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.     

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.     

SRA-MSDU: Enhanced A-MSDU frame aggregation with selective retransmission in 802.11n wireless networks

The main goal of the IEEE 802.11n standard is to achieve more than 100 Mbps of throughput at the MAC service access point. This high throughput has been achieved via many enhancements in both the physical and MAC layers. One of the MAC enhancements is the frame aggregation in which multiple frames are concatenated into a single large frame before being transmitted. The 802.11n MAC layer defines two types of aggregation, aggregate MAC service data unit (A-MSDU) and aggregate MAC protocol data unit (A-MPDU). The A-MPDU outperforms A-MSDU due to its large aggregation size and the subframes retransmission in erroneous channels. However, in error free channels and under the same aggregation size the A-MSDU performs better than the A-MPDU due to its smaller headers. Thus, adding a selective retransmission capability to the A-MSDU would improve the system performance. In this paper, we have proposed an MSDU frame aggregation scheme that enables selective retransmission at the MSDU level without altering the original MAC header. In this proposed scheme an implicit sequence control mechanism has been introduced in order to keep the frames in sequence and preserve their correct order at the receiver side. The results show that the proposed scheme improves the system performance in terms of throughput and delay even under highly erroneous channels.     

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...     

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.     

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.

     

Gas amplification factor in neon-nitrogen filled proportional counters

The gas amplification factor in a cylindrical proportional counter filled with Ne---N₂ Penning mixtures has been measured (over the range 1 < A < 6.4 × 10³) to verify the validity of the gas gain formulae of Rose and Korff, Khristov, Williams and Sara, Diethorn and Zastawny. This factor has been found to obey Zastawny's formula over the range of variables studied. The formula of Diethorn can be fitted only for small nitrogen concentrations (below 5 × 10⁻⁴).

Constants appropriate to the Zastawny and Diethorn formulae have been determined over a wide range of N₂ concentrations from spectroscopically pure Ne up to 11.1% N₂.     

The Effect of Bases and Formulation on the Release of Indomethacin from Suppositories

The in vitro release characteristics of indomethacin from different suppository formulations were investigated using a dialysis method. Suppositories containing 100 mg of indomethacin were prepared by the fusion method in a variety of Witepsol and Novata bases with different hydroxyl values. The rate of release of indomethacin was found to be unexpectedly higher from oily bases with low hydroxyl values.

Furthermore, the effect of surface active agents and some excipients commonly used in suppository formulations on the release properties of indomethacin was determined. Colloidal silicon dioxide, sodium lauryl sulphate and cetyl alcohol had a slight effect, on the release of indomethacin, whereas dioctyl sodium sulphosuccinate significantly increased the amount of indomethacin released. White beeswax and Tween-80, however, resulted in a marked decrease in the release of indomethacin.

The in vitro release of indomethacin from five commercially available preparations was also determined using the same method. Suppositories formulated in PEG bases gave better release properties than those in oily bases.     

Improvements and limitation of soy protein-based adhesive: A review

Soy protein is known for its eco-friendly, sustainable, and biodegradable qualities that are likely used as raw material in producing bioadhesive. However, soy protein-based adhesive are lacking in terms of adhesive strength and water-resistance compared to commercial formaldehyde-based adhesives such as phenol and urea-formaldehyde resin. Therefore, continuous research has been done to improve adhesive performance. This can be done via physical or modification methods, including the usage of cross-linking agents, structural modification, enzymatic modification, and the addition of additives. This review will cover these modification methods that give significant enhancement to the water-resistance and adhesive strength of soy protein-based adhesives.