Miracle-dz/UniFAR-FTUs huggingface dataset:
Staying Healthy: Industry organisations' influence on behaviours and services used by fishers

This study emerged in response to a previous report that used a strength-based approach to identify best-practice good-health behaviours in a number of farming and fishing communities, titled Staying Healthy: Behaviours and services used by farmers and fishers. This earlier report found there was a need for specific attention to the physical and particularly the mental health of those in the Australian commercial fishing industry. This included a better understanding of the issues, logistical and social barriers to good-health practices, and avenues of health communication and treatment. The 2013 Staying Healthy study echoed the Rural Industries Research and Development Corporation (RIRDC) research and development Plan for 2008-2012, Collaborative Partnership for Farming and Fishing Health and Safety, which advocated research into health, well-being and resilience in rural primary industries. These reports are coupled with widespread anecdotal accounts describing very poor physical and mental health within the fishing industry, including accounts of suicide and attempted suicide. As a contributor to a body of scholarly research, this study is supported by other literature, which is addressed, below. In this short section we introduce research that is most relevant to appreciating the findings of this study on fisher health and well-being.
Material handling optimization in warehousing operations

Distribution and warehousing activities are important pillars to an effective supply chain. They ensure the regulation of the operational flow and the synchronization of all actors in the network. Hence, distribution centers (DCs) act as crossover points between the supply, the production and the demand. The distribution includes a wide range of activities to ensure the integrity of the demand satisfaction. These activities range from the reception and storage of finished or semi-finished products to the preparation of orders and delivery. Distribution has been long seen as an operation with no or low added value; this has changed, and nowadays it is perceived as one of the critical areas for improvement. These activities are responsible for the satisfaction of an evolving market, requiring ever faster and more reliable delivery times, exact orders and highly customized products. This leads to an increased research interest on operations management focused on warehousing. For several years, we have witnessed strong advances in warehousing and order picking operations. The order picking activity is the process of retrieving items within the storage locations for the purpose of fulfilling orders. This problem has long been solved as a variant of the travelling salesman problem, where the order picker moves through aisles. However, modern warehouses with more and more product families may have special characteristics that make conventional methods irrelevant or inefficient. The first part of this thesis presents two practical and challenging material handling problems for the order picking within DCs. Since there are many research axes in the field of warehousing operations, we concentrated our efforts on the order picking problem and the repositioning of the products within the picking area. The order picking problem has been intensively studied in the literature. Our research widens the spectrum of this problem by including a set of characteristics associated with the physical facilities of the picking area and characteristics of the product, such as its weight, volume, category, fragility, etc. This means that a more applied perspective on the reality of operations is used in our algorithms development. The order picking workload is strongly influenced by the positioning of the products. The position of products within the picking area is determined by a storage assignment strategy. Many of these strategies use product sales information in order to facilitate access to the most popular items. In today’s competitive environment, the profitable lifetime of a product can be
. (2020). Computer Vision for Rapid Updating of the Highway Asset Inventory. Transportation Research Record.

In this paper, a decision support system is proposed to assist an analyst in updating the highway roadside asset inventory. The feasibility of the system is tested with assets along an eight kilometre section of the A27 highway on the south coast of England, UK. Survey data from a vehicle equipped with a single forward-facing camera and a GPS-enabled inertial measurement unit (IMU), aerial imagery of the highway, and the asset inventory is fused to develop the system. The camera on the vehicle is calibrated, so that assets may be automatically located within the survey images. The assets are then classiﬁed by a state of the art convolutional neural network. Therefore, those assets recorded correctly in the inventory, and those needing further manual inspection are automatically identiﬁed. Three different asset types are considered (trafﬁc signs, matrix signs and reference marker posts) and overall 91% of the assets in a withheld test-set are veriﬁed automatically. Thus the analyst is presented with a much smaller set of assets for which the inventory is incorrect and which require further inspection. We therefore demonstrate the value in fusing multiple data sources to develop decision support systems for transportation asset monitoring.
Contribution to the modelisation and failure prognosis in a synchrone permanent magnet motor

