Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between Injury and Spirituality in Pilgrims on the Camino de Santiago

The purpose of this research was to determine whether there is a relationship between spirituality and the likelihood of injury and the impact of that injury on pilgrims walking along the Camino de Santiago. The Camino de Santiago is an ancient, 791 km Christian pilgrimage route along the north of Spain from the French Pyrenees to Santiago de Compostela in Galicia, Spain. A total of 418 participants aged 18-80 were recruited along the Camino, and through social media, from May 15 to August 20, 2017. Participants on the walk completed a ten minute survey with questions pertaining to general demographics, injury and illness experiences, physical well-being and general fitness. Spirituality was assessed using the FACIT-Sp-12 questionnaire. Data was analyzed using Qualtrics, Excel, and Stata platforms. Results show that a participant’s spirituality score was not able to predict whether they would experience an injury or not on the pilgrimage (p=0.498; 95% CI [0.954, 1.023]). However, the data did demonstrate that participants who were more spiritual were better able to cope with an injury or illness while walking the pilgrimage (P<0.05; OR=1.253; 95% CI [1.165, 1.349]). These data suggest that pilgrims who walk the Camino de Santiago for spiritual or religious reasons may feel less impacted by any injuries or illnesses they experience on their journey.
Explorer Functional Dependencies for Graphs

We propose a class of functional dependencies for graphs, referred to as GFDs. GFDs capture both attribute-value dependencies and topological structures of entities, and subsume conditional functional dependencies (CFDs) as a special case. We show that the satisfiability and implication problems for GFDs are coNP-complete and NP-complete, respectively, no worse than their CFD counterparts. We also show that the validation problem for GFDs is coNP-complete. Despite the intractability, we develop parallel scalable algorithms for catching violations of GFDs in large-scale graphs. Using reallife and synthetic data, we experimentally verify that GFDs provide an effective approach to detecting inconsistencies in knowledge and social graphs.
AG: Adaptive Switching Granularity for Load Balancing with Asymmetric Topology in Data Center Network

Modern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipments widely exist in production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make full use of available paths, they are prone to experience packet reordering problem under asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called AG, which adaptively adjusts switching granularity according to the asymmetric degree of multiple paths. AG increases switching granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing switching granularity to obtain high link utilization under small degrees of topology asymmetry. AG is deployed on the switches with negligible overhead, while making no modification on end-hosts. We evaluate AG through both Mininet testbed and large-scale NS2 simulations. The experimental results show that AG reduces the average and 99th flow completion time by up to 51% and 56% over the state-of-the-art load balancing schemes, respectively.
Detection of Narrow Gaps Using Hessian Eigenvalues for Shape Segmentation of a CT Volume of Assembled Parts

Extracting each part of an assembled object from a CT volume is important for analyzing the assembled parts, e.g., evaluating part deformation, measuring the clearance between different parts, and checking the relative positions of the parts. However, separating parts sharing narrow gaps is difficult because the changes in the CT values across narrow gaps are too small to detect using conventional methods, e.g., peak detection of the gradient norms of the CT values. In this study, a method used in detecting a narrow gap whose size is comparable to the voxel-size is proposed. The proposed method is based on peak detection of the Hessian eigenvalues of the CT values. Combining the proposed method with a conventional gradient-based method, we developed a system to segment an assembly composed parts made of the same material.
Antenna allocation and pricing in virtualized massive MIMO networks via Stackelberg game

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while the minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO’s problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations.
METHOD FOR DISTORTION MEASUREMENTS USING PSEUDO-RANDOM SIGNAL

Distortion measurements should give us objective evaluation and comparison of different electroacoustical devices. Modern methods for distortion measurements give us partial information about certain devices, because used test signals don't have characteristics of natural signals – speech and music. Proposed combined audio test signal (CATS) consists of three sawtooth signals, and has characteristics of natural signals. Frequency components of the CATS spectrum are unevenly distributed, so intermodulation components fall between components of the original spectrum at the input of the measured system. Using peak search method intermodulation components can be extracted form original signal, and their comparison can be made.
Tracking momentary attention fluctuations with an EEG-based cognitive brain-machine interface

