A new interval diagnosis method: Application to the spacecraft rendezvous phase of the Mars sample return mission

This paper presents a new optimized interval principal component analysis applied to detect and isolate actuators faults of an autonomous spacecraft involved in the rendezvous phase of the Mars sample return mission. Based on the exploitation of various arithmetic and interval analysis properties, the new interval model is built by solving the interval eigenpairs problem via a resolution of a parametric linear programming problem. The detection and isolation phases are performed by extending the classic methods to interval-valued data. The proposed method is applied to detect and isolate actuators faults that can occur on the spacecraft's thrusters. Based on data provided by a “high fidelity” industrial simulator developed by Thales Alenia Space, the obtained results proved the effectiveness of the proposed interval fault diagnosis method on detecting and isolating thrusters' faults.     

Probing the Structural and Antimicrobial Study on a Sol–Gel Derived Velosef-Loaded Bioactive Calcium Magneso-Silicate Xerogel

Silicon - The nature of the opening silicate- based surface affects the chemical interaction, spectroscopic and antimicrobial efficiency. The aim of this approach was to evaluate the spectroscopic...     

Modern Template Design and Biological Evaluation of Cephradine-loaded Magnesium Calcium Silicate Nanocomposites as an Inhibitor for Nosocomial Bacteria in Biomedical Applications

Silicon - Magnesium calcium silicate nanostructures (MCSNS) loaded with (0.0, 0.6, 0.9, and 1.2 wt%) of Cephradine-drug consisting of mesoporous particles were functionally prepared by sol-gel...     

Facile modification of polysulfone hollow-fiber membranes via the incorporation of well-dispersed iron oxide nanoparticles for protein purification

Protein existence in wastewater is an important issue in wastewater management because proteins are generally present as contaminants and foulants. Hence, in this study, we focused on designing a polysulfone (PSf) hollow-fiber membrane embedded with hydrophilic iron oxide nanoparticles (IONPs) for protein purification by means of ultrafiltration. Before membrane fabrication, the dispersion stability of the IONPs was enhanced by the addition of a stabilizer, namely, citric acid (CA). Next, PSf–IONP–CA nanocomposite hollow-fiber membranes were prepared via a dry–wet spinning process and then characterized in terms of their hydrophilicity and morphology. Ultrafiltration and adsorption experiments were then conducted with bovine serum albumin as a model protein. The results that an IONP/CA weight ratio of 1:20 contributed to the most stable IONP dispersion. It was also revealed that the membrane incorporated with IONP–CA at a weight ratio of 1:20 exhibited the highest pure water permeability (58.6 L m⁻² h⁻¹ bar⁻¹) and protein rejection (98.5%) while maintaining a low protein adsorption (3.3 μg/cm²). The addition of well-dispersed IONPs enhanced the separation features of the PSf hollow-fiber membrane for protein purification. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47502.     

Usability evaluation of a virtual reality smartphone app for a living museum

This paper elaborates the empirical evidence of a usability evaluation of a VR and non-VR virtual tour application for a living museum. The System Usability Scale (SUS) was used in between participants experiments (Group 1: non-VR version and Group 2: VR version) with 40 participants. The results show that the mean scores of all components for the VR version are higher compared to the non-VR version, overall SUS score (72.10 vs 68.10), usability score (75.50 vs 71.70), and learnability (58.40 vs 57.00). Further analysis using a two-tailed independent t test showed no difference between the non-VR and VR versions. Additionally, no significant difference was observed between the groups in the context of gender, nationality, and prior experience (other VR tour applications) for overall SUS score, usability score, and learnability score. Α two-tailed independent t test indicated no significant difference in the usability score between participants with VR experience and no VR experience. However, a significant difference was found between participants with VR experience and no VR experience for both SUS score (t(38) = 2.17, p = 0.037) and learnability score (t(38) = 2.40, p = 0.021). The independent t test results indicated a significant difference between participant with and without previous visits to SCV for the usability score (t(38) = −2.31, p = 0.027), while there was no significant differences observed in other components. It can be concluded that both versions passed based on the SUS score. However, the sub-scale usability and learnability scores indicated some usability issue.

     

Effect of fractional parameter on plane waves of generalized magneto–thermoelastic diffusion with reference temperature-dependent elastic medium

The present paper is concerned with the investigation of disturbances in a homogeneous, isotropic reference temperature-dependent elastic medium with fractional order generalized thermoelastic diffusion. The formulation is applied to the generalized thermoelasticity based on the fractional time derivatives under the effect of diffusion. The analytical expressions for displacement components, stresses, temperature field, concentration and chemical potential are obtained in the physical domain by using the normal mode analysis technique. These expressions are calculated numerically for a copper-like material and depicted graphically. Effect of fractional parameter and presence of diffusion is analyzed theoretically and numerically. Comparisons are made with the results predicted by the fractional and without fractional order in the presence and absence of diffusion.     

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

Extensions to emergency vehicle location models

This paper is concerned with extending models for the maximal covering location problem in two ways. First, the usual 0–1 coverage definition is replaced by the probability of covering a demand within the target time. Second, once the locations are determined, the minimum number of vehicles at each location that satisfies the required performance levels is determined. Thus, the problem of identifying the optimal locations of a pre-specified number of emergency medical service stations is addressed by goal programming. The first goal is to locate these stations so that the maximum expected demand can be reached within a pre-specified target time. Then, the second goal is to ensure that any demand arising located within the service area of the station will find at least one vehicle, such as an ambulance, available. Erlang's loss formula is used to identify the arrival rates when it is necessary to add an ambulance in order to maintain the performance level for the availability of ambulances. The model developed has been used to evaluate locations for the Saudi Arabian Red Crescent Society, Riyadh City, Saudi Arabia.     

Shortcoming, problems and analytical comparison for flooding-based search techniques in unstructured P2P networks

Peer-to-Peer networks attracted a significant amount of interest because of their capacity for resource sharing and content distribution. Content distribution applications allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content. Searching in unstructured P2P networks is an important problem, which has received considerable research attention. Acceptable searching techniques must provide large coverage rate, low traffic load, and optimum latency. This paper reviews flooding-based search techniques in unstructured P2P networks. It then analytically compares their coverage rate, and traffic overloads. Our simulation experiments have validated analytical results.     

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.