The core of the work is to build an accurate model of the electrical machine where the prognostic technique is applied. In this thesis we started by a literature review on hybrid electric vehicles (HEV), the different types of electrical machine used in HEV’s and the different types of faults that may occur in those electrical machine. We also identify the useful monitoring parameters that are beneficial for those different types of faults. Then, a survey is presented where all the prognostic techniques that can be applied on this application are enumerated. The electromagnetic, thermal and vibration finite element model (FEM) of the permanent magnet machine is presented. The model is built at healthy operation and when a fault is integrated. The considered types of faults are:demagnetization, turn to turn short circuit and eccentricity. A confrontation between analytical and FEM (numerical method) for electromagnetic machine modeling is illustrated. Fault indicators where useful measured parameters forfault identification are recognized and useful features from the measured parameters are extracted; torque, temperature and vibration signal are elaborated for healthy and faulty states. The strategy of the adopted prognostic approach which is Hidden Markov Model (HMM) is explained. The technical aspect of the method is presented and the prognostic model is formulated. HMM is applied to detect and localize small scale fault small scale faults were where a systematic strategy is developed. The aging of the machine’s equipment,specially the sensitive ones that are the stator coil’s and the permanent magnet, is a very important matter for RUL calculation. An estimation strategy for RUL calculation is presented and discussed for those mentioned machine’s components. Closed loop configuration is very important; it is adopted by all available vehicle systems. Hence, the same previously mentioned steps are applied for a closed loop configuration too. A global model where the input of the machine’s FEM comes from the modeled inverter is built.
A double cascaded framework based on 3D SEAU-Net for kidney and kidney tumor Segmentation

Accurate segmentation of kidney and kidney tumor from CT-volumes is vital to many clinical endpoints, such as differential diagnosis, prognosis and radiation therapy planning. While manual segmentation is subjective and time-consuming, fully automated extraction is quite imperative and challenging due to intrinsic heterogeneity of tumor structures. To address this problem, we propose a double cascaded framework based on 3D SEAU-Net to hierarchically and successively segment the subregions of the target. This double cascaded framework is used to decompose the complex task of multi-class segmentation into two simpler binary segmentation tasks. That is to say, the region of interest (ROI) including kidney and kidney tumor is trained and extracted in the first step, and the pre-trained weights are used as the initial weights of the network that is to segment the kidney tumor in second step. Our proposed network, 3D SEAU-Net, integrates residual network, dilated convolution, squeeze-and-excitation network and attention mechanism to improve segmentation performance. To speed training and improve network generalization, we take advantage of transfer learning (i.e., weight transfer) in the whole training phase. Meanwhile, we use 3D fully connected conditional random field to refine the result in post-processing phase. Eventually, our proposed segmentation method is evaluated on KiTS 2019 dataset and experimental results achieves mean dice scores 93.51% for the whole kidney and tumor, 92.42% for kidney and 74.34% for tumor on the training data.
CLINICAL STUDIES Left and Right Atrial Volume by Freehand Three-dimensional Echocardiography: In Vivo Validation Using Magnetic Resonance Imaging

Introduction: Measurement of left and right atrial size is important for the management of arrhythmias, valvular and congenital heart disease. We have demonstrated that freehand three-dimensional (3D) echocardiography is more accurate and reproducible than two-dimensional (2D) echocardiography for measurement of left ventricular mass and volume. However, no prior study has validated the accuracy of freehand 3D for the determination of left or right atrial volume Methods: End-systolic (maximum) left and right atrial volumes were determined in 21 volunteer patients and normal subjects by one, two, and freehand 3D transthoracic echocardiography and compared to volumes obtained by gradient recalled magnetic resonance imaging. Three-dimensional echocardiographic determination of atrial volume was obtained using an acoustic spatial locator, a line-of-intersection display, and a surface reconstruction algorithm. Two-dimensional echocardiographic atrial volumes were obtained from apical biplane images of the left atrium and an apical single plane image of the right atrium using a summation of disks method. Onedimensional (1D) estimates of left atrial volume were determined by cubing the M-mode 1D antero-posterior dimension obtained on the parasternal long axis view.
Diagnosis and Therapeutic Management of Lead Toxicosis in Falcons in Saudi Arabia

