Toward Reducing Construction Project Delivery Time under Limited Resources

One of the most vital construction project aspects is to complete a project in minimum time restricted to the time–cost trade-off. Overlapping activities’ planning and their impact on the project under limited resource constraints should be considered. This study aims to develop a model for optimizing the project schedule and cost regarding overlap activities and their impacts. This study reviews previous studies on changes in past activities likely to produce additional reworking of subsequent activities. In addition, an AHP model is developed to assess the reworking time of subsequent activities based on possible changes in previous activities. In addition, five realistic construction projects are applied. Finally, an optimizing model is developed for optimizing project time and cost using overlapping techniques by using the Java program. The results indicate that the proposed model can be used by project managers easily for solving time and cost optimization problems. In addition, it can be updated to continuously improve its functionality. Finally, it can be updated later to support AI for finding better solutions.

Using the AIS Data to enhance smart port congestion optimizing model Subtitle as needed (paper subtitle)

Nowadays, ports are in need to maximize their incomes, and this based on the fierce competition. For this reason, all ports stakeholders should be involved to contribute in the design and the development of a policy of scheduling and priority.This project owned by the Vessel Call Service Planning Service in the port of Tanger Med as the "Tanger Med Port Authority", and it output was to report the summarized the work done and the methods behind it.The main goal is to develop a simulator model that includes all kind of operations and the operational process by choosing the appropriate KPIs that are fully reflecting the congestion transferred by port vesselsFirst, it aims to define the operating assumptions and the fundamental concepts on which our simulation model will be based and to present the data that were used for the implementation of the simulation as well as the origin of these data.Secondly, it aims to validate and calibrate the model by presenting some improvements that could be made to the simulator in order to make it more precise and more representative and to ensure automation of the processing of inputs and outputs. While towards the end we conclude with the presentation of congestion situations, the results obtained and their use in decision making.AIS data is another factor has been added to help in getting best results, this helped in vessels planification in/out predictive process while automating the use of our simulator with the AIS Data receipt from SatelliteThis part of project is in to give the smart aspect to our simulator results by using smart technology.

NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model

The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.

Reducing hERG Toxicity Using hERG Classification Model and Fragment-growing Network

Drug-induced cardiotoxicity has become one of the major reasons leading to drug withdrawal in past decades, which is closely related to the blockade of human Ether-a-go-go-related gene (hERG) potassium channel. Developing reliable hERG predicting model and optimizing model can greatly reduce the risk faced in drug discovery. In this study, we constructed eight hERG classification models, the best of which shows desirable generalization ability on low-similarity clinical compounds, as well as advantages in perceiving activity gap caused by small structural changes. Furthermore, we developed a hERG optimizer based on fragment grow strategy and explored its usage in four cases. After reinforcement learning, our model successfully suggests same or similar compounds as chemists’ optimization. Results suggest that our model can provide reasonable optimizing direction to reduce hERG toxicity when hERG risk is corresponding to lipophilicity, basicity, the number of rotatable bonds and pi-pi interactions. Overall, we demonstrate our model as a promising tool for medicinal chemists in hERG optimization attempts.

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

Welding is an important process in terms of manufacturing components for various types of machines and structures. One of the vital and still unsolved issues is determining the quality and properties welded joint in an online manner. In this paper, a technique for prediction of joint hardness based on the sequence of thermogram acquired during welding process is proposed. First, the correspondence between temperature, welding linear energy and hardness was revealed and confirmed using correlation analysis. Using a linear regression model, relations between temperature and hardness were described. According to obtained results in the joint area, prediction error was as low as 1.25%, while for HAZ it exceeded 15%. Future work on optimizing model and input data for HAZ hardness prediction are planned.