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.     

A novel compact circuit for 4-PAM energy-efficient high speed interconnect data transmission and reception

Transmission of signals, whether on-chip or off-chip, places severe constraints on timing and extracts a large price in energy. New silicon device technologies, such as back-plane CMOS, provide a programmable and adaptable threshold voltage as an additional tool that can be used for low power design. We show that one particularly desirable use of this freedom is energy-efficient high-speed transmission across long interconnects using multi-valued encoding. Our multi-valued CMOS circuits take advantage of the threshold voltage control of the transistors, by using the signal-voltage-to-threshold-voltage span, in order to make area-efficient implementations of 4-PAM (pulse amplitude modulation) transceivers operating at high speed. In a comparison of a variety of published technologies, for signal transmission with interconnects of 10-15 mm length, we show up to 50% improvement in energy for on-chip signal transmission over binary encoding together with higher limits for operating speeds without a penalty in circuit noise margin.     

Delayed neurological symptoms from the spontaneous migration of a bullet in the lumbosacral spinal canal. Case report

In a patient wounded by a gunshot in the abdomen, the bullet was radiologically located intradurally at S1 level. Although she had no neurological deficit at admission, she developed pain and motor weakness a few days later. At operation the bullet was found at L4 level and its removal resulted in complete neurological recovery.     

Comparative study in chemistry of microbially and electrochemically induced pitting of 316L stainless steel

Ennoblement of stainless steel (SS) by microbially deposited manganese oxides can lead to pitting corrosion at low chloride concentrations, causing unexpected material failures. We exposed 316L SS to manganese oxidizing bacteria Leptothrix discophora under well-defined laboratory conditions, and then placed the ennobled coupons in a 0.5 M sodium chloride solution until pitting developed. Using time-of-flight secondary ion mass spectroscopy we demonstrated that the pits and their immediate vicinity associated with microbial influenced corrosion had different chemical signatures than those associated with electrochemically induced pitting, suggesting a possibility that the microorganisms were directly involved in pit initiation. Based on the differences in the chemical signatures we were able to distinguish the microbially induced pits from those induced by anodic polarization.     

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.     

Magnetoresistance behaviour in CoFe/Cu multilayers: thin Cu layer effect

The magnetoresistance properties of the CoFe/Cu multilayers have been investigated as a function of thin non-magnetic Cu layer thickness (from 2.5 to 0.3 nm). CoFe/Cu multilayers were electrodeposited on Ti substrates from a single electrolyte containing their metal ions under potentiostatic control. The structural analysis of the films was made using X-ray diffraction. The peaks appeared at 2θ ≈ 44°, 51°, 74° and 90° are the main Bragg peaks of the multilayers, arising from the (111), (200), (220) and (311) planes of the face-centered cubic structure, respectively. The magnetic characterization was performed by using vibration sample magnetometer in magnetic fields up to ±1600 kA/m. At 0.6, 1.2 and 2.0 nm Cu layer thicknesses, the high saturation magnetization values were observed due to antiferromagnetic coupling of adjacent magnetic layers. Magnetoresistance measurements were carried out using the Van der Pauw method in magnetic fields up to ±1000 kA/m at room temperature. All multilayers exhibited giant magnetoresistance (GMR), and the similar trend in GMR values and GMR field sensitivity was observed depending on the Cu layer thickness.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks

Wireless Personal Communications - Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS)....