Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of martian dust aerosol with mars science laboratory rover engineering cameras.

Planetary atmospheres other than that of Earth provide natural laboratories to test our theories and models for climate studies and can help to identify the physical processes involved in the behaviour and evolution of a planet? s climate. Mars has always played a predominant role in comparative studies with Earth. Extensive efforts placed in the robotic exploration of Mars have retrieved large amount of data its atmosphere. Dust aerosol is the main driver of Mars? atmospheric variability, and the determination of the particles? properties is of high relevance for estimating its climate forcing. In particular, the angular distribution of sky brightness can be evaluated to retrieve valuable information regarding the physical properties of the aerosol particles. In this study we show that images retrieved by the Mars Science Laboratory (MSL) engineering cameras (Navcam and Hazcam) can be used to constrain the size and shape of dust aerosol particles, and to derive the column dust optical depth. A radiative transfer based iterative retrieval method was implemented in order to determine the aerosol modelling parameters that best reproduce the sky radiance as a function of the scattering angle observed by MSL engineering cameras. Results show an overall good agreement with previous studies and have contributed to extend the available data and to parameterise dust phase functions. The tools and procedures developed during this research can be implemented for the analysis of retrievals from future Mars exploration missions.
Analysis of Feature Tracking Methods for Vision-Based Vibration Damping of Flexible Link Robots

—Computer vision is often used in robotics where image-based feature detection is an important part. The obtained features can be exploited, e.g. for path planning, process monitoring or feedback control. In this paper the focus is on vision-based vibration damping of robots with ﬂexible links. The measured values for control are obtained by extracting image features. The required image processing framerate depends on the link dynamics. Image processing in general is a computationally expensive task since the complexity for pixel operations is of order O ( n 2 ) . Efﬁcient algorithms for online feature tracking have to be used. In an experiment, image processing is performed on a low cost computer and results regarding the computational time are presented. The feature detection performance is validated by results of the vision-based vibration damping control.
An LSTM model for cloze-style machine comprehension

Machine comprehension is concerned with teaching machines to answer reading comprehension questions. In this paper we adopt an LSTM-based model we designed earlier for textual entailment and propose two new models for cloze-style machine comprehension. In our ﬁrst model, we treat the document as a premise and the question as a hypothesis, and use an LSTM with attention mechanisms to match the question with the document. This LSTM remembers the best answer token found in the document while processing the question. Furthermore, we observe some special properties of machine comprehension and propose a two-layer LSTM model. In this model, we treat the question as a premise and use LSTMs to match each sentence in the document with the question. We further chain up the ﬁnal states of these LSTMs using another LSTM in order to aggregate the results. When evaluated on the commonly used CNN/Daily Mail dataset, both of our models are quite competitive compared with the state of the art, and the second two-layer model outperforms the ﬁrst model. a-LSTMs of the We compare this with the average gate values of the other
Machine Learning in Python for Weather Forecast based on Freely Available Weather Data

Forecasting weather conditions is important for, e.g., operation of hydro power plants and for flood management. Mechanistic models are known to be computationally demanding. Hence, it is of interest to develop models that can predict weather conditions faster than traditional meteorological models. The field of machine learning has received much interest from the scientific community. Due to its applicability in a variety of fields, it is of interest to study whether an artificial neural network can be a good candidate for prediction of weather conditions in combination with large data sets. The availability of meteorological data from multiple online sources is an advantage. In order to simplify the retrieval of data, a Python API to read meteorological data has been developed, and ANN models have been developed using TensorFlow.
Predictive risk modelling of hospital emergency readmission, and temporal comorbidity index modelling using machine learning methods

This thesis considers applications of machine learning techniques in hospital emergency readmission and comorbidity risk problems, using healthcare administrative data. The aim is to introduce generic and robust solution approaches that can be applied to different healthcare settings. Existing solution methods and techniques of predictive risk modelling of hospital emergency readmission and comorbidity risk modelling are reviewed. Several modelling approaches, including Logistic Regression, Bayes Point Machine, Random Forest and Deep Neural Network are considered. Firstly, a framework is proposed for pre-processing hospital administrative data, including data preparation, feature generation and feature selection. Then, the Ensemble Risk Modelling of Hospital Readmission (ERMER) is presented, which is a generative ensemble risk model of hospital readmission model. After that, the Temporal-Comorbidity Adjusted Risk of Emergency Readmission (T-CARER) is presented for identifying very sick comorbid patients. A Random Forest and a Deep Neural Network are used to model risks of temporal comorbidity, operations and complications of patients using the T-CARER. The computational results and benchmarking are presented using real data from Hospital Episode Statistics (HES) with several samples across a ten-year period. The models select features from a large pool of generated features, add temporal dimensions into the models and provide highly accurate and precise models of problems with complex structures. The performances of all the models have been evaluated across different timeframes, sub-populations and samples, as well as previous models.
FUZYE: A Fuzzy ${c}$ -Means Analog IC Yield Optimization Using Evolutionary-Based Algorithms