Momentary fluctuations in attention (perceptual accuracy) correlate with neural 1 activity fluctuations in primate visual areas. Yet, the link between such momentary 2 neural fluctuations and attention state remains to be shown in the human brain. We 3 investigate this link using a real-time cognitive brain machine interface (cBMI) 4 based on steady state visually evoked potentials (SSVEPs): occipital EEG potentials 5 evoked by rhythmically flashing stimuli. Tracking momentary fluctuations in 6 SSVEP power, in real-time, we presented stimuli time-locked to when this power 7 reached (predetermined) high or low thresholds. We observed a significant increase 8 in discrimination accuracy (d′) when stimuli were triggered during high (versus 9 low) SSVEP power epochs, at the location cued for attention. Our results indicate 10 a direct link between attention’s effects on perceptual accuracy and and neural gain 11 in EEG-SSVEP power, in the human brain. 12
SAFETY AND SITTING ASSESSMENT FOR NPPs DEPLOYMENT IN INDONESIA PANDE

SAFETY AND SITTING ASSESSMENT FOR NPPs DEPLOYMENT IN INDONESIA. Study on national economic and energy sources of Indonesia had shown that nuclear energy will be included as part of energy sources in our national energy mix policy in addition to fossil energy source, gas, water and other new and renewable energy. Fukushima accident made people doubt about the safety of Nuclear Power Plant (NPP), which impact on the public perception of the safety of nuclear power plants. Lesson leant from Fukushima accident, the safety of nuclear power plants need to be highlighted back to improve the public acceptance of nuclear safety and security in Indonesia. Preliminary safety assessment and sitting preparing for Large Reactor (LWR), and technology assessment on various technology of SMR has been started. The paper will describe the assessment of safety and radiation consequences on site for normal operation and Design Basis Accident postulation of PWR-1000 MWe in Bangka Island. Consequences of radiation for normal operation simulated for 2 units NPPs. The source term was generated from an inventory of peak radioisotope activities released by using ORIGEN-2 software and the consequence calculated by PC-Cream (routine release) and PC Cosyma (accident release). The adopted methodology used was based on the predominant site-specific meteorological data. The result of calculation are: doses on public for normal operation below 1 mSv/yr, and no evacuation of emergency planning for DBA postulation.
Scotland's Rural College The relationship of dairy farm eco-efficiency with intensification and self-sufficiency. Evidence from the French dairy sector using Life Cycle Analysis, Data Envelopment Analysis and Partial Least Squares Structural Equation Modelling

We aimed at quantifying the extent to which agricultural management practices linked to animal production and land use affect environmental outcomes at a larger scale. Two practices closely linked to farm environmental performance at a larger scale are farming intensity, often resulting in greater off-farm environmental impacts (land, non-renewable energy use etc.) associated with the production of imported inputs (e.g. concentrates, fertilizer); and the degree of self-sufficiency, i.e. the farm’s capacity to produce goods from its own resources, with higher control over nutrient recycling and thus minimization of losses to the environment, often resulting in greater on-farm impacts (eutrophication, acidification etc.). We explored the relationship of these practices with farm environmental performance for 185 French specialized dairy farms. We used Partial Least Squares Structural Equation Modelling to build, and relate, latent variables of environmental performance, intensification and self-sufficiency. Proxy indicators reflected the latent variables for intensification (milk yield/ cow, use of maize silage etc.) and self-sufficiency (home-grown feed/total feed use, on-farm energy/total energy use etc.). Environmental performance was represented by an aggregate ‘eco-efficiency’ score per farm derived from a Data Envelopment Analysis model fed with LCA and farm output data. The dataset was split into two spatially heterogeneous (biophysical conditions, production patterns) regions. For both regions, eco-efficiency was significantly negatively related with milk yield/cow and the use of maize silage and imported concentrates. However, these results might not necessarily hold for intensive yet more selfsufficient farms. This requires further investigation with latent variables for intensification and self-sufficiency that do not largely overlapa modelling challenge that occurred here. We conclude that the environmental ‘sustainability’ of intensive dairy farming depends on PLOS ONE | DOI:10.1371/journal.pone. 0166445 November 10, 2016 1 / 21 a11111
IDCL, International Diagnostic Checklists for ICD-19 and DSM-IV

This package of materials is designed to enable physicians, psychologists and other busy mental health professionals to quickly develop preliminary diagnoses of patients during an initial brief interview. The IDCL Checklists set contains the WHO Symptom Checklist, which helps the busy clinician to make a preliminary classification. Also contained are the 32 International Diagnostic Checklists (IDCL), which take into account the ICD-10 Research Criteria. These checklists can be used to make a careful evaluation of the symptoms and classification criteria, and thus help to arrive at precise diagnoses according to the ICD-10 classification. Also included is the symptom glossary, which provides succinct definitions of the various symptoms that should be evaluated when using the ICD-10 Symptom Checklist. The checklist system provides a virtual 1-2-3 flow chart which helps the clinician consider many possibilities and quickly focus in on a well-supported classification.
pores of Paenibacillus larvae , Ascosphaera apis , Nosema ceranae nd Nosema apis in bee products supervised by the Brazilian Federal nspection Service rica

