Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ROLE OF COMMUNITY BASED TOURISM BASED ON LOCAL WISDOM USING ONLINE MEDIA

This research aims to analyze the role of community based tourism based on local wisdom in ecotourism management using online media. The type of this research is qualitative research that relies on the ecotourism manager as participants involved in research. The data is collected through intensive interview using in-depth interview, cross check the data field with the secondary data, and photographs. The research result explained that community based tourism based on local wisdom using online media has an important role in the success of ecotourism management in order to increase the local and foreign tourists.
Denoising-Based Multiscale Feature Fusion for Remote Sensing Image Captioning

With the benefits from deep learning technology, generating captions for remote sensing images has become achievable, and great progress has been made in this field in the recent years. However, a large-scale variation of remote sensing images, which would lead to errors or omissions in feature extraction, still limits the further improvement of caption quality. To address this problem, we propose a denoising-based multi-scale feature fusion (DMSFF) mechanism for remote sensing image captioning in this letter. The proposed DMSFF mechanism aggregates multiscale features with the denoising operation at the stage of visual feature extraction. It can help the encoder–decoder framework, which is widely used in image captioning, to obtain the denoising multiscale feature representation. In experiments, we apply the proposed DMSFF in the encoder–decoder framework and perform the comparative experiments on two public remote sensing image captioning data sets including UC Merced (UCM)-captions and Sydney-captions. The experimental results demonstrate the effectiveness of our method.
Super Resolution for Light Field Images

Light field cameras are an interesting technology that has proven useful for applications including post-capture refocusing and single exposure 3D scene capture. However, their popularity has been limited due in part to the tradeoff between angular and spatial resolution that is present in light field images. To address this trade-off, this paper explores how super resolution methods may be applied to light field images. By exploiting the aliasing of the scene present in the light field sub-aperture images, these images can be combined into a single view higher resolution image using traditional super resolution techniques. In this paper, gradient descent is used to reconstruct high resolution images based on a light field image formation model. Results are presented for reconstruction of simulated light field data.
Effects of Local Thermotherapy on Chest Pain in Patients with Acute Coronary Syndrome: A Clinical Trial

Background: The chest pain is a very common complaint among patients with acute coronary syndrome (ACS). The local thermotherapy can reduce or relieve the heart pain by suppressing metabolites as pain mediators. Objectives: This study aimed to determine the effect of local thermotherapy on the chest pain in patients with ACS. Methods: This clinical trial was conducted on 78 patients with ACS hospitalized in the ICU of Golestan Hospital of Ahvaz in 2016. Based on the sample size and inclusion criteria, and by a convenience sampling, the eligible patients were enrolled and randomized into two equal-sized groups, namely control and intervention, each containing 39 participants. During the local thermotherapy sessions, the patients received local heat by using a hot pack warmed to 50°C for five days. The control group only received the routine treatment. Data collection instruments included a demographic questionnaire and the Numerical Rating Scale (NRS). The NRS was completed by the participants before the intervention and five days after it. Data were analyzed using the descriptive tests, correlation coefficient, independent t-test, Chi-square, and logistic regression in SPSS 20. Results: The mean pain severity in the case group before the intervention was 3.22 ± 0.86 and after the intervention, it decreased to 2.61 ± 0.7, indicating the effect of local thermotherapy. However, it was not statistically significant (P > 0.05). There was no significant relationship between age, gender, diabetes, hypertension, and hyperlipidemia, and the pain severity in this study (P > 0.05). Logistic regression analysis showed that women experienced more pain than men did. Conclusions: This study suggested the slight effectiveness of local thermotherapy in reducing the severity of pain in these patients. As a result, further studies are recommended.
Korean Journal for Food Science of Animal Resources

Characterization and utilization of the bioactive compounds from natural resources is one of the most concerns to maintain quality properties of foods, especially to prevent the oxidation of lipids in meat products. Phytochemical components and antioxidant activities of Cudrania tricuspidata (CT) leaves extracted using various solvents and their effects on physicochemical properties of pork patties during refrigerated storage were measured. The combined solvents of 80% ethanol, 80% methanol and pure double-distilled (dd)-water obtained the higher total phenolic compounds, flavonoids content, and antioxidant activities as compared to the pure solvent alone. Among the individual antioxidant components, catechin was the predominant polyphenol in CT leaves in all extracts. The addition of CT leaves extracts into pork patties showed high antioxidant activities since thiobarbituric acid-reactive substances (TBARS) values of added CT extracts were lower than those of the control (p<0.05). In conclusion, CT leaf phytochemical components displayed antioxidant activity that varied with the extract solvent used. CT extracts were superior to control in retarding lipid oxidation of pork patties, which was evident as reduced TBARS and peroxide values (POV).
Zentrum für Europäische Integrationsforschung Center for European Integration Studies