This paper presents fuzzy <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the genetic algorithm (GA). MC analysis is the most general and reliable technique for yield estimation, yet the considerable amount of time it requires has discouraged its adoption in population-based optimization tools. The proposed methodology reduces the total number of MC simulations that are required, since, at each GA generation, the population is clustered using a fuzzy <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-means (FCMs) technique, and, only the representative individual (RI) from each cluster is subject to MC simulations. This paper shows that the yield for the rest of the population can be estimated based on the membership degree of FCM and RIs yield values alone. This new method was applied on two real circuit-sizing optimization problems and the obtained results were compared to the exhaustive approach, where all individuals of the population are subject to MC analysis. The FCM approach presents a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. Moreover, a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-means-based clustering algorithm was also tested and compared with the proposed FUZYE, with the latest showing an improvement up to 13% in yield estimation accuracy.
Analyzing the impact of news trends on research publications and scientific collaboration networks

Scientific collaboration plays a vital role in generating novel ideas and innovative research progress among the researchers. Similarly, news diffusion also has an important role among the research communities. Though the collaboration networks have made an impact in scientific activities and attracted the attention of scientific communities, no work so far analyzed the cause which can determine the future research publications. The objective of this paper is to study influence of news trends on scientific collaboration and research publications. For this purpose, we have collected the top technological news trends and applied the LDA model to identify top research keywords from the articles. The results show that the news trends play a significant role on scientific collaborations and innovative research progress. It is found that the researchers identify their research gap, make future collaborations and does interdisciplinary research. Our results highlight the important role of diffusion of news, which influence the young researchers to generate novel ideas and tend to collaborate more with different scientific communities.
PROVIDING A NEW APPROACH TO ASSESSING TOURISM SUITABILITY IN IRAN (CASE STUDY: BADAREH CITY, ILAM PROVINCE, ZAGROS REGION)

Tourism suitability in Iran is evaluated using a systematic model in the form of two macro scales (extensive and intensive). The purpose of this study was to introduce a new approach in this regard based on the capabilities of all components of tourism land-uses and in accordance with new techniques and methods of assessment. Evaluation criteria were distributed in two general and specific groups using fuzzy logic. General criteria were involved in the evaluation of all tourist landuses, but specific criteria were specific to the evaluation of a particular land-use. The weight of land-use evaluation criteria, the weight of general criteria relative to the specific criteria as well as the weight of land-uses were determined in assessing the total tourism suitability of the region based on the ANP and Delphi techniques. Finally, the capability of each tourism specific land-use and the total tourism capabilities of the region were assessed based on the layers of specific and general criteria using the WLC linear approach, and then, along with other land-uses of the region, was explored using the MOLA exploratory method. The area allocated to five land-uses of climbing, picnic and camping, kite and gliders, rock climbing and snowboarding were 61.8, 2, 4.2 and 4.3 and 4 percent, respectively. The value for visiting archaeological and cultural assets and the average total value of tourism in the region were determined to be 178.5 and 47 (in the fuzzy range of 0-255), respectively. Providing accurate assessment of tourism suitability using the cluster structure based on their respective land-use capabilities and through the use of digital multi-factor evaluation techniques with fuzzy logic and ANP weighting technique are the benefits of this new approach. * Corresponding author Providing a New Approach to Assessing Tourism Suitability in Iran (Case Study: Badareh City, Ilam Province, Zagros Region) 781 Words: Analytic Network Process, Evaluation Model, Fuzzy Logic, Tourism, Weighted Linear Combination
The Method of Train Positioning Based on Digital Track Map Matching

A train positioning method based on GPS and digital rail line matching is proposed. Firstly, the digital track line is generated based on the fitting and interpolation algorithm of train track line. And then the GPS data are corrected by the track line positioning correction method, and the more accurate position estimation of the train is obtained. Finally, the data track line is simulated and analyzed with some measured data from Harbin to Qigihar track line. The analysis results show that cubic spline curve is better than cubic B-spline curve on the establishment of digital track map.
Aerodynamic Design Optimization of a Locomotive Nose Fairing for Reducing Drag