Due to their ecological and economic importance, honey bees have attracted much scientific attention, which has intensified due to the recent population decline of these insects in the several parts of the world. Among the factors related to these patterns, infection by pathogens are the most relevant, mainly because of the easy dissemination of these microorganisms. Although no zoonotic diseases are associated with these insects, the presence of infectious agents in bee products should still be considered because they play a role as disease dispersers, increasing the risk to animal health. Because of the possibility of dispersion of pathogens via bee products, this work aimed to identify the presence of spores of the pathogens Paenibacillus larvae, Ascosphaera apis and Nosema spp. in samples of honey, pollen and royal jelly that are registered with Brazil’s Federal Inspection Service (S.I.F.) and commercially available in the state of São Paulo. Of the 41 samples of bee products analyzed, only one showed no contamination by any of these pathogens. N. ceranae and P. larvae had the highest prevalence considering all the samples analyzed (present in 87.80% and 85.37% of the total, respectively), with N. apis present in 26.83% and A. apis present in 73.17% of the samples. These results provide support for the formulation of government regulations for sanitary control of exotic diseases by preventing dispersion of pathogens, including through illegal importation, since local and international trade and the transfer of colonies between regions play important roles in the dispersion of these microorganisms. © 2018 Sociedade Brasileira de Entomologia. Published by Elsevier Editora Ltda. This is an open he CC access article under t
Knowledge, awarness and practices regarding tuberculosis among gold miners in Tanzania

The overall aim of this study was to investigate the knowledge, awareness and practices regarding TB at a selected gold mine in Tanzania in order to enhance the paucity of knowledge in this area of public health. The mining population is considered to be at high risk of tuberculosis infection and illness. However, there is little data available on the knowledge, awareness and practices in the mining population in Tanzania. A quantitative, descriptive study, using the Health Belief Model as the conceptual framework, was conducted among 100 workers in order to give a detailed description of the knowledge and awareness of tuberculosis. The study confirms the role of the media, particularly radio broadcasting, health workers, teachers, and the community in promoting information and education on TB. Fever as a symptom was a problem. The study area is a malaria endemic area where fever is the commonest presentation hence every fever is regarded as malaria. This complicated picking up and identifying other causes of fever. Despite feeling compassion for and wanting to help TB sufferers, most avoided them, which emphasised people’s general fear of TB. This indicated the general isolation and stigmatisation of TB sufferers. The findings highlighted the need for on-going education about TB and its treatment, especially early diagnosis and adherence to treatment Key concepts Tuberculosis awareness, gold miners; Health Belief Model; knowledge and practices regarding TB
Discriminative Bayesian neural networks know what they do not know

Discriminative deep neural networks (NNs) that are trained in a supervised manner constitute a very successful approach to accurate, categorical classification of high-dimensional data. A major downside however is their limited perceptual horizon, given by the number of classes. Instances of unkown classes are erroneously, but often confidently associated with a known class. The actively studied integration of Bayesian modelling and neural networks [1–11] enables a new perspective: predictions can be associated with uncertainties, where the latter ideally would be high in face of data from classes not trained to recognize.
Estimating the Political Orientation of Twitter Users in Homophilic Networks

There have been many efforts to estimate the political orientation of citizens and political actors. With the burst of online social media use in the last two decades, this topic has undergone major changes. Many researchers and political cam-paigns have attempted to measure and estimate the political orientation of online social media users. In this paper, we use a combination of metric learning algorithms and label propagation methods to estimate the political orientation of Twitter users. We argue that the metric learning algorithm dramatically increases the accuracy of our model by accentuating the effect of homophilic networks. Homophilic networks are user clusters formed due to cognitive motivational processes linked with cognitive biases. We apply our method to a sample of Twitter users in Germany’s six-party political sphere. Our method obtains a signiﬁcant accuracy of 62% using only 40 observations of training data for each political party.
OptoNet: a MATLAB-based toolbox for cortical network analyses using functional near-infrared spectroscopy

