sub-criteria of health risk, including safety view, safety efficiency, understanding the risk, and risk investigation, were assessed to find the most influential factors for the optimization of the health risk of the workers. Results: Accordingly, the results indicated that “understanding the risk” followed by “safety efficiency” affected the health risk of the construction workers the most. However, among the sub-criteria, the most effective ones were “worker knowledge”, “manager knowledge”, “logistic specialized managers”, “modern facilities”, and “modern technology”. The selection of the linear and non-linear models was conducted according to the F values, and the model parameters were estimated using the Newton-Raphson test. Most coefficients were significant at P= 0.99. The model also has a high describing value of 0.97. The final estimated homogeneity coefficient was equal to 1.31, and it ranged from 0.86 to 2.72 for the critical risk investigation. The significance of the present research is to increase the health and safety of workers during work resulting in a more sustainable and healthier environment. Conclusion: Accordingly, the managers could handle the risks, determine the tolerable risks, indicate the risk of each process to control expenses, and take the essential measures for optimization.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Background: Investigating human health during work importantly affect the safety and efficiency of the society.
In this study, the Multi-Objective Decision-Making (MODM) method was combined with fuzzy TOPSIS to investigate and optimize the air pollution risk affecting the health and safety of construction workers of Lar City, Fars Province, Iran.
In this study, the Multi-Objective Decision-Making (MODM) method was combined with fuzzy TOPSIS to investigate and optimize the air pollution risk affecting the health and safety of construction workers of Lar City, Fars Province, Iran.
Trade-offs between peak accuracy and memory overhead remain under investigation across diverse hardware environments.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. We propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output.
We propose Low-Rank Adaptation, or LoRA, which freezes the pretrained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.