In this paper, the demand for real money M1, M2, and M3 is estimated for Austria. The modelling takes place within the framework of a small vector autoregression. To estimate the demand for money, two-equation error-correction models are constructed, which contain the short-run dynamics and the long-run economic equilibrium. It is found that a stable money demand exists for all monetary aggregates. The long-run equilibrium of M1, after accounting for a structural break in 1979, can be characterised as a classical type of money demand, with no interest rate effects and a unity elasticity of real GDP. In the case of M2 and M3, we find a unit coefficient on income and a significantly negative influence of a long-term interest rate. The statistical properties of the estimated short-run money demand equations – considering insample and out-of-sample (35 observations) tests – are generally very good.
ASSESSING THE DISTRIBUTION OF COURSE CONCEPTS IN LEARNING MATERIALS

This paper describes a simple scheme to assess the distribution of course concepts in a learning material. The scheme uses relations in the WordNet lexical knowledge-base as the basis for four proposed metrics; size, usability, complexity and cohesion. The metrics are formed according to the existing ontology metrics, and they are for educators to comparably assess the distribution of course concepts in learning materials. Using this scheme, educators could author learning materials that covers course concepts without constructing concept maps and without the involvement of domain experts. Inexperienced educators could ensure that their learning materials which are obtained from the Web meet their courses objectives. We conducted a quantitative study to determine the feasibility of the scheme to assess the distribution of concepts in an Information System course. The metrics was computed on a three-page case study provided by the Information Systems Audit and Control Association (ISACA). Then, the learning material was evaluated by 55 undergraduates at the end of a 36-contacthours course. Both results from the metrics computation and the learners’ perception are agreeable in term of the size, usability, and cohesion.
Reuse of Neural Modules for General Video Game Playing

A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.
Complex Multiple Support Vector Machine Regression for Frequency Selective MIMO-OFDM System

This paper proposes a new scheme to track the frequency selective time variant channel induced by multipath fading wireless Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system under high mobility conditions in the presence of Gaussian and non-Gaussian impulsive noise interfering with reference signals. The estimation of the channel is performed by using a nonlinear channel estimator based on a complex Multiple Support Vector Machine Regression (M-SVR) which is developed and applied to MIMO Long Term Evolution (LTE) Downlink with Vertical-Bell Labs Advanced Space Time (V-BLAST) detection algorithm. The obtained results confirm the effectiveness of the proposed technique to track the fading channel under high mobility conditions (350 Km/h) in the presence of different nonlinearities.
THE CHOICE OF PARAMETERS FOR WELDING OF STEEL S355NL

Research work is focused on welding of fine grained nomalised steel S355NL with high thickness 50 mm for crane construction. Double sided V-groove butt weld done by GMAW with filler OK Autrod 12.60. Welding parameters and cycle were designed according to norm EN 1011-2. Carbon equivalent, parameter U.C. S. served to set preheat and interpass temperature. No weld heat treatment was used. Destructive and non-destructive testing served to certify used welding process.
Wireless embedded systems: time, location, and applications

