The categories L-\mathbf{Top}_{0} and L-\mathbf{Sob} as epireflective hulls

We show that the category \(L\) - \(\mathbf{Top}_{0}\) of \(T_{0}\) - \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}\) of \(L\) -topological spaces and the category \(L\) - \(\mathbf{Sob}\) of sober \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}_{0}\) .     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Oxidation stability and compositional characteristics of oils from microwave roasted pumpkin seeds during thermal oxidation

The oxidative stability and compositional characteristics of the pumpkin seed oil (PSO) exposed to microwaves were studied during heating at 170°C. The oxidative indices such as free fatty acid (FFA), peroxide value (PV), p-anisidine value (p-AV), TOTOX, specific extinctions and thiobarbituric acid (TBA) value of oils were significantly increased, and the increments were found to be significantly higher (P < 0.05) in unroasted seed oil as compared to roasted seed oil. The relative contents of polyunsaturated fatty acids (PUFAs) were decreased to 84.7%, and saturated fatty acids (SFAs) were increased to 119.5% in unroasted sample, after 9 h of heating. On the other hand, in 12 min roasted samples, the relative contents of PUFAs were decreased to 97.0%, and SFAs were increased to 102.6% after 9 h of heating. The triacylglycerol species LLL and OLL levels were decreased as a consequence of increased heating time, and the reduction tended to be significantly higher in unroasted samples as compared to roasted ones. The oxidation products formed were also investigated by FTIR. The present results indicated that microwave roasting of pumpkin seeds markedly enhanced the oxidative stability of the oils during heating.     

A new spatial spherical pattern model into interactive cartography pattern: multi-dimensional data via geostrategic cluster

A growing amount of research conducted in digital, cooperative with advances in Artificial Intelligence, Computer Vision including Machine learning, has managed to the advance of progressive techniques that aim to detect and process affective information contained in multi-modal evidences. This research intends to bring together for theoreticians and practitioners from academic fields, professionals and industries and extends to be visualizing cries such epidemic, votes, social Phenomena in spherical representation interactive model working in the broad range of topics relevant to multi - modal data processing and forensics tools developing. Furthermore, progress has been made in this research besides that in this research conducted progression of mapping claims in present epoch necessitate the capacities of virtual guide of any understandable Geo-Visualization of spatial features that talented to convert the quantities of spatial pattern into cartography. The enlargement of a novel approaches fit for visualization of spatial pattern constituencies Starting exclusive Input Set of object O, set associated with feature F for regenerating Output the set C , interested region I special target C Even so, as indicated by the construction of the prototype as listed earlier in this thread, does it have the incentive for improvements: Representation could be used by Google Earth can Using Project enhancement representation whereby provides a 3D or 4D interaction with life measures with a view to cartography. In addition, the initiative suggests that a tool not accessible for disseminating information to the public can be addressed by the use of online mapping, which fuses with trends visualization for political circles and electors. But as mentioned above the framework is developed and it's also possible in the current example, for improvements: The project's representation 3D or 4D interacting Earth can use measures of life Earth From the map viewpoint. That's what that says. That means that. Which just means. Developers have concerns that. So it. Designers concern about that. This study supports the new, multi - demission and deployed countries in conjunction with another data is processed. Comprehensive, well-interpreted source data for the Data like Malaysia Jabatan Pendaftaran (JPN).

     

Implementation of genetic algorithm in an embedded microcontroller-based polarization control system

Implementation of genetic algorithm in a PIC32MX microcontroller-based polarization control system is proposed and demonstrated. The controller measures the signal intensity that is used to estimate the genetic value. This process is controlled by the genetic algorithm rather than referring to the Look-Up-Table as implemented in existing solutions. To reach optimum performance, the code is optimized by using the best genetic parameters so that the fastest execution time can be achieved. An ability of genetic algorithm to work efficiently in polarization control system possesses many advantages including easy code construction, low memory consumption and fast control speed. Genetic algorithm is intelligent enough to be used for endless polarization stabilization and in the worst case, able to stabilize the polarization changes in 300 μs. In the best case the response time can reach 17 μs.     

