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.     

Evaluation of antioxidant level and protein oxidation of rainbow trout (Oncorhynchus mykiss) fillets during rigor and post-rigor

In this study, the relationship between stunning techniques and protein oxidation which are accepted as the main cause of food spoilage was investigated. For this purpose, the antioxidant status, in vivo myofibrillary protein (MP) oxidation and sensitivity, and postmortem oxidation (inducted with hydroxyl radical system) of rainbow trout (Oncorhynchus mykiss) fillets killed by hitting to head (T1), neck crushing (T2), and convulsion (T3) methods, were investigated. Statistically significant differences (p < .05) were found among all parameters examined and it was observed that the most stressing technique was the convulsion method. It was determined that in protein profiles myosin were influenced too much from stunning technique and in actin observed oxidation-induced reductions. Reductions in S-S and S-H were also found to be increased in carbonyl concentrations, but the most effective values in both processes were determined by the convulsive technique. Our results show that short-term stunning techniques (hitting to head, neck crushing) give better meat quality results in terms of O. mykiss welfare and low MP oxidation rates. In general, we can say that T3 group fillets are more sensitive to oxidative damage, while T1 and T2 groups give better results in maintaining meat quality with low MP oxidation rates.     

Photoelectrocatalytic humic acid degradation kinetics and effect of pH,applied potential and inorganic ions

This study addresses the removal of humic acid (HA) dissolved in an aqueous medium by a photoelectrocatalytic process. UV₂₅₄ removal and the degradation of color (Vis₄₀₀) followed pseudo‐first order kinetics. Rate constants were 1.1 × 10^?1 min^?1, 8.3 × 10^?2 min^?1 and 2.49 × 10^?2 min^?1 (R² > 0.97) for UV₂₅₄ degradation and 1.7 × 10^?1 min^?1, 6.5 × 10^?2 min^?1 and 2.0 × 10^?2 min^?1 for color removal from 5 mg dm^?3, 10 mg dm^?3 and 25 mg dm^?3 HA respectively. Following a 2 h irradiation time, 96% of the color, 98% of the humic acid and 85% of the total organic carbon (TOC) was removed from an initial 25 mg dm^?3 HA solution in the photoanode cell. Photocatalytic removal on the same photoanode was also studied in order to compare the two methods of degradation. Results showed that the photoelectrocatalytic method was much more effective than the photocatalytic method especially at high pH values and with respect to UV₂₅₄ removal. The effect of other important reaction variables, eg pH, external potential and electrolyte concentration, on the photoelectrocatalytic HA degradation was also studied. Copyright © 2003 Society of Chemical Industry     

Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.     

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.     

Biodegradable Microparticles for Simultaneous Detection of Counterfeit and Deteriorated Edible Products

In an era of globalized trade relations where food and pharmaceutical products cross borders effortlessly, consumers face counterfeit and deteriorated products at elevated rates. This paper presents multifunctional, biodegradable hydrogel microparticles that can provide information on the authenticity and the potential deterioration of the tagged food or pharmaceutical formulations. These microparticles integrate spatially patterned authenticity code with two sensors—the first one detects possible presence of pathogenic microbes through monitoring pH while the second one identifies products stored above optimal temperatures via optical monitoring of the microparticle degradation. Particles are synthesized from a biocompatible polymer and a photoinitiator, dextran modified with 2‐hydroxyethylmethacrylate and riboflavin, respectively, using a continuous, high throughput method stop‐flow lithography. The proposed synthesis approach also enables crosslinking with visible light bringing about additional flexibility to flow lithography. Model liquid and solid food and pharmaceutical products are successfully labeled with microparticles and the functionality of the sensors in aqueous solutions is demonstrated.     

Silver Nanoflower Decorated Graphene Oxide Sponges for Highly Sensitive Variable Stiffness Stress Sensors

Soft conductive materials should enable large deformation while keeping high electrical conductivity and elasticity. The graphene oxide (GO)‐based sponge is a potential candidate to endow large deformation. However, it typically exhibits low conductivity and elasticity. Here, the highly conductive and elastic sponge composed of GO, flower‐shaped silver nanoparticles (AgNFs), and polyimide (GO‐AgNF‐PI sponge) are demonstrated. The average pore size and porosity are 114 µm and 94.7%, respectively. Ag NFs have thin petals (8–20 nm) protruding out of the surface of a spherical bud (300–350 nm) significantly enhancing the specific surface area (2.83 m² g^?1). The electrical conductivity (0.306 S m^?1 at 0% strain) of the GO‐AgNF‐PI sponge is increased by more than an order of magnitude with the addition of Ag NFs. A nearly perfect elasticity is obtained over a wide compressive strain range (0–90%). The strain‐dependent, nonlinear variation of Young's modulus of the sponge provides a unique opportunity as a variable stiffness stress sensor that operates over a wide stress range (0–10 kPa) with a high maximum sensitivity (0.572 kPa^?1). It allows grasping of a soft rose and a hard bottle, with the minimal object deformation, when attached on the finger of a robot gripper.