Nanoscale friction and wear of a polymer coated with graphene

Friction and wear of polymers at the nanoscale is a challenging problem due to the complex viscoelastic properties and structure. Using molecular dynamics simulations, we investigate how a graphene sheet on top of the semicrystalline polymer polyvinyl alcohol affects the friction and wear. Our setup is meant to resemble an AFM experiment with a silicon tip. We have used two different graphene sheets, namely an unstrained, flat sheet, and one that has been crumpled before being deposited on the polymer. The graphene protects the top layer of the polymer from wear and reduces the friction. The unstrained flat graphene is stiffer, and we find that it constrains the polymer chains and reduces the indentation depth.

Anesthesia Management in Cesarean Section for Close Contact Parturient with COVID-19 Patient: A Case Report

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak, which produced a disease that had been termed COVID-19, safely treating patients that have contracted COVID-19 has become a very challenging problem for both patients and healthcare workers alike. The case we will be dealing with concerns a surgery of a full-term parturient who tested negative for COVID-19 at the time of surgery but had been living with a husband who contracted COVID-19. The parturient was taken up for an elective caesarean section under spinal anesthesia in an isolated operating room. It is necessary to consider how to manage the patient who was a close contact even if their COVID-19 test result is negative and how to set up the protocols to protect healthcare workers themselves in such situation. Keywords ---- (COVID-19, Caesarean section, Parturient, Spinal anesthesia)

Chemical Biology Tools to Study Lipids and their Metabolism with Increased Spatial and Temporal Resolution

Lipids are important cellular components providing many essential functions. To fulfill these various functions evolution has selected for a diverse set of lipids and this diversity is seen at the organismal, cellular and subcellular level. Understanding how cells maintain this complex lipid organization is a very challenging problem, which for lipids, is not easily addressed using biochemical and genetic techniques. Therefore, chemical tools have an important role to play in our quest to understand the complexities of lipid metabolism. Here we discuss new chemical tools to study lipids, their distribution and metabolism with increased spatial and temporal resolution.

TP-MV: Therapeutic protein prediction by multi-view learning

Background: Therapeutic peptide prediction is critical for drug development and therapy. Researchers have been studying this essential task, developing several computational methods to identify different therapeutic peptide types. Objective: Most predictors are the specific methods for certain peptides. Currently, developing methods to predict the presence of multiple peptides remains a challenging problem. Moreover, it is still challenging to combine different features to make the therapeutic prediction. Method: In this paper, we proposed a new ensemble method TP-MV for general therapeutic peptide recognition. TP-MV is developed using the stacking framework in conjunction with the KNN, SVM, ET, RF, and XGB. Then TP-MV constructs a multi-view learning model as meta-classifiers to extract the discriminative feature for different peptides. Results: In the experiment, the proposed method outperforms the other existing methods on the benchmark datasets, indicating that the proposed method has the ability to predict multiple therapeutic peptides simultaneously. Conclusion: The TP-MV is a useful tool for predicting therapeutic peptides.

LPWAN-Based Real-Time 2D SLAM and Object Localization for Teleoperation Robot Control

In this study, the teleoperation robot control on a mobile robot with 2D SLAM and object localization using LPWAN is proposed. The mobile robot is a technology gaining popularity due to flexibility and robustness in a variety of terrains. In search and rescue activities, the mobile robots can be used to perform some missions, assist and preserve human life. However, teleoperation control becomes a challenging problem for this implementation. The robust wireless communication not only allows the operator to stay away from dangerous area, but also increases the mobility of the mobile robot itself. Most of teleoperation mobile robots use Wi-Fi having high-bandwidth, yet short communication range. LoRa as LPWAN, on the other hand, has much longer range but low-bandwidth communication speed. Therefore, the combination of them complements each other’s weaknesses. The use of a two-LoRa configuration also enhances the teleoperation capabilities. All information from the mobile robot can be sent to the PC controller in relatively fast enough for real-time SLAM implementation. Furthermore, the mobile robot is also capable of real-time object detection, localization, and transmitting images. Another problem of LoRa communication is a timeout. We apply timeout recovery algorithms to handle this issue, resulting in more stable data. All data have been confirmed by real-time trials and the proposed method can approach the Wi-Fi performance with a low waiting time or delay.

AN INTEGRATED FUZZY MCDM MODEL FOR EVALUATION AND SELECTION OF A SUITABLE TUGBOAT

Tugboats are of vital importance in ports where a significant portion of world trade takes place. Selection of a tugboat that suitable for different operations in a port is a challenging problem that requires many different criteria to be evaluated at the same time. This selection requires high experience as well as technical knowledge of the tugboats and the operations to be carried out. In the present paper, an integrated model for evaluation and selection of tugboats is given. Based on the statistical data available in the study, assessment of the effect of different criteria on different harbour tugboats categorized according to the propulsion systems were carried out. The criteria for the tugboat alternatives were assessed through a questionnaire by subject-matter-experts containing comparative technical, financial and operational questions. The weights of each criteria were calculated using fuzzy Shannon’s entropy and fuzzy TOPSIS was utilized to rank the alternatives. Finally, the most suitable tugboat according to propulsion system was selected.

Backpressure Minimization as Objective Function for Production Optimization in Absence of Wells Performance.

In the Oil & Gas sector, the production optimization is one of the most challenging problem, since it involves many operational variables linked by complex relationships. Moreover, during the asset life cycle those parameters could change. Conflicts and interactions between variables, constraints, and operational limitations could be solved holistically by an optimization tool based on an Evolutionary Algorithm. The algorithm searches for the optimum field configuration from the operational point of view, leading to the production maximization. Eni Production Department developed a tool based on this algorithm for management and optimization of surface asset hardly focused on field viewpoint. e-Rabbit (Risked Algorithm for Biogenetical Balance Integration Tool) provides integration for reservoir, well, network and process models. The present work has been developed to overcome a recurrent problem in the Oil & Gas business: the lack of data. There are many cases in which this situation occurs, for example in old fields where measurement tools and digitalization are not so widespread or in assets characterized by many wells and complex gathering systems, in which the detail on well performances could not be available. e-Rabbit, to perform its optimization, requires that information so, under those conditions, somehow, it is necessary to find another way to be able to optimize and manage the surface asset. A novel technique to optimize the whole production system has been introduced, whose objective function rely on backpressure minimization. To verify its effectiveness two case studies have been analyzed comparing the proposed optimal configuration with the output of the classical e-Rabbit - optimization.

HMBI: A New Hybrid Deep Model Based on Behavior Information for Fake News Detection

Fake news can cause widespread and tremendous political and social influence in the real world. The intentional misleading of fake news makes the automatic detection of fake news an important and challenging problem, which has not been well understood at present. Meanwhile, fake news can contain true evidence imitating the true news and present different degrees of falsity, which further aggravates the difficulty of detection. On the other hand, the fake news speaker himself provides rich social behavior information, which provides unprecedented opportunities for advanced fake news detection. In this study, we propose a new hybrid deep model based on behavior information (HMBI), which uses the social behavior information of the speaker to detect fake news more accurately. Specifically, we model news content and social behavior information simultaneously to detect the degrees of falsity of news. The experimental analysis on real-world data shows that the detection accuracy of HMBI is increased by 10.41% on average, which is the highest of the existing model. The detection accuracy of fake news exceeds 50% for the first time.