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

A multi-sector multi-region economic growth model of drought and the value of water: A case study in Pakistan

This study integrates ecohydrological vegetation and multi-sector multi-region economic growth models to evaluate the impacts of drought on markets and value the economic value of water. The values of several parameters of the agricultural production function are identified by applying leaf area indices that are simulated by the ecohydrological model, AgriCLVDAS. The three-sector three-region closed-economy model with the agricultural production functions of both irrigable and rainfed farmland as well as the stochastic process of precipitation and availability of river water are formulated to analyze the water rent as well as GDP growth in Pakistan under drought stress. According to the characteristics of the closed-economy model, the crop price is increased during drought periods because of the price hike in water (i.e., an increase in the marginal productivity of water, which is double that in high-water periods in Pakistan). The study further presents a way of investigating water resource management policies by applying comparative dynamics

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

Stock Performance, Sector’s Nature and Macroeconomic Environment

The existing literature on stock performance has focused on the viability of asset pricing theories, macroeconomic and microeconomic variations, and institutional disparities. Yet, whether any additional factors influence SP (Stock Performance) remains unanswered. To address this question, the study aims to provide fresh insights into industry factors concerning firm stock performance. The study adds to the existing research literature by focusing on these issues in the context of a developing economy. Data from 80 organizations were evaluated using a multiple regression model for 12 years to study the problem. The findings back up the importance of sector nature in stock performance. According to the results, company size, munificence, and HHI negatively link with financial performance, but growth, GDP, exchange rate, money supply, and oil prices have a positive link. The findings can help firms and individual investors better understand the factors that influence share prices, allowing them to assess their investment options better. Other financial institutions can provide better advice and products to investors seeking funding to finance share purchases

Surface acoustic waves induced micropatterning of cells in gelatin methacryloyl (GelMA) hydrogels

Acoustic force patterning is an emerging technology that provides a platform to control the spatial location of cells in a rapid, accurate, yet contactless manner. However, very few studies have been reported on the usage of acoustic force patterning for the rapid arrangement of biological objects, such as cells, in a three-dimensional (3D) environment. In this study, we report on a bio-acoustic force patterning technique, which uses surface acoustic waves (SAWs) for the rapid arrangement of cells within an extracellular matrix-based hydrogel such as gelatin methacryloyl (GelMA). A proof-of-principle was achieved through both simulations and experiments based on the in-house fabricated piezoelectric SAW transducers, which enabled us to explore the effects of various parameters on the performance of the built construct. The SAWs were applied in a fashion that generated standing SAWs (SSAWs) on the substrate, the energy of which subsequently was transferred into the gel, creating a rapid, and contactless alignment of the cells (<10 s, based on the experimental conditions). Following ultraviolet radiation induced photo-crosslinking of the cell encapsulated GelMA pre-polymer solution, the patterned cardiac cells readily spread after alignment in the GelMA hydrogel and demonstrated beating activity in 5-7 days. The described acoustic force assembly method can be utilized not only to control the spatial distribution of the cells inside a 3D construct, but can also preserve the viability and functionality of the patterned cells (e.g. beating rates of cardiac cells). This platform can be potentially employed in a diverse range of applications, whether it is for tissue engineering, in vitro cell studies, or creating 3D biomimetic tissue structures

Design Methods for Artificial Intelligence Fairness and Transparency

Fairness and transparency in artificial intelligence (AI) continue to become more prevalent as topics for research, design and development. General principles and guidelines for designing ethical and responsible AI systems have been proposed, yet there is a lack of design methods for these kinds of systems. In this paper, we present CoFAIR, a novel method to design user interfaces for exploring fairness, consisting of series of co-design workshops, and wider evaluation. This method can be readily applied in practice by researchers, designers and developers to create responsible and ethical AI systems

Lactobacillus rhamnosus GG-supplemented formula expands butyrate-producing bacterial strains in food allergic infants.

Dietary intervention with extensively hydrolyzed casein formula supplemented with Lactobacillus rhamnosus GG (EHCF+LGG) accelerates tolerance acquisition in infants with cow's milk allergy (CMA). We examined whether this effect is attributable, at least in part, to an influence on the gut microbiota. Fecal samples from healthy controls (n=20) and from CMA infants (n=19) before and after treatment with EHCF with (n=12) and without (n=7) supplementation with LGG were compared by 16S rRNA-based operational taxonomic unit clustering and oligotyping. Differential feature selection and generalized linear model fitting revealed that the CMA infants have a diverse gut microbial community structure dominated by Lachnospiraceae (20.5±9.7%) and Ruminococcaceae (16.2±9.1%). Blautia, Roseburia and Coprococcus were significantly enriched following treatment with EHCF and LGG, but only one genus, Oscillospira, was significantly different between infants that became tolerant and those that remained allergic. However, most tolerant infants showed a significant increase in fecal butyrate levels, and those taxa that were significantly enriched in these samples, Blautia and Roseburia, exhibited specific strain-level demarcations between tolerant and allergic infants. Our data suggest that EHCF+LGG promotes tolerance in infants with CMA, in part, by influencing the strain-level bacterial community structure of the infant gut