Abstract A total of 3251 falcons were examined at the Falcon Medical Research Hospital of the Fahad bin Sultan Falcon Center, Riyadh, Kingdom of Saudi Arabia, between September 1999 and March 2001. Of this total, 96 falcons (2.9%), including 78 saker (Falco cherrug), 12 peregrine (Falco peregrinus), and 6 lanner (Falco biarmicus) falcons, were admitted for treatment of lead toxicosis. Lead toxicosis was diagnosed by evaluating the anamnesis, clinical signs, radiographic findings, and blood lead concentrations as measured by an electrochemical analysis system that reported blood lead concentrations within several minutes. Thirty-four (35.4%) of the falcons treated had radiographic evidence of lead pellets or fragments in the gastrointestinal tract. In all cases with lead pellets present in the ventriculus, the blood lead concentration was >65 μg/dl (0.65 ppm). However, in 62 (64.5%) of the treated falcons, no radiographic evidence was found of lead pellets or fragments in the ventriculus. The blood lead concentrations in these cases ranged from 25.5 to >65 μg/dl (0.255–>0.65 ppm). Calcium disodium ethylenediaminetetracetate (Ca EDTA) was administered (50 mg/kg, undiluted, IM q12h) for 2–23 consecutive days, depending on the bird's blood lead concentration. The mean reduction in blood lead concentration after treatment was 83.9% (range = 72.6–94.2%, n = 22). Birds treated with Ca EDTA for as many as 23 consecutive days did not demonstrate any deleterious effects.
SENTINEL-1 IMAGE CLASSIFICATION FOR CITY EXTRACTION BASED ON THE SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS

Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.
A Modified Comprehensive Learning Particle Swarm Optimizer

To overcome premature convergence and falling into local optima of particle swarm optimization (PSO), a comprehensive learning particle swarm optimizer (CLPSO) has been proposed. However, it is not good at solving unimodal problems. In this paper, we propose a modified CLPSO (MCLPSO) with three improvements. Firstly, we use opposition-based learning (OBL) to improve the initial population. Secondly, we add the best solution of the population to the list of selected particles in order to improve the convergence ability while maintaining the population diversity. Finally, we use the mean velocity of the population with a linearly decreasing probability to update the particle velocity to further improve the performance of CLPSO. The MCLPSO algorithm is tested on CEC2005 in 30 dimensions. Furthermore, the MCLPSO is conducted to solve hydrothermal scheduling problems. The experimental results demonstrate that the solution accuracy of MCLPSO is overall better than those of comparison algorithms.
Simultaneous Localization and Mapping Solutions Using Monocular and Stereo Visual Sensors with Baseline Scaling System

In this paper, SLAM systems are introduced using monocular and stereo visual sensors. The SLAM solutions are implemented in both indoor and outdoor. The SLAM samples have been taken in different modes, such as a straight line that enables us to measure the drift, in addition to the loop sample that is used to test the loop closure and its corresponding trajectory deformation. In order to verify the trajectory scale, a baseline method has been used. In addition, a ground truth has been captured for both indoor and outdoor samples to measure the biases and drifts caused by the SLAM solution. Both monocular and stereo SLAM data have been captured with the same visual sensors which in the stereo situation had a baseline of 20.00 cm. It has been shown that, the stereo SLAM localization results are 75% higher precision than the monocular SLAM solution. In addition, the indoor results of the monocular SLAM are more precise than the outdoor. However, the outdoor results of the stereo SLAM are more precise than the indoor results by 30%, which is a result of the small stereo baseline cameras. In the vertical SLAM localization component, the stereo SLAM generally shows 60% higher precision than the monocular SLAM results.
Statistical Thinking from Scratch

In virtually every field, researchers find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires much more than the rote application of traditional statistical methods. This book will train researchers to be creative and confident users of statistics by thinking hard about the application of simple methods to a small dataset. In particular, this book focuses on simple linear regression—a method with strong connections to the most important tools in applied statistics—using it as a detailed case study for teaching resampling-based, likelihood-based, and Bayesian approaches to statistical inference. This exercise imparts an idea of how statistical procedures are designed and implemented, a flavor for the philosophical positions one implicitly assumes when applying statistics, and an opportunity to probe the strengths and weaknesses of one’s statistical approach. Key to the book’s novel approach is its mathematical level, which is gentler than most texts for statisticians but more rigorous than most introductory texts for non-statisticians. Statistical Thinking from Scratch is suitable for senior undergraduate and beginning graduate students, professional researchers, and practitioners seeking to improve their understanding of statistical methods across the natural and social sciences, medicine, psychology, public health, business, and other fields.
Design implications for adaptive augmented reality based interactive learning environment for improved concept comprehension in engineering paradigms

ABSTRACT Augmented reality (AR) has tremendous potential as a teaching and learning tool in engineering education to enhance students’ learning experience; it influences the students’ spatial ability for real-time visualization. Furthermore, this helps to attain better concept comprehension pertaining to improved understanding of the topics. The present study provides evidence by developing an AR learning environment (ARLE) suitable for complicated theoretical topics of electronics engineering, which otherwise cannot be demonstrated using practical experiments. The idea of using different design variants; such as mobile and table-top for adaptive AR is also implemented. For this, 60 undergraduate students of electronics and electrical engineering were introduced to the ARLE system, in two different case studies. The first case validates the efficacy of using AR for learning the concept of stability in linear control systems through a questionnaire based survey where students reported the ARLE as an effective learning system for theoretical topics. Second case study provides comparative analysis for usability of two design variants of the ARLE in terms of manipulability and comprehensibility. Finally, the design implications for developed ARLE are discussed, which may be helpful for other researchers in creating learning environments for different courses of engineering education.
LFSR-based generation of boundary-functional broadside tests