Abstract. Functional brain network analysis is important for understanding the causes of neurological disorders and relevant brain mechanisms. Lately, functional near-infrared spectroscopy (fNIRS) yields outputs similar to the blood-oxygen-level-dependent signals of functional magnetic resonance imaging (fMRI), and numerous studies have been conducted on functional connectivity and causality using fMRI and fNIRS. Despite the existence of numerous analysis toolboxes for fNIRS, most of them are difficult to use because they involve numerous steps, coefficients, and related files. In this study, we developed a MATLAB toolbox called OptoNet, to analyze cortical networks in the brain for fNIRS. Given that OptoNet consists of a simple and intuitive graphical user interface, users can readily analyze the cortical networks of the brain for fNIRS signals. To evaluate the efficacy of the developed toolbox, the finger tapping task experiment—extensively used in brain functional activities and causal connectivity studies—was employed. The experiment was performed using the right and left hands, and both hands simultaneously, and the consequently elicited brain cortical network activity was analyzed using developed OptoNet.
Response of Canola Plants to Antitranspirant Levels and Limited Irrigation

Water scarcity is the most limiting factor for crop production. Antitranspirant potassium chloride (KCl) was proposed to increase water use efficiency and support canola plants to tolerate water stress. The experiment was carried out in pots filled with sandy loam soil at the wire net house of National Research Centre, Cairo, Egypt. Three irrigation intervals (5, 10, 15 days) and three concentrations of KCl antitranspirant (0, 100, 200 ppm) were applied. Results assured that extension of irrigation interval to 15 days affected seriously plant growth, yield, osmotic pressure, relative water content, photosynthetic pigments, total soluble sugars and carbohydrate percentage. Use of KCl antitranspirant at 100 ppm and 10 days irrigation interval improved the above mentioned parameters and constituents compared to untreated plants. The oil percentage reached 34.17 and 37.87 at 100 and 200 ppm KCl and irrigation interval of 10 days. Saturated fatty acids were affected by extension of irrigation intervals to 15 days although at 10 days irrigation interval archidic fatty acid reached its maximum value of 14.25% and all unsaturated fatty acids were affected except for olic and erucic acids.
Hybrid joist floor constructions . Evaluation of measurement results

Lightweight building systems in general suffer from limited sound insulation, especially at low frequencies. Furthermore, theoretical models have severe limitations regarding prediction of the impact sound insulation, and the design is to a high extent based upon experience and measurements. Recommendations in Norway includes the frequency range down to 50 Hz, which means to include the spectrum adaptation term CI,50-2500 and C50-5000. One possible solution to improve the properties at low frequencies is to add mass and increase the stiffness of the construction. We refer to lightweight building systems combining wood and cement-based materials as hybrid constructions. The paper will present analysis of relevant measurement results of hybrid wood joist floor constructions from laboratory and field objects. The paper will discuss possible improvements of such floor constructions. From the "Silent Timber Build project", some relevant laboratory measurement data exist. Due to the impact sound insulation focus, some apartments with hybrid joist floors have been built and data collected. This paper is a part of a project at SINTEF Building & Infrastructure aiming to develop robust solutions also involving HVAC components inside the partition structure. Further progress in this project will include new laboratory measurements and a fire test.
Non-destructive Testing Techniques Applied to Valuables of our Technical Cultural Heritage

Among our cultural heritage (CH) are objects related to the developments and improvements in engineering. Similar to other artifacts these objects are due to degradation. Sometimes there is also little knowledge about their history, material composition, former use or exposures and impacts. Non-destructive Testing (NDT) techniques can reveal details about artifacts without any disturbance of these valuable objects and sometimes without even getting in contact. In the following, an example of a successful NDT application is given along with some more general remarks including recommendations for further NDT applications in the field.
3 Method Dictionary Matching End Input Text OOV Correction Correct Words OOV in Text Multi-tokens Polyphone Identification Start Shape Character Conversion

Spelling errors in the classical Mongolian text are mainly caused by misuse of polyphonic letters which present the same shape in the certain position of the word. About half to three-quarters of the classical Mongolian words are misspellings which have the correct appearances but wrong codes. In this paper, we code the Mongolian words by glyph codes to map the words to their shapes one-to-one. In addition, we also proposed the correction of out-of-vocabulary words (OOV) based on the Evolved Transformer by formalizing the correction task as a translation from misspellings to target spellings. The experimental results show that this approach achieves the new state-of-the-art performance.