Wireless embedded systems combine sensors and actuators with process-ing, storage and communication capabilities. Networks of small, low-power wireless devices, so called nodes, oﬀer the potential to collect observations of the physical world at unprecedented ﬁdelity and scale. Time and location are of fundamental importance in the context of wireless sensor networks. Accurate time and location information are crucial for many tasks, e.g., sensor data fusion, spatiotemporal coordination, low-power operation, and wireless medium access. In the ﬁrst part of this dissertation, we study the problem of time synchronization . As clock sources in wireless embedded systems often exhibit severe drift , and message exchange is subject to a delay , sophisticated algorithms are mandatory to keep clocks in synchronization. Existing time synchronization algorithms are designed to provide network-wide synchronization, i.e., between arbitrary nodes in the network. However, as we demonstrate in this dissertation, large synchronization errors may become visible with increas-ing distance from a reference node, but also close-by nodes in a network may be synchronized poorly. To address these issues, we propose two clock synchronization protocols: the Gradient Time Synchronization Protocol to achieve local synchronization, and the PulseSync protocol for global synchronization. We evaluate the performance of both protocols by simulations and experiments in diﬀerent wireless sensor network testbeds. The second part of this thesis is dedicated to the question how we can provide accurate location information in the context of wireless embedded systems. We present the SpiderBat platform, which provides node localization for sensor nodes using ultrasound pulses. By employing multiple transmitters and receivers, we can estimate the distance and angle between neighboring nodes. Using both distance and angle information allows to localize nodes with an accuracy of a few centimeters, even in sparse networks where other approaches fail. In the third part, we discuss design considerations for the architecture of future wireless embedded systems, as they have evolved from pure research tools to ubiquitous smart things. To this end, we present a resource-oriented approach to facilitate the connection between heterogeneous devices and ser-vices, and a novel wearable sensor platform that utilizes mobile phones for personalized sensing applications.
Detection of Lattice-points inside Triangular Mesh for Variable-viscosity Model of Red Blood Cells

: In this work we introduce the extension of an existing computational model for red blood cells that enables modelling of different viscosity inside and outside the cells. The extension is based on an algorithm for detection of ﬂuid lattice-points inside the cell given the membrane of the cell as a closed triangular mesh. This algorithm enables the setting of variable viscosity in the underlying lattice-Boltzmann method for computation of ﬂuid dynamics.
Quantifying Information Content in Multispectral Remote-Sensing Images Based on Image Transforms and Geostatistical Modelling

Quantifying information content in remote-sensing images is fundamental for information-theoretic characterization of remote sensing information processes, with the images being usually information sources. Information-theoretic methods, being complementary to conventional statistical methods, enable images and their derivatives to be described and analyzed in terms of information as defined in information theory rather than data per se. However, accurately quantifying images’ information content is nontrivial, as information redundancy due to spectral and spatial dependence needs to be properly handled. There has been little systematic research on this, hampering wide applications of information theory. This paper seeks to fill this important research niche by proposing a strategy for quantifying information content in multispectral images based on information theory, geostatistics, and image transformations, by which interband spectral dependence, intraband spatial dependence, and additive noise inherent to multispectral images are effectively dealt with. Specifically, to handle spectral dependence, independent component analysis (ICA) is performed to transform a multispectral image into one with statistically independent image bands (not spectral bands of the original image). The ICA-transformed image is further normal-transformed to facilitate computation of information content based on entropy formulas for Gaussian distributions. Normal transform facilitates straightforward incorporation of spatial dependence in entropy computation for the aforementioned double-transformed image bands with inter-pixel spatial correlation modeled via variograms. Experiments were undertaken using Landsat ETM+ and TM image subsets featuring different dominant land cover types (i.e., built-up, agricultural, and hilly). The experimental results confirm that the proposed methods provide more objective estimates of information content than otherwise when spectral dependence, spatial dependence, or non-normality is not accommodated properly. The differences in information content between image subsets obtained with ETM+ and TM were found to be about 3.6 bits/pixel, indicating the former’s greater information content. The proposed methods can be adapted for information-theoretic analyses of remote sensing information processes.
Comparison of Gadolinium Contrast Agent Retention in Patients Receiving Multiple Contrast-enhanced MRI Exams

: Gadolinium-based contrast agents have long been utilized in magnetic resonance imaging (MRI) to enhance image quality. Aside from the few reported cases of Nephrogenic Systemic Fibrosis in patients with severely compromised renal function, these contrast agents have generally been viewed as safe. However, recent studies have shown evidence of the retention of potentially toxic gadolinium well beyond the previously recognized clearing times in patients with normal renal function. This retention has been shown via persistent hyper-intense signal in certain brain regions in unenhanced MRI exams. The exact form of retained gadolinium and its long-term potential health effects remain unknown at this time. Due to concerns over retained gadolinium, our hospital switched to a more stably bound contrast agent in the spring of 2018. This study examined brain MRI images from patients with multiple contrast-enhanced exams using either the older, more unstable, linear agent, and the newer, more stable, macrocyclic agent. Signal intensities were measured in the globus pallidus and dentate nucleus; regions of the brain that have previously been shown to accumulate heavy metals such as gadolinium. Statistically significant increases in signal intensity were seen in the dentate nucleus in the linear contrast agent group, but not in the macrocyclic agent group. No significant signal increases were seen with either agent in the globus pallidus region of the brain. No correlation was seen between signal increase and the volume of contrast agent administered for either region or contrast agent.
An Empirical Model to Estimate Abundance of Nanophase Metallic Iron (npFe0) in Lunar Soils

