Changes in the quality of fishburger produced from Tilapia (<Emphasis Type="Italic">Oreochromis niloticus</Emphasis>) during frozen storage (−18 °C)

In this study, the chemical and sensory qualities of fishburger produced from tilapia (Oreochromis niloticus) were investigated during frozen storage (–18 °C) over 8 months. The ratios of crude protein, lipid, moisture, crude ash, and polyunsaturated fatty acids in tilapiaburger were found to be 17.82%, 5.29%, 66.68%, 2.56% and 7.95% respectively. At the end of the study, the total volatile basic nitrogen value was found to have fluctuated throughout the storage periods. The highest thiobarbituric acid value was found at the 7th month as 0.142 mg malonaldehyde/kg. The peroxide value was 0.18 meq/kg at the beginning of the storage but increased to 5.03 meq/kg at 6th month of storage and then decreased to 0.82 meq/kg at the 8th month. The level of free fatty acids was 2.73% oleic acid at the beginning but reached 4.14% in the first month of storage and then increased to 5.92% at the end of the storage. pH values ranged between 8.01 and 7.97. At the end of the storage period, sensory quality criteria were scored as quite near those of the fresh samples.     

An improved adaptive FastSLAM algorithm with time-varying noise estimator

In this study, an adaptive FastSLAM (AFastSLAM) algorithm, which is obtained by estimating the time-varying noise statistics and improving FastSLAM algorithm, is proposed. This improvement was accomplished by using maximum likelihood estimation and expectation maximization criterion and a one-step smoothing algorithm in importance sampling. In addition, innovation covariance estimation (ICE) method was used to prevent loss of positive definiteness of the process and measurement noise covariance matrices. The proposed method was compared with FastSLAM by calculating the root mean square error (RMSE) using different particle numbers at varying initial process and measurement noise values. Simulation studies have shown that AFastSLAM provides much more accurate, consistent, and successful estimates than FastSLAM for both robot and landmark positions.     

Accessibility,usability, and security evaluation of universities’ prospective student web pages: a comparative study of Europe,North America,and Oceania

Universal Access in the Information Society - Universities' prospective student web pages aim to disseminate information about their academic and social opportunities to their stakeholders;...     

Numerical modeling of anode-bleeding PEM fuel cells: Effects of operating conditions and flow field design

Operating the PEM fuel cell in the dead-ended anode mode reduces the overall cost and complexity of the system but causes a voltage loss and carbon corrosion in the cathode catalyst layer due to hydrogen starvation in the anode. Whereas allowing an ultra-low flowrate at the anode outlet offers a very high utilization of hydrogen and achieves a stable voltage transient. Here, a time-dependent pseudo-three-dimensional, two-phase, and non-isothermal model is developed to study the optimum bleeding rate, which maximizes the hydrogen utilization, achieves a stable cell voltage and avoids carbon corrosion, which is commonly observed when the bleed rate is set to zero, i.e. the dead-ended mode. The model is validated against the experimental data by comparing the polarization curves and cell voltage transients during the dead-ended anode operation of small experimental cells with serpentine and straight anode flow channels. Moreover, the effects of operating conditions on cell performance during the anode bleeding operation mode are investigated. Results demonstrate that the hydrogen utilization exceeds 99% in the anode-bleeding mode without hydrogen starvation, and the cell performance improves significantly for higher anode pressure, lower cell temperature, and lower relative humidity at the cathode inlet. Lastly, it is found that serpentine channels in the anode improve the uniformity of the distribution of hydrogen compared to straight and interdigitated channels in the anode-bleeding mode while the cathode flow field consists of serpentine channels.     

Hybrid IT2 fuzzy modelling with alpha cuts for hydrogen energy investments

The framework of this study is to weight 8 selected determinants and rank energy alternatives for hydrogen investments. For this purpose, different criteria that are based on two dimensions are identified with supported literature and interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) with alpha cuts is considered to measure the significant criteria. Additionally, renewable and non-renewable energy alternatives are ranked regarding the appropriateness for hydrogen energy investments with the help of IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) and IT2 fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) with alpha cuts. It is concluded that the weights of the criteria are quite similar for different alpha cuts. Also, ranking results of different energy alternatives are almost the same for both IT2 fuzzy TOPSIS and IT2 fuzzy VIKOR. Thus, it can be concluded that the analysis results are reliable and coherent. The principal results indicate that cost-efficiency and reserve adequacy play a key role in hydrogen investments since they have the highest weight (0.129). Moreover, it is also found that technological capacity also plays a critical role in this regard with the weight of 0.127. However, legal regulation has the lowest importance weight (0.121) in comparison with other factors. Additionally, the weights of personnel competency (criterion 3) and storage (criterion 4) are also low (0.122). The major conclusion show that renewable energy alternatives are more suitable to generate hydrogen energy in comparison with non-renewable ones. Within this context, it is identified that solar and geothermal energies are more appropriate alternatives for hydrogen production whereas coal and nuclear are on the last rank. Hence, the main strategy should be lowering the costs by following up-to-date technological developments. Another important issue is that it becomes more logical to produce hydrogen in renewable energy sources that will not be consumed forever so that sustainable production of the hydrogen can be provided.     

Polypropylene/Spray Dried and Silane-Treated Nanofibrillated Cellulose Composites

In this study, the spray dried nanofibrillated cellulose (NFC) was modified by a silane coupling agent to be used in polypropylene (PP)-copolymer matrix in the presence of maleic anhydride grafted PP (PP-g-MAH) compatibilizer. The PP/NFC composites were melt compounded and injection molded. In the first stage, the NFC was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM). It was shown that fibrillation in aqueous media through high-pressure homogenization was successfully achieved. The nanofibrils having diameter smaller than 50 nm were obtained. The X-ray photo electron spectroscopy (XPS) showed the presence of the silane groups on the NFC surface. Elastic modulus improved by the addition of the high modulus NFC to PP, but the yield strength was not improved. On the other hand, different than many inorganic micron-sized fillers, the elongation-at-break value did not drop much. Differential scanning calorimeter experiments indicated that the NFC acted as a nucleating agent. Rheological investigations exhibited that NFC decreased the viscosity of the PP. The addition of NFC to PP improved the thermal resistance of the PP as can be understood from the TGA experiments. It was demonstrated that renewable nanofiller can be a feasible method to functionalize the existing synthetic polymers. POLYM. ENG. SCI., 60: 352–361, 2019. © 2019 Society of Plastics Engineers     

Online video scene clustering by competitive incremental NMF

Efficient clustering and categorizing of video are becoming more and more vital in various applications including video summarization, content-based representation and so on. The large volume of video data is the biggest challenge that this task presents, for most the clustering techniques suffer from high dimensional data in terms of both accuracy and efficiency. In addition to this, most video applications require online processing; therefore, clustering should also be done online for such tasks. This paper presents an online video scene clustering/segmentation method that is based on incremental nonnegative matrix factorization (INMF), which has been shown to be a powerful content representation tool for high dimensional data. The proposed algorithm (Comp-INMF) enables online representation of video content and increases efficiency significantly by integrating a competitive learning scheme into INMF. It brings a systematic solution to the issue of rank selection in nonnegative matrix factorization, which is equivalent to specifying the number of clusters. The clustering performance is evaluated by tests on TRECVID video sequences, and a performance comparison to baseline methods including Adaptive Resonance Theory (ART) is provided in order to demonstrate the efficiency and efficacy of the proposed video clustering scheme. Clustering performance reported in terms of recall, precision and F1 measures shows that the labeling accuracy of the algorithm is notable, especially at edit effect regions that constitute a challenging point in video analysis.