Controlling Argan Seed Quality by NIR

The suitability of using visible/near infrared spectroscopy (Vis/NIR), as a rapid and non‐destructive technique for monitoring the quality of argan seeds (Argania spinosa Skeels) was studied. The analyzed parameters were the fatty acid composition of argan seed oil, seed moisture content, seed oil content and oil stability index (OSI). The ratio between major unsaturated and saturated fatty acids (U/S) during the oxidation assay at constant temperature was studied. Values from infrared drying were used as a laboratory reference for the moisture. Argan seed oil content was determined by Soxhlet extraction. A fatty acid analysis was carried out by gas chromatography and the OSI was determined by the Rancimat test. Predictive models of argan seed moisture, ratio U/S and OSI showed good accuracy. Therefore, Vis/NIR measurements can be used for controlling several argan seed quality parameters. This procedure might be of interest to the argan oil industry, which is currently in the process of modernization and expansion.     

Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

Multimedia Tools and Applications - Skin lesion recognition is one of the most important tasks in dermoscopic image analysis. Current Convolutional Neural Network (CNN) algorithms based recognition...     

Pooling spike neural network for fast rendering in global illumination

Neural Computing and Applications - The generation of photo-realistic images is a major topic in computer graphics. By using the principles of physical light propagation, images that are...     

Multi-scale CNN based on region proposals for efficient breast abnormality recognition

Multimedia Tools and Applications - Mammographic pattern recognition is one of the most essential tasks in breast cancer diagnosis, and has been studied for several years now to make it suitable...     

Enhanced proton transport properties of Nafion via functionalized halloysite nanotubes

The introduction of naturally occurring fibrillary structured halloysite clays (HNTs) into the Nafion matrix resulted in an enhanced proton conductivity and a reduced activation energy for proton transport. To assess the effect of the various states of the HNTs as a membrane additive, the σ_H⁺ of the composite membranes (Nafion/HNTs, Nafion/HNTs-NH₂, Nafion/HNTs-SO₃H) was measured at 30 °C and 80 °C and at different levels of relative humidity (%RH). An overall improvement of the σ_H⁺ of the Nafion was observed in the presence of the nanotubular additives (HNTs or HNTs-NH₂) which can be attributed to the high specific surface area accommodating hydrophilic functional groups and therefore contributing to the water retention/management in the composite membrane. However, the Nafion/HNT-SO₃H composites are distinguished in terms of enhanced performance in σ_H⁺ which persist in the whole range of % RH levels and noted that the σ_H⁺ values are almost 2 times higher than native Nafion in the low humidity region (30%–50%) and at 80 °C. In agreement with the σ_H⁺ enhancement, the activation energy is lower than that of native Nafion indicating that the proton transport is facilitated in the presence of -SO₃H modified HNTs, probably due to an improved connectivity and arrangement of ionic conducting domains.     

Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning

TCP is suboptimal in heterogeneous wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. In this paper, we propose to improve TCP performance in wired/wireless networks by endowing it with a classifier that can distinguish packet loss causes. In contrast to other proposals we do not change TCP’s congestion control nor TCP’s error recovery. A packet loss whose cause is classified as link error will simply be ignored by TCP’s congestion control and recovered as usual, while a packet loss classified as congestion loss will trigger both mechanisms as usual. To build our classification algorithm, a database of pre-classified losses is gathered by simulating a large set of random network conditions, and classification models are automatically built from this database by using supervised learning methods. Several learning algorithms are compared for this task. Our simulations of different scenarios show that adding such a classifier to TCP can improve the throughput of TCP substantially in wired/wireless networks without compromizing TCP-friendliness in both wired and wireless environments.     

Network Virtualization: A Viable Path Towards the Future Internet

New technologies as well as new ways of using network services are rapidly changing the Internet’s landscape. These developments will have far-reaching implications for the architecture of the networks of the future. However, the current Internet design is plagued with a number of fundamental limitations, which makes its use as the sole basis for the networking applications of the future questionable. We believe that the Future Internet must allow the co-existence of diverse network designs and paradigms, both new and old, to remain open to innovation and meet the challenges of the future. In this paper, we propose to use network virtualization, embedded in an architectural framework, to achieve this goal and to lay the foundation for the deployment of novel concepts such as content-centric networking.

Electrochemically deposited poly(3,4-propylenedioxythiophene) for dye-sensitized solar cell

Journal of Applied Electrochemistry - In this work, poly(3,4-propylenedioxythiophene) (PProDOT)-based counter electrode (CE) was elaborated. The PProDOT polymer was electropolymerized by using...     

Correction to: Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

Multimedia Tools and Applications - The Eqs. 5 and 9 in the original publication of this article contained an error. The original article has been corrected.