Lunar soils gradually become mature when they are exposed to a space environment, and nanophase metallic iron (npFe0) generates within them. npFe0 significantly changes the optical properties of lunar soils and affects the interpretation of the remotely sensed data of the lunar surface. In this study, a correlation analysis was conducted between npFe0 abundance and reflectance spectra at short wavelengths for lunar soil samples in four size groups based on their spectral and compositional data, collected by the Lunar Soil Characterization Consortium (LSCC). Results show that 540 nm single scattering albedo (SSA) of lunar soils correlates well with their corresponding npFe0 abundance for each size group of lunar soil samples. However, it is poorly correlated with npFe0 abundance when all size groups were considered because of the strong interference from grain size variation of lunar soils. To minimize the effect of grain size, the correlation of npFe0 abundance with the spectral ratio of 540 nm/810 nm SSA of all size groups for LSCC samples was calculated and results show that a higher correlation existed between them (R2 = 0.91). This ratio can serve as a simple empirical model for estimating npFe0 abundance in lunar soils. However, bias could be introduced to the estimation result when lunar soils possess a high content of agglutinitic glass and ilmenite. Our future work will focus on improving the model’s performance for these lunar soils.
Satellite-Observed Variations and Trends in Carbon Monoxide over Asia and Their Sensitivities to Biomass Burning

As the carbon monoxide (CO) total column over Asia is among the highest in the world, it is important to characterize its variations in space and time. Using Measurements of Pollution in the Troposphere (MOPITT) and Atmospheric InfraRed Sounder (AIRS) satellite data, the variations and trends in CO total column over Asia and its seven subregions during 2003–2017 are investigated in this study. The CO total column in Asia is higher in spring and winter than in summer and autumn. The seasonal maximum and minimum are in spring and summer respectively in the regional mean over Asia, varying between land and oceans, as well as among the subregions. The CO total column in Asia shows strong interannual variation, with a regional mean coefficient of variation of 5.8% in MOPITT data. From 2003 to 2017, the annual mean of CO total column over Asia decreased significantly at a rate of (0.58 ± 0.15)% per year (or −(0.11 ± 0.03) × 1017 molecules cm−2 per year) in MOPITT data, resulting from significant CO decreases in winter, summer, and spring. In most of the subregions, significant decreasing trends in CO total column are also observed, more obviously over areas with high CO total column, including eastern regions of China and the Sichuan Basin. The regional decreasing trends in these areas are over 1% per year. Over the entire Asia, and in fire-prone subregions including South Siberia, Indo-China Peninsula, and Indonesia, we found significant correlations between the MOPITT CO total column and the fire counts from the Moderate Resolution Imaging Spectroradiometer (MODIS). The variations in MODIS fire counts may explain 58%, 60%, 36%, and 71% of the interannual variation in CO total column in Asia and these three subregions, respectively. Over different land cover types, the variations in biomass burning may explain 62%, 52%, and 31% of the interannual variation in CO total column, respectively, over the forest, grassland, and shrubland in Asia. Extremes in CO total column in Asia can be largely explained by the extreme fire events, such as the fires over Siberia in 2003 and 2012 and over Indonesia in 2006 and 2015. The significant decreasing trends in MODIS fire counts inside and outside Asia suggest that global biomass burning may be a driver for the decreasing trend in CO total column in Asia, especially in spring. In general, the variations and trends in CO total column over Asia detected by AIRS are similar to but smaller than those by MOPITT. The two datasets show similar spatial and temporal variations in CO total column over Asia, with correlation coefficients of 0.86–0.98 in the annual means. This study shows that the interannual variation in atmospheric CO in Asia is sensitive to biomass burning, while the decreasing trend in atmospheric CO over Asia coincides with the decreasing trend in MODIS fire counts from 2003 to 2017.