فهرست مطالب hadi shakibian

To ensure employee safety and health, it is crucial to choose and use personal protective equipment correctly. An investigation of the respiratory and hearing protection equipment used in the industries of Tehran was conducted in the present study.

This study was a descriptive-analytical study conducted in 2023 on 400 occupational health and safety experts in Tehran province. A researcher-made checklist and questionnaire (physical and online versions) were used to determine the criteria that are considered when selecting protective equipment. Statistical analysis and graphing were performed using SPSS-v21 and Excel-2016.

Participants had an average age of 38.66 and an average work experience of 11.78 years. The participants reported 48.3% and 47.7% disapproval of the quality and 42.5% and 39.5% disapproval respectively of the quantity of respiratory and hearing protection equipment available in their industrial units. In addition, 56.3% of them admitted that they have no responsibility for providing or approving the equipment in their workplace. Overall, 27 and 29 criteria were mentioned by study participants pertaining to respiratory and hearing protection equipment, respectively. In the identified criteria, the frequency distribution ranged from 0.3% to 38%.

A high rate of expert dissatisfaction with protective equipment and a low frequency of most of the criteria mentioned indicates that industries aren’t paying much attention to the issue of choosing the right respiratory and hearing protection equipment. To improve the situation, more investigation is needed and practical and operational solutions will be provided. This study highlights the importance of proper selection and use of personal protective equipment in ensuring the safety and health of employees.

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average.

The transportation networks analysis aims to investigate the system's structural characteristics and dynamical evolution to evaluate the transit services. In this regard, the topological characteristics of the Iran railway network have been studied and compared to two well-studied railway networks, China and Spain. Also, the network vulnerability to station failures has been studied based on different attacks. Accordingly, in the first step of this work, the city stations have been extracted from Iran railway information to construct the network. Then, some structural properties, including the degree distribution, betweenness centrality, clustering coefficient, and distance distribution, have been analyzed for three networks. Finally, the network reliability has been evaluated using a random as well as adversarial attack. The structural analysis reveals that the Iran railway network would require some structural optimization to improve the economic benefits. Based on the vulnerability investigation, the network efficiency of the network will be dropped more quickly utilizing the maximum betweenness attack. In addition, as it were, little parts of the network seem to keep their usefulness as the estimate of the giant component is diminished exceptionally strongly when less than 20% of nodes are expelled from the network haphazardly or intentioned.

Link prediction in complex networks aims to explore similarities between node pairs. Recently, link prediction has been considered in the presence of network heterogeneity which makes the majority of the homogeneous link prediction approaches infeasible. A meta-structure, known as meta-path, has been proposed to explore such networks. Generating good meta-paths and selecting the best of them introduce some new challenges to link prediction problem. In this paper, a new ensemble-based link prediction approach is proposed in heterogeneous complex networks. This approach consists of three steps: (i) a set of meta-paths are selected such that each of them represents a different semantic between the target node pairs; (ii) a feature vector is extracted for each node pair using each meta-path; (iii) an ensemble of learners would be established on different feature sets. The final link predictor is obtained after the ensemble aggregation. The results on DBLP network show that the proposed approach has more accurate predictions than a single meta-path based link predictor.

Regression modeling in sensor networks is a difficult task due to (i) the network data is distributed among the nodes and (ii) the restricted capabilities of the sensor nodes, such as limited power supply and bandwidth capacity. Recently, some distributed approaches have been proposed based on gradient descent and Nelder-Mead simplex methods. Although in these methods, the energy consumption is low, but the accuracy is still far from the centralized approach. Also, they suffer from a high latency. In this paper, a two-fold distributed approach has been proposed for doing regression analysis in wireless sensor networks. After clustering the network, the regressor of each cluster is learned by the integration of particle swarm optimization and harmony search. Afterwards, cluster heads collaborate to construct the global network regressor using a weighted averaging combination rule. The experimental results show the proposed approach improves the accuracy and latency significantly while its energy consumption is considerably acceptable in comparison with its popular counterparts.