An inverse solution methodology for estimating the diffusion coefficient of gases in Athabasca bitumen from pressure-decay data

An inverse solution methodology is developed for the estimation of diffusion coefficient of gases in highly viscous, oil-sands bitumens from isothermal, pressure-decay measurements. The approach involves modeling the rate of change in pressure using the diffusion equation for the liquid phase coupled with a mass balance equation for the gas phase. The inverse solution framework is utilized to arrive at two graphical techniques for estimating the diffusion coefficient. Both techniques involve the determination of the slope of a straight line resulting from plotting the experimental data in accordance with the developed model. An advantage of the proposed techniques is that the diffusion coefficient is estimated directly, i.e. without making it an adjustable parameter. The novelty of the proposed method is in its simplicity as well as its ability to isolate portions of the pressure-decay data affected by experimental fluctuations. The effect of the initial pressure on the predicted diffusion coefficient and pressure-decay profile was also investigated. The diffusion coefficients of CO₂, CH₄, C₂H₆ and N₂ in Athabasca bitumen at 50–90 °C and about 8 MPa were estimated and compared with literature values.     

A low order,torsion deformable spatial beam element based on the absolute nodal coordinate formulation and Bishop frame

Multibody System Dynamics - Heretofore, the Serret–Frenet frame has been the ubiquitous choice for analyzing the elastic deformations of beam elements. It is well-known that this frame is...     

A selective modified bentonite–porphyrin carbon paste electrode for determination of Mn(II) by using anodic stripping voltammetry

A novel voltammetric sensor based on chemically modified bentonite–porphyrin carbon paste electrode (MBPCE) has been introduced for the determination of trace amount of Mn(II) in wheat flour, wheat rice and vegetables. In this method Mn(II) gives well-defined voltammetric peak at the pH range of 3.5–7.5. For the preliminary screening purpose, the catalyst was prepared by modification of bentonite with porphyrin and characterized by thermogravimetric method (TG) and UV–vis spectroscopy. The detection limit (three times signal-to-noise) with 4 min accumulation is 1.07 × 10⁻⁷ mol L⁻¹ Mn(II). The peak currents increases linearly with Mn(II) concentration over the range of 6.0 × 10⁻⁷ to 5.0 × 10⁻⁴ mol L⁻¹ (r² = 0.9959). Statistical treatment of the results gave a relative standard deviation lower than 2.30%. The chemical and instrumental parameters have been optimized and the results showed that 1000-fold excess of the additive ions had not interferences on the determination of Mn(II).     

Scalability of MANET routing protocols for heterogeneous and homogenous networks

In Mobile Ad hoc Network (MANET), mobility, traffic and node density are main network conditions that significantly affect performance of routing protocols. Much of the previous research in MANET routing have focused on developing strategies, which suit one specific networking scenario. Therefore, there is no existing protocol that can work well in all different networking scenarios. This paper reviews characteristics of several different classes of routing protocols. Moreover, most of current routing protocols assume homogeneous networking conditions where all nodes have the same capabilities and resources. This paper presents extensive studies simulations for DSR, AODV, LAR1, FSR and WRP in homogenous and heterogeneous networks that consist of different nodes with different resources. The results showed that while all protocols perform reasonably well in homogenous networking conditions, their performance suffer significantly over heterogonous networks.     

A new dynamic cellular learning automata-based skin detector

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

Collaboration barriers in BIM-based construction networks: A conceptual model

Building information modelling (BIM) teams, hereafter referred to as BIM-based construction networks, are teams whose possibly geographically dispersed members from various organisations and disciplines, perform project tasks on BIM-enabled projects. In recent years, BIM-based construction networks have progressively become the norm in executing BIM activities on projects. However, even though achieving BIM's full capabilities relies on effective collaboration among the team members in BIM-based construction networks, it is still a struggle for these members to collaborate. Nonetheless, only a few studies have been conducted to identify the barriers to strengthening team collaboration in BIM-based construction networks. To address this gap, the current study, by examining 73 journal articles on collaboration in BIM-based construction networks, builds upon a theoretical review of the literature. A conceptual model is presented to capture the main barriers to collaboration in BIM-based construction networks. The study benefits researchers as well as project managers. For researchers, the conceptual model provides an intermediate theory, namely, a theoretical basis to direct further knowledge creation attempts on the topic. In addition, the conceptual model supports project managers on BIM-enabled projects. That is, it simplifies the knowledge now available for practical applications enabling it to be translated into guidelines and practical instructions on real-life projects.