Effects of Surface Roughness and Supply Inertia on Steady Performance of Hydrostatic Thrust Bearings Lubricated with Non-Newtonian Fluids

The objective of present theoretical analysis is to study the combined effects of surface roughness and fluid inertia (including the inertia of the fluid in the supply region) on the steady performance of stepped circular hydrostatic thrust bearings lubricated with non-Newtonian pseudoplastic fluids using Rabinowitsch stress-strain model. To account for the effects of surface roughness, the classical Christensen theory of rough surface has been taken. Analytic expressions for film pressure in bearing regions have been established for radial and circumferential patterns of roughness. Numerical results for film pressure, load carrying capacity and lubricant flow rate has been plotted and analysed. Due to surface roughness and fluid inertia in overall regions, significant improvement in performance properties have been observed.

Water deteriorates lubricating oils: removal of water in lubricating oils using a robust superhydrophobic membrane

Lubricating oil failure caused by water is solved by a robust membrane that shows steady performance in regard to extreme water repellency, high-efficiency purification of lubricating oils, and low wear volume even after harsh mechanical damage.

Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a presegmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet’18 DenseCaption dataset (Krishna et al. 2017) and Charades-STA dataset (Sigurdsson et al. 2016; Gao et al. 2017) while observing only 10 or less clips per video.

Android-based Mobile Application for Door-to-Door Product Delivery

Many companies, such as Aramex, FedEx, and SMSA, offer product delivery services. However, the delivery process through these companies is costly and/or requires the customer’s physical attendance at the company to get the sent shipments. There is a persistent need to improve the delivery process in Saudi Arabia to reduce the effort, cost, and time that the customer spends to get the shipped products. This paper presents a new delivery approach in Saudi Arabia by developing an Android-based MobApp that allows the customers to use their mobile devices to send and receive shipped products at their doorstep by submitting online requests through the developed MobApp. The proposed MobApp, named as Door-to-Door (D2D) product delivery MobApp, guarantees fast and costless service among its competitors. The MobApp will provide its customers a reliable delivery process. It aims to provide a domestic delivery chain with whomever to wherever within Saudi Arabia. In addition, the proposed delivery MobApp allows the customers to create, update and track the delivery orders. Moreover, the proposed D2D delivery MobApp is easy to install and use, it provides a friendly GUI and has a powerful steady performance.

A Field Study of a Low-Flow Internally Cooled/Heated Liquid Desiccant Air Conditioning System: Quasi-Steady and Transient Performance

The field performance of a low-flow internally cooled/heated liquid desiccant air conditioning (LDAC) system is investigated in this paper. The quasi-steady performance (sensible and latent heat transfer rates, coefficient of performance (COP), and uncertainties) of the LDAC system is quantified under different ambient air conditions. A major contribution of this work is a direct comparison of the transient and quasi-steady performance of the LDAC system. This paper is the first to quantify the importance of transients and shows that, for the environmental and operating conditions in this paper, transients can be neglected when estimating the energy consumption of the LDAC system. Another major contribution of this work is the development and verification of a new method that quantifies (with acceptable uncertainties) the quasi-steady performance of a LDAC system from transient field data using average data.