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.     

An experimental evaluation of visual similarity for HDR images

In this paper, we investigate visual similarity for high dynamic range (HDR) images. We collect crowdsourcing data through a web-based experimental interface, in which the participants are asked to choose one of the two candidate images as being more similar to the query image. Triplets forming the query-and-candidates sets are obtained by random sampling from existing HDR data sets. Experimental control factors include choice of tone mapping operator (TMO), choice of distance metric, and choice of image feature. The image features that we experiment with are chosen from the features that are commonly used in the usual low dynamic range setting including features learned via Convolutional Neural Networks. The set of image features also includes combined features where the combination coefficients are estimated using logistic regression. We compute correlations between human judgments and quantitative features to understand how much each feature contributes to visual similarity. Combined features yield nearly 84% agreement with human judgments when applied on tone mapped images. Though we observed that using common features directly on raw or linearly scaled HDR images yield subpar correlation estimates compared to using them on tone mapped HDR images, we did not observe significant effect due to the choice of TMO on the estimates. As an application, we propose an improvement to style-based tone mapping for more correctly imparting desired styles to HDR images with different characteristics.

     

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.     

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.     

Evaluation of a university website’s usability for visually impaired students

Today websites are the tools most commonly used to access information. People with disabilities face difficulties accessing or using information, and the importance of website usability in their lives needs to be recognized. Visually impaired students need to be able to use university websites that inform them about the opportunities and events taking place on campus. This study aims to evaluate the usability of a university website by visually impaired students. In this research, six visually impaired students were interviewed. The assistive technologies they use, as well as the various web pages they wished to use unaided were identified. Following data collection, usability tests were conducted and satisfaction surveys were completed. The usability test was done with five visually impaired students. They were asked to think aloud while performing 11 tasks involving their university’s web pages, including the main page and the pages of student affairs, library and departments and then to accomplish these tasks. In this test, five tasks were not successfully completed by all students. According to the test results, finding final exam dates on the academic calendar posed major difficulties, and accessing the course schedule web page was the task that required the most time. The test results indicated the need for a search engine on each page, a text version for all pages, rearrangement of the web link sequences with tabs and more information about visuals. Suggestions related to the visually impaired students’ needs were offered.