This study considers the compression of a type of close-to-functional broadside tests called boundary-functional broadside tests when the on-chip decompression logic consists of a linear-feedback shift register (LFSR). Boundary-functional broadside tests maintain functional operation conditions on a set of lines (called a boundary) in a circuit. This limits the deviations from functional operation conditions by ensuring that they do not propagate across the boundary. Functional vectors for the boundary are obtained from functional broadside tests. Seeds for the LFSR are generated directly from functional boundary vectors without generating tests or test cubes. Considering the tests that the LFSR produces, the seed generation procedure attempts to obtain the lowest possible Hamming distance between their boundary vectors and functional boundary vectors. It considers multiple LFSRs with increasing lengths to achieve test data compression. The procedure is structured to explore the trade-off between the level of test data compression and the Hamming distances or the proximity to functional operation conditions.
MCMC for a hyperbolic Bayesian inverse problem in motorway traffic flow

We study the LWR model: a hyperbolic conservation law used to model traffic flow on motorways. This is an old model dating back to the 1950s, but has been shown to be robust and is parametrised by the so-called Fundamental Diagram (FD) which provides the relationship between flow and density. We consider the boundary conditions as nuisance parameters to be estimated but neglect the initial conditions as their effect on data is quickly washed out. // The data we use to estimate the parameters in the model is MIDAS data on a section of motorway that does not include any on/off ramps, thus conforming with the nature of the model as a conservation law. Little statistically sound work has been done so far on this inverse problem to estimate the FD parameters as well as the boundary conditions. // We consider two families of FDs, Del Castillo’s FD and the exponential FD – which have 4 and 2 parameters respectively – and perform inference for these along with the boundary conditions. We assume as prior that the boundary conditions follow a log Ornstein Uhlenbeck process which corresponds surprisingly well to practitioners’ prior belief. // We use standard MCMC methods (Gibbs, RWMH, parallel tempering, functional preconditioned RWMH) to sample from the posterior distribution. For some models, the posterior is highly correlated, multimodal and non-Gaussian, so we introduce novel proposals and find that while these are underpinned by clear intuition and show great promise in preliminary studies, they do not seem to appreciably accelerate mixing judging from the studies carried out so far.
The effects of manipulating graphical properties of LineSets on user comprehension

The aim of this research is to establish whether manipulating graphical properties of LineSets affects user performance. LineSets are a technique for visualizing networks of data items which lie in overlapping sets. Information about sets is represented by drawing one line for each set over an existing network of data items. Despite a wealth of knowledge of perceptual and cognitive theories being readily available, little work has been done to understand how manipulating graphical properties of LineSets affects user performance. In turn, little guidance is available to visualization designers for effectively visualising data for different types of tasks. Therefore, to draw more effective visualizations, an understanding of the impact of altering graphical properties on task performance is needed. There are six graphical choices to which humans are known to be perceptually sensitive: size, value, texture, colour, orientation and shape. The effectiveness of these choices is dependent on the type of information that is conveyed and task to be performed. As a result, it seems important to bridge the gap between the graphical choices that are made and the tasks that users perform. This would allow for more effective visualizations to be drawn if they can be optimised for a type of task. To determine the effect of graphical manipulations in LineSets, a series of empirical experiments that measured participant task completion time and accuracy were performed. Our first experiment, which investigated the effect of the set-line colour on user performance, established that applying unique colour hues to the set-lines was the most effective colour treatment regardless of the type of task being performed. The effects of varying the thickness of the set-lines, when the thickness reflects the number of items in a given set, was then evaluated. The results suggested that varying set-line thickness improved task performance. Similar results were found in a further experiment where we varied the diameter of the nodes to reflect the number of incident edges. A final experiment to evaluate the impact of these changes as a whole, comparing automatically generated LineSets to those thought to be improved by our results, provides evident that adopting our graphical choices significantly improves performance. Therefore, our contributions give insight to both designers and users of set visualisations into which graphical choices to make when conveying information in order to generate diagrams that can be more accurately and quickly interpreted.