Natural Palm Olein Polyol as a Replacement for Polyether Polyols in Viscoelastic Polyurethane Foam

The impact of replacing three polyether polyols with different levels of a single palm olein‐based natural oil polyol (NOP) was systematically correlated with the changes in foaming reactivity, cell structure, physico‐mechanical properties, and morphology of viscoelastic (VE) foams. The data show that replacing the polyether polyols with the NOP slightly increased the rate of the foaming reactivity. Increasing the NOP content resulted in increased cell size and cells remained fully open. Increased NOP content contributed to higher load bearing properties of VE foam, which can be attributed to higher functionality of NOP compared to polyether polyols. Addition of the NOP slightly increased the resilience of the foams, however, the hysteresis which is the measure of energy absorption remained mostly unaffected. Age properties, characterized by dry and humid compression sets, were mostly unaffected by the replacement of the polyether polyol with the NOP. The addition of NOP did not impact the morphology of the VE foam polymer matrix, which appears to retain a low degree of hard and soft segment domain separation. Overall, the results demonstrate a feasibility that the NOP can be used to partially replace the polyether polyols in VE polyurethane foams without significant impact on the functional performance.     

Preparation of presintered zirconia blocks for dental restorations through colloidal dispersion and cold isostatic pressing

This work proposes an effective method for dispersion of zirconia suspension for dental block preparation and optimizes the cold isostatic pressing (CIP) pressure to improve the densification of slip-casted zirconia blocks. Two batches of 44 wt% zirconia suspension were prepared using distilled water in a pH 2 medium containing 0.5 wt% polyethyleneimine as dispersant. The first batch was sonicated for different durations (from 5 min to 30 min), and the second batch was dispersed through ball milling at rotational speeds of 200, 300, and 400 rpm for 60, 90, and 120 min. All suspensions were subjected to sedimentation test and particle size measurement. Results revealed that the optimum ultrasonication duration was 10 min, which yielded the smallest particle size of 133 nm. Ball milling at 300 rpm for 120 min achieved the maximum dispersion of particles, with an average size of 75 nm. Under the optimum conditions of ultrasonication duration, ball milling duration, and ball milling speed, the particle size decreased to 48 nm, which is close to the primary particle size. These dispersion techniques and parameters were selected for preparing a suspension to be consolidated into blocks through slip casting and were enhanced through CIP at pressure ranging from 100 MPa to 300 MPa. CIP compaction at 250 MPa significantly increased the shrinkage percentage of green zirconia blocks, with pore radius decreased to 18 nm. The density of zirconia pressed at 250 MPa and presintered at a low temperature of 950 °C was 59% of the theoretical density and was higher than that of commercial presintered blocks. Thus, CIP should be conducted under a compaction pressure of 250 MPa to produce dense and homogeneous zirconia blocks.     

Hybrid neural network for prediction of CO<Subscript>2</Subscript> solubility in monoethanolamine and diethanolamine solutions

The solubility of CO₂ in single monoethanolamine (MEA) and diethanolamine (DEA) solutions was predicted by a model developed based on the Kent-Eisenberg model in combination with a neural network. The combination forms a hybrid neural network (HNN) model. Activation functions used in this work were purelin, logsig and tansig. After training, testing and validation utilizing different numbers of hidden nodes, it was found that a neural network with a 3-15-1 configuration provided the best model to predict the deviation value of the loading input. The accuracy of data predicted by the HNN model was determined over a wide range of temperatures (0 to 120 °C), equilibrium CO₂ partial pressures (0.01 to 6,895 kPa) and solution concentrations (0.5 to 5.0M). The HNN model could be used to accurately predict CO₂ solubility in alkanolamine solutions since the predicted CO₂ loading values from the model were in good agreement with experimental data.     

Easily made and handled carbon nanocones for scanning tunneling microscopy and electroanalysis

Well-formed carbon nanocones at the ends of micrometer-diameter carbon fibers (CFs) were fashioned into functional tips for scanning tunneling microscopy (STM) and miniaturized voltammetric sensors. Sharpening of single graphite filaments was achieved by simple DC electrochemical etching in 0.1 N NaOH. Operated as STM tips, pointed CFs resolved in air the contour and surface morphology of a nanoscopic Au line pattern and imaged in vacuum a Si (1 1 1) surface with clear atomic resolution. Subjecting already etched CFs to tip-sparing insulation with electrodeposited paint produced conical carbon ultramicroelectrodes (UMEs) with effective radii down to about 900 nm. Comparative cyclic voltammetry trials in alkaline, neutral and acidic solutions showed that the conical carbon UME’s had a wider practical potential window for electroanalytic applications than, for instance, Pt disk UMEs. The CF-based conical sensors described here are exceptionally easy to make with simple laboratory equipment and perform well in STM topography imaging and voltammetry. The inherent simplicity of sensor production widens the field of potential users, and offers clear advantages over existing types of UMEs, in particular those based on carbon nanotubes, which are especially hard to handle in an optical microscope setting.