Aerodynamic Design Optimization of a Locomotive Nose Fairing for Reducing Drag Chad Lamar Stucki Department of Mechanical Engineering, BYU Master of Science Rising fuel cost has motivated increased fuel efficiency for freight trains. At cruising speed, the largest contributing factor to the fuel consumption is aerodynamic drag. As a result of stagnation and flow separation on and around lead and trailing cars, the first and last railcars experience greater drag than intermediate cars. Accordingly, this work focused on reducing drag on lead locomotives by designing and optimizing an add-on nose fairing that is feasible for industrial operation. The fairing shape design was performed via computational fluid dynamic (CFD) software. The simulations consisted of two in-line freight locomotives, a stretch of rails on a raised subgrade, a computational domain, and a unique fairing geometry that was attached to the lead locomotive in each non-baseline case. Relative motion was simulated by fixing the train and translating the rails, subgrade, and ground at a constant velocity. An equivalent uniform inlet velocity was applied at zero degree yaw to simulate relative motion between the air and the train. Five fairing families—Fairing Families A–E (FFA–FFE)—are presented in this thesis. Multidimensional regressions are created for each family to approximate drag as a function of the design variables. Thus, railroad companies may choose an alternative fairing if the recommended fairing does not meet their needs and still have a performance estimate. The regression for FFE is used as a surrogate model in a surrogate based optimization. Results from a wind tunnel test and from CFD are reported on an FFE geometry to validate the CFD model. The wind tunnel test predicts a nominal drag reduction of 16%, and the CFD model predicts a reduction of 17%. A qualitative analysis is performed on the simulations containing the baseline locomotive, the optimal fairings from FFA–FFC, and the hybrid child and parent geometries from FFA & FFC. The analysis reveals that optimal performance is achieved for a narrow geometry from FFC because suction behind the fairing is greatly reduced. Similarly, the analysis reveals that concave geometries boost the flow over the top leading edge of the locomotive, thus eliminating a vortex upstream of the windshields. As a result, concave geometries yield greater reductions in drag. The design variable definitions for each family were strategically selected to improve manufacturability, operational safety, and aerodynamic performance relative to the previous families. As a result, the optimal geometry from FFE is believed to most completely satisfy the constraints of the design problem and should be given the most consideration for application in the railroad industry. The CFD solution for this particular geometry suggests a nominal drag reduction of 17% on the lead locomotive in an industrial freight train.
PROLONGING STORAGE AND SHELF LIFE OF “ANNA” APPLE FRUITS BY USING CHITOSAN AND SOME NATURAL ANTIOXCIDANTS

This study was carried out during two successive seasons of 2016 and 2017 in a private orchard located in Abo Hommos district, Beheira Governorate, Egypt to evaluate the effect of some postharvest treatments included: 2% (W/V) chitosan, 200 ppm citric acid, 200 ppm ascorbic acid, 2 mmol/l salicylic acid, chitosan plus citric acid, chitosan plus ascorbic acid and finally chitosan plus salicylic acid, all at the abovementioned concentrations on decay, firmness as well as other quality parameters of “Anna” apple fruits during cold storage at 2±1C with 9095% RH for three months and after poststorage shelf life at ambient temperature for five days. The results revealed that fruit decay, fresh weight loss, electrolyte leakage, total sugars, total soluble solids (TSS) as well as anthocyanin and carotene contents were increased, while firmness, vitamin C and acidity were decreased with the progress of cold storage and poststorage shelf life periods. Furthermore, the results indicated that all applied treatments reduced the incidence of fruit decay and fruit fresh weight loss. Furthermore, postharvest treatments retarded the loss of fruit firmness, lessened electrolyte leakage of fruits and were more effective on keeping the physical and chemical quality properties relative to the control treatment. Finally, this study emphasized that the addition of chitosan to antioxidant acids was more effective than the sole applications of each component on mitigating the incidence of decay, retarding the loss of firmness, reducing weight loss and electrolyte leakage beside keeping other physical and chemical characteristics, thus prolonging the storage and poststorage shelf life of “Anna” apples, especially the combination included chitosan and salicylic acid. Therefore, this strategy is easy to apply, new, cheap and safe that can ensure increases of “Anna” apple fruits marketability and the growers income.
NAIVE BAYES CLASSIFIER FOR WORD SENSE DISAMBIGUATION OF PUNJABI LANGUAGE

