A Comparison of Electric‐Field‐Driven and Pressure‐Driven Fiber Generation Methods for Drug Delivery

Polymeric fibers are prepared by using electric field driven fiber production technology—electrospinning and pressure driven fiber production technology—pressurized gyration. Fibers of four different polymers: polyvinylidene fluoride (PVDF), poly(methyl methacrylate (PMMA), poly(N‐isopropylacrylamide), and polyvinylpyridine (PVP), are spun by both techniques and differences are analyzed for their suitability as drug carriers. The diameters of electrospun fibers are larger in some cases (PVDF and PMMA), producing fibers with lower surface area. Pressurized gyration allows for a higher rate of fiber production. Additionally, drug‐loaded PVP fibers are prepared by using two poorly water‐soluble drugs (Amphotericin B and Itraconazole). In vitro dissolution studies show differences in release rate between the two types of fibers. Drug‐loaded gyrospun fibers release the drugs faster within 15 min compared to the drug‐loaded electrospun fibers. The findings suggest pressurized gyration is a promising and scalable approach to rapid fiber production for drug delivery when compared to electrospinning.     

Implementing regularly structured neural networks on the DREAMmachine

High-throughput implementations of neural network models are required to transfer the technology from small prototype research problems into large-scale "real-world" applications. The flexibility of these implementations in accommodating for modifications to the neural network computation and structure is of paramount importance. The performance of many implementation methods today is greatly dependent on the density and the interconnection structure of the neural network model being implemented. A principal contribution of this paper is to demonstrate an implementation method which exploits maximum amount of parallelism from neural computation, without enforcing stringent conditions on the neural network interconnection structure, to achieve this high implementation efficiency. We propose a new reconfigurable parallel processing architecture, the Dynamically Reconfigurable Extended Array Multiprocessor (DREAM) machine, and an associated mapping method for implementing neural networks with regular interconnection structures. Details of the system execution rate calculation as a function of the neural network structure are presented. Several example neural network structures are used to demonstrate the efficiency of our mapping method and the DREAM machine architecture on implementing diverse interconnection structures. We show that due to the reconfigurable nature of the DREAM machine, most of the available parallelism of neural networks can be efficiently exploited.     

Multi-focus images fusion via residual generative adversarial network

Multimedia Tools and Applications - Recently, most existing learning-based fusion methods are not fully end-to-end, which still predict the decision map and recover the fused image by the refined...     

Effect of Co-channel Interference on Two-Way Untrustworthy Relay Selection

Wireless Personal Communications - In wireless sensor networks (WSNs), traffic flow congestion can reduce network performance. We propose a congestion control method for WSNs that use a cache state...     

A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework

Students’ interaction and collaboration with the fellows and teachers using the Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of the educational assessment for students’ interaction. As new learning styles develop, new tools and assessment methods are also needed. The focus in this paper is to develop IoT based interaction framework and analysis of the student experience in electronic learning (eLearning) so that the students can take full advantage of the modern interaction technology and their learning can increase to a high level. This setup has a data collection module, which is implemented using Visual C# programming language and computer vision library. The number of faces, number of eyes, and status of eyes are extracted from the video stream, which is taken from a video camera. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to consider student-learning behaviors when assessing electronic learning strategies. The tools are also developed for the data collection on both student and teacher ends. Correlation of data and hidden meaning are extracted to make the learning experience and teaching performance better and adaptable. IoT based infrastructure provides the facilities to fellow students about location awareness, fellows’ accessibility, social behavior and helping hand.     

Simultaneous optimization of repair and control-limit policy in condition-based maintenance

In condition-based maintenance (CBM) planning, collected information from system condition monitoring is the basis of making decision about conducting the maintenance and repair activities. Recently, ample number of studies has been conducted in CBM field especially, in control-limit policy. In control-limit policy, using proportional Hazards model and results of monitoring system condition, one can estimate hazard rate function and its condition’s transition probability matrix. Then, considering replacement costs, optimal control-limit can be determined minimizing the average cost in the long run. The presented model considers repair policy and their implementation cost, and the assumptions of repair during interval inspection is ignored. Then, a model is presented to determine the optimal control-limit and the best repair policy, in which the average total cost per unit time in the long-run, is minimized. At the end, a numerical example is illustrated.     

PLA/metal oxide biocomposites for antimicrobial packaging application

ABSTRACT

The effects of metal oxide addition in polylactic acid (PLA) film were investigated in this study. PLA/Metal Oxide biocomposites were prepared by solvent casting method. The incorporation of metal oxide resulted in no chemical structure change of neat PLA. PLA/ZnO and PLA/MgO exhibited the widest inhibition while PLA/TiO₂ resulted in the smallest inhibition zone. The addition of metal oxides caused fracture and void on the surface of PLA. The addition of metal oxide into PLA films decreased the tensile strength at the concentration higher than 0.1 per hundred resin (phr). Incorporation of metal oxides accelerated the thermal degradation of PLA and tends to increase the biodegradability of PLA.     

A high sensitive MEMS capacitive fingerprint sensor using slotted membrane

In this paper a novel single-chip microelectromechanical systems (MEMS) capacitive fingerprint sensor with slotted membrane is developed to improve the sensitivity. The capacitive sensor consists of a thin, flexible membrane and a rigid back plate with air gap. In this study with making slots in upper electrode to decrease the mechanical stiffness of the membrane, using proportional T-shaped protrusion on diaphragm in order to concentrate the force from finger ridges, making holes in lower electrode to reduce the air damping and using low stress material for diaphragm, we have been succeeded to design a novel MEMS fingerprint sensor with high sensitivity compared with the previous works (Sato et al., IEEE Trans Electron Devices 52:1026–1032, 2005; Damghanian and Majlis, 2008 IEEE International Conference on Semiconductor Electronics (ICSE 2008), pp 634–638 2008). The behaviors of the fingerprint sensor with clamped and slotted membranes are analyzed using the finite element method (FEM). The results yield a sensitivity of 1.44 fF/Mpa for the clamped and 3.22 fF/Mpa for the slotted fingerprint sensor with a 50 × 50 μm² diaphragm. The sensitivity of the slotted structure is increased 2.236 times.