Hanging In, Stepping up and Stepping Out: Livelihood Aspirations and Strategies of the Poor Development in Practice

In recent years understanding of poverty and of ways in which people escape from or fall into poverty has become more holistic. This should improve the capabilities of policy analysts and others working to reduce poverty, but it also makes analysis more complex. This paper describes a simple schema which integrates multidimensional, multilevel and dynamic understandings of poverty, of poor people’s livelihoods, and of changing roles of agricultural systems. The paper suggests three broad types of strategy pursued by poor people: ‘hanging in’; ‘stepping up’; and ‘stepping out’. This simple schema explicitly recognises the dynamic aspirations of poor people; diversity among them; and livelihood diversification. It also brings together aspirations of poor people with wider sectoral, inter-sectoral and macro-economic questions about policies necessary for realisation of those aspirations

Faulty wind farm simulation: An estimation/control-oriented model

A large percentage of the electricity generation cost of wind turbines (WTs) is related to the operation and maintenance of the WTs. To reduce such costs, fault detection and isolation (FDI) and fault tolerant control (FTC) methods have become popular over the last decade, but most works focus on single WTs or WT subsystems. The present paper introduces a Simulink-based simulator able to simulate WT farms with the capacity to recreate different fault scenarios on the subsystems composing the WTs. The objective of the simulator is to be used by researchers to develop FDI and FTC strategies for wind farms. By way of example, the paper shows a case study illustrating the effects that different faults have on a wind farm

T-violation in Kμ3K_{\mu3} decay in a general two-Higgs doublet model

We calculate the transverse muon polarization in the $K^+_{\mu3}$ process arising from the Yukawa couplings of charged Higgs boson in a general two-Higgs doublet model where spontaneous violation of CP is presentComment: 6 pages, latex, accepted for publication in Phys. Rev.

A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.Versión del edito

Automatic Detection of Nephrops norvegicus Burrows in Underwater Images Using Deep Learning

Autonomous Underwater Vehicles and Remotely Operated Vehicles equipped with HD cameras are used by the scientist to capture the underwater footages efficiently and accurately. The abundance of the Norway Lobster Nephrops norvegicus stock in the Gulf of Cadiz is assessed based on the identification and counting of the burrows where they live, using underwater videos. The Instituto Espa˜ nol de Oceanograf´ıa (IEO) conducts an annual standard underwater television survey (UWTV) to generate burrow density estimates of Nephrops within a defined area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the experts. This is quite hectic and time consuming job. Computer Vision and Deep learning plays a vital role now a days in detection and classification of objects. The proposed system introduces a deep learning based automated way to identify and classify the Nephrops burrows. The proposed work is using current state of the art Faster RCNN models Inception v2 and MobileNet v2 for objects detection and classification. Tensorflow is used to evaluate the Inception and MobileNet performance with different numbers of training images. The average mean precision of Inception is more than 75% as compared to MobileNet which is 64%. The results show the comparison of Inception and MobileNet detections, as well as the calculation of True Positive and False Positive detections along with undetected burrows.Universidad de Málaga, IEEE, Sir SYED University Karachi-Pakistán, Mehran University Jamshoro-Pakistán, Riphah International Universit

Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) andMarine Institute Ireland (MIIreland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine experts. This is quite a time-consuming job. As a solution, we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision. The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows. This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification. We conduct experiments on two data sets, namely, the Smalls Nephrops survey (FU 22) and Cadiz Nephrops survey (FU 30), collected by Marine Institute Ireland and Spanish Oceanographic Institute, respectively. From the results, we observe that the Inception model achieved a higher precision and recall rate than theMobileNetmodel. The best mean Average Precision (mAP) recorded by the Inception model is 81.61% compared toMobileNet, which achieves the best mAP of 75.12%.Versión del edito

Titulos de letras del doctor Gabriel Antonio Madridano y Nava, canonigo penitenciario de la Santa Iglesia de Calahorra, Colegial y rector ... de dicho Colegio Mayor y Universidad y cathedratico de ... artes

Título tomado de la portadillaTexto fechado en Alcalá de Henares el 28 de julio de 1746, certificado por Luis de Haro y Cisneros, secretario de la Universida

Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization

Peer reviewedPostprin