Trans-symphyseal approach in the treatment of cervico-urethro-vaginal fistulas. Apropos of 2 cases

We opted for a symphysiotomy approach in the management of cervico-urethro-vaginal fistulae in two patients. This approach offers an excellent exposure of the region of the bladder neck. After section of the symphysis pubis with a Gigli's wire saw, the anterior wall of the bladder was taken down to the fistulae and we excised the fistulous tract. Longitudinal closure of the fistulae and the bladder followed. Results were satisfactory and there were no urinary incontinence.     

Arabic isolated word recognition system using hybrid feature extraction techniques and neural network

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg–Marquardt “Trainlm” and the scaled conjugate gradient “Trainscg”. Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.     

Tensile behaviour of superelastic NiTi alloys charged with hydrogen under applied strain

ABSTRACT

The tensile behaviour of NiTi alloys is investigated after hydrogen charging during the austenite, half-transformation and martensite phases. The specimens are charged with different current densities and charging durations. During the tensile tests, the strain of the plateau transformation decreases due to hydrogen-induced residual martensite variants. This decrease becomes important when the charging happens during the martensite phase. Accordingly, the hydrogen ensures the stability of the phase in which the charging process occurs. Moreover, a heightening of transformation stress is noticed during the plateau. The transformation stress increases when the current density grows and the charging duration rises. This occurrence is caused by the interaction between the hydrogen and NiTi structures, where hydrogen delays the NiTi martensite transformation.

This paper is part of a thematic issue on Hydrogen in Metallic Alloys     

Effect of extraction systems and cultivar on the quality of virgin olive oils

This research was carried out to compare the effects of two industrial scale processes (super press and triple phase systems) and a traditional Tunisian family-sized operation with laboratory milling for two varieties of olives: Chemlali and Chemchali. Analyses of the effect of the extraction system, on the oils' characteristics, revealed statistically significant differences in some parameters, mainly in antioxidant compounds ( P  < 0.05). Major fatty acids presented significant differences ( P  < 0.05) with respect to the cultivar kind (55.93% vs. 72.21%). Oxidative stability of Chemchali was nearly two times higher than Chemlali (94.58 vs. 50.37 day kg⁻¹). Results appear to confirm the general consensus that Chemchali oils are of much higher quality compared with the tolerable quality of Chemlali . The traditional method is a valid form of producing high quality oil with very high stability (48.87 and 109.07 day kg⁻¹ for Chemlali and Chemchali oil, respectively). However, industrial mills showed that triple-phase scale could cause considerable loss in polyphenols. Finally, olive oil quality is mainly dependant on the quality of the olives and on the extraction method.     

New linewidth enhancement determination method in semiconductor lasers based on spectrum analysis above and below threshold

Linewidth measurements of singlemode and multimode InGaAsP lasers were made above and below threshold with adapted interferometers. Linewidth against inverse power plots Delta nu =f(1/P), show different Schawlow-Townes slopes above and below threshold. Differences have been observed depending on singlemode or multimode laser behaviour. In the singlemode case the linewidth enhancement factor has been determined ( alpha =2.6) by extrapolating and correlating results above and below threshold.<>     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.