Word Sense Disambiguation (WSD) is the process of identifying the correct sense of the word in the context. The most leading scheme used by WSD is machine learning approach, where a human expert provides examples of correctly disambiguated words, and a machine learning algorithm is used to induce a model from these examples. In this paper, Naive Bayes supervised classifier has been used to disambiguate words of Punjabi language. The feature extraction process plays a vital role in building the supervised machine learning models. For the proposed Punjabi WSD system, Bag of Words (BoW) and collocation models are used separately to extract relevant features. BoW model has used all words around target word while collocation model has used two words before and two words after the target word as features. Both the models have used a common training data set to build the model. It has been observed that the selection of smoothing parameter for Naive Bayes has a significant impact on its performance. This proposed work has been tested on 150 most ambiguous noun words selected form Punjabi WordNet having 6 or more senses. During the process of building the model, fine senses of ambiguous words have been merged to produce coarse sense on the basis of manual analysis of lexical relations of WordNet. The accuracy of the proposed system has been calculated independently for BoW and collocation model. The proposed WSD system achieves an accuracy of 89% for BoW model and 81% for collocation model. It has been concluded that BoW model performs better than the collocation model for WSD task for Punjabi language.
Views and Attitudes of Local Farmers towards Planting, Growing and Managing Trees in Agroforestry System in Basona Worena District, Ethiopia

Agroforestry is a dynamic ecological-based natural resources management practice that integrates food crops, trees, and livestock in an agricultural landscape where the components may have ecological, economical, and social impacts. This study aimed at examining the views and attitudes of local farmers towards ‘planting, growing, and managing trees in agroforestry system’ in Basona Worena District, Ethiopia. As there was already a traditional agroforestry adopted by the farmers, the study kebele was purposely selected. A structured questionnaire was developed by accounting different socioeconomic variables. A total of 94 respondents were randomly contacted to collect the questionnaire data. Multiple linear regression technique was employed to analyze and interpret the data. Contrary to expectation, the findings showed that a greater proportion (about 60%) of the respondents did not practice agroforestry. However, those farmers who used to practice traditional agroforestry mainly exercised it to meet their household wood demands. Based on their economic values, the most commonly preferred tree species planted and grown by the farmers in descending order, included eucalyptus species, Acacia abyssinica, Croton macrostachyus, Sesbania sesban, Cupressus lusitanica, and Olea africana. The results also suggested that the farmers had conscious perceptions to determine the negative and positive effects of tree species on food crops, soils, and water. Generally, a greater proportion of the farmers strongly agreed (73.33%) that they had developed positive attitudes towards ‘planting, growing, and managing trees in agroforestry system’. Moreover, the multiple linear regression models showed that different socioeconomic variables significantly influenced the attitudes of farmers towards ‘planting, growing, and managing trees in agroforestry system’. Generally, the regression model explained about 36% of the variance in attitudes of the respondents towards ‘planting, growing, and managing trees’. Hence, providing improved agroforestry extension services, including incentives (e.g., seeds, tree seedlings, technical supports, and credits) may help increase the active participation of the farmers to adopt plant, grow, and manage trees in agroforestry system. Moreover, provision of adequate technical trainings on agroforestry technologies and establishing demonstration site is indispensable to increase the awareness level of the local farmers and thereby promote the adoption of agroforestry technologies in Basona Worena District and elsewhere.
Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management

A site-scale integrated decision support tool (i-DSTss) is developed for selection and sizing of stormwater Best Management Practices (BMPs). The tool has several component modules—hydrology, BMP selection, BMP sizing, and life-cycle cost analysis (LCCA)—integrated into a single platform. The hydrology module predicts runoff from small catchment on event and continuous basis using the Green-Ampt and Curve Number methods. The module predicted runoff from a small residential area and a parking lot with R2 value of 0.77 and 0.74, respectively. The BMP selection module recommends a BMP type appropriate for a site based on economic, technical, social and environmental criteria using a multi-criteria optimization approach. The BMP sizing module includes sizing options for green roofs, infiltration-based BMPs, and storage-based BMPs. A mass balance approach is implemented for all types of BMPs. The tool predicted outflow rates from a permeable pavement with R2 value of 0.89. A cost module is included where capital, operation and maintenance, and rehabilitation costs are estimated based on BMP size obtained from the sizing module. The i-DSTss is built on an accessible platform (Microsoft Excel VBA) and can be operated with a basic skillset. The i-DSTss is intended for designers, regulators, and municipalities for quick analysis of scenarios involving interaction among several factors.
A novel gradient based method for RGB-D human action recognition

Most action recognition approaches proposed over the years that were designed for RGB sequences cannot utilize the rich 3D-structural information to reduce large intra-class variations. This paper addresses the issue of handling sole depth structural information for accurate human action recognition. It presents and evaluates the saliency based P-DmHOG features for human action representation. Saliency based P-DmHOG features are depth features inspired by the well-known HOG descriptor. Good results namely 96.78%, 97.78% and 93.13% are achieved on three public benchmark datasets: MSR Actions 3D, MSR Action Pairs 3D, and MSR Daily Activity 3D, which show the efficiency of proposed method to spatio-temporal and shape information.