Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

An experimental study on air window collector having a vertical blind for active solar heating

In this study, an air window collector having vertical black blinds was investigated experimentally under actual outdoor conditions. The vertical blind was installed between two 120 × 80 cm glass panes. It can rotate about the vertical axis. The fully open position makes a 90° angle with the vertical plane. A commercially available blind made of cloth and coated with black paint was tested. The air circulation in the system is provided by a fan parallel to blinds. Various parameters were measured and recorded using a data acquisition system. The effect of these parameters on the thermal performance of the system were determined experimentally.     

An experimental and three-dimensional numerical study of natural convection heat transfer between two horizontal parallel plates

In this study, experimental and three-dimensional numerical studies were performed to investigate the effects of plate spacing and temperature difference on natural convection between isothermally heated upward-facing lower horizontal plate and externally insulated horizontal upper plate. Air is used as the heat transfer medium. Rayleigh number varied in the 1108–2.339 × 10⁵ range. Several numerical simulations for three-dimensional steady laminar and turbulent flows heat transfer were carried out using a commercial CFD code Fluent 6.2. Results have shown that there is a good agreement between the numerical and present experimental results as well as with available data in literature.     

Laser-induced fluorescence visualization of ion transport in a pseudo-porous capacitive deionization microstructure

In this paper, a microfluidic experimental set-up is introduced to study the ionic transport in an artificial capacitive deionization (CDI) cell. CDI is a promising desalination technique, which relies on the application of an external electric field and high surface area porous electrodes for ion separation and storage. Photolithography and deep reactive ion etching were used to fabricate a micro-CDI channel with pseudo-porous electrodes on a silicon-on-insulator substrate. Laser-induced fluorescence was performed using cationic Sulforhodamine B (SRB) fluorescent dye to measure ion concentration within the bulk solution and more importantly, within the porous electrodes during the desalination process, with an average normalized root mean square deviation of 8.2 %. Using this set-up, electromigration of ions within the electrode was visualized and the effect of applied electric potential on bulk solution concentration distribution is quantified. In addition, SRB and Fluorescein were used together to visualize anion and cation concentrations simultaneously. The method presented in this study can be used for solution concentrations up to approximately 0.7 mM. The ionic concentration profiles obtained by this approach can be used to test and validate the existing electrosorption models, and pseudo-porous electrodes can be modified to observe the effects of pore size, shape and distribution on electrosorption performance. Furthermore, with proper modifications, the microfabricated structure and experimental set-up can be used for CDI-on-a-chip applications and bio-separation devices.     

Image blur and energy broadening effects in XPEEM

We report image blurring and energy broadening effects in energy-filtered XPEEM when illuminating the specimen with soft X-rays at high flux densities. With a flux of 2×10¹³ photons/s, the lateral resolution in XPEEM imaging with either core level or secondary electrons is degraded to more than 50 nm. Fermi level broadening up to several hundred meV and spectral shift to higher kinetic energies are also systematically observed. Simple considerations suggest that these artifacts result from Boersch and Loeffler effects, and that the electron-electron interactions are strongest in the initial part of the microscope optical path. Implications for aberration corrected instruments are discussed.     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Revisiting Slotted ALOHA: Density Adaptation in FANETs

Unmanned aerial vehicles have been widely used in many areas of life. They communicate with each other or infrastructure to provide ubiquitous coverage or assist cellular and sensor networks. They construct flying ad hoc networks. One of the most significant problems in such networks is communication among them over a shared medium. Using random channel access techniques is a useful solution. Another important problem is that the variations in the density of these networks impact the quality of service and introduce many challenges. This paper presents a novel density-aware technique for flying ad hoc networks. We propose Density-aware Slotted ALOHA Protocol that utilizes slotted ALOHA with a dynamic random access probability determined using network density in a distributed fashion. Compared to the literature, this paper concentrates on proposing a three-dimensional, easily traceable model and stabilize the channel utilization performance of slotted ALOHA with an optimized channel access probability to its maximum theoretical level, 1/e, where e is the Euler’s number. Monte-Carlo simulation results validate the proposed approach leveraging aggregate interference density estimator under the simple path-loss model. We compare our protocol with two existing protocols, which are Slotted ALOHA and Stabilized Slotted ALOHA. Comparison results show that the proposed protocol has 36.78% channel utilization performance; on the other hand, the other protocols have 24.74% and 30.32% channel utilization performances, respectively. Considering the stable results and accuracy, this model is practicable in highly dynamic networks even if the network is sparse or dense under higher mobility and reasonable non-uniform deployments.